Causal relationship between gut microbiota and rosacea: a two-sample Mendelian randomization study

BackgroundRosacea, a chronic inflammatory skin condition affecting millions worldwide, is influenced by complex interactions between genetic and environmental factors. Although gut microbiota’s role in skin health is well-acknowledged, definitive causal links between gut microbiota and rosacea remain under-explored.MethodsUsing a two-sample Mendelian randomization (MR) design, this study examined potential causal relationships between gut microbiota and rosacea. Data was sourced from the largest Genome-Wide Association Study (GWAS) for gut microbiota and the FinnGen biobank for rosacea. A total of 2078 single nucleotide polymorphisms (SNPs) associated with gut microbiota were identified and analyzed using a suite of MR techniques to discern causal effects.ResultsThe study identified a protective role against rosacea for two bacterial genera: phylum Actinobacteria and genus Butyrivibrio. Furthermore, 14 gut microbiota taxa were discovered to exert significant causal effects on variant categories of rosacea. While none of these results met the strict False Discovery Rate correction threshold, they retained nominal significance. MR outcomes showed no pleiotropy, with homogeneity observed across selected SNPs. Directionality tests pointed toward a robust causative path from gut microbiota to rosacea.ConclusionThis study provides compelling evidence of the gut microbiota’s nominal causal influence on rosacea, shedding light on the gut-skin axis’s intricacies and offering potential avenues for therapeutic interventions in rosacea management. Further research is warranted to validate these findings and explore their clinical implications

The influence of cognitive ability in Chinese reading comprehension: can working memory updating change Chinese primary school students’ reading comprehension performance?

With the development of educational cognitive neuroscience, language instruction is no longer perceived as mechanical teaching and learning. Individual cognitive proficiency has been found to play a crucial role in language acquisition, particularly in the realm of reading comprehension. The primary objective of this study was to investigate two key aspects: firstly, to assess the predictive effects of the central executive (CE) on the Chinese reading comprehension scores of Chinese primary school students, and secondly, to explore the influence of CE training on the Chinese reading comprehension performance of Chinese primary school students. Chinese primary school students were recruited as participants. Experiment 1 used a Chinese N-back task, a Chinese Stroop task, and a number-pinyin conversion task to investigate the predictive effect of the CE components on Chinese reading comprehension. Experiment 2, based on the results of Experiment 1, used the Chinese character N-back training to explore the influence of updating training on Chinese reading comprehension. The findings from Experiment 1 underscored that CE had a predictive effect on Chinese reading comprehension scores. And updating had a prominent role in it. Experiment 2 revealed that the experimental group exhibited an enhancement in their updating performance following N-back training. Although the reading comprehension performance of the two groups after training did not produce significant differences in total scores, the experimental group showed maintained and higher microscopic reading comprehension scores than the control group in the more difficult post-test. In summary, this study yields two primary conclusions: (1) CE was able to predict Chinese reading comprehension scores. Updating has an important role in prediction. (2) Updating training enhances students’ updating performance and positively influences students’ Chinese microscopic reading comprehension performance

Minimum-Energy Bivariate Wavelet Frame with Arbitrary Dilation Matrix

In order to characterize the bivariate signals, minimum-energy bivariate wavelet frames with arbitrary dilation matrix are studied, which are based on superiority of the minimum-energy frame and the significant properties of bivariate wavelet. Firstly, the concept of minimum-energy bivariate wavelet frame is defined, and its equivalent characterizations and a necessary condition are presented. Secondly, based on polyphase form of symbol functions of scaling function and wavelet function, two sufficient conditions and an explicit constructed method are given. Finally, the decomposition algorithm, reconstruction algorithm, and numerical examples are designed

Automatic Detection, Validation and Repair of Race Conditions in Interrupt-Driven Embedded Software

Interrupt-driven programs are widely deployed in safety-critical embedded systems to perform hardware and resource dependent data operation tasks. The frequent use of interrupts in these systems can cause race conditions to occur due to interactions between application tasks and interrupt handlers (or two interrupt handlers). Numerous program analysis and testing techniques have been proposed to detect races in multithreaded programs. Little work, however, has addressed race condition problems related to hardware interrupts. In this paper, we present SDRacer, an automated framework that can detect, validate and repair race conditions in interrupt-driven embedded software. It uses a combination of static analysis and symbolic execution to generate input data for exercising the potential races. It then employs virtual platforms to dynamically validate these races by forcing the interrupts to occur at the potential racing points. Finally, it provides repair candidates to eliminate the detected races. We evaluate SDRacer on nine real-world embedded programs written in C language. The results show that SDRacer can precisely detect and successfully fix race conditions.Comment: This is a draft version of the published paper. Ke Wang provides suggestions for improving the paper and README of the GitHub rep

Decreased oocyte quality in patients with endometriosis is closely related to abnormal granulosa cells

Infertility and menstrual abnormalities in endometriosis patients are frequently caused by aberrant follicular growth or a reduced ovarian reserve. Endometriosis typically does not directly harm the oocyte, but rather inhibits the function of granulosa cells, resulting in a decrease in oocyte quality. Granulosa cells, as oocyte nanny cells, can regulate meiosis, provide the most basic resources required for oocyte development, and influence ovulation. Endometriosis affects oocyte development and quality by causing granulosa cells apoptosis, inflammation, oxidative stress, steroid synthesis obstacle, and aberrant mitochondrial energy metabolism. These aberrant states frequently interact with one another, however there is currently relatively little research in this field to understand the mechanism of linkage between abnormal states

ECA-TFUnet: A U-shaped CNN-Transformer network with efficient channel attention for organ segmentation in anatomical sectional images of canines

Automated organ segmentation in anatomical sectional images of canines is crucial for clinical applications and the study of sectional anatomy. The manual delineation of organ boundaries by experts is a time-consuming and laborious task. However, semi-automatic segmentation methods have shown low segmentation accuracy. Deep learning-based CNN models lack the ability to establish long-range dependencies, leading to limited segmentation performance. Although Transformer-based models excel at establishing long-range dependencies, they face a limitation in capturing local detail information. To address these challenges, we propose a novel ECA-TFUnet model for organ segmentation in anatomical sectional images of canines. ECA-TFUnet model is a U-shaped CNN-Transformer network with Efficient Channel Attention, which fully combines the strengths of the Unet network and Transformer block. Specifically, The U-Net network is excellent at capturing detailed local information. The Transformer block is equipped in the first skip connection layer of the Unet network to effectively learn the global dependencies of different regions, which improves the representation ability of the model. Additionally, the Efficient Channel Attention Block is introduced to the Unet network to focus on more important channel information, further improving the robustness of the model. Furthermore, the mixed loss strategy is incorporated to alleviate the problem of class imbalance. Experimental results showed that the ECA-TFUnet model yielded 92.63% IoU, outperforming 11 state-of-the-art methods. To comprehensively evaluate the model performance, we also conducted experiments on a public dataset, which achieved 87.93% IoU, still superior to 11 state-of-the-art methods. Finally, we explored the use of a transfer learning strategy to provide good initialization parameters for the ECA-TFUnet model. We demonstrated that the ECA-TFUnet model exhibits superior segmentation performance on anatomical sectional images of canines, which has the potential for application in medical clinical diagnosis