ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
The political system through a partisan lens: Within-person changes in support for political parties precede political system attitudes

Although political party support and attitudes towards the political system are closely related, the temporal ordering of these associations is unclear. Indeed, prior research identifies both partisan-led change in system attitudes and system attitude-led change in party support. Using a ten-year (2010-2020) national probability sample of New Zealand adults (N = 66,359), we test these associations by modelling the within-person cross-lagged effects between conservative and liberal party support, and political system justification. During conservative-led governments, increases in conservative party support predicted increases in political system justification more strongly than vice versa. The 2017 shift to a liberal-led government was met with an immediate reversal of the effects of party support on system justification, but the effect of system justification on party support took a full year to reverse. These results demonstrate people's perceptions of the fairness of the political system depend on their support for the party in power.
Platelet Reactivity: Is There a Role to Switch?

Antiplatelet agents are essential to prevent thrombotic events in patients with coronary artery disease, especially in those with acute coronary syndrome or undergoing percutaneous coronary intervention (PCI). However, the benefits of antiplatelet therapy always come at a price of increased risk for bleeding, and the clinical values of antiplatelet strategies depend on this characteristic benefit/risk ratio. Platelet function testing aiming at determining an individual's response to the administered agent was hoped to help balance bleeding and thrombosis in order to maximize benefit/risk ratio. However, randomized trials failed to demonstrate an improved clinical outcome of a platelet function-based treatment selection and consequently platelet function testing has not become a routine part of the management of antiplatelet therapies. This review aims to discuss results and shortcomings of available trials and registries regarding the potential role of platelet reactivity testing in guiding antiplatelet treatment selection in patients undergoing PCI.
Enhanced Multi-Task Learning Architecture for Detecting Pedestrian at Far Distance

Existing pedestrian detection methods suffer from performance degradation in the presence of small-scale pedestrians who are positioned at far distance from the camera. We present a pedestrian detection framework that is not only robust to small- and large-scale pedestrians, but is also significantly faster than state-of-the-art methods. The proposed framework incorporates semantic segmentation to confidence modules for RPN (Region Proposal Network) head and R-FCN (Region-based Fully Convolutional Networks) head, and a cascaded R-FCN head. The semantic segmentation confidence is extracted and utilized as auxiliary classification prior knowledge for RPN proposal selection and R-FCN head prediction. Finally, the cascaded R-FCN head progressively refine the pedestrian prediction accuracy with negligible computation overhead. The proposed framework is also capable of maintaining high detection performance on down-sampled input images, which leads to further reduction in overall computational complexity. Experiment results on CityPersons and MOT17Det datasets show that the proposed framework achieves competitive detection performance with about 3x speedup over state-of-the-art methods.
A review of magnetostatic moment method

This paper proposes a review of the magnetostatic moments method (MoM) applied to model electromagnetic devices. This method is now well-known for its "light weight" and its simplicity of implementation. Its main advantages are the nonrequirement of an air region mesh and a coarse mesh of the ferromagnetic material. It leads to very fast resolution and very accurate field, force, and moment computations. The paper proposes a state of the art of this approach and shows some efficient realizations.
Factors controlling sediment yield at the catchment scale in NW Mediterranean geoecosystems

Purpose This study aimed to (1) increase understanding of the relation between sediment yield and environmental variables at the catchment scale; (2) test and validate existing and newly developed regression equations for prediction of sediment yield; and (3) identify how better predictions may be obtained. Materials and methods A correlation and regression analysis was performed between sediment yield and over 40 environmental variables for 61 Spanish catchments. Variables were selected based on availability and expected relation with diverse soil erosion and sediment transport processes. For comparison, the Area Relief Temperature (ART) sediment delivery model was applied to the same catchments. Sediment yield estimates obtained from reservoir surveys were used for model calibration and validation. Results and discussion Catchment area, catchment perimeter, stream length, relief ratio, Modified Fournier Index, the RUSLE's R factor, and catchments percentage with poor vegetation cover showed highest correlations with sediment yield. Stepwise linear regression revealed that variables representing topography, climate, vegetation, lithology, and soil characteristics are required for the best prediction equation. Although calibration results were relatively good, validation showed that the models were unstable and not suitable for extrapolation to other catchments. Reasons for this unstable model performance include (1) lack of detail and quality of the data sources; (2) large variation in catchment characteristics; (3) insufficient representation of all relevant erosion and sediment transport processes; and (4) the presence of nonlinear relations between sediment yield and environmental variables. The nonlinear ART model performed relatively well but systematically overpredicted sediment yield. A model reflecting human impacts, including dams and conservation measures, is expected to provide better results. This, however, requires significantly more input data. Conclusions Although important insight is obtained into the relation between sediment yield and environmental factors, prediction of sediment yield at the catchment scale requires alternative approaches. More detailed information is required on land cover (change), and the effect of soil conservation measures. Validation of regression equations is a necessity, and better predictions are obtained by nonlinear models.
Delineating the transcriptional landscape and clonal diversity of virus-specific CD4+ T cells during chronic viral infection

Although recent evidence indicates that CD4+ T cells responding to chronic viral infection are functionally heterogenous, our understanding of the developmental relationships between these subsets, and a determination of how their transcriptional landscape compares to their acute infection counterparts remains unclear. Additionally, whether cell-intrinsic factors such as TCR usage influence CD4+ T cell fate commitment during persistent infection has not previously been studied. Herein, we perform single-cell RNA sequencing (scRNA-seq) combined with single-cell T cell receptor sequencing (scTCR-seq) on virus-specific CD4+ T cells isolated from mice infected with chronic lymphocytic choriomeningitis virus (LCMV) infection. We identify several transcriptionally distinct states among the Th1, Tfh, and memory-like T cell subsets that form at the peak of infection, including the presence of a previously unrecognized Slamf7+ subset with cytolytic features. We further show that the relative distribution of these populations differs substantially between acute and persistent LCMV infection. Moreover, while the progeny of most T cell clones displays membership within each of these transcriptionally unique populations, overall supporting a one cell-multiple fate model, a small fraction of clones display a biased cell fate decision, suggesting that TCR usage may impact CD4+ T cell development during chronic infection. Importantly, comparative analyses further reveal both subset-specific and core gene expression programs that are differentially regulated between CD4+ T cells responding to acute and chronic LCMV infection. Together, these data may serve as a useful framework and allow for a detailed interrogation into the clonal distribution and transcriptional circuits underlying CD4+ T cell differentiation during chronic viral infection.
Clinical practice guidelines for duodenal cancer 2021

Duodenal cancer is considered to be a small intestinal carcinoma in terms of clinicopathology. In Japan, there are no established treatment guidelines based on sufficient scientific evidence; therefore, in daily clinical practice, treatment is based on the experience of individual physicians. However, with advances in diagnostic modalities, it is anticipated that opportunities for its detection will increase in future. We developed guidelines for duodenal cancer because this disease is considered to have a high medical need from both healthcare providers and patients for appropriate management. These guidelines were developed for use in actual clinical practice for patients suspected of having non-ampullary duodenal epithelial malignancy and for patients diagnosed with non-ampullary duodenal epithelial malignancy. In this study, a practice algorithm was developed in accordance with the Minds Practice Guideline Development Manual 2017, and Clinical Questions were set for each area of epidemiology and diagnosis, endoscopic treatment, surgical treatment, and chemotherapy. A draft recommendation was developed through a literature search and systematic review, followed by a vote on the recommendations. We made decisions based on actual clinical practice such that the level of evidence would not be the sole determinant of the recommendation. This guideline is the most standard guideline as of the time of preparation. It is important to decide how to handle each case in consultation with patients and their family, the treating physician, and other medical personnel, considering the actual situation at the facility (and the characteristics of the patient).
Treatment with an antigen-specific dual microparticle system reverses advanced multiple sclerosis in mice

Antigen-specific therapies hold promise for treating autoimmune diseases such as multiple sclerosis while avoiding the deleterious side effects of systemic immune suppression due to delivering the disease-specific antigen as part of the treatment. In this study, an antigen-specific dual-sized microparticle (dMP) treatment reversed hind limb paralysis when administered in mice with advanced experimental autoimmune encephalomyelitis (EAE). Treatment reduced central nervous system (CNS) immune cell infiltration, demyelination, and inflammatory cytokine levels. Mechanistic insights using single-cell RNA sequencing showed that treatment impacted the MHC II antigen presentation pathway in dendritic cells, macrophages, B cells, and microglia, not only in the draining lymph nodes but also strikingly in the spinal cord. CD74 and cathepsin S were among the common genes down-regulated in most antigen presenting cell (APC) clusters, with B cells also having numerous MHC II genes reduced. Efficacy of the treatment diminished when B cells were absent, suggesting their impact in this therapy, in concert with other immune populations. Activation and inflammation were reduced in both APCs and T cells. This promising antigen-specific therapeutic approach advantageously engaged essential components of both innate and adaptive autoimmune responses and capably reversed paralysis in advanced EAE without the use of a broad immunosuppressant.
Region-Enhancing Network for Semantic Segmentation of Remote-Sensing Imagery

Semantic segmentation for high-resolution remote-sensing imagery (HRRSI) has become increasingly popular in machine vision in recent years. Most of the state-of-the-art methods for semantic segmentation of HRRSI usually emphasize the strong learning ability of deep convolutional neural network to model the contextual relationship in the image, which takes too much consideration on every pixel in images and subsequently causes the problem of overlearning. Annotation errors and easily confused features can also lead to the confusion problem while using the pixel-based methods. Therefore, we propose a new semantic segmentation network-the region-enhancing network (RE-Net)-to emphasize the regional information instead of pixels to solve the above problems. RE-Net introduces the regional information into the base network, to enhance the regional integrity of images and thus reduce misclassification. Specifically, the regional context learning procedure (RCLP) can learn the context relationship from the perspective of regions. The region correcting procedure (RCP) uses the pixel aggregation feature to recalibrate the pixel features in each region. In addition, another simple intra-network multi-scale attention module is introduced to select features at different scales by the size of the region. A large number of comparative experiments on four different public datasets demonstrate that the proposed RE-Net performs better than most of the state-of-the-art ones.
Tensor Factorization for Low-Rank Tensor Completion

Recently, a tensor nuclear norm (TNN) based method was proposed to solve the tensor completion problem, which has achieved state-of-the-art performance on image and video inpainting tasks. However, it requires computing tensor singular value decomposition (t-SVD), which costs much computation and thus cannot efficiently handle tensor data, due to its natural large scale. Motivated by TNN, we propose a novel low-rank tensor factorization method for efficiently solving the 3-way tensor completion problem. Our method preserves the low-rank structure of a tensor by factorizing it into the product of two tensors of smaller sizes. In the optimization process, our method only needs to update two smaller tensors, which can be more efficiently conducted than computing t-SVD. Furthermore, we prove that the proposed alternating minimization algorithm can converge to a Karush-Kuhn-Tucker point. Experimental results on the synthetic data recovery, image and video inpainting tasks clearly demonstrate the superior performance and efficiency of our developed method over state-of-the-arts including the TNN and matricization methods.
Degraded Image Semantic Segmentation With Dense-Gram Networks

Degraded image semantic segmentation is of great importance in autonomous driving, highway navigation systems, and many other safety-related applications and it was not systematically studied before. In general, image degradations increase the difficulty of semantic segmentation, usually leading to decreased semantic segmentation accuracy. Therefore, performance on the underlying clean images can be treated as an upper bound of degraded image semantic segmentation. While the use of supervised deep learning has substantially improved the state of the art of semantic image segmentation, the gap between the feature distribution learned using the clean images and the feature distribution learned using the degraded images poses a major obstacle in improving the degraded image semantic segmentation performance. The conventional strategies for reducing the gap include: 1) Adding image-restoration based preprocessing modules; 2) Using both clean and the degraded images for training; 3) Fine-tuning the network pre-trained on the clean image. In this paper, we propose a novel Dense-Gram Network to more effectively reduce the gap than the conventional strategies and segment degraded images. Extensive experiments demonstrate that the proposed Dense-Gram Network yields state-of-the-art semantic segmentation performance on degraded images synthesized using PASCAL VOC 2012, SUNRGBD, CamVid, and CityScapes datasets.
Pseudomonas oryzagri sp. nov., isolated from a rice field soil

A Gram-stain-negative, aerobic, rod-shaped and non-motile novel bacterial strain, designated MAHUQ-58T, was isolated from soil sample of a rice field. The colonies were observed to be light pink-coloured, smooth, spherical and 0.6-1.0 mm in diameter when grown on nutrient agar (NA) medium for 2 days. Strain MAHUQ-58T was found to be able to grow at 15-40 °C, at pH 5.5-10.0 and with 0-1.0 % NaCl (w/v). Cell growth occurred on tryptone soya agar, Luria-Bertani agar, NA, MacConkey agar and Reasoner's 2A agar. The strain was found to be positive for both oxidase and catalase tests. The strain was positive for hydrolysis of Tween 20 and l-tyrosine. According to the 16S rRNA gene sequence comparisons, the isolate was identified as a member of the genus Pseudomonas and to be closely related to Pseudomonas oryzae WM-3T (98.9 % similarity), Pseudomonas linyingensis LYBRD3-7T (97.7 %), Pseudomonas sagittaria JCM 18195 T (97.6 %) and Pseudomonas guangdongensis SgZ-6T (97.2 %). The novel strain MAHUQ-58T has a draft genome size of 4 536 129 bp (46 contigs), annotated with 4064 protein-coding genes, 60 tRNA genes and four rRNA genes. The average nucleotide identity (ANI) and digital DNA-DNA hybridization (dDDH) values between strain MAHUQ-58T and four closely related type strains were in the range of 85.5-89.5 % and 29.5-38.0 %, respectively. The genomic DNA G+C content was determined to be 67.0 mol%. The predominant isoprenoid quinone was ubiquinone 9. The major fatty acids were identified as C16:0, summed feature 3 (C16 : 1  ω6c and/or C16 : 1  ω7c) and summed feature 8 (C18 : 1  ω6c and/or C18 : 1  ω7c). On the basis of dDDH and ANI values, genotypic results, and chemotaxonomic and physiological data, strain MAHUQ-58T represents a novel species within the genus Pseudomonas, for which the name Pseudomonas oryzagri sp. nov. is proposed, with MAHUQ-58T (=KACC 22005T=CGMCC 1.18518T) as the type strain.
A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

Ensemble learning techniques have achieved state-of-the-art performance in diverse machine learning applications by combining the predictions from two or more base models. This paper presents a concise overview of ensemble learning, covering the three main ensemble methods: bagging, boosting, and stacking, their early development to the recent state-of-the-art algorithms. The study focuses on the widely used ensemble algorithms, including random forest, adaptive boosting (AdaBoost), gradient boosting, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost). An attempt is made to concisely cover their mathematical and algorithmic representations, which is lacking in the existing literature and would be beneficial to machine learning researchers and practitioners.
Quadrant-based contour features for accelerated shape retrieval system

Shape representation and retrieval are essential research topics of computer vision. This paper proposes a novel feature set to be used in content-based image retrieval systems. The proposed method is an extended version of our previous study which uses contour information of shapes. The previous study calculated the center of mass (CoM) of the shape. By taking the CoM as origin, we created imaginary vectors in every angular direction. From each vector, we extracted three features which are the number of intersections between vector and contour, average distance of intersection points to CoM, and standard deviation of these points. In this method, we extract novel features and decrease the size of the feature set to decrease the computation time. We divide the shape into quadrants and represent each quadrant by nine features. Each shape image is represented by a 4x9 feature vector. We tested the proposed method on MPEG-7 and ETH-80 datasets and compared it with the state-of-art. According to the results, our method decreased the computation time dramatically while giving a state-of-art level retrieval accuracy.
Stochastic Depth Residual Network for Hyperspectral Image Classification

The convolutional neural network (CNN) is a feedforward neural network with deep structure and convolution operation. In the hyperspectral image (HSI) classification, CNN has demonstrated excellent performance in extracting spectral and spatial information. However, the inherent complexity and high dimension of HSIs still limit the performance of most neural network models. The powerful feature extraction ability of CNN is normally achieved by dozens or more layers, which brings a series of problems such as gradient vanishing, overfitting, and slow training speed. In order to address these problems, this article presents a CNN architecture-based stochastic depth residual network (SDRN), which is specially designed for HSI data. This model takes the original 3-D cube as the input and 3-D convolution is used to extract abundant spectral and spatial features through corresponding residual blocks. In order to reduce the training time, we adopt a stochastic depth strategy. For each small batch, a sublayer is randomly discarded by an identity function. During the testing stage, the residual network with complete depth is used. Experiments on three datasets and a comparison of the state-of-art methods show that SDRN has great advantages in accuracy and training time compared with state-of-the-art HSI classification methods.
Phase separation of the microtubule-associated protein tau

The aggregation and misfolding of the neuronal microtubule-associated protein tau is closely linked to the pathology of Alzheimer's disease and several other neurodegenerative diseases. Recent evidence suggest that tau undergoes liquid-liquid phase separation in vitro and forms or associates with membrane-less organelles in cells. Biomolecular condensation driven by phase separation can influence the biological activities of tau including its ability to polymerize tubulin into microtubules. In addition, the high concentrations that tau can reach in biomolecular condensates provide a mechanism to promote its aggregation and the formation of amyloid fibrils potentially contributing to the pathology of different tauopathies. Here, the authors discuss the role of tau phase separation in physiology and disease.
Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art

Recently, research concerning electrical machines and drives condition monitoring and fault diagnosis has experienced extraordinarily dynamic activity. The increasing importance of these energy conversion devices and their widespread use in uncountable applications have motivated significant research efforts. This paper presents an analysis of the state of the art in this field. The analyzed contributions were published in most relevant journals and magazines or presented in either specific conferences in the area or more broadly scoped events.