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94, p = 0.008, OR 9.24, p = 0.037, and OR 3.25, p = 0.041, respectively). As a haplotype, the combination of the three alleles was significantly more frequent in the PER-PAE group than in both the PER-tolerant group and the general Korean population. PCC1 DQB1*0601 and B*5401 also demonstrated higher docking scores with PER than other alleles. This is the first study to analyze the association of PER-PAEs with specific HLA genotypes. Our results suggest that an HLA-associated genetic predisposition and a possible immunological mechanism are involved in the occurrence of PER-PAEs.Repetitive head impacts (RHI) are a growing concern due to their possible neurocognitive effects, with research showing a season of RHI produce white matter (WM) changes seen on neuroimaging. We conducted a secondary analysis of diffusion tensor imaging (DTI) data for 28 contact athletes to compare WM changes. We collected pre-season and post-season DTI scans for each subject, approximately 3 months apart. We collected helmet data for the athletes, which we correlated with DTI data. We adapted the SPatial REgression Analysis of DTI (SPREAD) algorithm to conduct subject-specific longitudinal DTI analysis, and developed global inferential tools using functional norms and a novel robust p value combination test. At the individual level, most detected injured regions (93.3%) were associated with decreased FA values. Using meta-analysis techniques to combine injured regions across subjects, we found the combined injured region at the group level occupied the entire WM skeleton, suggesting the WM damage location is subject-specific. Several subject-specific functional summaries of SPREAD-detected WM change, e.g., the [Formula see text] norm, significantly correlated with helmet impact measures, e.g. cumulative unweighted rotational acceleration (adjusted p = 0.0049), time between hits rotational acceleration (adjusted p value 0.0101), and time until DTI rotational acceleration (adjusted p = 0.0084), suggesting RHIs lead to WM changes.Physical fatigue crucially influences our decisions to partake in effortful action. However, there is a limited understanding of how fatigue impacts effort-based decision-making at the level of brain and behavior. We use functional magnetic resonance imaging to record markers of brain activity while human participants engage in uncertain choices for prospective physical effort, before and after bouts of exertion. Using computational modeling of choice behavior we find that fatiguing exertions cause participants to increase their subjective cost of effort, compared to a baseline/rested state. We describe a mechanism by which signals related to motor cortical state in premotor cortex influence effort value computations, instantiated by insula, thereby increasing an individual’s subjective valuation of prospective physical effort while fatigued. Our findings provide a neurobiological account of how information about bodily state modulates decisions to engage in physical activity.Mung bean starch (MBS)-based edible films with incorporation of guar gum (GG) and sunflower seed oil (SSO) were developed in this study. MBS, GG, and SSO were used as the main filmogenic biopolymer, thickener, and hydrophobicity-imparting substance, respectively. To investigate the effect of SSO content on the physicochemical, mechanical, and optical properties of the films, they were supplemented with various concentrations (0, 0.5, 1, and 2%, w/w) of SSO. Increasing SSO content tended to decrease tensile strength, elongation at break, crystallinity, water solubility, and the water vapor permeability; in contrast, it increased the oxygen transmission rate and water contact angle. Consequently, the incorporation of SSO into the matrix of MBS-based films decreased their mechanical strength but effectively enhanced their water-resistance properties. Therefore, the MBS-based film developed here can be properly used as an edible film in settings that require high water-resistance properties but do not call for robust mechanical strength.Chest radiographs are among the most frequently acquired images in radiology and are often the subject of computer vision research. However, most of the models used to classify chest radiographs are derived from openly available deep neural networks, trained on large image datasets. These datasets differ from chest radiographs in that they are mostly color images and have substantially more labels. Therefore, very deep convolutional neural networks (CNN) designed for ImageNet and often representing more complex relationships, might not be required for the comparably simpler task of classifying medical image data. Sixteen different architectures of CNN were compared regarding the classification performance on two openly available datasets, the CheXpert and COVID-19 Image Data Collection. Areas under the receiver operating characteristics curves (AUROC) between 0.83 and 0.89 could be achieved on the CheXpert dataset. On the COVID-19 Image Data Collection, all models showed an excellent ability to detect COVID-19 and non-COVID pneumonia with AUROC values between 0.983 and 0.998. It could be observed, that more shallow networks may achieve results comparable to their deeper and more complex counterparts with shorter training times, enabling classification performances on medical image data close to the state-of-the-art methods even when using limited hardware.In real world applications, data sets are often comprised of multiple views, which provide consensus and complementary information to each other. Embedding learning is an effective strategy for nearest neighbour search and dimensionality reduction in large data sets. This paper attempts to learn a unified probability distribution of the points across different views and generates a unified embedding in a low-dimensional space to optimally preserve neighbourhood identity. Probability distributions generated for each point for each view are combined by conflation method to create a single unified distribution. The goal is to approximate this unified distribution as much as possible when a similar operation is performed on the embedded space. As a cost function, the sum of Kullback-Leibler divergence over the samples is used, which leads to a simple gradient adjusting the position of the samples in the embedded space. The proposed methodology can generate embedding from both complete and incomplete multi-view data sets.