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Image enhancement by spline interpolation and adaptive power spectrum cut-off of filtered images This paper presents a new approach to enhance resolution of the image using interpolation on filtered image. It is evident that low frequency filtered image smoothes the image, though the high frequency filtered image presents sharp change in intensity of the image. Interpolating high frequency filtered image and combining with interpolated low frequency image preserves edge information. Cubic spline interpolation is used to interpolate high and low frequency filtered image. Moreover the cut-off frequency of filter is decided on the basis of power spectrum for better result. The proposed method is tested on satellite and medical images. The quantitative and subjective results has established superiority of the proposed scheme over linear and nonlinear interpolation methods.
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n, m, a = list(map(int, input().split(' ')))
length = int(n/a) if n%a==0 else int(n/a)+1
width = int(m/a) if m%a==0 else int(m/a)+1
ans = length * width
print(ans)
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def clear_step_buffers(self):
self._num_train_frames.assign(0)
self._num_eval_frames.assign(0)
self._replay_buffer.Clear()
self._demo_buffer.clear()
self._eval_datastore.clear()
self._reinitialize_dataset()
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Some say Confucianism is not a religion, since there are no Confucian deities and no teachings about the afterlife. Confucius himself was a staunch supporter of ritual, however, and for many centuries there were state rituals associated with Confucianism. Most importantly, the Confucian tradition was instrumental in shaping Chinese social relationships and moral thought. Thus even without deities and a vision of salvation, Confucianism plays much the same role as religion does in other cultural contexts. The founder of Confucianism was Kong Qiu (K'ung Ch'iu), who was born around 552 B.C.E. in the small state of Lu and died in 479 B.C.E. The Latinized name Confucius, based on the honorific title Kong Fuzi (K'ung Fu-tzu), was created by 16th-century Jesuit missionaries in China. Confucius was a teacher to sons of the nobility at a time when formal education was just beginning in China. He traveled from region to region with a small group of disciples, a number of whom would become important government officials. Confucius was not particularly famous during his lifetime, and even considered himself to be a failure. He longed to be the advisor to a powerful ruler, and he believed that such a ruler, with the right advice, could bring about an ideal world. Confucius said heaven and the afterlife were beyond human capacity to understand, and one should therefore concentrate instead on doing the right thing in this life. The earliest records from his students indicate that he did not provide many moral precepts; rather he taught an attitude toward one's fellow humans of respect, particularly respect for one's parents, teachers, and elders. He also encouraged his students to learn from everyone they encountered and to honor others' cultural norms. Later, his teachings would be translated by authoritarian political philosophers into strict guidelines, and for much of Chinese history Confucianism would be associated with an immutable hierarchy of authority and unquestioning obedience.
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Combined low initial DNA damage and high radiation-induced apoptosis confers clinical resistance to long-term toxicity in breast cancer patients treated with high-dose radiotherapy Background Either higher levels of initial DNA damage or lower levels of radiation-induced apoptosis in peripheral blood lymphocytes have been associated to increased risk for develop late radiation-induced toxicity. It has been recently published that these two predictive tests are inversely related. The aim of the present study was to investigate the combined role of both tests in relation to clinical radiation-induced toxicity in a set of breast cancer patients treated with high dose hyperfractionated radical radiotherapy. Methods Peripheral blood lymphocytes were taken from 26 consecutive patients with locally advanced breast carcinoma treated with high-dose hyperfractioned radical radiotherapy. Acute and late cutaneous and subcutaneous toxicity was evaluated using the Radiation Therapy Oncology Group morbidity scoring schema. The mean follow-up of survivors (n = 13) was 197.23 months. Radiosensitivity of lymphocytes was quantified as the initial number of DNA double-strand breaks induced per Gy and per DNA unit (200 Mbp). Radiation-induced apoptosis (RIA) at 1, 2 and 8 Gy was measured by flow cytometry using annexin V/propidium iodide. Results Mean DSB/Gy/DNA unit obtained was 1.70 ± 0.83 (range 0.63-4.08; median, 1.46). Radiation-induced apoptosis increased with radiation dose (median 12.36, 17.79 and 24.83 for 1, 2, and 8 Gy respectively). We observed that those "expected resistant patients" (DSB values lower than 1.78 DSB/Gy per 200 Mbp and RIA values over 9.58, 14.40 or 24.83 for 1, 2 and 8 Gy respectively) were at low risk of suffer severe subcutaneous late toxicity (HR 0.223, 95%CI 0.073-0.678, P = 0.008; HR 0.206, 95%CI 0.063-0.677, P = 0.009; HR 0.239, 95%CI 0.062-0.929, P = 0.039, for RIA at 1, 2 and 8 Gy respectively) in multivariate analysis. Conclusions A radiation-resistant profile is proposed, where those patients who presented lower levels of initial DNA damage and higher levels of radiation induced apoptosis were at low risk of suffer severe subcutaneous late toxicity after clinical treatment at high radiation doses in our series. However, due to the small sample size, other prospective studies with higher number of patients are needed to validate these results. Background Locally advanced breast cancer (LABC) is a relatively infrequently tumour which poses a significant clinical challenge. The management of LABC has evolved considerably. Initially, patients with LABC were treated with radical mastectomy ; thereafter, systemic therapy was subsequently incorporated along with surgery and radiotherapy (RT). However, even with such combined modality therapy, the long-term survival rate is approximately 50% among patients with LABC. In cases with inadequate response to neoadjuvant systemic therapies and inability to perform surgery, RT is the only possible treatment. Better local control outcomes, with acceptable toxicity, have been obtained by using high total doses of radiation administered in two small fractions per day (hyperfractionation, HF). HF allows escalation of the biologically effective dose to the tumour without a significant increase in late complications. The radio therapeutic doses received by the patient are limited by the tolerance of the normal tissues. Different patients given a standardized treatment can exhibit a range of normal acute and/or late tissue reactions. Thus, there is both a dose dependence and a variability in individual radiosensitivity, where genetic and constitutional factors inherit to each patient could exert an influence. The prediction of radiation-induced toxicity could help to select the most appropriate treatment for each patient. Many predictive factors have been described, including initial DNA damage, cell apoptosis, or gene expression patterns. In previous studies, we have reported an association between the initial number of DNA double-strand breaks (DSB) induced by x-rays in peripheral blood lymphocytes (PBL) and radiation-toxicity. Thus, increasing numbers of radiation induced DSB were related to severe late subcutaneous toxicity in LABC patients treated with HF. In the other hand, determination of radiation-induced apoptosis (RIA) in PBL by flow cytometry analysis has also been proposed as an approach for predicting normal tissue responses following radiotherapy. Patients suffering of late toxicity after RT showed reduced rates of RIA in several tumour locations. Moreover, we have recently reported an inverse association between the initial DNA damage and RIA in LABC patients. Taking into account the above background and our previously observations, we explored the clinical association between initial DNA damage and RIA in relation to radiation-induced toxicity in the set of LABC patients treated with high dose HF radical RT with long-term follow-up where this association have been previously observed. Characteristics of Patients Twenty-six consecutive patients diagnosed in our institution with locally advanced/inflammatory breast cancer were recruited prospectively for the study after they signed informed consent to their participation. The study was approved by the Research and Ethics Committee of our Institution. All patients were treated between 1992 and 1997; blood samples for radiosensitivity testing were extracted between February and December 1998. All the analyses were double-blinded to ensure their reliability. Mean age of patients was 57.62 ± 12.9 years (range 30-83). The majority of patients were postmenopausal (69.2%), presented bra size over 100 (65.4%), and non-inflammatory LABC (73.1%). Characteristics of patients are detailed in Table 1. Evaluation of clinical toxicity was made in each visit. The Radiotherapy Oncology Group (RTOG) morbidity score system was used to classify the toxicity of patients. Acute toxicity was evaluated during and at the end of RT. Late cutaneous and subcutaneous toxicity was evaluated every three months during the first two years, every six months to five years, and thereafter annually. At the end of the analysis (January 2011), the mean clinical follow-up of survivors (n = 13) was 197.23 months (range 155-228). The time point finally used for analysis corresponds to the last evaluation. Clinical toxicities of patients are detailed in Table 2. Radiation Treatment Patients were treated with a dose-escalation radiation therapy schedule using hyperfractionation. All patients received 60 Gy to the whole breast over a period of 5 weeks in two daily fractions of 1.2 Gy, separated by at least 6 h on 5 days each week. A boost covering the tumour plus margins was prescribed at a dose of 9.4-21.6 Gy. Peripheral nodes were treated by conventional fractionation (1.8/2Gy/day) at doses of 50-70 Gy. Supraclavicular and axillary lymph node areas were treated with an anterior field and a posterior axillary compensating field. Doses were prescribed to the mid-plane of the axilla and at a depth of 3 cm in the supraclavicular area. The internal mammary chain was treated by a direct anterior field with the dose prescribed at depth of 3 cm. Doses to the breast ranged from 64.8 Gy to 81.6 Gy (mean 77.5 ± 5.7 Gy; median 81.6 Gy). Maximum point doses ranged from 62.8 to 101.7 Gy (mean 87.4 ± 8.8; median 89.7 Gy). Analysis of Initial DNA Damage Data related to initial DNA damage were obtained from our files. Shortly, mononuclear cells were isolated from blood of patients, resuspended in cold DMEM, and mixed with 1% ultra-low-melting-point agarose to obtain 250 l plugs. Irradiation on ice was performed using a 60 Co source (rate dose 1.5 Gy/min, approximately) as previously reported. Plugs were held 1 hour at 4°C and incubated at 37°C for 24 hours. Initial radiation-induced DNA damage in PBL was measured by pulsed-field gel electrophoresis (PFGE) as previously described, and data are summarized in Table 3. Apoptosis assay and flow cytometry RIA analyses were performed as previously reported. PBL were irradiated with 0, 1, 2 and 8 Gy. After irradiation, samples were incubated for 24 hours at 37°C and 5% CO 2. After extraction of cellular pellet, it was resuspended in 100 l Annexin V buffer Kit (Pharmingen, Becton Dickinson). After the addition of 4 l of Annexin-V-FITC and 10 l of propidium iodure (PI), cells were incubated during 15 minutes at room temperature in the dark. Finally, 400 l of Annexin V buffer Kit were added. Every assay was made in triplicate. The flow cytometry analysis was performed in a FACScalibur (Becton Dickinson, San Jos, CA) using a 488 nm argon laser, and each sample was analyzed in a Macintosh Quadra 650 minicomputer (Apple computer Inc., Cupertino, CA) as previously reported. Data were analyzed using the CellQuest program (Becton Dickinson, San Jos, CA) calculating early and late apoptosis levels. RIA is defined as the percentage of total PBL death induced by the radiation dose minus the spontaneous cell death (control, 0 Gy). Statistical analyses Statistical analyses were performed using the SPSS Statistical Package (version 15.0 for Windows). The cut-off values for continuous variables were the median and the tertiles of the distribution, as previously reported. Univariate and multivariate analyses were performed using Cox regression. All tests were two sided and statistical significance level was established for a P value less than 0.05. All samples were processed anonymously. Radiation-induced toxicity in breast cancer patients The actuarial probability of being free of severe late cutaneous toxicity, nine-teen years after radiation therapy, was 61.5%, while only 19.2% were free of severe late subcutaneous toxicity. In a previous observation, 10 years after RT, 65% of patients were free of severe late cutaneous toxicity ( 2 test, P = 0.463); while 29% were free of severe late subcutaneous toxicity ( 2 test, P = 0.031). Severe subcutaneous toxicity is related to breast shrinkage, fibrosis and sometimes pain. Late radiation-induced reaction occurs after a latency period of >90 days (typical range 0.5-5 years). The latency period in animals is known to be shorter after higher doses, and in humans, it is even >5 years for moderate doses or for very late reacting tissues. Late damage progresses over time, and it is important to highlight that doses believed safe at 5 years may result in serious late side effects beyond the 5-year period with any treatment protocol. For this, the ability to predict late effects in the treated breast is of great importance, especially when an unconventional treatment schedule is prescribed. In univariate analysis (simple Cox regression), severe subcutaneous late toxicity (grades 3-4) was related to bra size-estimated breast volume (P = 0.037) ( Table 4). Breast size is strongly related to late changes in breast appearance possible because greater radiation changes are related to greater dose inhomogeneity in women with large breasts. Initial DNA damage levels in breast cancer patients Initial DNA damage was determined as radiationinduced double-strand breaks (DSB) in irradiated lymphocyte from all 26 LABC patients. There was a wide variation in DSB among patients (. These results support the suggestion that variation in cell radiosensitivity can be detected in vitro using radiosensitivity assays on lymphocytes derived from normal tissues of cancer patients prior to radiotherapy [18,. This wide variation in DNA DSB can be attributed to variation between individuals more than to variation due to technical or sampling errors. Initial DNA damage followed a normal distribution (Kolmogorov-Smirnov test, P > 0.05), and data obtained from the present group of patients matched previously published results for breast cancer patients. However, other molecular events such as DNA repair foci or DNA-loops should be taken into account for the correct interpretation of data. It has been observed that DNA DSB in residual foci and relaxation of DNA-loops may be linked to induction of radiation-induced apoptosis in lymphocytes. We have previously demonstrated a relation between the sensitivity of in vitro-irradiated peripheral blood lymphocytes and the risk of developing late toxic effects after RT in the present set of patients. However, the predictive value of initial DNA damage is controversial and different findings have been reported on this regard. Thus, we agree with some authors and we disagree with some others. Moreover, more initial DSB have been detected in lymphocytes from normal patients as compared to radiosensitive. In our opinion, it is important to highlight that the predictive role of initial DNA damage was observed in patients treated with high-dose of radiation, where the toxicity reactions are more evident. Differences in the protocol treatment (RT schedule: dose and type of fractionation) and in the methodology used (PFGE, comet assay, gamma-H2AX induction) could help to explain the discrepancies observed. Radiation-induced apoptosis in breast cancer patients Data of RIA were available in all 26 breast cancer patients as shown in Table 3. RIA increased with radiation dose and data fitted to a semi logarithmic model as follows: RIA = ln(Gy) +. This mathematical model was defined by two constants: the coefficient in origin (determining the spontaneous apoptosis) and the coefficient (defining the slope of the curve). As expected, RIA at 1, 2 and 8 Gy, as well as and constants followed a normal distribution (Kolmogorov-Smirnov test, P > 0.05). There is an important variation in the ex vivo susceptibility of normal cells against ionizing radiation. It has been suggested that the radiation-induced damage measured on lymphocytes could be proportional to the acute damage evaluated on the skin of treated patients. Anyhow, it is possible to estimate the cellular radiosensitivity of PBL of patients analyzing the RIA rate by annexin V/PI staining flow cytometric analysis, defining an intrinsic individual value of radiosensitivity inherit to each patient. Radiation-induced apoptosis has been proposed as a reliable method for prediction of normal tissue toxicity after radiotherapy by us and other authors. However, some other studies reported no correlations between individual radiosensitivity of cancer patients and radiation-induced apoptosis in PBLs. The lack of uniformity in experimental design helps to understand these differences. Thus, the cells used in the assay (total PBL, Epstein-Barr virus-transformed Table 4 Distribution of patients according to expected radiation sensitivity after the irradiation of peripheral blood lymphocytes at 1, 2 and 8 Gy lymphoblastoid cell lines, CD(3+) lymphocytes), the radiation protocol, or the analysis strategy are critical to make possible the comparison among studies. Association of initial DNA damage and radiation-induced apoptosis with normal tissue toxicity As previously published, increasing numbers of radiation induced DSB were related to severe late toxicity in breast cancer patients. Thus, among patients receiving the highest radiation doses (81.6 Gy), those who showed higher levels of initial DNA damage had a greater risk of severe subcutaneous toxicity. In the present set of patients, no association was observed between DNA DSB or RIA (at any radiation dose), or constants and normal tissue toxicity, possibly due to the small sample size (data not shown). An association between the initial DNA damage and the radiationinduced apoptosis, as a consequence of x-ray, may exist. DNA DSB are assumed to be the most important lesion to induce apoptosis. Depending on the severity of the DNA damage and the cell type involved, cells may undergo apoptosis instead of attempting to repair the damage. Lymphocytes are particularly sensitive to apoptosis, partly because they induce Bax expression in response to ionizing radiation exposure. Lymphocytes from patients who suffered Ataxiatelangiectasia, Bloom syndrome, or Fanconi anaemia showed absence of induction of p53 and lower levels of Bax. Apoptosis is initiated following DSB through an ATM-directed pathway. This could explain the fact that patients affected by the Ataxia-Telangiectasia syndrome show the lowest rates of RIA. In that sense, we have recently reported an inverse association between the initial DNA damage and RIA in LABC patients. Defective apoptotic response to radiation in PBLs could help to explain this inverse relation. According to the above observations, high initial DNA damage or low radiation-induced apoptosis would confer sensitivity to long-term toxicity, separately. In the present study, we tried to disclose the predictive value of both parameters in a combined form. The percentage of patients developing severe late toxicity determines the maximum acceptable radiation dose. Generally, an adverse effect frequency of 5%-10% is considered acceptable. We observed that 7.6% (range 3.8-11.5%) of our patients suffered from severe complications (2, 1, and 3 out of 26 patients analyzed at 1, 2 and 8 Gy respectively) ( Table 4). Because this subset of patients is too small, we focused on the expected most resistant patients to RT: those who presented low initial DNA damage and high radiationinduced apoptosis (Table 4). Thus, we considered "resistant patients" those who presented DSB values lower than 1.78 DSB/Gy per 200 Mbp (two lower thirds of the distribution) and RIA values over 9.58, 14.40 or 24.83 for 1, 2 and 8 Gy respectively (two upper thirds of the distribution) (Table 3). We did not observe any association with late toxicity in the whole series, in univariate analysis. However, order to the higher received dose (≥81.6 Gy), we observed that severe subcutaneous late toxicity (grades 3-4) was related to this radiationresistance profile in patients treated with higher dose of radiation (simple Cox regression, Table 5). Those patients treated at very high doses (≥81.6 Gy) and who presented this radiation-resistance pattern were at low risk of suffer severe subcutaneous late toxicity (Table 5). Furthermore, in multivariate analysis in the whole series, severe subcutaneous late toxicity was related to the received dose (HR 1.138, 95%CI 1.003-1.291, P = 0.045), the bra size-estimated volume (HR 1.073, 95%CI 1.004-1.147, P = 0.038), and with this radiation-resistant profile (HR 0.223, 95%CI 0.073-0.678, P = 0.008; HR 0.206, 95%CI 0.063-0.677, P = 0.009; HR 0.239, 95%CI 0.062-0.929, P = 0.039, for RIA at 1, 2 and 8 Gy, respectively) ( Table 6). Thus, those patients who presented lower levels of initial DNA damage and higher levels of radiation induced apoptosis were at low risk of suffer severe subcutaneous late toxicity. No relation was found with acute or late cutaneous toxicity. The close relation between chromosome fragment production and killing in many cell systems has been important in linking DNA DSB to death, because it is a natural step to relate DNA strand breakage to chromosome breakage. However, the recognition that apoptosis may be an important mode of radiation-induced death in some cell types raise the possibility that other types of damage may induce apoptosis. A significant association was observed for the first time between these variables, both considered as predictive factors for radiation toxicity, and normal tissue damage. Conclusions Initial DNA double-strand breaks and radiationinduced apoptosis in peripheral blood lymphocytes have been proposed as reliable methods for prediction of radiation-induced late toxicity in normal tissues. We have observed, for the first time, a combined role of both parameters. Thus, we propose a radiation-resistance profile where those patients who present lower levels of initial DNA damage and higher levels of radiation induced apoptosis were at low risk of suffer severe subcutaneous late toxicity in our series. This finding opens the possibility to develop new predictor assays taking into account the initial DNA damage and radiation-induced apoptosis levels, and introduces new data which may help to understand and define the complex mechanisms behind the normal tissue toxicity. Nonetheless, due to the small sample size, the present results need to be validated in bigger clinical series. Authors' contributions LAHH has written the manuscript, has participated in the statistical analysis, has made tables and has been involved in type of packaging likewise in the submission process. RCV has made the last revision of patients as well as the update of the medical records. BP and ML have made the selection of patients, the evaluation of clinical variables and grade of toxicity as well as all the aspects related with the patients selected, including the treatment. EB and CRG have made the cell experiments with lymphocytes, irradiation of cells, flow cytometry experiments and data acquisition. MIN has been involved in conception and design of the study and has made the DNA-DSB experiments and analyses. PCL has been involved in conception and design of the study and in drafting the manuscript and has given final approval of the version to be published. All authors read and approved the final manuscript. Abbreviations: HR = hazard ratio; CI = confidence interval.
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Trajectories of relationship quality in dementia: a longitudinal study in eight European countries Abstract Objectives Relationship quality (RQ) between a person with dementia and a family carer may influence their health and quality of life. However, evidence regarding its course and influencing factors is limited. We aimed to explore RQ trajectories in dementia, and identify predictors of change. Methods We analysed longitudinal data from a cohort of 350 community-dwelling people with dementia and their informal carers, participating in the Actifcare study in eight European countries. The Positive Affect Index, rated separately by both people with dementia and their carers, assessed RQ. Other measures included the Neuropsychiatric Inventory Questionnaire (regarding persons with dementia), and the Relative Stress Scale, Sense of Coherence Scale and Lubben Social Network Scale (for carers). Trajectories and influencing factors were explored applying a latent growth model (LGM). Results RQ in the group of carers declined over 1year, but RQ scores for the persons with dementia did not change. Higher stress in carers negatively influenced their baseline RQ ratings. Carer sense of coherence and being a spouse were associated with more positive baseline RQ carer assessments. Higher levels of neuropsychiatric symptoms were linked to decline in carers RQ, whereas social support was associated with more positive RQ trajectories. Conclusion This study provides a valuable insight into the course of RQ. LGM proved useful to explore the factors that influence RQ trajectories and variability within- and between-persons. Our findings emphasise the importance of carer-perceived social support and sense of coherence, and of reducing neuropsychiatric symptoms, in maintaining a good RQ.
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The involvement of microtubules and actin filaments in the intracellular transport of non-viral gene delivery system It is known that two cytoskeleton components, microtubules and actins filaments, are required for efficient endocytosis. The relative importance of these two components in the cellular uptake of 2-(dimethylamino)ethyl methacrylate (DMAEMA)-DNA polyplexes was investigated in this study by applying microtubule depolymerising agent, colchicine, and actin polymerising inhibitor, cytochalasin D, in a cell transfection study. The effect of colchicine on transfection efficiency of polyplexes was found to be a time-dependent phenomenon, whereby the level of gene expression was inhibited at early stage, presumably to the disruption of a transport of vesicles along microtubules by colchicine. As time progressed, the level of gene expression was significantly enhanced relative to the control, possibly due to the failure in transport of vesicles from endosomes to late lysosomes, or due to the breakdown of nuclear membrane when mitosis was arrested at metaphase by colchicine. On the other hand, transfection efficiency was significantly reduced at all time points by cytochalasin D, which is considered to primarily affects invagination of vesicles at the early stage of endocytosis by inhibiting actin polymerisation. Further investigation to identify the endocytotic route of DMAEMA polyplexes was conducted applying clathrin- and caveolae- pathways inhibitors in cell transfection study. The results indicate that DMAEMA polyplexes were internalized primarily through clathrin-mediated pathway, with a minor fraction possibly entering cells via a caveolae-mediated pathway.
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export * from './LoadingCache';
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TFB has long had a good reputation of being a high standard, nutritional remedy in Chinese history. Thus, in ancient medicinal literature TFB has been ascribed certain curative properties, such as: Promoting saliva secretion, moistening lungs and stopping dry cough, decreasing itching in the throat, inhibiting the coughing blood, relieving stomach pain, stopping constipation and blood in the stool, restoring tired muscles, supporting good spirit and memory, keeping skin young end hair shiny etc.
During the last 15-20 years scientific studies of TFB have been carried out in China end Japan- Thus, In Journal of Medicine and Material Medics, 1978, p. 21-25, San Ming Research Station, describe the treatment of chronic bronchitis end chronic pulmonary disease using TFB.
Liu zhi-bin et al reported that, oral or subcutaneous injection of TFB to mice raised the macrophage and enhanced the phagocytic function (Proceeding of Beijing Medical University, 14(1); 14-15, 1982).
Wang Zia-oan et al in Chinese Medical Journal of Radiation end Protection, pp. 65-66, 1983, reported that TFB could prevent the harmful influence of Co-60 irradiation in monkeys by restoring their leucocyte counts back to normal level.
Cheng Zi-qi et al in Chinese Medical Journal of Radiation end Protection, pp 4(3), 54-55, 1984, reported that TFB when given to patients, who had received radiation, or chemotherapy treatment for cancer, raised the B and T lymphocyte count with 8.6% and 11.0% respectively.
Liu shu-hua et al reported (J. Zhonghue Fangshe Yixue Yu Fanghu 5(4), pp. 262-265, 1985), that injection of AET, 5-HT, TFB to donor mice before Co-60 irradiation protected the haemopoietic function of bone marrow. But there was no studies directly towards the effect of TFB on mircovascular endothelial function.
Dang Wen-long et al Immuno-pharmacological study on the polysaccharide of Tremella (Zhongcaoyao 1984, 15(9) 23-6, 22) reported that i.v. injection of Tremella polysaccharide (TP) enhanced phagocytotic clearance of C particles and 32P-labeled Staphylococcus aureus from the circulation by macrophages of the reticuloendothelial system (RES). It was also found, that TP antagonized the inhibited phagocytotic function of the RES by immune inhibitors. However, there was no disclosure, about the effect of TFB on endothelial cells per se, still less on vascular endothelium, which is different from RES.
Endothelial cells which belong to the reticuloendothelial system (RES) are called "Reticuloendothelial Cells". The name "RES" was proposed 1924 by Dr. Aschoff, who suggested that those mononuclear cells, which possess function of phagocytosis, or of storage some granules and dyes are united as a whole system. These cells have a common defense function in the body, and thus he called these cells: "The Reticuloendothelial System". This system includes fixed or movable macrophages and mononuclear cells in the blood flow. But nowadays this expression has been replaced by "mononuclear phagocytotic system".
Several Japanese patent publications disclose the use of extracts from mycelium end fruit bodies of TFB for antitumor and carcinostatic treatment, cf. the Japanese published patent applications JP57017518-B4; JP6057835-B4; JP54011250-A; JP53107407-A: JP53107406-A.
In 1988, it was reported in JP63183537 that TFB was found to be an anti-inflammatory drug, particularly in the cosmetic field, but there were no experimental and clinical data for showing and proving the anti-inflammatory effect of TFB introduced by this report.
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<reponame>Gabriel-Baril/random-java-projects
package state;
public class PropertyNotOwnedState extends PropertyState
{
@Override
public void landOnBy(Player player, Property property)
{
System.out.print(" - not owned\n" + player.getName());
if(player.getMoney() < property.getPrice())
{
System.out.println(" does not have enough money to purchase");
}
else
{
player.debit(property.getPrice());
property.setOwner(player);
System.out.println(" bought " + property.getName());
}
}
}
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DOMINO-AD protocol: donepezil and memantine in moderate to severe Alzheimer's disease a multicentre RCT Background Alzheimer's disease (AD) is the commonest cause of dementia. Cholinesterase inhibitors, such as donepezil, are the drug class with the best evidence of efficacy, licensed for mild to moderate AD, while the glutamate antagonist memantine has been widely prescribed, often in the later stages of AD. Memantine is licensed for moderate to severe dementia in AD but is not recommended by the England and Wales National Institute for Health and Clinical Excellence. However, there is little evidence to guide clinicians as to what to prescribe as AD advances; in particular, what to do as the condition progresses from moderate to severe. Options include continuing cholinesterase inhibitors irrespective of decline, adding memantine to cholinesterase inhibitors, or prescribing memantine instead of cholinesterase inhibitors. The aim of this trial is to establish the most effective drug option for people with AD who are progressing from moderate to severe dementia despite treatment with donepezil. Method DOMINO-AD is a pragmatic, 15 centre, double-blind, randomized, placebo controlled trial. Patients with AD, currently living at home, receiving donepezil 10 mg daily, and with Standardized Mini-Mental State Examination (SMMSE) scores between 5 and 13 are being recruited. Each is randomized to one of four treatment options: continuation of donepezil with memantine placebo added; switch to memantine with donepezil placebo added; donepezil and memantine together; or donepezil placebo with memantine placebo. 800 participants are being recruited and treatment continues for one year. Primary outcome measures are cognition (SMMSE) and activities of daily living (Bristol Activities of Daily Living Scale). Secondary outcomes are non-cognitive dementia symptoms (Neuropsychiatric Inventory), health related quality of life (EQ-5D and DEMQOL-proxy), carer burden (General Health Questionnaire-12), cost effectiveness (using Client Service Receipt Inventory) and institutionalization. These outcomes are assessed at baseline, 6, 18, 30 and 52 weeks. All participants will be subsequently followed for 3 years by telephone interview to record institutionalization. Discussion There is considerable debate about the clinical and cost effectiveness of anti-dementia drugs. DOMINO-AD seeks to provide clear evidence on the best treatment strategies for those managing patients at a particularly important clinical transition point. Trial registration Current controlled trials ISRCTN49545035 Background Acetylcholinesterase inhibitors are widely prescribed to patients with mild to moderate Alzheimer's disease (AD), with studies showing they improve cognition and stabilize cognition, function and behaviour for up to 6-12 months (see below). Although there is evidence that these drugs are effective in the mild to moderate range of severity, there is at present little evidence to guide clinicians at the critical decision point when patients deteriorate beyond moderate to severe dementia. Memantine has systematic review level evidence to support efficacy in late stage AD and has been widely prescribed at this stage. Again, however, there is not an adequate evidence base to guide decisions about patients compliant with a cholinesterase inhibitor but who have reached the moder-ate to severe point where they might be considered for memantine treatment. There is therefore a pressing clinical need to provide an evidence base for physicians on which to base decisions about continued prescribing of cholinesterase inhibitors in patients as they reach the moderate to severe stage of AD but there have been no clinical trials that can provide this evidence. There is also a need to evaluate the use of memantine, which already has a licence for the treatment of moderate to severe AD, alone and in combination with a cholinesterase inhibitor at the point where patients make the transition to moderate to severe disease. To inform clinical practice and relevant decision-making bodies (such as, for England and Wales, the National This article is available from: http://www.trialsjournal.com/content/10/1/57 © 2009 Jones et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Institute for Health and Clinical Excellence, NICE) most effectively, any trials that examine these issues should be pragmatic and based on a representative patient population in whom clinicians currently have genuine uncertainty about how to proceed with treatment. Important outcome measures for such a trial should include preservation of activities of daily living and independence as well as cost-utility data. By recruiting patients who have reached the transition point between moderate and severe dementia, and who are already receiving anticholinesterase treatment, DOM-INO-AD is designed to answer clinical questions about efficacy and cost-effectiveness that have real relevance to clinicians and policy making organisations. Clinical Studies A number of studies have demonstrated that acetylcholinesterase inhibitors modestly improve cognition in a subgroup of patients with mild to moderate AD and stabilize cognition, function and behaviour for 6 months and may continue to exert benefit for 12 months. Evidence has also been presented to suggest that these drugs may continue to have a beneficial effect for up to 2 years although a recent influential systematic review concluded that benefits of treatment are modest and that the methodological quality of most published trials could be questioned. Acetylcholine and choline acetyltransferase levels do not begin to fall significantly until dementia is advanced. Hence, there would be good theoretical reasons to anticipate a therapeutic effect of cholinesterase inhibition at later stages of disease than the currently licensed indication of mild to moderate severity. Trial evidence for this comes from four particular sources. Firstly, randomized double-blind placebo-controlled trials of cholinesterase inhibitors that have included moderate to severely affected patients have shown significant benefits over 24 weeks in cognitive, behavioural and functional outcomes in a group of patients whose SMMSE scores ranged from 5-17 (mean score for patients receiving donepezil = 11.7). Secondly, secondary analyses of trial data from mild to moderate patients examining the subgroup of patients with more severe illness have also shown benefits. Wilkinson performed a post hoc analysis on pooled data from 124 patients with SMMSE scores of 10-12 from the 4 pivotal galantamine studies. Cognitive and functional abilities were significantly improved in galantamine treated participants. Burns retrospectively analyzed pooled data from 117 patients selected from three RCTs of rivastigmine on the basis of a SMMSE score of 10-12 points. Rivastigmine treatment over 6 months showed significant benefits in cognitive and behavioural domains. Further, Gauthier and Feldman reanalyzed earlier Feldman data focussing on 145 patients with a SMMSE score of 5-12. At week 24, using the last observation carried forward principle for imputing missing data, the mean differences in mean change from baseline scores were 2.0 points for the SMMSE and 7.4 points for the Severe Impairment Battery (SIB) in favour of the donepezil treated participants and Clinician's Interview-Based Impression of Change-Plus (CIBIC-plus) scores were significantly improved compared with placebo with a 0.70 point mean treatment difference. Thirdly, long term trial data within which participants have progressed to more severe disease stages of illness can also demonstrate apparent continued efficacy in moderate to severe patients. In the AD2000 trial 49% of participants randomized to treatment with donepezil had SMMSE scores of 10-18 points at study entry. Over the 2year study period, the donepezil group averaged SMMSE scores 0.8 points higher than the placebo group. This benefit was not restricted to any subgroup of patients in terms of severity rating and was maintained over the trial period. Raskind demonstrated benefits of continuing galantamine treatment for 36 months in a group of patients whose mean SMMSE score at entry had been 19.7 points and a proportion of whom would have entered the moderate to severe category during the course of the study. Fourthly, trials examining the effects of cholinesterase inhibitor withdrawal provide some information. Typically, following a placebo washout period of 6 weeks at the end of a trial, the benefits of 24 weeks of donepezil treatment in terms of cognition and global function are lost. Most of such washouts have been at the end of relatively short trials at which point patients have still been only mildly to moderately affected. There are no published randomized controlled trials examining the effects of treatment withdrawal in patients at the moderate to severe boundary that would help clinicians to make decisions at the point where NICE guidance advises stopping. Holmes showed behavioural benefits of continuing donepezil in a group of patients selected on the basis of marked neuropsychiatric symptoms (NPI score>11) at baseline and with a mean SMMSE score of 21.1 points. Additionally, there is intriguing preliminary evidence that a combination of a cholinesterase inhibitor and memantine might be particularly beneficial in patients at this severity point. For example, a study of 404 patients with moderate to severe AD (SMMSE 5-14) who had been stabilized on donepezil treatment for at least 6 months, investigated the effect of adding memantine 10 mg b.d. for 6 months. The change in total mean (standard error) scores favoured memantine over placebo for the SIB; 1.0 (0.7) vs. - 4.64 (0.087). All other secondary measures showed significant benefits of memantine treatment. Treatment discontinuations because of adverse events were seen in 7.4% receiving memantine and 12.4% in those receiving placebo. An unpublished Phase III study in mild to moderate AD patients, however, has suggested that combinations of a variety of cholinesterase inhibitors and memantine may not provide additional benefits over monotherapy in this less impaired group. Overall, the preliminary and post-hoc evidence of an effect on behaviour has not yet been backed up by RCT evidence which tests this question and there is a clear need for more data. NICE Guidance NICE recommended in 2001 that cholinesterase inhibitors (donepezil, rivastigmine and galantamine) should be offered to patients with mild to moderate AD whose MMSE score was above 12 points. NICE guidance from 2001-2005 was that prescription should only be continued while the MMSE score remained above 12 points and the patient's global, functional and behavioural condition remained at a level where the drug was considered to have a worthwhile effect. This recommendation was made on the grounds of cost-containment, rather than clinical efficacy, since many of the patients who entered the trials that established efficacy had MMSE scores of as low as 10 points. Current NICE guidance for England and Wales, which although amended in response to judicial review since its original publication remains substantially the same in its effect, recommends that the three acetylcholinesterase inhibitors donepezil, galantamine and rivastigmine are used as options in the management of people with AD of moderate severity (MMSE score of between 10 and 20 points). Patients continuing on the drugs should be reviewed six monthly by MMSE score and global, functional and behavioural assessment, incorporating the views of carers. Drug treatment should only be continued while patients' MMSE score remains above 10 points and their global, functional and behavioural condition remains at a level where the drug is considered to be having a worthwhile effect. Memantine is not recommended as a treatment option for people with moderately severe to severe AD except as part of a clinical study. Aim of the study The aim of the DOMINO-AD study is to determine, in a factorial (2 2) design, whether there is worthwhile benefit for patients, for whom there is uncertainty on whether or not to continue cholinesterase inhibitors, from: 1) adding memantine to donepezil, 2) switching to memantine or 3) continuing donepezil compared to 4) placebo. Primary Objectives The trial will test a number of hypotheses in memory clinic patients who have declined in terms of cognitive function to reach the transition point to moderate-tosevere AD. These hypotheses are:a) patients with AD who continue donepezil beyond the moderate to severe transition point will show a significantly smaller decline on ratings of cognitive function and activities of daily living over the following 12 months than those discontinuing donepezil, with analysis using all trial participants; b) patients with AD who commence memantine therapy at the moderate to severe transition point will show a significantly smaller decline on ratings of cognitive function and activities of daily living over the following 12 months than those who do not, with analysis using all trial participants; c) patients given the combination of memantine and donepezil at the moderate to severe transition point will show additive or synergistic significant benefits on measures of activities of daily living and cognitive function after 12 months compared to those patients continuing on either monotherapy. Secondary Objectives Secondary hypotheses to be tested are:a) patients with AD who continue donepezil beyond the moderate to severe transition point will show a significantly smaller deterioration on ratings of non-cognitive symptoms and health related quality of life over the following 12 months than those discontinuing donepezil, with analysis using all trial participants; b) patients with AD who commence memantine therapy at the moderate to severe transition point will show a significantly smaller deterioration on ratings of non-cognitive symptoms and health related quality of life over the following 12 months than those who do not, with analysis using all trial participants; c) patients given the combination of memantine and donepezil at the moderate to severe transition point will show additive or synergistic significant benefits on measures of non-cognitive symptoms and health related quality of life after 12 months compared to those patients continuing on either monotherapy; d) treatment of patients with donepezil beyond the moderate to severe transition point will be more costeffective than discontinuing donepezil; memantine therapy will be more cost-effective than placebo; the combination of memantine and donepezil will be more cost-effective than monotherapy; e) patients who continue on donepezil beyond the moderate to severe transition point will be institutionalized later than those who do not; patients who commence memantine therapy will be institutionalized later than those taking placebo; patients who commence the combination of memantine and donepezil will be institutionalized later than those on monotherapy. For carers, parallel secondary objectives concern changes in psychological morbidity and health related quality of life. Design This is a pragmatic, multicentre, double-blind (with patient, carer, clinician, outcome assessor and investigators blinded), randomized, placebo controlled (double dummy), parallel group, 2 2 factorial clinical trial. Figure 1 illustrates the trial design. All participants receive trial interventions for 52 weeks. Participants are randomized to one of four arms centrally by the Medical Research Council (MRC) Clinical Trials Unit in London via telephone. Randomization is by dynamic allocation using minimization to ensure balanced allocation across the following factors: centre, duration of donepezil treatment prior to randomization, baseline SMMSE score and age. (Arm 1) Combination of donepezil plus memantine Participants in this arm continue with their current donepezil 10 mg/day regimen and immediately commence active memantine at a dose of 5 mg per day, increasing in 5 mg increments weekly until 20 mg per day is achieved from week 4 onwards. (Arm 2) Withdrawal of donepezil and prescription of memantine Participants in this arm immediately commence active memantine at a dose of 5 mg per day, increasing in 5 mg increments weekly until 20 mg per day is achieved from week 4 onwards. The donepezil dose is reduced to 5 mg daily in weeks 1 to 4 and replaced with placebo donepezil in week 5. (Arm 3) Continued prescription of donepezil monotherapy Participants in this arm continue with their current donepezil 10 mg/day regimen and immediately commence placebo memantine. (Arm 4) Withdrawal of donepezil Participants in this arm immediately commence placebo memantine dose escalation and switch to donepezil 5 mg daily in weeks 1 to 4, which is replaced with placebo donepezil in week 5. Planned inclusion/exclusion criteria Inclusion criteria People are eligible to participate if they are patients who meet standardized clinical McKhann criteria for probable or possible AD, have been continuously prescribed donepezil for at least 3 months and continuously prescribed 10 mg donepezil for the previous 6 weeks. They must have had no changes in prescription of any psychotropic drugs (antipsychotic, antidepressant, benzodiazepine) in the previous 6 weeks, the prescribing clinician must consider (based on NICE guidance, discussions with patient and carer and clinical judgement) that change of drug treatment (i.e. stop donepezil or introduce memantine) may be appropriate and on testing with the SMMSE, the standardized assessment of cognitive function, the score is between 5 and 13. Also, to be eligible, the participant must be community resident with a family or professional carer or be visited on at least a daily basis by a carer. Participants must have agreed to take part if considered capable and the main carer (informal or professional) must also have given consent to his/her own involvement and to the participant's involvement. Exclusion criteria These include severe, unstable or poorly controlled medical conditions apparent from physical examination or clinical history, current prescription of memantine, contra-indications or previous adverse or allergic reactions to trial drugs, involvement in another clinical trial or that the clinician considers the patient would not be compliant. Recruitment/consent procedures Participants are identified from patients with AD meeting study eligibility criteria who are being followed up in memory clinics, out patient clinics or other components of specialist mental health, geriatric medicine or neurology services. Once a potential participant has been identified, the clinician obtains verbal consent to pass his/her details to the research worker who then recruits the patient in line with the trial standard operating procedures. Where possible, fully informed consent is obtained from the patient. However, the majority of patients with moderate to severe dementia lack the necessary mental capacity to give fully informed consent. In this situation, agreement to participate in the study is still obtained to the patient's best level of understanding and the patient is not enrolled if they refuse or show significant distress. For Assessments Study assessment measures will be applied at baseline prior to randomization, at week 6 to assess the acute effects of donepezil withdrawal, at week 18, week 30 and at week 52. Participants will then be followed up every 26 weeks for 3 years by telephone interview to establish whether and on what date they entered a care institution. Trial completion is defined as completion of 52 weeks on the trial medication or discontinuation of follow-up for any cause. Participants who discontinue taking the trial medication are encouraged to remain in follow-up. Participants may not formally discontinue their follow-up and remain on the trial medication. Arrangements for continued provision of the trial medication at the end of the trial will be made on an individual basis by the clinician responsible for the participant's care. Primary Outcomes Measures Standardized Mini-Mental State Examination (SMMSE) The Mini-Mental State Examination is a well-established measure of cognitive function in elderly people. It shows good test-retest and inter-rater reliability and performs satisfactorily against more detailed measures of cognitive function. The Standardized Mini-Mental State Examination (SMMSE) has been developed to improve the reliability of the original instrument and will be used to assess decline in cognitive function during the trial. Scores range from 30 (unimpaired) to 0 (impaired). Bristol Activities of Daily Living Scale (BADLS) The BADLS was specifically designed for use with dementia patients living in the community and participating in clinical trials. The BADLS is sensitive to change, correlates well with economic outcomes and, despite being a carer rated instrument, appears to have good test-retest reliability, on a stringent measure, and, additionally, the levels of disability between which the scale aims to discriminate were also carer generated, giving some perspective on the value of change. The BADLS will be used to assess activities of daily living during the trial. Scores range from 0 (unimpaired) to 60 (impaired). Secondary Outcome Measures Client Service Receipt Inventory (CSRI) The CSRI describes service use, informal care and other aspects of accommodation and care pertinent to the costing of interventions and their implications. Parallel sections of the CSRI will be used for those in: residential care, at home with co-resident carer, and at home without co-resident carer. EuroQol EQ-5D (EQ-5D) The EQ-5D instrument is a generic, utility-based health related quality of life (HRQoL) measure. It can be simply administered to patients or carers in the form of a self-completed questionnaire and has been used in patients with neurological disorders. In this trial, the carer will be completing the questionnaire. There are two core components to the instrument: a description of the respondent's own health using a health state classification system with five dimensions, and a rating on a visual analogue thermometer scale. DEMQOL-Proxy DEMQOL-Proxy is a 31 item, disease specific instrument for evaluating HRQoL in dementia, which shows comparable psychometric properties to the best available instruments and has been validated in a UK population, and given doubts about how well the EQ-5D performs with people with dementia this usefully complements it. Neuropsychiatric Inventory (NPI) The NPI assesses twelve domains of possible behavioural disturbance in dementia -using a screening strategy to save time. The NPI will be used in DOMINO-AD to measure the caregiver's assessment of nature, frequency and severity of Behavioural Psychological Symptoms of Dementia (BPSD). NPI scores range from 0 (no disturbance) to 144 (maximum disturbance). General Health Questionnaire 12 (GHQ-12) The GHQ-12 is a well validated, widely used, self-rated instrument for detecting psychological morbidity and psychiatric disorder. In DOMINO-AD GHQ-12 will be used to measure levels of psychological distress in the carers of study patients. Scores range from 0 (not distressed) to 12 (distressed) using the GHQ scoring method, with a cut off of 2/3 utilised to discriminate cases from non-cases. Institutionalization This will be assessed via a simple question as to where the patient is living. Sample Size Calculation The trial aims to recruit 800 patients over a period of 2 years. Even allowing for a 25% loss to follow-up, this sample size will provide over 90% power to detect small (0.25 standard deviations) overall treatment effects on the primary outcome measures with 90% power at p < 0.01. It will also be sufficient to detect small differences (0.25 standard deviations) in the primary outcome measures at any one assessment point with 80% power at p < 0.05, and small to moderate differences (0.3 standard deviations) with 90% power at p < 0.05. These differences are equivalent to 1-2 point improvements in the SMMSE and BADLS, which are considered the minimal clinically relevant differences. The sample size calculations assume a correlation between serial measurements of the SMMSE/ BADLS of around 0.6 (multiplying factor of 0.34 based on 1 baseline and 4 post-randomization measurements, as described by Machin et al ) anticipating an analysis of covariance with repeated measures. Analyses The primary analyses of the effect of donepezil and memantine on BADLS and SMMSE will be analyzed using multilevel modelling repeated measures (MMRM) regression methods, adjusted for baseline scores. This approach gives greater power to detect differences than simple ttests on differences and will allow investigations to be made to determine how long any benefits of treatment persist, and whether the mode of action is one of symptomatic relief or disease modification. By including all time points, MMRM analyses minimise the effect of any missing data. Nevertheless, vigorous efforts will be made to minimise missing data, and sensitivity analyses will be undertaken to explore potential bias from missing data. The secondary outcome of time to institutionalization will be analyzed using standard stratified log-rank and Mantel-Haenszel tests, as used by the Early Breast Cancer Trialists' Collaborative Group study, and results presented as odds ratio plots. Any exploratory analyses here will use multivariate logistic and proportional hazards regression. Excessive subgroup analyses can give rise to misleading results and therefore all subgroup investigations will be interpreted cautiously. Health Economic Evaluation Service utilisation patterns, carer inputs and all associated costs will be calculated for each patient, based on data collected using a modified version of the CSRI, completed by a family carer or professional carer. Unit costs to reflect long-run marginal opportunity costs will be attached using national figures where available. Each cost-effectiveness analysis will be conducted from the perspective of (a) the NHS and social services, and (b) society. The BADLS, DEMQOL and a utility measure generated from the EQ-5D will be used in turn in a series of cost-effectiveness analyses, the last of these to generate Quality Adjusted Life Year (QALY) measures (with societal weights). The associations between EQ-5D, DEMQOL, SMMSE and BADLS scores, and changes therein, will also be examined, given uncertainty about the validity of EQ-5D measures as QALY generators within this population. Cost-effective-ness acceptability curves will be plotted using bootstrap analyses to locate the findings of the economic evaluation in their wider decision-making context. Sensitivity analyses will also examine the consequences of key assumptions in the cost-effectiveness analysis. In addition, a mathematical model, developed from the AD2000 database and using BADLS and NPI data, will be used to estimate risks of institutionalization in treatment groups over four years. Ethical considerations The protocol has been approved by the An Independent Data Monitoring Committee (IDMC) will monitor the progress of the trial including: recruitment, protocol adherence, serious adverse events and side effects of treatment as well as the difference between the trial treatments on the primary outcome measures. The IDMC will produce a report to the Trial Steering Committee (TSC) after every meeting and can recommend premature closure of the trial following clear evidence of benefit or harm in accordance with the IDMC charter. The main ethical issue here is that the severity of cognitive impairment may significantly interfere with the individual patient's ability to give fully informed consent. With patients entering this study having SMMSE scores of between five and thirteen, the majority with this degree of dementia will lack the necessary mental capacity to give fully informed consent. However, the aims of the study mean it is vital that a representative patient group is randomized including those that lack capacity. The DOMINO-AD study involves minimal risk to the patient and offers the potential of significant clinical benefit, so it is ethically permissible to randomize patients whose capacity is impaired. Approaches to and legislation relating to obtaining patients' agreement to participate in the study in this situation were described earlier. Discussion There is considerable debate about the clinical and cost effectiveness of anti-dementia drugs. DOMINO-AD seeks to provide clear evidence on the best treatment strategies at a particularly important clinical transition point from moderate to severe AD. The design of the study, with multiple centres, a double-blind placebo controlled design, and central randomization, maximises recruitment opportunities and minimises the risk of selection or allocation bias. The results of existing trials have been influential in determining policy on prescribing for AD, but evidence for those people deteriorating to this transition point is lacking. This condition is certain to become more prevalent in ageing populations and therefore the decision on continuing treatment in people showing deterioration will be faced far more frequently in the future. The results of this study will make a substantial contribution to clinical decision-making in a situation currently characterised by uncertainty. Competing interests Many of the investigators have received support from pharmaceutical companies, for example, to attend conferences, for giving lectures, for the provision of consultancy, for the conduct of research, or for the development of research infrastructure. Many investigators had no competing interests and the list of investigators with competing interests, and the details which they declared, is as follows:-Professor Clive Ballard, King's College: Received honoraria from Novartis, Pfizer, Shire, Lundbeck, Myriad, Janssen-Cilag, Astra Zeneca and Servier pharmaceutical companies and research grants from Novartis, Lundbeck, Astra-Zeneca and Janssen-Cilag pharmaceuticals; Professor Sube Banerjee, London, Maudsley: Development of the DEMQOL system; Received speaker and consultancy fees from all companies involved in making anti-dementia medication and has had an educational grant from Pfizer; Has worked for Department of Health; Dr Peter Bentham, Birmingham: Is a paid consultant to Tau Therapeutics Pte Ltd; Professor Alistair Burns, Manchester: Received research funding and honoraria and expenses for consultancy work from companies involved in the manufacturing and marketing of drugs for dementia -Eisai, Pfizer, Shire, Baxter, Janssen-Cilag. Does occasional lectures for companies hosting meetings on behalf of these industries -Pharam-Ed and Phase-V; Gets paid expenses from the Alzheimer's Society in the UK and Alzheimer's Australia (for lectures in 2008); Received an honorarium from John Wiley for role as editor of the International Journal of Geriatric Psychiatry and receive honoraria from a number of publishers for books written and edited; Assoc. Professor Rob Jones, Nottingham: Has received educational travel and expenses support for a conference attendance from Pfizer and on another occasion from Boots; Has spoken at and/or organised educational events which have received educational funding support from companies with products in the field of Alzheimer's disease and related conditions; Professor Roy Jones, Bath: His research institute has received grant support, consulting fees and honoraria from companies with products in the field of Alzheimer's disease and related conditions; The Research Institute for Care of the Elderly has recently completed the development of a new research building which has been funded as a result of donations to a major capital appeal. A number of pharmaceutical companies including Lundbeck, Merz, Eisai and Pfizer working in the field of Alzheimer's disease and dementia have contributed to this appeal; Professor James Lindsay, Leicester: Has received speaker and consultancy fees from companies involved in the manufacturing and marketing of drugs for dementia -Eisai, Pfizer, Shire, Janssen-Cilag; Professor John O'Brien, Newcastle: Received honoraria from Pfizer, Shire, Lundbeck, Janssen-Cilag and GE Healthcare, and provided consultancy to GE Healthcare, Servier and Bayer. Dr Peter Passmore, Belfast: Has been a member of speaker bureaus in international meetings and conferences for Eisai, Janssen-
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<filename>Testing/Code/Numerics/NeuralNetworks/itkNeuralNetworksHeaderTest.cxx
/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: itkNeuralNetworksHeaderTest.cxx
Language: C++
Date: $Date$
Version: $Revision$
Copyright (c) Insight Software Consortium. All rights reserved.
See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
#if defined(_MSC_VER)
#pragma warning ( disable : 4786 )
#endif
#include <iostream>
// This file has been generated by BuildHeaderTest.tcl
// Test to include each header file for Insight
#include "itkBackPropagationLayer.h"
#include "itkBackPropagationLayer.txx"
#include "itkBatchSupervisedTrainingFunction.h"
#include "itkBatchSupervisedTrainingFunction.txx"
#include "itkCompletelyConnectedWeightSet.h"
#include "itkCompletelyConnectedWeightSet.txx"
#include "itkErrorBackPropagationLearningFunctionBase.h"
#include "itkErrorBackPropagationLearningFunctionBase.txx"
#include "itkErrorBackPropagationLearningWithMomentum.h"
#include "itkErrorBackPropagationLearningWithMomentum.txx"
#include "itkErrorFunctionBase.h"
#include "itkGaussianRadialBasisFunction.h"
#include "itkGaussianRadialBasisFunction.txx"
#include "itkGaussianTransferFunction.h"
#include "itkHardLimitTransferFunction.h"
#include "itkHardLimitTransferFunction.txx"
#include "itkIdentityTransferFunction.h"
#include "itkIdentityTransferFunction.txx"
#include "itkInputFunctionBase.h"
#include "itkLayerBase.h"
#include "itkLayerBase.txx"
#include "itkLogSigmoidTransferFunction.h"
#include "itkLogSigmoidTransferFunction.txx"
#include "itkMeanSquaredErrorFunction.h"
#include "itkMeanSquaredErrorFunction.txx"
#include "itkMultilayerNeuralNetworkBase.h"
#include "itkMultilayerNeuralNetworkBase.txx"
#include "itkMultiquadricRadialBasisFunction.h"
#include "itkMultiquadricRadialBasisFunction.txx"
#include "itkNeuralNetworkObject.h"
#include "itkNeuralNetworkObject.txx"
#include "itkOneHiddenLayerBackPropagationNeuralNetwork.h"
#include "itkOneHiddenLayerBackPropagationNeuralNetwork.txx"
//#include "itkPerceptron.h"
//#include "itkPerceptron.txx"
//#include "itkPerceptronLayer.h"
//#include "itkPerceptronLayer.txx"
//#include "itkPerceptronLearningFunction.h"
//#include "itkPerceptronLearningFunction.txx"
#include "itkProductInputFunction.h"
#include "itkProductInputFunction.txx"
#include "itkQuickPropLearningRule.h"
#include "itkQuickPropLearningRule.txx"
#include "itkRadialBasisFunctionBase.h"
#include "itkRBFBackPropagationLearningFunction.h"
#include "itkRBFBackPropagationLearningFunction.txx"
#include "itkRBFLayer.h"
#include "itkRBFLayer.txx"
#include "itkRBFNetwork.h"
#include "itkRBFNetwork.txx"
#include "itkSigmoidTransferFunction.h"
#include "itkSigmoidTransferFunction.txx"
#include "itkSignedHardLimitTransferFunction.h"
#include "itkSignedHardLimitTransferFunction.txx"
#include "itkSquaredDifferenceErrorFunction.h"
#include "itkSquaredDifferenceErrorFunction.txx"
#include "itkSumInputFunction.h"
#include "itkSumInputFunction.txx"
#include "itkSymmetricSigmoidTransferFunction.h"
#include "itkSymmetricSigmoidTransferFunction.txx"
#include "itkTanHTransferFunction.h"
#include "itkTanHTransferFunction.txx"
#include "itkTanSigmoidTransferFunction.h"
#include "itkTanSigmoidTransferFunction.txx"
#include "itkTrainingFunctionBase.h"
#include "itkTrainingFunctionBase.txx"
#include "itkTransferFunctionBase.h"
#include "itkTwoHiddenLayerBackPropagationNeuralNetwork.h"
#include "itkTwoHiddenLayerBackPropagationNeuralNetwork.txx"
#include "itkWeightSetBase.h"
#include "itkWeightSetBase.txx"
int main ( int , char ** )
{
return 0;
}
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This note is for all Tamils in Sri Lanka and in the Tamil Diasporas throughout the World (it does not apply to the 60 million Tamils in Tamil Nadu, South Indian).
This article is a suggestion to all Tamils to take stock of the Tamil situation as it stands today. To examine all factors from a fresh perspective that is not tainted by LTTE propaganda. If you consider deeply, perhaps you might decide that the time has come for a change. Remember that everything the LTTE has said before, and is still saying is disinformation and either complete falsehoods or based on deliberate provocation designed to incite the security forces to react so that their actions can be photographed and video’d to be used in their propaganda.
The government or the security forces did not cause or start the troubles that prevail in Sri Lanka today. These troubles arose because a group of Tamils, for whatever reason, decided to take up arms against the sovereign State of Sri Lanka. The government is merely defending the sovereignty of the country as any government will do, must do, to protect its citizens. The actions of the security forces are the means by which the country is defending itself. Neither the government (past or present) nor the security forces did anything before or during this conflict to provoke or cause reason for this or any other group to seek to divide the nation.
First of all, it must be understood that the call for a Tamil Homeland in Sri Lanka will never come about, not only because the Sinhalese people, however meek and mild they are, will never accept it (and any government that tries to force such a thing will be chased out of Parliament), but because it is morally wrong, there is no reason for it and it is totally unjustified. It is morally wrong because Sri Lanka is equally the land of the Sinhalese, Tamils, Muslims, and Burghers who now constitute its population, and each must be free to go anywhere in the land as they please. Today, most citizens of Sri Lanka are not free to visit the areas given to the LTTE by a weak and naïve Prime Minister. It is morally wrong because no one race or creed or religion should enjoy special privileges, and should not have the need for special privileges. Everyone should be treated alike by the State. If not, redress should be through legislation or the courts, not by taking up arms.
There is no justification for such a ‘Homeland’ because there never was. The call for a federal state was merely a token political idea that was used at election time by Tamil politicians. There is no justification because there was never any discrimination by any government, past or present, and there was never any harassment by the security forces before 1978. In 1975, as we all know, the Tamil politicians decided to force the issue of a federal state by taking up arms. A federal state that was only in their imagination, and not at all a popular demand of the Tamil population. They made a fatal mistake by hiring the criminal elements in Jaffna to carry out their intentions and were the first to suffer the consequences when the criminals turned upon their erstwhile masters and murdered almost all of them. Whatever the background, there is no justification because all the demands of the Tamils (except that for a federal state, which was an impossibility) were acceded to by one government or the other.
Try to identify even one act by the security forces Before 1978, that could be called harassment, and any act ever, even after 1978, that could be called genocide. You will find that there isn’t even a single incident. Note that the photographs in the propaganda packs the LTTE sells are the result of ethnic cleansing of Sinhalese villages by the LTTE, which they are portraying as Tamils butchered by the Sinhalese. Don’t take my word for it, just find out the name of the village through official records.
Today, as a result of this grave mistake on the part of the Tamil politicians, there is an uneducated and uncivilised criminal controlling the destinies of almost all the Tamils in the diaspora. Those Tamils living under the protection of the Sri Lanka government are perhaps the only Tamils who are safe from this madman (and also those in South India, who have the protection of the Indian government). How does the LTTE control? They control through intimidation. Is that the way a lawful organisation should behave? You all know what happens if a Tamil family does not make the monthly contribution.
You have heard about the kidnap and ransom cases, the credit card scams, the protection rackets and the ships that have been hijacked by the LTTE. You have heard about gun running, perhaps excusable through tenuous logic when you consider they are fighting the Sri Lankan security forces, but what about the drug running and human smuggling? Do you know of any legitimate government doing these things? Do you condone such acts as being necessary to raise funds to fight for a Tamil Homeland? Do you know that the LTTE is one of the richest organisations in the World, through the money that you, the Tamil diasporas contribute? The collection is estimated to be between $10 million to $100 million per month.
I need hardly ask whether you condone the forced conscription of children as young as 10 years old. Of course you don’t, not even to fight for a homeland, because that is adult stuff. The children must be left to enjoy their childhood, not be brainwashed and worse by persons who know nothing else but killing. They have warped the minds and turned the heads of a generation of Tamil youth, and I do not mean only those who have been conscripted. Included are all the Tamil children since 1983, particularly in the diasporas, who have been forced to listen to the false propaganda spouted by the LTTE. Those adults who lived among their Sinhalese, Muslim and Burgher friends know how false this propaganda is, but cannot openly say it, not even to another Tamil because the LTTE has turned their own children against their parents to inform on anyone ‘betraying’ the LTTE. Children who are gullible enough and impressionable enough to believe everything forced on them by LTTE propaganda.
Today, the word ‘Tamil” is being increasingly associated with terrorism, kidnapping and financial scams, the same way that ‘Muslim’ is being identified with terrorism because of Al Quaida. It won’t be long before ‘Tamil’ will be treated with contempt and loathing, not so much in Sri Lanka as in the rest of the World.
The Homeland concept and Eelam being false and impossible, the Tamil community should start disassociating itself from the concept as soon as possible. For a start, the remaining Tamil political parties in Sri Lanka should change their names, removing “Eelam” and “Liberation”. There is nothing to liberate from, just ask the one million Tamils who continue to live happily among their Sinhalse neighbours in the government-controlled areas. Or do the Tamil people want to be ‘liberated’ to live under Prabhakaran? From what I hear, there are more Tamils and Tamil businesses in Colombo and suburbs than Sinhalese and Sinhalese businesses. Does that sound as if these Tamils are being discriminated against?
So please take a fresh look, without the blinkers that LTTE propaganda has placed over your eyes, search for the truth and decide for yourself whether you should blindly follow the propaganda of an acknowledged criminal and terrorist organisation, or do the right thing by condemning the LTTE and the call for a Tamil Homeland. The Tamils started this absurd nonsense; they should at least attempt to stop it. There are a few Tamils with character in certain diaspora who are attempting to do so, Give them your support.
Australia – 21 July 2007.
|
For anyone trying to lose weight, one of the first indulgences to get cut from their diet is alcohol. After all, alcohol packs 7 kcal/g a good-size glass of wine contains more than 150 calories an extravagance that could stymie efforts to slim down.
But maybe it's time to put that wineglass back on the table. New findings from researchers at Brigham and Women's Hospital in Boston reveal that women who drink moderately are less likely to gain weight over time than those who don't. Before you start stocking your kitchen with vodka instead of vegetables, however, experts caution that the relationship between alcohol and weight may not be that simple.
Led by Dr. Lu Wang, preventive-health experts at Brigham conducted the first long-term study of women's drinking habits and weight gain. The study involved 19,220 women over the age of 38 who were of normal weight. Researchers asked the women about their alcohol consumption over the past year and recorded how much of four different types of alcoholic beverages they consumed beer, red wine, white wine and liquor. The researchers measured the average ethanol content of each beverage and then calculated each volunteer's average alcohol intake; they also weighed each woman five times over the course of the follow-up period.
After 13 years, women consuming the highest amount of alcohol per day (more than two drinks daily) were 30% less likely to be overweight and nearly 70% less likely to be obese than nondrinkers, the team found. "We certainly don't want to encourage nondrinkers to adopt alcohol as a method for weight control, but we were surprised by the strength of the association," says Dr. JoAnn Manson, chief of preventive medicine at Brigham and Women's and a co-author of the study, published in the Archives of Internal Medicine.
The scientists controlled for a suite of obvious factors that could have separately contributed to the women's weight, such as age, smoking, physical activity and other lifestyle and behavioral habits. But even after accounting for these potential confounders, the link remained between higher alcohol consumption and a lower risk of being overweight or obese.
The association led the team to consider several possible explanations. First, it could be that women who drink more simply substitute alcohol for other sources of calories in essence adopting a form of the liquid diet. Indeed, when the researchers analyzed the data, it appeared that the women who drank the most got fewer of their total calories from nonalcoholic sources than other women, but also consumed the most calories overall. Women having one to two drinks daily, for example, consumed 1,738 kcal/day, compared to the 1,670 kcal/day of teetotalers, but they took in 177 fewer kcal/day from nonalcoholic sources. Whether or not this substitution is a conscious decision on the women's part still isn't clear and the study wasn't designed to find out.
Second, there is evidence that alcohol may cause physiologic changes to appetite and metabolism that may drive women to lose weight as they drink more. Women may metabolize alcohol differently from men, using a more inefficient, high-energy process that causes them to burn more of the calories from alcohol than men, which in turn leads to a net loss in caloric intake. But more research is needed to determine exactly how women process alcohol and the different ways in which the liquid calories are absorbed by the body. "It's very likely there is a combination of physiologic, metabolic and some behavioral changes," says Manson regarding the association between drinking and weight.
It's worth noting that while replacing some foods with alcohol may seem like an enticing weight-loss loophole, it isn't necessarily good for health. "Displacing 200 calories or so from food with alcohol probably has a detrimental effect on diet quality and on overall health," notes Dr. David Katz, director and co-founder of the Yale University Prevention Research Center. "If you look meticulously at nutrient intake, there might be important deficiencies there."
That underscores the complex effect that alcohol has on the body, especially in women: excess alcohol can lead to a greater risk of developing breast cancer, while moderate consumption of a glass of wine a day may help reduce heart disease risk. So whatever potential gains a nightly beer or glass of red may have on slimming down love handles, the benefits must be balanced against the other potential gains and risks of alcohol consumption.
Putting these results in the context of previous work showing the heart benefits of moderate drinking, Katz prefers to look at it this way: "This study suggests that you can probably make room for moderate alcohol consumption and not have it result in weight gain. But we certainly don't want to suggest to people to go out and drink more alcohol as a weight-control strategy."
|
<reponame>clazaro/Kratos
// | / |
// ' / __| _` | __| _ \ __|
// . \ | ( | | ( |\__ `
// _|\_\_| \__,_|\__|\___/ ____/
// Multi-Physics
//
// License: BSD License
// Kratos default license: kratos/license.txt
//
// Main authors: <NAME> (https://github.com/philbucher)
// <NAME>
//
// System includes
// External includes
// Project includes
#include "testing/testing.h"
#include "geometries/pyramid_3d_13.h"
#include "tests/cpp_tests/geometries/test_geometry.h"
namespace Kratos {
namespace Testing {
typedef GeometryType::Pointer BaseGeometryPtrType;
typedef Pyramid3D13<NodeType> Pyramid3D13GeometryType;
/** Generates a sample Pyramid3D13.
* Generates a trirectangular pyramid on the origin with positive volume and side 1.
* @return Pointer to a Pyramid3D13
*/
BaseGeometryPtrType GenerateRegularPyramid3D13() {
return BaseGeometryPtrType(new Pyramid3D13GeometryType(
GeneratePoint<NodeType>(-1.0, 1.0, 0.0),
GeneratePoint<NodeType>(-1.0, -1.0, 0.0),
GeneratePoint<NodeType>( 1.0, -1.0, 0.0),
GeneratePoint<NodeType>( 1.0, 1.0, 0.0),
GeneratePoint<NodeType>( 0.0, 0.0, 1.5),
GeneratePoint<NodeType>(-1.0, 0.0, 0.0),
GeneratePoint<NodeType>( 0.0, -1.0, 0.0),
GeneratePoint<NodeType>( 1.0, 0.0, 0.0),
GeneratePoint<NodeType>( 0.0, 1.0, 0.0),
GeneratePoint<NodeType>(-0.5, 0.5, 0.75),
GeneratePoint<NodeType>(-0.5, -0.5, 0.75),
GeneratePoint<NodeType>( 0.5, -0.5, 0.75),
GeneratePoint<NodeType>( 0.5, 0.5, 0.75)
));
}
KRATOS_TEST_CASE_IN_SUITE(Pyramid3D13EdgesNumber, KratosCoreGeometriesFastSuite)
{
auto geomRegular = GenerateRegularPyramid3D13();
KRATOS_CHECK_EQUAL(geomRegular->EdgesNumber(), 8);
}
KRATOS_TEST_CASE_IN_SUITE(Pyramid3D13FacesNumber, KratosCoreGeometriesFastSuite)
{
auto geomRegular = GenerateRegularPyramid3D13();
KRATOS_CHECK_EQUAL(geomRegular->FacesNumber(), 5);
}
KRATOS_TEST_CASE_IN_SUITE(Pyramid3D13Volume, KratosCoreGeometriesFastSuite)
{
//KRATOS_SKIP_TEST << "NOT IMPLEMENTED!";
auto geomRegular = GenerateRegularPyramid3D13();
KRATOS_CHECK_NEAR(geomRegular->Volume(), 2.0, TOLERANCE);
}
KRATOS_TEST_CASE_IN_SUITE(Pyramid3D13Center, KratosCoreGeometriesFastSuite)
{
auto geomRegular = GenerateRegularPyramid3D13();
array_1d<double, 3> center{0, 0, 4.5/13};
KRATOS_CHECK_VECTOR_NEAR(geomRegular->Center(), center, TOLERANCE);
}
/** Checks the inside test for a given point respect to the pyramid
* Checks the inside test for a given point respect to the pyramid
* It performs 4 tests:
* A Point inside the pyramid: Expected result TRUE
* A Point outside the pyramid: Expected result FALSE
* A Point over a vertex of the pyramid: Expected result TRUE
* A Point over an edge of the pyramid: Expected result TRUE
*/
KRATOS_TEST_CASE_IN_SUITE(Pyramid3D13IsInside, KratosCoreGeometriesFastSuite)
{
auto geom = GenerateRegularPyramid3D13();
Point PointInside(0.0, 0.0, 0.3);
Point PointOutside(0.0, 0.0, 1.6);
Point PointOutside2(0.1, 0.1, 1.4);
Point PointInVertex(-1.0, 1.0, 0.0);
Point PointInEdge(-1.0, 0.0, 0.0);
Point LocalCoords;
KRATOS_CHECK(geom->IsInside(PointInside, LocalCoords, EPSILON));
KRATOS_CHECK_IS_FALSE(geom->IsInside(PointOutside, LocalCoords, EPSILON));
KRATOS_CHECK_IS_FALSE(geom->IsInside(PointOutside2, LocalCoords, EPSILON));
KRATOS_CHECK(geom->IsInside(PointInVertex, LocalCoords, EPSILON));
KRATOS_CHECK(geom->IsInside(PointInEdge, LocalCoords, EPSILON));
}
/** Checks the point local coordinates for a given point respect to the
* pyramid. The centre of the pyramid is selected due to its known
* solution.
*/
KRATOS_TEST_CASE_IN_SUITE(Pyramid3D13PointLocalCoordinates, KratosCoreGeometriesFastSuite)
{
auto geom = GenerateRegularPyramid3D13();
// Compute the global coordinates of the centre
auto points = geom->Points();
auto centre = geom->Center();
// Compute the centre local coordinates
array_1d<double, 3> centre_local_coords;
geom->PointLocalCoordinates(centre_local_coords, centre);
KRATOS_CHECK_NEAR(centre_local_coords(0), 0.0, TOLERANCE);
KRATOS_CHECK_NEAR(centre_local_coords(1), 0.0, TOLERANCE);
KRATOS_CHECK_NEAR(centre_local_coords(2), -0.538461538, TOLERANCE);
}
KRATOS_TEST_CASE_IN_SUITE(Pyramid3D13ShapeFunctionsValues, KratosCoreGeometriesFastSuite)
{
auto geom = GenerateRegularPyramid3D13();
array_1d<double, 3> coord(3);
coord[0] = 1.0 / 2.0;
coord[1] = 1.0 / 4.0;
coord[2] = 1.0 / 16.0;
KRATOS_CHECK_NEAR(geom->ShapeFunctionValue(0, coord), -0.146942, TOLERANCE);
KRATOS_CHECK_NEAR(geom->ShapeFunctionValue(1, coord), -0.203934, TOLERANCE);
KRATOS_CHECK_NEAR(geom->ShapeFunctionValue(2, coord), -0.230026, TOLERANCE);
KRATOS_CHECK_NEAR(geom->ShapeFunctionValue(3, coord), -0.169373, TOLERANCE);
KRATOS_CHECK_NEAR(geom->ShapeFunctionValue(4, coord), 0.0332031, TOLERANCE);
KRATOS_CHECK_NEAR(geom->ShapeFunctionValue(5, coord), 0.149345, TOLERANCE);
KRATOS_CHECK_NEAR(geom->ShapeFunctionValue(6, coord), 0.242043, TOLERANCE);
KRATOS_CHECK_NEAR(geom->ShapeFunctionValue(7, coord), 0.190544, TOLERANCE);
KRATOS_CHECK_NEAR(geom->ShapeFunctionValue(8, coord), 0.139046, TOLERANCE);
KRATOS_CHECK_NEAR(geom->ShapeFunctionValue(9, coord), 0.0933838, TOLERANCE);
KRATOS_CHECK_NEAR(geom->ShapeFunctionValue(10, coord), 0.280151, TOLERANCE);
KRATOS_CHECK_NEAR(geom->ShapeFunctionValue(11, coord), 0.466919, TOLERANCE);
KRATOS_CHECK_NEAR(geom->ShapeFunctionValue(12, coord), 0.15564, TOLERANCE);
}
KRATOS_TEST_CASE_IN_SUITE(Pyramid3D13ShapeFunctionsLocalGradients, KratosCoreGeometriesFastSuite)
{
auto geom = GenerateRegularPyramid3D13();
Matrix gradient;
// Compute the global coordinates of the centre
auto points = geom->Points();
Point centre = geom->Center();
// Compute the centre local coordinates
array_1d<double, 3> centre_local_coords;
geom->PointLocalCoordinates(centre_local_coords, centre);
gradient = geom->ShapeFunctionsLocalGradients(gradient, centre_local_coords);
KRATOS_CHECK_NEAR(gradient(0,0), +0.0443787, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(0,1), +0.0443787, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(0,2), -0.00961538, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(1,0), -0.0443787, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(1,1), +0.0443787, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(1,2), -0.00961538, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(2,0), -0.0443787, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(2,1), -0.0443787, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(2,2), -0.00961538, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(3,0), +0.0443787, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(3,1), -0.0443787, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(3,2), -0.00961538, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(4,0), 0.0, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(4,1), 0.0, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(4,2), -0.0384615, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(5,0), 0.0, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(5,1), -0.295858, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(5,2), -0.25, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(6,0), 0.295858, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(6,1), 0.0, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(6,2), -0.25, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(7,0), 0.0, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(7,1), +0.295858, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(7,2), -0.25, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(8,0), -0.295858, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(8,1), 0.0, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(8,2), -0.25, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(9,0), -0.177515, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(9,1), -0.177515, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(9,2), +0.269231, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(10,0), +0.177515, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(10,1), -0.177515, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(10,2), +0.269231, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(11,0), +0.177515, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(11,1), +0.177515, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(11,2), +0.269231, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(12,0), -0.177515, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(12,1), +0.177515, TOLERANCE);
KRATOS_CHECK_NEAR(gradient(12,2), +0.269231, TOLERANCE);
}
/** Tests the area using 'GI_GAUSS_1' integration method.
* Tests the area using 'GI_GAUSS_1' integration method.
*/
KRATOS_TEST_CASE_IN_SUITE(Pyramid3D13GaussPoint1, KratosCoreGeometriesFastSuite)
{
auto geom = GenerateRegularPyramid3D13();
const double expected_vol = 2.0;
KRATOS_CHECK_NEAR(CalculateAreaByIntegration(*geom, GeometryData::IntegrationMethod::GI_GAUSS_1), expected_vol, TOLERANCE);
VerifyStrainExactness(*geom, GeometryData::IntegrationMethod::GI_GAUSS_1);
}
/** Tests the area using 'GI_GAUSS_2' integration method.
* Tests the area using 'GI_GAUSS_2' integration method.
*/
KRATOS_TEST_CASE_IN_SUITE(Pyramid3D13GaussPoint2, KratosCoreGeometriesFastSuite)
{
auto geom = GenerateRegularPyramid3D13();
const double expected_vol = 2.0;
KRATOS_CHECK_NEAR(CalculateAreaByIntegration(*geom, GeometryData::IntegrationMethod::GI_GAUSS_2), expected_vol, TOLERANCE);
VerifyStrainExactness(*geom, GeometryData::IntegrationMethod::GI_GAUSS_2);
}
/** Tests the area using 'GI_GAUSS_3' integration method.
* Tests the area using 'GI_GAUSS_3' integration method.
*/
KRATOS_TEST_CASE_IN_SUITE(Pyramid3D13GaussPoint3, KratosCoreGeometriesFastSuite)
{
auto geom = GenerateRegularPyramid3D13();
const double expected_vol = 2.0;
KRATOS_CHECK_NEAR(CalculateAreaByIntegration(*geom, GeometryData::IntegrationMethod::GI_GAUSS_3), expected_vol, TOLERANCE);
VerifyStrainExactness(*geom, GeometryData::IntegrationMethod::GI_GAUSS_3);
}
/** Tests the area using 'GI_GAUSS_4' integration method.
* Tests the area using 'GI_GAUSS_4' integration method.
*/
KRATOS_TEST_CASE_IN_SUITE(Pyramid3D13GaussPoint4, KratosCoreGeometriesFastSuite)
{
auto geom = GenerateRegularPyramid3D13();
const double expected_vol = 2.0;
KRATOS_CHECK_NEAR(CalculateAreaByIntegration(*geom, GeometryData::IntegrationMethod::GI_GAUSS_4), expected_vol, TOLERANCE);
VerifyStrainExactness(*geom, GeometryData::IntegrationMethod::GI_GAUSS_4);
}
/** Tests the area using 'GI_GAUSS_5' integration method.
* Tests the area using 'GI_GAUSS_5' integration method.
*/
KRATOS_TEST_CASE_IN_SUITE(Pyramid3D13GaussPoint5, KratosCoreGeometriesFastSuite)
{
auto geom = GenerateRegularPyramid3D13();
const double expected_vol = 2.0;
KRATOS_CHECK_NEAR(CalculateAreaByIntegration(*geom, GeometryData::IntegrationMethod::GI_GAUSS_5), expected_vol, TOLERANCE);
VerifyStrainExactness(*geom, GeometryData::IntegrationMethod::GI_GAUSS_5);
}
} // namespace Testing
} // namespace Kratos.
|
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Earlier this year, I wrote about how The Five-Year Engagement over-indulged its actors’ improvisational comedy, which caused scenes to run on for far too long. Director Jay Roach wisely dodges this problem by cutting his new film, The Campaign, down to a lean 85 minutes. There’s clearly plenty of more material as stars Will Ferrell and Zach Galifianakis‘ strong chemistry and comic mastery play brilliantly off each other, especially when the humor gets particularly crass. It’s a film that can deliver big laughs, and yet it features an odd concoction of broad comedy that grazes up against targeted political commentary. The brew tastes a bit off as it highlights a missed opportunity to truly take advantage of the political arena.
Cam Brady (Ferrell) is a four-term congressman from North Carolina who looks like he’ll easily cruise to an uncontested fifth term even though he practically flaunts his extramarital affair. However, there’s enough dirt to make his political backers, the Motch Brothers (Dan Aykroyd and John Lithgow), nervous enough to put their money into another challenger—the sweet but dimwitted Marty Huggins (Galifianakis). Huggins truly loves his district and desperately wants to impress his disappointed father (Brian Cox), so he decide to run for Brady’s seat. The two political rivals then proceed to tear each other apart as Marty plays puppet to an intense political operative (Dylan McDermott), and Cam retaliates by finding new levels of depravity.
Politics in America has always been a ridiculous endeavor. In 1920, rivals tried to besmirch future president Warren G. Harding by claiming he had African-American ancestry. In 2000, George W. Bush was able to win South Carolina by spreading the rumor that John McCain had a black baby out of wedlock (I suppose Karl Rove decided not to mess with the classics). Despite the ludicrous and at times repulsive nature of politics, The Campaign puts this nature on steroids. The film mandates that we suspend our disbelief to reach the understanding that in this world, no scandal can kill a campaign. It’s a necessary concession to the comedy, and it’s also a bit ahead of its time. Eventually, the electorate will have to stop being squeamish about minor indiscretions because the politicians of tomorrow are all on Facebook.
When you set aside the notion that a candidate couldn’t get away with tricks like manipulating his rival’s family, it allows the audience to just have fun with the strange and crude behavior of its lead characters. The Campaign is at its best when it gives the characters free reign to riff off each other and play a joke to perfection. It’s can get a bit awkward at times when the movie wants to find a heart when its characters can be so unrelentingly heartless. Politics is shameless, which is why it can be so funny. Marty finds his conscience in a way that’s organic to the story, but the script never quite manages to convince us of Cam’s moments of vulnerability.
It’s this kind of unbalance that throws The Campaign off its oddball certainty. There’s not a second of hesitation in how funny it is that Cam could punch a baby and still be in the race, but then the movie wants to pull back and make a sharp critique of campaign finance reform. There are no direct parodies in the film except for the Motch Brothers standing in for the Koch Brothers. For those unfamiliar with the Kochs, they’re rabid industrialists who have taken full advantage of the Citizens United ruling to fund the bejeezus out of the Republican candidates. The Motches’ plan is to turn Cam and Marty’s district into a sweatshop with Chinese laborers (“Insourcing!” one of the brothers proudly calls it), and it’s a stab at satire the film doesn’t attempt to make anywhere else.
This attempt slightly diminishes the rest of the film because we’re left to wonder what The Campaign could be with a little fine tuning. Sharp political comedies are in short supply (The Daily Show and The Colbert Report fit the bill on a nightly basis, but in terms of feature films, the last great satire was In the Loop), and Ferrell and Galifianakis are talented enough to do more than simply go broad. The Campaign shouldn’t be criticized for something it’s not trying to be, except it occasionally wants to be that thing. It’s as if Jimmy McMillan (“The Rent Is Too Damn High” guy) wanted credit for making a comment on nuclear disarmament. You’re too preposterous to be taken seriously, so your other point is diminished and ignored despite its validity.
By making a serious point in a goofy comedy, we see the easiness of The Campaign and are left to wonder why we’re watching politics as opposed to any other world where Ferrell and Galifianakis could play rivals. But it’s hard to be angry at a movie that wants to make its audience laugh and then does so exceedingly well. Ferrell and Galifianakis are singular comic talents and we’re lucky to have them sharing the screen. Candidates rarely deliver on their promises, but the pairing of these two comic talents squaring off in an absurd situation is worthy of our vote.
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// Copyright (C) 2016 The Qt Company Ltd.
// Copyright (C) 2016 Intel Corporation.
// SPDX-License-Identifier: LicenseRef-Qt-Commercial OR LGPL-3.0-only OR GPL-2.0-only OR GPL-3.0-only
//
// W A R N I N G
// -------------
//
// This file is not part of the public API. This header file may
// change from version to version without notice, or even be
// removed.
//
// We mean it.
//
//
#ifndef QDBUSPENDINGCALL_P_H
#define QDBUSPENDINGCALL_P_H
#include <QtDBus/private/qtdbusglobal_p.h>
#include <qlist.h>
#include <qmutex.h>
#include <qpointer.h>
#include <qshareddata.h>
#include <qwaitcondition.h>
#include "qdbusmessage.h"
#include "qdbus_symbols_p.h"
#ifndef QT_NO_DBUS
QT_BEGIN_NAMESPACE
class QDBusPendingCall;
class QDBusPendingCallWatcher;
class QDBusPendingCallWatcherHelper;
class QDBusConnectionPrivate;
class QDBusPendingCallPrivate: public QSharedData
{
public:
// {
// set only during construction:
const QDBusMessage sentMessage;
QDBusConnectionPrivate * const connection;
// for the callback mechanism (see setReplyCallback and QDBusConnectionPrivate::sendWithReplyAsync)
QPointer<QObject> receiver;
QList<QMetaType> metaTypes;
int methodIdx;
// }
mutable QMutex mutex;
QWaitCondition waitForFinishedCondition;
// {
// protected by the mutex above:
QDBusPendingCallWatcherHelper *watcherHelper;
QDBusMessage replyMessage;
DBusPendingCall *pending;
QString expectedReplySignature;
// }
QDBusPendingCallPrivate(const QDBusMessage &sent, QDBusConnectionPrivate *connection)
: sentMessage(sent), connection(connection), watcherHelper(nullptr), pending(nullptr)
{ }
~QDBusPendingCallPrivate();
bool setReplyCallback(QObject *target, const char *member);
void waitForFinished();
void setMetaTypes(int count, const QMetaType *types);
void checkReceivedSignature();
static QDBusPendingCall fromMessage(const QDBusMessage &msg);
};
class QDBusPendingCallWatcherHelper: public QObject
{
Q_OBJECT
public:
void add(QDBusPendingCallWatcher *watcher);
void emitSignals(const QDBusMessage &replyMessage, const QDBusMessage &sentMessage)
{
if (replyMessage.type() == QDBusMessage::ReplyMessage)
emit reply(replyMessage);
else
emit error(QDBusError(replyMessage), sentMessage);
emit finished();
}
Q_SIGNALS:
void finished();
void reply(const QDBusMessage &msg);
void error(const QDBusError &error, const QDBusMessage &msg);
};
QT_END_NAMESPACE
#endif // QT_NO_DBUS
#endif
|
#include "treeview.h"
#include "statview.h"
#include "include.h"
#include "common.h"
#include "resource.h"
#include "trade.h"
#pragma warning(disable: 4706)
using namespace std;
LPARAM TreeView_GetItemParam(HWND hwnd, HTREEITEM htItem) {
TVITEMW tvi = {0};
tvi.hItem = htItem;
tvi.mask = TVIF_PARAM;
TreeView_GetItem(hwnd, &tvi);
return tvi.lParam;
}
string TreeView_GetItemText(HWND hwnd, HTREEITEM htItem) {
static const size_t len = 240;
WCHAR buffer[len + 1];
TVITEMW tvi = {0};
tvi.hItem = htItem;
tvi.mask = TVIF_TEXT;
tvi.cchTextMax = len;
tvi.pszText = &buffer[0];
TreeView_GetItem(hwnd, &tvi);
return wastr_to_str(tvi.pszText, len);
}
string TreeView_GetParentText(HWND hwnd, HTREEITEM htItem, int depth/* = 1*/) {
for (int i = 1; i < depth; i++)
htItem = TreeView_GetParent(hwnd, htItem);
return TreeView_GetItemText(hwnd, htItem);
}
HTREEITEM TreeView_FindSibling(HWND hwnd, HTREEITEM start, string match) {
HTREEITEM current = start;
do {
if (TreeView_GetItemText(hwnd, current) == match)
return current;
} while ((current = TreeView_GetNextSibling(hwnd, current)) != NULL);
return NULL;
}
HTREEITEM TreeView_FindSibling(HWND hwnd, HTREEITEM start, LPARAM ptr) {
HTREEITEM current = start;
do {
if (TreeView_GetItemParam(hwnd, current) == ptr)
return current;
} while ((current = TreeView_GetNextSibling(hwnd, current)) != NULL);
return NULL;
}
HTREEITEM TreeView_FindSiblingItemCode(HWND hwnd, HTREEITEM start, string code) {
HTREEITEM current = start;
do {
ItemCode* item = (ItemCode*)TreeView_GetItemParam(hwnd, current);
if (item && item->code == code)
return current;
} while ((current = TreeView_GetNextSibling(hwnd, current)) != NULL);
return NULL;
}
bool TreeView_IsSelectedD2Item() {
return TreeView_GetChild(GetDlgItem(g_TAB.getTab(0).wnd, IDC_TREE1), TreeView_GetSelection(GetDlgItem(g_TAB.getTab(0).wnd, IDC_TREE1))) == NULL;
}
bool TreeView_IsSelectedD2Store() {
HWND tree = GetDlgItem(g_TAB.getTab(0).wnd, IDC_TREE1);
HTREEITEM cur = TreeView_GetChild(tree, TreeView_GetSelection(tree));
if (!cur) return false;
return TreeView_GetChild(tree, cur) == NULL;
}
bool TreeView_IsSelectedD2Char() {
HWND tree = GetDlgItem(g_TAB.getTab(0).wnd, IDC_TREE1);
HTREEITEM cur = TreeView_GetChild(tree, TreeView_GetSelection(tree));
if (!cur) return false;
cur = TreeView_GetChild(tree, TreeView_GetSelection(tree));
if (!cur) return false;
return TreeView_GetChild(tree, cur) == NULL;
}
HTREEITEM TreeView_GetHTVofD2item(const ItemData* item) {
vector<string> traverse = {item->realm, item->account, item->character, item->store};
HWND htv = GetDlgItem(g_TAB.getTab(0).wnd, IDC_TREE1);
HTREEITEM current = TreeView_GetRoot(htv);
for (UINT i = 0; i < traverse.size(); i++) {
current = TreeView_GetChild(htv, current);
current = TreeView_FindSibling(htv, current, traverse[i]);
}
current = TreeView_GetChild(htv, current);
return TreeView_FindSibling(htv, current, (LPARAM)*&item);
}
int TreeView_GetChildCount(HWND tree, HTREEITEM hItem) {
int count = 0;
while (hItem = TreeView_GetChild(tree, hItem))//generates compiler warning C4706
count++;
return count;
}
void TreeView_SetCheckStateForAllParents(HWND tree, HTREEITEM child) {
while (child != NULL) {
int check = 0;
int total = 1;
HTREEITEM current = TreeView_GetChild(tree, TreeView_GetParent(tree, child));
check = TreeView_GetCheckState(tree, current) ? check + 1 : check;
while (current != NULL) {
current = TreeView_GetNextSibling(tree, current);
check = TreeView_GetCheckState(tree, current) ? check + 1 : check;
total++;
}
TreeView_SetCheckState(tree, TreeView_GetParent(tree, child), total == check);
child = TreeView_GetParent(tree, child);
}
}
HTREEITEM TreeView_SetCheckStateForAllChildren(HWND tree, HTREEITEM hItem, BOOL checkstate) {
HTREEITEM current = hItem;
HTREEITEM sibling = NULL;
while (current != NULL && sibling == NULL) {
TreeView_SetCheckState(tree, current, checkstate);
sibling = TreeView_SetCheckStateForAllChildren(tree, TreeView_GetChild(tree, current), checkstate);
current = TreeView_GetNextSibling(tree, current);
}
return sibling;
}
HTREEITEM getfiltercharacters(HWND tree, HTREEITEM hItem, map<string, vector<string>> *list) {
HTREEITEM current = hItem;
HTREEITEM child = NULL;
while (current != NULL && child == NULL) {
child = getfiltercharacters(tree, TreeView_GetChild(tree, current), list);
if ((TreeView_GetChild(tree, current) == NULL) && (TreeView_GetCheckState(tree, current) == 1)) {
string character = TreeView_GetItemText(tree, current);
string realm = TreeView_GetItemText(tree, TreeView_GetParent(tree, TreeView_GetParent(tree, current)));
list->operator[](realm).push_back(character);
}
current = TreeView_GetNextSibling(tree, current);
}
return child;
}
HTREEITEM getfilteritems(HWND tree, HTREEITEM hItem, vector<string>* list) {
HTREEITEM current = hItem;
HTREEITEM sibling = NULL;
while (current != NULL && sibling == NULL) {
sibling = getfilteritems(tree, TreeView_GetChild(tree, current), list);
if ((TreeView_GetChild(tree, current) == NULL) && (TreeView_GetCheckState(tree, current) == 1)) {
ItemCode* ic = (ItemCode*)TreeView_GetItemParam(tree, current);
list->push_back(ic->code);
}
current = TreeView_GetNextSibling(tree, current);
}
return sibling;
}
LRESULT FinderTreeCustomDraw(HWND tree, LPNMTVCUSTOMDRAW pNMTVCD) {
if (pNMTVCD == NULL) return -1;
switch (pNMTVCD->nmcd.dwDrawStage) {
case CDDS_PREPAINT:{
/*HBRUSH background = CreateSolidBrush(RGB(30, 30, 30));
HBRUSH border = CreateSolidBrush(RGB(0, 0, 0));
RECT rc = getclientrect(tree);
FillRect(pNMTVCD->nmcd.hdc, &rc, background);
FrameRect(pNMTVCD->nmcd.hdc, &rc, border);
DeleteObject(background);
DeleteObject(border);*/
return (CDRF_NOTIFYPOSTPAINT | CDRF_NOTIFYITEMDRAW);
}
case CDDS_ITEMPREPAINT: {
TV_ITEM tvi = {0};
tvi.mask = TVIF_TEXT | TVIF_PARAM;
tvi.hItem = (HTREEITEM)pNMTVCD->nmcd.dwItemSpec;
TreeView_GetItem(tree, &tvi);
if (tvi.lParam > 0 && TreeView_GetChild(tree, tvi.hItem) == NULL) {
ItemData* pItemData = (ItemData*)tvi.lParam;
if (pItemData) {
SetTextColor(pNMTVCD->nmcd.hdc, getitemqualitycolor(pItemData->quality));
if (pNMTVCD->nmcd.uItemState & CDIS_SELECTED)
SetBkColor(pNMTVCD->nmcd.hdc, RGB(128, 0, 0));
else {
if (TreeView_GetSelection(tree) == tvi.hItem)
SetBkColor(pNMTVCD->nmcd.hdc, RGB(80, 80, 200));//selection no focus
else
SetBkColor(pNMTVCD->nmcd.hdc, g_cust_color);
}
}
}
/*else
SetBkColor(pNMTVCD->nmcd.hdc, RGB(100, 0, 0));*/
return (CDRF_NOTIFYPOSTPAINT | CDRF_NEWFONT);
}
case CDDS_ITEMPOSTPAINT: {
if (trade::is_mytradefile()) {
TV_ITEM tvi = {0};
tvi.mask = TVIF_TEXT | TVIF_PARAM;
tvi.hItem = (HTREEITEM)pNMTVCD->nmcd.dwItemSpec;
TreeView_GetItem(tree, &tvi);
ItemData* pItemData = (ItemData*)tvi.lParam;
if (pItemData && TreeView_GetChild(tree, tvi.hItem) == NULL && pItemData->is_trade) {
RECT rc;
TreeView_GetItemRect(tree, tvi.hItem, &rc, TRUE);
HBRUSH highlight = CreateSolidBrush(RGB(255, 0, 255));
FrameRect(pNMTVCD->nmcd.hdc, &rc, highlight);
DeleteObject(highlight);
}
}
return CDRF_DODEFAULT;
}
}
return CDRF_DODEFAULT;
}
|
def parse_exported(moma_path):
if not os.path.exists(moma_path):
print('no such file')
return None
file = open(moma_path, 'r')
tline = file.readline()
while not re.search('trackRegionInterval',tline):
tline = file.readline()
pixlim = re.findall('(\d+)',tline)
tracklim = int(pixlim[0])
time_mat={}
while tline:
if re.search('id=',tline):
index = int(re.search('(\d+)',tline).group(0))
addNameToDictionary(time_mat, index,{})
addNameToDictionary(time_mat[index], 'tracklim',[])
addNameToDictionary(time_mat[index], 'born',[])
addNameToDictionary(time_mat[index], 'genealogy',[])
addNameToDictionary(time_mat[index], 'pixlim',[])
addNameToDictionary(time_mat[index], 'pos_GL',[])
addNameToDictionary(time_mat[index], 'exit_type',[])
time_mat[index]['tracklim'] = tracklim
time_mat[index]['born'] = int(re.search('(?<=birth_frame=)-*(\d+)',tline).group(0))
tline = file.readline()
while re.search('(frame=)|(output=)',tline):
if re.search('frame=',tline):
frame = int(re.search('(?<=frame=)(\d+)',tline).group(0))+1
time_mat[index]['genealogy'] =re.search('(?<=genealogy=)([0-9TB]*)',tline).group(0)
pix_low = int(re.findall('pixel_limits=\[(\d*),',tline)[0])
pix_high = int(re.findall('pixel_limits=\[\d*,(\d*)\]',tline)[0])
time_mat[index]['pixlim'].append([pix_low,pix_high])
pos_GL = int(re.findall('pos_in_GL=\[(\d*),',tline)[0]);
num_GL = int(re.findall('pos_in_GL=\[\d*,(\d*)\]',tline)[0]);
time_mat[index]['pos_GL'].append([pos_GL,num_GL])
tline = file.readline()
if re.search('DIVISION',tline):
time_mat[index]['exit_type'] = 'DIVISION'
elif re.search('EXIT',tline):
time_mat[index]['exit_type'] = 'EXIT'
elif re.search('USER_PRUNING',tline):
time_mat[index]['exit_type'] = 'USER_PRUNING'
elif re.search('ENDOFDATA',tline):
time_mat[index]['exit_type'] = 'ENDOFDATA'
time_mat[index]['pixlim'] = np.array(time_mat[index]['pixlim'])
time_mat[index]['pos_GL'] = np.array(time_mat[index]['pos_GL'])
else:
tline = file.readline()
time_mat = pd.DataFrame(time_mat).T
return time_mat
|
Resonant Frequencies of Irregularly Shaped Microstrip Antennas Using Method of Moments This paper describes an application of the method of moments to determine resonant frequencies of irregularly shaped microstrip patches embedded in a grounded dielectric slab. For analysis, the microstrip patch is assumed to be excited by a linearly polarized plane wave that is normal to the patch. The surface-current density that is induced on the patch because of the incident field is expressed in terms of subdomain functions by dividing the patch into identical rectangular subdomains. The amplitudes of the subdomain functions, as a function of frequency, are determined using the electric-field integral equation (EFIE) approach in conjunction with the method of moments. The resonant frequencies of the patch are then obtained by selecting the frequency at which the amplitude of the surface-current density is real. The resonant frequencies of the equilateral triangular and other nonrectangular patches are computed using the present technique, and these frequencies are compared with measurements and other independent calculations.
|
Bird Flu Flies Again, Prompting UN Advisory : Shots - Health News The United Nations' Food and Agriculture Organization warned of a "possible major resurgence" of H5N1 influenza, including a mutant virus that appears to be unfazed by available vaccines.
Workers at the Jurong Bird Park in Singapore catch flamingos last year as part of a drive to vaccinate them against avian flu.
Out of the public eye, the bird flu has been making a comeback.
The United Nations' Food and Agriculture Organization warned today about a "possible major resurgence" of H5N1 influenza, including a mutant virus that appears to be unfazed by available vaccines.
The latest fatality from the infection occurred in Cambodia earlier this month. A 6-year-old girl became the eighth person to die from avian flu there this year, the World Health Organization said.
In 2003, the H5N1 virus that usually affected only birds was passed from birds to humans. Spread of the virus from human to human remains rare, but a mutation that would make the lethal virus easily transmissible from one person to another would raise an even bigger hazard to public health.
The culling of hundreds of millions of domestic poultry helped eliminate the avian flu virus in most countries where it had been found. But the virus has remained entrenched in a half-dozen countries, including Vietnam and Indonesia, the UN's Food and Agriculture Organization says.
Now migrating birds appear to be spreading the H5N1 virus to countries where it hasn't been seen in years. "Wild birds may introduce the virus, but peoples' actions in poultry production and marketing spread it," FAO Chief Veterinary Officer Juan Lubroth said in a statement.
So far this year, the WHO says, there have been 49 human cases of avian flu reported, resulting in 25 deaths. In all of 2010, there were 48 cases and 24 deaths.
The FAO called on countries to step up their surveillance and preparations against the avian flu.
Lubroth has sounded the alarm about H5N1 before. "As long as it is present in even one country, there is still a public health risk to be taken seriously," he said in April 2010.
|
Inman, the leader in independent real estate and technology news and events, is thrilled to announce Joyce Rey as a speaker for the highly anticipated Luxury Connect in Beverly Hills, California, Oct. 21-22.
In 1978 when Joyce Rey sold Owlwood (the Sonny-and-Cher mansion) for $4.2 million — more than double the amount anyone had ever paid for a home in the United States at the time — she established her reputation for negotiating historic landmark estates and luxury residential sales.
The following year, she formed the first company in the United States to specialize in estates valued at more than $1 million. Since then, Rey has spent three decades selling some of America’s most significant residences, setting record after record along the way. She is currently setting yet another record as the listing agent for the most expensive home on the market listed on the MLS in the U.S.: Palazzo di Amore, priced at $149 million.
As someone whose business is laser-focused on the luxury market, Rey is uniquely positioned to spot trends and see what’s coming down the pike, which gives her the advantage to stay ahead of the game.
She should know. Rey’s luxury real estate expertise, negotiating power, integrity and passion for personal service are nothing short of inspiring, and the proof is in the pudding. One of the most respected names in luxury real estate, and arguably the foremost leader for Beverly Hills to Malibu, Rey has amassed nearly $3 billion in career sales.
Today, she remains as dedicated to her celebrated clients’ best interests as her own — which encompass film, art, music, world travel, yoga and philanthropy.
We’re honored to welcome Joyce Rey to Luxury Connect on Oct. 22, where she’ll be sharing her knowledge and expertise on the topic of luxury real estate.
|
Instruction on health care malpractice issues in entry-level physical therapy curricula. In the face of an ongoing health care malpractice crisis, instruction on malpractice issues in entry-level health care professional education programs is vital for the legal well-being of prospective clinicians. A 1978 survey of US medical schools revealed that less than 40% required instruction in medical law. By 1989, 76% of US medical schools required medicolegal instruction. This article summarizes the results of a survey of entry-level physical therapy educational programs to determine whether a majority currently offer required malpractice instruction. The study found that the majority do offer such instruction. Half of the physical therapy programs with instruction in malpractice employ attorney instructors, essential for improving relations between health care and legal professionals. Graduate entry-level programs offer 11 or more hours of medicolegal instruction with greater frequency than undergraduate programs. Additional surveys of other allied health disciplines are recommended to ascertain other standards for entry-level malpractice-related instruction.
|
def check_existing_passwords(number):
return User.password_exist(number)
|
/**
*
* Generated by JaxbToStjsAssimilater.
* Assimilation Date: Thu Sep 12 10:06:01 CDT 2019
*
**/
package s3000l;
public enum LanguageCodeValues {
CS,
DA,
DE,
EL,
EN,
ES,
FI,
FR,
HI,
IS,
IT,
JA,
KO,
NL,
NO,
PL,
PT,
RU,
SV,
ZH;
}
|
<reponame>Shashi-rk/azure-sdk-for-java<filename>sdk/labservices/azure-resourcemanager-labservices/src/main/java/com/azure/resourcemanager/labservices/models/OperationStatusResponse.java
// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
// Code generated by Microsoft (R) AutoRest Code Generator.
package com.azure.resourcemanager.labservices.models;
import com.azure.resourcemanager.labservices.fluent.models.OperationStatusResponseInner;
/** An immutable client-side representation of OperationStatusResponse. */
public interface OperationStatusResponse {
/**
* Gets the status property: status of the long running operation for an environment.
*
* @return the status value.
*/
String status();
/**
* Gets the inner com.azure.resourcemanager.labservices.fluent.models.OperationStatusResponseInner object.
*
* @return the inner object.
*/
OperationStatusResponseInner innerModel();
}
|
def save_document_content(self, name: str, content: str):
spath = os.path.join(self.opath, name)
with open(spath, "w", encoding="utf-8", newline="\n") as f:
f.write(content)
|
// Copyright (C) 2010,2011,2012 GlavSoft LLC.
// All rights reserved.
//
//-------------------------------------------------------------------------
// This file is part of the TightVNC software. Please visit our Web site:
//
// http://www.tightvnc.com/
//
// This program is free software; you can redistribute it and/or modify
// it under the terms of the GNU General Public License as published by
// the Free Software Foundation; either version 2 of the License, or
// (at your option) any later version.
//
// This program is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License along
// with this program; if not, write to the Free Software Foundation, Inc.,
// 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
//-------------------------------------------------------------------------
//
#ifndef __ANSISTRINGSTORAGE_H__
#define __ANSISTRINGSTORAGE_H__
#include "StringStorage.h"
class AnsiStringStorage
{
public:
AnsiStringStorage();
AnsiStringStorage(const char *string);
AnsiStringStorage(const StringStorage *string);
AnsiStringStorage(const AnsiStringStorage &stringBuffer);
~AnsiStringStorage();
// Fills internal buffer by the string.
virtual void setString(const char *string);
// Returns pointer to the first symbol.
const char *getString() const;
// Returns length of string (in characters, not including terminating null character).
size_t getLength() const;
// Returns size of string in bytes, including terminating null character.
size_t getSize() const;
// Checks if string is empty.
// @return true if string is empty.
bool isEmpty() const;
void fromStringStorage(const StringStorage *src);
void toStringStorage(StringStorage *dst) const;
void format(const char *format, ...);
void appendString(const char *string);
private:
typedef std::vector<char> BufferType;
BufferType m_buffer;
};
#endif // __ANSISTRINGSTORAGE_H__
|
//
// ConnectAPIInternalDelegate.h
// RDP SDK
//
// Created by <NAME> on 5/3/17.
// Copyright © 2017 d'Amigos. All rights reserved.
//
#ifndef ConnectAPIInternalDelegate_h
#define ConnectAPIInternalDelegate_h
@protocol ConnectAPIInternalDelegate <NSObject>
- (void) onWebViewContentLoaded:(NSString*) content;
- (void) onRequestFailed;
@end
#endif /* ConnectAPIInternalDelegate_h */
|
Characterization of the Poly--1,6-N-Acetylglucosamine Polysaccharide Component of Burkholderia Biofilms ABSTRACT We demonstrated the production of poly--1,6-N-acetylglucosamine (PNAG) polysaccharide in the biofilms of Burkholderia multivorans, Burkholderia vietnamiensis, Burkholderia ambifaria, Burkholderia cepacia, and Burkholderia cenocepacia using an immunoblot assay for PNAG. These results were confirmed by further studies, which showed that the PNAG hydrolase, dispersin B, eliminated immunoreactivity of extracts from the species that were tested (B. cenocepacia and B. multivorans). Dispersin B also inhibited biofilm formation and dispersed preformed biofilms of Burkholderia species. These results imply a role for PNAG in the maintenance of Burkholderia biofilm integrity. While PNAG was present in biofilms of all of the wild-type test organisms, a pgaBC mutant of B. multivorans (Mu5) produced no detectable PNAG, indicating that these genes are needed for Burkholderia PNAG formation. Furthermore, restoration of PNAG production in PNAG negative E. coli TRXWMGC (pgaC) by complementation with B. multivorans pgaBCD confirmed the involvement of these genes in Burkholderia PNAG production. While the confocal scanning laser microscopy of untreated wild-type B. multivorans showed thick, multilayered biofilm, Mu5 and dispersin B-treated wild-type biofilms were thin, poorly developed, and disrupted, confirming the involvement of PNAG in B. multivorans biofilm formation. Thus, PNAG appears to be an important component of Burkholderia biofilms, potentially contributing to its resistance to multiple antibiotics and persistence during chronic infections, including cystic fibrosis-associated infection.
|
<filename>app/src/main/java/com/softekapp/whatstool/recycler/VideoViewHolder.java
package com.softekapp.whatstool.recycler;
import android.view.View;
import android.widget.ImageView;
import androidx.recyclerview.widget.RecyclerView;
import com.softekapp.whatstool.R;
/**
* Created by umer on 01-May-18.
*/
public class VideoViewHolder extends RecyclerView.ViewHolder {
public ImageView imageView,imageViewCheck,imageViewPlay;
public VideoViewHolder(View view) {
super(view);
this.imageView = (ImageView) view.findViewById(R.id.imageView_wa_image);
this.imageViewCheck = (ImageView) view.findViewById(R.id.imageView_wa_checked);
this.imageViewPlay = (ImageView) view.findViewById(R.id.imageView_wa_play);
}
}
|
<reponame>taikai/taikai-design-system
export type ButtonVariant = 'solid' | 'outline' | 'text';
export type ButtonColor =
| 'primary'
| 'danger'
| 'info'
| 'purple'
| 'white'
| 'dark'
| 'magic'
| 'pulse';
|
An analysis of the role of HnRNP C dysregulation in cancers Heterogeneous nuclear ribonucleoproteins C (HnRNP C) is part of the hnRNP family of RNA-binding proteins. The relationship between hnRNP C and cancers has been extensively studied, and dysregulation of hnRNP C has been found in many cancers. According to existing public data, hnRNP C could promote the maturation of new heterogeneous nuclear RNAs (hnRNA s, also referred to as pre-mRNAs) into mRNAs and could stabilize mRNAs, controlling their translation. This paper reviews the regulation and dysregulation of hnRNP C in cancers. It interacts with some cancer genes and other biological molecules, such as microRNAs (miRNAs), long noncoding RNAs (lncRNAs), and double-stranded RNAs (dsRNAs). Even directly binds to them. The effects of hnRNP C on biological processes such as alternative cleavage and polyadenylation (APA) and N6-methyladenosine (m6A) modification differ among cancers. Its main function is regulating stability and level of translation of cancer genes, and the hnRNP C is regarded as a candidate biomarker and might be valuable for prognosis evaluation. Introduction Post-transcriptional processes are the main mechanisms that control gene expression in mammalian cells. RNAbinding proteins (RBPs) is a class of proteins that can mediate post-transcriptional regulation. In human cells, RBPs have their RNA-binding domains that can interact with RNAs to participate in transcriptional processes. Heterogeneous nuclear ribonucleoproteins (hnRNPs) are a large family of RBPs that are rich in the human body. Studies have revealed that hnRNPs contain more than 30 proteins. HnRNPs share some common functions because of their common structural features. These proteins contain a highly conserved RNA-binding domain at their amino terminus, and the carboxyl end has a unique functional region that could interact with each of the RBPs. They are functionally diverse and complex. They take part in the maturation of new heterogeneous nuclear RNAs (hnRNAs, also referred to as pre-mRNAs) into mRNAs and can also stabilize mRNAs, controlling their translation. The C protein tetramer, which is called hnRNP C in this article, was identified as a core component of hnRNP particles that form on all nascent transcripts. In its natural state, it is a tetramer that includes 3 C1 proteins (41 kDa) and 1 C2 protein (43 kDa) (Fig. 1A). Furthermore, it has been proven that C1 and C2 come from a single coding sequence. HnRNP C is located in the cell nucleus, and binds with pre-RNA to formulate complexes, modulating splicing efficiency. A study in 1986 showed that an hnRNP C antibody called 4F4 could inhibit the splicing of pre-mRNA and that 4F4 can be immunoprecipitated with a 60S splicing complex (spliceosome) that contains a C protein. Splicing of pre-mRNA was not inhibited by 4F4 deactivates or by the use of other hnRNP proteins antibodies. HnRNP C could stabilize mRNA and modulate the level of translation by interacting with poly-U tracts of the 3-uncoding region (UTR) or 5-UTR of mRNA. In addition, hnRNP C is an important N6-methyladenosine (m6A) reader. The binding of these components could be facilitated by the 'm6A-switch' mechanism. Studies on the function of hnRNP C indicate that it is necessary at the organism level but not at the cell level. Knocking out hnRNP C in mice arrests development at the egg cylinder stage. HnRNP C knockout in murine stem cells only resulted in a low differentiation rate, 1and yeast, another eukaryote, also lacks this gene. This means that hnRNP C1/ C2 may influence the rate and/or fidelity of one or more biological processes. Indeed, many studies illuminate hnRNP C and play key roles in many human diseases. It is not clear what kind of systemic role hnRNP C plays in humans and how it is related to human disease. Herein, we reviewed the functions of hnRNP C in cancers. Structure HnRNP C exhibits a unique supramolecular assembly; it is composed of four subunits: 3 C1 subunits and 1 C2 subunit (Fig. 1A). In 1995, images of hnRNP C from different directions were obtained using an electron microscope. Approximately 50% of the images showed that these four subunits form a plane quadrilateral. However, a triangle three-subunit structure was also shown in the images. In addition, hnRNP C has a stable structure in nature. All of these results illustrate that the macroscopic structure of hnRNP C most likely involved a tetrahedral configuration (Fig. 1A). Isoform C2 contains 306 amino acids (aa). Isoform C1 differs from C2 in that it is missing 13 aa in the 108-120 aa region ; the total amino acid sequence is shown in Fig. 1B. In the N-terminus (Fig. 1C), located in the 16-87 aa region, the RNA recognition motif (RRM) is an RNA-binding domain (RBD) that is 72 aa in length Fig. 1 Structure of hnRNP C. A Tetrahedral structure of hnRNP C in electron microscope. B The amino acid sequence of hnRNP C 1, the red part is the 13aa different from hnRNP C 2.13aa:13 animo acids. C Functional motifs of hnRNPC 1 and C 2.RRM:RNA recognition motif;bZLM:Basic leucine zipper-like motif;NLS:Nuclear localization signal;CLZ: Leucine zipper-like oligomer domain;CTD:C-terminal domain. Structurally, the RRM consists of four -sheets and two -helices (). It contains conserved RNP1 octameric and RNP2 hexameric sequences, and they are 30 amino acids apart. The variable loops connecting the -sheets contribute to the RNA-binding specificity of hnRNP C. RRM has five binding pockets, and they can recognize uridines with an unusual 5-to-3 base selectivity gradient. Moreover, five successive fragments of U residues were screened, and binding analysis showed that this sequence constituted the binding site with high affinity (Kd = 170 nM) for hnRNP C1. It has been confirmed that full-length hnRNP C tends to bind with sequences that are rich in "U". The 155-161 aa sequence is a nuclear localization signal (NLS) (Fig. 1C), which is typically a short peptide sequence responsible for the nuclear entry of nucleophilic proteins. Typically, these sequences contain 4 ~ 8 amino acids, and the classical NLS consists of one (onepart) or two (two-part) basic amino acid chains. This result is in line with the principle that this NLS sequence is Pro-Ser-Lys-Arg-Cln-Arg-Vla. Residues 140-214 constitute a particular domain called the basic leucine zipper-like motif (bZLM) (Fig. 1C). The functions of hnRNP C, such as stabilizing pre-RNA and mediating splicing, are dependent on the RRM and bZLM. Additionally, bZLM is a major determinant of hnRNP C's high-affinity interaction with RNA, oligomerization and its highly synergistic RNA binding activity. In the C-terminus of the bZLM, there is a 28-aa helical region (residues 180-207), which is called the leucine zipper-like oligomer domain (CLZ) (Fig. 1C). The CLZ domain of C2 and C2 itself are the binding partners of hnRNP C, and three C1 particles act as the receptors. It should be noted that the specificity of RNA binding is largely mediated by the 3D structure of the protein, in which structural regions around the RNA-binding domain fine-tune the interactions of RNA proteins. The CLZ is one of these structural regions. Like many charged proteins, hnRNP C1 and C2 use alpha-helices as an oligomerization mechanism and are partially stabilized by continuous helix contacts formed between amphiphilic helix hydrophobic surfaces, and for each set of seven residues along the helix as a cyclic unit, a 230-nt (nucleotide) region of RNA could have four identical contacts with the RRM. C1 and C2 monomers have only one RBD, and they have to oligomerize with each other to form specific and powerful RNA interactions. This polymerization capacity is mediated by the CLZ domain. This synergistic interaction between these four particles of hnRNP C is necessary to form hnRNP C tetramers to measure the length of newly formed transcripts. Moreover, mutation of the CLZ domain in hnRNP C proteins results in low-affinity binding to pre-RNA. The RNA site size of a single C protein tetramer is 230 to 240 nt, and three tetramers, hnRNP A, B and C, could constitute a unique 19S triangular complex that folds a single particle length of pre-RNA (700 nt). The formation of the hnRNP C triangular complex is a primary event for the assembly of other 40S hnRNP core particles in vitro and in vivo. At the end of the C-terminus of hnRNP C, there is a domain called the C-terminal domain (CTD, residues 208-290) (Fig. 1C). It is worth mentioning that this domain has four phosphorylation sites that could participate in phosphorylation and dephosphorylation of hnRNP C, and studies have shown that dephosphorylation of hnRNP C proteins is necessary for their binding to some pre-RNAs. These discoveries highlight that CTD phosphorylation and dephosphorylation are important for pre-spliceosome assembly. Because of these functional structures, hnRNP C is able to bind to certain biomolecules and perform its functions in biological processes. Role of hnRNP C dysregulation in cancers High expression of hnRNP C has been found in many kinds of cancers, and upregulation of hnRNP C always indicates a poorer prognosis; this has been demonstrated in cancers such as breast cancer, glioblastoma multiforme (GBM), and gastric cancer. Therefore, hnRNP C is regarded as a candidate biomarker and might be valuable for prognosis evaluation. In glioma, the upregulation of hnRNP C is related to a high degree of malignancy, and clinical research has shown that the upregulation of hnRNP C might be associated with a good prognosis for glioma patients. Another Kaplan-Meier survival analysis from non-smallcell lung cancer (NSCLC) showed that patients with the higher hnRNP C expression levels were predicted to have shorter survival times and to have a worse prognosis. It also showed no significance in some situations, such as a study for colorectal cancer. but a study for metastatic colon cancer cells showed that overexpression of hnRNP C plays a critical role in the alternative cleavage and polyadenylation (APA) profile, which has been linked to cancer progression. The overview of the global functions of hnRNP C dysregulation in cancer (Table 1) shows that hnRNP C is a negative element for cancer treatment. However, it is not clear how it exerts these effects and what role it plays. Next, we will explain how it acts on different biomolecules (Fig. 2) and what regulates its expression in cancer. HnRNP C and proteins P53 acts as a tumour suppressor in many tumour types. HnRNP C was also discovered to interact with p53 by directly binding to p53 and could make p53 unstable, prevent its activation, and downregulate its protein level. Moreover, it has been found that RNA can negatively regulate the hnRNP C-p53 interaction. has a potential as a biomaker. The expression levels of hnRNP C and KH typesplicing regulatory protein (KHSRP) in NSCLC tissues were significantly higher than those in paracancerous noncancerous tissues; KHSRP is also a pre-mRNA splicing protein. Increased expression of hnRNP C was found to be significantly associated with advanced tumour stage and metastasis The overexpression of hnRNP C significantly promoted the proliferation, migration and invasion of lung cancer cells in vitro and in vivo. Western blotting revealed that hnRNP C is a downstream protein of KHSRP and may induce the invasion and metastasis of human lung cancer cells through activation of the IFN--JAK-STAT1 signalling pathway (Fig. 2). HnRNP C and microRNAs (miRNAs) MiRNAs are a kind of noncoding RNA with a length of approximately 22 nt. They are important endogenous RNAs that can regulate gene expression and are promising candidates for biomarker development. A study of GBM showed that hnRNP C could bind directly to primary miRNA and promote miRNA expression in T98G cells. When hnRNP C is silenced, miR-21 is expressed at lower levels, and programmed cell death 4 (PDCD4), which is the target gene of miR-21, is upregulated. This effect on miR-21 may be due to the RNA splicing function of hnRNP C, which in turn inhibits the migration and invasion of T98G cells. Indeed, the upregulation of hnRNP C in highly aggressive U87MG cells also supported the potential value of hnRNP C as a prognostic and therapeutic marker for GBM. This example shows that hnRNP C can act on miRNAs and that hnRNP C and miRNAs can interact with each other. MiR-744-5p binds to hnRNP C, and hnRNP C influences the miR-21 expression level. MiR-744-5p could lead to the downregulation of Bcl2 levels, which has pro-apoptotic effects in ovarian serous cystadenocarcinoma. MDA-MB-435-LvBr2 (LvBr2) is a kind of Braintrophic metastatic cell, hnRNP C has a high expressive in it, and virtually, miR-146a absence from brain metastases. miR-146a in LvBr2 cells could interact with hnRNP C, promoting the migration and invasion of LVBR2 cells. Therefore, in human cancer cells, miR-NAs downregulate hnRNP C expression. Perhaps this is a mechanism of cancer growth and metastasis. Moreover, hnRNP C shortens UTRs in mRNA APA isoforms. This shortening may improve the translational output of key genes such as cell cycle regulators by avoiding exposure to suppressor modules such as miRNAs. Overall, hnRNP C directly binds and shortens UTRs, promoting the expression of miRNAs and thus influencing other cancer-related genes. HnRNP C and lncRNAs Long noncoding RNAs (lncRNAs) are RNAs consisting of more than 200 nt, and many lncRNAs considered highly connected with cancer. Protein-lncRNA interactions play key roles in many cellular processes, such as splicing, polyadenylation, transport, stability and translation. One study showed that m6A could change the local structure of mRNA and lncRNA, promoting hnRNP C binding. LncRNA SNHG1 is retained in the nucleus by nucleolar binding and binds to p53-competing hnRNP C, which promotes p53-dependent apoptosis by disrupting the regulation of p53 activity by hnRNP C and upregulating p53 levels. LBX2-AS1 is a lncRNA that is highly expressed in oesophageal squamous cell carcinoma (ESCC) samples. Through interacting with hnRNP C, LBX2-AS1 could enhance the stability of zinc finger E-box binding protein 1 (ZEB1) and zinc finger E-box binding protein 2 (ZEB2) mRNAs, which are the most critical epithelial-mesenchymal transition (EMT) conversion molecules, promoting ESCC cell migration, and it also confirmed knockdown of hnRNP C could suppress cell migration and reversed EMT progress. Similarly, LINC00662, which is overexpressed in oral squamous cell carcinoma (OSCC), recruited hnRNPC protein to increase AK4 expression. And AK4 has been demonstrated as a carcinogen. It reduces the radiosensitivity of OSCC cells. These lncRNAs can effectively inhibit the binding of hnRNP C, which may explain why hnRNP C is highly expressed in carcinomas. Many cancers have been demonstrated to exhibit overexpression of hnRNP C. It is possible that Possibly hnRNP C is a downstream molecule of lncRNA. HnRNP C and Alu elements In the breast cancer cell lines MCF7 and T47D, hnRNP C repression inhibited cell proliferation and tumour growth, and the supernatant from hnRNP C knockdown cells inhibited breast cancer growth. The repression of hnRNP C induced the upregulation of endogenous double-stranded RNA (dsRNA), which is nonviral and known as one of the binding ligands of retinoic acid-inducible gene I (RIG-I) (gene name DDX58). However, in other tumour cell lines (MDA-MB-231 and BT549) or non-tumour MCF10A cells, hnRNP C knockout did not induce interferon (IFN) response or dsRNA accumulation in these unresponsive cells. This suggests that there may be a complementary mechanism that helps retain control of the IFN response and dsRNA accumulation by compensating for the absence of hnRNP C in these cells. This discovery of dsRNA inhibition by hnRNP C is a novel extension of the previously characterized function of hnRNP C, which binds to pre-mRNA introns and regulates RNA splicing. Endogenous dsRNA can trigger the IFN response. This work is so interesting that some scientists published a comment. IFN injection has been a treatment since the 1970s. The activation of IFN by endogenous retroviral dsRNA was observed in hypomethylated testicular germ cell tumours, and the expression of IFN was only limited in neoplastic seminoma cells. In breast cancer, endogenous nucleic acid and hnRNP C promote IFN production in cancer cells, indicating that dsRNA could at least be regulated by the hnRNP C/dsRNA axis. Moreover, the intermediate transmitter dsRNA is truly endogenous. It is a complementary mechanism product instead of a retroviral product. That is, IFN could be influenced by external factors and intracellular nucleic acids. Perhaps this finding could explain the difference in the effectiveness of IFN therapies. These up-regulated dsRNA species are rich in the Alu sequences, which were known for harbouring hnRNP C binding sites. It could be considered that the interaction between hnRNP C and dsRNA can be seen as a part of the interaction between hnRNP C and Alu. The Alu element is a major target of the RNA editing enzyme adenosine deaminase, RNA specific (ADAR). Alu elements can act as splicing receptors, inhibit mRNA translation and cause genetic instability. HnRNP C can prevent some splicing factors (such as U2AF65) from binding to Alu elements to protect against "Alu exonization" so that Alu elements are not at risk of abnormal incorporation into mature transcripts. This phenomenon indicates that hnRNP C has a function in maintaining transcriptome stability. However, it is unclear how hnRNP C protects against "Alu exonization". Knockdown of hnRNP C results in the separation of the two arm exons of the Alu element and almost complete skipping of upstream replacement exons. Research has found that competition between hnRNP C and U2AF65 prevents the transcriptome from facilitating the exonization of Alu elements. Deletion of hnRNP C leads to the formation of previously suppressed Alu exons, which severely disrupts transcriptional function. The inhibition of hnRNP C is Alu dependent. U-bundle mutation of Alu elements mitigates hnRNP C inhibition, resulting in strong inclusion of Alu exons and skipping replacement exons. In contrast, in the hnRNP C knockdown process, the complete removal of the Alu element eliminates any regulation of the upstream optional exon. These observations are consistent with the model of dynamic processing competition between Alu exons and upstream replacement exons, and the same phenomenon occurs within the gene and downstream regions. It plays a key role in facilitating the therapeutic effects of antitumour drugs such as DNA methyltransferase inhibitors and CDK4/6 inhibitors on many kinds of cancers. HnRNP C and APA events We still do not know the mechanism by which hnRNP C promotes miRNA expression and then changes the cell phenotype. APA are general mechanisms of mammalian transcriptional diversification and have recently been associated with proliferative status and cancer. Between normal and cancer cells, the most prominent APA profile changes have been found, and cancer cells tend to express mRNA APA isoforms. APA facilitates the inclusion or exclusion of these sites (RBP sites and miRNA target sites), providing an opportunity for cells to regulate gene expression at the posttranscriptional level by affecting transcriptional stability, translation output, and subcellular localization. 3-UTR truncation of growth-promoting mRNA transcripts alleviates inhibition mediated by intrinsic miRNAs and Au-rich elements to promote facilitative translation of key genes. For example, the expression of shorter mRNA subtypes of the proto-oncogene IGF2BP1/IMP-1 resulted in more oncogenic transformations than the expression of full-length annotated mRNAs. In addition, this is related to the immune microenvironment in pancreatic adenocarcinoma. In addition, elevated levels of hnRNP C in metastatic colon cancer cells drive coding region (CR) and UTR APA of a group of genes, including methylenetetrahydrofolate dehydrogenase (NADP+-dependent) 1-like (MTHFD1L), and these changes are closely associated with cancer progression. Knockdown of hnRNP C can lengthen UTR-APA, which is shorter in colorectal carcinoma cells than in normal cells. Mihaela Zavolan's study showed that the frequency of hnRNP C binding to poly (U) bundles peaks near the poly(A) sites, and the apparent effect of hnRNP C binding sites on regulating polyadenylation decreased with increasing distance from poly(A) sites. It has been reported that mRNA APA isoforms with shorter UTRs tend to be expressed in cancer cells. This may be accomplished by controlling poly(A) site selection through hnRNP C to upregulate the production of fulllength MTHFD1L mRNA. This inference is consistent with the observation that knockdown of hnRNP C can lengthen UTR-APA in colon cancer cells. Importantly, hnRNP C could hide poly(A) sites through the tight binding of hnRNP C to the three-terminal processing sites to obscure their cleavage and polyadenylation. Both the number and length of uridine bundles contribute to the use of hnRNP C-dependent aggregation (A) sites. The downregulation of hnRNP C decreased with increasing distance between the poly(A) sites and hnRNP C binding sites. When hnRNP C was knocked down, the use of intron poly(A) sites increased, which cannot be explained by alternative splicing events. When hnRNP C was knocked out, the incidence of intron site cleavage and polyadenylation also increased. However, in the terminal exon, the U-rich poly(A) sites used during hnRNP C knockout tend to be distally located. In these transcripts, hnRNP C may play the role of "blocking" the "stronger" signal at the distal end, allowing the use of the "weaker" proximal poly(A) site. They also found that the intron aggregation (A) site is most likely to be deleted, which shortens the length of transcripts. This is how hnRNP C regulates APA events. It is worth mentioning that the 3-UTRs regulated by hnRNP C are rich in ELAV-like RBP 1 (ELAVL1) binding sites, including CD47 gene binding sites involved in the recently discovered 3-UTR-dependent protein localization mechanism (UDPL). Indeed, hnRNP C knockout promotes the expression of the long CD47 3-UTR. This confirms that 3-UTR-dependent proteins are hnRNP C response transcripts. CD47 protein is a hot tumour target. This also confirms the importance of hnRNP C in cancer research. HnRNP C and m6A HnRNP C is one of the m6A methylation RNA regulators ("readers"). HnRNP C could alter mRNA and lncRNA in an m6A-dependent manner. M6A-related bioinformatics analysis revealed that overexpression of hnRNP C facilitates the progression of OSCC via EMT. It also has multiple other functions, such as increasing differentiation in type II testicular germ cell tumours (TGCTs), inducing cell death in ovarian cancer, promoting chemotherapy resistance, indicating overall survival (OS) in gastric cancer, and promoting the progression of colorectal cancer. It also showed value in diagnosis, progression and prognosis evaluation in lung adenocarcinoma, oesophageal cancer, adrenocortical carcinoma, urothelial carcinoma of the bladder, and kidney renal papillary cell carcinoma. HnRNP C is significantly related to the OS of many kinds of cancers, such as pancreatic cancer, so it is an effective prognostic marker. LncRNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), which has an m6A site, was recently shown to induce local structural changes that increase the recognition of the U5 channel and are recognized and bound by hnRNP C. In addition, as an m6A regulator, hnRNP C gives rise to a malignant phenotype in pancreatic ductal adenocarcinoma (PDAC) cells by antagonizing TAF8L (antimetastatic isoform) and increasing TAF8S (prometastatic alternative splicing isoform). When an m6A mutation occurs in TAF8, the interaction between hnRNP C and the TAF8 transcript weakens, and TAF8S expression decreases. This indicates that the interaction between hnRNP C and m6A mediates shearing events that affect PDAC. The binding activity of hnRNP C regulated by the m6A switch affects the abundance and selective splicing of target RNAs. RBPs regulate RBM pathways through m6A-dependent RNA structural remodelling and provide a new direction for the study of epigenetics by RNA modification. Conclusions/expectations In recent years, hnRNP C has been seen as a promising biomarker in different kinds of cancers, as a prognostic marker for cancer. It influences many biological molecules to exert its effects. In most of those studies mentioned, elevated hnRNP C usually indicates a poor prognosis. However, in a few cases, hnRNP C does not show any significance. This could explain why there is no perfect biomarker to estimate the prognosis of cancer patients. It has been proven to have value in most cases, so we could say it has the potential to be used in the clinic. However, with this optimistic perspective, contradicting results should also be noted. A study on GBM found that hnRNP C was positively correlated with malignancy, but when assessing the OS time of patients with high expression and low expression, hnRNP C was negatively correlated with prognosis. In other research, high expression of hnRNP C indicated a poor prognosis. As usual, the higher the degree of malignancy was, the worse the prognosis of the patients. This does not seem to be due to sampling error. Another study on GBM found that hnRNP C promotes migration and invasion. Overall, more data on the effect of hnRNP C on OS time would help support these conclusions. Given that hnRNP C is located in the nucleus, it may not have an advantage as a clinical tumour marker. Like liver cancer biomarker AFP, a clinical biomarker should have high specificity and sensitivity and is also detectable due to its peripheral distribution. However, hnRNP C does not have these features. In the future, it is important to focus on methods for targeting hnRNP C to treat cancer. In addition to its role in cancer, hnRNP C plays an important role in other human diseases. In the nervous system, hnRNP C binds with a 29-nt sequence in the 3-UTR of amyloid precursor protein (APP) mRNA, whose cleavage product A is highly correlated with degenerative neuropathy, such as Alzheimer's disease, and regulates neuronal synapse growth. These results proved its effects in stabilizing and enhancing the translation of mRNA. In addition, hnRNP C plays an important role in spinal muscular atrophy (SMA) and viral diseases such as hepatitis B virus (HBV). In addition, hnRNP C participates in ageing and regeneration. It can interact with the human telomerase holoenzyme and is related to the ability of telomerase to access telomeres. After all, the maintenance of telomere length may indicate cancers instead of longer lifespan. In acute promyelocytic leukaemia (APL), a novel new fusion between the HNRNPC gene and the RARG gene has been found. One thing has been verified that HNRNPC-RARA is a regular genetic event instead of a random one and it is a refractory case. Indeed, hnRNP C plays an important role in many transcription-related events and maintains the stability of mRNA in normal cells. Based on the above findings, it is likely that the dysregulation of hnRNP C is a negative factor for human health. In summary, as an RBP, hnRNP C binds with miRNA, promoting its expression. HnRNP C could bind lncR-NAs, altering their effects on other molecules. HnRNP C could interact with Alu elements to prevent dsRNA from stimulating the immune response. In addition, it could interact with other genes, such as P53, to exert its effects. It also plays a splicing function, regulating APA events and m6A events, thus affecting the tumour process.
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/**
* Data container for the configuration setting domain object.
*
* @author jaeger
*/
@Embeddable
public class ConfigurationSettingData extends DomainDataContainer {
private static final long serialVersionUID = 4236417344577567732L;
@Convert( converter=CKConverter.class )
@Column(name = "INFORMATION_ID", nullable = false)
private ConfigurationKey informationId;
@Column(name = "CONTEXT_ID", nullable = false)
private String contextId;
@Column(name = "ENV_VALUE")
private String value;
public ConfigurationSettingData() {
}
public ConfigurationSettingData(ConfigurationKey informationId,
String contextId, String value) {
this.informationId = informationId;
this.contextId = contextId;
this.value = value;
}
public ConfigurationKey getInformationId() {
return informationId;
}
public void setInformationId(ConfigurationKey informationId) {
this.informationId = informationId;
}
public String getContextId() {
return contextId;
}
public void setContextId(String contextId) {
this.contextId = contextId;
}
public String getValue() {
if (value != null) {
return value.trim();
}
return value;
}
public void setValue(String value) {
this.value = value;
}
}
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Juniper's second quarter outlook looks light, but Broadcom blows away its first quarter targets. Polycom also better-than-expected.
Networking gear maker Juniper’s first quarter was better than expected, but the outlook was light. The company’s earnings---along with Polycom and Broadcom---presented a mixed technology picture.
Juniper Networks reported first quarter earnings of $91 million, or 18 cents a share, on revenue of $1.06 billion, up 3 percent from a year ago. The earnings tally includes a 5 cents a share tax benefit, 2 cents a share litigation charge and penny a share restructuring charge. Non-GAAP earnings for the first quarter were 24 cents a share.
Wall Street was looking for first quarter earnings of 21 cents a share on revenue of $1.06 billion.
As for the outlook, Juniper projected second quarter revenue between $1.07 billion and $1.1 billion with non-GAAP earnings of 22 cents a share and 26 cents a share.
Wall Street was expecting Juniper to report second quarter earnings of 27 cents a share on revenue of $1.11 billion.
Juniper noted that it continues to expect enterprise spending to be week with U.S. service providers spending well with improved demand in EMEA.
Polycom reported first quarter earnings of $3 million, or a penny a share, on revenue of $339 million, down 2 percent from a year ago. Non-GAAP earnings were 13 cents a share in the first quarter.
Wall Street was looking for first quarter earnings of 11 cents a share on revenue of $336.3 million.
The company said that its revenue growth in unified communication personal devices was 8 percent in the first quarter with 10 percent growth in services. Fifty one percent of revenue was in the Americas followed by 26 percent in EMEA and 23 percent in Asia Pacific.
Broadcom reported a strong first quarter that handily topped expectations due to mobile chip demand.
The company reported first quarter earnings of $191 million, or 33 cents a share, on revenue of $2.01 billion, up 10 percent from a year ago. Non-GAAP earnings were 65 cents a share.
Wall Street was expecting first quarter earnings of 56 cents a share on revenue of $1.91 billion.
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Dan Butler (civil servant)
Air Force Office of Special Investigations
Prior to joining the ODNI, Mr. Butler was Executive Director of the U.S. Air Force Office of Special Investigations (AFOSI), at Andrews Air Force Base, MD. In this capacity he served as adviser to the AFOSI Commander, oversaw the AFOSI Special Investigations Career Program and was responsible for executive-level policy coordination, liaison, and representation to national and international organizations.
Naval Criminal Investigative Service
Mr. Butler worked for seven years at Headquarters Naval Criminal Investigative Service in Washington, D.C., where he was Deputy Assistant Director for Government Liaison and Public Affairs, and executive assistant to the Director and Coordinator of Strategic Planning. He was appointed to the Senior Executive Service in December 2000.
US Navy
Mr. Butler served on active duty with the U.S. Navy from 1981 to 1991 as an intelligence officer. After his release from the Navy in 1991, he served as a Naval Reserve officer assigned to the Joint Military Intelligence College as an adjunct professor. He retired from the Naval Reserve in 2002.
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<reponame>henesy/plan9-1e<gh_stars>0
typedef struct Mouseinfo Mouseinfo;
typedef struct Cursorinfo Cursorinfo;
struct Mouseinfo{
/*
* First three fields are known in some l.s's
*/
int dx; /* interrupt-time delta */
int dy;
int track; /* update cursor on screen */
Mouse;
int changed; /* mouse structure changed since last read */
Rendez r;
int newbuttons; /* interrupt time access only */
int clock; /* check mouse.track on RTE */
};
struct Cursorinfo{
Cursor;
Lock;
int visible; /* on screen */
int disable; /* from being used */
Rectangle r; /* location */
};
extern Mouseinfo mouse;
extern Cursorinfo cursor;
void mouseupdate(int);
void _gbitblt(GBitmap*, Point, GBitmap*, Rectangle, Fcode);
void _gtexture(GBitmap*, Rectangle, GBitmap*, Fcode);
void _gsegment(GBitmap*, Point, Point, int, Fcode);
void _gpoint(GBitmap*, Point, int, Fcode);
void hwscreenwrite(int, int);
/* for devbit.c */
#define gbitblt _gbitblt
#define gtexture _gtexture
#define gsegment _gsegment
#define gpoint _gpoint
#define mbbpt(x)
#define mbbrect(x)
#define screenupdate()
#define mousescreenupdate()
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<gh_stars>0
import * as React from 'react'
import styled from 'styled-components'
import { Button as BasicButton, Icon, Image, Modal } from 'semantic-ui-react'
import { Button } from '../basic-components'
import Identicon from '@polkadot/react-identicon'
import 'react-tippy/dist/tippy.css'
import { Tooltip } from 'react-tippy'
import t from '../../services/i18n'
import { colorSchemes } from '../styles/themes'
import { IExtrinsic } from '@polkadot/types/types'
import { formatBalance } from '@polkadot/util'
import BN = require('bn.js')
import { chains } from '../../constants/chains'
interface IConfirmProps {
chain: string,
trigger: any,
fromAddress: string,
amount: BN,
tip: BN,
toAddress: string,
fee: BN,
extrinsic?: IExtrinsic | null,
color: string,
senderAvailable: BN,
confirm: any,
open: boolean,
handleModal: any
}
interface IConfirmState {
addressCopied: boolean,
copiedTimeout?: any
}
const formatOptions = { withSi: true }
const delay = 1500
export default class Confirm extends React.Component<IConfirmProps, IConfirmState> {
state: IConfirmState = {
addressCopied: false
}
componentWillUnmount () {
if (this.state.copiedTimeout) {
clearTimeout(this.state.copiedTimeout)
}
}
handleClose = () => this.props.handleModal(false)
handleConfirm = () => {
this.handleClose()
this.props.confirm()
}
truncate = (address: string) => {
return `${address.slice(0, 13)}...${address.slice(-3)}`
}
copyToClipboard = (val: string) => {
const el = document.createElement('textarea')
el.value = val
el.setAttribute('readonly', '')
el.style.position = 'absolute'
el.style.left = '-9999px'
document.body.appendChild(el)
el.select()
document.execCommand('copy')
document.body.removeChild(el)
this.setState({ addressCopied: true })
const timeout = setTimeout(() => {
this.setState({ addressCopied: false })
}, delay)
this.setState({ copiedTimeout: timeout })
}
copyFromAddressToClipboard = () => this.copyToClipboard(this.props.fromAddress)
copyToAddressToClipboard = () => this.copyToClipboard(this.props.toAddress)
render () {
const chain = chains[this.props.chain]
const identiconTheme = chain.identiconTheme
const totalFee = this.props.amount.add(this.props.fee).add(this.props.tip)
return (
<Modal
trigger={this.props.trigger}
style={{ 'zIndex': 3 }}
onClose={this.handleClose}
open={this.props.open}
>
<UpperSection>
<Offset>
<Status>
<Identicon value={this.props.fromAddress} size={48} theme={identiconTheme}/>
</Status>
</Offset>
<Tooltip
title={!this.state.addressCopied ? t('copyToClipboard') : t('copiedExclam')}
duration={delay}
position='bottom'
trigger='mouseenter'
arrow={true}
>
<FromAddress>
<span onClick={this.copyFromAddressToClipboard}>
{this.truncate(this.props.fromAddress)}
</span>
</FromAddress>
</Tooltip>
<Heading>Confirm Extrinsic</Heading>
<Subheading>Review your extrinsic details</Subheading>
</UpperSection>
<OverlaySection>
<Image
src='/assets/overlay.svg'
centered={true}
/>
</OverlaySection>
<Section>
<FromTo color={colorSchemes[this.props.color].backgroundColor}>
<Icon name='arrow circle right' size={'big'} style={{ 'marginLeft': '10px' }}/>
<Identicon
value={this.props.toAddress}
size={28}
style={{ 'marginRight': '5px' }}
theme={identiconTheme}
/>
<To>
<Tooltip
title={!this.state.addressCopied ? t('copyToClipboard') : t('copiedExclam')}
position='bottom'
trigger='mouseenter'
arrow={true}
>
<span onClick={this.copyToAddressToClipboard}>
{this.truncate(this.props.toAddress)}
</span>
</Tooltip>
<AvailableBalance>
<p>Available: {formatBalance(this.props.senderAvailable, formatOptions)}</p>
</AvailableBalance>
</To>
</FromTo>
</Section>
<Section style={{ 'marginTop': '8px' }}>
<Info>
<Key>Fee</Key>
<Value>{formatBalance(this.props.fee, formatOptions)}</Value>
</Info>
<div style={{ 'border': '1px solid gray' }}/>
</Section>
<Section style={{ 'marginTop': '8px' }}>
<Info>
<Key>Amount</Key>
<Value>{formatBalance(this.props.amount, formatOptions)}</Value>
</Info>
</Section>
<Section style={{ 'marginTop': '8px' }}>
<Info>
<Key>Tip</Key>
<Value>{formatBalance(this.props.tip, formatOptions)}</Value>
</Info>
</Section>
<Section style={{ 'marginTop': '8px', 'marginBottom': '16px' }}>
<Info>
<Key>Total</Key>
<Value>
{formatBalance(totalFee, formatOptions)}
</Value>
</Info>
</Section>
<Section style={{ 'marginTop': '20px' }}>
<Info>
<BasicButton style={{ width: '45%' }} onClick={this.handleClose}>Cancel</BasicButton>
<Button style={{ width: '45%' }} onClick={this.handleConfirm}>Confirm</Button>
</Info>
</Section>
</Modal>
)
}
}
const UpperSection = styled.div`
width: 100%;
margin: 8px 0 9px;
text-align: center;
`
const Offset = styled.div`
width: 100%;
margin-top:-34px;
display: flex;
justify-content: center;
margin-bottom: 8px;
`
const Heading = styled.h3`
margin-top: 8px;
margin-bottom: 4px;
font-family: Nunito;
font-size: 19px;
font-weight: bold;
font-style: normal;
font-stretch: normal;
line-height: normal;
letter-spacing: normal;
color: #30383b;
`
const Subheading = styled.p`
font-family: Nunito;
font-size: 11px;
font-weight: normal;
font-style: normal;
font-stretch: normal;
line-height: normal;
letter-spacing: normal;
color: #a0aeb4;
`
const OverlaySection = styled.div`
width: 100%;
margin-top: 7px;
text-align: center;
`
const Status = styled.div`
width: 50px;
height: 50px;
border-radius: 100%;
background-color: #fff;
display: flex;
justify-content: center;
align-items: center;
`
const Key = styled.p`
margin-top: 3px;
font-family: Nunito;
font-size: 11px;
font-weight: bold;
color: #a0aeb4;
margin-bottom: 0px;
`
const FromAddress = styled.span`
font-family: Nunito;
font-size: 8px;
font-weight: bold;
color: #a0aeb4;
`
const Value = styled.p`
font-family: Nunito;
font-size: 14px;
line-height: 1.43;
color: #30383b;
`
const Section = styled.div`
width: 100%;
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
`
const FromTo = styled.div`
width: 80%;
border-radius:20px;
height: 50px;
display: flex;
flex-direction: row;
align-items: center;
background-color: ${props => props.color};
color: #fff;
margin-top: 15px;
`
const Info = styled.div`
width: 80%;
display: flex;
flex-direction: row;
align-items: center;
justify-content: space-between;
`
const To = styled.div`
display: 'flex';
alignItems: 'center';
fontSize: '13px';
marginLeft: '-10px'
`
const AvailableBalance = styled.div`
display: 'flex';
alignItems: 'center';
fontSize: '11px';
marginLeft: '10px'
`
|
FANTASY: fully automatic network emulation architecture with cross-layer support Testing and evaluating real-world wireless and mobile systems is very difficult. The volatile nature of the wireless medium and mobility complicates their evaluation. The access to system information hindered by the operating system further increases the evaluation of a real-world system. In contrast, a simulator allows to easily set up complex wireless and mobile scenarios, log protocol variables of interest and to repeat the whole test easily if desired. Developers of real-world systems also want to perform tests with the simplicity and convenience of a simulation without loosing the ability to execute arbitrary networking software in its genuine environment (an operating system). In this paper, we present fantasy, a new network emulation architecture that allows the fully automated setup and execution of an experiment, enables the convenient access to system information and the collection of test results. With the integration of the cross-layer architecture crawler, we demonstrate that we are able to monitor parameters across protocol layers and to evaluate network emulation scenarios where cross-layer optimization is involved.
|
LOS ANGELES – The Los Angeles Kings have named Mark Hardy as an Assistant Coach, Kings President/General Manager Dean Lombardi and new Kings Head Coach Terry Murray announced Monday. In addition, the Kings have promoted Jamie Kompon to Assistant Coach and Nelson Emerson to Assistant Coach/Development Coordinator.
"I am excited to help welcome Mark back to the Kings organization," said Murray. "During his time in Chicago as an Assistant Coach, Mark did a great job working with their young players and their young defensemen in particular, and he was instrumental in helping that club make the great strides it has made the last couple of years. In addition, Mark was an important player for the Kings and I feel that his all-round experience in this game as a coach and as a player will really assist me as we work together with our players.
"I am also excited about the promotions for Jamie and Nelson. In the time I have spent with Jamie, I have come to admire his work ethic, his passion and the knowledge he possesses. At the same time, Nelson and Bill were professional players, and very good players, and their knowledge of our club will be invaluable to me and our entire staff as we move forward."
Hardy, 49, joins Kompon, Emerson and Bill Ranford (Goaltending Coach) on Murray’s coaching staff. Hardy has followed a 15-year NHL playing career – which included two stints as a Kings defenseman (1979-88 and 1992-94) – with a successful coaching career that includes serving as a Kings Assistant Coach from 1999-2006. During his first coaching tenure with the Kings, Hardy’s responsibilities focused on defensive play, penalty killing and overall play without the puck. Hardy’s penalty killing unit ranked third in the NHL for the 2001-02 season with an 86.6 percent success rate (the second best in Kings history).
Hardy the past two seasons has served as an Assistant Coach with the Chicago Blackhawks, where he successfully oversaw the development of a young blueline that played a major role in the Blackhawks team goals-against-average going from 3.40 during the 2005-06 season to 2.82 during the 2007-08 season. He also served as an Assistant Coach with the Long Beach Ice Dogs (IHL) for four seasons (1995-96 through 1998-99).
As a player, Hardy ranks 16th on the Kings all-time scoring list (and third on the Kings all-time defensemen scoring list) with 303 points in his 11 seasons with the Kings. The Semaden, Switzerland, native also played in 41 career postseason games with the Kings, and he was a member of the Kings club that advanced to the Stanley Cup Finals in 1993.
Originally selected by the Kings in the second round (30th overall) in the 1979 NHL Entry Draft, Hardy recorded 368 points (62-306=368) and 1,293 penalty minutes in 915 career regular season games with the Kings, Minnesota North Stars and New York Rangers. With the Kings, he was twice named the Kings Outstanding Defenseman (1984-85 and 1986-87). In addition, he ranks second in assists (250), third in games played (616), fourth in penalty minutes (858) and fifth in goals (53) amongst Kings defensemen all-time.
Kompon, 40, has served the past two seasons on the Kings coaching staff, most recently holding the title of Assistant Coach and Director of Player Development. Prior to joining the Kings, he spent nine seasons in the St. Louis Blues organization (1997-06) where he served as the club’s Video Coach before adding Strength and Conditioning Coach to his title during the 2002-03 season. In addition, Kompon was on the coaching staff of Team Canada at the 2006 World Championship in Latvia where he served as Video Coach.
Emerson, 40, has served the past two seasons on the Kings coaching staff, most recently holding the title of Assistant Coach. Like Hardy, Emerson played for the Kings (2000-02) during his 12-year NHL playing career. Emerson has also served as Head Coach of the Los Angeles Junior Kings Midget AAA Under-18 Team where he also held the title of Director of Hockey Operations. Emerson also worked for the Kings as a Coaches Aide (alongside Hardy) during the 2003-04 season.
|
Preparation and physical evaluation of microcapsules of hydrophilic drug-cyclodextrin complexes. An emulsion-solvent evaporation method for preparation of microcapsules containing water-soluble 2-hydroxypropyl-beta-cyclodextrin complex of a lipophilic water-insoluble drug, hydrocortisone, is described. The release of the drug from the microcapsules was determined in simulated gastric fluid. The drug release rate from the microcapsules could be controlled by addition of a plasticizer and it was sustained over extended time. Addition of solubilizing compounds to the dissolution medium did not affect the drug release rate.
|
/**
* fills the initialInstance with the properties from the Json-data intput stream
*/
public static Properties fromJson(InputStream jsonIS, DefinitionRegistryService defRegistryService) {
try {
JsonNode jsonNode = mapper.readTree(jsonIS);
return fromJsonNode(defRegistryService, jsonNode);
} catch (Exception e) {
throw TalendRuntimeException.createUnexpectedException(e);
}
}
|
1. Technical Field
This invention relates to sound generating devices and, more particularly, to a sound generating hand wear device for generating and emitting musical sounds.
2. Prior Art
The generation of sounds in conventional musical instruments, such as a piano, an electronic organ, a guitar, a flute, or the like, is controlled by operating a keyboard, plucking strings, or blowing a pipe. These conventional musical instruments, however, may restrict the location of performance and/or the posture of the performer. For example, pianos and electronic organs are too large to be carried, so it is impossible for a performer to move his body with the instrument freely during a performance. Guitars and wind instruments can be carried and so do not restrict the location of performance, but they can limit the posture of a performer because these instruments must be hand-held. Thus, unencumbered movement by a performer during performance cannot be expected using conventional instruments.
A tone generating glove that includes switches and a tone generating circuit has been proposed in the prior art. In such a glove the switches are connected to the tone generating circuit, and both the switches and the tone generating circuit are mounted in the glove. The tone generating circuit produces a tone or tones in response to the actuation of one or more of the switches. Preferably, a switch is positioned at each finger joint of the glove, and the tone generating circuit produces a different tone in response to the actuation of each different switch. In other words, each of the switches uniquely corresponds to each of the tones. Thus, the musical tones are controlled by the bending of fingers.
This device, worn on the hand, makes it possible for a performer to enjoy the generation of musical tones in response to hand movement because the musical tones are controlled merely by bending the fingers, which does not hinder other motions of the body. However, the device does have various disadvantages as shown herein below. Unfortunately, the device cannot accurately respond to the bending of fingers. This is because each switch and other components are not interconnected and are individually attached to the glove, so that the bending of one finger causes sagging on a part of the glove, which hinders the maintenance of contact of the other switches corresponding to the other digits. Furthermore, a performer cannot achieve the expressive musical performance which the performer wishes for. This is because the device can only generate musical tones in response to ON/OFF signals of the switches, and cannot control tone volumes, tone colors, sound effects, etc. in response to the signals of the switches.
Accordingly, a need remains for a sound generating hand wear device in order to overcome the above-noted shortcomings. The present invention satisfies such a need by providing sound generating hand wear that is easy to use, light weight and durable in design, is reasonably priced and has great entertainment value for persons of all ages. Such a sound generating hand wear device allows an individual to create musical and rhythmic patterns by simply tapping their fingers on any suitable surface. This not only provides a fun, inventive, and entertaining novelty item, but also helps individuals who have little or no musical training to create melodies and musical/rhythmic phrases. The sound generating hand wear is also a convenient training and practice aid, and provides for a novel way to train inexperienced and novice musicians about the importance of rhythm.
|
<reponame>dvt32/cpp-journey
/*
30.
Да се състави програма, която последователно въвежда цели числа.
Когато броят на въведените ненулеви числа стане N (N e зададено)
или
броят на въведените нулеви числа стане 2,
въвеждането се прекратява и
се извежда средно аритметичното на четните и броят на нечетните от въведените числа.
*/
#include "stdafx.h"
using namespace std;
#include <iostream>
#include <stdlib.h>
#include <conio.h>
#include <math.h>
void main(){
system("chcp 1251");
int i;
int a[1337];
int brNulevi = 0; // Брой нулеви числа
int brNenulevi = 0; // Брой ненулеви числа
int N; // Необходим брой ненулеви числа за излизане от цикъла.
int brChetni = 0; // Брой четни числа
int sumChetni = 0; // Сума на четните числа
int brNechetni = 0; // Брой нечетни числа.
cout << endl << "Enter N: "; cin >> N;
cout << endl << "ЗАБЕЛЕЖКА: 0 не се смята за число.";
cout << endl << endl;
// Въвеждане на числата
for (i = 0; i < 1337; i++)
{
cout << "Enter element number " << i << ": ";
cin >> a[i];
if (a[i] != 0)
{
brNenulevi++;
if (brNenulevi == N) {
if (a[i] % 2 == 0)
{
brChetni++;
sumChetni += a[i];
}
if (a[i] % 2 == 1)
{
brNechetni++;
}
break;}
if (a[i] % 2 == 0)
{
brChetni++;
sumChetni += a[i];
}
if (a[i] % 2 == 1)
{
brNechetni++;
}
}
if (a[i] == 0)
{
brNulevi++;
if (brNulevi == 2) { break; }
}
}
cout << endl << "Броят на нечетните числа е " << brNechetni << endl;
if (brChetni > 0)
{
cout << "Средно аритметичното на четните числа е " << (double)sumChetni / brChetni;
}
_getch();
}
|
<filename>ppapi/proxy/tracked_callback_unittest.cc
// Copyright (c) 2012 The Chromium Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.
#include <stdint.h>
#include "base/bind.h"
#include "base/location.h"
#include "base/memory/ref_counted.h"
#include "base/run_loop.h"
#include "base/single_thread_task_runner.h"
#include "base/synchronization/waitable_event.h"
#include "base/threading/simple_thread.h"
#include "ppapi/c/pp_completion_callback.h"
#include "ppapi/c/pp_errors.h"
#include "ppapi/proxy/ppapi_proxy_test.h"
#include "ppapi/proxy/ppb_message_loop_proxy.h"
#include "ppapi/shared_impl/callback_tracker.h"
#include "ppapi/shared_impl/proxy_lock.h"
#include "ppapi/shared_impl/resource.h"
#include "ppapi/shared_impl/resource_tracker.h"
#include "ppapi/shared_impl/scoped_pp_resource.h"
#include "ppapi/shared_impl/test_globals.h"
#include "ppapi/shared_impl/tracked_callback.h"
#include "testing/gtest/include/gtest/gtest.h"
// Note, this file tests TrackedCallback which lives in ppapi/shared_impl.
// Unfortunately, we need the test to live in ppapi/proxy so that it can use
// the thread support there.
namespace ppapi {
namespace proxy {
namespace {
class CallbackThread : public base::SimpleThread {
public:
explicit CallbackThread(PP_Instance instance)
: SimpleThread("CallbackThread"), instance_(instance) {}
~CallbackThread() override {}
// base::SimpleThread overrides.
void BeforeStart() override {
ProxyAutoLock acquire;
// Create the message loop here, after PpapiGlobals has been created.
message_loop_ = new MessageLoopResource(instance_);
}
void BeforeJoin() override {
ProxyAutoLock acquire;
message_loop()->PostQuit(PP_TRUE);
message_loop_ = nullptr;
}
void Run() override {
ProxyAutoLock acquire;
// Make a local copy of message_loop_ for this thread so we can interact
// with it even after the main thread releases it.
scoped_refptr<MessageLoopResource> message_loop(message_loop_);
message_loop->AttachToCurrentThread();
// Note, run releases the lock to run events.
base::RunLoop().Run();
message_loop->DetachFromThread();
}
MessageLoopResource* message_loop() { return message_loop_.get(); }
private:
PP_Instance instance_;
scoped_refptr<MessageLoopResource> message_loop_;
};
class TrackedCallbackTest : public PluginProxyTest {
public:
TrackedCallbackTest() : thread_(pp_instance()) {}
CallbackThread& thread() { return thread_; }
private:
// PluginProxyTest overrides.
void SetUp() override {
PluginProxyTest::SetUp();
thread_.Start();
}
void TearDown() override {
thread_.Join();
PluginProxyTest::TearDown();
base::RunLoop run_loop;
run_loop.RunUntilIdle();
}
CallbackThread thread_;
};
// All valid results (PP_OK, PP_ERROR_...) are nonpositive.
const int32_t kInitializedResultValue = 1;
const int32_t kOverrideResultValue = 2;
struct CallbackRunInfo {
explicit CallbackRunInfo(base::ThreadChecker* thread_checker)
: run_count_(0),
result_(kInitializedResultValue),
completion_task_run_count_(0),
completion_task_result_(kInitializedResultValue),
thread_checker_(thread_checker),
callback_did_run_event_(
base::WaitableEvent::ResetPolicy::MANUAL,
base::WaitableEvent::InitialState::NOT_SIGNALED) {}
void CallbackDidRun(int32_t result) {
CHECK(thread_checker_->CalledOnValidThread());
if (!run_count_)
result_ = result;
++run_count_;
callback_did_run_event_.Signal();
}
void CompletionTaskDidRun(int32_t result) {
CHECK(thread_checker_->CalledOnValidThread());
if (!completion_task_run_count_)
completion_task_result_ = result;
++completion_task_run_count_;
}
void WaitUntilCompleted() { callback_did_run_event_.Wait(); }
unsigned run_count() { return run_count_; }
int32_t result() { return result_; }
unsigned completion_task_run_count() { return completion_task_run_count_; }
int32_t completion_task_result() { return completion_task_result_; }
private:
unsigned run_count_;
int32_t result_;
unsigned completion_task_run_count_;
int32_t completion_task_result_;
// Weak; owned by the creator of CallbackRunInfo.
base::ThreadChecker* thread_checker_;
base::WaitableEvent callback_did_run_event_;
};
void TestCallback(void* user_data, int32_t result) {
CallbackRunInfo* info = static_cast<CallbackRunInfo*>(user_data);
info->CallbackDidRun(result);
}
// CallbackShutdownTest --------------------------------------------------------
class CallbackShutdownTest : public TrackedCallbackTest {
public:
CallbackShutdownTest() : info_did_run_(&thread_checker_),
info_did_abort_(&thread_checker_),
info_didnt_run_(&thread_checker_) {}
// Cases:
// (1) A callback which is run (so shouldn't be aborted on shutdown).
// (2) A callback which is aborted (so shouldn't be aborted on shutdown).
// (3) A callback which isn't run (so should be aborted on shutdown).
CallbackRunInfo& info_did_run() { return info_did_run_; } // (1)
CallbackRunInfo& info_did_abort() { return info_did_abort_; } // (2)
CallbackRunInfo& info_didnt_run() { return info_didnt_run_; } // (3)
private:
base::ThreadChecker thread_checker_;
CallbackRunInfo info_did_run_;
CallbackRunInfo info_did_abort_;
CallbackRunInfo info_didnt_run_;
};
} // namespace
// Tests that callbacks are properly aborted on module shutdown.
TEST_F(CallbackShutdownTest, DISABLED_AbortOnShutdown) {
ProxyAutoLock lock;
scoped_refptr<Resource> resource(
new Resource(OBJECT_IS_PROXY, pp_instance()));
// Set up case (1) (see above).
EXPECT_EQ(0U, info_did_run().run_count());
// TODO(dmichael): Test this on a background thread?
scoped_refptr<TrackedCallback> callback_did_run = new TrackedCallback(
resource.get(),
PP_MakeCompletionCallback(&TestCallback, &info_did_run()));
EXPECT_EQ(0U, info_did_run().run_count());
callback_did_run->Run(PP_OK);
EXPECT_EQ(1U, info_did_run().run_count());
EXPECT_EQ(PP_OK, info_did_run().result());
// Set up case (2).
EXPECT_EQ(0U, info_did_abort().run_count());
scoped_refptr<TrackedCallback> callback_did_abort = new TrackedCallback(
resource.get(),
PP_MakeCompletionCallback(&TestCallback, &info_did_abort()));
EXPECT_EQ(0U, info_did_abort().run_count());
callback_did_abort->Abort();
EXPECT_EQ(1U, info_did_abort().run_count());
EXPECT_EQ(PP_ERROR_ABORTED, info_did_abort().result());
// Set up case (3).
EXPECT_EQ(0U, info_didnt_run().run_count());
scoped_refptr<TrackedCallback> callback_didnt_run = new TrackedCallback(
resource.get(),
PP_MakeCompletionCallback(&TestCallback, &info_didnt_run()));
EXPECT_EQ(0U, info_didnt_run().run_count());
GetGlobals()->GetCallbackTrackerForInstance(pp_instance())->AbortAll();
// Check case (1).
EXPECT_EQ(1U, info_did_run().run_count());
// Check case (2).
EXPECT_EQ(1U, info_did_abort().run_count());
// Check case (3).
EXPECT_EQ(1U, info_didnt_run().run_count());
EXPECT_EQ(PP_ERROR_ABORTED, info_didnt_run().result());
}
// CallbackResourceTest --------------------------------------------------------
namespace {
class CallbackResourceTest : public TrackedCallbackTest {
public:
CallbackResourceTest() {}
};
class CallbackMockResource : public Resource {
public:
static scoped_refptr<CallbackMockResource> Create(PP_Instance instance) {
ProxyAutoLock acquire;
return scoped_refptr<CallbackMockResource>(
new CallbackMockResource(instance));
}
~CallbackMockResource() {}
// Take a reference to this resource, which will add it to the tracker.
void TakeRef() {
ProxyAutoLock acquire;
ScopedPPResource temp_resource(ScopedPPResource::PassRef(), GetReference());
EXPECT_NE(0, temp_resource.get());
reference_holder_ = temp_resource;
}
// Release it, removing it from the tracker.
void ReleaseRef() {
ProxyAutoLock acquire;
reference_holder_ = 0;
}
// Create the test callbacks on a background thread, so that we can verify
// they are run on the same thread where they were created.
void CreateCallbacksOnLoop(MessageLoopResource* loop_resource) {
ProxyAutoLock acquire;
// |thread_checker_| will bind to the background thread.
thread_checker_.DetachFromThread();
loop_resource->task_runner()->PostTask(
FROM_HERE, RunWhileLocked(base::Bind(
&CallbackMockResource::CreateCallbacks, this)));
}
int32_t CompletionTask(CallbackRunInfo* info, int32_t result) {
// The completion task must run on the thread where the callback was
// created, and must hold the proxy lock.
CHECK(thread_checker_.CalledOnValidThread());
ProxyLock::AssertAcquired();
// We should run before the callback.
CHECK_EQ(0U, info->run_count());
info->CompletionTaskDidRun(result);
return kOverrideResultValue;
}
void CheckInitialState() {
callbacks_created_event_.Wait();
EXPECT_EQ(0U, info_did_run_.run_count());
EXPECT_EQ(0U, info_did_run_.completion_task_run_count());
EXPECT_EQ(0U, info_did_run_with_completion_task_.run_count());
EXPECT_EQ(0U,
info_did_run_with_completion_task_.completion_task_run_count());
EXPECT_EQ(0U, info_did_abort_.run_count());
EXPECT_EQ(0U, info_did_abort_.completion_task_run_count());
EXPECT_EQ(0U, info_didnt_run_.run_count());
EXPECT_EQ(0U, info_didnt_run_.completion_task_run_count());
}
void RunCallbacks() {
callback_did_run_->Run(PP_OK);
callback_did_run_with_completion_task_->Run(PP_OK);
callback_did_abort_->Abort();
info_did_run_.WaitUntilCompleted();
info_did_run_with_completion_task_.WaitUntilCompleted();
info_did_abort_.WaitUntilCompleted();
}
void CheckIntermediateState() {
EXPECT_EQ(1U, info_did_run_.run_count());
EXPECT_EQ(PP_OK, info_did_run_.result());
EXPECT_EQ(0U, info_did_run_.completion_task_run_count());
EXPECT_EQ(1U, info_did_run_with_completion_task_.run_count());
// completion task should override the result.
EXPECT_EQ(kOverrideResultValue,
info_did_run_with_completion_task_.result());
EXPECT_EQ(1U,
info_did_run_with_completion_task_.completion_task_run_count());
EXPECT_EQ(PP_OK,
info_did_run_with_completion_task_.completion_task_result());
EXPECT_EQ(1U, info_did_abort_.run_count());
// completion task shouldn't override an abort.
EXPECT_EQ(PP_ERROR_ABORTED, info_did_abort_.result());
EXPECT_EQ(1U, info_did_abort_.completion_task_run_count());
EXPECT_EQ(PP_ERROR_ABORTED, info_did_abort_.completion_task_result());
EXPECT_EQ(0U, info_didnt_run_.completion_task_run_count());
EXPECT_EQ(0U, info_didnt_run_.run_count());
}
void CheckFinalState() {
info_didnt_run_.WaitUntilCompleted();
EXPECT_EQ(1U, info_did_run_with_completion_task_.run_count());
EXPECT_EQ(kOverrideResultValue,
info_did_run_with_completion_task_.result());
callback_did_run_with_completion_task_ = nullptr;
EXPECT_EQ(1U, info_did_run_.run_count());
EXPECT_EQ(PP_OK, info_did_run_.result());
callback_did_run_ = nullptr;
EXPECT_EQ(1U, info_did_abort_.run_count());
EXPECT_EQ(PP_ERROR_ABORTED, info_did_abort_.result());
callback_did_abort_ = nullptr;
EXPECT_EQ(1U, info_didnt_run_.run_count());
EXPECT_EQ(PP_ERROR_ABORTED, info_didnt_run_.result());
callback_didnt_run_ = nullptr;
}
private:
explicit CallbackMockResource(PP_Instance instance)
: Resource(OBJECT_IS_PROXY, instance),
info_did_run_(&thread_checker_),
info_did_run_with_completion_task_(&thread_checker_),
info_did_abort_(&thread_checker_),
info_didnt_run_(&thread_checker_),
callbacks_created_event_(
base::WaitableEvent::ResetPolicy::MANUAL,
base::WaitableEvent::InitialState::NOT_SIGNALED) {}
void CreateCallbacks() {
// Bind thread_checker_ to the thread where we create the callbacks.
// Later, when the callback runs, it will check that it was invoked on this
// same thread.
CHECK(thread_checker_.CalledOnValidThread());
callback_did_run_ = new TrackedCallback(
this, PP_MakeCompletionCallback(&TestCallback, &info_did_run_));
// In order to test that the completion task can override the callback
// result, we need to test callbacks with and without a completion task.
callback_did_run_with_completion_task_ = new TrackedCallback(
this,
PP_MakeCompletionCallback(&TestCallback,
&info_did_run_with_completion_task_));
callback_did_run_with_completion_task_->set_completion_task(
Bind(&CallbackMockResource::CompletionTask,
this,
&info_did_run_with_completion_task_));
callback_did_abort_ = new TrackedCallback(
this, PP_MakeCompletionCallback(&TestCallback, &info_did_abort_));
callback_did_abort_->set_completion_task(
Bind(&CallbackMockResource::CompletionTask, this, &info_did_abort_));
callback_didnt_run_ = new TrackedCallback(
this, PP_MakeCompletionCallback(&TestCallback, &info_didnt_run_));
callback_didnt_run_->set_completion_task(
Bind(&CallbackMockResource::CompletionTask, this, &info_didnt_run_));
callbacks_created_event_.Signal();
}
// Used to verify that the callback runs on the same thread where it is
// created.
base::ThreadChecker thread_checker_;
scoped_refptr<TrackedCallback> callback_did_run_;
CallbackRunInfo info_did_run_;
scoped_refptr<TrackedCallback> callback_did_run_with_completion_task_;
CallbackRunInfo info_did_run_with_completion_task_;
scoped_refptr<TrackedCallback> callback_did_abort_;
CallbackRunInfo info_did_abort_;
scoped_refptr<TrackedCallback> callback_didnt_run_;
CallbackRunInfo info_didnt_run_;
base::WaitableEvent callbacks_created_event_;
ScopedPPResource reference_holder_;
};
} // namespace
// Test that callbacks get aborted on the last resource unref.
TEST_F(CallbackResourceTest, DISABLED_AbortOnNoRef) {
// Test several things: Unref-ing a resource (to zero refs) with callbacks
// which (1) have been run, (2) have been aborted, (3) haven't been completed.
// Check that the uncompleted one gets aborted, and that the others don't get
// called again.
scoped_refptr<CallbackMockResource> resource_1(
CallbackMockResource::Create(pp_instance()));
resource_1->CreateCallbacksOnLoop(thread().message_loop());
resource_1->CheckInitialState();
resource_1->RunCallbacks();
resource_1->TakeRef();
resource_1->CheckIntermediateState();
// Also do the same for a second resource, and make sure that unref-ing the
// first resource doesn't much up the second resource.
scoped_refptr<CallbackMockResource> resource_2(
CallbackMockResource::Create(pp_instance()));
resource_2->CreateCallbacksOnLoop(thread().message_loop());
resource_2->CheckInitialState();
resource_2->RunCallbacks();
resource_2->TakeRef();
resource_2->CheckIntermediateState();
// Double-check that resource #1 is still okay.
resource_1->CheckIntermediateState();
// Kill resource #1, spin the message loop to run posted calls, and check that
// things are in the expected states.
resource_1->ReleaseRef();
resource_1->CheckFinalState();
resource_2->CheckIntermediateState();
// Kill resource #2.
resource_2->ReleaseRef();
resource_1->CheckFinalState();
resource_2->CheckFinalState();
{
ProxyAutoLock lock;
resource_1 = nullptr;
resource_2 = nullptr;
}
}
// Test that "resurrecting" a resource (getting a new ID for a |Resource|)
// doesn't resurrect callbacks.
TEST_F(CallbackResourceTest, DISABLED_Resurrection) {
scoped_refptr<CallbackMockResource> resource(
CallbackMockResource::Create(pp_instance()));
resource->CreateCallbacksOnLoop(thread().message_loop());
resource->CheckInitialState();
resource->RunCallbacks();
resource->TakeRef();
resource->CheckIntermediateState();
// Unref it and check that things are in the expected states.
resource->ReleaseRef();
resource->CheckFinalState();
// "Resurrect" it and check that the callbacks are still dead.
resource->TakeRef();
resource->CheckFinalState();
// Unref it again and do the same.
resource->ReleaseRef();
resource->CheckFinalState();
{
ProxyAutoLock lock;
resource = nullptr;
}
}
} // namespace proxy
} // namespace ppapi
|
<reponame>J3FALL/LASSO-Regression
//
// Created by user on 05.10.2018.
//
#include <chrono>
#include <cmath>
#include <random>
#include <iostream>
#include "DataSet.h"
DataSet::DataSet() {
std::uniform_real_distribution<double> uniform(0, 0.15);
std::default_random_engine engine;
engine.seed(std::chrono::system_clock::now().time_since_epoch().count());
//engine.seed(10);
for (int deg = 60; deg < 300; deg+=4) {
sample.push_back(polynomial(deg * M_PI / 180.0, 15));
target.push_back(sin(sample.back()[0]) + uniform(engine));
}
}
std::vector<double> DataSet::polynomial(double x, int maxPower) {
std::vector<double> result;
result.push_back(x);
for (int power = 1; power <= maxPower; power++) {
double last = result.back();
result.push_back(last * x);
}
return result;
}
|
package lafzi
import "strings"
func queryFromArabic(arabicText string) string {
query := strings.Join(strings.Fields(arabicText), "")
query = adjustEndSentence(query)
query = replaceTanween(query)
query = toPhonetic(query)
query = removeUnvoweled(query)
query = removeMadd(query)
query = strings.ReplaceAll(query, "0", "")
query = removeShadda(query)
query = adjustTajweed(query)
return query
}
func adjustEndSentence(str string) string {
strRunes := []rune(str)
if len(strRunes) <= 1 {
return str
}
lastIdx := len(strRunes) - 1
lastRune := strRunes[lastIdx]
switch lastRune {
case alef:
prev, exist := peek(strRunes, lastIdx-1)
if exist && prev == fathatan {
newRunes := append(strRunes[:lastIdx-1], fatha)
return string(newRunes)
}
case tehMarbuta:
newRunes := append(strRunes[:lastIdx], heh)
return string(newRunes)
case fatha, damma, kasra:
prev, exist := peek(strRunes, lastIdx-1)
if exist && prev != alef && prev != alefMaksura {
newRunes := append(strRunes[:lastIdx], sukun)
return string(newRunes)
}
}
return str
}
func replaceTanween(str string) string {
noonSukun := string(noon) + string(sukun)
str = strings.ReplaceAll(str, string(fathatan), string(fatha)+noonSukun)
str = strings.ReplaceAll(str, string(dammatan), string(damma)+noonSukun)
str = strings.ReplaceAll(str, string(kasratan), string(kasra)+noonSukun)
return str
}
func toPhonetic(str string) string {
runes := []rune(str)
phoneticRunes := []rune{}
for _, r := range runes {
if phonetic, exist := phonetics[r]; exist {
phoneticRunes = append(phoneticRunes, phonetic)
}
}
return string(phoneticRunes)
}
func removeMadd(str string) string {
str = strings.ReplaceAll(str, "iy0", "i")
str = strings.ReplaceAll(str, "uw0", "u")
return str
}
func removeShadda(str string) string {
runes := []rune(str)
newRunes := []rune{}
for idx, r := range runes {
nextRune, exist := peek(runes, idx+1)
if exist && r == nextRune {
continue
}
newRunes = append(newRunes, r)
}
return string(newRunes)
}
func removeUnvoweled(str string) string {
runes := []rune(str)
newRunes := []rune{}
for idx, r := range runes {
// If it's already vowel, just put it back
switch r {
case 'a', 'i', 'u', '0':
newRunes = append(newRunes, r)
continue
}
// If it's not vowel and the next is not vowel as well, skip
nextRune, exist := peek(runes, idx+1)
if exist {
switch nextRune {
case 'a', 'i', 'u', '0':
default:
continue
}
}
newRunes = append(newRunes, r)
}
return string(newRunes)
}
func adjustTajweed(str string) string {
// Replace iqlab
str = strings.ReplaceAll(str, "nb", "mb")
// Replace idgham
str = strings.ReplaceAll(str, "ny", "y")
str = strings.ReplaceAll(str, "nn", "n")
str = strings.ReplaceAll(str, "nm", "m")
str = strings.ReplaceAll(str, "nw", "w")
str = strings.ReplaceAll(str, "nl", "l")
str = strings.ReplaceAll(str, "nr", "r")
return str
}
|
We have each played this game for 10 or more years. Like a majority of the feedback appears to be for all servers, we think the game by itself doesn't need much changing to be most enjoyable. We think that balance requires constant involvement keeping things in check. The reasons that RF appeared to be a success to us fall into a few areas. PVP is the best part of this game! Sure it can be easy to point out how this PVP and targetting system is outdated and buggy but we're stuck with the engine that we have. Sometimes it's really enjoyable because of how simple and straight-forward fights can be and it comes down to char strength, utility, and teamwork. The only way to get stronger is by upgrading items, getting rare items, or reaching a higher level to use new items. Upgrading(almost of any kind) requires materials obtainable through ore. The ore is generally only attainable through chip war which continuously re-encourages pvp in the game. Utility has fallen out for some time but it was one of my favorite things. Some people really enjoy playing unique(and less popular) classes. Runes and Charms largely ruined the viability of "middle" pure classes. Aside from the pvp and gameplay itself, everything else that RF offers is due to it's community. From launch and into the future we will always have the players as our focus and have communication together. We are opening this server so that hopefully there's a decent place to play classic RF. We believe that the majority of players feel the same way about RF as our staff. Why can't there just be a server that has the plain old game without people screwing it up. People grind, farm, kill rare bosses, and upgrade weapons. The plain game was great and just needs attention to maintain balance. Below we'll address how we plan to achieve this. Everything should be default unless otherwise mentioned on the details page. Below is the concept of of what we're going for.
OUR PLAN
All upgrades in this server will be manual. Knowing this, int+4/5 weapons are what we consider to be good weapons. We know +6/7's exist on lots of servers but we all know that it's dumb to play against because the damage is really not balanced with all the mechanics of the game. +6/7 are possible in our server but since they require manual upgrading, they are obviously very difficult. As the winning race, we want you to have to decide whether you will spend the next 6 hours mining or grinding XP. We do not want the winning race to be able to deplete the ore deposits in 20 minutes and go grind more. We want the winning race to have both options available but shouldn't be able to get both every CW win.
HQ and settlement pit bosses will drop talics again like they used to back in the day. This will give a reasonable source for manual upgrades as well as superior armor for players.
Int+4 should be pretty attainable through manual upgrades but will be a little bit of a challenge, +5 probably a lot more rare without lots of failures.
We would like to point out that we have consciously made 55+ difficult to achieve. We don't want 90% of the player-base to reach max level and get bored. We remember playing chip wars on Codemasters' 55 cap where all players 40-55 were relevant. There were people in 55 gear that were powerhouses and there were lower level people with decent gear that were able to contribute. Given these points, we'd like the majority of players to hit 55 requiring some effort and activity. 56+ to be attained by the players that worked hard and grinded a lot. On this game version and level cap good superior items are viable against 60+ players. If people choose, they should be able to stay 55 and use their time to farm materials and make better and better 55 gear and stay relevant if they really wanted to.
We have no desire to make any profit from this server. We are adults and can keep the server up ourselves cost-wise. We really do not want to have anything pay to win but since we want to ensure some revenue coming in for costs we tried to be careful what would be accessible via Altruism Points and cash shop. We came up with the idea that whatever we allow people to get through cash shop, it should always be tradable items and encourage active playing. This will make sure free-to-play players have access to all the same content and it gives an in-game value for cash shop items for people who choose to use it.
We will always be evaluating what is in the cash shop. We just want you to know what will always be going through our minds when deciding those items.
We always accept and encourage feedback. The best way to do so is inside of our discord!
|
// GetValue returns expression value, if it's a constant.
func (expr ExprNode) GetValue() (interface{}, bool) {
if expr.Type == NodeTypeLiteral {
return expr.Value, true
}
return nil, false
}
|
Luspatercept for myelodysplastic syndromes/myeloproliferative neoplasm with ring sideroblasts and thrombocytosis Myelodysplastic syndromes/myeloproliferative neoplasm with ring sideroblasts and thrombocytosis (MDS/MPN-RS-T) is a myeloid disorder with myelodysplastic and myeloproliferative features. MDS/MPN-RS-T-associated anemia causes fatigue, reduced quality of life, and worse survival . Patients with MDS/MPN-RS-T have favorable overall survival compared to patients with MDS-RS ; however, ~50% of patients require red blood cell (RBC) transfusions resulting in protracted transfusion dependence. Patients with MDS/MPN-RS-T also have a fourfold higher thrombotic event risk compared to patients with MDS with ring sideroblasts (MDS-RS). Treatment of MDS/MPN-RS-T aims to improve anemia, reduce thrombotic event risk, lower platelets, and/or modify the disease course. However, data supporting the ef fi cacy transfusion requirements and adverse events. The diagnosis of patients with MDS/MPN-RS-T in the intention-to-treat population was performed using cytomorphologic, cytogenetic, and molecular genetic results and blood counts. The primary endpoint in the MEDALIST study was RBC transfusion independence (RBC-TI) ≥8 weeks during weeks 1-24. Secondary endpoints included: modified hematologic response-erythroid (mHI-E; mean hemoglobin increase ≥1.5 g/dL or a reduction of ≥4 units RBC transfusion , over 56 consecutive days) ; ≥1.0 g/dL hemoglobin increase from baseline over 56 consecutive days during weeks 1-24; rates of progression to acute myeloid leukemia (AML); and incidence of treatment-emergent adverse events (TEAEs). A post hoc analysis of clinical benefit (defined as RBC-TI ≥8 weeks and/or mHI-E during weeks 1-24) was also performed. All P values are descriptive and not adjusted for multiplicity. Despite limited numbers, the value of luspatercept for patients with MDS/MPN-RS-T is supported by comparisons with data from the entire MEDALIST study population. The achievement of RBC-TI ≥8 weeks during weeks 1-24 among patients with MDS/MPN-RS-T randomized to luspatercept vs placebo (64.3 vs 22.2%) was higher than in the overall MEDALIST population (37.9 vs 13.2%). Similarly, mHI-E was achieved in 71.4 vs 11.1% of patients with MDS/MPN-RS-T randomized to luspatercept vs placebo, compared to 52.9 vs 11.8% in the overall MEDALIST population. After 24 weeks of treatment, patients randomized to luspatercept had increases from baseline in mean hemoglobin ( 2B). Although the increase in hemoglobin levels after 24 weeks among patients with MDS/MPN-RS-T was not significantly different between luspatercept and placebo, the absolute magnitude of increase was nominally higher (+1.7 vs +0.9 g/dL). Patients randomized to luspatercept vs placebo had a significantly greater increase in mean leukocyte count but not mean platelet or neutrophil counts. At baseline, patients with MDS/MPN-RS-T had a higher median platelet count than the overall MEDALIST population (447 vs 234 10 9 /dL) as expected, had lower median sEPO (59.9 vs 153.2 U/L), were less likely to have received iron chelation therapy (26.1 vs 48.5%), and had lower median transfusion burden (4.0 vs 5.0 units/8 weeks), consistent with their higher RBC-TI and mHI-E response rates. The most common TEAEs of any grade in the luspatercept arm were dizziness, nausea, diarrhea, and asthenia (Fig. 2C). TEAEs leading to discontinuation occurred in 2 of 14 (14.3%) patients in the luspatercept arm and 3 of 9 (33.3%) in the placebo arm. One patient randomized to luspatercept experienced a transient ischemic attack. One patient randomized to placebo experienced progression to AML (P = 0.202) vs none randomized to luspatercept. Recommendations for the treatment of patients with MDS/ MPN-RS-T include ESAs and transfusions for anemia, and lenalidomide for anemia and platelet-level reduction. Recommendations for the use of lenalidomide for patients with MDS/MPN-RS-T are based on case reports totaling 12 patients and a retrospective analysis of 167 patients, rather than clinical trials; the use of ESAs is based on a single retrospective study which included 40 patients with MDS/MPN-RS-T, of whom 45% achieved an erythroid response (hemoglobin increase ≥2.0 g/ dL or RBC-TI ≥8 weeks for patients who required ≥4 units/8 weeks), compared to 71.4% of patients treated with luspatercept (refractory or ineligible for ESAs) in the current study. However, this comparison should be undertaken with caution, given the different definitions of erythroid response. In conclusion, this subgroup analysis provides the first clinical trial data to support the efficacy of luspatercept in patients with MDS/MPN-RS-T, a population who currently have no proven effective treatment options. Overall, luspatercept was found to be effective-significantly reducing transfusion burden and improving mHI-E and leukocyte levels-with a generally well-tolerated safety profile.
|
Resuscitation with amiodarone increases survival after hemorrhage and ventricular fibrillation in pigs Supplemental digital content is available in the text. BACKGROUND The aim of this experimental study was to compare survival and hemodynamic effects of a low-dose amiodarone and vasopressin compared with vasopressin in hypovolemic cardiac arrest model in piglets. METHODS Eighteen anesthetized male piglets (with a weight of 25.3 kg) were bled approximately 30% of the total blood volume via the femoral artery to a mean arterial blood pressure of 35 mm Hg in a 15-minute period. Afterward, the piglets were subjected to 4 minutes of untreated ventricular fibrillation followed by 11 minutes of open-chest cardiopulmonary resuscitation. At 5 minutes, circulatory arrest amiodarone 1 mg/kg was intravenously administered in the amiodarone group (n = 9), while the control group received the same amount of saline (n = 9). At the same time, all piglets received vasopressin 0.4 U/kg intravenously administered and hypertonic-hyperoncotic solution 3-mL/kg infusion for 20 minutes. Internal defibrillation was attempted from 7 minutes of cardiac arrest to achieve restoration of spontaneous circulation. The experiment was terminated 3 hours after resuscitation. RESULTS Three-hour survival was greater in the amiodarone group (p = 0.02). After the successful resuscitation, the amiodarone group piglets had significantly lower heart rate as well as greater systolic, diastolic, and mean arterial pressure. Troponin I plasma concentrations were lower and urine output was greater in the amiodarone group. CONCLUSION Combined resuscitation with amiodarone and vasopressin after hemorrhagic circulatory arrest resulted in greater 3-hour survival, better preserved hemodynamic parameters, and smaller myocardial injury compared with resuscitation with vasopressin only.
|
<filename>zssmodel/src/org/zkoss/zss/range/impl/imexp/ExcelExportFactory.java<gh_stars>100-1000
/*
{{IS_NOTE
Purpose:
Description:
History:
2013/12/01 , Created by dennis
}}IS_NOTE
Copyright (C) 2013 Potix Corporation. All Rights Reserved.
{{IS_RIGHT
}}IS_RIGHT
*/
package org.zkoss.zss.range.impl.imexp;
import org.zkoss.lang.Library;
import org.zkoss.zss.range.SExporterFactory;
import org.zkoss.zss.range.SExporter;
/**
*
* @author dennis
* @author Hawk
* @since 3.5.0
*/
public class ExcelExportFactory implements SExporterFactory{
/**
* @since 3.5.0
*/
public enum Type{
XLS,XLSX;
}
private Type _type;
public ExcelExportFactory(Type type){
this._type = type;
}
@Override
public SExporter createExporter() {
AbstractExcelExporter exporter = _type == Type.XLSX ?
new ExcelXlsxExporter() : new ExcelXlsExporter();
exporter.setExportCache(isExportCache()); //ZSS-873
return exporter;
}
//ZSS-873
private boolean isExportCache() {
String importCache = Library.getProperty("org.zkoss.zss.export.cache", "false");
return "true".equalsIgnoreCase(importCache.trim());
}
}
|
Public-Private Partnership Networks: Exploring Business-Government Relationships in United Kingdom Transportation Projects Abstract Since the early 1990s, U.K. governmental policy has formally encouraged the delivery of infrastructure through private finance initiatives, a model of public-private partnership in which the design, construction, financing, operation, and maintenance of facilities are bundled into a long-term contract with a single consortium of firms. Drawing on an analysis of governmental records of the firms that participated in every U.K. project between 1987 and 2009, this article traces the extent to which stable partnerships are used to produce private finance transportation initiatives. The findings highlight an important tension between the benefits and drawbacks of repeat collaborations on one-off projects. On the one hand, the extensive use of repeat-partnership relationships lowers transaction costs, encourages innovation, and supports learning from past experiences. On the other hand, deep embeddedness within social networks that encourages frequent repeat collaborations can reduce competition within the industry and contribute to higher delivery costs and lower-quality public services. Through this analysis, the article addresses the key economic geography literatures that are related to project ecologies, embeddedness, and repeat collaborations within networked production processes.
|
<filename>browser/src/vscode.ts
const vscode =window['vscode'];
const postMessage = (message:any) => { if (vscode) { vscode.postMessage(message) } }
export const getVscodeEvent = () => {
let events = {}
let init:boolean = false;
function receive({ data }) {
if (!data || !data.type)
return;
if (events[data.type]) {
events[data.type](data.content);
}
}
return {
on(event:string, callback) {
this.tryInit();
events[event] = callback
return this;
},
emit(event:string, data?:any) {
this.tryInit();
postMessage({ type: event, content: data })
return this;
},
tryInit() {
if (init) return;
init = true;
window.addEventListener('message', receive)
},
destroy() {
window.removeEventListener('message', receive)
init = false;
}
}
}
|
from extras.plugins import PluginConfig
class NATConfig(PluginConfig):
name = 'netbox_nat_plugin'
verbose_name = 'NetBox NAT Plugin'
description = 'A NetBox plugin to document NAT related things.'
version = '0.0.0'
author='<NAME>',
author_email='<EMAIL>',
base_url = 'nat'
required_settings = []
default_settings = {}
min_version = "3.0"
config = NATConfig
|
Identifying Influences in Patient Decision-making Processes in Online Health Communities: Data Science Approach Background In recent years, an increasing number of users have joined online health communities (OHCs) to obtain information and seek support. Patients often look for information and suggestions to support their health care decision-making. It is important to understand patient decision-making processes and identify the influences that patients receive from OHCs. Objective We aimed to identify the posts in discussion threads that have influence on users who seek help in their decision-making. Methods We proposed a definition of influence relationship of posts in discussion threads. We then developed a framework and a deep learning model for identifying influence relationships. We leveraged the state-of-the-art text relevance measurement methods to generate sparse feature vectors to present text relevance. We modeled the probability of question and action presence in a post as dense features. We then used deep learning techniques to combine the sparse and dense features to learn the influence relationships. Results We evaluated the proposed techniques on discussion threads from a popular cancer survivor OHC. The empirical evaluation demonstrated the effectiveness of our approach. Conclusions It is feasible to identify influence relationships in OHCs. Using the proposed techniques, a significant number of discussions on an OHC were identified to have had influence. Such discussions are more likely to affect user decision-making processes and engage users participation in OHCs. Studies on those discussions can help improve information quality, user engagement, and user experience. Background In recent years, online health communities (OHCs) such as the Cancer Survivors Network (CSN), MedHelp, DoctorLounge, WebMD, and Health-boards message boards have become one of the most important resources that patients leverage. An OHC is defined as an asynchronous web-based message board system for patients that contains multiple message boards, each of which typically focuses on 1 disease. OHCs provide a web-based channel that enables information exchange, facilitates communication, and provides support to patients and caregivers. They are especially valuable for patients with chronic diseases to learn about their conditions and seek social support. Empowering and supporting patients to make informed health care decisions is a key component of patient-centered health care and is a social, economic, and technical necessity. A lot of patients seek information and advice on OHCs. Existing work has found that nearly half of the threads in a breast cancer forum are related to patient decision-making. Studies have also shown that patients are often influenced by web-based sources and social media in their health care decision-making. Objectives The goal of this study was to identify the influence relationship of posts in discussion threads related to health care decision-making. Specifically, we defined the influence relationships and identified post replies that influenced the initial author, who had questions posted on OHCs. The outcomes of this study are important for health care professionals to help patients make informed decisions for several reasons. First, analyzing the writing style and pattern of posts that have influence may help explain why they have influence and provide insights to health care professionals on effective communication with patients. Second, if the information provided by posts that have an influence is not accurate, it will mislead patients. It is important to check the information quality in such posts to improve the quality of influence. Furthermore, a patient who has questions but does not receive any replies that have an influence may need further help. Literature Review There is a lot of research conducted on OHC analysis, although with limited study on identifying influence relationships of posts. Several studies have been conducted on analyzing the reciprocal patterns between users' replies in discussion forums. There is also work on analyzing the patterns between post views and post replies. Many studies have been conducted on identifying influential users in a community. In those applications, a post, blog, or tweet typically expresses an opinion of the author, and the replies are considered as an indication of being influenced by the opinion of the original post. That is, the reply relationship is considered as an influence relationship. The focus is on judging the influential power of an author based on activeness of post writing and social network features such as PageRank-like algorithms or clustering algorithms. Finding influence relationships among posts in discussion forums is different from finding influential users and requires different techniques. In an OHC, the initial author of a thread typically expresses a question, not an opinion. The influence happens when a reply to the question affects the initial author. There are only 2 existing studies that consider the influence of the replier on the initial author. This influence is identified when the sentiment of the initial author is changed to be similar to that of the replier. However, this definition may not be accurate. Let us look at an example of a discussion thread related to patient decision-making, shown in Figure 1. An OHC user initialized a thread asking for advice on whether to have chemotherapy before surgery for her mother's treatment plan in post p A. In Figure 1A, a user replied by comforting her in post p B1. The reply was not informative. Even though the initial author expressed gratefulness to the author of post p B1, with sentiment changing to be positive in post p C1, she was not influenced by post p B1. Indeed, studies show that 75% to 85% of CSN forum participants change their sentiment in a positive direction through web-based interactions with other community members. A change in sentiment is not necessarily an indicator of being influenced. In contrast, in Figure 1B, a user shared her experience in a similar situation suggesting to have chemotherapy before a surgery in post p B2. The initial author expressed her gratitude and indicated that she would consider this suggestion in determining her mother's treatment plan (the sentences in italics) in p C2, showing her being influenced. Contribution Instead of considering sentiment changes, we propose using questions or future actions on relevant replies as an indicator of being influenced, as illustrated in the aforementioned example. There are 2 major challenges in identifying influence relationships. First, we need to define influence relationships of posts. We examined the semantics of post content to define influence relationships. Unlike influential users, who are defined by network features in the existing work, text content is the key to determine whether posts have influence. Second, it is hard to identify influence relationships. Unlike typical text classification problems, influence relationships involve multiple posts with reply relationships rather than a single paragraph of text. In addition, influence is an abstract concept. It is challenging to extract relevant features to capture the influence patterns considering both content and the reply relationship. This study makes novel contributions to identifying influence relationships in discussion threads in OHCs related to patient decision-making. Specifically, we defined the influence relationship between the posts based on the semantics of the post content, an extensible deep learning model that extracts and combines both sparse and dense features was proposed to identify the influence relationships in OHC decision-making threads, and the proposed model achieved good performance in identifying influence relationships in empirical evaluation. Methods In this section, we first model the OHC data and define the influence relationship in discussion threads. We then propose a deep learning-based model to identify the influence relationships. Figure 2 presents an overview of the OHC data structure. We modeled an OHC as a set of discussion threads T = {t 1, t 2,..., t n }. Each thread t i is composed of a set of posts and a function R that represents the reply relationship. For example, Figure 2 illustrates a thread that contains a set of 5 posts {p A, p B, p C, p B', p C' }. One of the reply relationships, R(p B ) = p A, represents that post p B replies to post p A. Each post p i consists of a sequence of sentences p i = {s 1, s 2,..., s l }. Each post has an author. We denoted the author relationship using a function U. U(p i ) represents the author of post p i. Note that a post only has a single author; however, an author may write ≥0 posts in a thread. We used p A to present the first post of a thread and named it the initial post. The author of the initial post, U(p A ), is referred to as the initial author of the thread. Existing work has studied the thread discussions in OHCs and identified that a subset of threads is related to patient decision-making. Such a thread is characterized by questions in the initial post and replies with suggestions of options. Techniques have been developed to identify decision-making threads in OHCs. Definition of Discussion Threads In this paper, we study how to identify the cases where the initial author of a decision-making thread is influenced by a reply post. Note that our study is general to any thread discussions related to decision-making. The definition and identification of decision-making threads can be handled using the approach developed in existing work or other approaches. In the rest of this paper, we use threads to refer to decision-making threads for simplicity. The defined influence relationship may not be applicable to discussion threads that are not related to decision-making, such as discussion threads for casual communication or experience-sharing threads providing social support. Overview Before introducing the definition of influence relationships, we first introduce relationships. A relationship is defined on a triple of posts in a thread with reply relationships: an initial post, a reply to the initial post, and the initial author's subsequent reply. Definition 1 (Relationship) We define the relationship among three posts p A, p B, and p C, in a thread as r i = (p A, p B, and p C ), where post p A is the initial post of the thread, post p B replies to p A, post p C replies to p B, and the authors of p A and p C are the same person. That is, R(p B ) = p A, R(p C ) = p B, and U(p A ) = U(p C ). We used r i = (p A, p B, p C ) to denote the relationship among p A, p B, and p C. Note that there are many such relationships in a thread, and we considered all such triples. For instance, Figure 2 shows a thread with 2 relationships, r 1 = (p A, p B, p C ) and r 2 = (p A, p B', p C' ). Also, note that existing work on identifying influential users does not consider the relationships among post triples but only considers the reply relationship between 2 posts. Definition of Influence Relationships Intuition Now, we discuss how to define influence relationships on relationship (p A, p B, p C ), where post p B has an influence on the initial author U(p A ). First, intuitively, if post p B influences the initial author U(p A ), then the content of these 3 posts must be relevant. Second, we referred to the definition of influence in Merriam-Webster -"to affect or alter by indirect or intangible means"-and the reaction of being influenced is to sway rather than being convinced. If the initial author considers the suggestion given in post p B, even if she eventually does not take the suggestion, she is considered to have been influenced by post p B. On the basis of this definition, we observed 2 indications that the initial author, U(p A ), was influenced by p B. An observation of being influenced is that the initial author may ask questions in p C based on the suggestions in p B. Curiosity is a motivator for learning and influential in decision-making. An existing study used a statistically large sample of learning forum posts to investigate whether student participation in the forum could be influenced. They observed that students who were influenced by others' interesting answers were more likely to ask follow-up questions. This indicates that asking further questions is a sign of being influenced. The same pattern also exists in OHCs. Let us look at the example in Figure 1C. The initial author expressed concerns about hair loss in p A. Another user replied in post p B3 suggesting the use of wigs. The initial author then replied in post p C3 with questions (the sentences in italics) for more details about the suggestion given in post p B3. These questions indicate that the initial author was thinking about the suggestion given in post p B ; that is, being influenced. The second indication that the initial author was influenced by a post p B is that she expressed her intention to take action in post p C. Adjei et al found that member-to-member communication in web-based brand communities greatly influenced the members' future purchase behavior. Similarly, the communication through discussion threads in OHCs may also affect the initial author's future actions. Let us look at the example in Figure 1B again. For the treatment question asked in p A, a forum user shared her experience and discussed the treatment in post p B2. The initial author then replied with a planned action (the sentence in italics) in p C2. The intention of future action based on the communications in the thread is an indicator of the influence relationship. On the basis of these observations, we define influence relationships in decision-making threads in the following section. To identify influence relationships, we modeled it as a classification task. Given a set of relationships R = {r 1, r 2,..., r n }, for each relationship r i, we predicted its label to be either 1 or −1, where label 1 indicated that r i was an influence relationship and label -1 indicated that r i was not an influence relationship. The goal was to learn a model from the labels of known relationships and predict the labels for unlabeled relationships. Overview In this section, we present the method to identify the influence relationships in decision-making threads in OHCs. Figure 3 presents the framework of the proposed method. Given a set of discussion threads as the input, we first extracted the triple relationships using the relationship extraction module. Text relevance features, question probability features, and action probability features were then calculated using the text relevance measurement module, the question probability calculation module, and the action probability calculation module, respectively. Finally, all the features were combined using a deep learning model in the feature combination module to generate the probability of a relationship being an influence relationship. Relationship Extraction Module In this section, we introduce the relationship extraction module, which extracted all relationships defined in definition 1. In the first step of relationship extraction, we built the reply tree structure based on the indented format in html files. For each adjacent post pair, the post that was posted earlier was treated as the parent of the latter post. The ancestor-descent distance between a post and the initial post was represented by the number of tab characters. The reply structure of a thread is illustrated in Figure 2. Each post is a node in the thread tree, and each edge represents a reply relationship. The root of the thread tree is the initial post (ie, p A ) in definition 1. Existing work observes that, in some forums, the reply structure in a discussion thread may not be fully available and proposes techniques to construct full reply structures. The OHCs used in our experiments had a full reply structure. Existing techniques can be leveraged if needed for other forums. We then navigated the thread tree to extract all relationship triples, as defined in definition 1. Each triple started with the initial post followed by a reply to the initial post written by another author and then a subsequent reply by the initial author, all of which were on the same path in the thread tree. For example, r 1 = (p A, p B, p C ) and r 2 = (p A, p B', p C' ) are 2 relationships in the thread tree in Figure 2. Text Relevance Measurement Module The text relevance measurement module measures the content relevance, or text semantic similarity, of 2 posts using a relevance score between 0 and 1. There are mainly 2 types of deep learning-based methods in the literature that measure text relevance. The first type of method extracts content feature vectors of 2 input texts and then combines them to make a prediction, such as the Deep Structured Semantic Models (DSSM), the Convolutional DSSM, and Architecture-I (ARC-I). The intuition of this method is to highlight the important information of the original texts so that irrelevant content can be removed before the feature combination phase. However, the drawback of this type of method is that it runs the risk of losing detail. The second type generates the word-level relevance first and then uses neural networks to learn the hierarchical interaction patterns for content-level relevance, such as DeepMatch, Architecture-II (ARC-II), and MatchPyramid. The motivation is that making a good relevance judgment requires considering the interactions in the text relevance measurement process, starting from the interactions between words to patterns in phrases and those in whole sentences. However, the training process for the second type is much more expensive than for the first one. We evaluated both approaches to measure text relevance in experiments. We chose 2 state-of-the-art representative methods for the text relevance measurement module in the evaluation. For the first type, we chose ARC-I, which uses a multilayer perceptron to combine relevance feature vectors. It shows better performance than the DSSM and Convolutional DSSM, both of which use cosine similarity. We chose MatchPyramid to represent the second type of method as it exhibits better performance than the other 2 methods (DeepMatch and ARC-II ) in experiments on multiple data sets. We further proposed the adaptation of Bidirectional Encoder Representations from Transformers (BERT) as the embedding layer in the ARC-I and MatchPyramid models. BERT is a state-of-the-art embedding method for word representation in many natural language understanding tasks, trained on BookCorpus and English Wikipedia. We considered both BERT (trained on Wikipedia) and word2vec (trained on the training data set) as the embedding methods for both ARC-I and MatchPyramid. Different variations of the text relevance measurement module are evaluated in the Text Relevance Evaluation section. Question Probability Calculation Module We now discuss how to calculate the probability of a post containing a question using the question probability calculation module. There are 2 types of methods to identify question sentences in forums: a rule-based approach and a learning-based approach. In a rule-based approach, question marks and 5W1H words (what, who, when, where, why, and how) are used to identify question sentences. A learning-based approach uses sequential question patterns to train a binary classifier on labeled data. Liu and Jansen used the question mark to extract question posts from Sina Weibo. In the studies by Ranganath et al, frameworks were proposed to identify rhetorical questions by modeling the motivation of the user for posting them. In the study by Ojokoh et al, questions from ResearchGate were identified based on the maximum probability value of a nave Bayes classification with part-of-speech tag features. Both rule-based and learning-based approaches can achieve excellent performances. A study shows that a rule-based approach can outperform complicated learning-based approaches. Thus, we followed a rule-based method to identify question presence in the posts. In total, 2 types of rules were considered: question marks and 5W1H words. We made adaptations of this approach for OHCs. As a question mark is the most significant sign of a question, we gave a higher confidence score to a sentence with a question mark. We also set some constraints on 5W1H words to simulate the pattern of question sentences. First, 5W1H must appear at the beginning of a sentence. Second, auxiliary words were added to the original words for more specific patterns. After the question probability of each sentence in a post p i was calculated, the maximum probability was used as the likelihood of post p i containing at least one question, denoted as Q(p i ). Action Probability Calculation Module This section presents the action probability calculation module, which generated the probability of action presence in a post. The indication of a future action can be captured by the presence of verbs and appropriate sentence tense. The Natural Language Toolkit (NLTK) tagger module defines a standard interface for augmenting each token of a text with supplementary information, such as its part of speech or its WordNet synset tag, and provides several different implementations for this interface. We leveraged the NLTK tagger module to assess the likelihood of a post containing future actions by checking the existence of words with a future tense verb tag (eg, will consider in Figure 1B) or a modal auxiliaries tag (eg, can, could, may, and must). To count on the cases where future tenses may not be identified because of forum users' typos or informal writing, we set the probability of future action to be 0.5 when the rules failed to identify future actions. Equation 1 shows the calculation formula to generate the action probability of a post p i. Note that we did not consider negation in the action probability calculation module. For example, in post p C, the initial author disagrees with the suggestions proposed in p B and decides to do something different. For those cases, the overall meaning of p B and p C would be the opposite and, therefore, would be captured by the relevance vectors generated in the text relevance measurement module. Thus, we did not consider negations in this phase to avoid double counting. Feature Combination Module Overview Referring to Figure 4, the text relevance measurement module calculated P AB -the relevance score between p A and p B -and P BC -the relevance score between p B and p C. The question probability calculation module and action probability calculation module calculated the question probability Q(p C )-or Q in short-and action probability A(p C )-or A in short-based on the text of p C. We now discuss the feature combination module that measures the influence score based on these features. We discuss 2 alternative methods: a baseline approach and a deep learning model. Baseline Approach Recall that, according to definition 2, the presence of an influence relationship requires the relevance between post p A and post p B, the relevance between post p B and post p C, and the presence of a question or action in post p C. We started with an intuitive method to detect influence relationships based on the definition using Equation 2. We set the thresholds to 0.5, 0.5, and 0.9 for each component. Deep Learning Approach We further proposed a deep learning model that combines the text relevance, the likelihood of question presence, and the likelihood of future action presence to identify influence relationships. The architecture of this model is shown in Figure 4. Compared with the baseline approach, there are 3 major benefits of using a deep learning model. First, it is labor-intensive, time-consuming, and difficult to determine appropriate thresholds for cutting off the probabilities using a rule-based approach such as the baseline approach. A threshold that works well for one data set may not be optimal for another. Both a rule-based approach and a deep learning model require different thresholds for different data sets. A rule-based approach requires manual parameter tuning for each data set. In contrast, a deep learning approach learns thresholds from the ground truth and, thus, can easily adapt to a new data set with minimal human intervention. Second, the question and action features may have different interactions with the relevance features. We observed that questions are often relevant, but actions are not necessarily. People typically express appreciation in post p C or sometimes even mention actions totally irrelevant to post p B, such as the plan to travel or shop. Being relevant is more important to consider in the presence of actions compared with in the presence of questions. However, in the baseline approach, the question and action features are merged before being combined with the relevance features, resulting in the loss of important information. Furthermore, we used relevance vectors as inputs to the deep learning model to calculate the influence score. Compared with the baseline approach, which uses the relevance scores as input to measure the influence score, relevance vectors provide much richer information. This can be especially helpful when there are several topics involved in the discussion. The relevance information is also leveraged during the phase of combining the relevance features with the question or action features. Let V AB denote the relevance vector between p A and p B and V BC denote the relevance vector between p B and p C. We generated V AB,V BC from p A, p B, and p C and calculated Q and A from p C. These features were then connected. The question or future action in p C must be related to the content of p A and p B. Thus, we combined V AB and V BC with Q and A using one of the following two operators: cat (concatenating each relevance vector with question or action probability) and dot (multiplying each relevance vector with question or action probability). There are 2 major differences between these 2 operators for connecting the features: cat and dot. First, dot makes sure that Q and A affect each dimension in the relevance vectors, whereas cat cannot guarantee this as some neurons or nodes are dropped out. Some interactions between questions or actions and text relevance may be ignored by the cat operator. Second, the training process of the cat is more expensive than that of the dot because, for each dense layer 1 to 4, there is an additional dimension for the cat compared with for the dot. In Figure 4, we use ⊗ to present the combination operator, which can be either cat or dot. The combination step produces 4 feature vectors: V AB ⊗ Q, V AB ⊗ A, V BC ⊗ Q, and V BC ⊗ A. To extract the key information from these combined feature vectors, 4 dense (fully connected) layers were used to populate the summarized feature vectors (S 1, S 2, S 3, S 4 ). The concatenation of these 4 summarized feature vectors was passed through 2 dense layers. The first one was used to further combine the summarized feature vectors. The second one aimed to generate the probability distribution over the labels. To avoid gradient vanishing and exploding, we chose the Relu function as the activation function for all the dense layers except the output layer, which uses the softmax function to populate the probabilities. We trained the model using the binary cross-entropy loss function defined in Equation 3, which minimizes the distance between the probability distributions of the ground truth and those of the predicted score. Where y i is the ground truth label of the ith training sample and s i is the score predicted by the model. The Adam optimizer was leveraged for optimization because of its advantage of processing sparse features and obtaining faster convergence compared with the normal stochastic gradient descent with momentum. Ethics Approval All materials were obtained from anonymous open-source data. Thus, ethics approval was not required. Experiment Setting and Evaluation Metrics We implemented a prototype system for influence relationship identification on discussion threads. The prototype system and data sets used in the evaluation are publicly available at GitHub. For empirical evaluation, we collected 25,208 threads that were publicly available in the CSN breast cancer forum. The webpages were collected and processed by a web crawler we developed leveraging the Spider Crawler library. There were 321,000 posts with 1.9 million sentences in total. We applied the classifier proposed by Li et al on all 25,208 threads to identify the ones that were related to patient decision-making and obtained 11,815 (46.87%) such threads. Note that other models for classifying decision-making threads can also be plugged in. We then extracted relationships from the decision-making threads using the relationship extraction module and obtained 9053 relationships. We randomly picked 853 (9.42%) of them to label. A total of 4 PhD students worked on the manual labeling. All the relationship triples and post pairs were first independently labeled. In case of disagreement, a consensus was reached after discussion. A total of 261 relationships were labeled as influence relationships. Recall that, per definition 1, each relationship is presented as a triple (p A, p B, p C ). We also labeled whether posts p A and p B were relevant (ie, P AB ) and whether posts p B and p C were relevant (ie, P BC ). We observed some reply posts with content expressing only comfort or wishes. Although they express care about the initial author's conditions and seem relevant, they are generic. After discussion, we reached an agreement that, when the initial post and reply post shared similar medical terms (such as chemotherapy and chemo), we would label them as relevant. All 1706 post pairs (p A, p B ) and (p B, p C ) of the 853 relationships were labeled. Of the 1706 pairs, 1210 (70.93%) were relevant pairs, and the remaining 496 (29.07%) were irrelevant. We split the set of relationships into a training set (90%) and a testing set (10%). The post pairs in the aforementioned training and test sets were used for text relevance training and testing, respectively. The metrics used for evaluation included precision, recall, F 1 score, accuracy, area under the receiver operating characteristic curve (ROC AUC), and area under the precision-recall curve (PR AUC). They evaluated the effectiveness of a system using different aspects: precision, also known as positive predictive value, is the fraction of relevant instances among the retrieved instances; recall, also known as sensitivity, is the fraction of relevant instances that are retrieved among all relevant instances; F 1 score measures a model's performance by calculating the harmonic mean of the precision and recall, as shown in the following equation: ; accuracy is a common evaluation metric for binary classification problems and is defined as the fraction of corrected predictions among the total number of predictions; ROC AUC is a common evaluation metric for binary classification problems and is created by plotting the true positive rate against the false positive rate at various threshold settings; and PR AUC is commonly used to evaluate the performance of a model on data sets with imbalanced labels. Table 1 presents the classification results of the text relevance measurement module. In total, 2 observations were made. The first observation was that the models using BERT achieved high recall but low precision, whereas the models with word-embedding vectors trained on OHC data obtained balanced precision and recall values. There are 2 reasons for these results. First, OHC data are domain-sensitive and can benefit from domain-specific word representation. Second, the BERT transformer tends to link words in adjacent sentences by mistake. Text Relevance Evaluation In the text relevance measurement module, precision was more important than recall as the accuracy of influence relationship identification depended on the precision of relevance classification. Thus, we used the word vectors trained on OHC data instead of BERT in the following experiments. The second observation was that, with word vector embedding, ARC-I achieved a better performance than MatchPyramid in most of the evaluation metrics. In the ARC-I model, each input text goes through an embedding layer, a convolution layer, and a max pooling layer, and the extracted feature vectors are then concatenated together as the input to a fully connected layer that calculates the predicted relevance scores. MatchPyramid populates the local word relevance matrix first. Each cell of the matrix presents the dot product of the word-embedding vectors of the words in the text input. The patterns of these interactions are then extracted using a convolutional neural network. Thus, ARC-I focuses on checking relevance based on the meaning of the whole text, whereas MatchPyramid focuses on summarizing the important relevance features based on local word similarity. For OHC data sets, posts were relatively long and often contained noisy information; thus, considering the meaning of the entire post text was more important than focusing on adjacent words. This is why the performance of ARC-I was better than that of MatchPyramid in our evaluation. We also observed that ARC-I with word2vec outperformed MatchPyramidwith word2vec in both ROC AUC and PR AUC but had an inferior F 1 score. Note that F 1 averages the performance of all the samples by combining the precision and recall, whereas the ROC AUC and PR AUC cumulate the precisions among all samples with different recall thresholds. This indicates that the average performance of MatchPyramidwith word2vec was better, but the overall performance of ARC-Iwith word2vec was better. Question and Action Probability Evaluation Now, we present the evaluation of the question probability calculation module and the action probability calculation module. The performance is shown in Table 2. Good performance was achieved for question identification. For future action identification, a high score was achieved on recall but not on precision. The following are a few examples of posts that are classified as containing future actions but actually do not have action intent: I will tell you though I hated my silicone or I would worry about it. These sentences have verbs in the future tense, but those verbs only convey opinions or feelings rather than taking action on health care. We plan to improve action detection by training action sentence models as future work. Recall that in the deep learning approach, question and action probabilities are considered as input features instead of imposing a strict requirement on their presence. We conducted an analysis on the test data in terms of their presence. All positive cases either had a probability of action presence of 1.0 or had a high probability of question presence, with an average probability of 0.986 (SD 0.033). This indicates that the deep learning approach captures definition 2 well, ensuring the high likelihood that either a question or a future action is present. Table 3 shows the performance of the baseline and deep learning approaches with alternative ways to combine text relevance vectors, question features, and action features. Recall that, for the feature combination module, baseline combines the text relevance score, the likelihood of question presence, and the likelihood of future action presence to identify influence relationships. MatchPyramid+cat Q/A represents the model using MatchPyramid to calculate the text relevance score and cat as the combination operator ⊗, whereas MatchPyramid+dot Q/A uses dot as the combination operator ⊗. ARC-I+cat Q/A represents the model using ARC-I to calculate the relevance score and cat as the combination operator ⊗, whereas ARC-I+dot Q/A uses dot as the combination operator ⊗. We also visualized the operating characteristic curves of all methods, as shown in Figure 5. From Table 3 and Figure 5, we have the following observations. First, all proposed deep learning methods, which use relevance features and consider the interaction between relevance and the presence of questions or actions, significantly outperformed the baseline approach. This indicates that the relevance feature vectors generated by the text relevance measurement module were effective in capturing relevant content. Combining these feature vectors with the features of question presence and action presence helped capture their interactions and achieved good performance in influence relationship classification. In contrast, the baseline approach, which directly follows definition 2, did not perform well. This was due to the inability to capture the interactions between text relevance and question or action presence and the challenge of manually setting an appropriate cutoff threshold for each module. Influence Relationship Classification Evaluation Second, the models using the dot operator performed better than those using the cat operator. There are mainly 2 reasons for this. First, question probability and action probability may interact with V AB and V BC relevance vectors, which can be captured well by the dot operator. Figure 1B shows an example in which the action in p C is related to the discussion in p A and p B. The action in p C2 is related to chemo, which is the common content of p A and p B2. In this case, the action probability needs to be combined with V AB. Although, in another case, the action refers to an option mentioned in p B, the interaction between p B and p C is more likely to be the context of the action and, thus, the action probability needs to be combined with V BC. In contrast, the cat operator ignores some interactions between questions (actions) and the context because of the dropout of some neutrals. Therefore, the cat-based methods had a much lower recall than the dot-based methods. The results show that interactions between action and context are important for influence identification. Furthermore, the ARC-I+dot Q/A had a much better precision, accuracy, ROC AUC, and PR AUC than MatchPyramid+dot Q/A but had lower recall and slightly lower F 1. This is because ARC-I achieved a better performance than MatchPyramid in the text relevance measurement module. ARC-I+dot Q/A was stricter than MatchPyramid+dot Q/A when fitting the model to the relevance factor. For applications that want to analyze the writing style and patterns of posts that have influence, precision is critical. ARC-I+dot Q/A is effective for locating such discussions. In contrast, for applications that want to check the information quality of the posts that have influence to prevent and mitigate the spread of misleading information, MatchPyramid+dot Q/A is more suitable because of its higher recall. A Case Study Figure 1 shows an example of 3 relationships, (p A, p B1, p C1 ), (p A, p B2, p C2 ), and (p A, p B3, p C3 ), where (p A is the initial post of the thread. The scores of these 3 relationships calculated using our system were 0.282, 0.793, and 0.622, respectively. Our system identified (p A, p B2, p C2 ) and (p A, p B3, p C3 ) as each containing an influence relationship, and (p A, p B1, p C1 ) does not. As we can see from the post content, p B2 provides suggestions to the initial author regarding the treatment decision. In post p C2, the initial author expresses actions to take based on the suggestions in p B2. In post p B3, the replier recommends that the author use wigs. The initial author then asks further questions about the wig information. Both relationships indicate that the initial author was influenced. In contrast, p B1 discusses general information and comforts the initial author, and the initial author expresses thanks in p C1, but there is no indication of being influenced. Principal Findings To the best of our knowledge, this is the first study that defines the influence relationships of discussion posts related to decision-making in OHCs. We proposed a deep learning-based natural language processing prototype to identify influence relationships. We then applied the developed techniques to identify the influence relationships in an OHC, the CSN breast cancer forum. There were 2 major observations. First, we found that there is a significant amount of influence relationships in the OHC. Of the 9052 relationships in decision-making threads identified by Li et al, 3069 (33.9%) were identified as influence relationships. That is, approximately one-third of the communications influence the initial authors on their decision-making. Furthermore, of the 5143 decision-making threads, which have at least one relationship, 2417 (47%) contain at least one influence relationship. Owing to the prevalence, it is important to study posts that have influence. Second, we also observed that posts that have influence may contribute to engaging users in discussions. The average number of posts in threads containing at least one influence relationship was 15.5, whereas the average number of posts in threads containing no influence relationship was 12.6. Our conjecture is that posts that have an influence likely provide helpful information or good reasoning, which are thought-provoking and help engage users in discussions. On the basis of these observations, there are several applications that can benefit from the identification and analysis of influence relationships. First, analyzing the quality of posts that have influence helps improve the quality of the influence. As discussed in the first observation, influence relationships are common. Quality checking of those posts is more critical than that of other posts in terms of improving the effect of influences and mitigating the spread of misleading information. On the basis of the identification of influence relationships, we can further identify influential users in OHCs. We can use existing techniques that analyze the network features to identify influential users, where this work calculates the edge weights (ie, the influence of a post). Identifying and checking influential users contributes to high-quality information dissemination. Second, based on the second observation, analyzing the writing style of posts that have influence provides insights to health care professionals about effective communication for patient engagement. Furthermore, identifying influence relationships contributes to effective information recommendations for addressing the information overload problem. When a user searches for information in OHCs, it is important to rank discussion threads and posts and recommend to users the most relevant and helpful discussions. On the basis of the analysis of influence relationships and the second observation, discussions that contain influence relationships are more likely to provide helpful information and encourage patient engagement. Thus, the presence of influence relationships is a positive factor in ranking. Limitations Our results are not without limitations. First, our definition of relationship was based on 3 posts, including the initial post in the thread. Therefore, we only identified the posts that had an influence on the initial author. However, any 3 posts that have a sequential reply relationship with the first and third posts from the same author can represent a relationship. We conjecture that the proposed techniques can be used to identify influence relationships among the generalized relationships and plan to study that problem in the future. Second, in this study, we considered text relevance between the posts in the relationship. Sometimes, even though 2 posts, p B and p C, are relevant overall, the specific sentence that has a question or future action indication in p C may not be relevant to the suggestions in p B. In addition, the current technique for future action detection sometimes generates false positives. To address these issues, we will investigate how to leverage part-of-speech and reference resolution techniques to improve natural language understanding. Conclusions and Future Work We studied the problem of identifying influence relationships of web-based discussions and developed techniques and a prototype system for identifying influence relationships in OHCs. The proposed deep learning model demonstrates the performance advantage of the compared methods. As future work, we will address the aforementioned limitations to improve the generality and accuracy of the proposed techniques.
|
. Methanotrophs could degrade methane and various chlorinated hydrocarbons. The analysis on methane monooxygenase gene cluster sequence would help to understand its catalytic mechanism and enhance the application in pollutants biodegradation. The methanotrophs was enriched and isolated with methane as the sole carbon source in the nitrate mineral salt medium. Then, five chlorinated hydrocarbons were selected as cometabolic substrates to study the biodegradation. The phylogenetic tree of 16S rDNA using MEGE5.05 software was constructed to identify the methanotroph strain. The pmoCAB gene cluster encoding particulate methane monooxygenase (pMMO) was amplified by semi-nested PCR in segments. ExPASy was performed to analyze theoretical molecular weight of the three pMMO subunits. As a result, a strain of methanotroph was isolated. The phylogenetic analysis indicated that the strain belongs to a species of Methylocystis, and it was named as Methylocystis sp. JTC3. The degradation rate of trichloroethylene (TCE) reached 93.79% when its initial concentration was 15.64 mol/L after 5 days. We obtained the pmoCAB gene cluster of 3 227 bp including pmoC gene of 771 bp, pmoA gene of 759 bp, pmoB gene of 1 260 bp and two noncoding sequences in the middle by semi-nested PCR, T-A cloning and sequencing. The theoretical molecular weight of their corresponding gamma, beta and alpha subunit were 29.1 kDa, 28.6 kDa and 45.6 kDa respectively analyzed using ExPASy tool. The pmoCAB gene cluster of JTC3 was highly identical with that of Methylocystis sp. strain M analyzed by Blast, and pmoA sequences is more conservative than pmoC and pmoB. Finally, Methylocystis sp. JTC3 could degrade TCE efficiently. And the detailed analysis of pmoCAB from Methylocystis sp. JTC3 laid a solid foundation to further study its active sites features and its selectivity to chlorinated hydrocarbon.
|
Stockholm (AFP) - Russia became the world's third largest military spender in 2016 despite low oil prices and economic sanctions, as the global expenditure rose for a second consecutive year, a study said on Monday.
Russian soldiers patrol in a small Syrian village near the city of Hama on May 4, 2016.
Russia's military spending was $69.2 billion (around 64 billion euros) in 2016, a 5.9 percent rise over 2015, the Stockholm International Peace Research Institute (SIPRI) said in a report, adding this was the highest proportion of its GDP since it became an independent state.
"This increased spending and heavy burden on the economy comes at a time when the Russian economy is in serious trouble due to low oil and gas prices and the economic sanctions imposed since 2014," (by the West over the Ukraine conflict), SIPRI said.
Saudi Arabia was the third largest spender in 2015 but dropped to fourth place in 2016 as its expenditure fell by 30 percent to $63.7 billion, "despite its continued involvement in regional wars", it added.
"Falling oil revenue and associated economic problems attached to the oil-price shock has forced many oil-exporting countries to reduce military spending," SIPRI researcher Nan Tian said, adding Saudi Arabia had the largest drop in spending between 2015 and 2016.
The US remained the top spender as its expenditure grew by 1.7 percent between 2015 and 2016 to $611 billion while China boosted its expenditure by 5.4 percent to $215 billion, a lower rate than in previous years.
US tanks, trucks and other military equipment, which arrived by ship, are unloaded in the harbor of Bremerhaven, Germany, January 8, 2017.
SIPRI said the rise in US military spending in 2016 "may signal the end of a trend of decreases in spending" caused by the 2008 economic crisis and the withdrawal of US troops from Afghanistan and Iraq.
On April 13 the US dropped its largest ever non-nuclear bomb, hitting Islamic State group positions in a remote area of eastern Nangarhar province in Afghanistan.
"Future spending patterns remain uncertain due to the changing political situation in the USA," Aude Fleurant, Director of the SIPRI Arms and Military Expenditure (AMEX) programme, said in a statement.
Hit by a series of terror attacks since 2015, Western Europe raised its military expenditure for the second consecutive year, up by 2.6 percent in 2016.
Overall military spending in Central Europe jumped by 2.4 percent in 2016.
"The growth in spending by many countries in Central Europe can be partly attributed to the perception of Russia posing a greater threat," said senior SIPRI researcher Siemon Wezeman in the statement.
"This is despite the fact that Russia's spending in 2016 was only 27 per cent of the combined total of European NATO members," he added.
|
import { SplitSchema, Step } from '../../types/interfaces'
import { setStepState } from '../../utils/formUtils'
import { styled } from '@mui/system'
import Box from '@mui/material/Box'
import Stack from '@mui/material/Stack'
import Grid from '@mui/material/Grid'
import Typography from '@mui/material/Typography'
import Button from '@mui/material/Button'
import Divider from '@mui/material/Divider'
export default function RenderFileTab({
currentStep: step,
splitSchema,
setSplitSchema,
}: {
currentStep: Step
splitSchema: SplitSchema
setSplitSchema: Function
}) {
const { state } = step
const { binary, code } = state
const codeId = 'select-code-file'
const binaryId = 'select-binary-file'
const Input = styled('input')({
display: 'none',
})
const handleCodeChange = (e: any) => {
setStepState(splitSchema, setSplitSchema, step, { ...state, code: e.target.files[0] })
}
const handleBinaryChange = (e: any) => {
setStepState(splitSchema, setSplitSchema, step, { ...state, binary: e.target.files[0] })
}
const displayFilename = (filename: string) => {
const parts = filename.split('.')
const ext = parts.pop()
const base = parts.join('.')
return base.length > 12 ? `${base}...${ext}` : filename
}
return (
<Grid container justifyContent='center'>
<Stack direction='row' spacing={2} sx={{ p: 3 }}>
<Box sx={{ textAlign: 'center' }}>
<label htmlFor={codeId}>
<Typography sx={{ p: 1 }} variant='h5'>
Uploade a code file (.zip)
</Typography>
<Input style={{ margin: '10px' }} id={codeId} type='file' onChange={handleCodeChange} accept={'.zip'} />
<Button variant='outlined' component='span'>
{code ? displayFilename(code.name) : 'Upload file'}
</Button>
</label>
</Box>
<Divider orientation='vertical' flexItem />
<Box sx={{ textAlign: 'center' }}>
<label htmlFor={binaryId}>
<Typography sx={{ p: 1 }} variant='h5'>
Upload a binary file (.zip)
</Typography>
<Input style={{ margin: '10px' }} id={binaryId} type='file' onChange={handleBinaryChange} accept={'.zip'} />
<Button variant='outlined' component='span'>
{binary ? displayFilename(binary.name) : 'Upload file'}
</Button>
</label>
</Box>
</Stack>
</Grid>
)
}
export function FileTabComplete(step: Step) {
return step.state.binary && step.state.code
}
|
<gh_stars>0
#ifndef __CLOCK_H__
#define __CLOCK_H__
void init_system_clock(void);
#endif
|
/**
* Class User for create user (object) with params: id, name, role and version.
* @author Didyk Andrey ([email protected]).
* @since 21.07.2017.
* @version 1.0.
*/
class User {
/**
* @param id - user id.
*/
private int id;
/**
* @param name - name user.
*/
private String name;
/**
* @param role - user role.
*/
private String role;
/**
* @param version - user version.
*/
private volatile int version;
/**
* User - constructor.
* @param id - user id.
* @param name - user name.
* @param role - user role.
**/
User(int id, String name, String role) {
this.id = id;
this.name = name;
this.role = role;
}
/**
* setRole - sets role for user and change role.
* @param role - user role.
*/
synchronized void setRole(String role) {
this.role = role;
this.version++;
}
/**
* setVersion - sets version for user.
* @param version - is version.
*/
synchronized void setVersion(int version) {
this.version = version;
}
/**
* getId - returns id for user.
* @return - returns id for user.
*/
int getId() {
return id;
}
/**
* getName - returns name for user.
* @return - returns name for user.
*/
String getName() {
return this.name;
}
/**
* getRole - returns role for user.
* @return - returns role for user.
*/
String getRole() {
return this.role;
}
/**
* getVersion - returns version for user.
* @return - returns version for user.
*/
int getVersion() {
return this.version;
}
/**
* equals - return boolean result.
* @param obj - object of class User.
* @return - returns boolean result "true" if id of user is same, and returns "false" - isn`t same.
*/
@Override
public boolean equals(Object obj) {
if (this == obj) {
return true;
}
if (!(obj instanceof User)) {
return false;
}
User user = (User) obj;
return id == user.id;
}
/**
* hashCode - returns hashCode for user.
* @return - returns hashCode for user.
*/
@Override
public int hashCode() {
return id;
}
/**
* toString - returns string format.
* @return - returns all information for user.
*/
@Override
public String toString() {
return String.format("%s%s%s%s%s%s%s%s%s%s%s", "User{", "id=", this.id, ", name=", this.name, ", ",
"role=", this.role, ", version=", this.version, "}");
}
}
|
<reponame>rokudo5262/phone<gh_stars>0
import { createReducer, on } from '@ngrx/store';
import { categoryInitialState, categoryAdapter } from '../states/categories.state';
import { CategoriesActions, CategoriesApiActions } from '../actions';
export const categoriesFeatureKey = 'categories';
export const reducer = createReducer(
categoryInitialState,
on(
CategoriesActions.loadCategories,
CategoriesApiActions.loadCategoriesSuccess,
(state, { categories }) => {
return categoryAdapter.addMany(categories, state);
},
),
on(
CategoriesActions.getCategoryDetail,
CategoriesApiActions.getCategoryDetailSuccess,
(state, { category }) => {
return categoryAdapter.addOne(category, state);
},
),
on(
CategoriesActions.addCategory,
CategoriesApiActions.addCategorySuccess,
(state, { category }) => categoryAdapter.addOne(category, state),
),
on(
CategoriesActions.updateCategory,
(state, { update }) => categoryAdapter.updateOne(update, state),
),
on(
CategoriesActions.deleteCategory,
(state, { update }) => categoryAdapter.updateOne(update, state),
),
on(
CategoriesActions.removeCategory,
CategoriesApiActions.removeCategorySuccess,
(state, { categoryId }) => categoryAdapter.removeOne(categoryId, state),
),
);
|
Kuala Lumpur Inner Ring Road
Edinburgh flyover
Construction began in late 2007 and was completed in the end of 2009. The project is led by the Kuala Lumpur City Hall (Dewan Bandaraya Kuala Lumpur (DBKL)).
Jalan Pudu-Hang Tuah intersections
The 114-year-old Pudu Prison's wall between Jalan Pudu and Jalan Hang Tuah was demolished on 20 June 2010 by the Kuala Lumpur City Hall (Dewan Bandaraya Kuala Lumpur (DBKL)) to make way for a road expansion and tunnel project on Jalan Pudu.
Section between Raja Chulan and Imbi
The section of the Inner Ring Road between Raja Chulan and Imbi intersections was changed to one-way road in 2007 because of the opening of the SMART Tunnel and the Sultan Ismail–Kampung Pandan Link. As a result, motorists travelling in clockwise direction are diverted to Jalan Raja Chulan and Jalan Imbi. However, the road divider along the section remained intact to retain the support of the overhead KL Monorail tracks. As a result, motorists travelling at the wrong side of the road may tend to cross illegally to the other carriageway, exposing them to risks of accidents.
|
""" Underlying Abstraction module for Docker.
This module abstracts [Python
Docker](https://docker-py.readthedocs.io/en/stable/) by wrapping it in a
`Driver` Class. This class forms the foundation of all different application
drivers. This is the primary module of the project.
Typical interfacing is done through inheritance.
``` python
from integration_tester import driver
class SomeNewDriver(driver.Driver):
...
```
This module will raise a `DockerNotAvailable` exception on import if Docker is
not running or incorrectly configured.
"""
import time
from typing import Dict, Optional, Tuple, Union
import docker
import requests
from integration_tester import errors
# Test Docker client connection
try:
docker.from_env().images.list()
except requests.exceptions.ConnectionError as error:
raise errors.DockerNotAvailable() from error
class Driver:
""" Base Docker Abstraction.
This Class abstracts the Python Docker SDK by starting, stopping and
cleaning Docker containers and images. This driver should only be used as a
base Class to other higher level Classes.
"""
_status = True
def __init__(self,
tag: str,
ports: Optional[Dict[int, Tuple[str, int]]] = None,
remove_image: bool = False):
""" Initialise the driver.
Initialisation includes creating and starting a detached instance of
the Docker container associated to the `tag` provided.
Args:
tag: Reference to the specific version of Docker Image to pull from
Docker Hub.
ports: Ports to expose from the container.
remove_image: Flag to delete the Docker Image from the local machine
on object deconstruction.
Tags refer to the Docker Image version "tag" which can be found on the
[Docker Hub](https://hub.docker.com/) for any given public image.
Ports should be provided in the following format:
``` python
{
port_from: (address_to, port_to),
}
```
This will bind `port_from` to the address `address_to` and to the port
`port_to`. I.E. `port_from` -> `address_to:port_to`.
"""
self.tag = tag
self._remove_image = remove_image
self._ports = {}
if ports is not None:
self._ports = ports
# Client has to be recreated each time it is used. See issue#5:
# https://github.com/Liamdoult/integration_tester/issues/5
client = self._get_docker_client()
container = client.containers.run(self.tag,
detach=True,
ports=self._ports)
self._container_id = container.id
def __del__(self) -> None:
""" Ensure proper removal of docker resources.
The container and its associated volume is stopped and *Force* deleted.
If `_remove_image` is set to True on initialisation this will delete
the downloaded (or existing) local image. This is a soft delete
(Meaning if there is an already linked container existing, it will
*not* delete the image). This is to ensure that we don't get any "YouR
CoDe BroKE mY dAtA ConTainEr" messages.
"""
# Client has to be recreated each time it is used. See issue#5:
# https://github.com/Liamdoult/integration_tester/issues/5
client = self._get_docker_client()
container = client.containers.get(self._container_id)
# Image needs to be retrieved prior to container object deconstruction.
image = container.image
container.stop()
container.remove(v=True, force=True)
# Image needs to be deleted after container deconstruction due to
# referencing issues.
if self._remove_image is True:
try:
client.images.remove(image.id)
except docker.errors.APIError as error:
if error.status_code != 409:
raise error
@staticmethod
def _get_docker_client() -> docker.DockerClient:
""" Create a new docker instance.
Returns:
A DockerClient object which is used to interact with the docker
service.
Exceptions:
errors.DockerNotAvailable: Raised if the connection to the Docker
service is interrupted.
"""
try:
client = docker.from_env()
client.images.list()
return client
except requests.exceptions.ConnectionError as error:
raise errors.DockerNotAvailable() from error
def ready(self) -> bool:
""" Container ready check.
This method *should* be overridden.
This method should return `True` when the software inside the container
has correctly loaded and is ready to receive connections.
"""
return self._status
# Pylint disabled: this method should be overridden and `self` may be
# required.
def reset(self) -> bool: # pylint: disable=R0201
""" Reset the service.
This method *should* be override.
This method should reset the software inside the container to `factory`
settings (original state).
"""
return
def wait_until_ready(self,
wait_interval: Union[float, int] = 1,
timeout: int = 60) -> None:
""" Block until container is ready to be used.
This blocking method extends `ready`. This means that it is not just a
check of when the Docker container is running but when the software
inside the container is "ready". i.e. Docker says the MongoDB container
is ready but the mongo application inside the container is starting.
Args:
wait_interval: Gaps between checks of the container (Not
recommended to change).
timeout: Timeout if the container does not become `ready`.
"""
start_time = time.time()
while not self.ready():
interval = time.time() - start_time
if interval >= timeout:
raise errors.ReadyTimeout("Container failed to start.")
# Make sure the wait will not be longer than the timeout
if timeout - interval < wait_interval:
time.sleep(timeout - interval)
else:
time.sleep(wait_interval)
|
Justice Minister Urmas Reinsalu (Pro Patria) said on Thursday that a potential no-deal exit of United Kingdom from the European Union won't mean new visa requirements for tourists. However, there are several other legal areas where the communication between the EU and UK would change.
In the event of a hard Brexit, Brits arriving in Estonia after 29 March this year will be subject to legislation intended for third country citizens. This means that if they intend to stay in Estonia for a longer period of time, they need to apply for a residency permit after three months, when the visa-free period ends.
EU citizens living in the UK, Estonians among them, will have to reapply for the status of a permanent UK resident, a requirement that also extends to family members. Estonian citizens travelling to the UK after 29 March with the intention to stay will also have to apply for a residency permit.
Mr Reinsalu pointed out that the UK has declared that visa requirements will not be imposed on students studying at British higher education institutions as well as on tourists.
In the case of a no-deal Brexit, anyone driving to the UK with their own vehicle will need the international Green Card to prove they have sufficient international insurance coverage. The same will apply to cars with a British registration plate travelling to Estonia.
Mr Reinsalu also warned that there is no telling yet how communications will be organised, meaning that those frequently phoning numbers in the UK might have to expect roaming charges and should thus be careful.
Other changes include the use of the EU's European health insurance card, which would no longer be valid in the UK. Mr Reinsalu considers it "reasonable" for travellers to get private health insurance coverage for trips to the UK in such a situation.
Concerning online purchases from UK companies, people need to keep in mind that all legal disputes would have to be resolved in the UK in the case of a hard Brexit, Mr Reinsalu added, as all trade agreements between the EU and the UK would expire. On top of that, as the UK is in fact leaving the common market and customs area, the Tax and Customs Board after a no-deal Brexit will also treat the UK as a third country, with all the customs regulations implied.
|
/**
* Validates the content url controller
*
* @return true if the control contains valid data; otherwise, false will be
* returned
*/
protected boolean validateContentUrlControl() {
ContentType contentType = (ContentType) mActionContentTypeControl.getSelectedItem();
if (contentType == ContentType.APP_URL || contentType == ContentType.WEB_URL) {
if (mContentUrlControl.getText().length() < 1) {
mContentUrlControl.setError("the beacon action content url cannot be empty");
return false;
}
}
return true;
}
|
<reponame>timothee-haudebourg/bottle
#![feature(arbitrary_self_types)]
use bottle::{Receiver, Remote, Handler, EventQueue};
struct Foo {
value: i32
}
struct Reflect;
impl bottle::Event for Reflect {
type Response = Remote<dyn Handler<Event>>;
}
enum Event {
Foo
}
impl bottle::Event for Event {
type Response = ();
}
impl Handler<Reflect> for Foo {
fn handle(self: Receiver<Self>, _event: Reflect) -> Remote<dyn Handler<Event>> {
let remote = self.as_remote();
remote as Remote<dyn Handler<Event>>
}
}
impl Handler<Event> for Foo {
fn handle(self: Receiver<Self>, _event: Event) {
println!("my value is {}", self.value)
}
}
#[async_std::main]
async fn main() {
let queue = EventQueue::new();
let foo = Remote::new(queue.reference(), Foo { value: 42 });
std::thread::spawn(move || {
async_std::task::block_on(queue.process())
});
let remote = foo.send(Reflect).await;
remote.send(Event::Foo).await;
}
|
Self-assembled magnetic nanowire arrays Programmable defect-free self-assembled magnetic nanowire arrays were produced by using specially designed nano-magnet arrays and external magnetic field. Nickel nanowires were grown on nanoporous template using electrodeposition technique. Magnetic properties, structure and morphology were studied carefully by using vibrating sample magnetometer (VSM), X-ray diffraction (XRD), and scanning electron microscopy (SEM). Individual magnetic nanowires were successfully trapped between the nanomagnet pairs with a width of 200 nm and a thickness of 100 nm. Templated self-assembly of magnetic nanowires was successfully demonstrated with Permalloy nano-magnet arrays in both 1D and 2D.
|
Vasodynamic and angiogenic effects of eicosanoids in the eye. In the eye, the main vascular response to PGs is vasodilation. Although its effect has not been studied in detail, TxA2 can be expected to cause vasoconstriction in the eye, as it does in other tissues. When applied in high doses, PGs also increase the permeability of the microvasculature in the anterior segment, especially in rabbits. This may contribute to protein leakage associated with disruption of the BAB. The peptidoleukotrienes seem to reduce blood flow in the anterior segment, although more data will be required to substantiate this observation. It has been suggested over the years that PGs have a physiologic role in the autoregulation of the retinal blood flow. It has also been hypothesized that PGs, particularly those of the E series, play an important role in corneal neovascularization. The evidence obtained thus far in support of such hypothesis is not conclusive. It is possible that in the course of corneal vascularization or its induction, PGs are released, together with other mediators such as lipoxygenase products, but the pathophysiologic mechanisms are poorly understood. The role of eicosanoids in CME remains speculative. It is possible that other compounds in the cyclooxygenase pathway such as thromboxane and prostacyclin or some lipoxygenase products play a more important role in CME than the classical PGs, but, there is insufficient data so far to implicate any one eicosanoid or any other autacoids in the initiation of the vascular changes that are associated with CME.
|
Ginsenoside Rb1 prevents interleukin-1 beta induced inflammation and apoptosis in human articular chondrocytes Purpose Osteoarthritis (OA) is an age-related joint disease that is characterised by the degeneration of articular chondrocytes. Ginsenosides, the most important pharmacological ingredients of ginseng, have been proven to provide effective therapy for neurodegenerative diseases and can inhibit cell apoptosis. We investigated whether ginsenoside Rb1 can modulate inflammation and apoptosis in human chondrocytes. Methods Chondrocytes were isolated from OA patients undergoing total knee replacement surgery. Apoptosis was assessed by TUNEL (terminal deoxyribonucleotide transferasemediated dUTP nick end-labelling)-positive staining. Levels of PGE2 and NO2- were detected by ELISA. Gene expression levels were measured for type II collagen (Col2A1), aggrecan, MMP-13, COX-2, iNOS, caspase-3, and PARP. Results The results showed that TUNEL-positive staining chondrocytes were decreased by Rb1 compared with IL-1. Both 10 or 100 g/ml Rb1 inhibited the effect of IL-1 on chondrocytes by decreasing levels of PGE2, NO2-, MMP-13, COX-2, iNOS, caspase-3 and PARP and increasing aggrecan and Col2A1 gene expression levels, to block IL-1-induced cell inflammation and apoptosis. Conclusions The results suggest that Rb1 possesses potential anti-inflammatory and anti-apoptotic properties in human chondrocytes, possibly by binding to oestrogen receptors to exert its pharmacological effects. Introduction Osteoarthritis (OA) is an age-related joint disease that is characterised by the degeneration of articular chondrocytes. Chondrocytes are only one cell type which are responsible for the maintenance of the extracellular matrix (ECM). Interleukin-1 beta (IL-1) causes inflammation of articular cartilage, stimulates the production of matrix metalloproteinases (MMPs), cyclo-oxygenase-2 (COX-2) and prostaglandin E2 (PGE2), and is therefore a target for therapeutic strategies. These inflammatory cytokines can further inhibit the synthesis of the main constituents of the ECM, type II collagen (Col2A1) and aggrecan (ACAN), and can disrupt the balance of metabolism in articular cartilage. Apoptosis is thought to have a pivotal role in human and animal OA. It has been reported that apoptosis of chondrocytes can be induced by different agents, such as caspase-3 and inducible nitric oxide synthase (iNOS). During apoptotic cell death caspases activate the cleavage of the DNA repair enzyme poly(ADPribose) polymerase (PARP) in chondrocytes. Ginsenosides, the most important pharmacological ingredients of ginseng, have been proven to provide effective therapy for neurodegenerative diseases and can inhibit cell apoptosis. To date, more than 40 different ginsenosides have been identified and isolated from the root of ginseng. Ginsenosides are generally divided into two groups, panaxadiols and panaxatriols, based on the chemical structure. Panaxadiols include compounds called ginsenoside Rb1 (Rb1), which is the most abundant among more than 40 ginsenosides. A previous study has shown that Rb1 can exert a suppressive effect on local inflammation in rats with cerebral ischemia. Furthermore, Rb1 protects PC12 cells from apoptosis through stimulation of the oestrogen receptor. However, no data yet has been reported concerning the effect of Rb1 on apoptosis and inflammation in chondrocytes or its therapeutic role in OA. In this study we investigated whether Rb1 is able to inhibit apoptosis and inflammatory responses in OA by establishing an in vitro model in human chondrocytes. Cell culture All experiments were approved by the Ethical Committee of Nanjing Medical University. Articular cartilage samples were obtained from OA patients undergoing total knee replacement surgery. All patients were diagnosed using the criteria of the American College of Rheumatology. Harvested cartilage was minced into small pieces and incubated in a trypsin-containing solution for two hours at 37°C. The pieces were then washed with phosphatebuffered saline (PBS) and incubated at 37°C overnight in 0.2 % collagenase. After digestion, the chondrocytes were collected and cultured in DMEM/F12 medium containing 10 % FBS and 100U/ml of penicillin-streptomycin at 37°C in a humidified 5 % CO 2 atmosphere. Cells were used at passage 0 or 1 to avoid dedifferentiation. Experimental design First-generation human chondrocytes were cultured in DMEM medium without serum and with 2 % serumfree bovine serum albumin (BSA) for 24 hours after washing three times with PBS to prevent the influence of other cytokines. Cells were treated with 10 ng/ml IL-1 and Rb1 at 1, 10 and 100 g/ml. A positive control group consisted of cells treated with 10 ng/ml IL-1 alone. A negative control group was untreated except for a change in the medium. Cells were harvested after incubation for 24 hours. Cell apoptosis According to the manufacturer's instructions, terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate-biotin nick end-labelling (TUNEL) was performed to detect cell apoptosis. Images were randomly selected from two sections of each specimen and the stained cells were counted under 400 magnification. Chondrocytes were stained with DAPI at 37°C for 30 minutes Apoptotic chondrocytes were recognised with dual TUNEL and DAPI staining. For each group in experiments, three images were randomly selected using an inverted fluorescence microscope. TRIzol and chloroform reagents were used to extract total RNA according to the manufacturer's instructions. RNA concentrations were measured using a spectrophotometer; samples with values of 1.7-2.0 were used. Complimentary DNA (cDNA) was synthesised from RNA using reverse transcriptase and a PrimeScript RT reagent kit (Fermentas, Lithuania). Real-time PCR was carried out by combining 2 l of cDNA with reagents from the SYBR Premix Ex Taq II to give a total volume of 20 l. The generation of specific PCR products was confirmed by melting-curve analysis; mRNA encoding actin served as an internal control. Gene expression data for the proteins of interest were standardised against -actin. The primers (TaKaRa, Japan) used are shown in Table 1. Analysis Results are expressed as mean ± standard deviation (SD). All analyses were performed using SPSS version 16.0 software (SPSS Inc., Chicago, IL, USA). For multiple comparisons, one-way analysis of variance was used depending on the experiment design. A p value of <0.05 was considered statistically significant. Effect of Rb1 on chondrocyte apoptosis TUNEL staining confirmed the effect of Rb1 on chondrocyte apoptosis (Fig. 1). Percentage of TUNEL positive cells after 48 hours of IL-1 exposure was 37.30 %, which was higher than control (p<0.05). When the chondrocytes were cocultured with 10 g/ml of IL-1 and Rb1 the percentage of TUNEL positive cells (20.33 %) was lower than the IL-1 group (p<0.05). Effects of Rb1 on IL-1-induced Col2A1 and AGAN gene expression Quantitative real-time PCR was used to analyse gene expression. Stimulation with IL-1 (10 ng/ml) led to a 2.9-fold decrease of Col2A1 and ACAN gene expression compared to the negative control (p<0.05) (Fig. 2). They were increased 1.7and 2.3-fold (p<0.05) after co-treatment with 10 g/ml Rb1, and 2.4-and 2.0-fold (p<0.05) after co-treatment with 100g/ml Rb1. However, no effect was found with 1 g/ml Rb1 (p>0.05). The effect of Rb1 was found to be dose-dependent. Rb1 inhibited iNOS, caspase-3 and PARP gene expression and IL-1-induced nitric oxide (NO) production Stimulation with IL-1 (10 ng/ml) for 24 hours led to a 111.2-fold increase in iNOS gene expression as well as a six-fold increase in NO production in the supernatant (p<0.05) (Fig. 4). At a concentration of 1 g/ml, Rb1 slightly decreased the gene expression of iNOS and production of NO (p>0.05), whereas at a concentration of 10 g/ml the gene expression of iNOS and production of NO decreased by 6.5-and 2.6-fold, respectively (p<0.05). Rb1 at a concentration of 100 g/ml decreased the gene expression of iNOS and production of NO 6.5-and 4.0-fold, respectively (p<0.05). Stimulation with IL-1 (10 ng/ml) led to a 5.4fold increase in caspase-3 and a 3.0-fold increase in PARP (p<0.05) (Fig. 4). They were decreased 2.3-and 2.7-fold after co-treatment with 10 g/ml Rb1 and 1.9-and 2.0-fold after co-treatment with 100 g/ml Rb1 (p<0.05). However, no effect was found at 1 g/ml Rb1 (p>0.05). Discussion Ginsenoside Rb1 is one of the richest subtypes in quantity among 30 ginsenosides, which exert multiple biological actions including anti-inflammatory, anti-apoptosis, and neuroprotective activities. In this paper, our findings suggest that Rb1 can inhibit the production of inflammatory agents such as MMP-13, COX-2, PGE2, iNOS and NO, and decrease Col2A1 and ACAN degrad a t i o n i n d u c e d b y I L -1 i n h u m a n a r t i c u l a r chondrocytes. Real-time PCR results showed that Rb1 inhibited the gene expression of the apoptotic biomarkers, caspase-3 and PARP, in chondrocytes. IL-1 leads to the production and accumulation of high levels of pro-inflammatory cytokines in OA synovial cells and chondrocytes. These trigger the production of additional proinflammatory cytokines which induce genes encoding COX-2 and matrix-degrading enzymes such as MMPs. Furthermore, Fig. 2 Effect of Rb1 on IL-1induced Col2A1 and AGAN gene expression. The normalised gene expression levels are expressed as ratios of the copy number of the mRNA and that of -actin cDNA. Data were obtained from three independent experiments and are presented as the mean ± SD. ★★p<0.05 vs. the control. ★p<0.05 Fig. 1 Effect of Rb1 on chondrocyte apoptosis. All data are mean ± SD. ★★p<0.05 vs. the IL group. ★p<0.05 vs. the control COX-2 converts arachidonic acid into PGE2, which together with COX-2, sensitise peripheral receptors and cause pain. In the present study, we found that elevated COX-2 and MMP-13 gene expression and PGE2 production in IL-1-induced cells was suppressed by the addition of Rb1. This suggests that Rb1 may act as an anti-inflammatory similar to NSAIDs, which have been shown to ameliorate OA symptoms by inhibiting the expression of COX-2 and PGE2. Fig. 3 Effect of Rb1 on IL-1induced MMP-13 and COX-2 gene expression and IL-1induced PGE2 production. The normalised gene expression levels are expressed as ratios of the copy number of the mRNA and that of -actin cDNA. Data were obtained from three independent experiments and are presented as the mean ± SD. ★★p<0.05 vs. the control. ★p<0.05 vs. the IL-1 group Fig. 4 Effect of Rb1 on IL-1induced iNOS caspase-3 and PARP gene expression and IL-1-induced NO production. The normalized gene expression levels are expressed as ratios of the copy number of the mRNA and that of -actin cDNA. Culture media were analysed for NO2-concentration. All data shown were the mean ± SD of NO2-concentration as a percentage of the control. ★★p<0.05 vs. the control. ★p<0.05 vs. the IL-1 It is widely accepted that increased IL-1 levels can trigger apoptosis in chondrocytes, which leads to further degenerative changes in cartilage. Of the 12 caspases in mammals, caspase-3 is a crucial biomarker of apoptosis that also acts as an apoptotic executor. PARP, a downstream target of caspase-3, is a nuclear enzyme normally involved in DNA repair, but extensive activation of PARP promotes cell death. We found that levels of mRNAs encoding caspase-3 and PARP in human chondrocytes treated with IL-1 were upregulated, whereas co-treatment with Rb1 downregulated the expression levels of these genes in IL-1-stimulated chondrocytes. Our findings indicate that Rb1 could inhibit chondrocyte apoptosis induced by IL-1. Evidence has confirmed the existence of two oestrogen receptors (ERs) in the articular cartilage, indicating that the cartilage can respond to estrogen. Oestrogen has a protective effect in OA. Rb1 could exert an oestrogen-like effect by binding to ERs. Hashimoto et al. found that ginsenoside Rb1 protects PC12 cells from caspase-3-dependent apoptosis through stimulation of oestrogen receptors. We suggest that Rb1 acts in a similar way to oestrogen in treating OA. In conclusion, our work shows that Rb1 possesses potential anti-inflammatory and anti-apoptotic properties in human chondrocytes, possibly through binding to ERs to exert pharmacological effects. These findings suggest that Rb1 may help protect against the degeneration of cartilage in patients with OA. Conflict of interest The authors declare that they have no conflict of interest. Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
|
class AssertValidDirection:
"""AssertValidDirection(direction: Direction)
Raised when direction not upward/downward
"""
def __init__(self, direction: Direction):
options = [Direction.UPWARD, Direction.DOWNWARD]
if direction not in options:
raise KeyError(
f'direction expected from {options}, '
f'found {direction}')
|
Process intensification for the enhancement of growth and chlorophyll molecules of isolated Chlorella thermophila: A systematic experimental and optimization approach. In our current work, we have optimized six physicochemical parameters (light intensity, light period, pH, inoculum size, culture period, and salt concentration) toward growth and chlorophyll synthesis using isolated fresh water microalgae Chlorella thermophila . Here, both experimental and computational approaches were employed for the process intensification. Results revealed that the content of biomass and chlorophyll were enhanced by 118% and 95%, respectively, with productivity enhancement of 30% for biomass and 61% for chlorophyll from the optimization of physicochemical parameters. Further, optimum light intensity was found to be 128mol m-2 s-1 after conducting experiments in optimized chemical and physicochemical conditions, contributing to the enhancement of productivity of 46% for biomass and 106% for chlorophyll. Urea was found to be the most effective nitrogen source with an increase of 70% and 160% biomass and chlorophyll productivity, respectively. Moreover, sucrose as a carbon source contributed to an increase of 97% and 264% biomass and chlorophyll productivity.
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<reponame>AdrianBueno/ShareTripServices<gh_stars>0
package com.sdi.business.impl;
import java.util.List;
import javax.ejb.Stateless;
import javax.jws.WebService;
import com.sdi.business.impl.commands.CommandExecutor;
import com.sdi.business.impl.commands.seat.ListSeatsFromTrip;
import com.sdi.business.impl.commands.seat.ListSeatsFromUser;
import com.sdi.business.impl.face.local.LocalSeatsService;
import com.sdi.business.impl.face.remote.RemoteSeatsService;
import com.sdi.infrastructure.exception.BusinessException;
import com.sdi.infrastructure.model.Seat;
import com.sdi.infrastructure.model.types.TravelStatus;
@Stateless
@WebService(name = "SeatsService")
public class EjbSeatsService implements LocalSeatsService, RemoteSeatsService {
@SuppressWarnings("unchecked")
@Override
public List<Seat> listSeatsFromTrip(Long t,TravelStatus s)
throws BusinessException {
return (List<Seat>) CommandExecutor.execute(new ListSeatsFromTrip(t,s));
}
@SuppressWarnings("unchecked")
@Override
public List<Seat> listSeatsFromUser(Long u, TravelStatus s)
throws BusinessException {
return (List<Seat>) CommandExecutor.execute(new ListSeatsFromUser(u,s));
}
}
|
<gh_stars>0
package org.javaswift.joss.model;
public class FormPost {
public long expires;
public String signature;
}
|
Validity of Calibrated Photoluminescence Lifetime Measurements of Crystalline Silicon Wafers for Arbitrary Lifetime and Injection Ranges We investigate the validity of calibrated photoluminescence lifetime measurements of crystalline silicon wafers for arbitrary lifetime and injection ranges. Absolute lifetime images are obtained from steady-state photoluminescence measurements by relating the photoluminescence signal to the excess carrier density. Since the luminescence signal is expected to be related to the integral of the depth distribution of the excess carrier density, an adequate calibration of the luminescence signal requires a secondary method which yields the integral of the depth distribution of the excess carrier density in absolute units. In this paper, we investigate the applicability of steady-state photoconductance measurements for the calibration of the photoluminescence signal. We derive a generalized relation linking the photoluminescence signal with the excess carrier density, considering the impact of an inhomogeneous carrier concentration profile. We experimentally verify the impact of the carrier distribution on the photoluminescence calibration by investigating two silicon wafers with different electronic bulk properties. Finally, we propose an iterative correction procedure reducing the deviations due to an inhomogeneous carrier density profile of calibrated photoluminescence-based lifetime measurements significantly.
|
<reponame>jstoffan/box-annotations
import { Annotation, Collaborator, NewAnnotation, Permissions, Token } from '../@types';
export type APICollection<R> = {
entries: R[];
limit: number;
next_marker: string | null;
previous_marker: string | null;
};
export type APIError = {
code: string;
context_info: unknown;
help_url: string;
message: string;
request_id: string;
status: number;
type: 'error';
};
export type APIOptions = {
apiHost?: string;
clientName?: string;
token: Token;
};
export interface AnnotationsAPI {
createAnnotation(
fileId: string | null,
fileVersionId: string | null,
payload: NewAnnotation,
permissions: Permissions,
successCallback: (result: Annotation) => void,
errorCallback: (error: APIError) => void,
): Promise<void>;
getAnnotations(
fileId: string | null,
fileVersionId: string | null,
permissions: Permissions,
successCallback: (result: APICollection<Annotation>) => void,
errorCallback: (error: APIError) => void,
limit?: number,
shouldFetchAll?: boolean,
): Promise<void>;
destroy(): void;
}
export interface CollaboratorsAPI {
getFileCollaborators(
fileId: string | null,
successCallback: (result: APICollection<Collaborator>) => void,
errorCallback: (error: APIError) => void,
requestData?: {
filter_term?: string;
include_groups?: boolean;
include_uploader_collabs?: boolean;
},
limit?: number,
): Promise<void>;
destroy(): void;
}
|
const Requirements: string[] = [
"At Least 13 years of age",
"Valid Infinite Flight Pro subscription",
"At Least Grade 3 in Infinite Flight",
"Must have access to Discord and be able to maintain maturity there",
"Able to file at least 1 flight per month",
"Landing / Violation ratio must be no higher than 0.25",
];
export default Requirements;
|
Sinn Féin Matt Carthy has said Prime Minister Theresa May's announcement of a snap Westminster General Election is “further evidence” that concerns of people in the North of Ireland “do not register” on the British Government agenda. However, the European MEP stated that Irish republicans must seize the opportunity to further to build political progress towards a referendum on Irish unity.
“The British Government will always put its own interests above any others and, at this time, that means the concerns of a very reactionary Tory Right Wing. Brexit has highlighted, in stark terms, the undemocratic, unnatural and unjust nature of Partition,” Mr Carthy said.
Unification
The former Monaghan Mayor stated Brexit and the recent Assembly election, which heralded an end to unionist political majority, have changed the context of the argument for a United Ireland.
“Despite the narrow political motivation behind the calling of a Westminster election, Irish republicans must seize the opportunity to further to build political progress towards a referendum on Irish unity. It is an opportunity to reject the Tory political agenda, to re-assert the North’s vote to remain within the EU, and to advance the cause of a shared inclusive and United Ireland,” Mr Carthy added.
Instability
But while sitting MP for Fermanagh and South Tyrone Tom Elliott admits rumours of a snap election have “ebbed and flowed” for sometime, its planned arrival on June 8 is “not perfect nor ideal” for political stability in Northern Ireland.
Mr Elliott, who assumed office of MLA from Sinn Fein's Michelle Gildernew in 2015, believes a Westminster election will only serve to heighten political tensions and potentially cause further instability. “Politically Northern Ireland is not a stable place at this time. Sinn Fein for their part wants that, which is unfortunate and doesn't help anyone. A Westminster election will only serve to add to that sense of instability,” he said.
“One-off chance”
In her address outside 10 Downing Street earlier today, Ms May said her government had the right plan for negotiating the terms of Britain's exit from the EU. The move comes despite the Prime Minister repeatedly denying she would call an election before the next scheduled poll in 2020.
“We need a general election and we need one now. We have at this moment a one-off chance to get this done ... before the detailed talks begin,” said Mrs May, who if her party emerges triumphant, would have a mandate both for her leadership and her negotiating position on Brexit.
Explaining the decision, Mrs May said: “The country is coming together but Westminster is not.”
She furthermore laid down the gauntlet to opposition parties, stating: “This is your moment to show you mean it - to show you're not opposing the government for the sake of it, to show that you do not treat politics as a game.”
|
package com.mangopay.core.enumerations;
/**
* Sort direction enumeration.
*/
public enum SortDirection {
none,
asc,
desc
}
|
Newly released documents from an investigation show the Wisconsin governor and potential presidential candidate tried to evade campaign laws.
Wisconsin Gov. Scott Walker allegedly was at the center of a criminal scheme to illegally coordinate fundraising with conservative groups and his recall-election campaign, according to newly released documents from a Wisconsin investigation.
Prosecutors allege that Walker, his chief of staff, and others on his campaign tried to bypass state election laws by working with 12 national conservative groups to raise money for his election effort in 2011 and 2012.
Charges, however, have not been filed against Walker or any member of his staff.
The investigation found an email from May 4, 2011, between Walker and former Bush adviser Karl Rove, in which Walker says that one of his top deputies, R.J. Johnson, would lead the coordination effort.
Bottom-line: R.J. helps keep in place a team that is wildly successful in Wisconsin. We are running 9 recall elections and it will be like 9 congressional markets in every market in the state (and Twin Cities).
The documents come from part of a "John Doe" investigation — called that because it is conducted in private and is sealed from the public. A federal judge unsealed the investigation Thursday.
The investigation has been a thorn in the side of Walker in the past several years, especially after investigators released emails from Walker aides recently showing racist and homophobic jokes.
Democrats in Wisconsin are convinced that Walker wants to run for president in 2016, and that he has the skills to do so. As National Journal's Tim Alberta writes in a new profile, Walker's an unlikely character for the job.
Walker doesn't seem like a man who is likely to be elected governor this November for the third time in four years — let alone one who is considered a top-tier contender for the Republican presidential nomination. He's not a scholarly policy wonk like Bobby Jindal. He doesn't have the dynastic resources of Jeb Bush or Rand Paul. He isn't a skillful orator like Marco Rubio or Ted Cruz. Nor is he a commanding, charismatic presence like his friend Chris Christie. He is, in fact, more like a reverse Christie: The New Jersey governor is belligerent on the outside and moderate on the inside; Walker is a rock-ribbed conservative in a genial, unexceptional package.
Being a pleasant guy — agreeable, inoffensive — isn't an obvious political strength, but it has been one for Walker. His soft-spoken nature and his humdrum personal style have led his opponents repeatedly to underestimate his ambition, his determination, and his strategic skill. ...Walker's innocuous bearing has allowed him to move calmly toward his prey without startling it.
But now, like Christie, he's finding himself at the center of a scandal that could shatter his hopes for the presidency.
|
<filename>src/main/java/com/strandls/taxonomy/dao/SpeciesGroupMappingDao.java
/**
*
*/
package com.strandls.taxonomy.dao;
import java.util.ArrayList;
import java.util.HashSet;
import java.util.List;
import java.util.Set;
import org.hibernate.Session;
import org.hibernate.SessionFactory;
import org.hibernate.query.Query;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import javax.inject.Inject;
import com.strandls.taxonomy.pojo.SpeciesGroupMapping;
import com.strandls.taxonomy.util.AbstractDAO;
/**
* @author <NAME>
*
*/
public class SpeciesGroupMappingDao extends AbstractDAO<SpeciesGroupMapping, Long> {
private final Logger logger = LoggerFactory.getLogger(SpeciesGroupMappingDao.class);
/**
* @param sessionFactory
*/
@Inject
protected SpeciesGroupMappingDao(SessionFactory sessionFactory) {
super(sessionFactory);
}
@Override
public SpeciesGroupMapping findById(Long id) {
Session session = sessionFactory.openSession();
SpeciesGroupMapping entity = null;
try {
entity = session.get(SpeciesGroupMapping.class, id);
} catch (Exception e) {
logger.error(e.getMessage());
} finally {
session.close();
}
return entity;
}
@SuppressWarnings("unchecked")
public List<SpeciesGroupMapping> getTaxonomyId(Long sGroup) {
String qry = "from SpeciesGroupMapping where speciesGroupId = :sGroup";
Session session = sessionFactory.openSession();
List<SpeciesGroupMapping> result = new ArrayList<>();
try {
Query<SpeciesGroupMapping> query = session.createQuery(qry);
query.setParameter("sGroup", sGroup);
result = query.getResultList();
} catch (Exception e) {
logger.error(e.getMessage());
} finally {
session.close();
}
return result;
}
public Set<String> getTaxonIds(Long speciesGroupId) {
String qry = "from SpeciesGroupMapping where speciesGroupId = :sGroup";
Session session = sessionFactory.openSession();
List<SpeciesGroupMapping> speciesGroupMappings = new ArrayList<>();
try {
Query<SpeciesGroupMapping> query = session.createQuery(qry, SpeciesGroupMapping.class);
query.setParameter("sGroup", speciesGroupId);
speciesGroupMappings = query.getResultList();
} catch (Exception e) {
logger.error(e.getMessage());
} finally {
session.close();
}
Set<String> result = new HashSet<>();
for(SpeciesGroupMapping s : speciesGroupMappings) {
result.add(s.getTaxonConceptId().toString());
}
return result;
}
}
|
A burglary investigation in rural Monroe County resulted in the arrest of one person and an ongoing search for a second suspect.
Monroe County Sheriff’s Deputies made contact with Jeremy Pipes at a residence on Monroe County Road 1009 on Friday, Jan. 25. A second person, Shawn Reese, 42, of Moberly, is believed to have fled.
The Monroe County Sheriff’s Office, the Missouri State Highway Patrol, the Randolph County Sheriff’s Office and the K-9 unit with the Shelbina Police Department assisted in the search for Reese. A subsequent search of the property resulted in the recovery of multiple stolen items.
Monroe County Prosecuting Attorney Talley Kendrick has charged both subjects with burglary. Pipes is being held on a $30,000 cash-only bond.
Authorities are asking for the assistance of the public in locating Shawn Reese, who is currently wanted on a $30,000 cash-only warrant.
“Reese may be driving a stolen maroon 2005 Ford Expedition with license plate #CN8W5L,” said Monroe County Chief Deputy Joe Colston.
Anyone with any information can contact the Monroe County Sheriff’s Office at 660-327-4060.
|
<gh_stars>1000+
/**
* @worklet
*/
export const move = <T>(input: T[], from: number, to: number) => {
"worklet";
const offsets = input.slice();
while (from < 0) {
from += offsets.length;
}
while (to < 0) {
to += offsets.length;
}
if (to >= offsets.length) {
let k = to - offsets.length;
while (k-- + 1) {
offsets.push();
}
}
offsets.splice(to, 0, offsets.splice(from, 1)[0]);
return offsets;
};
|
def factory(sheet: 'Sheet', index: int, reference_index: int) -> 'Row':
row_key = sheet, index
try:
row = Row._all[row_key]
except KeyError:
row = Office.get_row_class()(sheet, index, reference_index)
return row
|
<filename>fuzzer/src/black_box.rs
/*
* Copyright (c) 2014 <NAME>
* Licensed under the MIT license
*/
// stolen from criterion
// https://github.com/bheisler/criterion.rs/blob/c21de8397a1e879321ab4ffbf3d5a6b0997300c3/src/lib.rs#L157
pub fn black_box<T>(dummy: T) -> T {
unsafe {
let ret = std::ptr::read_volatile(&dummy);
std::mem::forget(dummy);
ret
}
}
|
// Checks if the given ref should be ignored
func (s *Server) ignoreRef(rawRef string) bool {
if rawRef[:10] == "refs/tags/" && !s.IgnoreTags {
return false
}
return rawRef[:11] != "refs/heads/"
}
|
Researchers at Rice University's Department of Mechanical Engineering and Materials Science have successfully created single-atom sheets of an insulator: hexagonal Boron Nitride (h-BN).The breakthrough could help graphene kick silicon back into the 20th century, paving the way for nanoscale field-effect transistors, quantum capacitors or biosensors.
Researchers at Rice University's Department of Mechanical Engineering and Materials Science have successfully created single-atom sheets of an insulator: hexagonal Boron Nitride (h-BN).
The breakthrough could help graphene kick silicon back into the 20th century, paving the way for nanoscale field-effect transistors, quantum capacitors or biosensors.
The used vapour deposition to deposit a layer of h-BN between one and five atoms thick onto a copper substrate. It can then be tranferred to other materials.
More in the announcement here.
|
Sex reversal in medaka treated in vitro with 17methyldihydrotestosterone during oocyte maturation Using the SrR strain of the medaka Oryzias latipes, we examined the effect of a nonaromatizable androgen on sex determination. Intrafollicular immature oocytes isolated before breakdown of the germinal vesicle were incubated in the presence of 17methyldihydrotestosterone (MDHT) for about 10 h during their maturational period. At the end of incubation, mature oocytes were rinsed and then artificially inseminated in regular saline. The fertilized eggs were then allowed to develop in tap water, and the fry were reared on a regular powdered diet until adulthood. Sex reversal of female to male was observed in a manner dependent on the dose of MDHT. In the solvent control group in which intrafollicular oocytes were matured in medium containing no exogenous androgen, no sex reversal was observed. The present finding, that the sex of medakas can be reversed by a single in vitro exposure of immature oocytes to androgen during the preovulatory period, suggests the existence in the oocyte of a sex determinant sensitive to sex steroids. This method for controlling the sex of eggs before fertilization may establish sexdetermined eggs as potent material for investigating the mechanism of sex determination in the medaka.
|
Development of a new nanocrystalline alloy for X-ray shielding ABSTRACT The purpose of this study was to develop a new nanocrystalline alloy material, which can replace lead for the purposes of radiation shielding as it is not hazardous to the human body and it is light in weight, to use the developed alloy in a fiber, and to evaluate its performance. This study used tungsten carbide and cobalt as the base metals and developed a new nanocrystalline alloy material. Then, radiation-shielding fibers 0.2 and 0.4mm thick were created from the prepared tungsten carbide and cobalt powder. Equivalent dose was measured and shielding rate was obtained by the lead-equivalent test method for X-ray protection of goods suggested in the Korean Standard. According to our results, the shielding rate of the 0.2-mm-thick WCCo alloy was 96.52% at a tube voltage of 50kVp, 94.86% at a tube voltage of 80kVp, and 94.10% at a tube voltage of 100kVp. The shielding rate of the 0.4-mm-thick WCCo alloy was 97.47% at a tube voltage of 50kVp, 96.57% at a tube voltage of 80kVp, and 95.63% at a tube voltage of 100kVp. It is believed that the nanocrystalline WCCo alloy developed for radiation shielding in this study will contribute to a decrease in primary X-ray exposure as well as exposure to low-dose secondary X-rays, such as scattered rays. Furthermore, the use of a nanocrystalline WCCo alloy oxide rather than lead will allow for the development of shielding wear that is lighter and contribute to the development of various radiation-shielding products made of environmentally friendly materials.
|
<gh_stars>0
import { EthSignature } from "@connext/types";
import {
sign,
encrypt,
decrypt,
keccak256,
serialize,
deserialize,
hexToBuffer,
bufferToHex,
utf8ToBuffer,
bufferToUtf8,
concatBuffers,
addHexPrefix,
} from "eccrypto-js";
export const ETH_SIGN_PREFIX = "\x19Ethereum Signed Message:\n";
export function hashMessage(message: Buffer | string): Buffer {
const data = Buffer.isBuffer(message) ? message : utf8ToBuffer(message);
return keccak256(concatBuffers(utf8ToBuffer(ETH_SIGN_PREFIX), utf8ToBuffer(String(data.length)), data));
}
export function splitSignature(sig: Buffer): EthSignature {
return {
r: sig.slice(0, 32).toString("hex"),
s: sig.slice(32, 64).toString("hex"),
v: sig.slice(64, 65).toString("hex"),
};
}
export function joinSignature(sig: EthSignature): Buffer {
return concatBuffers(hexToBuffer(sig.r), hexToBuffer(sig.s), hexToBuffer(sig.v));
}
export async function signDigest(privateKey: Buffer, digest: Buffer): Promise<Buffer> {
const sig = await sign(privateKey, digest);
return sig;
}
export async function signMessage(privateKey: Buffer | string, message: Buffer | string): Promise<string> {
privateKey = Buffer.isBuffer(privateKey) ? privateKey : hexToBuffer(privateKey);
const hash = hashMessage(message);
const sig = await signDigest(privateKey, hash);
return addHexPrefix(bufferToHex(sig));
}
export async function encryptWithPublicKey(publicKey: string, message: string): Promise<string> {
const encrypted = await encrypt(hexToBuffer(publicKey), utf8ToBuffer(message));
return bufferToHex(serialize(encrypted));
}
export async function decryptWithPrivateKey(privateKey: string, message: string): Promise<string> {
const encrypted = deserialize(hexToBuffer(message));
const decrypted = await decrypt(hexToBuffer(privateKey), encrypted);
return bufferToUtf8(decrypted);
}
|
package store
import (
"database/sql"
"errors"
"fmt"
)
// ErrNoRows is returned if expected result is empty
var ErrNoRows = errors.New("No rows found")
// DBError returns basic error plus original query
type DBError interface {
error
Query() string
}
type dbError struct {
err error
query string
}
func (e *dbError) Query() string {
return e.query
}
func (e *dbError) Error() string {
return fmt.Sprintf("Err: %v\r\nSQL: %v", e.err.Error(), e.Query())
}
func handleError(err error, query string) error {
if err == nil {
return nil
}
if err == sql.ErrNoRows {
return ErrNoRows
}
return &dbError{err, query}
}
|
package loops;
public class Continue {
public static void main(String[] args) {
for(int i = 1; i<=100; i++) {
if(i>=40 && i<=50)
continue;
System.out.println(i);
}
}
}
|
def should_reset(self, current_time_step: ts.TimeStep) -> bool:
handle_auto_reset = getattr(self, '_handle_auto_reset', False)
return handle_auto_reset and np.all(current_time_step.is_last())
|
Clinical utility of saline solution sonohysterography in the diagnosis of uterine pathology Sonohysterography involves the instillation of sterile saline solution under continuous sonographic visualization to assess the endometrial cavity. It is the objective of this study to determine the diagnostic accuracy of saline solution infusion sonohysterography and compare the reliability of transvaginal ultrasonography with and without saline solution infusion sonohysterography in detecting intrauterine pathology. The reliability of saline solution infusion sonohysterography were evaluated in 31 patients seen in our institution. These were then compared with the pathological results on available specimens. The sensitivity of transvaginal ultrasonography improved after saline solution infusion sonohysterography from 56 to 94% and the specificity from 40 to 60%. The positive predictive value increased from 75 to 88% and the negative predictive value from 22 to 75%. The diagnostic accuracy also increased from 52 to 86%. When the transvaginal ultrasonography and saline solution infusion sonohysterography results were combined, they had sensitivity of 100%, specificity of 100%, positive predictive value of 89%, negative predictive value of 100% and a diagnostic accuracy of 90%. The use of saline solution infusion sonohysterography also improved the quality of information about the location and size of uterine mass. This development has implications for the management of uterine bleeding disorders. It can distinguish women who only require medical therapy from those who require surgery. The method is easy to learn and is well tolerated by the patients.
|
From a distance they look like white toothpicks standing up out of the ocean. Up close they are towering engines of motion, whirring in the face of unforgiving gusts of wind. Offshore wind farms are a blossoming field of green energy, with trillions of watts of untapped energy flowing along our coasts everyday. Pioneering farms in the EU and China have already begun to harvest a small fraction of this power, and these nations have made large investments in capturing even more. Yet the numbers of watts available is less impressive than simply seeing these farms in action. They look like something straight out of a science fiction film. See for yourself in the videos below. Early success in offshore wind farms are powering the EU towards ever larger projects, set to supply a substantial portion of that continent’s energy needs in the decades ahead. The United States lags far, far, far behind, but recent developments may help push us towards tapping or own vast offshore wind reserves.
Apparently it takes years for change to blow its way across the Atlantic. While EU nations and corporations have worked together to build dozens of successful wind farms off their coastal shores, the United States has produced exactly zero so far. This is in spite of about 21% of the world’s wind energy being produced in the US. Look at a list of the world’s top 25 onshore wind projects and the USA is dominating. Look at the same list for offshore sites and we don’t even appear. Despite the great similarity between the two types of renewable energy resources, the US is lagging years behind the EU.
But we may finally be ready to catch up. The American Wind Energy Association held their annual Offshore Wind Expo recently in Baltimore, with several key figures in the EU wind community attending. Jens Eckhoff president of the German Offshore Foundation discussed the 250 million Euro Alpha Ventus wind farm that is exceeding expectations and produced over 190 gigawatt hours in 2011 so far. Maria McCaffery, Chief Executive of the British Wind Energy Association bragged about the continued success of Thanet, the world’s largest offshore wind farm. And there were many more European wind experts touting their recent successes. It isn’t surprising then, that US Secretary of the Interior Ken Salazar used his speech at the AWEA meeting to renew his dedication to offshore wind energy and promise that the US would propel itself forward to catch up with the rest of the world. The combination of EU encouragement and Salazar’s vision has been potent enough to get the press worked up. Mainstream media feeds and green energy blogs have been abuzz with speculation that American offshore wind farms may have received the kick in the pants they need to succeed.
Yet the opposition they face hasn’t gone away. There are two large obstacles to the construction of offshore wind farms in the US. The first is cost. While the technology behind these facilities has improved dramatically since Denmark first installed offshore turbines in 1991, start up costs are still considerable. We’re talking hundreds of millions of dollars for even moderate sized farms, and billions of dollars for projects that may put an actual dent in US energy needs. And that’s just for turbines. Several US companies, including Google, are investing billions more in the infrastructure needed to carry offshore electricity towards users on land. The Atlantic Wind Connection is an ongoing project in development to create a ‘backbone’ grid 15 miles off the coast that will connect 7000 megawatts of offshore wind to 1.9 million homes in the US. If you want billions of watts of power, you have to invest many billions of dollars, and while the US is ready to do so, the tough economic times have slowed projects down to some degree.
The other large(r) hurdle is public opposition, usually based in localities where these projects have been proposed. So far, only one lease has been signed for an offshore wind farm in the US. Salazar green lighted the Cape Wind project to much fan fare in last year’s AWEA meeting. Yet Cape Wind has also been one of the most vigorously opposed wind projects, with a recent documentary focused on the conflict. Why? There are some environmental concerns, largely around impact on marine ecosystems and commercial fishing. Perhaps more important to the political rhetoric however, the project could screw up the aesthetic beauty of Nantucket Sound. The Daily Show had a humorous take on that objection when it was first raised years ago.
Cape Wind moves forward with strong support from Salazar and other government officials, but opposition isn’t going away. The UK, Germany, and Denmark have all faced objections from vocal constituents to their many offshore wind farm projects. I understand some of the hesitation. While wind turbines are located and spaced so as not to seriously impact sailing and other ship traffic, clearly having no turbines would be safer (even if the risk is marginal). Environmental concerns around the wind farms include pollution from subsonic and audible noise, erosion of materials, construction waste, and service teams. When windmills die (and they eventually will) there’s debate over whether it’s better to haul the turbines back to shore or let them be used as reefs on the ocean floor. Then there’s the very valid concern that investments in wind energy, offshore or not, will not be as profitable or reliable as investments in fossil fuel efficiency, solar energy, biofuels, etc. Offshore wind energy is still being tested, and it would be irresponsible of me not to point out that its opposition has valid ground to stand on (pardon the pun).
Yet I laugh at the aesthetic concerns. I’ve seen traditional wind farms in person in the US, and seen countless videos of offshore farms in the EU and China. While noisy, I think these structures are beautiful. Absolutely so. There’s a real awe they inspire when you consider that these slowly turning turbines are generating millions of watts of power for people, seemingly with no real effort. Adding a few to the beach horizon is a plus in my book.
It looks like enough people in the US agree with me. Rumors are that Salazar will sign another lease for an offshore wind farm soon. Investments in the Atlantic Wind Connection continue to stack up, and valuable projects keep moving forward including a testing facility off the coast of Virginia, and a farm for Rhode Island. Manufacturers like Siemens improve the efficiency and cost of their turbines every year. While projects in the EU aren’t worry free, they have largely been successful. So much so that Europe is going to dwarf its current capacity by orders of magnitude in the upcoming decade with new proposed wind farms. The US can use that model to finally launch its own offshore wind energy grid in the next few years, and I hope it does. Water-logged watts from windmills aren’t worry free, but they make sense. There’s terrawatts out there on the waves! A gold rush is in order.
It may sound strange that I have favorite videos of offshore wind farms, but I do. These fields of turbines spaced hundreds of meters apart strike me as both futuristic and strangely mesmerizing. Here are a couple of very interesting looks at the modern offshore windmill:
Sailing through the Nysted Wind Farm in Denmark:
A great HD aerial view of the Horns Rev Wind Farm, also in Denmark. Is this music cheesy or fitting? Don’t know:
Here is a quick but cool video of the construction of an offshore wind turbine, this one at the new Alpha Ventus farm in Germany:
I’ll leave you with one last video, this one by Energinet, one of the forces behind offshore wind energy in Denmark. It’s the first short video in a series of clips designed to inform and sway the public. Propaganda? Sure, but well made, and well worth a watch. We’ll probably see similar campaigns in the US before offshore wind farms get the support they need to thrive:
[image credits: Atlantic Wind Connection, NordNordWest via WikiCommons]
[source: Atlantic Wind Connection, Alpha Ventus, Cape Wind, Dong Energy]
|
/*
Copyright 2018 Turbine Labs, Inc.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
package updater
import (
"testing"
"time"
tbnflag "github.com/turbinelabs/nonstdlib/flag"
"github.com/turbinelabs/test/assert"
)
func TestNewFromFlags(t *testing.T) {
flagset := tbnflag.NewTestFlagSet()
ff := NewFromFlags(flagset)
flagset.Parse([]string{"-delay=1m"})
ffImpl := ff.(*fromFlags)
assert.NonNil(t, ffImpl.diffOpts)
assert.Equal(t, ffImpl.delay, 60*time.Second)
}
func TestNewFromFlagsDefault(t *testing.T) {
flagset := tbnflag.NewTestFlagSet()
ff := NewFromFlags(flagset)
ffImpl := ff.(*fromFlags)
assert.Equal(t, ffImpl.delay, 30*time.Second)
}
func TestNewFromFlagsWithDefault(t *testing.T) {
flagset := tbnflag.NewTestFlagSet()
ff := NewFromFlags(flagset, WithDefaultDelay(5))
ffImpl := ff.(*fromFlags)
assert.Equal(t, ffImpl.delay, 5*time.Second)
}
func TestFromFlagsValidateDelay(t *testing.T) {
flagset := tbnflag.NewTestFlagSet()
ff := NewFromFlags(flagset)
flagset.Parse([]string{"-delay=500ms"})
assert.ErrorContains(t, ff.Validate(), "delay may not be less than 1 second")
}
func TestFromFlagsValidateSkipMinDelay(t *testing.T) {
flagset := tbnflag.NewTestFlagSet()
ff := NewFromFlags(flagset, SkipMinDelay())
flagset.Parse([]string{"-delay=500ms"})
ffImpl := ff.(*fromFlags)
assert.Equal(t, ffImpl.delay, 500*time.Millisecond)
}
|
<reponame>brettdavidson3/eclipselink.runtime<gh_stars>0
/*******************************************************************************
* Copyright (c) 1998, 2013 Oracle and/or its affiliates. All rights reserved.
* This program and the accompanying materials are made available under the
* terms of the Eclipse Public License v1.0 and Eclipse Distribution License v. 1.0
* which accompanies this distribution.
* The Eclipse Public License is available at http://www.eclipse.org/legal/epl-v10.html
* and the Eclipse Distribution License is available at
* http://www.eclipse.org/org/documents/edl-v10.php.
*
* Contributors:
* James - initial impl
******************************************************************************/
package org.eclipse.persistence.testing.models.plsql;
import java.math.BigDecimal;
import java.util.ArrayList;
import java.util.List;
/**
* Used to test simple PLSQL record types.
*
* @author James
*/
public class Employee {
protected BigDecimal id;
protected String name;
protected boolean active;
protected Address address;
protected List<Phone> phones = new ArrayList<Phone>();
public BigDecimal getId() {
return id;
}
public void setId(BigDecimal id) {
this.id = id;
}
public String getName() {
return name;
}
public void setName(String name) {
this.name = name;
}
public Address getAddress() {
return address;
}
public void setAddress(Address address) {
this.address = address;
}
public List<Phone> getPhones() {
return phones;
}
public void setPhones(List<Phone> phones) {
this.phones = phones;
}
public boolean equals(Object object) {
if (!(object instanceof Employee)) {
return false;
}
Employee employee = (Employee)object;
if (this.id != null && !this.id.equals(employee.id)) {
return false;
}
if (this.name != null && !this.name.equals(employee.name)) {
return false;
}
if (this.address != null && !this.address.equals(employee.address)) {
return false;
}
return true;
}
public boolean isActive() {
return active;
}
public void setActive(boolean active) {
this.active = active;
}
}
|
Väinö Kohtanen
Early life and education
Born in 1889 in Finland, Kohtanen joined the Young Men's Christian Association (YMCA) in his teens and it was here that he met Aarne Rintala. Rintala had joined the Seventh-day Adventist faith and after his baptism began to hold Bible studies with his friends from the Young Men's Christian Association, including Kohtanen and Kaarlo Soisalo. This resulted in Kohtanen accepting the Adventist faith and prompted him to pursue academic study and a career in ministry. Rintala, Kohtanen, and Soisalo all went on to become evangelists and administrators in the Seventh-day Adventist Church in Europe. Kohtanen undertook his education as a minister at Stanborough College, now known as Newbold College. in Watford, England from 1909 - 1912.
Friendship with Arthur S. Maxwell
While studying in England, Kohtanen spent his school breaks with the family of Arthur S. Maxwell, who later became well known Christian children's author “Uncle Arthur.” Prior to meeting Kohtanen, Maxwell had not shown any interest in Christianity. Such was his objection that once when a minister had come to give the Maxwell family a Bible study, he had escaped the family home by sliding down a drain-pipe. When Maxwell met Kohtanen, however, his perspective on Christianity totally changed and was described by Maxwell's mother as "complete as that of the Apostle Paul". Kohtanen and Maxwell later became roommates at Stanborough College and they remained friends throughout their lives. In 1933, Maxwell traveled from the US to Finland to attend Kohtanen's first Conference Session as the newly appointed President of the Seventh-day Adventist Church Conference in Finland.
Personal life
Upon completion of his studies in 1912, Kohtanen returned to Finland and married Kirsti Grundström, who came from a wealthy family. The marriage was opposed by her family as they felt as a minister of religion he would not be able to provide for her. Kohtanen was able to prove them wrong and they had one son, Spencer, who died in infancy and one daughter, Mirjam (Sigvartsen) who became a physiotherapist and later moved to Norway. His great-grandson Jan Åge Sigvartsen is an Old Testament and Second Temple Period academic and is author of the popular scholarly exegesis website ExegesisPaper.com.
|
<filename>maven/src/test/java/sorting/SortingTest.java
package sorting;
import sorting.*;
import org.junit.jupiter.api.Test;
import static org.junit.jupiter.api.Assertions.assertTrue;
import java.util.Arrays;
import java.util.stream.*;
public class SortingTest {
private void sortAndAssert(Sorter sorter, Comparable[] arr) {
String unsortedString = Arrays
.stream(arr)
.map(item -> item.toString())
.collect(Collectors.joining(","));
sorter.sort(arr);
String sortedString = Arrays
.stream(arr)
.map(item -> item.toString())
.collect(Collectors.joining(","));
assertTrue(
Sorter.sorted(arr),
unsortedString + " <> " + sortedString);
}
private void runTests(Sorter sorter) {
sortAndAssert(sorter, new Integer[] { });
sortAndAssert(sorter, new Integer[] { 0, 1, 2, 3 });
sortAndAssert(sorter, new Integer[] { 3, 2, 1, 0 });
sortAndAssert(sorter, new Integer[] { 4, 7, 2, 1 });
sortAndAssert(sorter, new Integer[] { 4, 7, 2, 3, 5, 1, 2, 6, 20, 1 });
sortAndAssert(sorter, new String[] { "x", "a", "b" });
}
@Test
public void testSelectionSorter() {
runTests(new SelectionSorter());
}
@Test
public void testInsertionSorter() {
runTests(new InsertionSorter());
}
@Test
public void testShellSorter() {
runTests(new ShellSorter());
}
@Test
public void testRecursiveMergeSorter() {
runTests(new RecursiveMergeSorter());
}
@Test
public void testBottomUpMergeSorter() {
runTests(new BottomUpMergeSorter());
}
@Test
public void testQuickSorter() {
runTests(new QuickSorter());
}
}
|
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