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This result suggests a general deficit in recognizing the positive expression in ASD children. Additionally, we found that the SampEn measurements computed from the alpha and theta bands and the linear features extracted from the delta band can be considered biomarkers for disturbances in Emotional Facial Expression (EFE) processing in ASD children.Public speaking is a common type of social evaluative situation and a significant amount of the population feel uneasy with it. It is of utmost importance to detect public speaking stress so that appropriate action can be taken to minimize its impacts on human health. In this study, a multimodal human stress classification scheme in response to real-life public speaking activity is proposed. Electroencephalography (EEG), galvanic skin response (GSR), and photoplethysmography (PPG) signals of forty participants are acquired in rest-state and during public speaking activity to divide data into a stressed and non-stressed group. Frequency domain features from EEG and time-domain features from GSR and PPG signals are extracted. The selected set of features from all modalities are fused to classify the stress into two classes. Classification is performed via a leave-one-out cross-validation scheme by using five different classifiers. The highest accuracy of 96.25% is achieved using a support vector machine classifier with radial basis function. Statistical analysis is performed to examine the significance of EEG, GSR, and PPG signals between the two phases of the experiment. Statistical significance is found in certain EEG frequency bands as well as GSR and PPG data recorded before and after public speaking supporting the fact that brain activity, skin conductance, and blood volumetric flow are credible measures of human stress during public speaking activity.To mitigate the spread of the current coronavirus disease 2019 (COVID-19) pandemic, it is crucial to have an effective screening of infected patients to be isolated and treated. Chest X-Ray (CXR) radiological imaging coupled with Artificial Intelligence (AI) applications, in particular Convolutional Neural Network (CNN), can speed the COVID-19 diagnostic process. In this paper, we optimize the data augmentation and the CNN hyperparameters for detecting COVID-19 from CXRs in terms of validation accuracy. This optimization increases the accuracy of the popular CNN architectures such as the Visual Geometry Group network (VGG-19) and the Residual Neural Network (ResNet-50), by 11.93% and 4.97%, respectively. We then proposed CovidXrayNet model that is based on EfficientNet-B0 and our optimization results. We evaluated CovidXrayNet on two datasets, including our generated balanced COVIDcxr dataset (960 CXRs) and the benchmark COVIDx dataset (15,496 CXRs). With only 30 epochs of training, CovidXrayNet achieves state-of-the-art accuracy of 95.82% on the COVIDx dataset in the three-class classification task (COVID-19, normal or pneumonia). The CovidXRayNet model, the COVIDcxr dataset, and several optimization experiments are publicly available at https//github.com/MaramMonshi/CovidXrayNet.Paroxysmal atrial fibrillation (PAF) is a cardiac arrhythmia that can eventually lead to heart failure or stroke if left untreated. Early detection of PAF is therefore crucial to prevent any further complications and avoid fatalities. An implantable defibrillator device could be used to both detect and treat the condition though such devices have limited computational capability. With this constraint in mind, this paper presents a novel set of features to accurately predict the presence of PAF. The method is evaluated using ECG signals from the widely used atrial fibrillation prediction database (AFPDB) from PhysioNet. We analysed 106 signals from 53 pairs of ECG recordings. Each pair of signals contains one 5-min ECG segment that ends just before the onset of a PAF event and another 5-min ECG segment at least 45 min distant from the PAF event, to represent a non-PAF event. Seven novel features are extracted through the Poincaré representation of R-R interval signals, and are prioritised through feature ranking schemes. The features are used with four standard classification techniques for PAF prediction and compared to the existing state of the art from the literature. Using only the seven proposed features, classification performance outperforms those of the classical state-of-the-art feature set, registering sensitivity and specificity measurements of over 96%. The results further improve when the features are combined with several of the classical features, with an accuracy increasing to 98% using a linear kernel SVM. The results show that the proposed features provide a useful representation of the PAF condition and achieve good prediction with off-the-shelf classification techniques that would be suitable for ICU deployment.Precision Nutrition research aims to use personal information about individuals or groups of individuals to deliver nutritional advice that, theoretically, would be more suitable than generic advice. Machine learning, a subbranch of Artificial Intelligence, has promise to aid in the development of predictive models that are suitable for Precision Nutrition. As such, recent research has applied machine learning algorithms, tools, and techniques in precision nutrition for different purposes. However, a systematic overview of the state-of-the-art on the use of machine learning in Precision Nutrition is lacking. Therefore, we carried out a Systematic Literature Review (SLR) to provide an overview of where and how machine learning has been used in Precision Nutrition from various aspects, what such machine learning models use as input features, what the availability status of the data used in the literature is, and how the models are evaluated. Nine research questions were defined in this study. We retrieved 4930 f high-performance Precision Nutrition approaches.Staphylococcus aureus is a deadly human bacterial pathogen that causes a wide variety of clinical manifestations. Invasive S. see more aureus infections in hospitals and the community are one of the main causes of mortality and morbidity, as virulent and multi-drug-resistant strains have evolved. There is an unmet and urgent clinical need for immune-based non-antibiotic approaches to treat these infections as the growing antibiotic resistance poses a significant public health danger. Subtractive proteomics assisted reverse vaccinology-based immunoinformatics pipeline was used in this study to target the suitable antigenic proteins for the development of multi-epitope vaccine (MEV). Three essential virulent and antigenic proteins were identified including Glycosyltransferase, Elastin Binding Protein, and Staphylococcal secretory antigen. A variety of immunoinformatics tools have been used to forecast T-cell and B-cell epitopes from target proteins. Seven CTL, five HTL, and eight LBL epitopes, connected through suitabletivity of proposed MEV in protection against infections associated with S. aureus. However, further experimental validations are required to fully evaluate the potential of proposed vaccine candidate.This paper explores police operations "pacifying" Rio de Janeiro's favelas to estimate if positive shocks of policing affect birth outcomes. Estimates show that pregnancies residing within official "pacification" borders had 0.07 standard deviation better birth outcomes than pregnancies on the same street but giving birth shortly before the police's arrival. Pacification effects concentrate in the third trimester of gestation and are followed by increases in the number of prenatal visits. No evidence of spillovers is found in areas immediately circumventing pacification borders. Hospital-level estimates indicate no impacts on the supply of health services, stress/anxiety among women, or abortions.Development of novel multimodality radiotherapy treatments in metastatic breast cancer, especially in the most aggressive triple negative (TNBC) subtype, is of significant clinical interest. Here we show that a novel inhibitor of Polo-Like Kinase 4 (PLK4), CFI-400945, in combination with radiation, exhibits a synergistic anti-cancer effect in TNBC cell lines and patient-derived organoids in vitro and leads to a significant increase in survival to tumor endpoint in xenograft models in vivo, compared to control or single-agent treatment. Further preclinical and proof-of-concept clinical studies are warranted to characterize molecular mechanisms of action of this combination and its potential applications in clinical practice.Asthma is a chronic airway inflammation that caused by many factors. The voltage-gated proton channel Hv1 has been proposed to extrude excessive protons produced by NADPH oxidase (NOX) from cytosol to maintain its activity during respiratory bursts. Here, we showed that loss of Hv1 aggravates ovalbumin (OVA)-induced allergic lung asthma in mice. The numbers of total cells, eosinophils and neutrophils in bronchoalveolar lavage fluid (BALF) of Hv1-deficiency (KO) mice are obviously increased after OVA challenge compared with that of wild-type (WT) mice. Histopathological staining reveals that Hv1-deficiency aggravates OVA-induced inflammatory cell infiltration and goblet cell hyperplasia in lung tissues. The expression of IL-4, IL-5 and IL-13 are markedly increased in lung tissues of OVA-challenged KO mice compared with that of WT mice. Furthermore, the expression levels of NOX2, NOX4 and DUOX1 are dramatically increased, while the expression levels of SOD2 and catalase are significantly reduced in lung tissues of OVA-challenged KO mice compared with that of WT mice. The production of ROS in lung tissues of KO mice is significantly higher than that of WT mice after OVA challenge. Our data suggest that Hv1-deficiency might aggravate the development of allergic asthma through increasing ROS production.

In recent years, more and more studies have been focusing on the association between Cytotoxic T lymphocyte antigen-4 (CTLA-4) (+49 A/G) gene polymorphism and autoimmune diseases. However, the results of previous studies are still controversial. The meta-analysis is aiming at determining the association in CTLA-4 (+49 A/G) gene rs231775 polymorphism and ankylosing spondylitis (AS), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE).

We searched PubMed, Web of Science, Chinese National Knowledge Infrastructure (CNKI) and Chinese Biomedical Database (CBM) up to November 2020, use random or fixed-effect models to perform meta-analysis to compare alleles and other genetic models, including homozygous, heterozygous, recessive and dominant models. The odds ratio (OR) with a 95% confidence interval (95% CI) was used to assess the correlation between CTLA-4 (+49 A/G) gene polymorphism and the genetic affectability of AS, RA, and SLE. Meanwhile, we used sequential trial analysis (TSA) to analyze the relesigned studies are needed to elucidate the relationship in CTLA-4 (+49 A/G) gene rs231775 G allele and autoimmune diseases, especially AS, SLE.

CTLA-4 (+49 A/G) gene rs231775 G allele increases the risk of autoimmune diseases in Caucasian populations. And it also increases the risk of RA in Caucasian and Mongolian populations. More sample size and more elaborately designed studies are needed to elucidate the relationship in CTLA-4 (+49 A/G) gene rs231775 G allele and autoimmune diseases, especially AS, SLE.