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4,100 | Gangliosides in Neurodegenerative Diseases | The main purpose of this chapter is to summarize the chief findings on ganglioside changes/interactions with some of the neurodegenerative disorders. For the latter we have focused on three diseases that have seen especially intensive study in that regard: Parkinson's, Alzheimer's, and Huntington's diseases. Parkinson's disease (PD) has received the most intensive study with revelation of systemic deficiency of GM1 in brain and all peripheral tissues that have been analyzed to date; this pointed to GM1 replacement as a promising therapy which proved only partially successful when tried for reasons that are discussed. Huntington's disease resembles PD in also manifesting GM1 deficiency, which did, however, respond to GM1 replacement therapy - apparently due to GM1 being administered directly into the brain. Alzheimer's disease was more complex in relation to gangliosides, with b-series (GD1b, GT1b) apparently depressed along with a-series. GM1 administered in brain appeared to induce improvement, but in a limited number of patients. We summarize studies showing why GM1 is of critical importance in neuronal function, and we also briefly point to a few additional neurological disorders in which one or more ganglioside changes have been implicated. |
4,101 | Study on categorization of factors affecting smallholder dairy production in Siltie Zone, Southern Ethiopia, applying multivariate analysis approaches | This study aims to categorize smallholder dairy farming systems through multivariate analysis. Nonlinear categorical principal component analysis (NLPCA) was used to lessen 35 variables into 4 sets of uncorrelated components. These four categories are environment-genetic interaction, management, hygiene, and genetic-related factors. Besides, within the two-step cluster analysis, a variable cluster membership was created that identified which family belonged to which cluster. For comparison purpose, hierarchical cluster analysis was used. A two-step cluster analysis results showed that most farms (41.50% and 31.90% in peri-urban and urban areas, respectively, had been in cluster 1 at the same time as most farms (66.70%) of urban areas had been in cluster 2. Overall, most (31.00% and 34.00%) of farms have been in clusters 1 and 2, respectively. Most farms in peri-urban areas had been challenged with scarcity of feed, mastitis, and animal sickness than farms within the urban and rural farming systems. Forming farmer groups as a cooperative to supply offerings together with feed processing devices, artificial insemination, and health services is usually recommended to triumph over such hassle. In addition, on account that it is primarily based on a couple of criteria, the category system evolved in this study depicts a lot better dairy farming systems with admiration to the variety of the components and the relative contribution of each component to dairy farming than do the single-criterion classifications. Hence, the results of such classifications should be seen as a start line from which to efficiently compare the modern extension system and eventually design the high-quality-fit extension models for a heterogeneous populace of smallholder dairy farmers. |
4,102 | Appendiceal schwannoma - report of a case and literature review | Appendiceal tumours encompass a wide spectrum of differential diagnoses and frequently present with clinical features of appendicitis. We report the case of a 43-year-old woman who presented with epigastric pain, dyspepsia and bloating. An atypical right para-iliac mass was detected on abdominal ultrasound, and computed tomography (CT) identified an appendiceal tumour. The tumour subtype remained indeterminate following Gallium-68 Dotatate positron emission tomography (PET); however, an appendiceal neuroendocrine tumour was suspected. Surgical resection with laparoscopic en bloc appendicectomy and limited caecectomy was performed, and histopathological assessment confirmed an appendiceal schwannoma. The report is followed by a review of the literature. To our knowledge, there have been fourteen reported cases of appendiceal schwannoma. The preoperative diagnosis can be challenging and appendiceal schwannoma had not been suspected in any of the reported cases, while a suspected diagnosis of neuroendocrine tumour or gastrointestinal stromal tumour was common. Definitive diagnosis requires immunohistochemical assessment and S100 is the hallmark. No personal or family history of underlying neurofibromatosis (NF) type 1 or type 2 has been reported to date. As for other gastrointestinal schwannomas, complete surgical resection is the recommended treatment for appendiceal schwannoma. Following this, despite lack of long-term follow-up, no cases of recurrence have been reported thus far. |
4,103 | Cobalt Cyclopentadienyl-Phosphine Dinitrogen Complexes | By applying the potassium salts of cyclopentadienyl-phosphine ligands LK to CoCl2 , the corresponding cobalt chlorides (1, LCoII Cl) were prepared. By reducing complexes 1 with KHBEt3 under a N2 atmosphere, bridging end-on complexes, LCoI -N2 -CoI L (2 a and 2 b), were successfully obtained. 15 N2 -labeled [15 N2 ]-2 a was prepared under 15 N2 /14 N2 exchange in THF solution. LCoI -N2 -CoI L complex 2 a could react with P4 molecules to release N2 and generate a Co-P4 -Co moiety 4. Further reduction of complex 2 b led to cleavage of a P-C bond in the cyclopentadienyl-phosphine ligand to provide novel μ-PCy2 -bridged Co0 -N2 complex 5. DFT calculations confirmed the experimental observations. |
4,104 | Reducing the question burden of patient reported outcome measures using Bayesian networks | Patient Reported Outcome Measures (PROMs) are questionnaires completed by patients about aspects of their health status. They are a vital part of learning health systems as they are the primary source of information about important outcomes that are best assessed by patients such as pain, disability, anxiety and depression. The volume of questions can easily become burdensome. Previous techniques reduced this burden by dynamically selecting questions from question item banks which are specifically built for different latent constructs being measured. These techniques analyzed the information function between each question in the item bank and the measured construct based on item response theory then used this information function to dynamically select questions by computerized adaptive testing. Here we extend those ideas by using Bayesian Networks (BNs) to enable Computerized Adaptive Testing (CAT) for efficient and accurate question selection on widely-used existing PROMs. BNs offer more comprehensive probabilistic models of the connections between different PROM questions, allowing the use of information theoretic techniques to select the most informative questions. We tested our methods using five clinical PROM datasets, demonstrating that answering a small subset of questions selected with CAT has similar predictions and error to answering all questions in the PROM BN. Our results show that answering 30% - 75% questions selected with CAT had an average area under the receiver operating characteristic curve (AUC) of 0.92 (min: 0.8 - max: 0.98) for predicting the measured constructs. BNs outperformed alternative CAT approaches with a 5% (min: 0.01% - max: 9%) average increase in the accuracy of predicting the responses to unanswered question items. |
4,105 | Large-scale document image retrieval and classification with runlength histograms and binary embeddings | We present a new document image descriptor based on multi-scale runlength histograms. This descriptor does not rely on layout analysis and can be computed efficiently. We show how this descriptor can achieve state-of-the-art results on two very different public datasets in classification and retrieval tasks. Moreover, we show how we can compress and binarize these descriptors to make them suitable for large-scale applications. We can achieve state-of-the-art results in classification using binary descriptors of as few as 16-64 bits. (C) 2012 Elsevier Ltd. All rights reserved. |
4,106 | Surgical Management of Massive Pulmonary Embolism Presenting with Cardiopulmonary Arrest: How Far Is Too Far? | The incidence of diagnosed massive pulmonary embolism presenting to the Emergency Department is between 3% and 4.5% and it is associated with high mortality if not intervened timely. Cardiopulmonary arrest in this subset of patients carries a very poor prognosis, and various treating pathways have been applied with modest rate of success. Systemic thrombolysis is an established first line of treatment, but surgeons are often involved in the decision-making because of the improving surgical pulmonary embolectomy outcomes. |
4,107 | Large-Scale Malicious Software Classification With Fuzzified Features and Boosted Fuzzy Random Forest | Classification of malicious software, especially in a very large dataset, is a challenging task for machine intelligence. Malware can have highly diversified features, each of which has highly heterogeneous distributions. These factors increase the difficulties for traditional data analytic approaches to deal with them. Although deep learning based methods have reported good classification performance, the deep models usually lack interpretability and are fragile under adversarial attacks. To solve these problems, fuzzy systems have become a competitive candidate in malware analysis. In this article, a new fuzzy-based approach is proposed for malware classification. We focused on portable executable files in the Windows platform and analyzed the distributions of static features and content-oriented features. Fuzzification was used to reduce the ubiquitous impact of noise and outliers in a very large dataset. Finally, a novel boosted classifier consisted of fuzzy decision trees and support vector machine is proposed to perform the malware classification. By using fuzzy decision trees, the inner structure of the classifier can be readily interpreted as discriminative rules, whereas the novel boosting strategy provides state-of-the-art classification performance. Extensive experimental results showed that our method significantly outperformed several state-of-the-art classifiers. |
4,108 | Attention Residual Learning for Skin Lesion Classification | Automated skin lesion classification in dermoscopy images is an essential way to improve the diagnostic performance and reduce melanoma deaths. Although deep convolutional neural networks (DCNNs) have made dramatic breakthroughs in many image classification tasks, accurate classification of skin lesions remains challenging due to the insufficiency of training data, inter-class similarity, intra-class variation, and the lack of the ability to focus on semantically meaningful lesion parts. To address these issues, we propose an attention residual learning convolutional neural network (ARL-CNN) model for skin lesion classification in dermoscopy images, which is composed of multiple ARL blocks, a global average pooling layer, and a classification layer. Each ARL block jointly uses the residual learning and a novel attention learning mechanisms to improve its ability for discriminative representation. Instead of using extra learnable layers, the proposed attention learning mechanism aims to exploit the intrinsic self-attention ability of DCNNs, i.e., using the feature maps learned by a high layer to generate the attention map for a low layer. We evaluated our ARL-CNN model on the ISIC-skin 2017 dataset. Our results indicate that the proposed ARL-CNN model can adaptively focus on the discriminative parts of skin lesions, and thus achieve the state-of-the-art performance in skin lesion classification. |
4,109 | Local Coordinate Concept Factorization for Image Representation | Learning sparse representation of high-dimensional data is a state-of-the-art method for modeling data. Matrix factorization-based techniques, such as nonnegative matrix factorization and concept factorization (CF), have shown great advantages in this area, especially useful for image representation. Both of them are linear learning problems and lead to a sparse representation of the images. However, the sparsity obtained by these methods does not always satisfy locality conditions. For example, the learned new basis vectors may be relatively far away from the original data. Thus, we may not be able to achieve the optimal performance when using the new representation for other learning tasks, such as classification and clustering. In this paper, we introduce a locality constraint into the traditional CF. By requiring the concepts (basis vectors) to be as close to the original data points as possible, each datum can be represented by a linear combination of only a few basis concepts. Thus, our method is able to achieve sparsity and locality simultaneously. We analyze the complexity of our novel algorithm and demonstrate the effectiveness in comparison with the state-of-the-art approaches through a set of evaluations based on real-world applications. |
4,110 | Through the Looking Glass: The Paradoxical Evolution of Targeted Temperature Management for Comatose Survivors of Cardiac Arrest | For the past two decades, targeted temperature management (TTM) has been a staple in the care of comatose survivors following cardiac arrest. However, recent clinical trials have failed to replicate the benefit seen in earlier studies, bringing into question the very existence of such clinical practice. In this review, we explore clinical scenarios within critical care that appeared to share a similar fate, but in actuality changed the landscape of practice in a modern world. Accordingly, clinicians may apply these lessons to the utilization of TTM among comatose survivors following cardiac arrest, potentially paving way for a re-framing of clinical care amidst an environment where current data appears upside down in comparison to past successes. |
4,111 | Sensory neural hearing loss in Behcet's disease successfully controlled with infliximab, case report and review of literature | Behcet's disease is a systemic autoimmune disorder occasionally associated with otological manifestations, including sensorineural hearing loss. We are reporting a case of Behcet's disease, which was complicated by sensorineural hearing loss and managed successfully with anti-TNF agent Infliximab. |
4,112 | Footedness for scratching itchy eyes in rodents | The neural bases of itchy eye transmission remain unclear compared with those involved in body itch. Here, we show in rodents that the gastrin-releasing peptide receptor (GRPR) of the trigeminal sensory system is involved in the transmission of itchy eyes. Interestingly, we further demonstrate a difference in scratching behaviour between the left and right hindfeet in rodents; histamine instillation into the conjunctival sac of both eyes revealed right-foot biased laterality in the scratching movements. Unilateral histamine instillation specifically induced neural activation in the ipsilateral sensory pathway, with no significant difference between the activations following left- and right-eye instillations. Thus, the behavioural laterality is presumably due to right-foot preference in rodents. Genetically modified rats with specific depletion of Grpr-expressing neurons in the trigeminal sensory nucleus caudalis of the medulla oblongata exhibited fewer and shorter histamine-induced scratching movements than controls and eliminated the footedness. These results taken together indicate that the Grpr-expressing neurons are required for the transmission of itch sensation from the eyes, but that foot preference is generated centrally. These findings could open up a new field of research on the mechanisms of the laterality in vertebrates and also offer new potential therapeutic approaches to refractory pruritic eye disorders. |
4,113 | ARTS: A Framework for AI-Rooted IoT System Design Automation | IoT systems are used for performing a variety of essential tasks in wide-ranging sectors, such as healthcare, smart cities, agriculture, and industrial automation. Most of these systems incorporate smartness for intelligent decision making using artificial intelligence (AI) approaches. To reduce the network bandwidth usage and load on the cloud server, there is a push to relegate most of these AI-related computations to edge IoT devices/systems. Hence, to ensure good performance, the edge devices must be systematically designed with an emphasis on the respective AI requirements from exploration to deployment. State-of-the-art IoT device and system design practices place little importance on the AI specifications during the early stages of the system development resulting in systems, which are unable to meet the AI specifications without additional redesigning and optimization efforts. In this letter, we propose an automated framework for AI-rooted IoT system design approach, where the AI specifications play a vital role in deciding the system components, design, and implementation from a very early stage of the design life cycle. The proposed framework employs an expert system and a machine-readable knowledge-base to automate the design process. Using a set of case studies, we demonstrate the benefits of the proposed framework. |
4,114 | Energy-aware strategy for collaborative target-detection in wireless multimedia sensor network | Energy-efficiency in visual surveillance is the most important issue for wireless multimedia sensor network (WMSN) due to its energy-constraints. This paper addresses the trade-off between detection-accuracy and power-consumption by presenting an energy-aware scheme for detecting moving target based on clustered WMSN. The contributions of this paper are as follows; 1- An adaptive clustering and nodes activation approach is proposed based on residual energy of detecting nodes and the location of the object at the camera's field of view (FoV). 2- An effective cooperative features-pyramid construction method for collaborative target identification with low communication cost. 3- An in-network collaboration mechanism for cooperative detection of the target is proposed. The performance of this scheme is evaluated using both standard datasets and personal recorded videos in terms of detection-accuracy and power-consumption. Compared with state-of-the-art methods, our proposed strategy greatly reduces energy-consumption and saves more than 65% of the network-energy. Detection-accuracy rate of our strategy is 11% better than other recent works. We have increased the Precision of classification up to 49% and 65% and the Recall of classification up to 53% and 71% for specific-target and object-type respectively. These results demonstrate the superiority of our scheme over the recent state-of-the-art works. |
4,115 | The Effect of Long-Term Iron Chelator Therapy on Serum Levels of Hepcidin and Ferritin in Patients with Thalassemia Major and Intermediate | Serum hepcidin is a good predictor of iron overload compared with serum ferritin. However, serum hepcidin levels may change under different conditions. The current study aims to determine the role of long-term iron chelator therapy on serum levels of hepcidin and ferritin in patients with thalassemia major (TM) and intermediate (TI). In this cross-sectional study 91 patients with thalassemia TM and TI, who referred to the thalassemia center were chosen. The serum levels of hepcidin and ferritin were measured after two years of iron chelator therapy by ELISA and ECL methods, respectively. The patients' demographic information was extracted from their records. After treatment with iron chelator, ferritin levels decreased in 44 patients (48.4%), and increased in 47 patients (%51.6). Median serum levels of hepcidin decreased in all patients (%100). Also, there was a significant association between serum levels of hepcidin and ferritin (p value = 0.034). Furthermore, while a significant difference was observed between ferritin changes (p = 0.01), no difference was found between changes in hepcidin based on the type of iron chelator (p value = 0.94). Increased levels of hepcidin and ferritin in β-thalassemia patients are significantly ameliorated by iron chelator. |
4,116 | Towards Finite File Packetizations in Wireless Device-to-Device Caching Networks | We consider wireless device-to-device (D2D) caching networks with single-hop transmissions. Previous work has demonstrated that caching and coded multicasting can significantly increase per user throughput. However, the state-of-the-art coded caching schemes for D2D networks are generally impractical because content files are partitioned into an exponential number of packets with respect to the number of users if both library and memory sizes are fixed. In this paper, we present two combinatorial approaches of D2D coded caching network design with reduced packetizations and desired throughput gain compared to the conventional uncoded unicasting. The first approach uses a "hypercube" design, where each user caches a "hyperplane" in this hypercube and the intersections of "hyperplanes" represent coded multicasting codewords. In addition, we extend the hypercube approach to a decentralized design. The second approach uses the Ruzsa-Szemeredi graph to define the cache placement. Disjoint matchings on this graph represent coded multicasting codewords. Both approaches yield an exponential reduction of packetizations while providing a per-user throughput that is comparable to the state-of-the-art designs in the literature. Furthermore, we apply spatial reuse to the new D2D network designs to further reduce the required packetizations and significantly improve per user throughput for some parameter regimes. |
4,117 | Role of C-terminal domain of Mycobacterium tuberculosis PE6 (Rv0335c) protein in host mitochondrial stress and macrophage apoptosis | PE/PPE proteins of Mycobacterium tuberculosis (Mtb) target the host organelles to dictate the outcome of infection. This study investigated the significance of PE6/Rv0335c protein's unique C-terminal in causing host mitochondrial perturbations and apoptosis. In-silico analysis revealed that similar to eukaryotic apoptotic Bcl2 proteins, Rv0335c had disordered, hydrophobic C-terminal and two BH3-like motifs in which one was located at C-terminal. Also, Rv0335c's N terminal had mitochondrial targeting sequence. Since, C-terminal of Bcl2 proteins are crucial for mitochondria targeting and apoptosis; it became relevant to evaluate the role of Rv0335c's C-terminal domain in modulating host mitochondrial functions and apoptosis. To confirm this, in-vitro experiments were conducted with Rv0335c whole protein and Rv0335c∆Cterm (C-terminal domain deleted Rv0335c) protein. Rv0335c∆Cterm caused significant reduction in mitochondrial perturbations and Caspase-mediated apoptosis of THP1 macrophages in comparison to Rv0335c. However, the deletion of C-terminal domain didn't affect Rv0335c's ability to localize to mitochondria. Nine Ca2+ binding residues were predicted within Rv0335c and four of them were at the C-terminal. In-vitro studies confirmed that Rv0335c caused significant increase in intracellular calcium influx whereas Rv0335c∆Cterm had insignificant effect on Ca2+ influx. Rv0335c has been reported to be a TLR4 agonist and, we observed a significant reduction in the expression of TLR4-HLA-DR-TNF-α in response to Rv0335c∆Cterm protein also suggesting the role of Rv0335c's C-terminal domain in host-pathogen interaction. These findings indicate the possibility of Rv0335c as a molecular mimic of eukaryotic Bcl2 proteins which equips it to cause host mitochondrial perturbations and apoptosis that may facilitate pathogen persistence. |
4,118 | An Examination of Predictors of Prejudice against Transgender Individuals | We examined associations between prejudice toward transgender people, aggression proneness, history of family violence, contact and closeness with transgender people, and education about issues that impact transgender individuals. We also examined the moderating effects of contact, education, and closeness on the relations between aggression and history of family violence with prejudice. There were 360 participants (M age = 31.34, SD = 12.47, range 18-75) who completed the survey online. Participants were recruited through social media, websites, and MTurk. Higher levels of aggression proneness were related to higher levels of prejudice. Higher levels of education about issues that impact transgender people and prior contact with a transgender person were associated with less prejudice. In a multiple regression analysis, the strongest predictor of prejudice was education about transgender people and topics. Moderation analyses revealed that prior contact may buffer the effects of aggression proneness on prejudiced beliefs. |
4,119 | More Than Meets the Eyes: Bringing Attention to the Eyes Increases First Impressions of Warmth and Competence | The present research examined how face masks alter first impressions of warmth and competence for different racial groups. Participants were randomly assigned to view photographs of White, Black, and Asian targets with or without masks. Across four separate studies (total N = 1,012), masked targets were rated significantly higher in warmth and competence compared with unmasked targets, regardless of their race. However, Asian targets benefited the least from being seen masked compared with Black or White targets. Studies 3 and 4 demonstrate how the positive effect of masks is likely due to these clothing garments re-directing attention toward the eyes of the wearer. Participants viewing faces cropped to the eyes (Study 3), or instructed to gaze into the eyes of faces (Study 4), rated these targets similarly to masked targets, and higher than unmasked targets. Neither political affiliation, belief in mask effectiveness, nor explicit racial prejudice moderated any hypothesized effects. |
4,120 | An Energy Aware Adaptive Sampling Algorithm for Energy Harvesting WSN with Energy Hungry Sensors | Wireless sensor nodes have a limited power budget, though they are often expected to be functional in the field once deployed for extended periods of time. Therefore, minimization of energy consumption and energy harvesting technology in Wireless Sensor Networks (WSN) are key tools for maximizing network lifetime, and achieving self-sustainability. This paper proposes an energy aware Adaptive Sampling Algorithm (ASA) for WSN with power hungry sensors and harvesting capabilities, an energy management technique that can be implemented on any WSN platform with enough processing power to execute the proposed algorithm. An existing state-of-the-art ASA developed for wireless sensor networks with power hungry sensors is optimized and enhanced to adapt the sampling frequency according to the available energy of the node. The proposed algorithm is evaluated using two in-field testbeds that are supplied by two different energy harvesting sources (solar and wind). Simulation and comparison between the state-of-the-art ASA and the proposed energy aware ASA (EASA) in terms of energy durability are carried out using in-field measured harvested energy (using both wind and solar sources) and power hungry sensors (ultrasonic wind sensor and gas sensors). The simulation results demonstrate that using ASA in combination with an energy aware function on the nodes can drastically increase the lifetime of a WSN node and enable self-sustainability. In fact, the proposed EASA in conjunction with energy harvesting capability can lead towards perpetual WSN operation and significantly outperform the state-of-the-art ASA. |
4,121 | Segmentation-Based Background-Inference and Small-Person Pose Estimation | Despite encouraging results have been achieved in human pose estimation in recent years, it remains challenging problems. When the background is similar to the human body parts, and there are small persons with low-resolution in the image, the performance may degrade dramatically. This paper addresses problems in background-inference and small-person pose estimation. To achieve this, a novel pose estimation algorithm is proposed on the basis of person semantic segmentation deep neural network. Different from most previous methods with a single pose estimation model, we generate mixture models with pose estimation and semantic segmentation. We introduce novel generative adversarial model and auxiliary model to realize the semantic segmentation network, which can handle the confusion of the similar regions in the background. In addition, to address the problem of the scale differences between big and small persons' keypoints, we add additional position and channel attention modules to the first two stages of OpenPose. We conduct extensive experiments on COCO and VOC datasets. And we compare the proposed method with the most popular state-of-the-art human pose estimation and semantic segmentation frameworks, including MultiPoseNet, Deterton2 and DeepLab V3. Our experimental results show that the proposed method is more accurate than the state-of-the-art algorithms and performs effectively in tackling the complex situations. |
4,122 | Probing the importance of lipid diversity in cell membranes via molecular simulation | Lipid membranes in prokaryotes and eukaryotes have a wide array of lipids that are necessary for proper membrane structure and function. In this paper, an introduction to lipid diversity in biology and a mini-review on how molecular simulations have been used to model biological membranes (primarily limited to one to three lipid types in most simulation-based models) is provided, which motivates the use of all-atom molecular dynamics (MD) simulations to study the effect of lipid diversity on properties of realistic membrane models of prokaryotes and eukaryotes. As an example, cytoplasmic membrane models of Escherichia coli were developed at different stages of the colony growth cycle (early-log, mid-log, stationary and overnight). The main difference between lipid compositions at each stage was the concentration of a cyclopropane-containing moiety on the sn-2 lipid acyl chain (cyC17:0). Triplicate MD simulations for each stage were run for 300 ns to study the influence of lipid diversity on the surface area per lipid, area compressibility modulus, deuterium order parameters, and electron density profiles. The overnight stage (also known as the death stage) had the highest average surface area per lipid, highest rigidity, and lowest bilayer thickness compare to other stages of E. coli cytoplasmic membrane. Although bilayer thickness did depend on the growth stage, the changes between these were small suggesting that the hydrophobic core of transmembrane proteins fit well with the membrane in all growth stages. Although it is still common practise in MD simulations of membrane proteins to use simple one- or two-component membranes, it can be important to use diverse lipid model membranes when membrane protein structure and function are influenced by changes in lipid membrane composition. |
4,123 | Recommendations from the association for professionals in infection control and epidemiology health inequalities & disparities task force | In June 2021, a task force commissioned by the Board of Directors of the Association for Professionals in Infection Prevention and Epidemiology (APIC) evaluated the landscape of health inequity and health disparities as they apply to infection prevention in health care settings. This task force, consisting of infection preventionists across the country, convened to evaluate current literature, identify relevant issues and make recommendations to the APIC Board of Directors for action steps to be taken. |
4,124 | Selections from the history of environmental pollution, with special attention to air pollution. Part 2: From medieval times to the 19th century | Several comprehensive publications have been issued recently on the environmental pollution of past times, especially from medieval times (e.g. Brimblecombe, 1987a; Goldstein, 1988; Brimblecombe and Pfister, 1990; Hughes, 1993; Markham, 1994; Brimblecombe, 1995). The aim of this paper is to give an over-view of information on the subject-mainly related to the period between medieval times and the 19th century. |
4,125 | Effect of queen cell numbers on royal jelly production and quality | Royal jelly (RJ) is a popular functional food with a wealth of health-promoting effects. Over 90% of the global RJ is produced in China mainly by a high RJ-producing honeybee (RJB) strain that can accept and feed a great number of queen larvae for RJ production. To elucidate RJ changes due to queen cell numbers (QCNs), we compared the yield, larval acceptance rate, metabolic and proteomic profiles, and antioxidant activities of RJ from 1 to 5 strips of queen cells (64 per strip) in RJB colonies. As QCNs increased, the larval acceptance rate was not found to vary (p = 0.269) whereas the RJ weight per cell began to significantly decline in the 5-strip colonies (p < 0.05). Increased QCNs had a profound impact on RJ metabolic profiles and mainly reduced fatty acid levels. Remarkably, the 10-hydroxy-2-decenoic acid (10-HDA) content, a most important indicator of RJ quality, declined gradually from 2.01% in the 1-strip colonies to 1.52% in the 5-strip colonies (p < 0.001). RJ proteomic profiles were minimally altered and antioxidant activities were not significantly changed by QCNs. Collectively, the metabolomics and proteomics data and the antioxidant activity test represent a global evaluation of the quality of RJ produced with different QCNs. Our findings gain new insights into higher-quality RJ production using the high-yielding RJBs. |
4,126 | Erosion rates and weathering history of rock surfaces associated with Aboriginal rock art engravings (petroglyphs) on Burrup Peninsula, Western Australia, from cosmogenic nuclide measurements | The Burrup Peninsula and surrounding Dampier Archipelago, in Western Australia, contain the world's largest known gallery of rock art engravings (petroglyphs), estimated to number up to 1 million images. The peninsula is also the site of major industrial development and there are concerns that industrial emissions may adversely affect the stability and longevity of the rock art. We have studied the natural processes and rates of weathering and erosion, including the effects of fire, that affect the stability of rock surfaces and hence the longevity of the rock art, using cosmogenic nuclides. The concentration of Be-10 in quartz yields erosion rates in the range 0.15-0.48 mm/1000 years on horizontal rock surfaces and 0.34-2.30 mm/1000 years on vertical rock faces. The former, largely caused by mm-scale surface flaking, are amongst the lowest erosion rates measured by cosmogenic nuclides anywhere in the world. The latter are inferred to represent a combination of mm-scale flaking and very rare centimetre-to metre-scale block falls, controlled by failure along joint planes. Such low erosion rates result from a combination of resistant rocks, low relief and low rainfall, favouring long-term preservation of the petroglyphs - long enough to encompass the known period of human settlement in Australia. (c) 2013 Elsevier Ltd. All rights reserved. |
4,127 | A Cross-Domain Metal Trace Restoring Network for Reducing X-Ray CT Metal Artifacts | Metal artifacts commonly appear in computed tomography (CT) images of the patient body with metal implants and can affect disease diagnosis. Known deep learning and traditional metal trace restoring methods did not effectively restore details and sinogram consistency information in X-ray CT sinograms, hence often causing considerable secondary artifacts in CT images. In this paper, we propose a new cross-domain metal trace restoring network which promotes sinogram consistency while reducing metal artifacts and recovering tissue details in CT images. Our new approach includes a cross-domain procedure that ensures information exchange between the image domain and the sinogram domain in order to help them promote and complement each other. Under this cross-domain structure, we develop a hierarchical analytic network (HAN) to recover fine details of metal trace, and utilize the perceptual loss to guide HAN to concentrate on the absorption of sinogram consistency information of metal trace. To allow our entire cross-domain network to be trained end-to-end efficiently and reduce the graphic memory usage and time cost, we propose effective and differentiable forward projection (FP) and filtered back-projection (FBP) layers based on FP and FBP algorithms. We use both simulated and clinical datasets in three different clinical scenarios to evaluate our proposed network's practicality and universality. Both quantitative and qualitative evaluation results show that our new network outperforms state-of-the-art metal artifact reduction methods. In addition, the elapsed time analysis shows that our proposed method meets the clinical time requirement. |
4,128 | Does Pacemaker Implantation After Surgical Aortic Valve Replacement Impact Long-Term Morbidity and Mortality? A Focused Review | Permanent pacing remains a serious complication that can occur in the postoperative period of surgical aortic valve replacement. The reported incidence is variable, and there are many perioperative factors that have been linked with a greater need for permanent pacing. Permanent pacing can also be associated with late lead-related and cardiac complications that can affect late outcome. However, the degree of late dependence on pacemakers is varied, and some studies have shown that a substantial proportion of patients do not need long-term pacing. Some groups have found that permanent pacing was associated with a negative impact on long-term survival in these patients. A common finding among these studies is that the groups of patients with pacemakers had higher preoperative surgical risk and comorbidity status. This makes it difficult to establish whether permanent pacing on its own represents a risk factor for late mortality or whether it is simply a marker that reflects the higher complexity and comorbidities in this group of patients. |
4,129 | Chordae Tendineae Approximation Technique for Severe Tricuspid Regurgitation with Severe Leaflet Tethering Using a Totally Endoscopic Beating-Heart Strategy: A Case Report | Untreated severe tricuspid regurgitation (TR) is associated with poor outcomes. Functional TR occurs secondary to dilatation of the annulus and tethering of the leaflets. Ring annuloplasty alone can correct most cases, but is insufficient in cases of severe annular dilatation due to severe leaflet tethering. In such cases, a tricuspid edge-to-edge technique may be an option. However, stitching of the leaflet tips alone is likely to result in tearing of the leaflets. Approximation of the durable chordae tendineae is considered helpful for this problem. Herein, we present the case of a 39-year-old man who had undergone openheart surgery for acute type A aortic dissection 13 months earlier. A right mini-thoracotomy approach with a beating-heart strategy was used, which did not require unnecessary pericardial adhesiolysis and dissection. This technique had the advantage of reducing the operation time and the risk of bleeding. To summarize, we present a case of tricuspid valve repair in a high-risk patient with severe leaflet tethering that was successfully managed using these methods. |
4,130 | Molecular insight into pyrolysis processes via reactive force field molecular dynamics: A state-of-the-art review | Molecular simulations based on reactive force-fields (ReaxFF) have been applied as a powerful tool for exploring the pyrolysis process of complex carbonaceous materials. It can describe the breakage and formation of bonds during the chemical reaction processes based on Bond-Order so as to explore the complex reaction mechanisms from microscale, which can offset experimental flaws. In this review, we attempt to provide an overview of the state-of-the-art advances in the pyrolysis process of coal, biomass, polymer and fuel at molecular level. First the major constituent elements, development, force field optimization, diverse application of ReaxFF MD is intro-duced. Then, the kinetics models, pyrolysis mechanisms and products reaction pathways of coal, biomass, polymer and fuel are discussed. In addition, the train and optimization of force field and post-processing of simulation are discussed, which can improve reaction analysis capability and provide new ideas to extract in-formation. We conclude the pyrolysis application using ReaxFF MD from microscopic level with an eye toward the future challenges and developments in those fields. |
4,131 | Image Restoration Using Gaussian Mixture Models With Spatially Constrained Patch Clustering | In this paper, we address the problem of recovering degraded images using multivariate Gaussian mixture model (GMM) as a prior. The GMM framework in our method for image restoration is based on the assumption that the accumulation of similar patches in a neighborhood are derived from a multivariate Gaussian probability distribution with a specific covariance and mean. Previous methods of image restoration with GMM have not considered spatial (geometric) distance between patches in clustering. Our conducted experiments show that in the case of constraining Gaussian estimates into a finite-sized windows, the patch clusters are more likely to be derived from the estimated multivariate Gaussian distributions, i.e., the proposed statistical patch-based model provides a better goodness-of-fit to statistical properties of natural images. A novel approach for computing aggregation weights for image reconstruction from recovered patches is introduced which is based on similarity degree of each patch to the estimated Gaussian clusters. The results admit that in the case of image denoising, our method is highly comparable with the state-of-the-art methods, and our image interpolation method outperforms previous state-of-the-art methods. |
4,132 | Applying the AHP to Conflict Resolution: A Russia-NATO Case Study | In this paper, we apply the Analytic Hierarchy Process approach to conflict resolution in the context of the Russia-Ukraine conflict. We build models that illustrate the evaluation criteria, strategic and sub-criteria, and concessions for each party in this negotiation. Ratings are used to evaluate the degree to which concessions contribute or take away from successful resolution of the conflict. Afterwards, gain ratios are built to determine the benefit-cost scores so that concessions may be traded that result in equitable solutions. The approach presented here demonstrates for the first time why all concessions that parties to a conflict may offer might not trade all at once. A Max-Min optimization approach is used to maximize the gain to both parties of the conflict while minimizing the disparity in gain between the two. |
4,133 | Taphonomic Patterning of Cemetery Remains Received at the Office of the Chief Medical Examiner, Boston, Massachusetts | A sample of 49 cases of cemetery remains received at the Office of the Chief Medical Examiner, Massachusetts (OCME-MA), in Boston was compared with published taphonomic profiles of cemetery remains. The present sample is composed of a cross section of typical cases in this region that ultimately are derived from modern to historical coffin burials and get turned over to or seized by law enforcement. The present sample was composed of a large portion of isolated remains, and most were completely skeletonized. The most prevalent taphonomic characteristics included uniform staining (77.6%), coffin wear (46.9%), and cortical Exfoliation (49.0%). Other taphonomic changes occurring due to later surface exposure of cemetery remains included subaerial weathering, animal gnawing, algae formation, and excavation marks. A case of one set of skeletal remains associated with coffin artifacts and cemetery offerings that was recovered from transported cemetery fill is also presented. |
4,134 | Kaempferol: A potential agent in the prevention of colorectal cancer | Colorectal cancer (CRC) is the third most prevalent cancer in relation to incidence and mortality rate and its incidence is considerably increasing annually due to the change in the dietary habit and lifestyle of the world population. Although conventional therapeutic options, such as surgery, chemo- and radiotherapy have profound impacts on the treatment of CRC, dietary therapeutic agents, particularly natural products have been regarded as the safest alternatives for the treatment of CRC. Kaempferol (KMP), a naturally derived flavonol, has been shown to reduce the production of reactive oxygen species (ROS), such as superoxide ions, hydroxyl radicals, and reactive nitrogen species (RNS), especially peroxynitrite. Furthermore, this flavonol inhibits xanthine oxidase (XO) activity and increases the activities of catalase, heme oxygenase-1 (HO), and superoxide dismutase (SOD) in a wide range of cancer and non-cancer cells. Based on several studies, KMP is also a hopeful anticancer which carries out its anticancer action via suppression of angiogenesis, stimulation of apoptosis, and cell cycle arrest. Due to various applications of KMP as an anticancer flavonol, this review article aims to highlight the current knowledge regarding the role of KMP in CRC. |
4,135 | CNN weight sharing based on a fast accuracy estimation metric | The computational workload involved in CNNs is typically out of reach for low-power embedded devices. The Approximate Computing paradigm can be exploited to reduce the CNN complexity since it improves performances and energy-efficiency by relaxing the need for fully accurate operations. In this work, we target weightsharing as an approximate technique to reduce the memory footprint of a CNN. More in detail, we prove that optimizing the number of shared weights can enable significant network memory compression without noticeable accuracy loss without retraining or fine-tuning steps. However, we observe that the exploration time can easily explode in state-of-the-art CNNs. We thus propose the use of a fast accuracy estimation metric to guide the design space exploration and drastically reduce the exploration time up to 12x. Compared with state-of-the-art CNN approximation methods, we obtained more than 4x compression on GoogleNet on the ImageNet dataset with less than 1% accuracy loss in less than 5 h and without any retraining step. |
4,136 | Urinary phthalate metabolite concentrations and hot flash outcomes: Longitudinal associations in the Midlife Women's Health Study | Midlife in women is an understudied time for environmental chemical exposures and menopausal outcomes. Recent cross-sectional research links phthalates with hot flashes, but little is known regarding such associations over time. Our objective was to estimate longitudinal associations between repeated measures of urinary phthalate metabolite concentrations and hot flash outcomes in midlife women. Using data from the Midlife Women's Health Study (MWHS), a prospective longitudinal study, we fit generalized linear mixed-effects models (GLMMs) and Cox proportional hazards regression models to repeated measures over a 4-year period. Recruitment occurred in Baltimore and surrounding counties, Maryland, USA between 2006 and 2015. Participants were premenopausal/perimenopausal women (n = 744) aged 45-54 years, who were not pregnant, not taking menopausal symptom medication or oral contraceptives, did not have hysterectomy/oophorectomy, and irrespective of hot flash experience. Baseline mean (SD) age was 48.4 (2.45), and 65% were premenopausal. Main outcome measures included adjusted odds ratios (ORs) for 4 self-reported hot flash outcomes (ever experienced, past 30 days experience, weekly/daily, and moderate/severe), and hazard ratios (HRs) for incident hot flashes. We observed mostly increased odds of certain hot flash outcomes with higher concentrations of metabolites of di (2-ethylhexyl) phthalate (DEHP), monoisobutyl phthalate (MiBP), and a molar summary measure of plasticizer phthalate metabolites (DEHP metabolites, mono-(3-carboxypropyl) phthalate (MCPP), monobenzyl phthalate (MBzP)). Some associations between exposures and outcomes indicated decreased odds. In conclusion, phthalate metabolites were associated with certain hot flash outcomes in midlife women. Midlife may be a sensitive period for higher phthalate metabolite concentrations with respect to menopausal symptoms. |
4,137 | Highly Undersampled Magnetic Resonance Image Reconstruction Using Two-Level Bregman Method With Dictionary Updating | In recent years Bregman iterative method (or related augmented Lagrangian method) has shown to be an efficient optimization technique for various inverse problems. In this paper, we propose a two-level Bregman Method with dictionary updating for highly undersampled magnetic resonance (MR) image reconstruction. The outer-level Bregman iterative procedure enforces the sampled k-space data constraints, while the inner-level Bregman method devotes to updating dictionary and sparse representation of small overlapping image patches, emphasizing local structure adaptively. Modified sparse coding stage and simple dictionary updating stage applied in the inner minimization make the whole algorithm converge in a relatively small number of iterations, and enable accurate MR image reconstruction from highly undersampled k-space data. Experimental results on both simulated MR images and real MR data consistently demonstrate that the proposed algorithm can efficiently reconstruct MR images and present advantages over the current state-of-the-art reconstruction approach. |
4,138 | Creative Mural Landscapes, Building Communities and Resilience in Uruguayan Tourism | The purpose of this research was to analyze open-air mural painting museums in Uruguay as a model of tourism resilience, sustainability, and social development, being one of the first Latin American examples to demonstrate the ability to adapt to change and overcome external shocks through the creation of creative community landscapes. To do so, documentary research, photographic documentation, and field research were carried out in order to explore the opportunities of mural tourism in small locations in Uruguay. In the nineties, a new type of artistic production was created in Uruguay, initially characterized by its decentralization. This was somewhat of a revolution in the muralist field as, until this time, Montevideo had been the center of cultural tradition, considered the intellectual focus of the country, and had concentrated the largest number of murals. For this reason, the birth of new muralist nuclei in small rural enclaves, which traditionally had not had much access to culture and no link to muralism, is remarkable. Secondly, this new movement sought to diversify economic activity given the consequences of the severe economic crises and environmental catastrophes that were and are still prevalent in these areas. Therefore, these new creative landscapes were conceived as important examples of the resilience of cultural tourist destinations. The results emphasize that, until now, the idea of giving muralism a new use as a tool for local economic development had not been envisaged with reference to mural art in Uruguay. This new rethinking has given rise to the so-called Regionalization Processes of Uruguayan wall production. The most relevant cases are those developed in the municipalities of San Gregorio de Polanco (1993), Rosario (1994), and Pan de Azucar (1998). |
4,139 | Sign Detection and Number Comparison on RNS 3-Moduli Sets {2(n)-1, 2(n+x), 2(n)+1} | Number comparison, sign identification and overflow detection are important operations, especially for digital signal processing, but hard to perform using the residue number system (RNS). In this paper, a new method is proposed for sign identification and number comparison based on an optimized version of the mixed radix conversion for the augmented 3-moduli sets . Notably, most of the computations are directly performed on the moduli channels, thus allowing to easily adapt this new method to any RNS processor. Accordingly, this paper proposes an efficient unified very large scale integration architecture based on the presented methodology, which can be used not only to design application specific integrated circuits (ASICs) but also to configure field-programmable gate arrays (FPGAs). The implementation results that were obtained using CMOS technologies show that the proposed architecture provided comparators that are more efficient than the related state of the art, by considering as a figure of merit the area time product. More specifically, the considered ASIC and FPGA implementations provide relative improvements in the efficiency of up to 57 and , respectively. The experimental assessment also shows that the power consumption of the proposed circuits is significantly lower than the related state of the art, with relative reductions of up to . |
4,140 | Connected Bicycles-State-of-the-Art and Adoption Decision | This paper provides a snapshot of the current state-of-the-art on connected bicycles as discussed in the published literature as well as real-world implementations. We discuss several facets of connected bicycles, including those associated with quantified-self, synergies with social media, network effect, environmental sustainability, big data, among others. We then consider extensions and what might be possible in this general area based on existing and proposed systems as well as extant literature. We also discuss some of the issues and challenges that are associated with connected bicycles. A connected bicycle's utility increases with the number of other connected bicycles in its immediate vicinity. Through a Hotelling model, we consider the price and profit dynamics for a connected bicycle retailer. Our results indicate that the equilibrium price of a connected (regular) bicycle increases (decreases) with associated connected bicycle network effects. The unit connectivity cost for a connected bicycle has a direct effect on regular bicycle price. We derive expressions on when it is profitable for a retailer to switch from regular to connected bicycles as well as when the lowest profit levels are reached. |
4,141 | Ketones: the double-edged sword of SGLT2 inhibitors? | Sodium-glucose cotransporter 2 (SGLT2) inhibitors are a class of medications used by individuals with type 2 diabetes that reduce hyperglycaemia by targeting glucose transport in the kidney, preventing its reabsorption, thereby inducing glucosuria. Besides improving HbA1c and reducing body weight and blood pressure, the SGLT2 inhibitors have also been demonstrated to improve cardiovascular and kidney outcomes, an effect largely independent of their effect on blood glucose levels. Indeed, the mechanisms underlying these benefits remain elusive. Treatment with SGLT2 inhibitors has been found to modestly increase systemic ketone levels. Ketone bodies are an ancillary fuel source substituting for glucose in some tissues and may also possess intrinsic anti-oxidative and anti-inflammatory effects. Some have proposed that ketones may in fact mediate the cardio-renal benefits of this drug category. However, a rare complication of SGLT2 inhibition is ketoacidosis, sometimes with normal or near-normal blood glucose concentrations, albeit occurring more frequently in patients with type 1 diabetes who are treated (predominately off-label) with one of these agents. We herein explore the notion that an underpinning of one of the more serious adverse effects of SGLT2 inhibitors may, in fact, explain, at least in part, some of their benefits-a potential 'double-edged sword' of this novel drug category. |
4,142 | Unification of Blockchain and Internet of Things (BIoT): requirements, working model, challenges and future directions | The Internet of Things (IoTs) enables coupling of digital and physical objects using worthy communication technologies and introduces a future vision where computing systems, users and objects cooperate for convenience and economic benefits. Such a vision requires seamless security, data privacy, authentication and robustness against attacks. These attributes can be introduced by blockchain, a distributed ledger that maintains an immutable log of network transactions. In this paper, we present a comprehensive review on how to remodel blockchain to the specific IoT needs in order to develop Blockchain based IoT (BIoT) applications and aim to shape a coherent picture of the current state-of-the-art efforts in this direction. After describing the basic characteristics and requirements of IoT, evolution of blockchain is presented. In this regard, we start with the fundamental working principles of blockchain and how such systems achieve auditability, security and decentralization. Further, we describe the most relevant BIoT applications, its architecture design and security aspects. From there, we build our narrative on the centralized IoT challenges followed by recent advances towards solving them. Finally, some future directions are enumerated with the aim to guide future BIoT researchers on challenges that needs to be considered ahead of deploying the next generation of BIoT applications. |
4,143 | Multiple feature kernel hashing for large-scale visual search | Recently hashing has become attractive in large-scale visual search, owing to its theoretical guarantee and practical success. However, most of the state-of-the-art hashing methods can only employ a single feature type to learn hashing functions. Related research on image search, clustering, and other domains has proved the advantages of fusing multiple features. In this paper we propose a novel multiple feature kernel hashing framework, where hashing functions are learned to preserve certain similarities with linearly combined multiple kernels corresponding to different features. The framework is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also general for both supervised and unsupervised scenarios. We present efficient alternating optimization algorithms to learn both the hashing functions and the optimal kernel combination. Experimental results on three large-scale benchmarks CIFAR-10, NUS-WIDE and a-TRECVID show that the proposed approach can achieve superior accuracy and efficiency over state-of-the-art methods. (C) 2013 Elsevier Ltd. All rights reserved. |
4,144 | PoPPL: Pedestrian Trajectory Prediction by LSTM With Automatic Route Class Clustering | Pedestrian path prediction is a very challenging problem because scenes are often crowded or contain obstacles. Existing state-of-the-art long short-term memory (LSTM)-based prediction methods have been mainly focused on analyzing the influence of other people in the neighborhood of each pedestrian while neglecting the role of potential destinations in determining a walking path. In this article, we propose classifying pedestrian trajectories into a number of route classes (RCs) and using them to describe the pedestrian movement patterns. Based on the RCs obtained from trajectory clustering, our algorithm, which we name the prediction of pedestrian paths by LSTM (PoPPL), predicts the destination regions through a bidirectional LSTM classification network in the first stage and then generates trajectories corresponding to the predicted destination regions through one of the three proposed LSTM-based architectures in the second stage. Our algorithm also outputs probabilities of multiple predicted trajectories that head toward the destination regions. We have evaluated PoPPL against other state-of-the-art methods on two public data sets. The results show that our algorithm outperforms other methods and incorporating potential destination prediction improves the trajectory prediction accuracy. |
4,145 | Insights into the characteristics and mechanism of vacuum drying technology for municipal sludge processing | Vacuum drying is an effective approach for sludge treatment and valorization. However, the vacuum drying of sludge has not been industrialized at present. The objective of this study was to elucidate the vacuum drying characteristics of static sludge and crack initiation mechanism. Our results indicate that crusting on the sludge surface under a high vacuum inhibited drying by reducing major cracks at sludge thicknesses of 13.6 and 10.2 mm. The inhibition effect weakened with decreasing sludge thickness. At 6.8 mm, the mean drying rate (VM) was the lowest at 0.08 MPa, while VM decreased with increasing vacuum degree at thicknesses of 13.6 and 10.2 mm. The decrease in drying rate could be attributed to rapid evaporation on the sludge surface under a high vacuum, leading to crusting, which inhibited crack initiation. VM was raised by 67.9-162.2% from 10.2 to 6.8 mm because the suction force of vacuum on water was much higher than the resistance to water diffusion of small isolation piles at 6.8 mm. Additionally, this study provided essential information to improve existing sludge treatment methods. |
4,146 | Modeling uncertain data using Monte Carlo integration method for clustering | Nowadays, data clustering is an important task to the mining research community since the availability of uncertain data is increasing rapidly in many applications such as weather forecasting, business information management systems. In this work, proposed Monte Carlo integration based uncertain objects modeling technique is compared with three state-of-the-art methods namely, kernel density estimation, Dempster-Shafer, and Monte Carlo simulation. Then Kullback-Leibler and Jeffrey divergences are used to measure the similarity between uncertain objects and merge them with modified DBSCAN and k-medoids clustering algorithms. A heuristic algorithm is proposed to find the optimum radius, which is one of the inputs of DBSCAN. All the experiments are performed on one synthesized dataset and three real datasets namely, weather data, Japanese vowels and activity of daily living data. Five performance measures namely, accuracy, precision, recall, F-score, and Jaccard index are considered for comparing proposed method with state-of-the-art methods. Two non-parametric tests namely, Wilcoxon rank sum and sign test are also conducted. These results denote the effectiveness and efficiency of the proposed method over state-of-the-art methods. (C) 2019 Elsevier Ltd. All rights reserved. |
4,147 | Double trouble: A case of fraternal twins with iron-refractory iron-deficiency anemia | Iron-refractory iron-deficiency anemia (IRIDA) is a rare autosomal recessive disease that presents in childhood. We report the case of fraternal twins presenting with severe hypochromic microcytic anemia and hypoferritinemia. Two missense mutations affecting the TRMPSS6 gene were identified, consistent with IRIDA. Subsequent parenteral iron therapy improved clinical and blood parameters. |
4,148 | Impact of Virtual Learning on Mothers With Children in Elementary School: A Psychosocial Viewpoint | Background Measuring the impact of virtual learning (VL), specifically psychosocially, and its consequences have been poorly studied because VL has never been implemented in this way before worldwide. To our knowledge, no studies in Saudi Arabia have addressed this topic, with very limited available literature internationally. This study aims to evaluate the psychosocial effects of VL on mothers of children in elementary school and its relation to psychosocial factors. Methodology Using an anonymous online questionnaire posted on social media, a quantitative, observational, cross-sectional study was conducted from May through December 2021 in Riyadh, Kingdom of Saudi Arabia. A total of 460 mothers consented to complete the study questionnaire. The questionnaire consisted of a socioeconomic section and collected information on perceived stress via the Perceived Stress Scale (PSS-14). Results The overall PSS-14 score showed a moderate stress level. Our results showed that as age groups tended to rise, stress scores tended to decline. Diabetes was a significant variable contributing to high stress. Verbal abuse toward a child essentially reflects an increase in stress. Mothers with familial conflicts were more prone to high stress. Conclusions The consequences of VL on mothers and the whole family are alarming. Stress, verbal and physical abuse, and unhealthy family dynamics are strongly associated with such a way of learning. The impact of emotional and behavioral changes among this group of individuals needs to be further investigated. |
4,149 | RSP-DST: Revisable State Prediction for Dialogue State Tracking | Task-oriented dialogue systems depend on dialogue state tracking to keep track of the intentions of users in the course of conversations. Although recent models in dialogue state tracking exhibit good performance, the errors in predicting the value of each slot at the current dialogue turn of these models are easily carried over to the next turn, and unlikely to be revised in the next turn, resulting in error propagation. In this paper, we propose a revisable state prediction for dialogue state tracking, which constructs a two-stage slot value prediction process composed of an original prediction and a revising prediction. The original prediction process jointly models the previous dialogue state and dialogue context to predict the original dialogue state of the current dialogue turn. Then, in order to avoid the errors existing in the original dialogue state continuing to the next dialogue turn, a revising prediction process utilizes the dialogue context to revise errors, alleviating the error propagation. Experiments are conducted on MultiWOZ 2.0, MultiWOZ 2.1, and MultiWOZ 2.4 and results indicate that our model outperforms previous state-of-the-art works, achieving new state-of-the-art performances with 56.35, 58.09, and 75.65% joint goal accuracy, respectively, which has a significant improvement (2.15, 1.73, and 2.03%) over the previous best results. |
4,150 | Gray whale habitat use and reproductive success during seismic surveys near their feeding grounds: comparing state-dependent life history models and field data | We used a stochastic dynamic programming (SDP) model to quantify the consequences of disturbance on pregnant western gray whales during one foraging season. The SDP model has a firm basis in bioenergetics, but detailed knowledge of minimum reproductive length of females (Lmin) and the relationship between length and reproductive success (Rfit) was lacking. We varied model assumptions to determine their effects on predictions of habitat use, proportion of animals disturbed, reproductive success, and the effects of disturbance. Smaller Lmin values led to higher predicted nearshore habitat use. Changes in Lmin and Rfit had little effect on predictions of the effect of disturbance. Reproductive success increased with increased Lmin and with higher probability of reproductive success by length. Multiple seismic surveys were conducted in 2015 off the northeast coast of Sakhalin Island, with concomitant benthic prey surveys, photo-identification studies, and whale distribution sampling, thus providing a unique opportunity to compare output from SDP models with empirical observations. SDP model predictions of reproductive success and habitat use were similar with and without acoustic disturbance, and SDP predictions of reproductive success and large-scale habitat use were generally similar to values and trends in the data. However, empirical estimates of the proportion of pregnant females nearshore were much higher than SDP model predictions (a large effect, measured by Cohen's d) during the first week, and the SDP model overestimated whale density in the south and underestimated density around the mouth of Piltun Bay. Such differences in nearshore habitat use would not affect SDP predictions of reproductive success or survival under the current seismic air gun disturbance scenario. |
4,151 | Reducing microbial and human contamination in DNA extractions from ancient bones and teeth | Although great progress has been made in improving methods for generating DNA sequences from ancient biological samples, many, if not most, samples are still not amenable for analyses due to overwhelming contamination with microbial or modern human DNA. Here we explore different DNA decontamination procedures for ancient bones and teeth for use prior to DNA library preparation and high-throughput sequencing. Two procedures showed promising results: (i) the release of surface-bound DNA by phosphate buffer and (ii) the removal of DNA contamination by sodium hypochlorite treatment. Exposure to phosphate removes on average 64% of the microbial DNA from bone powder but only 37% of the endogenous DNA (from the organism under study), increasing the percentage of informative sequences by a factor of two on average. An average 4.6-fold increase, in one case reaching 24-fold, is achieved by sodium hypochlorite treatment, albeit at the expense of destroying 63% of the endogenous DNA preserved in the bone. While both pretreatment methods described here greatly reduce the cost of genome sequencing from ancient material due to efficient depletion of microbial DNA, we find that the removal of human DNA contamination remains a challenging problem. |
4,152 | Outward Movement of Targeting Ligands from a Built-In Reserve Pool in Nuclease-Resistant 3D Hierarchical DNA Nanocluster for in Vivo High-Precision Cancer Therapy | Nanostructures made entirely of DNAs display great potential as chemotherapeutic drug carriers but so far cannot achieve sufficient clinic therapy outcomes due to off-target toxicity. In this contribution, an aptamer-embedded hierarchical DNA nanocluster (Apt-eNC) is constructed as an intelligent carrier for cancer-targeted drug delivery. Specifically, Apt-eNC is designed to have a built-in reserve pool in the interior cavity from which aptamers may move outward to function as needed. When surface aptamers are degraded, ones in reserve pool can move outward to offer the compensation, thereby magically preserving tumor-targeting performance in vivo. Even if withstanding extensive aptamer depletion, Apt-eNC displays a 115-fold enhanced cell targeting compared with traditional counterparts and at least 60-fold improved tumor accumulation. Moreover, one Apt-eNC accommodates 5670 chemotherapeutic agents. As such, when systemically administrated into HeLa tumor-bearing BALB/c nude mouse model, drug-loaded Apt-eNC significantly inhibits tumor growth without systemic toxicity, holding great promise for high precision therapy. |
4,153 | End to End Segmentation of Canola Field Images Using Dilated U-Net | Semantic segmentation is used in many fields like agriculture, medical imaging, and autonomous driving. The paper proposes an end to end solution for efficient weeds and crop segmentation in field environment application. The crop/weeds segmented output is utilized to generate a decision map for variable rate fertilizer and herbicide application. Currently available models are memory expensive and do not have real time performance unless enough computational power is accessible in field. We use Maximum Likelihood Classification (MLC) and image processing techniques to label field images in three classes; background, crop, and weeds. This data is processed through our modified U-Net, which improves the semantic accuracy with reduced memory cost. We train our model with DICE loss and compare the results with state of the art. We achieve 89.12% mean Intersection Over Union (mIOU) with 86.11%, 82.99%, and 98.23% individual IOU for crop, weeds, and background, respectively. Our proposed model uses only 15M parameters which are 57M less than the state-of-the-art models with a compromise of 1% mIOU score. |
4,154 | I must go down to the seas again: Or, what happens when the sea comes to you? Murujuga rock art as an environmental indicator for Australia's north-west | The Dampier Archipelago (properly known as Murujuga) is a rich rock art province in north-western Australia which documents an arid-maritime cultural landscape. But this Archipelago of 42 islands has only existed since the mid-Holocene. When people started to engrave art here, the granophyre bedrock was part of an inland range, more than 160 km from the coast. The Pilbara archaeological record demonstrates human responses through over forty thousand years of environmental change. This paper discusses how rock art across Murujuga can give insights to changing dynamics of people in place as well as depicting major environmental change. A predictive model is developed to assist in understanding the social changes which have been wrought by sea-level rise and consequential environmental changes. (C) 2014 Elsevier Ltd and INQUA. All rights reserved. |
4,155 | From Perception to Navigation in Environments with Persons: An Indoor Evaluation of the State of the Art | Research in the field of social robotics is allowing service robots to operate in environments with people. In the aim of realizing the vision of humans and robots coexisting in the same environment, several solutions have been proposed to (1) perceive persons and objects in the immediate environment; (2) predict the movements of humans; as well as (3) plan the navigation in agreement with socially accepted rules. In this work, we discuss the different aspects related to social navigation in the context of our experience in an indoor environment. We describe state-of-the-art approaches and experiment with existing methods to analyze their performance in practice. From this study, we gather first-hand insights into the limitations of current solutions and identify possible research directions to address the open challenges. In particular, this paper focuses on topics related to perception at the hardware and application levels, including 2D and 3D sensors, geometric and mainly semantic mapping, the prediction of people trajectories (physics-, pattern- and planning-based), and social navigation (reactive and predictive) in indoor environments. |
4,156 | Integrated clustering approach to developing technology for functional feature and engineering specification-based reference design retrieval | Engineering design is a complex activity, and is heavily reliant on the know-how of engineering designers. Hence, capturing, storing, and reusing design information, design intent, and underlining design knowledge to support design activities is a key issue in engineering knowledge management. To meet the demand for engineering designers regarding functional feature and engineering specification-based knowledge resources, this study proposes a novel scheme for functional feature and engineering specification-based reference design retrieval using an integrated clustering approach for providing engineering designers with easy access to relevant reference design and associated knowledge. The research objectives can be achieved by performing the following five tasks: (i) designing a functional feature and engineering specification-based reference design retrieval process, (ii) developing a functional feature and engineering specification representation, (iii) investigating and integrating ART1 (adaptive resonance theory 1) neural network, GA (genetic algorithm), and fuzzy ART (fuzzy adaptive resonance theory) clustering techniques, and (iv) implementing a functional feature and engineering specification-based reference design retrieval mechanism and experimenting with an example. The retrieval process involves three steps: functional feature and engineering specification-based query, similar design case search and retrieval, and similar design case ranking. The techniques involved include: (i) a binary code-based representation for functional feature and an EXPRESS language-based representation for engineering specification, (ii) ART1 neural network and genetic algorithm for functional feature-based similar design case clustering, (iii) fuzzy ART for engineering specification-based similar design ease clustering, (iv) similarity calculation for ranking similar design cases, and (v) a case-based representation for designed entities. |
4,157 | A Survey on Imitation Learning Techniques for End-to-End Autonomous Vehicles | The state-of-the-art decision and planning approaches for autonomous vehicles have moved away from manually designed systems, instead focusing on the utilisation of large-scale datasets of expert demonstration via Imitation Learning (IL). In this paper, we present a comprehensive review of IL approaches, primarily for the paradigm of end-to-end based systems in autonomous vehicles. We classify the literature into three distinct categories: 1) Behavioural Cloning (BC), 2) Direct Policy Learning (DPL) and 3) Inverse Reinforcement Learning (IRL). For each of these categories, the current state-of-the-art literature is comprehensively reviewed and summarised, with future directions of research identified to facilitate the development of imitation learning based systems for end-to-end autonomous vehicles. Due to the data-intensive nature of deep learning techniques, currently available datasets and simulators for end-to-end autonomous driving are also reviewed. |
4,158 | Inference of Cerebrovascular Topology With Geodesic Minimum Spanning Trees | A vectorial representation of the vascular network that embodies quantitative features-location, direction, scale, and bifurcations-has many potential cardio-and neuro-vascularapplications. We present VTrails, an end-to-end approach to extract geodesic vascular minimum spanning trees from angiographic data by solving a connectivity-optimized anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Evaluating real and synthetic vascular images, we compare VTrails against the state-of-the-art ridge detectors for tubular structures by assessing the connectedness of the vesselness map and inspecting the synthesized tensor field. The inferred geodesic trees are then quantitatively evaluated within a topologically aware framework, by comparing the proposed method against popular vascular segmentation tool kits on clinical angiographies. VTrails potentials are discussed towards integrating group-wise vascular image analyses. The performance of VTrails demonstrates its versatility and usefulness also for patient-specific applications in interventional neuroradiology and vascular surgery. |
4,159 | Deep-Full-Range: A Deep Learning Based Network Encrypted Traffic Classification and Intrusion Detection Framework | With the rapid evolution of network traffic diversity, the understanding of network traffic has become more pivotal and more formidable. Previously, traffic classification and intrusion detection require a burdensome analyzing of various traffic features and attack-related characteristics by experts, and even, private information might be required. However, due to the outdated features labeling and privacy protocols, the existing approaches may not fit with the characteristics of the changing network environment anymore. In this paper, we present a light-weight framework with the aid of deep learning for encrypted traffic classification and intrusion detection, termed as deep-full-range (DFR). Thanks to deep learning, DFR is able to learn from raw traffic without manual intervention and private information. In such a framework, our proposed algorithms are compared with other state-of-the-art methods using two public datasets. The experimental results show that our framework not only can outperform the state-of-the-art methods by averaging 13.49% on encrypted traffic classification's F1 score and by averaging 12.15% on intrusion detection's F1 score but also require much lesser storage resource requirement. |
4,160 | Preliminary Determination of Activation Products for a Varian Truebeam Linear Accelerator | Medical linear accelerators used to treat various forms of cancers are operated at a number of different energies. A by-product of the high-energy photons produced by accelerators is activation of components within the machine itself and its surrounding bunker. The activation products pose radiological and regulatory challenges during the operation of the accelerator as well as when it is time for final decommissioning. The Varian TrueBeam is a new state-of-the-art linear accelerator now operating in the Canadian market. There is currently limited information on the production of its activation products and the resulting impacts on operation and decommissioning. In this paper, activation products in the Varian TrueBeam accelerator are experimentally determined by performing gamma spectroscopy using a portable high purity germanium detector. A total of 10 isotopes are identified for the conditions tested, which include Na-24, Al-28, Mn-56, Ni-57, Cu-64, Cu-66, Br-82, Sb-122, Sb-124, W-187. The half-lives of these isotopes range from 2.3 min to 60.2 d. These preliminary results indicate that a decommissioning case similar to other radiotherapy accelerators can be made. |
4,161 | Uniform Embedding for Efficient JPEG Steganography | Steganography is the science and art of covert communication, which aims to hide the secret messages into a cover medium while achieving the least possible statistical detectability. To this end, the framework of minimal distortion embedding is widely adopted in the development of the steganographic system, in which a well designed distortion function is of vital importance. In this paper, a class of new distortion functions known as uniform embedding distortion function (UED) is presented for both side-informed and non side-informed secure JPEG steganography. By incorporating the syndrome trellis coding, the best codeword with minimal distortion for a given message is determined with UED, which, instead of random modification, tries to spread the embedding modification uniformly to quantized discrete cosine transform (DCT) coefficients of all possible magnitudes. In this way, less statistical detectability is achieved, owing to the reduction of the average changes of the first-and second-order statistics for DCT coefficients as a whole. The effectiveness of the proposed scheme is verified with evidence obtained from exhaustive experiments using popular steganalyzers with various feature sets on the BOSSbase database. Compared with prior arts, the proposed scheme gains favorable performance in terms of secure embedding capacity against steganalysis. |
4,162 | Structural basis for mouse receptor recognition by SARS-CoV-2 omicron variant | The sudden emergence and rapid spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) omicron variant has raised questions about its animal reservoir. Here, we investigated receptor recognition of the omicron's receptor-binding domain (RBD), focusing on four of its mutations (Q493R, Q498R, N501Y, and Y505H) surrounding two mutational hotspots. These mutations have variable effects on the RBD's affinity for human angiotensin-converting enzyme 2 (ACE2), but they all enhance the RBD's affinity for mouse ACE2. We further determined the crystal structure of omicron RBD complexed with mouse ACE2. The structure showed that all four mutations are viral adaptations to mouse ACE2: three of them (Q493R, Q498R, and Y505H) are uniquely adapted to mouse ACE2, whereas the other one (N501Y) is adapted to both human ACE2 and mouse ACE2. These data reveal that the omicron RBD was well adapted to mouse ACE2 before omicron started to infect humans, providing insight into the potential evolutionary origin of the omicron variant. |
4,163 | Effects of nanocapsules of poly-epsilon-caprolactone containing artemisinin on zebrafish early-life stages and adults | Artemisinin extracted from Artemisia annua L. plants has a range of properties that qualifies it to treat several diseases, such as malaria and cancer. However, it has short half-life, which requires making continuous use of it, which has motivated the association of artemisinin (ART) with polymeric nanoparticles to increase its therapeutic efficiency. However, the ecotoxicological safety of this association has been questioned, given the scarcity of studies in this area. Thus, in this work the toxicity of Poly (epsilon-Caprolactone) nanocapsules added with ART (ART-NANO) in zebrafish (Danio rerio), embryos and adults was studied. Different endpoints were analyzed in organisms exposed to ART-NANO, including those predictive of embryotoxicity and histopatoxicity. Embryotoxicity was analyzed based on Organization for Economic Co-operation and Development (OECD) test guideline (236) for fish embryo acute toxicity applied to zebrafish (Danio rerio) at 96 hpf under five nominal logarithmic concentrations (0.125 to 2.0 mg/L). Our results demonstrate, mainly, that fertilized eggs presented increased coagulation, lack of heart rate, vitelline sac displacement and lack of somite formation. On the other hand, adult individuals (exposed to the same concentrations and evaluated after 24 and 96 h of exposure) have shown increased pericarditis. Therefore, the treatment based on ART, poly (epsilon-caprolactone) nanocapsules and on their combination at different concentrations have shown toxic effects on zebrafish embryos and adult individuals. (C) 2020 Elsevier B.V. All rights reserved. |
4,164 | User-assisted image shadow removal | This paper presents a novel user-aided method for texture-preserving shadow removal from single images requiring simple user input. Compared with the state-of-the-art, our algorithm offers the most flexible user interaction to date and produces more accurate and robust shadow removal under thorough quantitative evaluation. Shadow masks are first detected by analysing user specified shadow feature strokes. Sample intensity profiles with variable interval and length around the shadow boundary are detected next, which avoids artefacts raised from uneven boundaries. Texture noise in samples is then removed by applying local group bilateral filtering, and initial sparse shadow scales are estimated by fitting a piecewise curve to intensity samples. The remaining errors in estimated sparse scales are removed by local group smoothing. To relight the image, a dense scale field is produced by in-painting the sparse scales. Finally, a gradual colour correction is applied to remove artefacts due to image post-processing. Using state-of-the-art evaluation data, we quantitatively and qualitatively demonstrate our method to outperform current leading shadow removal methods. (C) 2017 Elsevier B.V. All rights reserved. |
4,165 | luvHarris: A Practical Corner Detector for Event-Cameras | There have been a number of corner detection methods proposed for event cameras in the last years, since event-driven computer vision has become more accessible. Current state-of-the-art have either unsatisfactory accuracy or real-time performance when considered for practical use, for example when a camera is randomly moved in an unconstrained environment. In this paper, we present yet another method to perform corner detection, dubbed look-up event-Harris (luvHarris), that employs the Harris algorithm for high accuracy but manages an improved event throughput. Our method has two major contributions, 1. a novel "threshold ordinal event-surface" that removes certain tuning parameters and is well suited for Harris operations, and 2. an implementation of the Harris algorithm such that the computational load per event is minimised and computational heavy convolutions are performed only 'as-fast-as-possible', i.e., only as computational resources are available. The result is a practical, real-time, and robust corner detector that runs more than 2.6x the speed of current state-of-the-art; a necessity when using a high-resolution event-camera in real-time. We explain the considerations taken for the approach, compare the algorithm to current state-of-the-art in terms of computational performance and detection accuracy, and discuss the validity of the proposed approach for event cameras. |
4,166 | Teaching application of watercolor light color technique | As a branch of watercolor painting, watercolor light color has been widely used in different fields. In the field of design, designers use the convenience, quickness, transparency and brilliance of watercolor to draw a design drawing. In the field of art creation, watercolor is usually the best choice for painters to go out to sketch and create large-scale drawings. In the field of art education, watercolor tools are easy to carry, low-cost and easy to operate, which can facilitate students' repeated practice and outside Sketching is helpful to cultivate students' sense of color and observation ability. Therefore, as a branch of art curriculum, watercolor light color has a wide range of uses and great practicability, which is worth exploring and studying. |
4,167 | Unobtrusive Behavioral and Activity-Related Multimodal Biometrics: The ACTIBIO Authentication Concept | Unobtrusive Authentication Using ACTIvity-Related and Soft BIOmetrics (ACTIBIO) is an EU Specific Targeted Research Project (STREP) where new types of biometrics are combined with state-of-the-art unobtrusive technologies in order to enhance security in a wide spectrum of applications. The project aims to develop a modular, robust, multimodal biometrics security authentication and monitoring system, which uses a biodynamic physiological profile, unique for each individual, and advancements of the state of the art in unobtrusive behavioral and other biometrics, such as face, gait recognition, and seat-based anthropometrics. Several shortcomings of existing biometric recognition systems are addressed within this project, which have helped in improving existing sensors, in developing new algorithms, and in designing applications, towards creating new, unobtrusive, biometric authentication procedures in security-sensitive, Ambient Intelligence environments. This paper presents the concept of the ACTIBIO project and describes its unobtrusive authentication demonstrator in a real scenario by focusing on the vision-based biometric recognition modalities. |
4,168 | A Supervised Framework for the Registration and Segmentation of White Matter Fiber Tracts | A supervised framework is presented for the automatic registration and segmentation of white matter (WM) tractographies extracted from brain DT-MRI. The framework relies on the direct registration between the fibers, without requiring any intensity-based registration as preprocessing. An affine transform is recovered together with a set of segmented fibers. A recently introduced probabilistic boosting tree classifier is used in a segmentation refinement step to improve the precision of the target tract segmentation. The proposed method compares favorably with a state-of-the-art intensity-based algorithm for affine registration of DTI tractographies. Segmentation results for 12 major WM tracts are demonstrated. Quantitative results are also provided for the segmentation of a particularly difficult case, the optic radiation tract. An average precision of 80% and recall of 55% were obtained for the optimal configuration of the presented method. |
4,169 | Gene Extraction of Leizhou Kiln Porcelain Patterns Based on Safety Internet of Things and Its Application in Modern Design | Porcelain art is a classic art treasure in the history of human civilization. After thousands of years of development, it carries the wisdom and sweat of the ancients and draws us a masterpiece that spans thousands of years. China is the hometown of porcelain. As the essence of Chinese art, porcelain has retained the traditional artistic characteristics of our country for thousands of years. Porcelain design can not only capture people's living experience and cultural understanding, but also express the thoughts or feelings of predecessors. This thesis aims to study the application of Leizhou kiln porcelain pattern gene extraction and modern design in the secure Internet of Things. The extraction of Leizhou kiln porcelain patterns in this paper is a very rigorous technical work. First, a security IoT monitoring and early warning system must be established. Secondly, a field survey of Leizhou Museum and Zhanjiang City Museum will be carried out to take photos of Leizhou kiln porcelain from various angles, combined with literature. Sort out the first-hand porcelain materials and classify the porcelain according to the age of production. Then use software such as photoshop, Adobe illustrator, CorelDRAW and other software to extract unit patterns from the patterns on the Leizhou kiln porcelain in turn, use symmetry, exaggeration and other basic skeleton deformation processing, refer to the popular color matching, to form a modern and unique pattern. The results of the empirical analysis show that we must use the Internet of Things technology to take safety measures before Leizhou kiln porcelain pattern gene extraction, which can make the safety reach 91%, and we must also apply the extracted patterns to modern designs. It does not mean that the porcelain patterns are directly used. What we want to achieve is an innovative way of inheritance, so that these porcelain patterns will be presented to everyone with a brand-new mental outlook. |
4,170 | Improving Object Tracking by Added Noise and Channel Attention | CNN-based trackers, especially those based on Siamese networks, have recently attracted considerable attention because of their relatively good performance and low computational cost. For many Siamese trackers, learning a generic object model from a large-scale dataset is still a challenging task. In the current study, we introduce input noise as regularization in the training data to improve generalization of the learned model. We propose an Input-Regularized Channel Attentional Siamese (IRCA-Siam) tracker which exhibits improved generalization compared to the current state-of-the-art trackers. In particular, we exploit offline learning by introducing additive noise for input data augmentation to mitigate the overfitting problem. We propose feature fusion from noisy and clean input channels which improves the target localization. Channel attention integrated with our framework helps finding more useful target features resulting in further performance improvement. Our proposed IRCA-Siam enhances the discrimination of the tracker/background and improves fault tolerance and generalization. An extensive experimental evaluation on six benchmark datasets including OTB2013, OTB2015, TC128, UAV123, VOT2016 and VOT2017 demonstrate superior performance of the proposed IRCA-Siam tracker compared to the 30 existing state-of-the-art trackers. |
4,171 | Handwriting recognition using cohort of LSTM and lexicon verification with extremely large lexicon | In this article, a handwriting recognition model whose complexity does not depend on the lexicon size is proposed. It is an alternative to lexicon-driven decoding, based on alexicon verificationprocess that allows to deal with millions of words, without any time consuming decoding stage. This lexicon verification is included in a cascade framework that uses complementary LSTM RNN classifiers. An original and very efficient method to obtain hundreds of complementary LSTM RNN extracted from a single training, calledcohort, is proposed. The proposed approach achieves new state-of-the art performance on the Rimes and IAM datasets, and provides 90% of accuracy on the Rimes dataset when dealing with a gigantic lexicon record of 3 millions of words. The last contribution extends the idea of cohort and lexicon verification in a ROVER combination for handwriting line recognition, and achieves state-of-the-art results on the Rimes dataset. |
4,172 | Betulinic acid alleviates zearalenone-induced uterine injury in mice | Zearalenone (ZEA) is a mycotoxin with estrogen-like biological activity, which widely present in feed and raw materials, with strong reproductive system toxicity and a major threat to animal reproduction. Betulinic acid (BA) is a natural plant compound with antioxidant, anti-inflammatory and other pharmacological activities. However, the mechanism of ZEA-induced uterine injury and the protective effect of BA have not been reported. Our results show that ZEA could cause uterine histopathological damage and cellular ultrastructural damage, affecting the secretion of sex hormones, such as estradiol (E2) and progesterone (P4), and increase the mRNA and protein expression of estrogen receptor α (ERα). ZEA could inhibit the activities of catalase (CAT) and superoxide dismutase (SOD), increase the production of malondialdehyde (MDA) and reactive oxygen species (ROS), and cause uterine oxidative stress. Furthermore, ZEA affected the homeostasis of uterine cell proliferation and death by regulating the expression of proliferating cell nuclear antigen (PCNA) and activating the mitochondrial apoptotic pathway. ZEA-induced uterine injury might be related to the activation of p38/ERK MAPK signaling pathway. However, the regulatory effect of ZEA on the uterus was reversed after BA treatment. In conclusion, the uterus is an important target organ attacked by ZEA, and BA showed a good therapeutic effect. |
4,173 | Adaptive Weighting Landmark-Based Group-Wise Registration on Lung DCE-MRI Images | Image registration of lung dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is challenging because the rapid changes in intensity lead to non-realistic deformations of intensity-based registration methods. To address this problem, we propose a novel landmark-based registration framework by incorporating landmark information into a group-wise registration. Robust principal component analysis is used to separate motion from intensity changes caused by a contrast agent. Landmark pairs are detected on the resulting motion components and then incorporated into an intensity-based registration through a constraint term. To reduce the negative effect of inaccurate landmark pairs on registration, an adaptive weighting landmark constraint is proposed. The method for calculating landmark weights is based on an assumption that the displacement of a good matched landmark is consistent with those of its neighbors. The proposed method was tested on 20 clinical lung DCE-MRI image series. Both visual inspection and quantitative assessment are used for the evaluation. Experimental results show that the proposed method effectively reduces the non-realistic deformations in registration and improves the registration performance compared with several state-of-the-art registration methods. |
4,174 | The BACE1-generated C-terminal fragment of the neural cell adhesion molecule 2 (NCAM2) promotes BACE1 targeting to Rab11-positive endosomes | Beta-site amyloid precursor protein cleaving enzyme 1 (BACE1), also known as β-secretase, is an aspartic protease. The sorting of this enzyme into Rab11-positive recycling endosomes regulates the BACE1-mediated cleavage of its substrates, however, the mechanisms underlying this targeting remain poorly understood. The neural cell adhesion molecule 2 (NCAM2) is a substrate of BACE1. We show that BACE1 cleaves NCAM2 in cultured hippocampal neurons and NCAM2-transfected CHO cells. The C-terminal fragment of NCAM2 that comprises the intracellular domain and a small portion of NCAM2's extracellular domain, associates with BACE1. This association is not affected in cells with inhibited endocytosis, indicating that the interaction of NCAM2 and BACE1 precedes the targeting of BACE1 from the cell surface to endosomes. In neurons and CHO cells, this fragment and BACE1 co-localize in Rab11-positive endosomes. Overexpression of full-length NCAM2 or a recombinant NCAM2 fragment containing the transmembrane and intracellular domains but lacking the extracellular domain leads to an increase in BACE1 levels in these organelles. In NCAM2-deficient neurons, the levels of BACE1 are increased at the cell surface and reduced in intracellular organelles. These effects are correlated with increased levels of the soluble extracellular domain of BACE1 in the brains of NCAM2-deficient mice, suggesting increased shedding of BACE1 from the cell surface. Of note, shedding of the extracellular domain of Sez6, a protein cleaved exclusively by BACE1, is reduced in NCAM2-deficient animals. These results indicate that the BACE1-generated fragment of NCAM2 regulates BACE1 activity by promoting the targeting of BACE1 to Rab11-positive endosomes. |
4,175 | Contour Transformer Network for One-Shot Segmentation of Anatomical Structures | Accurate segmentation of anatomical structures is vital for medical image analysis. The state-of-the-art accuracy is typically achieved by supervised learning methods, where gathering the requisite expert-labeled image annotations in a scalable manner remains a main obstacle. Therefore, annotation-efficient methods that permit to produce accurate anatomical structure segmentation are highly desirable. In this work, we present Contour Transformer Network (CTN), a one-shot anatomy segmentation method with a naturally built-in human-in-the-loop mechanism. We formulate anatomy segmentation as a contour evolution process and model the evolution behavior by graph convolutional networks (GCNs). Training the CTN model requires only one labeled image exemplar and leverages additional unlabeled data through newly introduced loss functions that measure the global shape and appearance consistency of contours. On segmentation tasks of four different anatomies, we demonstrate that our one-shot learning method significantly outperforms non-learning-based methods and performs competitively to the state-of-the-art fully supervised deep learning methods. With minimal human-in-the-loop editing feedback, the segmentation performance can be further improved to surpass the fully supervised methods. |
4,176 | RTNet: Relation Transformer Network for Diabetic Retinopathy Multi-Lesion Segmentation | Automatic diabetic retinopathy (DR) lesions segmentation makes great sense of assisting ophthalmologists in diagnosis. Although many researches have been conducted on this task, most prior works paid too much attention to the designs of networks instead of considering the pathological association for lesions. Through investigating the pathogenic causes of DR lesions in advance, we found that certain lesions are closed to specific vessels and present relative patterns to each other. Motivated by the observation, we propose a relation transformer block (RTB) to incorporate attention mechanisms at two main levels: a self-attention transformer exploits global dependencies among lesion features, while a cross-attention transformer allows interactions between lesion and vessel features by integrating valuable vascular information to alleviate ambiguity in lesion detection caused by complex fundus structures. In addition, to capture the small lesion patterns first, we propose a global transformer block (GTB) which preserves detailed information in deep network. By integrating the above blocks of dual-branches, our network segments the four kinds of lesions simultaneously. Comprehensive experiments on IDRiD and DDR datasets well demonstrate the superiority of our approach, which achieves competitive performance compared to state-of-the-arts. |
4,177 | Using Temporal Convolutional Networks to estimate ball possession in soccer games | The use of tracking data in the field of sport analytics has increased in the last years as a starting point for in-depth tactical analyses. This work investigates the use of Temporal Convolutional Networks (TCNs), a powerful architecture for sequential data analysis, to extract ball possession information from tracking data. This task is a crucial step for many tactical analyses and is nowadays carried out manually by a human operator in the stadium, which is costly, difficult to implement, and prone to errors. In this work, several classification approaches are explored to classify the game state as dead, ball owned by the home team, or by the away team: as a single-branch, ternary prediction, or as two binary predictions, first detecting whether the game is dead or alive and then which team owns the ball. TCNs are exploited to create independent trajectory embeddings from tracking data of each object; since there is no semantic ordering among the tracked objects, we investigate different permutation-invariant layers to combine the embeddings, namely, an element-wise sum over the embeddings, a self-attention module, and the use of 2D convolutions. Performance evaluation on tracking data from professional soccer games shows that the proposed method outperforms state-of-the-art rule-based methods, achieving 86.2% accuracy in possession estimation (+7.3% compared to the state of the art) and 89.2% accuracy in dead-alive classification (+33.2% compared to the state of the art). Extensive ablation studies were conducted to investigate how different input data concur to the final prediction. |
4,178 | Indole-3-carbinol ameliorated the neurodevelopmental deficits in neonatal anoxic injury in rats | Neonatal anoxia is linked to long-lasting neurodevelopmental deficits. Due to the lack of pharmacological intervention to treat neonatal anoxia, there is interest in finding new molecules for its treatment. Indole-3-carbinol (I3C) has shown neuroprotective effects in some disease conditions. However, the neuroprotective role of I3C in neonatal anoxia has not been explored. Consequently, we have investigated the effect of I3C on neonatal anoxia-induced brain injury and neurodevelopmental deficits. Rat pups after 30 h of birth were subjected to two episodes of anoxia (10 min in each) at a time interval of 24 h by flowing 100% nitrogen. I3C was administered within 30 min of the second episode of anoxia on a postnatal day (PND) 3 and continued for PND 9. Neurodevelopmental deficits, cortical mitochondrial membrane potential (MMP), opening of mitochondrial permeability transition pore (MPTP), electron transport chain (ETC) enzyme activities, oxidative stress, hypoxia-inducible factor-1α (HIF-1α) levels, histopathological changes, and apoptosis were measured. I3C treatment dose-dependently ameliorated the neurodevelopmental deficits and somatic growth in anoxic pups. I3C improved mitochondrial function by enhancing the MMP, mitochondrial ETC enzymes, and antioxidants. It blocked the MPTP opening and release of cytochrome C in anoxic pups. Further, I3C reduced the elevated cortical HIF-1α in neonatal anoxic pups. Furthermore, I3C ameliorated histopathological abnormalities and mitochondrial-mediated apoptotic indicators Cyt C, caspase-9, and caspase-3. Our study concludes that I3C improved neuronal development in anoxic pups by enhancing mitochondrial function, reducing HIF-1α, and mitigating apoptosis. |
4,179 | Fecal microbiota transplantation improves intestinal inflammation in mice with ulcerative colitis by modulating intestinal flora composition and down-regulating NF-kB signaling pathway | Ulcerative colitis (UC) is a chronic inflammatory disease of the intestine. It is characterized with recurrent. The pathogenesis is mainly associated with environmental factors, genetic susceptibility, dysbiosis of the intestinal flora and autoimmunity. The role of intestinal flora disorders in the pathogenesis and progression of UC is becoming increasingly prominent. More and more studies have confirmed that fecal microbiota transplantation (FMT) could reshape the composition of UC intestinal flora and it is expected to be a new strategy for UC treatment. In this study, we used 2% Dextran sulfate sodium (DSS) for 7 days to induce acute colitis model in mice, and interfere with FMT and Enterotoxigenic Escherichia coli (ETEC). ELISA and immunohistochemistry were applied to detect the concentration and expression of NF-κB p65, STAT3 and IL-6. 16SrRNA high-throughput sequencing was performed to explore the composition of intestinal flora. The aim was to study the treatment effect of FMT on UC mice and explore its potential mechanism by observing the changes of intestinal flora composition and diversity, and its relationship with NF-κB p65, STAT3 and IL-6 expression. We conclude that FMT could improve intestinal flora disorder in mice with ulcerative colitis, regulate NF-κB signaling pathway, and significantly reduce intestinal inflammation in UC mice. |
4,180 | Neuroimaging Correlates of Post-Traumatic Stress Disorder in Traumatic Brain Injury: A Systematic Review of the Literature | Neuroimaging is widely utilized in studying traumatic brain injury (TBI) and post-traumatic stress disorder (PTSD). The risk for PTSD is greater after TBI than after non-TBI trauma, and PTSD is associated with worse outcomes after TBI. Studying the neuroimaging correlates of TBI-related PTSD may provide insights into the etiology of both conditions and help identify those TBI patients most at risk of developing persistent symptoms. The objectives of this systematic review were to examine the current literature on neuroimaging in TBI-related PTSD, summarize key findings, and highlight strengths and limitations to guide future research. A Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA) compliant literature search was conducted in PubMed (MEDLINE®), PsycINFO, Embase, and Scopus databases prior to January 2022. The database query yielded 4486 articles, which were narrowed based on specified inclusion criteria to a final cohort of 16 studies, composed of 854 participants with TBI. There was no consensus regarding neuroimaging correlates of TBI-related PTSD among the included articles. A small number of studies suggest that TBI-related PTSD is associated with white matter tract changes, particularly in frontotemporal regions, as well as changes in whole-brain networks of resting-state connectivity. Future studies hoping to identify reliable neuroimaging correlates of TBI-related PTSD would benefit from ensuring consistent case definition, preferably with clinician-diagnosed TBI and PTSD, selection of comparable control groups, and attention to imaging timing post-injury. Prospective studies are needed and should aim to further differentiate predisposing factors from sequelae of TBI-related PTSD. |
4,181 | Real-time 3D reconstruction using point-dependent pose graph optimization framework | As an essential core of structure from motion, full optimization and pose graph optimization are widely used in most of state-of-the-art 3D reconstruction systems, to estimate the motion trajectory of camera during scanning. Comparing to full optimization, the pose graph optimization has the advantages of low computational complexity and fast convergence, while the practical accuracy of pose graph optimization in applications is intrinsically limited by simple loss function independent of points in scene. In this paper, we proposed a point-dependent pose graph optimization (PDPGO) to address this problem and take it as core to construct a 3D high-precision reconstruction system. In our pipeline, we first construct a hierarchical pose graph by aligning the input frame to its overlapping frames searched by a spatial hashing scheme, which reduces the computational complexity of pairwise alignment. We then derive a loss function of PDPGO from global geometry loss, which improves the accuracy of previous methods. Our system is validated on public benchmarks, and experimental results demonstrate the competing performance against the state-of-the-art systems. And the average reconstruction accuracy in all scenes of ICL-NUIM is up to 0.9 cm. |
4,182 | Combating desertification: Building on traditional knowledge systems of the thar desert communities | The Thar Desert of western India is known for its rich and ancient culture system and traditions. The communities have long been part of the Thar Desert ecosystem and have evolved specific strategies to live in harmony with its hostile environment. This culture has provided several miracle plants of immense food and medicinal value to modem civilisation. The ancient rural livelihood knowledge system reflects time-tested techno-scientific knowledge with a proven track record of sustainability, especially during natural hazards like drought and famines. In addition, several of the traditional skills of local communities in arts and crafts, music and instruments have made modem man aware of the art and techniques of sustainably utilising local biological resources and preserving their biodiversity along with using waste products of the forests, without harming the desert ecosystem. Traditional cultural and socio-religious values are fast dwindling under the impact of materialistic approach, industrialisation and development. This paper endeavours to illustrate the need to assist and propagate indigenous rural livelihood systems rather than mindlessly replace or abandon them as a result of state bureaucracies. |
4,183 | Reconstructing 3D Shapes From Multiple Sketches Using Direct Shape Optimization | 3D shape reconstruction from multiple hand-drawn sketches is an intriguing way to 3D shape modeling. Currently, state-of-the-art methods employ neural networks to learn a mapping from multiple sketches from arbitrary view angles to a 3D voxel grid. Because of the cubic complexity of 3D voxel grids, however, neural networks are hard to train and limited to low resolution reconstructions, which leads to a lack of geometric detail and low accuracy. To resolve this issue, we propose to reconstruct 3D shapes from multiple sketches using direct shape optimization (DSO), which does not involve deep learning models for direct voxel-based 3D shape generation. Specifically, we first leverage a conditional generative adversarial network (CGAN) to translate each sketch into an attenuance image that captures the predicted geometry from a given viewpoint. Then, DSO minimizes a project-and-compare loss to reconstruct the 3D shape such that it matches the predicted attenuance images from the view angles of all input sketches. Based on this, we further propose a progressive update approach to handle inconsistencies among a few hand-drawn sketches for the same 3D shape. Our experimental results show that our method significantly outperforms the state-of-the-art methods under widely used benchmarks and produces intuitive results in an interactive application. |
4,184 | First report on the probiotic potential of Mammaliicoccus sciuri isolated from raw goat milk | Probiotics are considered effective microbial dietary supplements that provide beneficial effects to consumers, usually by restoring or improving gut microflora. Goat milk is one of the rich sources of probiotics as well as nutrients. Therefore, the primary aim of this research was to isolate and evaluate the potential of novel indigenous probiotic strains present in goat milk. Six different raw goat milk samples were collected from different areas of Multan, Pakistan. For bacterial characterization, samples were cultured and isolated on MRS agar plates for different morphological and biochemical tests. The probiotic potential of the six isolates, all of which were gram positive (G1, G2, G3, G4, G5, and G6) and five of which were catalase negative (all except G1), were assessed via a milk coagulation assay and antimicrobial activity, pH tolerance, phenol tolerance, and sodium chloride (NaCl) tolerance tests, which revealed that all the isolates coagulated in milk and showed protease and lipase activity, except G3. All six isolates showed tolerance against 0.2% phenol and 2-4% NaCl and were able to survive in both alkaline and acidic conditions. Only five isolates showed antimicrobial activity against indicator strain Aspergillus niger strain STA9, validating their probiotic nature. The most potent bile-tolerant and bacteriocin-producing isolate, G1, also showed γ-hemolytic activity and resistance to penicillin but showed susceptibility to other antibiotics. The lactic acid-producing (0.60% titratable acidity) G1 isolate was identified as a novel strain of Mammaliicoccus sciuri based on 16S rDNA sequencing. The above findings suggest that the potent M. sciuri GMN01 strain can serve as a potential probiotic strain. A potent probiotic strain isolated from raw goat milk could be utilized as a dietary supplement, and goat milk could become an alternative to other sources of milk, particularly cow milk. However, safety aspects of this strain require further investigation because the present safety tests are insufficient to conclude that the GMN01 isolate is safe. |
4,185 | The Vulnerability of Semantic Segmentation Networks to Adversarial Attacks in Autonomous Driving: Enhancing Extensive Environment Sensing | Enabling autonomous driving (AD) can be considered one of the biggest challenges in today?s technology. AD is a complex task accomplished by several functionalities, with environment perception being one of its core functions. Environment perception is usually performed by combining the semantic information captured by several sensors, i.e., lidar or camera. The semantic information from the respective sensor can be extracted by using convolutional neural networks (CNNs) for dense prediction. In the past, CNNs constantly showed stateof-the-art performance on several vision-related tasks, such as semantic segmentation of traffic scenes using nothing but the red-green-blue (RGB) images provided by a camera. Although CNNs obtain state-of-the-art performance on clean images, almost imperceptible changes to the input, referred to as adversarial perturbations, may lead to fatal deception. The goal of this article is to illuminate the vulnerability aspects of CNNs used for semantic segmentation with respect to adversarial attacks, and share insights into some of the existing known adversarial defense strategies. We aim to clarify the advantages and disadvantages associated with applying CNNs for environment perception in AD to serve as a motivation for future research in this field. |
4,186 | Research and Development on Phase-Shifting Surfaces (PSSs) | Phase-shifting surfaces (PSSs) developed in recent years are reported. Research and development on these phase-shifting surfaces are set in the historical context of prior-art free-standing lensing devices, as well as in the context of recent studies performed by other research groups. In addition, initial work on a phase-and amplitude-shifting surface (PASS), an extension of the phase-shifting surface, is demonstrated in a beam-shaping application. |
4,187 | Sequential beat-to-beat P and T wave delineation and waveform estimation in ECG signals: Block Gibbs sampler and marginalized particle filter | For ECG interpretation, the detection and delineation of P and T waves are challenging tasks. This paper proposes sequential Bayesian methods for simultaneous detection, threshold-free delineation, and waveform estimation of P and T waves on a beat-to-beat basis. By contrast to state-of-the-art methods that process multiple-beat signal blocks, the proposed Bayesian methods account for beat-to-beat waveform variations by sequentially estimating the waveforms for each beat. Our methods are based on Bayesian signal models that take into account previous beats as prior information. To estimate the unknown parameters of these Bayesian models, we first propose a block Gibbs sampler that exhibits fast convergence in spite of the strong local dependencies in the ECG signal. Then, in order to take into account all the information contained in the past rather than considering only one previous beat, a sequential Monte Carlo method is presented, with a marginalized particle filter that efficiently estimates the unknown parameters of the dynamic model. Both methods are evaluated on the annotated QT database and observed to achieve significant improvements in detection rate and delineation accuracy compared to state-of-the-art methods, thus providing promising approaches for sequential P and T wave analysis. (C) 2014 Elsevier B.V. All rights reserved. |
4,188 | Internalized HIV-related stigma in women of color obtaining care at an HIV specialty center in Los Angeles County, California | The current study examined the role of internalized HIV-related stigma in antiretroviral therapy adherence, viral load, and retention in care among women of color living with HIV in Los Angeles County, California. African American and Hispanic/Latino women 18 years of age and older completed a one-time brief survey between September 2017 and February 2018. Descriptive statistics, and univariable and multivariable logistic regressions were used to analyze the data. Seventy-six participants enrolled in the study and 74 completed the entire survey. Seventy-six percent of respondents were Hispanic/Latino, 24% were African American, 71% were unemployed, and 54% had less than a high school education. Thirty-five percent were defined as having "high" stigma with a score in the upper quartile of the scale. Being unemployed, having a high school education or less, and not meeting the Health Resources and Services Administration's annual retention in care measure were associated with "high" stigma. When controlling for education and employment status, those reporting "high" stigma vs. "low" stigma were 18.8 times more likely to not meet the criteria for annual retention in care (OR = 18.8, 95% CI = 1.9-189.2, p = 0.013). Stigma-reduction interventions targeting healthcare settings may be necessary to improve patient retention and engagement in care. |
4,189 | Structure-Guided Segmentation for 3D Neuron Reconstruction | Digital reconstruction of neuronal morphologies in 3D microscopy images is critical in the field of neuroscience. However, most existing automatic tracing algorithms cannot obtain accurate neuron reconstruction when processing 3D neuron images contaminated by strong background noises or containing weak filament signals. In this paper, we present a 3D neuron segmentation network named Structure-Guided Segmentation Network (SGSNet) to enhance weak neuronal structures and remove background noises. The network contains a shared encoding path but utilizes two decoding paths called Main Segmentation Branch (MSB) and Structure-Detection Branch (SDB), respectively. MSB is trained on binary labels to acquire the 3D neuron image segmentation maps. However, the segmentation results in challenging datasets often contain structural errors, such as discontinued segments of the weak-signal neuronal structures and missing filaments due to low signal-to-noise ratio (SNR). Therefore, SDB is presented to detect the neuronal structures by regressing neuron distance transform maps. Furthermore, a Structure Attention Module (SAM) is designed to integrate the multi-scale feature maps of the two decoding paths, and provide contextual guidance of structural features from SDB to MSB to improve the final segmentation performance. In the experiments, we evaluate our model in two challenging 3D neuron image datasets, the BigNeuron dataset and the Extended Whole Mouse Brain Sub-image (EWMBS) dataset. When using different tracing methods on the segmented images produced by our method rather than other state-of-the-art segmentation methods, the distance scores gain 42.48% and 35.83% improvement in the BigNeuron dataset and 37.75% and 23.13% in the EWMBS dataset. |
4,190 | Query Reorganization Algorithms for Efficient Boolean Information Filtering | In the information filtering paradigm, clients subscribe to a server with continuous queries that express their information needs and get notified every time appropriate information is published. To perform this task in an efficient way, servers employ indexing schemes that support fast matches of the incoming information with the query database. Such indexing schemes involve (i) main-memory trie-based data structures that cluster similar queries by capturing common elements between them and (ii) efficient filtering mechanisms that exploit this clustering to achieve high throughput and low filtering times. However, state-of-the-art indexing schemes are sensitive to the query insertion order and cannot adopt to an evolving query workload, degrading the filtering performance over time. In this paper, we present an adaptive trie-based algorithm that outperforms current methods by relying on query statistics to reorganise the query database. Contrary to previous approaches, we show that the nature of the constructed tries, rather than their compactness, is the determining factor for efficient filtering performance. Our algorithm does not depend on the order of insertion of queries in the database, manages to cluster queries even when clustering possibilities are limited, and achieves more than 96 percent filtering time improvement over its state-of-the-art competitors. Finally, we demonstrate that our solution is easily extensible to multi-core machines. |
4,191 | Endoscopic and external dacryocystorhinostomy: A therapeutic proposal for distal acquired lacrimal obstructions | Endoscopic (END-DCR) and external dacryocystorhinostomies (EXT-DCR) are nowadays considered the gold standard techniques for non-oncologic distal acquired lacrimal disorders (DALO). However, no unanimous consensus has been achieved on which of these surgeries is the most suitable to the individual patient. Herein, we review the available literature of the last 30 years with the aim of defining a simple and reproduceable treatment algorithm to treat DALO. A search of PubMed, EMBASE, Scopus and Cochrane databases was last performed in December 2021 to examine evidence regarding the role of END-DCR and EXT-DCR in primary and revision surgeries. If considered primary surgeries, END-DCR should be preferred in case of intranasal comorbidities, given the possibility to directly visualize and treat potential intranasal pathologies. Conversely, EXT-DCR should be chosen in case of need/preference for local anesthesia, given the major historical experience and wider surgical field that helps to resolve intra-operatory complications (e.g., bleeding) in an uncollaborative patient. In the absence of the abovementioned conditions, the decision of one or other approach should be discussed with the patient. In recurrent cases, END-DCR should be considered the treatment of choice given the major likelihood to visualize the causes of primary failure and directly resolve it. In conclusion, END-DCR should be considered the treatment of choice in revision cases or in primary ones associated with intranasal pathologies, whereas EXT-DCR should be chosen if local anesthesia is needed. In the absence of these scenarios, it is still open to debate which of these two approaches should be used. |
4,192 | Inductive Power Transfer for Automotive Applications: State-of-the-Art and Future Trends | The paper discusses the development status of the inductive power transmission for automotive applications. This technology is, in fact, gaining the interest of electric vehicle manufacturers as an effective strategy to improve themarket penetration of electric mobility. Starting from the origin of this technology, the paper presents an overview of the current state of the art as well as the current research and industrial projects. Particular attention is devoted to the description of a prototypal system for the dynamic inductive power transmission whose goal is to extend the battery range by a fast partial recharging during the movement of the vehicle. |
4,193 | Optimization, validation and application of a high-throughput 96-well elution protocol for the quantification of psychoactive substances in influent wastewater | Wastewater-based epidemiology (WBE) is based on the analysis of human metabolic excretion products (biomarkers) of xenobiotics in wastewater, to gain information about various lifestyles and health aspects of a population in an evidence-based manner. Due to the complex wastewater matrix and trace level occurrence of human biomarkers in the sewage network, it is crucial to have sensitive analytical procedures available. Additionally, to improve the value of WBE as a complementary epidemiological source, there is increasing pressure on the analysis of more compounds, more locations and more samples. A high-throughput method based on 96-well Oasis MCX solid-phase extraction (SPE), requiring less influent wastewater (2 mL), was developed in accordance with the European Medicines Agency guidelines. Validation was successful for 28 parent drugs and metabolites of antidepressants, opioids and drugs of abuse. The selection of biomarkers and quantification limit was chosen to be relevant for WBE and was predominantly 10 ng/L or below. The final method was successfully applied to 24-h composite samples of October 2019 (n = 27), obtained from an urban wastewater treatment plant in Leuven (Belgium). |
4,194 | Dense depth maps from correspondences derived from perceived motion | Many computer vision applications require finding corresponding points between images and using the corresponding points to estimate disparity. Today's correspondence finding algorithms primarily use image features or pixel intensities common between image pairs. Some 3-D computer vision applications, however, do not produce the desired results using correspondences derived from image features or pixel intensities. Two examples are the multimodal camera rig and the center region of a coaxial camera rig. We present an image correspondence finding technique that aligns pairs of image sequences using optical flow fields. The optical flow fields provide information about the structure and motion of the scene, which are not available in still images but can be used in image alignment. We apply the technique to a dual focal length stereo camera rig consisting of a visible light-infrared camera pair and to a coaxial camera rig. We test our method on real image sequences and compare our results with the state-of-the-art multimodal and structure from motion (SfM) algorithms. Our method produces more accurate depth and scene velocity reconstruction estimates than the state-of-the-art multimodal and SfM algorithms. (C) 2017 SPIE and IS&T |
4,195 | Zavarzinia marina sp. nov., a novel hydrocarbon-degrading bacterium isolated from deep chlorophyll maximum layer seawater of the West Pacific Ocean and emended description of the genus Zavarzinia | A Gram-stain-negative, motile, non-spore-forming, strictly aerobic and rod-shaped bacterial strain, Adcm-6AT, was isolated from a seawater sample collected from the deep chlorophyll maximum layer in the West Pacific Ocean. Strain Adcm-6AT grew at 20-37 °C (optimum, 28-32 °C), at pH 6-11 (pH 7) and in the presence of 0-6 % (1-2 %) NaCl (w/v). Phylogenetic analysis based on 16S rRNA gene sequences indicated that it belonged to the genus Zavarzinia and had 97.7 and 96.9 % sequence similarity to Zavarzinia compransoris DSM 1231T and Zavarzinia aquatilis JCM 32263T, respectively. Digital DNA-DNA hybridization and average nucleotide identity values between strain Adcm-6AT and the two type strains were 22.2-22.9 % and 79.7-80.4 %, respectively. The principal fatty acids were C19:0 cyclo ω8c, summed feature 8 (C18:1 ω6c and/or C18:1 ω7c) and C16:0. The predominant respiratory quinone was Q-10. The polar lipids were diphosphatidylglycerol, two phosphatidylethanolamines, two phosphatidyglycerols and an unidentified lipid. The genomic DNA G+C content of strain Adcm-6AT was 67.7 %. Based on phylogenetic analysis and genomic-based relatedness indices, as well as phenotypic and genotypic characteristics, strain Adcm-6AT represents a novel species within the genus Zavarzinia, for which the name Zavarzinia marina sp. nov. is proposed. The type strain is Adcm-6AT (=MCCC M24951T=KCTC 82849T). |
4,196 | Intramedullary spinal metastasis of a carcinoid tumor | We report an intramedullary spinal cord metastasis from a bronchial carcinoid, and discuss its mechanisms and management. Intramedullary spinal cord metastases from any cancer are rare, and bronchial carcinoids account for only a small fraction of lung cancers. To our knowledge, an intramedullary spinal cord metastasis from a bronchial carcinoid has been described only once previously. |
4,197 | Vibrational Properties of LaNb0.8 M0.2 O4-δ (M=As, Sb, V, and Ta) | LaNb0.8 M0.2 O4-δ (where M=As, Sb, V, and Ta) oxides with pentavalent elements of different ionic sizes were synthesized by a solid-state reaction method. The vibrational properties of these oxides have been investigated. These studies revealed that the substituent element influences both Debye temperature value as well as the Raman active vibrational modes. Additionally, the low-temperature vibrational properties of LaNb0.8 Sb0.2 O4-δ have been determined to show the phase transition occurrence at 260 K which is lower than previously reported. |
4,198 | [Ideas and Briefing about Regulatory Requirements for Laboratory Developed Tests in the US] | As a special kind of in vitro diagnostic devices（IVDs）, laboratory developed tests（LDTs） are of great significance to the development of clinical laboratories. This study aims to explore the regulatory requirements ideas of LDTs. By introducing the development of LDTs and the changing of regulatory requirements in the United States, combing the current regulatory framework and discussing relevant ideas in the regulatory requirements of LDTs. |
4,199 | VQA as a factoid question answering problem: A novel approach for knowledge-aware and explainable visual question answering | With recent advancements in machine perception and scene understanding, Visual Question Answering (VQA) has garnered much attraction from researchers in the direction of training neural models for jointly analyzing, grounding and reasoning over the multi-modal space of image visual context and natural language in order to answer natural language questions pertaining to the image contents. However, though recent works have achieved significant improvement over state-of-art models for answering questions that are answerable by solely referring to the visual context of the image, such models are often limited, being incapable of tackling questions involving external world knowledge beyond the visible contents. Though recently, research has been driven towards tackling external knowledge based VQA as well, there is significant room for improvement as limited studies exist in this area. Inspired by the aforementioned challenges involved, this paper is aimed at answering free form and open ended natural language questions, not limited to visual context of an image, but external world knowledge as well. With this motive, inspired by human cognitive abilities of comprehending and reasoning answers when given a set of facts, this paper proposes a novel model architecture to model VQA as a factoid question answering problem, leveraging state-of-the-art deep learning techniques for reasoning and inferring answers to free form questions, in an attempt of improving the state-of-art in open ended visual question answering. (c) 2021 Elsevier B.V. All rights reserved. |
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