A new multifunctional conductive hydrogel film, mAu/PDPPy, was developed using a freeze-thaw cycling-annealing method, enhancing its mechanical and electrochemical properties. This film is suitable for wearable devices, enabling wireless, real-time monitoring of electrophysiological signals and sweat glucose. The innovative preparation method simplifies hydrogel film production, potentially expanding their application in wearable technology.

The study explores the role of lysosomal TFEB-TRPML1 axis in astrocytes and its impact on depressive-like behaviors. It identifies a stress-sensing signaling pathway involving astrocytic lysosomes, revealing that chronic stress alters lysosomal morphology and decreases TFEB and TRPML1 protein levels in depression models. The research highlights the significance of lysosomal exocytosis-mediated ATP release in modulating depressive-like behaviors, suggesting lysosomes as potential targets for antidepressants. The findings provide insights into the relationship between astrocytic lysosomes and stress vulnerability to depression, emphasizing the role of astrocytic TRPML1 in stress-induced depressive-like behaviors.

The study explores the development of Prussian Blue-derived atomic Fe/Fe3C@N-doped C catalysts supported by carbon cloth, designed as an integrated air cathode for flexible Zn-air batteries. The catalysts exhibit high oxygen reduction reaction (ORR) activity and stability, attributed to the synergistic effects between Fe-N4-C and Fe3C species. The Fe/Fe3C@N-doped C/CC-700 catalyst demonstrates superior ORR catalytic activity, durability, and electrochemical performance in Zn-air batteries, outperforming commercial Pt/C and RuO2 catalysts. The research highlights the potential of these catalysts in enhancing the performance of Zn-air batteries, with implications for energy storage applications.

The article reviews the evolution of rotating internal channels in gas turbine blades, focusing on the influence of the Coriolis force on heat transfer. It highlights the development of bilaterally enhanced U-channels, which utilize the Coriolis force to improve heat transfer on both leading and trailing walls. The study emphasizes the importance of non-dimensional numbers Re and Ro in enhancing heat transfer performance and suggests that the novel channel design reduces pressure loss, making it a promising solution for turbine blade cooling.

The article presents a novel Direction of Arrival (DOA) estimation method for vector hydrophones using Sparse Bayesian Learning (SBL), termed Vector-SBL. This method enhances DOA estimation accuracy by leveraging the multidimensional sound field information captured by vector hydrophones and the high-resolution capabilities of SBL. The Vector-SBL algorithm outperforms traditional algorithms like OMP, MUSIC, and CBF, especially under conditions of low signal-to-noise ratio (SNR), small snapshots, and multiple coherent sources. Simulation experiments demonstrate its superior performance in terms of estimation accuracy, resolution, and robustness against noise. The method's practical applicability is validated through a lake test experiment using MEMS vector hydrophones, confirming its high estimation accuracy in real-world underwater environments.

The study explores the design and fabrication of biomimetic silk laminated and herringbone structures (BS-LSTMs) inspired by natural architectures. These structures were reinforced through a fiber 'welding' process using hexafluoroisopropanol (HFIP) to enhance their mechanical properties. The research demonstrates that the welding process significantly improves the toughness and strength of the silk fabrics, making them comparable to or even surpassing natural materials like animal horns. The findings suggest potential applications in various fields due to the enhanced performance of these bioinspired mesostructures.

Genome-wide association studies (GWAS) and metabolome genome-wide association studies (mGWAS) are pivotal in understanding complex traits in livestock and poultry breeding. These methods enhance genetic diversity comprehension and facilitate genetic improvement and breeding innovation. The article reviews the application of GWAS and mGWAS in animal genetic breeding, highlighting their roles in identifying genetic markers and candidate genes crucial for phenotypic traits. Despite challenges in interpretation due to genetic complexity, these methods offer significant potential for sustainable animal husbandry development. The integration of metabolomics with genomics provides refined insights into genetic bases, although mGWAS requires larger sample sizes and incurs higher costs compared to GWAS.

Astrocytes are identified as significant contributors to the pathophysiology of major depressive disorder, affecting blood-brain barrier integrity, gap junctions, gliotransmission, glutamate homeostasis, and energy metabolism. The study highlights the sensitivity of astrocytes to stress and their role in integrating peripheral and central stress responses, providing a comprehensive overview of the molecular, cellular, and circuit properties of astrocytes in the context of depression.

The study investigates the screening rates for primary aldosteronism (PA) in hypertensive patients presenting to emergency departments (EDs) in Victoria, Australia. It finds that PA is significantly underscreened, even in patients with classic symptoms. The study suggests that the ED is a valuable setting for opportunistic screening of high-risk patients and calls for increased awareness and education about PA screening. The authors recommend a feasible pipeline for testing to ensure appropriate referral, investigation, and diagnosis of this underrecognized condition.

The article presents a study on predicting sea surface temperature (SST) using a spatiotemporal UNet (ST-UNet) model. The model incorporates parallel convolution with different kernel sizes and ConvLSTM to capture spatial and temporal features, respectively. The Atrous Spatial Pyramid Pooling (ASPP) module is used to enhance multi-scale feature extraction. Experiments conducted on SST datasets from the Bohai Sea, Yellow Sea, and South China Sea demonstrate that the ST-UNet model outperforms other methods in terms of mean absolute error (MAE) and mean square error (MSE). The study highlights the model's effectiveness in capturing spatial correlations and accurately predicting SST, with potential applications in marine hydrographic studies.