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5,200 | [Surgical Treatment of Esophageal Cancer-New Technologies, Modern Concepts] | In Germany esophageal cancer is mostly treated in specialized centers according to national and international guidelines in a multimodal and interdisciplinary setting. In the next few years centralization of esophageal surgery will continue in Germany due to new national regulations on minimum case volumes. This article highlights new technologies for surgical treatment of esophageal cancer and also depicts the current oncological concepts from the perspective of a high-volume center. |
5,201 | Biogenic synthesis of gold nanoparticles using Satureja rechingeri Jamzad: a potential anticancer agent against cisplatin-resistant A2780CP ovarian cancer cells | Drug resistance of cancer cells is a major issue in cancer treatment. Plant-mediated nanoparticle synthesis has been applied in recent years to overcome this problem. In this study, the biogenic synthesis of AuNPs was explored using Satureja rechingeri Jamzad aqueous leaf extract, and their anticancer effects were evaluated in cisplatin-resistant A2780CP ovarian cancer cells. The chemical composition of S. rechingeri Jamzad was analyzed using gas chromatography-mass spectrometry. The characteristics of green-synthesized AuNPs were confirmed using XRD, FTIR, UV-visible spectroscopy, TEM, SEM, EDX, DLS, and zeta potential. The cytotoxic effects of AuNPs and S. rechingeri Jamzad aqueous extract on cisplatin-resistant A2780CP ovarian cancer cells were evaluated by MTT assay and flow cytometry. Real-time PCR analyzed gene expression. The chemical composition revealed that carvacrol (89%) was the main component of the S. rechingeri Jamzad extract. The average size of the spherical biosynthesized AuNPs was 15.1 ± 3.7 nm. The AuNPs and plant extract inhibited the growth of cisplatin-resistant ovarian cancer cells in a time- and dose-dependent manner. The apoptotic cell death was confirmed by flow cytometry and DAPI staining. The proapoptotic genes were upregulated, while anti-apoptotic and metastatic genes were downregulated. According to the cell cycle analysis, cancer cells were arrested in the G0/G1 phase. Considering the anticancer activity of the synthesized AuNPs using S. rechingeri Jamzad and the low side effects of AuNPs on normal cells, these AuNPs showed strong potential for use as biological agents in drug-resistant cancer cells treatment. |
5,202 | Addressing current barriers to autism diagnoses through a tiered diagnostic approach involving pediatric primary care providers | Formal autism diagnosis from a specialist trained in autism assessment is customary prior to a child accessing early, intensive autism-specific services. However, long wait lists for diagnostic evaluations and limited specialty workforce have created substantial delays. Additionally, lengthy multidisciplinary evaluations are costly to insurers, inconvenient to families, and disproportionally impact under-resourced families. Diagnostic delays can impede access to intervention services. These barriers, combined with evidence regarding the importance of receiving early, autism-specific treatment, demand new approaches enabling access to autism specific services before comprehensive evaluation. Pediatric primary care providers (PCPs) are often the only health care professionals with whom a family interacts during early childhood and can play a crucial role in helping children with autism symptoms access services. Many strategies for autism diagnosis in primary care are being developed and tested; however, they have yet to be broadly adopted by PCPs, primarily due to critical implementation barriers in primary care settings. There is also not enough evidence on the accuracy of PCPs' diagnostic impressions without extensive specialty support, resulting in PCP hesitancy in diagnosing ASD, as well as family and service provider hesitancy in accepting a PCP autism diagnosis. In this commentary, we explore the acute need for shortening waitlists for autism evaluations through a tiered diagnostic approach, in which PCPs can rule in or rule out autism in children, for whom diagnosis is clear, and refer more complex cases for specialist evaluations, and explore implementation challenges to this approach. |
5,203 | Perfusion culture of endothelial cells under shear stress on microporous membrane in a pressure-driven microphysiological system | This paper reports perfusion culture of human umbilical vein endothelial cells (HUVECs) on a microporous membrane in a pressure-driven microphysiological system (PD-MPS), which we developed previously as a multi-throughput perfusion culture platform. We designed fluidic culture unit with microporous membrane to culture HUVECs under fluidic shear stress and constructed a perfusion culture model in the PD-MPS platform. Four fluidic culture units were arranged in the microplate-sized device, which enables four-throughput assay for characterization of HUVECs under flow. Medium flow was generated above and below the membrane by sequential pneumatic pressure to apply physiological shear stress to HUVECs. HUVECs exhibited aligned morphology to the direction of the flow with shear stress of 11.5-17.7 dyn/cm2 under the flow condition, while they randomly aligned under static culture condition in a 6 well plate. We also observed 3.3- and 5.0-fold increase in the expression levels of the thrombomodulin and endothelial nitric oxide synthase mRNAs, respectively, under the flow condition in the PD-MPS compared to the static culture in 6 well plate. We also observed actin filament aligned to the direction of flow in HUVECs cultured under the flow condition. |
5,204 | Dual feature extraction based convolutional neural network classifier for magnetic resonance imaging tumor detection using U-Net and three-dimensional convolutional neural network | Analysis and monitoring of disease development rely heavily on automated segmentation of brain tumors using MRI data. Because gliomas are aggressive and diverse, effective and precise segmentation techniques are utilized to divide tumors into intra-tumoral groups. In the proposed work, the Gray Level Co-occurrence Matrix feature extraction is used to extract characteristics from the image. In the segmentation problem of Brain tumor, Convolutional Neural Networks, which are widely employed in biomedical image segmentation, have greatly enhanced the stateof-the-art accuracy. In this work, we propose a major but simple combinative approach that results in improved and more precise estimates by combining two segmentation networks: a U-Net and a 3D CNN. For each model, it was assessed independently on the dataset to produce segmentation maps that varied greatly in terms of segmented tumor sub-regions and were then ensembled to reach the final prediction. On the validation set, the accuracy (percentage) of 98.29, 98.45, and 99.4 for tumor core, enhanced tumor and whole tumor, respectively, performed well in contrast to state-of-the-art designs presently existing. |
5,205 | Myocardial Function Imaging in Echocardiography Using Deep Learning | Deformation imaging in echocardiography has been shown to have better diagnostic and prognostic value than conventional anatomical measures such as ejection fraction. However, despite clinical availability and demonstrated efficacy, everyday clinical use remains limited at many hospitals. The reasons are complex, but practical robustness has been questioned, and a large inter-vendor variability has been demonstrated. In this work, we propose a novel deep learning based framework for motion estimation in echocardiography, and use this to fully automate myocardial function imaging. A motion estimator was developed based on a PWC-Net architecture, which achieved an average end point error of (0.06 +/- 0.04) mm per frame using simulated data from an open access database, on par or better compared to previously reported state of the art. We further demonstrate unique adaptability to image artifacts such as signal dropouts, made possible using trained models that incorporate relevant image augmentations. Further, a fully automatic pipeline consisting of cardiac view classification, event detection, myocardial segmentation and motion estimation was developed and used to estimate left ventricular longitudinal strain in vivo. The method showed promise by achieving a mean deviation of (-0.7 +/- 1.6)% compared to a semi-automatic commercial solution for N=30 patients with relevant disease, within the expected limits of agreement. We thus believe that learning-based motion estimation can facilitate extended use of strain imaging in clinical practice. |
5,206 | Darinaparsin (ZIO-101) enhances the sensitivity of small-cell lung cancer to PARP inhibitors | Small-cell lung cancer (SCLC) is an aggressive high-grade neuroendocrine carcinoma of the lung associated with early metastasis and an exceptionally poor prognosis. Little progress has been made in developing efficacious targeted therapy for this recalcitrant disease. Herein, we showed that H3.3, encoded by two genes (H3F3A and H3F3B), was prominently overexpressed in SCLC. Darinaparsin (ZIO-101), a derivative of arsenic trioxide, dose- and time-dependently inhibited the viability of SCLC cells in an H3.3-dependent manner. More importantly, ZIO-101 treatment resulted in substantial accumulation of H3.3 and PARP1 besides induction of G2/M cell cycle arrest and apoptosis in SCLC cells. Through integrative analysis of the RNA-seq data from Cancer Cell Line Encyclopedia dataset, JNCI and Genomics of Drug Sensitivity in Cancer 2 datasets, we found that H3F3A expression was negatively correlated with the IC50 values of PARP inhibitors (PARPi). Furthermore, co-targeting H3.3 and PARP1 by ZIO-101 and BMN673/olaparib achieved synergistic growth inhibition against SCLC in vitro and in vivo. In conclusion, it is feasible to target H3.3 by ZIO-101 to potentiate the response rate of PARPi in SCLC patients. |
5,207 | Human trajectory prediction and generation using LSTM models and GANs | Human trajectory prediction is an important topic in several application domains, ranging from self-driving cars to environment design and planning, from socially-aware robots to intelligent tracking sys-tems. This complex subject comes with different challenges, such as human-space interaction, human-human interaction, multimodality, and generalizability. Currently, these challenges, especially generaliz-ability, have not been completely explored by state-of-the-art works. This work attempts to fill this gap by proposing and defining new methods and metrics to help understand trajectories. In particular, new deep learning models based on Long Short-Term Memory and Generative Adversarial Network architec-tures are used in both unimodal and multimodal contexts. These approaches are evaluated with new error metrics, which normalize some biases in standard metrics. Tests have been assessed using newly collected datasets characterized by a higher diversity and lower linearity than those used in state-of-the -art works. The results prove that the proposed models and datasets are comparable to and yield better generalizability than state-of-the-art works. Moreover, we also prove that our datasets better represent multimodal scenarios (allowing for multiple possible behaviors) and that human trajectories are moder-ately influenced by their spatial region and slightly influenced by their date and time. (c) 2021 Elsevier Ltd. All rights reserved. |
5,208 | Adaptive-Tree Multicast: Efficient Multidestination Support for CMP Communication Substrate | Multidestination communications are a highly necessary capability for many coherence protocols in order to minimize on-chip hit latency. Although CMPs share this necessity, up to now few suitable proposals have been developed. The combination of resource scarcity and the common idea that multicast support requires a substantial amount of extra resources is responsible for this situation. In this work, we propose a new approach suitable for on-chip networks capable of managing multidestination traffic via hardware in an efficient way with negligible complexity. We introduce a novel multicast routing mechanism, able to circumvent many of the limitations of conventional multicast schemes. Adaptive-tree multicasting is able to maintain correctness for multiflit multicast messages without routing restrictions, while also coupling correctness and performance in a natural way. Replication restrictions not only guarantee the presence of enough resources to avoid deadlock, but also dynamically adapt tree shape to network conditions, routing multicast messages through noncongested paths. The performance results, using a state-of-the-art full system simulation framework, show that it improves the average full system performance of a CMP by 20 percent and network ED2P by 15 percent, when compared to a state-of-the-art router with conventional multicast support and similar implementation cost. |
5,209 | Arguments for the biological and predictive relevance of the proportional recovery rule | The proportional recovery rule (PRR) posits that most stroke survivors can expect to reduce a fixed proportion of their motor impairment. As a statistical model, the PRR explicitly relates change scores to baseline values - an approach that arises in many scientific domains but has the potential to introduce artifacts and flawed conclusions. We describe approaches that can assess associations between baseline and changes from baseline while avoiding artifacts due either to mathematical coupling or to regression to the mean. We also describe methods that can compare different biological models of recovery. Across several real datasets in stroke recovery, we find evidence for non-artifactual associations between baseline and change, and support for the PRR compared to alternative models. We also introduce a statistical perspective that can be used to assess future models. We conclude that the PRR remains a biologically relevant model of stroke recovery. |
5,210 | Sequence analysis of Epstein-Barr virus RPMS1 gene in malignant hematopathy of Northern China | The RPMS1 gene is the only member of the BamHI-A rightward transcripts (BARTs) family for which a full-length complementary DNA has been identified, and RPMS1 transcript has been confirmed in many Epstein-Barr virus (EBV)-positive malignancies. However, the effects of sequence variations of RPMS1 in hematological malignancies and their biological significance are unclear. To explore the association between RPMS1 gene variations and hematological malignancy, the RPMS1 gene of 391 EBV-positive samples from patients with EBV-positive leukemia, myelodysplastic syndromes and lymphoma in northern China were sequenced. On the basis of phylogenetic tree and mutation characteristics of RPMS1, all the sequences were divided into five major types: RPMS1-A, RPMS1-B, RPMS1-C, RPMS1-E, and RPMS1-F. RPMS1-A type, similar to the prototype B95-8, was identified in 71.87% (281/391) of samples and was the major type in all subpopulations. The frequency of RPMS1-F type was significantly higher in all malignant hematopathy groups than in healthy donors. The Hodgkin lymphoma group contained more RPMS1-F than other malignant hematopathy groups, and acute myeloid leukemia contained more RPMS1-C type than other malignant hematopathy groups. Therefore, RPMS1-A is the main type of RPMS1 gene in northern China, and RPMS1-F may be associated with hematologic malignancies. |
5,211 | Low-Complexity State-Space-Based System Identification and Controller Auto-Tuning Method for Multi-Phase DC-DC Converters | The importance of online system identification (SI) in power electronics is ever increasing. It enables the tracking of system parameters, which in turn can be used for online controller tuning. Hence, SI is a key element for improving a converter's dynamic performance, stability, reliability. In this paper, a novel state-space-based SI approach utilizing the step-adaptive approximate least squares estimation algorithm with observation matrix randomization is proposed. The presented concept yields an accurate state-space model of the converter while simultaneously achieving a fast convergence rate and low computational complexity. Consequently, the estimated state-space model is utilized to automatically tune a full state feedback controller. This results in an improved converter performance in terms of overshoots, undershoots, and settling times. The proposed concept is verified by a prototype system comprising a two-phase buck converter and a field-programmable gate array. The provided measurement results highlight the effectiveness and benefits of the presented method over the state-of-the-art algorithms, as well as z-domain estimation. It is shown that the number of required estimation iterations is more than halved in comparison with the state-of-the-art parametric SI approaches, while accuracy is improved. |
5,212 | Texture and rheological features of strain and pH sensitive chitosan-imine graphene-oxide composite hydrogel with fast self-healing nature | Here we report a smart Chitosan-isophthalaldehyde-Graphene oxide (CS-ISD-GO) hydrogel as a "multicomponent hydrogel". We witnessed an unprecedented pH responsive changes in viscoelasticity, stretchability, adhesiveness, self-healing and self-adaptability upon changing the pH and concentration of CS and ISD that was authenticated by texture and rheological analysis. The GO provides physical crosslinks and antibacterial properties to the hydrogel. Taking the advantage of dynamic nature of covalent and non-covalent interactions, we tuned the hydrogel adhesion and stretchability in response to the pH changes. Further self-healing of hydrogels was fully investigated by measuring thixotropic response over more than three cycles of strain sweep and real time optical imaging and video recording techniques. The recorded videos display 100 % self-healing response within a time frame of 2-6 min. These properties were observed only over small range of pH (4.5-5.5). The hydrogel becomes mechanically strong above pH 5.5 and becomes unstable above pH 7 leading to subsequent disintegration. The characterization of hydrogel was carried by FTIR, FESEM and TGA analysis. In addition, the hydrogel was reduced using NaBH4 for drug release. The reduced gel appears to be stable at lower pH values also. The reduced hydrogel may potentially be used for drug release purpose with low toxicity. |
5,213 | Artistic Image Analysis Using Graph-Based Learning Approaches | We introduce a new methodology for the problem of artistic image analysis, which among other tasks, involves the automatic identification of visual classes present in an art work. In this paper, we advocate the idea that artistic image analysis must explore a graph that captures the network of artistic influences by computing the similarities in terms of appearance and manual annotation. One of the novelties of our methodology is the proposed formulation that is a principled way of combining these two similarities in a single graph. Using this graph, we show that an efficient random walk algorithm based on an inverted label propagation formulation produces more accurate annotation and retrieval results compared with the following baseline algorithms: bag of visual words, label propagation, matrix completion, and structural learning. We also show that the proposed approach leads to a more efficient inference and training procedures. This experiment is run on a database containing 988 artistic images (with 49 visual classification problems divided into a multiclass problem with 27 classes and 48 binary problems), where we show the inference and training running times, and quantitative comparisons with respect to several retrieval and annotation performance measures. |
5,214 | Assisting Example-Based API Misuse Detection via Complementary Artificial Examples | Application Programming Interfaces (APIs) allow their users to reuse existing software functionality without implementing it by themselves. However, using external functionality can come at a cost. Because developers are decoupled from the API's inner workings, they face the possibility of misunderstanding, and therefore misusing APIs. Prior research has proposed state-of-the-art example-based API misuse detectors that rely on existing API usage examples mined from existing code bases. Intuitively, without a varied dataset of API usage examples, it is challenging for the example-based API misuse detectors to differentiate between infrequent but correct API usages and API misuses. Such mistakes lead to false positives in the API misuse detection results, which was reported in a recent study as a major limitation of the state-of-the-art. To tackle this challenge, in this paper, we first undertake a qualitative study of 384 falsely detected API misuses. We find that around one third of the false-positives are due to missing alternative correct API usage examples. Based on the knowledge gained from the qualitative study, we uncover five patterns which can be followed to generate artificial examples for complementing existing API usage examples in the API misuse detection. To evaluate the usefulness of the generated artificial examples, we apply a state-of-the-art example-based API misuse detector on 50 open source Java projects. We find that our artificial examples can complement the existing API usage examples by preventing the detection of 55 false API misuses. Furthermore, we conduct a pre-designed experiment in an automated API misuse detection benchmark (MUBench), in order to evaluate the impact of generated artificial examples on recall. We find that the API misuse detector covers the same true positive results with and without the artificial example, i.e., obtains the same recall of 94.7 percent. Our findings highlight the potential of improving API misuse detection by pattern-guided source code transformation techniques. |
5,215 | Enhanced Peptide Stability Against Protease Digestion Induced by Intrinsic Factor Binding of a Vitamin B12 Conjugate of Exendin-4 | Peptide digestion from proteases is a significant limitation in peptide therapeutic development. It has been hypothesized that the dietary pathway of vitamin B12 (B12) may be exploited in this area, but an open question is whether B12-peptide conjugates bound to the B12 gastric uptake protein intrinsic factor (IF) can provide any stability against proteases. Herein, we describe a new conjugate of B12 with the incretin peptide exendin 4 that demonstrates picomolar agonism of the glugacon-like peptide-1 receptor (GLP1-R). Stability studies reveal that Ex-4 is digested by pancreatic proteases trypsin and chymotrypsin and by the kidney endopeptidase meprin β. Prebinding the B12 conjugate to IF, however, resulted in up to a 4-fold greater activity of the B12-Ex-4 conjugate relative to Ex-4, when the IF-B12-Ex-4 complex was exposed to 22 μg/mL of trypsin, 2.3-fold greater activity when exposed to 1.25 μg/mL of chymotrypsin, and there was no decrease in function at up to 5 μg/mL of meprin β. |
5,216 | Enzymatic potato starch modification and structure-function analysis of six diverse GH77 4-alpha-glucanotransferases | 4-α-glucanotransferase (EC 2.4.1.25) mediated glucan transfer in starch provides opportunities for production of clean label starch ingredients with unique gelling properties. 4-α-glucanotransferases can be found in glycoside hydrolase (GH) family GH13, GH57, and in the monospecific glycoside hydrolase family 77 (GH77). Here, pH-temperature optima, steady-state kinetics, potato starch modifying properties and structural folds are reported for six phylogenetically distinct GH77 members, representing four different domain architectures including a novel multi-domain 4-α-glucanotransferase from Lactococcus lactis. Four of the enzymes exhibited starch modifying activity leading to a gradual decrease of the amylose content, elongation of amylopectin chains, and enabled formation of firm starch gels. Unexpectedly, these diverse enzymes catalyzed similar changes in chain length distributions. However, the amylose depletion and amylopectin elongation rates spanned more than two orders of magnitude between the enzyme showing very different specific activities. Tt4αGT from Thermus thermophilus had highest temperature optimum (73 °C) and superior potato starch modifying efficacy compared to the other five enzymes. |
5,217 | Bitline Charge Sharing Suppressed Bitline and Cell Supply Collapse Assists for Energy-Efficient 6T SRAM | This paper proposes a bitline charge sharing suppressed bitline read assist (BCS RA) and a cell supply collapse write assist (BCS WA). The proposed BCS RA suppresses the bitline (BL) voltage to half of the supply voltage (V-DD) using the charge sharing BL precharger for improving read stability and energy efficiency. In the proposed BCS WA, the charge on cell V-DD (CVDD) is shared with that on the BL precharged to half-V-DD by the charge sharing write driver, which causes the collapse in CVDD. In cells with poor writability, CVDD can be collapsed more by the self-collapse paths when the write operation is performed. Thus, the BCS WA improves writability and reduces write energy consumption. The simulation results using 22-nm FinFET technology show that static random access memory (SRAM) using BCS RA and WA consumes much less read and write energy than SRAMs using state-of-the-art assists while achieving a comparable minimum operating V-DD to SRAMs using state-of-the-art assists. Even compared to the SRAM without assists, the read and write energy consumption is reduced by 31% and 26%, respectively. |
5,218 | Multielement determination (essential and potentially toxic elements) in eye shadows exposed to consumption in Brazil using ICP OES | Worldwide, cosmetics (especially eye shadows) are widely consumed and have a great impact on the economy. The aim of this study was to determine the multielement composition, focusing on essential and potentially toxic elements, in cosmetics (eye shadow) exposed to consumption in Brazil. Concentrations of 17 elements (Al, As, Ba, Cd, Co, Cr, Cu, Mn, Mo, Ni, Pb, Sb, Se, Sr, Ti, V and Zn) were determined in samples (produced in China and Brazil) using a sequential optical emission spectrometer with inductively coupled plasma (ICP OES) after acid digestion, assisted by a closed digester block (6 mL of HNO3 + 2 mL of H2O2 + 1 mL of Triton ×-100 + 1 mL of ultrapure water). The method was validated by linearity, precision, accuracy, limits of detection (LoD) and quantification (LoQ). The elements were quantified (in µg g-1): Al (852-21,900), Ba (3.47-104), Cd (1.70-6.93), Cr (< 8.53-66.6), Cu (< 0.480-14.5), Mn (92.20-1,190), Ni (< 4.23-40.7), Pb (< 2.16-5.06), Sb (1.10-10.5), Sr (0.760-46.0), Ti (32.0-440), V (< 0.85-1.7) and Zn (24.90-2,600). As, Co, Mo and Se in all the investigated samples were found to be below the LoQ values of ICP OES. In this study, regardless of sample compositions and origins (Brazilian or Chinese), high levels of Al, Cd, Cr, Cu, Mn, Ni, Pb, Sb, Ti, V and Zn were observed, exceeding the recommended maximum tolerable limits, according to Brazilian and global legislations, which may present potential risks to human health and the environment. |
5,219 | Improvement of Radio Frequency Identification Security Using New Hybrid Advanced Encryption Standard Substitution Box by Chaotic Maps | Radio Frequency Identification (RFID) technology is widely utilized by businesses, organizations and wireless communication systems. RFID technology is secured using different ways of data encryption, e.g., Advanced Encryption Standard (AES). The Substitution Box (S-Box) is the core of AES. In this paper, a new algorithm is proposed to generate a modified S-Box with new keys, specifically a key and plaintext-dependent S-Box using an improved RC4 encryption algorithm with Logistic Chaotic Maps (LCM). The strength of the proposed S-Box is tested throughout the paper, and compared against the state-of-the-art S-Box implementations, namely, the static S-Box, dynamic S-box, KSA and PRGA S-Box, and RC4 S-Boxes with Henon chaotic maps. The comparison between the state-of-the-art S-Boxes and the proposed S-Box demonstrates that the use of the Logistic Chaotic Map increases the security of the S-Box and makes the differential and linear cryptography more sturdy. In particular, using the strict avalanche test, we demonstrate that the proposed S-Box improves the security by achieving a cipher text bit-flip ratio of 0.4765, which is closer to 0.5 (where half the bits are flipped), while maintaining a minimum elapsed time of 19 milliseconds for encryption and decryption. |
5,220 | Lymphoepithelioma-Like Carcinoma of the Breast: A Case Report of a Rare Type of Invasive Carcinoma | Lymphoepithelioma carcinoma (LELC) is an extremely rare type of mammary cancer. Based on the histology, it can be misdiagnosed with inflammatory lesions like mastitis and medullary carcinoma or other hematopoietic neoplasms like lymphoma in the breast. Since LELC has a good response to chemotherapy with a good prognosis, t is prognostically important to recognize LELC. We report a rare case of LELC in a 51-year-old pre-menopausal female with a left breast mass, diagnosed with invasive ductal carcinoma (IDC), LELC type, treated with mastectomy, followed by adjuvant chemotherapy and radiotherapy, with a disease-free interval of 10 months. Herein, we present the case with its clinical presentation, radiologic imaging, histopathological features, and immunohistochemistry (IHC) findings. The rarity of this type of breast tumor warrants studying the behavior of these uncommon tumors to avoid misdiagnosis and establish well-defined criteria for diagnosis. |
5,221 | Mountain erosion and mitigation: global state of art | Mountain erosion mapping is one of the important aspects for monitoring environmental degradation. Global climate change coupled with human activities is eroding the mountain regions of the world at an alarming rate. The various types of erosion can be classified as water erosion, aeolian erosion, glaciered erosion, gravity erosion and man-made erosion. Tectonic activity also plays a major role towards mountain erosion. Soil erosion along with debris flow and rock falling not only causes loss of human life and property, but also affects the climatic condition of the mountain regions. Mountain erosion mitigation and protection methods include wire mesh fencing with inclusion of brush layers and brush mats on fascines, constructing check dams and slope benches and application of bioengineering works such as plantation and vegetation growth. This study consists of the global scenario with some of the state-of-the-art methods for mitigation and mappings of mountain erosion. The erosion in mountainous regions can be controlled with the help of suitable vegetation and plant growth, with installation of special bioengineered materials. However, public awareness is also considered as an important factor. Moreover, it is found that mapping the rate of erosion greatly helps to make proper action plan against the erosion process. |
5,222 | Feasibility of using PALSAR technology as a signal amplifier for antibody bridging assay | The immunogenicity testing of oligonucleotide drugs using an antibody bridging assay has been scarcely investigated. We developed a highly sensitive antibody bridging assay model and assessed it using probe alteration link self-assembly reactions (PALSAR) technology as a signal amplifier. Methods: The concentration of each probe was optimized, and the bridging assay model was compared with and without signal amplification. Cut-point and analytical sensitivity were determined, and accuracy, precision and drug tolerance were evaluated. Results: The PALSAR bridging assay achieved a net signal 21-36 times higher than that obtained with the conventional method. The analytical sensitivity achieved was 48.8 ng/ml, with adequate accuracy, precision and drug tolerance. Conclusion: PALSAR technology is feasible for developing an antibody bridging assay using oligonucleotides as capture and detection probes. |
5,223 | Semi-Supervised Learning With Deep Embedded Clustering for Image Classification and Segmentation | Deep neural networks usually require large labeled datasets to construct accurate models; however, in many real-world scenarios, such as medical image segmentation, labeling data are a time-consuming and costly human (expert) intelligent task. Semi-supervised methods leverage this issue by making use of a small labeled dataset and a larger set of unlabeled data. In this paper, we present a flexible framework for semi-supervised learning that combines the power of supervised methods that learn feature representations using state-of-the-art deep convolutional neural networks with the deeply embedded clustering algorithm that assigns data points to clusters based on their probability distributions and feature representations learned by the networks. Our proposed semi-supervised learning algorithm based on deeply embedded clustering (SSLDEC) learns feature representations via iterations by alternatively using labeled and unlabeled data points and computing target distributions from predictions. During this iterative procedure, the algorithm uses labeled samples to keep the model consistent and tuned with labeling, as it simultaneously learns to improve feature representation and predictions. The SSLDEC requires a few hyper-parameters and thus does not need large labeled validation sets, which addresses one of the main limitations of many semi-supervised learning algorithms. It is also flexible and can be used with many stateof-the-art deep neural network configurations for image classification and segmentation tasks. To this end, we implemented and tested our approach on benchmark image classification tasks as well as in a challenging medical image segmentation scenario. In benchmark classification tasks, the SSLDEC outperformed several state-of-the-art semi-supervised learning methods, achieving 0.46% error on MNIST with 1000 labeled points and 4.43% error on SVHN with 500 labeled points. In the iso-intense infant brain MRI tissue segmentation task, we implemented SSLDEC on a 3D densely connected fully convolutional neural network where we achieved significant improvement over supervised-only training as well as a semi-supervised method based on pseudo-labeling. Our results show that the SSLDEC can be effectively used to reduce the need for costly expert annotations, enhancing applications, such as automatic medical image segmentation. |
5,224 | Split-Dose Administration Enhances Immune Responses Elicited by a mRNA/Lipid Nanoparticle Vaccine Expressing Respiratory Syncytial Virus F Protein | mRNA vaccines have recently received significant attention due to their role in combating the SARS-CoV-2 pandemic. As a platform, mRNA vaccines have been shown to elicit strong humoral and cellular immune responses with acceptable safety profiles for prophylactic use. Despite their potential, industrial challenges have limited realization of the vaccine platform on a global scale. Critical among these challenges are supply chain considerations, including mRNA production, cost of goods, and vaccine frozen-chain distribution. Here, we assess the delivery of lipid nanoparticle-encapsulated mRNA (mRNA/LNP) vaccines using a split-dose immunization regimen as an approach to develop mRNA dose-sparing vaccine regimens with potential to mitigate mRNA supply chain challenges. Our data demonstrate that immunization by a mRNA/LNP vaccine encoding respiratory syncytial virus pre-F (RSV pre-F) over a 9 day period elicits comparable or superior magnitude of antibodies when compared to traditional bolus immunization of the vaccine. The split-dose immunization regimens evaluated in our studies were designed to mimic reported drug or antigen release profiles from microneedle patches, highlighting the potential benefit of pairing mRNA vaccines with patch-based delivery technologies to enable sustained release and solid-state stabilization. Overall, our findings provide a proof of concept to support further investigations into the development of sustained delivery approaches for mRNA/LNP vaccines. |
5,225 | De novo variants and recombination at 4q35: Hints for preimplantation genetic testing in facioscapulohumeral muscular dystrophy | Facioscapulohumeral muscular dystrophy (FSHD) has been associated with the deletion of an integral number of 3.3 kb units of the polymorphic D4Z4 repeat array at 4q35. The prenatal identification of this defect can be carried out on chorionic villi or amniocytes, whereas preimplantation genetic testing for monogenic disorders (PGT-M) requires molecular markers linked to the D4Z4 allele of reduced size. In this context the reliability of this association is crucial. To test the informativeness of the nearby polymorphic markers we investigated recombination at 4q35 using the polymorphic markers D4S1523, D4S163 and D4S139 positioned at 0.55, 0.5 and 0.21 Mb proximal to the D4Z4 array respectively. We determined the probability of recombination events to occur in the D4Z4-D4S1523 interval considering 86 subjects belonging to 12 FSHD families and found a recombination frequency of 14% between D4Z4 and D4S1523. Our study also revealed the occurrence of de novo variants and germline mosaicism. These findings highlight the recombinogenic nature of the 4q subtelomere and indicate that caution should be taken when interpreting PGT-M results. It is advisable that a woman who underwent a PGT-M cycle undertakes a prenatal DNA analysis to confirm the size of the D4Z4 alleles carried by the fetus. |
5,226 | High precision U/Th dating of the rock paintings at Mt. Huashan, Guangxi, southern China | The rock art and the associated natural scenery at 38 sites located in the Zuojiang River valley, in the southwest of Guangxi Zhuang Autonomous Region, southern China, were inscribed recently on UNESCO's World Heritage List. The painted panel at the site of Mt. Huashan is probably the largest known rock art panel in the world, consisting of approximately 1900 identifiable figures and occupying an area of approximately 8000 m(2). To determine a precise age on the rock art at Mt. Huashan, 56 secondary carbonate layers above and below the paintings were studied for their mineralogy, oxygen, and carbon isotopic compositions and dated by the Th-230/U method. The Th-230/U dating results demonstrate that ages of the rock paintings can be bracketed between 1856 +/- 16 and 1728 +/- 41yr BP corresponding to the middle to the end of the Eastern Han dynasty (AD 25 to 220). The results imply that the rock painting practices at Mt. Huashan probably lasted more than a century, and the Zuojiang rock art is younger than that at Baiyunwan and Cangyuan in Yunnan Province by 1 to 10 centuries. |
5,227 | New mechanisms for the kidney-protective effect of alkali in chronic kidney disease | Worldwide, more than one in ten adults are estimated to have chronic kidney disease (CKD). As CKD progresses, both the cost of treatment and associated risk of morbidity and mortality increase exponentially. As such, there is a great need for therapies that effectively slow CKD progression. Evidence from several small clinical trials indicates that alkali therapy may slow the rate of CKD progression. The biological mechanisms underlying this protective effect, however, remain unknown. In their recently published manuscript, Pastor Arroyo et al. (Clin Sci (Lond) (2022) 136(8): https://doi.org/10.1042/CS20220095) demonstrate that the alkali sodium bicarbonate protects against loss of renal function in a crystal nephropathy model in mice. Using unbiased approaches in both mice and human tissue, the authors go on to identify two novel mechanisms that may underly this protection. The first pathway is through promoting pathways of cell metabolism, which they speculate helps the remaining functional nephrons adapt to the greater metabolic needs required to maintain kidney filtration. The second pathway is by restoration of α-Klotho levels, which may limit the expression of adhesion molecules in the injured kidney. This, the authors speculate, may prevent inflammation from driving the functional decline of the kidney. Identifying these novel pathways represents an important step forward harnessing the potential benefits of alkali therapy in CKD. |
5,228 | Physical, mechanical, and biological characterization of robocasted carbon nanotube reinforced microwave sintered calcium phosphate scaffolds for bone tissue engineering | This study analyses the influence of the addition of Multi Walled Carbon Nanotubes (MWCNT) on the physical, mechanical, and biological behaviour of Calcium Phosphate (CP) bone scaffolds developed using the robocasting technique for bone regeneration. Three different mass percentages (0.5, 1, and 2 wt%) of MWCNT are added to the CP powder and a slurry is prepared using a CMC binder for printing the scaffolds. The scaffolds were printed in 2 infill ratios, 50 and 100%, and were sintered under an inert atmosphere in a microwave furnace which was then taken for various characterization studies. Physical characterisation studies revealed that the shrinkage rate of scaffolds is very low compared to other additive manufacturing techniques. The incorporation of 0.5 wt% of MWCNT produced the best results in mechanical characterization studies with a compressive strength of 10.38 MPa and 11.89 MPa for 50% and 100% infill ratios respectively. In Vitro Biocompatibility studies also proved that 0.5 wt% MWCNT samples are the most suitable for cell growth while the hemocompatibility tests showed that the samples are blood compatible. . The 100% infill samples fared better than the 50% samples in physical and mechanical properties. The results suggest that the MWCNT incorporated CP scaffolds can be used to treat critical size bone defects. |
5,229 | Novel features for art movement classification of portrait paintings | The increasing availability of extensive digitized fine art collections opens up new research directions. In particular, correctly identifying the artistic style or art movement of paintings is crucial for large artistic database indexing, painter authentication, and mobile recognition of painters. Even though the implementation of CNN on artwork classification improved the performance dramatically compared to tradition classifier, the feature extraction methods are still valuable to help establishing better image representation for both common classifiers and neural networks. The main goal of this article is to present three novel features and a mature model structure for artistic movement recognition of portrait paintings. The proposed features include two unique color features and one texture feature: (a) Modified Color Distance (MCD), (b) ColorRatio Feature and (c) Weber's law Based Texture Feature. We demonstrate the superiority of our proposed method over the state-of-the-art approaches, and how successful our features are to support features from various neural networks. Another contribution of our work is a new portrait database that consists of 927 paintings from 6 different art movements. Extensive computer evaluations on this database show that we achieved an average accuracy of 98% for classifying two categories and 82.6% for classifying all 6 categories. Besides, our novel features improved the performance of pre trained CNN significantly. (c) 2021 Elsevier B.V. All rights reserved. |
5,230 | Real-Time Deep Pose Estimation With Geodesic Loss for Image-to-Template Rigid Registration | With an aim to increase the capture range and accelerate the performance of state-of-the-art inter-subject and subject-to-template 3-D rigid registration, we propose deep learning-based methods that are trained to find the 3-D position of arbitrarily-oriented subjects or anatomy in a canonical space based on slices or volumes of medical images. For this, we propose regression convolutional neural networks (CNNs) that learn to predict the angle-axis representation of 3-D rotations and translations using image features. We use and compare mean square error and geodesic loss to train regression CNNs for 3-D pose estimation used in two different scenarios: slice-to-volume registration and volume-to-volume registration. As an exemplary application, we applied the proposed methods to register arbitrarily oriented reconstructed images of fetuses scanned in-utero at a wide gestational age range to a standard atlas space. Our results show that in such registration applications that are amendable to learning, the proposed deep learning methods with geodesic loss minimization achieved 3-D pose estimation with a wide capture range in real-time (<100ms). We also tested the generalization capability of the trained CNNs on an expanded age range and on images of newborn subjects with similar and different MR image contrasts. We trained our models on T2-weighted fetal brain MRI scans and used them to predict the 3-D pose of newborn brains based on T1-weighted MRI scans. We showed that the trained models generalized well for the new domain when we performed image contrast transfer through a conditional generative adversarial network. This indicates that the domain of application of the trained deep regression CNNs can be further expanded to image modalities and contrasts other than those used in training. A combination of our proposed methods with accelerated optimization-based registration algorithms can dramatically enhance the performance of automatic imaging devices and image processing methods of the future. |
5,231 | Uncertainty Quantification for Deep Unrolling-Based Computational Imaging | Deep unrolling is an emerging deep learning-based image reconstruction methodology that bridges the gap between model-based and purely deep learning-based image reconstruction methods. Although deep unrolling methods achieve state-of-the-art performance for imaging problems and allow the incorporation of the observation model into the reconstruction process, they do not provide any uncertainty information about the reconstructed image, which severely limits their use in practice, especially for safety-critical imaging applications. In this article, we propose a learning-based image reconstruction framework that incorporates the observation model into the reconstruction task and that is capable of quantifying epistemic and aleatoric uncertainties, based on deep unrolling and Bayesian neural networks. We demonstrate the uncertainty characterization capability of the proposed framework on magnetic resonance imaging and computed tomography reconstruction problems. We investigate the characteristics of the epistemic and aleatoric uncertainty information provided by the proposed framework to motivate future research on utilizing uncertainty information to develop more accurate, robust, trustworthy, uncertainty-aware, learning-based image reconstruction and analysis methods for imaging problems. We show that the proposed framework can provide uncertainty information while achieving comparable reconstruction performance to state-of-the-art deep unrolling methods. |
5,232 | Organ Preservation in Rectal Cancer: An Overview of the Dutch Perspective and Recent Developments | Although current guidelines on rectal cancer treatment often recommend rectal resection with or without neoadjuvant (chemo)radiotherapy, there is growing interest in organ-preserving treatment approaches among patients and clinicians in the Netherlands. Currently, multiple ongoing studies are investigating the value of different non-operative treatment modalities to improve tumour response rates and increase the chance of successful organ preservation. Papillon contact X-ray brachytherapy is a promising treatment modality to improve the chance of organ preservation, which seems especially relevant for elderly and frail patients unable or refusing to undergo total mesorectal excision surgery. The elderly and frail patient with rectal cancer poses a significant challenge and warrants a thorough multidisciplinary approach to provide the most optimal organ-preserving treatment. In this overview, an insight into the Dutch perspectives and developments within the field of organ preservation and the set-up of a Papillon facility to complete the spectrum of organ-preserving treatment options in a tertiary referral centre for rectal cancer treatment has been provided. |
5,233 | Improved compressive tracking based on pixelwise learner | This work expands upon state-of-the-art multiscale tracking based on compressive sensing (CT) by increasing the overall tracking accuracy. A pixelwise classification stage is incorporated in the CT-based tracker to obtain a relatively stable appearance model, by distinguishing object pixels from the background. In addition, we identify potential distracting regions that are used in a feedback strategy to handle occlusion and avoid drifting toward nearby regions with similar appearances. We evaluate our approach on several benchmark datasets to demonstrate its effectiveness with respect to the state-of-the-art tracking algorithms. (c) 2018 SPIE and IS&T |
5,234 | Myrciaria Genus: Bioactive Compounds and Biological Activities | The Myrtaceae family is of angiosperms, imposing its size and economic, cultural, and scientific importance. The genus Myrciaria, belonging to this family, has 33 species currently accepted, many of which are research targets aimed at elucidating their bioactive compounds and biological activities. Most species of the Myrciaria genus have terpenes in their composition, mainly mono and sesquiterpenes, and phenolic compounds such as tannins, phenolic acids, and flavonoids. Other secondary metabolites are also observed, such as alkaloids, steroids, coumarins, saponins, and naphthoquinones. These bioactive compounds are closely related to these species' most diverse biological activities: antioxidant, anti-inflammatory, analgesic, antiproliferative, antimicrobial, antiparasitic, insecticide, metabolic, protective, and nutraceutical. This work aims to provide a review of secondary metabolites and medicinal properties related to the genus Myrciaria, thus stimulating further studies on the species of this genus. |
5,235 | Hybrid of Harmony Search Algorithm and Ring Theory-Based Evolutionary Algorithm for Feature Selection | Feature Selection (FS) is an important pre-processing step in the fields of machine learning and data mining, which has a major impact on the performance of the corresponding learning models. The main goal of FS is to remove the irrelevant and redundant features, resulting in optimized time and space requirements along with enhanced performance of the learning model under consideration. Many meta-heuristic optimization techniques have been applied to solve FS problems because of its superiority over the traditional optimization approaches. Here, we have introduced a new hybrid meta-heuristic FS model based on a well-known meta-heuristic Harmony Search (HS) algorithm and a recently proposed Ring Theory based Evolutionary Algorithm (RTEA), which we have named as Ring Theory based Harmony Search (RTHS). Effectiveness of RTHS has been evaluated by applying it on 18 standard UCI datasets and comparing it with 10 state-of-the-art meta-heuristic FS methods. Obtained results prove the superiority of RTHS over the state-of-the-art methods considered here for comparison. |
5,236 | Commercial Aircraft Electrification-Current State and Future Scope | Electric and hybrid-electric aircraft propulsion are rapidly revolutionising mobility technologies. Air travel has become a major focus point with respect to reducing greenhouse gas emissions. The electrification of aircraft components can bring several benefits such as reduced mass, environmental impact, fuel consumption, increased reliability and quicker failure resolution. Propulsion, actuation and power generation are the three key areas of focus in more electric aircraft technologies, due to the increasing demand for power-dense, efficient and fault-tolerant flight components. The necessity of having environmentally friendly aircraft systems has promoted the aerospace industry to use electrically powered drive systems, rather than the conventional mechanical, pneumatic or hydraulic systems. In this context, this paper reviews the current state of art and future advances in more electric technologies, in conjunction with a number of industrially relevant discussions. In this study, a permanent magnet motor was identified as the most efficient machine for aircraft subsystems. It is found to be 78% and 60% more power dense than switch-reluctant and induction machines. Several development methods to close the gap between existing and future design were also analysed, including the embedded cooling system, high-thermal-conductivity insulation materials, thin-gauge and high-strength electrical steel and integrated motor drive topology. |
5,237 | Neuromorphic LIF Row-by-Row Multiconvolution Processor for FPGA | Deep Learning algorithms have become state-of-the-art methods for multiple fields, including computer vision, speech recognition, natural language processing, and audio recognition, among others. In image vision, convolutional neural networks (CNN) stand out. This kind of network is expensive in terms of computational resources due to the large number of operations required to process a frame. In recent years, several frame-based chip solutions to deploy CNN for real time have been developed. Despite the good results in power and accuracy given by these solutions, the number of operations is still high, due the complexity of the current network models. However, it is possible to reduce the number of operations using different computer vision techniques other than frame-based, e.g., neuromorphic event-based techniques. There exist several neuromorphic vision sensors whose pixels detect changes in luminosity. Inspired in the leaky integrate-and-fire (LIF) neuron, we propose in this manuscript an event-based field-programmable gate array (FPGA) multiconvolution system. Its main novelty is the combination of a memory arbiter for efficient memory access to allow row-by-row kernel processing. This system is able to convolve 64 filters across multiple kernel sizes, from 1 x 1 to 7 x 7, with latencies of 1.3 mu s and 9.01 mu s, respectively, generating a continuous flow of output events. The proposed architecture will easily fit spike-based CNNs. |
5,238 | In vitro evaluation of efficacy of nonstarch polysaccharides enzymes on wheat by simulating the avian digestive tract | In this study, the efficacy of different nonstarch polysaccharide (NSP) enzyme sources on wheat ingredients and wheat basal diets in vitro were evaluated by simulating the avian digestive tract. In Exp. 1, pH level was increased from 2.0 to 8.0 by simulating the avian digestive tract. The relative enzyme activities of xylanase A, B, and C and β-glucanase X at pH 3.0-3.5 were higher (P < 0.05) than those at pH 2.0 or 7.0-8.0. The optimal pH levels of 3.5 and 7.0 were screened by simulating the proventriculus and small intestine, respectively to evaluate the efficacy of NSP enzyme on wheat sources. In Exp. 2, wheat 1 contained the highest content of NSP fractions and the lowest digestibility in vitro dry matter (IVDMD) and energy (IVED) in wheat samples. Therefore, wheat 1 was selected for hydrolysis research under different NSP enzyme sources and levels (1,500, 4,500, 13,500, 40,500, 121,500 U xylanase/kg and 250, 500, 1,000, 2,000, 4,000 U β-glucanase/kg) in vitro. The hydrolysis of wheat on the basis of the released reducing sugar content was determined by xylanase sources A > B > C (P < 0.05) and β-glucanase sources of X > Y (P < 0.05). On the basis of the hydrolysis, the optimum dose of xylanase A and β-glucanase X were 40,500 U/kg and 2,000 U/kg, respectively. Subsequently, the completely randomized designs involving 2 NSP enzymes treatments × 2 endogenous digestive enzymes treatments (Exp. 3), as well as 2 wheat basal diets × 2 NSP enzymes treatments (Exp. 4) were used to evaluate the efficacy of NSP enzymes on dietary nutrient digestibility. The addition of NSP enzymes (40,500 U xylanase A/kg and 2,000 U β-glucanase X/kg) increased the IVDMD and IVED of wheat 1 without endogenous enzymes (P < 0.05), while the IVDMD and IVED of wheat 1 with endogenous enzyme were only slightly increased (P > 0.05). The addition of NSP enzymes could increase the IVDMD and IVED of corn-wheat-soybean meal diet (P < 0.05), but had no effect on those of wheat-cottonseed meal rapeseed meal diet (P > 0.05). In conclusion, xylanase and β-glucanase additions could effectively eliminate the adverse effects on wheat and wheat basal diets at the optimal pH levels of 3.5 and 7.0 by simulating the proventriculus and small intestine parts, respectively. The efficacy of NSP enzymes was influenced by the enzyme sources, dietary type, and the interaction of endogenous enzymes. |
5,239 | Theoretical analysis of heat pump assisted air gap membrane distillation | Three configurations for heat pumps for simultaneously heating and cooling of vacuum assisted air gap membrane distillation (V-AGMD) are proposed and theoretically analysed. The benefit of using heat pumps is that local heating is not needed. The first configuration is the current state of the art a heat pump is used for the heating and cooling of V-AGMD. In the second configuration, a heat pump is connected to a V-AGMD module where the membrane and condenser channel have a constant temperature. The second configuration has a total distillate production of 38251/m(2)/h, but has a specific electrical energy consumption (SEEC) ranging between 16 and 107 kWh(el)/m(3). The SEEC of the first configuration ranges between 9 and 22 kWh(el)/m(3), which is higher than the thermodynamic limit. In the third configuration, a zero liquid discharge setup was proposed based on the first configuration. Results indicate that 26.7 kWh(el)/m(3) is needed to raise the 35 g/l feed water to 250 g/l, which is only slightly higher than the 25 kWh(el)/m(3) of mechanical vapour compression. Ammonia was used as a refrigerant in the calculations, making results applicable to real setups. In conclusion, heat pumps can provide heating and cooling, making them a promising solution. |
5,240 | Low-Cost Multispectral System Design for Pigment Analysis in Works of Art | To better understand and preserve works of art, knowledge is needed about the pigments used to create the artwork. Various noninvasive techniques have been used previously to create pigment maps, such as combining X-ray fluorescence and hyperspectral imaging data. Unfortunately, most museums have limited funding for the expense of specialized research equipment, such as hyperspectral reflectance imaging systems. However, many museums have hand-held point X-ray fluorescence systems attached to motorized easels for scanning artwork. To assist museums in acquiring data that can produce similar results to that of HSI systems, while minimizing equipment costs, this study designed and modeled a prototype system to demonstrate the expected performance of a low-cost multispectral system that can be attached to existing motorized easels. We show that multispectral systems with a well-chosen set of spectral bands can often produce classification maps with value on par with hyperspectral systems. This study analyzed the potential for capturing data with a point scanning system through predefined filters. By applying the system and noise modeling parameters to HSI data captured from a 14th-Century illumination, the study reveals that the proposed multispectral imaging system is a viable option for this need. |
5,241 | Ontologies in the context of product lifecycle management: state of the art literature review | The use of ontologies in the context of product lifecycle management (PLM) is gaining importance and popularity, while at the same time it generates a lot of controversy in discussions within scientific and engineering communities. Yet, what is ontology? What challenges have been addressed so far? What role does ontology play? Do we really need ontology? These are the core questions this paper seeks to address. We propose to conduct a comprehensive study of the concept of Ontology firstly in its domain of origin, Philosophy, and secondly in information science. Based on the understanding of this concept and an in-depth analysis of the state of the art, seven key roles of ontology are defined. These roles serve as a framework describing the general state of research on the use of ontologies in the context of PLM. |
5,242 | Group testing for image compression using alternative transforms | This paper extends the group testing for wavelets (IEEE Trans. Image Process. 11 (2002) 901) algorithm to code coefficients from the wavelet packet transform, the discrete cosine transform, and various lapped transforms. Group testing offers a noticeable improvement over zerotree coding techniques on these transforms; is inherently flexible; and can be adapted to different transforms with relative ease. The new algorithms are competitive with many recent state-of-the-art image coders that use the same transforms. (C) 2003 Elsevier Science B.V. All rights reserved. |
5,243 | A case of Hashimoto's thyroiditis following Graves' disease | Graves' disease is characterized by the presence of circulating autoantibodies that stimulate the TSH receptor, inducing hyperthyroidism and goiter. Hashimoto's thyroiditis is an autoimmune disease leading to thyroid tissue destruction by cell and antibody-mediated immune processes. The occurrence of Hashimoto's thyroiditis following Graves' disease has been rarely reported. Its pathogenesis is not clear. Herein, we report the case of a 40-year-old woman who was referred to our department for thyrotoxicosis. Laboratory tests revealed overt hyperthyroidism. Thyroid scintigraphy showed an enlarged gland with diffusely increased tracer uptake, confirming the diagnosis of Graves's disease. The patient was treated with propranolol and thiamazole. Two months later, she received radioactive iodine therapy. Three years and 9 months later, the patient presented with hypothyroidism and very high levels of thyroperoxidase antibodies consistent with the diagnosis of Hashimoto's thyroiditis. She was treated with levothyroxine. The shift from Graves' disease to Hashimoto's thyroiditis was reported in the literature. However, its pathogenesis has not been clearly elucidated. |
5,244 | Assessment of state-of-the-art models for predicting the remobilisation of radionuclides following the flooding of heavily contaminated areas: the case of Pripyat River floodplain | The performances of models are assessed to predict the wash-off of radionuclides from contaminated flooded areas. This process should be accounted for in the proper management of the aftermath of a nuclear accident. The contamination of the Pripyat River water following the inundation of a floodplain heavily contaminated by Sr-90 and (CS)-C-137 of Chernobyl origin is used as the basis for modelling. The available experimental evidence demonstrated that remobilisation of radiostrontium is an important process implying a significant secondary radioactive load of water flowing over the contaminated floodplain. On the contrary, there is no empirical evidence of a similar behaviour for radiocaesium. In general, state-of-the-art models properly predicted the remobilisation of strontium, whereas they significantly overestimated radiocaesium concentrations in water. The necessary model improvements for a more accurate prediction of radiocaesium contamination levels include a reassessment of the values of the model parameters controlling the remobilisation process. (c) 2006 Elsevier Ltd. All rights reserved. |
5,245 | An improved architecture for designing modulo (2(n)-2(p)+1) multipliers | In this express, we have proposed an improved architecture for designing efficient modulo (2(n) - 2(p) + 1) multipliers on the condition n >= 2p. The proposed modulo (2(n) - 2(p) + 1) multipliers can improve the current state of the art by 7.7-42.5% in terms of area and 13.1-44.2% in terms of performance delay on the average or by 60.5% in terms of area with the equivalent delay performance. |
5,246 | Anomaly residual prediction with spatial-temporal and perceptual constraints | Anomaly detection algorithms based on deep neural networks have achieved favorable performance in finding abnormal events in surveillance video. Recently, end-to-end methods that combine feature extraction, model learning, and anomaly scoring into one training procedure have become popular. However, most existing research studies have relied on a deep convolutional structure, which faces overfitting problems for a limited training set. An anomaly detection algorithm based on the state-of-the-art prediction framework is proposed, leveraging the gap between frame prediction and its ground truth to detect abnormal events. The residual block is transferred from image classification, and we modify its modules to suit the prediction application in order to tackle the difficulties in training a deeper prediction network. As far as we know, the proposed method is the first anomaly detection residual network trained from scratch, which is different from several existing ones with fixed resnet-50 layers as feature extractor. Furthermore, a new perceptual constraint focusing on high-level information is proposed and combined with the commonly used spatial-temporal constraints. Experimental results on challenging public surveillance sequences verify that our proposed framework can effectively produce state-of-the-art performance. (C) 2019 SPIE and IS&T |
5,247 | Assessment of the immune-modulatory and antimicrobial effects of dietary chitosan on Nile tilapia (Oreochrmis niloticus) with special emphasis to its bio-remediating impacts | Fish, pathogen and environment are three counterparts who are sharing the same circle of life. To keep fish up to their optimal health, environment should be competently improved and pathogen count/virulence should be seized. Using of bioactive immunostimulants to achieve these objectives is the hypothesis under assessment. Thus, the present study was performed to evaluate the use of shrimp shells derived chitosan as an immunostimulant as well as preventive regime against Aeromonas hydrophila infection of Nile tilapia and to assess its antibacterial/aquatic bio-remediating effects. Results achieved by feeding 1% chitosan as preventive/therapeutic regimes have revealed a remarkably enhanced several innate immunological parameters (e.g., Phagocytic activity/index, NBT, Lysozyme activity and ACH50), increased resistance against A. hydrophila and strikingly improved water quality compared to the 0.5 and 2% chitosan containing diets. Conclusively, experimental results suggest the commercial usage of chitosan as an efficient immunostimulant and bio-remediating agent in aquaculture. |
5,248 | The effect of spraying bacterial and fungal solutions on Korean pine Pinus koraiensis Sieb. et Zucc. cone development and seed quality when sprayed during the flowering phase | Korean pine is an economically essential afforestation species limited by the unreasonable collection of cones, indiscriminate use of chemical pesticides and pest damage. This study aimed to determine whether spraying bacterial or fungal solutions affected insect pests, cone development, and the seed quality of Korean pine Pinus koraiensis Sieb. et Zucc. The experiment was conducted in a forest plantation in Linkou County (Heilongjiang, China) in 2019. Four fungal strains and one bacterial strain were applied during the flowering phase of Korean pine. The results after a year and a half of study indicated that a high concentration of Bacillus thuringiensis 223176 promoted cone development, increased seed weight, and reduced the proportion of damaged cones. Under this treatment, there were 15.873% damaged cones; the seed weight reached 0.829 g, and there were 82.738% fully developed cones. Trees treated with the second most effective strain, Beauveria bassiana 122077, had 30.556% damaged cones and an average seed weight of 0.810 g. Leucanicillium antillanum 01 performed the worst in this study. The seed weight was only 0.775 g, and the damaged and fully developed cones were 52.444 and 41.773%, respectively. In summary, spraying bacterial or fungal solutions during the flowering stage of Korean pine positively impacted seed quality and effectively decreased damage by the lepidopteran species that feed on the cones and seeds in this study. |
5,249 | Game-play affects hamstring but not adductor muscle fibre mechanics in elite U20 basketball athletes | Muscle tendon unit fibre mechanics of hamstring and adductor strain injuries are not well studied, with factors such as fatigue promoted as risk factors in the absence of mechanistic evidence. In this study, musculoskeletal modelling was used to estimate fibre mechanics of four hamstring (biceps femoris long head, biceps femoris short head, semimembranosus and semitendinosus) and four adductor (adductor brevis, adductor longus, adductor magnus and gracilis) muscles during an anticipated cut task. The cut task was performed by 10 healthy elite male U20 basketball players both before and immediately after they played in one (of four) competitive basketball game. Biceps femoris long head produced significantly lower (p = 0.032) submaximal force post-game in the latter part of swing (30.7% to 35.0% of stride), though its peak force occurred later (37%) and remained unchanged. Semimembranosus produced significantly lower (p = 0.006) force post-game (32.9% to 44.9% of stride), which encompassed the instance of peak force (39%). Neither fibre velocity nor fibre length of the investigated muscles were significantly affected by game-play. These finding suggest that if fatigue is a factor in hamstring and adductor muscle strain injuries and is brought about by game-play, it is unlikely through the fibre mechanisms investigated in this study. |
5,250 | Transcriptional Profile of the Developing Subthalamic Nucleus | The subthalamic nucleus (STN) is a small, excitatory nucleus that regulates the output of basal ganglia motor circuits. The functions of the STN and its role in the pathophysiology of Parkinson's disease are now well established. However, some basic characteristics like the developmental origin and molecular phenotype of neuronal subpopulations are still being debated. The classical model of forebrain development attributed the origin of STN within the diencephalon. Recent studies of gene expression patterns exposed shortcomings of the classical model. To accommodate these findings, the prosomeric model was developed. In this concept, STN develops within the hypothalamic primordium, which is no longer a part of the diencephalic primordium. This concept is further supported by the expression patterns of many transcription factors. It is interesting to note that many transcription factors involved in the development of the STN are also involved in the pathogenesis of neurodevelopmental disorders. Thus, the study of neurodevelopmental disorders could provide us with valuable information on the roles of these transcription factors in the development and maintenance of STN phenotype. In this review, we summarize historical theories about the developmental origin of the STN and interpret the gene expression data within the prosomeric conceptual framework. Finally, we discuss the importance of neurodevelopmental disorders for the development of the STN and its potential role in the pathophysiology of neurodevelopmental disorders. |
5,251 | 3D Multi-Attention Guided Multi-Task Learning Network for Automatic Gastric Tumor Segmentation and Lymph Node Classification | Automatic gastric tumor segmentation and lymph node (LN) classification not only can assist radiologists in reading images, but also provide image-guided clinical diagnosis and improve diagnosis accuracy. However, due to the inhomogeneous intensity distribution of gastric tumor and LN in CT scans, the ambiguous/missing boundaries, and highly variable shapes of gastric tumor, it is quite challenging to develop an automatic solution. To comprehensively address these challenges, we propose a novel 3D multi-attention guided multi-task learning network for simultaneous gastric tumor segmentation and LN classification, which makes full use of the complementary information extracted from different dimensions, scales, and tasks. Specifically, we tackle task correlation and heterogeneity with the convolutional neural network consisting of scale-aware attention-guided shared feature learning for refined and universal multi-scale features, and task-aware attention-guided feature learning for task-specific discriminative features. This shared feature learning is equipped with two types of scale-aware attention (visual attention and adaptive spatial attention) and two stage-wise deep supervision paths. The task-aware attention-guided feature learning comprises a segmentation-aware attention module and a classification-aware attention module. The proposed 3D multi-task learning network can balance all tasks by combining segmentation and classification loss functions with weight uncertainty. We evaluate our model on an in-house CT images dataset collected from three medical centers. Experimental results demonstrate that our method outperforms the state-of-the-art algorithms, and obtains promising performance for tumor segmentation and LN classification. Moreover, to explore the generalization for other segmentation tasks, we also extend the proposed network to liver tumor segmentation in CT images of the MICCAI 2017 Liver Tumor Segmentation Challenge. Our implementation is released at https://github.com/infinite-tao/MA-MTLN. |
5,252 | Tubby-like proteins (TLPs) transcription factor in different regulatory mechanism in plants: a review | Tubby-like proteins (TLPs) transcription factors are found in single-celled to multi-cellular eukaryotes in the form of large multigene families. TLPs are identified through a specific signature of carboxyl terminal tubby domain, required for plasma membrane tethering and amino terminal F-box domain communicate as functional SCF-type E3 ligases. The comprehensive distribution of TLP gene family members in diverse species indicates some conserved functions of TLPs in multicellular organisms. Plant TLPs have higher gene members than animals and these members reported important role in multiple physiological and developmental processes and various environmental stress responses. Although the TLPs are suggested to be a putative transcription factors but their functional mechanism is not much clear. This review provides significant recent updates on TLP-mediated regulation with an insight into its functional roles, origin and evolution and also phytohormones related regulation to combat with various stresses and its involvement in adaptive stress response in crop plants. |
5,253 | Direct pose estimation for planar objects | Estimating six degrees of freedom poses of a planar object from images is an important problem with numerous applications ranging from robotics to augmented reality. While the state-of-the-art Perspective-n-Point algorithms perform well in pose estimation, the success hinges on whether feature points can be extracted and matched correctly on target objects with rich texture. In this work, we propose a two-step robust direct method for six-dimensional pose estimation that performs accurately on both textured and textureless planar target objects. First, the pose of a planar target object with respect to a calibrated camera is approximately estimated by posing it as a template matching problem. Second, each object pose is refined and disambiguated using a dense alignment scheme. Extensive experiments on both synthetic and real datasets demonstrate that the proposed direct pose estimation algorithm performs favorably against state-of-the-art feature-based approaches in terms of robustness and accuracy under varying conditions. Furthermore, we show that the proposed dense alignment scheme can also be used for accurate pose tracking in video sequences. |
5,254 | Post-combustion CO2 capture applied to a state-of-the-art coal-fired power plant-The influence of dynamic process conditions | A dynamic model of the amine-based CO2-capture process is presented and applied to investigate the transient behavior of the absorption system during and after load changes in Nordjyllandsvaerket, a state-of-the-art coal-fired power plant with a thermal efficiency of 47.5%. Two scenarios of flexible operation in the power plant are investigated: part-load and peak load operation. Simulations of the load-variation scenarios show that implementation of active control strategies improves capture system performance with respect to capture efficiency and the heat requirement. The reboiler duty can be decreased considerably during part load operation compared to a case where no control strategy is applied. Integration of the capture process with the power plant results in an efficiency decrease of around 9 percentage points at full load and in the range of 8-12 percentage points during 60% part load operation, depending on if a process controller is used or not. Energy requirement for CO2 compression is not included in these numbers. In addition, the response time of the absorption system is significantly decreased in the cases where a process control strategy is implemented, both for part load and peak load operation. (C) 2014 Elsevier Ltd. All rights reserved. |
5,255 | POPDC1 scaffolds a complex of adenylyl cyclase 9 and the potassium channel TREK-1 in heart | The establishment of macromolecular complexes by scaffolding proteins is key to the local production of cAMP by anchored adenylyl cyclase (AC) and the subsequent cAMP signaling necessary for cardiac functions. We identify a novel AC scaffold, the Popeye domain-containing (POPDC) protein. The POPDC family of proteins is important for cardiac pacemaking and conduction, due in part to their cAMP-dependent binding and regulation of TREK-1 potassium channels. We show that TREK-1 binds the AC9:POPDC1 complex and copurifies in a POPDC1-dependent manner with AC9 activity in heart. Although the AC9:POPDC1 interaction is cAMP-independent, TREK-1 association with AC9 and POPDC1 is reduced upon stimulation of the β-adrenergic receptor (βAR). AC9 activity is required for βAR reduction of TREK-1 complex formation with AC9:POPDC1 and in reversing POPDC1 enhancement of TREK-1 currents. Finally, deletion of the gene-encoding AC9 (Adcy9) gives rise to bradycardia at rest and stress-induced heart rate variability, a milder phenotype than the loss of Popdc1 but similar to the loss of Kcnk2 (TREK-1). Thus, POPDC1 represents a novel adaptor for AC9 interactions with TREK-1 to regulate heart rate control. |
5,256 | Depth Completion and Super-Resolution with Arbitrary Scale Factors for Indoor Scenes | Depth sensing has improved rapidly in recent years, which allows for structural information to be utilized in various applications, such as virtual reality, scene and object recognition, view synthesis, and 3D reconstruction. Due to the limitations of the current generation of depth sensors, the resolution of depth maps is often still much lower than the resolution of color images. This hinders applications, such as view synthesis or 3D reconstruction, from providing high-quality results. Therefore, super-resolution, which allows for the upscaling of depth maps while still retaining sharpness, has recently drawn much attention in the deep learning community. However, state-of-the-art deep learning methods are typically designed and trained to handle a fixed set of integer-scale factors. Moreover, the raw depth map collected by the depth sensor usually has many depth data missing or misestimated values along the edges and corners of observed objects. In this work, we propose a novel deep learning network for both depth completion and depth super-resolution with arbitrary scale factors. The experimental results on the Middlebury stereo, NYUv2, and Matterport3D datasets demonstrate that the proposed method can outperform state-of-the-art methods. |
5,257 | Deep Learning-Based Computer Vision for Surveillance in ITS: Evaluation of State-of-the-Art Methods | Intelligent transportation system (ITS) collects numerous data for analysis of the transportation system. The data can be used for providing services for travellers and traffic controllers in the ITS and optimizing it, for the purpose of making the transportation more efficient and safer. Due to the wide and flexible employment of video cameras in visual surveillance system (VSS), mature edge-cloud resource scheduling for data transmission and analysis, and the fast development of deep learning, computer vision (CV) methods have been employed in the visual-based ITS services successfully. In this paper, we discuss the edge-cloud surveillance resource scheduling for the CV methods and review the deep learning-based CV methods in the VSS, including detection, classification, and tracking methods, for better understanding of the relationship between the CV-based ITS services and these methods. We experimentally compare several state-of-the-art deep learning-based methods, which have been successfully applied in the CV fields under the ITS scenario, on their performance, inference speed, computational quantity, and model size. According to the comparisons, we propose four main challenges of the deep learning-based CV methods applied in the services, as a discussion of the future research directions. Code are available at https://github.com/PRIS-CV/DL-CV-ITS. |
5,258 | Utterance Level Feature Aggregation with Deep Metric Learning for Speech Emotion Recognition | Emotion is a form of high-level paralinguistic information that is intrinsically conveyed by human speech. Automatic speech emotion recognition is an essential challenge for various applications; including mental disease diagnosis; audio surveillance; human behavior understanding; e-learning and human-machine/robot interaction. In this paper, we introduce a novel speech emotion recognition method, based on the Squeeze and Excitation ResNet (SE-ResNet) model and fed with spectrogram inputs. In order to overcome the limitations of the state-of-the-art techniques, which fail in providing a robust feature representation at the utterance level, the CNN architecture is extended with a trainable discriminative GhostVLAD clustering layer that aggregates the audio features into compact, single-utterance vector representation. In addition, an end-to-end neural embedding approach is introduced, based on an emotionally constrained triplet loss function. The loss function integrates the relations between the various emotional patterns and thus improves the latent space data representation. The proposed methodology achieves 83.35% and 64.92% global accuracy rates on the RAVDESS and CREMA-D publicly available datasets, respectively. When compared with the results provided by human observers, the gains in global accuracy scores are superior to 24%. Finally, the objective comparative evaluation with state-of-the-art techniques demonstrates accuracy gains of more than 3%. |
5,259 | Association between asthma, chronic bronchitis, emphysema, chronic obstructive pulmonary disease, and lung cancer in the US population | Lung cancer is one of the primary causes of death with poor life expectancy after diagnosis. History of past respiratory diseases such as asthma, chronic obstructive lung disease (COPD), emphysema, and chronic bronchitis can increase the risk of lung cancer. Very few studies are available to simultaneously assess multiple respiratory diseases and lung cancer. The objective of this study was to investigate correlations between asthma, emphysema, chronic bronchitis, and chronic obstructive lung disease with lung cancer in the US adult population. This was a cross-sectional study using data from a total of 23,523 adult participants from the National Health Examination and Nutrition Survey (NHANES) datasets for seven cycles ranging from 2003-2004 to 2015-2016. To analyze the data, specialized weighted complex survey logit regressions were conducted. Linear logit regression models using only main-effects were constructed first to assess the correlation between the selected demographic and lifestyle variables and asthma, emphysema, chronic bronchitis, and COPD. A second set of linear, main-effects logit regression models were constructed to examine the correlation between lung cancer and asthma, emphysema, chronic bronchitis, COPD when corrected for the selected covariates. The study identified positive correlations between emphysema, chronic bronchitis, COPD, and lung cancer. No correlation between asthma and lung cancer was established. Of the covariates studied, race/ethnicity, marital status, highest educational level, age, family income to poverty ratio, and lifetime smoking were also found to be correlated with the presence of lung cancer. Correlations between the covariates gender, body mass index, alcohol consumption, and country of birth and lung cancer were not found. The study established statistically significant correlations between lung cancer and the lung diseases emphysema, chronic bronchitis, and COPD. The lack of association between asthma and lung cancer may arise from the timeline of diagnosis asthma or type of lung cancer. The study also established significant correlations between lung cancer and several of the covariates included in the analysis. It also established correlations between the covariates and the lung diseases asthma, emphysema, chronic bronchitis, and COPD. |
5,260 | Temporal Patternization of Power Signatures for Appliance Classification in NILM | Non-Intrusive Load Monitoring (NILM) techniques are effective for managing energy and for addressing imbalances between the energy demand and supply. Various studies based on deep learning have reported the classification of appliances from aggregated power signals. In this paper, we propose a novel approach called a temporal bar graph, which patternizes the operational status of the appliances and time in order to extract the inherent features from the aggregated power signals for efficient load identification. To verify the effectiveness of the proposed method, a temporal bar graph was applied to the total power and tested on three state-of-the-art deep learning techniques that previously exhibited superior performance in image classification tasks-namely, Extreme Inception (Xception), Very Deep One Dimensional CNN (VDOCNN), and Concatenate-DenseNet121. The UK Domestic Appliance-Level Electricity (UK-DALE) and Tracebase datasets were used for our experiments. The results of the five-appliance case demonstrated that the accuracy and F1-score increased by 19.55% and 21.43%, respectively, on VDOCNN, and by 33.22% and 35.71%, respectively, on Xception. A performance comparison with the state-of-the-art deep learning methods and image-based spectrogram approach was conducted. |
5,261 | A Quantitative Approach to Screen for Nephrotoxic Compounds In Vitro | Nephrotoxicity due to drugs and environmental chemicals accounts for significant patient mortality and morbidity, but there is no high throughput in vitro method for predictive nephrotoxicity assessment. We show that primary human proximal tubular epithelial cells (HPTECs) possess characteristics of differentiated epithelial cells rendering them desirable to use in such in vitro systems. To identify a reliable biomarker of nephrotoxicity, we conducted multiplexed gene expression profiling of HPTECs after exposure to six different concentrations of nine human nephrotoxicants. Only overexpression of the gene encoding heme oxygenase-1 (HO-1) significantly correlated with increasing dose for six of the compounds, and significant HO-1 protein deregulation was confirmed with each of the nine nephrotoxicants. Translatability of HO-1 increase across species and platforms was demonstrated by computationally mining two large rat toxicogenomic databases for kidney tubular toxicity and by observing a significant increase in HO-1 after toxicity using an ex vivo three-dimensional microphysiologic system (kidney-on-a-chip). The predictive potential of HO-1 was tested using an additional panel of 39 mechanistically distinct nephrotoxic compounds. Although HO-1 performed better (area under the curve receiver-operator characteristic curve [AUC-ROC]=0.89) than traditional endpoints of cell viability (AUC-ROC for ATP=0.78; AUC-ROC for cell count=0.88), the combination of HO-1 and cell count further improved the predictive ability (AUC-ROC=0.92). We also developed and optimized a homogenous time-resolved fluorescence assay to allow high throughput quantitative screening of nephrotoxic compounds using HO-1 as a sensitive biomarker. This cell-based approach may facilitate rapid assessment of potential nephrotoxic therapeutics and environmental chemicals. |
5,262 | Self-Powered Signal Transduction of Ion-Selective Electrodes to an Electronic Paper Display | Mobile integrated electrochemical sensors normally require a power supply for operation. Unfortunately, the practice of discarding batteries associated with these devices runs counter to our desire for a sustainable world. Self-powered sensing concepts that draw the energy directly from the measurement itself would overcome this limitation. Potentiometric sensors for the measurement of pH, many electrolytes, and gases are ubiquitous in analytical practice. However, in potentiometry, the voltage is acquired in the absence of current flow, making it seemingly impossible to draw power. Fortunately, it has been recently established that transient currents may be tolerated across potentiometric measurement cells to charge a capacitive or electrochromic element such as Prussian blue integrated in the measurement cell and whose absorbance then directly follows the potential changes in a reversible manner. We have shown here that commercial electronic paper (e-paper), widely used to make electronic ink and ebook readers, can directly be driven by a potentiometric measurement cell in a reversible manner at mild potentials of >100 mV typical for such sensors. The capacitance of the e-paper pixel studied here was found to be 0.53 μF mm-2, 30 times smaller than that of Prussian blue films. The colorimetric absorbance of the e-paper was also more stable (observed drift over 2 h corresponding to 0.76 mV h-1) and reproducible (corresponding to 1 mV standard deviation). The e-paper pixel was directly driven by a polymeric pH electrode as a model system. Choosing a basic inner solution (pH 12.9) behind the membrane gave sufficiently positive cell potentials for driving visible absorbance change in a sample pH range of 4-10, while a more acidic pH of 3.4 and alternating the connections to the e-paper were more suited for more basic samples of pH > 10. This convenient and cost-effective approach makes it possible to directly drive an optical display from the potentiometric measurement itself and should be suitable for moderate sensing membrane resistances of less than about 100 kΩ, depending on the area of the chosen pixel. |
5,263 | Rigorous Assessment of Cl- -Based Anolytes on Electrochemical Ammonia Synthesis | Many challenges in the electrochemical synthesis of ammonia have been recognized with most effort focused on delineating false positives resulting from unidentified sources of nitrogen. However, the influence of oxidizing anolytes on the crossover and oxidization of ammonium during the electrolysis reaction remains unexplored. Here it is reported that the use of analytes containing halide ions (Cl- and Br- ) can rapidly convert the ammonium into N2 , which further intensifies the crossover of ammonium. Moreover, the extent of migration and oxidation of ammonium is found to be closely associated with external factors, such as applied potentials and the concentration of Cl- . These findings demonstrate the profound impact of oxidizing anolytes on the electrochemical synthesis of ammonia. Based on these results, many prior reported ammonia yield rates are calibrated. This work emphasizes the significance of avoiding selection of anolytes that can oxidize ammonium, which is believed to promote further progress in electrochemical nitrogen fixation. |
5,264 | State of the art and challenges of security SLA for cloud computing | There are users and organizations that resist adopting cloud computing solutions, due to concerns about the security and privacy of their data. A Service Level Agreement (SLA) can be used to address these concerns, increasing trust in the purchased services through the clear description of the guarantees offered by the provider to the subscribers. For this purpose, the authors performed a literature systematic mapping to enumerate existing solutions and open issues in security SLAs in cloud computing. This review is presented in this paper as well as an analysis of the state of art. This paper also presents a classification of the selected papers and a discussion about management of security SLAs in clouds. (C) 2016 Elsevier Ltd. All rights reserved. |
5,265 | On the cooperation of meta-heuristics for solving many-objective problems: An empirical analysis including benchmark and real-world problems | The performance of state-of-the-art evolutionary algorithms in solving many-objective problems varies ac-cording to different problem characteristics, which poses a challenge for many-objective optimization. In this study, we analyze the cooperative hyper-heuristic (HH-CO) for many-objective optimization. HH-CO tackles the challenge of dynamically finding the best MOEA (multi-objective evolutionary algorithm) for applying and, at the same time, exploiting the MOEAs cooperation for a given problem instance. This recently proposed hyper-heuristic (HH) showed results competitive to stand-alone MOEAs and a state-of-art hyper-heuristic. Our goal is to identify what leads HH-CO towards its competitive results and distinguishes it from other state-of -art hyper-heuristics. To answer those questions, we observed the choices made by HH-CO and a state-of-art HH. In addition, we analyzed how those choices are related to the quality of MOEAs applied stand-alone. Furthermore, we evaluated scenarios where HH-CO presented better and worse results and identified the main reasons for these outcomes. Overall, HH-CO presented better results in 80% of instances. We concluded that the greedy selection heuristic employed by HH-CO could be improved. Still, the positive influence of the cooperative migration procedure surpasses HH-CO deficiencies for most problem instances. Finally, we evaluated the capabilities of both strategies on a real-world problem. They achieved very similar hypervolume results, without a significant difference to the best MOEA, but better than some state-of-the-art MOEAs. |
5,266 | Affective Image Captioning for Visual Artworks Using Emotion-Based Cross-Attention Mechanisms | Within the museum community, the automatic generation of artwork description is expected to accelerate the improvement of accessibility for visually impaired visitors. Captions that describe artworks should be based on emotions because art is inseparable from viewers' emotional reactions. By contrast, artworks typically do not have unique interpretations; thus, it is difficult for systems to reflect the specified emotions in captions precisely. Most existing methods attempt to leverage predicted emotion labels from images to generate emotion-oriented captions; however, they do not allow users to specify arbitrary emotions. In this paper, we aim to build a model that generates emotion-conditioned captions that describe visual art. We propose an affective visual encoder, which integrates emotion attributes and cross-modal joint features of images into visual information over all encoder blocks. Moreover, we introduce affective tokens that fuse grid- and region-based image features to cover both contextual and object-level information. We validated our method on the ArtEmis dataset, and the results demonstrated that our method outperformed baseline methods on all metrics in the emotion-conditioned task. |
5,267 | [Characteristics and Clinical Application of Commonly Used Wound Dressings] | The pathological mechanism of wound healing is complicated and affected by multiple factors. Modern wound dressings are widely used in the clinical management of wound healing and have achieved good therapeutic effects. Clinically, wounds are often caused by different etiologies. However, there are few reviews focus on the selection of reasonable dressings for different types of wounds. This study mainly focuses on the characteristics of commonly used wound dressings and summarizes the characteristics of the most commonly used wound dressings in clinical practice and their effects. The advantages and disadvantages of pathology wounds: diabetic foot ulcers, pressure injuries, burns, and leg ulcers are reviewed. This study aims to provide references for the development and clinical selection of wound dressings for scientific researchers and first-line nursing staff who are engaged in wound dressings. |
5,268 | Clustered Regularly Interspaced short palindromic repeats-Based Microfluidic System in Infectious Diseases Diagnosis: Current Status, Challenges, and Perspectives | Mitigating the spread of global infectious diseases requires rapid and accurate diagnostic tools. Conventional diagnostic techniques for infectious diseases typically require sophisticated equipment and are time consuming. Emerging clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated proteins (Cas) detection systems have shown remarkable potential as next-generation diagnostic tools to achieve rapid, sensitive, specific, and field-deployable diagnoses of infectious diseases, based on state-of-the-art microfluidic platforms. Therefore, a review of recent advances in CRISPR-based microfluidic systems for infectious diseases diagnosis is urgently required. This review highlights the mechanisms of CRISPR/Cas biosensing and cutting-edge microfluidic devices including paper, digital, and integrated wearable platforms. Strategies to simplify sample pretreatment, improve diagnostic performance, and achieve integrated detection are discussed. Current challenges and future perspectives contributing to the development of more effective CRISPR-based microfluidic diagnostic systems are also proposed. |
5,269 | State-of-the-art of classical SPH for free-surface flows | Smoothed Particle Hydrodynamics (SPH) is the most widely established mesh-free method which has been used in several fields as astrophysics, solids mechanics and fluid dynamics. In the particular case of computational fluid dynamics, the model is beginning to reach a maturity that allows carrying out detailed quantitative comparisons with laboratory experiments. Here the state-of-the-art of the classical SPH formulation for free-surface flow problems is described in detail. This is demonstrated using dam-break simulations in 2-D and 3-D. The foundations of the method will be presented using different derivations based on the method of interpolants and on the moving least-squares approach. Different methods to improve the classic SPH approach such as the use of density filters and the corrections of the kernel function and its gradient are examined and tested on some laboratory cases. |
5,270 | Biosensor Characterization from Cratylia mollis Seed Lectin (Cramoll)-MOF and Specific Carbohydrate Interactions in an Electrochemical Model | Biosensors are small devices known for their selectivity, high specificity and sensitivity to the respective analyte, at low concentrations. We developed an electrochemical biosensor using the crystalline polymer MOF-[Cu3 (BTC)2 (H2 O)2 ]n to characterize Cratylia mollis seed lectin (Cramoll) and its interaction with free carbohydrate (glucose) and carbohydrates on the surface of rabbit erythrocytes. The electrochemical potentials presented by the exponential curves that vary from 96 to 142 mV in relation to concentrations of 10 to 20 mM of glucose are decisive for the use of the system containing gold electrode/MOF/Cramoll for the characterization of biological models due to its high sensitivity. As well as the kinetic behavior presented in the cyclic voltammograms, with a cathodic current response of 0.000 3 A for a glucose concentration of 15 mM. These results were due to the high specificity of Cramoll under these conditions, promoting stability of surface charges at the Cramoll/electrode interface. This phenomenon facilitates the monitoring of the interaction with free glucose present in the electrolyte medium by potentiometric and amperometric methods and with carbohydrates present on the surface of rabbit erythrocytes through the potentiometric method. Through scanning electron microscopy (SEM) it was possible to observe Cramoll immobilized on the MOF surface, proving the specificity of the ligand (glucose-lectin) through the morphological lectin changes in this process. This electrochemical model, Cramoll/MOF biosensor, is effective for evaluating free lectin/carbohydrate or in the erythrocyte membrane. |
5,271 | Self-Reported Cannabis Use and HIV Viral Control among Patients with HIV Engaged in Care: Results from a National Cohort Study | Background: The association between cannabis use and HIV-1 RNA (viral load) among people with HIV (PWH) engaged in care is unclear. Methods: We used data collected from 2002 to 2018 on PWH receiving antiretroviral therapy (ART) enrolled in the Veterans Aging Cohort Study. Generalized estimating equations were used to estimate associations between self-reported past-year cannabis use and detectable viral load (>= 500 copies/mL), with and without adjustment for demographics, other substance use, and adherence. Results: Among 2515 participants, 97% were male, 66% were Black, the mean age was 50 years, and 33% had detectable HIV viral load at the first study visit. In unadjusted analyses, PWH with any past-year cannabis use had 21% higher odds of a detectable viral load than those with no past-year use (OR = 1.21; 95% CI, 1.07-1.37). However, there was no significant association between cannabis use and viral load after adjustment. Conclusions: Among PWH engaged in care and receiving ART, cannabis use is associated with decreased adherence in unadjusted analyses but does not appear to directly impact viral control. Future studies are needed to understand other potential risks and benefits of cannabis use among PWH. |
5,272 | Supervised learning in automatic channel selection for epileptic seizure detection | Detecting seizure using brain neuroactivations recorded by intracranial electroencephalogram (iEEG) has been widely used for monitoring, diagnosing, and closed-loop therapy of epileptic patients, however, computational efficiency gains are needed if state-of-the-art methods are to be implemented in implanted devices. We present a novel method for automatic seizure detection based on iEEG data that outperforms current state-of-the-art seizure detection methods in terms of computational efficiency while maintaining the accuracy. The proposed algorithm incorporates an automatic channel selection (ACS) engine as a pre-processing stage to the seizure detection procedure. The ACS engine consists of supervised classifiers which aim to find iEEG channels which contribute the most to a seizure. Seizure detection stage involves feature extraction and classification. Feature extraction is performed in both frequency and time domains where spectral power and correlation between channel pairs are calculated. Random Forest is used in classification of interictal, ictal and early ictal periods of iEEG signals. Seizure detection in this paper is retrospective and patient-specific. iEEG data is accessed via Kaggle, provided by International Epilepsy Electro-physiology Portal. The dataset includes a training set of 6.5 h of interictal data and 41 min in ictal data and a test set of 9.14 h. Compared to the state-of-the-art on the same dataset, we achieve 2 times faster in run-time seizure detection. The proposed model is able to detect a seizure onset at 89.40% sensitivity and 89.24% specificity with a. mean detection delay of 2.63 s for the test set. The area under the ROC curve (AUC) is 96.94%, that is comparable to the current state-of-the-art with AUC of 96.29%. (C) 2017 The Authors. Published by Elsevier Ltd. |
5,273 | Oral Ibrexafungerp for Vulvovaginal Candidiasis Treatment: An Analysis of VANISH 303 and VANISH 306 | Background: Ibrexafungerp is a novel antifungal treatment for acute vulvovaginal candidiasis (VVC). Using pooled data from two phase three studies (VANISH 303 and 306) in the treatment of acute VVC, this analysis sought to determine the effectiveness of ibrexafungerp in various patient subgroups that may impact outcomes. Materials and Methods: Data from VANISH 303 (NCT03734991) and VANISH 306 (NCT03987620) evaluating ibrexafungerp 300 mg twice daily (BID) for 1 day versus placebo, were pooled and analyzed to determine clinical cure rate, clinical improvement, and mycological cure at the test-of-cure visit (day 11 ± 3) and symptom resolution at the follow-up visit (day 25 ± 4) in the overall population. Patient subgroups analyzed included race, body mass index (BMI), baseline vulvovaginal signs and symptoms (VSS) score, and Candida species. Results: At the test-of-cure visit, patients receiving ibrexafungerp, compared with those who received placebo, had significantly higher rates of clinical cure (56.9% [214/376 patients] vs. 35.7% [65/182 patients]), clinical improvement (68.4% [257/376 patients] vs. 45.1% [82/182 patients]), and mycological cure (54.0% [203/376 patients] vs. 24.2% [44/182 patients]; all p < 0.0001). At the follow-up visit, patients receiving ibrexafungerp had sustained responses with higher symptom resolution rates (66.8% [251/376 patients]) versus placebo (48.4% [88/182 patients]; p < 0.0001). Race, BMI, baseline VSS score (including VSS severity score 13-18), and Candida species infection did not adversely affect clinical cure rates. Safety analysis results were consistent with the individual studies. Conclusions: Ibrexafungerp provides a safe and well-tolerated first-in-class fungicidal, 1-day oral treatment for patients with acute VVC, the first new therapy in >20 years. Clinical Trial Registration Number: NCT03734991. |
5,274 | Using a Serious Game as an Elicitation Tool in Interview Research: Reflections on Methodology | It can be difficult to understand the process of making decisions in health care, because of both the complexity of health care systems and the demands faced by health care professionals. Serious games offer an underexplored opportunity to elicit data about decision-making. In this article, we present and reflect on a methodological case study where we used a serious game as part of a semistructured interview to study the process of decision-making. The game Resilience Challenge presented a patient's journey through a hospital, where a player had to make decisions that influenced patient care. The game was used during interviews with 20 nurses, both in person and remotely. Having a mini debrief with a participant after each game scenario provides to be a helpful technique to understand the participants' decision-making process and elicit tacit knowledge about their work. Serious games show promise as a methodological research tool to elicit the process of decision-making among health care professionals. |
5,275 | Sports, Executive Functions and Academic Performance: A Comparison between Martial Arts, Team Sports, and Sedentary Children | It is well known that curricular physical activity benefits children's executive functions and academic performance. Therefore, this study aimed to determine whether there is an influence of extracurricular sports on executive functions and academic performance. However, it is less known which specific types of the sport better enhance executive functions in children; to investigate this issue, this study compared the performance on executive functions tasks and academic performance in one hundred and two boys and girls with an average age of 11.84 years recruited from Italian schools and gyms (N = 102), who participated in martial arts or team sports or were sedentary children. Executive functions were measured with the tests: Attenzione e Concentrazione, Digit Span test, Tower of London, IOWA Gambling task BVN 5-11, and BVN 12-18. Results demonstrated that children practicing martial arts showed better executive functioning and higher school marks than those involved in team sports or not involved in any sports. Furthermore, participants aged 12 to 15 years old outperformed in cool and hot executive functions tasks and had a better academic performance. Thus, the present findings supported the view that regular practice of extracurricular sports enhances executive functions development and consequently influences academic performance. |
5,276 | Deep Convolutional Neural Networks for pedestrian detection | Pedestrian detection is a popular research topic due to its paramount importance for a number of applications, especially in the fields of automotive, surveillance and robotics. Despite the significant improvements, pedestrian detection is still an open challenge that calls for more and more accurate algorithms. In the last few years, deep learning and in particular Convolutional Neural Networks emerged as the state of the art in terms of accuracy for a number of computer vision tasks such as image classification, object detection and segmentation, often outperforming the previous gold standards by a large margin. In this paper, we propose a pedestrian detection system based on deep learning, adapting a general-purpose convolutional network to the task at hand. By thoroughly analyzing and optimizing each step of the detection pipeline we propose an architecture that outperforms traditional methods, achieving a task accuracy close to that of state-of-the-art approaches, while requiring a low computational time. Finally, we tested the system on an NVIDIA Jetson TK1, a 192-core platform that is envisioned to be a forerunner computational brain of future self-driving cars. (C) 2016 Elsevier B.V. All rights reserved. |
5,277 | Information Content Weighting for Perceptual Image Quality Assessment | Many state-of-the-art perceptual image quality assessment (IQA) algorithms share a common two-stage structure: local quality/distortion measurement followed by pooling. While significant progress has been made in measuring local image quality/distortion, the pooling stage is often done in ad-hoc ways, lacking theoretical principles and reliable computational models. This paper aims to test the hypothesis that when viewing natural images, the optimal perceptual weights for pooling should be proportional to local information content, which can be estimated in units of bit using advanced statistical models of natural images. Our extensive studies based upon six publicly-available subject-rated image databases concluded with three useful findings. First, information content weighting leads to consistent improvement in the performance of IQA algorithms. Second, surprisingly, with information content weighting, even the widely criticized peak signal-to-noise-ratio can be converted to a competitive perceptual quality measure when compared with state-of-the-art algorithms. Third, the best overall performance is achieved by combining information content weighting with multiscale structural similarity measures. |
5,278 | CO2 capture with potassium carbonate solutions: A state-of-the-art review | The potassium carbonate (PC) solution is an important chemical solvent to reduce CO2 emissions due to its advantages of low cost, little toxicity, ease of regeneration, slow corrosiveness, low degradation, and its high stability as well as CO2 absorption capacity. As a result, the PC process has been applied in more than 700 plants worldwide for CO2 and hydrogen sulphide removal from streams like ammonia synthesis gas, crude hydrogen, natural gas, and town gas. This paper provides a state-of-the-art review on the research works on CO2 capture using the PC solution. The studies related to the PC solution comprise three main areas: process, thermodynamics, and kinetics. Important experimental studies as well as modeling and simulation studies are reviewed. Future research directions on CO2 absorption by aqueous PC solution are highlighted and discussed. (C) 2015 Elsevier Ltd. All rights reserved. |
5,279 | SLIM-ADC: Spin-based Logic-In-Memory Analog to Digital Converter leveraging SHE-enabled Domain Wall Motion devices | This paper devises a novel Analog to Digital Converter (ADC) framework for energy-aware acquisition of analog signals with Logic-in-Memory capabilities. The beyond-CMOS hardware architecture has been designed to minimize the overall cost of signal acquisition. Spin-Hall Effect driven Domain Wall Motion (SHE-DWM) devices are utilized to realize the proposed framework called Spin-based Logic-In-Memory ADC (SLIM-ADC). Our simulation results indicate that the proposed SLIM-ADC offers similar to 200 fJ energy consumption on average for each analog conversion or logic operation with up to 1 GHz speed. Furthermore, our results indicate that the proposed SLIM-ADC outperforms other state of the art spin-based ADC designs by offering similar to 5.45 mW improved power dissipation on average. Additionally, a Majority Gate (MG)-based Full-Adder (MG-FA) is implemented using the proposed SLIM-ADC. Our results show that the proposed MG-FA offers similar to 2.9-fold reduced power dissipation on average and similar to 1.7-fold reduced delay on average compared to the state of the art Full-Adder designs reported herein. |
5,280 | A "signal-on" photoelectrochemical sensor for human epidermal growth factor receptor 2 detection based on Y6/CdS organic-inorganic heterojunction | A "signal-on" photoelectrochemical (PEC) immunosensor was successfully constructed for determination of human epidermal growth factor receptor 2 (HER2) based on organic-inorganic heterojunction Y6/CdS as photoactive material. Compared with single organic semiconductor, Y6, Y6/CdS exhibited higher photoelectric conversion efficiency due to the formation of heterojunction. In the presence of HER2, sandwich immune structure was formed between HER2 aptamer and anti-HER2 antibody (Ab) by specific recognition. The polydopamine (PDA) nanoparticles were used for signal amplification to enhance photocurrent intensity as PDA can act as electron donor to eliminate holes and promote electron-hole pairs separation. The developed PEC sensor displayed a wide detection range of 5-1000 pg mL-1 and a low detection limit of 2.2 pg mL-1 for HER2 (S/N = 3). The sensor was successfully used for the detection of HER2 in serum with recoveries between 94.8 and 104% and relative standard deviations (RSDs) in the range of 1.2-4.3%. Furthermore, the designed immunosensor possessed good stability, selectivity, and reproducibility, which can find potential clinical applications for disease diagnosis. A "signal-on" photoelectrochemical sensor was reported for human epidermal growth factor receptor 2 detection based on Y6/CdS organic-inorganic heterojunction. |
5,281 | Emerging Evidence on Coronary Heart Disease Screening in Kidney and Liver Transplantation Candidates: A Scientific Statement From the American Heart Association: Endorsed by the American Society of Transplantation | Coronary heart disease is an important source of mortality and morbidity among kidney transplantation and liver transplantation candidates and recipients and is driven by traditional and nontraditional risk factors related to end-stage organ disease. In this scientific statement, we review evidence from the past decade related to coronary heart disease screening and management for kidney and liver transplantation candidates. Coronary heart disease screening in asymptomatic kidney and liver transplantation candidates has not been demonstrated to improve outcomes but is common in practice. Risk stratification algorithms based on the presence or absence of clinical risk factors and physical performance have been proposed, but a high proportion of candidates still meet criteria for screening tests. We suggest new approaches to pretransplantation evaluation grounded on the presence or absence of known coronary heart disease and cardiac symptoms and emphasize multidisciplinary engagement, including involvement of a dedicated cardiologist. Noninvasive functional screening methods such as stress echocardiography and myocardial perfusion scintigraphy have limited accuracy, and newer noninvasive modalities, especially cardiac computed tomography-based tests, are promising alternatives. Emerging evidence such as results of the 2020 International Study of Comparative Health Effectiveness With Medical and Invasive Approaches-Chronic Kidney Disease trial emphasizes the vital importance of guideline-directed medical therapy in managing diagnosed coronary heart disease and further questions the value of revascularization among asymptomatic kidney transplantation candidates. Optimizing strategies to disseminate and implement best practices for medical management in the broader end-stage organ disease population should be prioritized to improve cardiovascular outcomes in these populations. |
5,282 | Hyperspectral Panoramic Image Stitching Using Robust Matching and Adaptive Bundle Adjustment | Remote-sensing developments such as UAVs heighten the need for hyperspectral image stitching techniques that can obtain information on a large area through various parts of the same scene. State-of-the-art approaches often suffer from accumulation errors and high computational costs for large-scale hyperspectral remote-sensing images. In this study, we aim to generate high-precision hyperspectral panoramas with less spatial and spectral distortion. We introduce a new stitching strategy and apply it to hyperspectral images. The stitching framework was built as follows: First, a single band obtained by signal-to-noise ratio estimation was chosen as the reference band. Then, a feature-matching method combining the SuperPoint and LAF algorithms was adopted to strengthen the reliability of feature correspondences. Adaptive bundle adjustment was also designed to eliminate misaligned artifact areas and occasional accumulation errors. Lastly, a spectral correction method using covariance correspondences is proposed to ensure spectral consistency. Extensive feature-matching and image-stitching experiments on several hyperspectral datasets demonstrate the superiority of our approach over the state of the art. |
5,283 | HDSR-Flor: A Robust End-to-End System to Solve the Handwritten Digit String Recognition Problem in Real Complex Scenarios | Automatic handwriting recognition systems are of interest for academic research fields and for commercial applications. Recent advances in deep learning techniques have shown dramatic improvement in relation to classic computer vision problems, especially in Handwritten Text Recognition (HTR). However, several approaches try to solve the problem of deep learning applied to Handwritten Digit String Recognition (HDSR), where it has to deal with the low number of trainable data, while learning to ignore any writing symbol around the digits (noise). In this context, we present a new optical model architecture (Gated-CNN-BGRU), based on HTR workflow, applied to HDSR. The International Conference on Frontiers of Handwriting Recognition (ICFHR) 2014 competition on HDSR were used as baselines to evaluate the effectiveness of our proposal, whose metrics, datasets and recognition methods were adopted for fair comparison. Furthermore, we also use a private dataset (Brazilian Bank Check - Courtesy Amount Recognition), and 11 different approaches from the state-of-the-art in HDSR, as well as 2 optical models from the state-of-the-art in HTR. Finally, the proposed optical model demonstrated robustness even with low data volume (126 trainable data, for example), surpassing the results of existing methods with an average precision of 96.50%, which is equivalent to an average percentage of improvement of 3.74 points compared to the state-of-the-art in HDSR. In addition, the result stands out in the competition's CVL HDS set, where the proposed optical model achieved a precision of 93.54%, while the best result so far had been from Beijing group (from the competition itself), with 85.29%. |
5,284 | Protective role of vitamin D status against COVID-19: a mini-review | An outbreak of pneumonia caused by a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is called COVID-19 and has led to a pandemic worldwide. It is reasonable to investigate and control factors affecting disease severity and mortality. The relation between vitamin D and viral pneumonia has been previously reported. Vitamin D deficiency is common and may increase hospital admission and mortality rate in patients with COVID-19. This mini-review examines the pathways that show the association between vitamin D and COVID-19. On the other hand, it deals with the available evidence related to the relationship between vitamin D deficiency and the effect of vitamin D supplementation on the prevalence, severity, and mortality of COVID-19. Also, we described the pathophysiology of the organs' involvement in COVID-19 and the effect of vitamin D on these outcomes. Vitamin D strengthens the innate and adaptive immune system, modulates immune responses, prevents lung and cardiovascular system damage, and reduces thrombotic events. Vitamin D exerts these effects in several pathways. Vitamin D prevents virus entry and replication by maintaining the integrity of the body's physical barrier. Vitamin D reduces the damage to vital organs and thrombotic events by increasing the level of Angiotensin-converting enzyme 2 (ACE2), nitric oxide, and antioxidants or by reducing inflammatory cytokines and free radicals. Sufficient vitamin D may be reduced morbidity and mortality due to COVID-19. However, this issue should be investigated and confirmed by further research in the future. |
5,285 | Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring | Mammographic risk scoring has commonly been automated by extracting a set of handcrafted features from mammograms, and relating the responses directly or indirectly to breast cancer risk. We present a method that learns a feature hierarchy from unlabeled data. When the learned features are used as the input to a simple classifier, two different tasks can be addressed: i) breast density segmentation, and ii) scoring of mammographic texture. The proposed model learns features at multiple scales. To control the models capacity a novel sparsity regularizer is introduced that incorporates both lifetime and population sparsity. We evaluated our method on three different clinical datasets. Our state-of-the-art results show that the learned breast density scores have a very strong positive relationship with manual ones, and that the learned texture scores are predictive of breast cancer. The model is easy to apply and generalizes to many other segmentation and scoring problems. |
5,286 | Actions Do Not Always Speak Louder Than Words | Financial (dis)incentives (e.g., bonuses, taxes) and social incentives (e.g., public praise) have typically been proposed as methods to encourage greater cooperation for the benefit of all. However, when cooperation requires exertion of effort, such interventions might not always be effective. While incentives tend to be highly motivating when choosing to exert effort, evidence suggests that they have less of an effect on behavior during effort execution. The aim of this exploratory study was to incorporate these insights into empirical investigation of the effects of social incentives on cooperative effort. To this end, we modified a public goods game task to require effort contributions to a common good. Crucial manipulation involved incorporating social incentives into this task and linking them to (a) choices that people made or (b) effortful actions they exerted. Our findings suggest, in line with recent effort-based decision-making models, that social incentives have a stronger effect on cooperative effort when they are linked to choices that people make, rather than the actual effort they exert. This study demonstrates potential benefits of eliciting a priori declarations of cooperative effort tied to social incentives to encourage greater effort for the benefit of all. |
5,287 | Lightweight convolutional neural networks for player detection and classification | Vision-based player detection and classification are important in sports applications. Accuracy, efficiency, and low memory consumption are desirable for real-time tasks such as intelligent broadcasts and event classification. In this paper, we present a convolutional neural network (CNN) that satisfies all these requirements. The network contains a three-branch proposal network and a four-cascade classification network. Our method first trains these cascaded networks from labeled image patches. Then, we efficiently apply the network to a whole image by using a dilation strategy in testing. We conducted experiments on soccer, basketball, ice hockey and pedestrian datasets. Experimental results demonstrate that our method can accurately detect players under challenging conditions. Compared with CNNs that are adapted from general object detection networks such as Faster-RCNN, our approach achieves state-of-the-art accuracy on three types of games (basketball, soccer and ice hockey) with 1000 x fewer parameters. The generality of our method is also demonstrated on a standard pedestrian detection dataset in which our method achieves competitive performance compared with state-of-the-art methods. |
5,288 | Instrument platforms for thin-layer chromatography | High performance column and thin-layer chromatography are both instrumental techniques but differ in that column chromatography requires a fully integrated instrument platform with high pressure capability while for thin-layer chromatography separate devices are used for each unit operation, usually at or close to atmospheric pressure, and afford higher flexibility supporting on-line or off-line operation. The unit operations of thin-layer chromatography are defined as sample application, development and evaluation with derivatization as an optional step. The diversity of equipment for each operation contributes to the flexibility of analysis by thin-layer chromatography and supports manual, semi-automated or full-automation of the separation process. Instrument platforms are more than a convenience as they affect performance, repeatability, sample detectability, and time management. The current trend in thin-layer chromatography is to make the unit operations independent of the user so that analysts can perform other tasks while each step is performed. In addition, in thin-layer chromatography it is general practice to separate several samples simultaneously, and instrument platforms are required to accommodate this feature. In this article, we review contemporary instrumentation employed in thin-layer chromatography for sample application, development, derivatization, photodocumentation, densitometric evaluation, and hyphenation with spectroscopic detectors with an emphasis on the variety and performance of commercially available systems. Some suggestions for best practices and avoidance of common mistakes are included. |
5,289 | RESEARCH ON ART DESIGN BASED ON THE CONCEPT OF LOW CARBON ECONOMY | Based on the introduction of the basic principle of deepening, this paper analyses the application of deepening in the selection of roads and art design based on the concept of low carbon economy. First, different information is processed on the basis of the spatial data acquisition and a technical knowledge base is created on the basis of the GIS. Then, the interactive genetic algorithm is used to optimise the highway, which reflects the roads design based on the concept of low carbon economy. Finally, the multiplicative decision model is created to make an intelligent decision in order to achieve the multi-objective coordination and unification of route selection. |
5,290 | Photoacoustic image-guided biomimetic nanoparticles targeting rheumatoid arthritis | The high level of reactive oxygen species (ROS) in the rheumatoid arthritis (RA) microenvironment (RAM) and its persistent inflammatory nature can promote damage to joints, bones, and the synovium. Targeting strategies that integrate effective RAM regulation with imaging-based monitoring could lead to improvements in the diagnosis and treatment of RA. Here, we report the combined use of small interfering RNAs (siRNAsT/I) and Prussian blue nanoparticles (PBNPs) to silence the expression of proinflammatory cytokines TNF-α/IL-6 and scavenge the ROS associated with RAM. To enhance the in vitro and in vivo biological stability, biocompatibility, and targeting capability of the siRNAsT/I and PBNPs, macrophage membrane vesicles were used to prepare biomimetic nanoparticles, M@P-siRNAsT/I. The resulting constructs were found to suppress tumor necrosis factor-α/interleukin-6 expression and overcome the hypoxic nature of RAM, thus alleviating RA-induced joint damage in a mouse model. The M@P-siRNAsT/I of this study could be monitored via near-infrared photoacoustic (PA) imaging. Moreover, multispectral PA imaging without the need for labeling permitted the real-time evaluation of M@P-siRNAsT/I as a putative RA treatment. Clinical microcomputed tomography and histological analysis confirmed the effectiveness of the treatment. We thus suggest that macrophage-biomimetic M@P-siRNAsT/I and their analogs assisted by PA imaging could provide a new strategy for RA diagnosis, treatment, and monitoring. |
5,291 | Boundary Coding Representation for Organ Segmentation in Prostate Cancer Radiotherapy | Accurate segmentation of the prostate and organs at risk (OARs, e.g., bladder and rectum) in male pelvic CT images is a critical step for prostate cancer radiotherapy. Unfortunately, the unclear organ boundary and large shape variation make the segmentation task very challenging. Previous studies usually used representations defined directly on unclear boundaries as context information to guide segmentation. Those boundary representations may not be so discriminative, resulting in limited performance improvement. To this end, we propose a novel boundary coding network (BCnet) to learn a discriminative representation for organ boundary and use it as the context information to guide the segmentation. Specifically, we design a two-stage learning strategy in the proposed BCnet: 1) Boundary coding representation learning. Two sub-networks under the supervision of the dilation and erosion masks transformed from the manually delineated organ mask are first separately trained to learn the spatial-semantic context near the organ boundary. Then we encode the organ boundary based on the predictions of these two sub-networks and design a multi-atlas based refinement strategy by transferring the knowledge from training data to inference. 2) Organ segmentation. The boundary coding representation as context information, in addition to the image patches, are used to train the final segmentation network. Experimental results on a large and diverse male pelvic CT dataset show that our method achieves superior performance compared with several state-of-the-art methods. |
5,292 | GA-Based Optimization of Irregular Subarray Layouts for Wideband Phased Arrays Design | The design of phased arrays generating low sidelobes and grating-lobes-free patterns over wide frequency bandwidths is addressed. The array structure is decomposed in subarrays with irregular polyomino tiles whose locations and orientations are optimized by means of a genetic algorithms-based approach. A set of representative results is reported and discussed to give some in-sights on the performance of the proposed approach also in comparison to state-of-the-art solutions. |
5,293 | Marginalizing Sample Consensus | A new method for robust estimation, MAGSAC++, is proposed. It introduces a new model quality (scoring) function that does not make inlier-outlier decisions, and a novel marginalization procedure formulated as an M-estimation with a novel class of M-estimators (a robust kernel) solved by an iteratively re-weighted least squares procedure. Instead of the inlier-outlier threshold, it requires only its loose upper bound which can be chosen from a significantly wider range. Also, we propose a new termination criterion and a technique for selecting a set of inliers in a data-driven manner as a post-processing step after the robust estimation finishes. On a number of publicly available real-world datasets for homography, fundamental matrix fitting and relative pose, MAGSAC++ produces results superior to the state-of-the-art robust methods. It is more geometrically accurate, fails fewer times, and it is often faster. It is shown that MAGSAC++ is significantly less sensitive to the setting of the threshold upper bound than the other state-of-the-art algorithms to the inlier-outlier threshold. Therefore, it is easier to be applied to unseen problems and scenes without acquiring information by hand about the setting of the inlier-outlier threshold. The source code and examples both in C++ and Python are available at https://github.com/danini/magsac. |
5,294 | Review and Comparison of Control Strategies in Active Power Decoupling | The active power-decoupling (APD) method is an effective solution to handle the inherent double-line frequency ripple power in single-phase power systems. It removes the bulky passive devices and facilitates the improvement of the system power density and even the reliability. This article provides a comprehensive review of the prior-art and state-of-the-art control strategies in APD. They are categorized into four groups according to the basic control ideas of "power balance," "harmonic suppression," "volt-second balance/charge balance," and "virtual impedance." And the specific control strategies under each control idea are discussed and compared. The pros and cons of each control idea are also presented . Finally, this article draws a sketch of the global trends in APD control. |
5,295 | Alpha-Particle Emission Energy Spectra From Materials Used for Solder Bumps | The emitted alpha particle energy distribution from solder bumps can show substantial surface emission which has a large impact on the modeled SEU rate. State-of-the art alpha-particle detectors are required to measure the low emissivity and energy distribution. |
5,296 | National Email Communication Platforms May Indicate or Contribute to Gender Disparities: Preliminary Analysis of an Academic Medicine Listserv | Background: Gender disparities are well documented in the academic medicine literature and have been shown to impact representation, rank, and leadership opportunities for women. Social media platforms, including electronic mailing lists (listservs), may contribute to disparities by differentially highlighting or promoting individuals' work in academic and public health settings. Because of this, they provide a record by which to assess the presence of gender disparities; therefore, they become tools to identify gender differences in the frequency or pattern of representation. This study examines the representation of women in academic medicine electronic communications by analyzing weekly email listserv announcements of the American Association of Medical Colleges (AAMC). Materials and Methods: A mixed methods approach was used to analyze listserv communications during two time periods, 2012-2014 and 2018-2019. Each email contained multiple announcements. Individual achievement messages were selected, categorized by gender, and coded with one of three action categories: departures, appointments, and other mentions. Additionally, each notice was coded by professional setting (media, professional organizations, medical school/research, health care systems, public health, and government). Results: We analyzed a total of 5701 announcements in the AAMC communication listserv. Men represented 73.2% (N = 4171) and women 26.8% (N = 1530) of the total announcements. During 2012-2014, 24.0% of announcements were about women, while in the 2018-2019 sample, 35.7% of announcements were about women (p < 0.001). Overall, women were underrepresented in departure-focused messages compared to messages with an appointment or other focus in the sample. The prevalence of women in announcements from the 2012-2014 and 2018-2019 samples also varied based on setting. Conclusions: Findings support the presence of gender disparities in these sets of listserv communications. While social media overall is not considered to be a source of complete information, this study analyzed the same listserv communication by the same organization over the entire period, thereby providing a window into the frequency and type of representation of women's professional activity in academic medicine. |
5,297 | Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets | Under the global pandemic of COVID-19, the use of artificial intelligence to analyze chest X-ray (CXR) image for COVID-19 diagnosis and patient triage is becoming important. Unfortunately, due to the emergent nature of the COVID-19 pandemic, a systematic collection of CXR data set for deep neural network training is difficult. To address this problem, here we propose a patch-based convolutional neural network approach with a relatively small number of trainable parameters for COVID-19 diagnosis. The proposed method is inspired by our statistical analysis of the potential imaging biomarkers of the CXR radiographs. Experimental results show that our method achieves state-of-the-art performance and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage. |
5,298 | Usability testing of three visual HMIs for assisted driving: How design impacts driver distraction and mental models | There is a variety of visual human-machine interfaces (HMI) designed across vehicle manufacturers that support drivers while supervising driving automation features, such as adaptive cruise control (ACC). These various designs communicate the same limited amount of information to drivers about their ACC system and it is unclear which HMI designs impact driver distraction the least or how their design could be modified to help drivers develop more accurate mental models of their ACC system. Using a user-centred design (UCD) approach, we designed a speedometer to inform drivers about some of the system's capabilities and then invited 23 drivers to use ACC in a low-fidelity driving simulator to compare the usability of three HMIs using eye-tracking, response times, and qualitative data. Our attempt at designing an intuitive and more informative speedometer received mixed results, but design recommendations are given regarding the indication of the set target speed, set time gap between vehicles (headway distance), and system mode (conventional or adaptive cruise). Practitioner summary: Manufacturers' heterogeneous designs of their visual HMIs for the ACC systems may impact driver distraction in different ways. We used usability testing to compare three HMIs in a driving simulator and make several design recommendations to indicate speed, time gap, and system mode in a more efficient way. Abbreviations: ACC: adaptive cruise control; ADAS: advanced driving assistance system; HMI: human-machine interface; ISO: international organisation for standardization; OEM: original equipment manufacturer; RSME: rating scale of mental effort; RT: response time; R-TLX: raw task load index; SUS: system usability scale; TGT: total glance time; UCD: user-centred design; UX: user experience; xTGT: extended total glance time. |
5,299 | How two extraembryonic epithelia became one: serosa and amnion features and functions of Drosophila's amnioserosa | The conservation of gene networks that specify and differentiate distinct tissues has long been a subject of great interest to evolutionary developmental biologists, but the question of how pre-existing tissue-specific developmental trajectories merge is rarely asked. During the radiation of flies, two extraembryonic epithelia, known as serosa and amnion, evolved into one, called amnioserosa. This unique extraembryonic epithelium is found in fly species of the group Schizophora, including the genetic model organism Drosophila melanogaster, and has been studied in depth. Close relatives of this group develop a serosa and a rudimentary amnion. The scuttle fly Megaselia abdita has emerged as an excellent model organism to study this extraembryonic tissue organization. In this review, development and functions of the extraembryonic tissue complements of Drosophila and Megaselia are compared. It is concluded that the amnioserosa combines cells, genetic pathway components and functions that were previously associated either with serosa development or amnion development. The composite developmental trajectory of the amnioserosa raises the question of whether merging tissue-specific gene networks is a common evolutionary process. This article is part of the theme issue 'Extraembryonic tissues: exploring concepts, definitions and functions across the animal kingdom'. |
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