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Front Cover: The free volume within materials is crucial for gas adsorption and diffusion. Size and distribution of internal fractional free volume significantly impact barrier performance. This study innovatively combines experiments and theoretical calculations to corroborate that methylene group growth in aliphatic units leads to decreased crystallinity, elevated free volume fraction, enhanced segment mobility, ultimately diminishing barrier properties. More details can be found in article 2400051 by Zhiyong Wei and co‐workers..By Lizheng Wang; Zhu Tu; Jiaming Liang and Zhiyong WeiReported by Author; Author; Author; Author [Extracted from the article]

Federated learning enables multiple clients to collaboratively learn machine learning models in a privacy-preserving manner. However, in real-world scenarios, a key challenge encountered in federated learning is the statistical heterogeneity among clients. Existing work mainly focused on a single global model shared across the clients, making it hard to generalize well to all clients due to the large discrepancy in the data distributions. To address this challenge, we propose pFedLT, a novel approach that can adapt the single global model to different data distributions. Specifically, we propose to perform a pluggable layer-wise transformation during the local update phase based on scaling and shifting operations. In particular, these operations are learned with a meta-learning strategy. By doing so, pFedLT can capture the diversity of data distribution among clients, therefore, can generalize well even when the data distributions among clients exhibit high statistical heterogeneity. We conduct extensive experiments on synthetic and real-world datasets (MNIST, Fashion_MNIST, CIFAR-10, and Office+Caltech10) under different Non-IID settings. Experimental results demonstrate that pFedLT significantly improves the model accuracy by up to 11.67% and reduces the communication costs compared with state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Background: Detecting pathogens in pediatric central nervous system infection (CNSI) is still a major challenge in medicine. In addition to conventional diagnostic patterns, metagenomic next-generation sequencing (mNGS) shows great potential in pathogen detection. Therefore, we systematically evaluated the diagnostic performance of mNGS in cerebrospinal fluid (CSF) in pediatric patients with CNSI. Methods: Related literature was searched in the Web of Science, PubMed, Embase, and Cochrane Library. We screened the literature and extracted the data according to the selection criteria. The quality of included studies was assessed by the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool and the certainty of the evidence was measured by the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) score system. Then, the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odd's ratio (DOR), and area under the curve (AUC) of the summary receiver operating characteristic curve (sROC) were estimated in Stata Software and MetaDisc. Subgroup analyses were performed to investigate the potential factors that influence the diagnostic performance. Results: A total of 10 studies were included in the meta-analysis. The combined sensitivity was 0.68 (95% confidence interval [CI]: 0.59 to 0.76, I2 = 66.77%, p < 0.001), and the combined specificity was 0.89 (95% CI: 0.80 to 0.95, I2 = 83.37%, p < 0.001). The AUC of sROC was 0.85 (95% CI, 0.81 to 0.87). The quality level of evidence elevated by the GRADE score system was low. Conclusions: Current evidence shows that mNGS presents a good diagnostic performance in pediatric CNSI. Due to the limited quality and quantity of the included studies, more high-quality studies are needed to verify the above conclusion. [ABSTRACT FROM AUTHOR]