Gut microbiota is involved in male reproductive function: a review.

Globally, ~8%-12% of couples confront infertility issues, male-related issues being accountable for 50%. This review focuses on the influence of gut microbiota and their metabolites on the male reproductive system from five perspectives: sperm quality, testicular structure, sex hormones, sexual behavior, and probiotic supplementation. To improve sperm quality, gut microbiota can secrete metabolites by themselves or regulate host metabolites. Endotoxemia is a key factor in testicular structure damage that causes orchitis and disrupts the blood-testis barrier (BTB). In addition, the gut microbiota can regulate sex hormone levels by participating in the synthesis of sex hormone-related enzymes directly and participating in the enterohepatic circulation of sex hormones, and affect the hypothalamic-pituitary-testis (HPT) axis. They can also activate areas of the brain that control sexual arousal and behavior through metabolites. Probiotic supplementation can improve male reproductive function. Therefore, the gut microbiota may affect male reproductive function and behavior; however, further research is needed to better understand the mechanisms underlying microbiota-mediated male infertility.

Gut microbiota affects obesity susceptibility in mice through gut metabolites.

Introduction: It is well-known that different populations and animals, even experimental animals with the same rearing conditions, differ in their susceptibility to obesity. The disparity in gut microbiota could potentially account for the variation in susceptibility to obesity. However, the precise impact of gut microbiota on gut metabolites and its subsequent influence on susceptibility to obesity remains uncertain. Methods: In this study, we established obesity-prone (OP) and obesity-resistant (OR) mouse models by High Fat Diet (HFD). Fecal contents of cecum were examined using 16S rDNA sequencing and untargeted metabolomics. Correlation analysis and MIMOSA2 analysis were used to explore the association between gut microbiota and intestinal metabolites. Results: After a HFD, gut microbiota and gut metabolic profiles were significantly different between OP and OR mice. Gut microbiota after a HFD may lead to changes in eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), a variety of branched fatty acid esters of hydroxy fatty acids (FAHFAs) and a variety of phospholipids to promote obesity. The bacteria g_Akkermansia (Greengene ID: 175696) may contribute to the difference in obesity susceptibility through the synthesis of glycerophosphoryl diester phosphodiesterase (glpQ) to promote choline production and the synthesis of valyl-tRNA synthetase (VARS) which promotes L-Valine degradation. In addition, gut microbiota may affect obesity and obesity susceptibility through histidine metabolism, linoleic acid metabolism and protein digestion and absorption pathways.

Facial Prior Guided Micro-Expression Generation.

This paper focuses on the facial micro-expression (FME) generation task, which has potential application in enlarging digital FME datasets, thereby alleviating the lack of training data with labels in existing micro-expression datasets. Despite obvious progress in the image animation task, FME generation remains challenging because existing image animation methods can hardly encode subtle and short-term facial motion information. To this end, we present a facial-prior-guided FME generation framework that takes advantage of facial priors for facial motion generation. Specifically, we first estimate the geometric locations of action units (AUs) with detected facial landmarks. We further calculate an adaptive weighted prior (AWP) map, which alleviates the estimation error of AUs while efficiently capturing subtle facial motion patterns. To achieve smooth and realistic synthesis results, we use our proposed facial prior module to guide motion representation and generation modules in mainstream image animation frameworks. Extensive experiments on three benchmark datasets consistently show that our proposed facial prior module can be adopted in image animation frameworks and significantly improve their performance on micro-expression generation. Moreover, we use the generation technique to enlarge existing datasets, thereby improving the performance of general action recognition backbones on the FME recognition task. Our code is available at https://github.com/sysu19351158/FPB-FOMM.

Who is sensitive to selection biases in inductive reasoning?

The samples of evidence we use to make inferences in everyday and formal settings are often subject to selection biases. Two property induction experiments examined group and individual sensitivity to one type of selection bias: sampling frames - causal constraints that only allow certain types of instances to be sampled. Group data from both experiments indicated that people were sensitive to the effects of such frames, showing narrower generalization when sample instances were selected because they shared a target property (property sampling) than when instances were sampled because they belonged to a particular group (category sampling). Group generalization patterns conformed to the predictions of a Bayesian model of property induction that incorporates a selective sampling mechanism. In each experiment, however, there was considerable individual variation, with a nontrivial minority showing little sensitivity to sampling frames. Experiment 2 examined correlates of frames sensitivity. A composite measure of working memory capacity predicted individual sensitivity to sampling frames. These results have important implications for current debates about people's ability to factor sample selection mechanisms into their inferences and for the development of formal models of inductive inference. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Prediction of atherosclerosis using machine learning based on operations research.

BACKGROUND: Atherosclerosis is one of the major reasons for cardiovascular disease including coronary heart disease, cerebral infarction and peripheral vascular disease. Atherosclerosis has no obvious symptoms in its early stages, so the key to the treatment of atherosclerosis is early intervention of risk factors. Machine learning methods have been used to predict atherosclerosis, but the presence of strong causal relationships between features can lead to extremely high levels of information redundancy, which can affect the effectiveness of prediction systems. OBJECTIVE: We aim to combine statistical analysis and machine learning methods to reduce information redundancy and further improve the accuracy of disease diagnosis. METHODS: We cleaned and collated the relevant data obtained from the retrospective study at Affiliated Hospital of Nanjing University of Chinese Medicine through data analysis. First, some features that with too many missing values are filtered out of the 34 features, leaving 25 features. 49% of the samples were categorized as the atherosclerosis risk group while the rest 51% as the control group without atherosclerosis risk under the guidance of relevant experts. We compared the prediction results of a single indicator that had been medically proven to be highly correlated with atherosclerosis with the prediction results of multiple features to fully demonstrate the effect of feature information redundancy on the prediction results. Then the features that could distinguish whether have atherosclerosis risk or not were retained by statistical tests, leaving 20 features. To reduce the information redundancy between features, after drawing inspiration from graph theory, machine learning combined with optimal correlation distances was then used to screen out 15 significant features, and the prediction models were evaluated under the 15 features. Finally, the information of the 5 screened-out non-significant features was fully utilized by ensemble learning to improve the prediction superiority for atherosclerosis. RESULTS: Area Under the Receiver Operating Characteristic (ROC) Curve (AUC), which is used to measure the predictive performance of the model, was 0.84035 and Kolmogorov-Smirnov (KS) value was 0.646. After feature selection model based on optimal correlation distance, the AUC value was 0.88268 and the KS value was 0.688, both of which were improved by about 0.04. Finally, after ensemble learning, the AUC value of the model was further improved by 0.01369 to 0.89637. CONCLUSIONS: The optimal distance feature screening model proposed in this paper improves the performance of atherosclerosis prediction models in terms of both prediction accuracy and AUC metrics. Code and models are available at https://github.com/Cesartwothousands/Prediction-of-Atherosclerosis.

Characteristics of intestinal microbiota in C57BL/6 mice with non-alcoholic fatty liver induced by high-fat diet.

Introduction: As a representation of the gut microbiota, fecal and cecal samples are most often used in human and animal studies, including in non-alcoholic fatty liver disease (NAFLD) research. However, due to the regional structure and function of intestinal microbiota, whether it is representative to use cecal or fecal contents to study intestinal microbiota in the study of NAFLD remains to be shown. Methods: The NAFLD mouse model was established by high-fat diet induction, and the contents of the jejunum, ileum, cecum, and colon (formed fecal balls) were collected for 16S rRNA gene analysis. Results: Compared with normal mice, the diversity and the relative abundance of major bacteria and functional genes of the ileum, cecum and colon were significantly changed, but not in the jejunum. In NAFLD mice, the variation characteristics of microbiota in the cecum and colon (feces) were similar. However, the variation characteristics of intestinal microbiota in the ileum and large intestine segments (cecum and colon) were quite different. Discussion: Therefore, the study results of cecal and colonic (fecal) microbiota cannot completely represent the results of jejunal and ileal microbiota.

CARes-UNet: Content-aware residual UNet for lesion segmentation of COVID-19 from chest CT images.

PURPOSE: Coronavirus disease 2019 (COVID-19) has caused a serious global health crisis. It has been proven that the deep learning method has great potential to assist doctors in diagnosing COVID-19 by automatically segmenting the lesions in computed tomography (CT) slices. However, there are still several challenges restricting the application of these methods, including high variation in lesion characteristics and low contrast between lesion areas and healthy tissues. Moreover, the lack of high-quality labeled samples and large number of patients lead to the urgency to develop a high accuracy model, which performs well not only under supervision but also with semi-supervised methods. METHODS: We propose a content-aware lung infection segmentation deep residual network (content-aware residual UNet (CARes-UNet)) to segment the lesion areas of COVID-19 from the chest CT slices. In our CARes-UNet, the residual connection was used in the convolutional block, which alleviated the degradation problem during the training. Then, the content-aware upsampling modules were introduced to improve the performance of the model while reducing the computation cost. Moreover, to achieve faster convergence, an advanced optimizer named Ranger was utilized to update the model's parameters during training. Finally, we employed a semi-supervised segmentation framework to deal with the problem of lacking pixel-level labeled data. RESULTS: We evaluated our approach using three public datasets with multiple metrics and compared its performance to several models. Our method outperforms other models in multiple indicators, for instance in terms of Dice coefficient on COVID-SemiSeg Dataset, CARes-UNet got the score 0.731, and semi-CARes-UNet further boosted it to 0.776. More ablation studies were done and validated the effectiveness of each key component of our proposed model. CONCLUSIONS: Compared with the existing neural network methods applied to the COVID-19 lesion segmentation tasks, our CARes-UNet can gain more accurate segmentation results, and semi-CARes-UNet can further improve it using semi-supervised learning methods while presenting a possible way to solve the problem of lack of high-quality annotated samples. Our CARes-UNet and semi-CARes-UNet can be used in artificial intelligence-empowered computer-aided diagnosis system to improve diagnostic accuracy in this ongoing COVID-19 pandemic.