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Zn metal holds grand promise as the anodes of aqueous batteries for grid‐scale energy storage. However, the rampant zinc dendrite growth and severe surface side reactions significantly impede the commercial implementation. Herein, a universal Zn‐metal oxide Ohmic contact interface model is demonstrated for effectively improving Zn plating/stripping reversibility. The high work function difference between Zn and metal oxides enables the building of an interfacial anti‐blocking layer for dendrite‐free Zn deposition. Moreover, the metal oxide layer can function as a physical barrier to suppress the pernicious side reactions. Consequently, the proof‐of‐concept CeO2‐modified Zn anode delivers ultrastable durability of over 1300 h at 0.5–5 mA cm−2 and improved Coulombic efficiency, the feasibility of which is also evidenced in MoS2//Zn full cells. This study enriches the fundamental comprehension of Ohmic contact interfaces on the Zn deposition, which may shed light on the development of other metal battery anodes. A universal Zn‐metal oxide Ohmic contact interface model is demonstrated to effectively enable improved Zn2+ diffusion kinetics and a reduced Zn nucleation barrier, thus achieving a dendrite‐free and side‐reaction‐free Zn deposition chemistry for significant improvement of the electrochemical performance in both the symmetric cells and full cells.

China implemented a strict lockdown policy to prevent the spread of COVID-19 in the worst-affected regions, including Wuhan and Shanghai. This study aims to investigate impact of these lockdowns on air quality index (AQI) using a deep learning framework. In addition to historical pollutant concentrations and meteorological factors, we incorporate social and spatio-temporal influences in the framework. In particular, spatial autocorrelation (SAC), which combines temporal autocorrelation with spatial correlation, is adopted to reflect the influence of neighbouring cities and historical data. Our deep learning analysis obtained the estimates of the lockdown effects as − 25.88 in Wuhan and − 20.47 in Shanghai. The corresponding prediction errors are reduced by about 47% for Wuhan and by 67% for Shanghai, which enables much more reliable AQI forecasts for both cities.

Commensal bacteria generate immensely diverse active metabolites to maintain gut homeostasis, however their fundamental role in establishing an immunotolerogenic microenvironment in the intestinal tract remains obscure. Here, we demonstrate that an understudied murine commensal bacterium, Dubosiella newyorkensis , and its human homologue Clostridium innocuum , have a probiotic immunomodulatory effect on dextran sulfate sodium-induced colitis using conventional, antibiotic-treated and germ-free mouse models. We identify an important role for the D. newyorkensis in rebalancing Treg/Th17 responses and ameliorating mucosal barrier injury by producing short-chain fatty acids, especially propionate and L-Lysine (Lys). We further show that Lys induces the immune tolerance ability of dendritic cells (DCs) by enhancing Trp catabolism towards the kynurenine (Kyn) pathway through activation of the metabolic enzyme indoleamine-2,3-dioxygenase 1 (IDO1) in an aryl hydrocarbon receptor (AhR)-dependent manner. This study identifies a previously unrecognized metabolic communication by which Lys-producing commensal bacteria exert their immunoregulatory capacity to establish a Treg-mediated immunosuppressive microenvironment by activating AhR-IDO1-Kyn metabolic circuitry in DCs. This metabolic circuit represents a potential therapeutic target for the treatment of inflammatory bowel diseases. Here, Zhang et al . identify a metabolic axis by which Lys-producing commensal bacterium Dubosiella newyorkensis mediates a Treg-mediated immunosuppressive microenvironment by activating AhR-IDO1-Kyn metabolic circuitry in dendritic cells.

Background Protein-protein interactions (PPIs) dominate intracellular molecules to perform a series of tasks such as transcriptional regulation, information transduction, and drug signalling. The traditional wet experiment method to obtain PPIs information is costly and time-consuming. Result In this paper, SDNN-PPI, a PPI prediction method based on self-attention and deep learning is proposed. The method adopts amino acid composition (AAC), conjoint triad (CT), and auto covariance (AC) to extract global and local features of protein sequences, and leverages self-attention to enhance DNN feature extraction to more effectively accomplish the prediction of PPIs. In order to verify the generalization ability of SDNN-PPI, a 5-fold cross-validation on the intraspecific interactions dataset of Saccharomyces cerevisiae (core subset) and human is used to measure our model in which the accuracy reaches 95.48% and 98.94% respectively. The accuracy of 93.15% and 88.33% are obtained in the interspecific interactions dataset of human-Bacillus Anthracis and Human-Yersinia pestis, respectively. In the independent data set Caenorhabditis elegans, Escherichia coli, Homo sapiens, and Mus musculus, all prediction accuracy is 100%, which is higher than the previous PPIs prediction methods. To further evaluate the advantages and disadvantages of the model, the one-core and crossover network are conducted to predict PPIs, and the data show that the model correctly predicts the interaction pairs in the network. Conclusion In this paper, AAC, CT and AC methods are used to encode the sequence, and SDNN-PPI method is proposed to predict PPIs based on self-attention deep learning neural network. Satisfactory results are obtained on interspecific and intraspecific data sets, and good performance is also achieved in cross-species prediction. It can also correctly predict the protein interaction of cell and tumor information contained in one-core network and crossover network.The SDNN-PPI proposed in this paper not only explores the mechanism of protein-protein interaction, but also provides new ideas for drug design and disease prevention.

Proton exchange membrane fuel cells convert hydrogen and oxygen into electricity without emissions. The high cost and low durability of Pt-based electrocatalysts for the oxygen reduction reaction hinder their wide application, and the development of non-precious metal electrocatalysts is limited by their low performance. Here we design a hybrid electrocatalyst that consists of atomically dispersed Pt and Fe single atoms and Pt–Fe alloy nanoparticles. Its Pt mass activity is 3.7 times higher than that of commercial Pt/C in a fuel cell. More importantly, the fuel cell with a low Pt loading in the cathode (0.015 mg Pt  cm −2 ) shows an excellent durability, with a 97% activity retention after 100,000 cycles and no noticeable current drop at 0.6 V for over 200 hours. These results highlight the importance of the synergistic effects among active sites in hybrid electrocatalysts and provide an alternative way to design more active and durable low-Pt electrocatalysts for electrochemical devices. The high cost of Pt severely limits fuel cell deployment, but alternative Pt-free catalysts suffer from a low activity and, especially, durability. Now, a low-Pt-content catalyst consisting of Pt and Fe single atoms, dispersed on a nitrogen-doped carbon matrix, and Pt–Fe nanoparticles is shown to exhibit excellent activity and durability in fuel cells.

In tissue engineering, scaffolding plays an important role in accommodating and stimulating new tissue growth. Chitosan and alginate are two widely used natural polymers in tissue engineering. Here, we prepared the chitosan-alginate (Chi-Alg) hydrogel from naturally derived chitosan and alginate polymers. The Fourier-transformed infrared spectroscopy and X-ray diffraction results demonstrated that a chitosan-alginate hydrogel was constructed due to the strong ionic interaction between the positively charged amino groups of chitosan and the negatively charged carboxyl groups of alginate. The scanning electron microscopy and contact angle results showed the inner porous structure and highly hydrophilic property of chitosan-alginate hydrogel. As the two most promising cell types in nerve tissue engineering, both olfactory ensheathing cells and neural stem cells proliferated well on the chitosan-alginate hydrogel. All results indicated the good potential application of a chitosan-alginate hydrogel for neural tissue engineering.

Background During the novel coronavirus epidemic, family physicians played a pivotal role in the primary health care system. They provided essential infrastructural support for the implementation of community prevention and control strategies and helped advance the frontline of epidemic containment. Objective This study aims to gather the latest research on the role of family doctors during the COVID‐19 pandemic, analyze publication trends and underlying thematic structures, and thereby provide a comprehensive overview of research hotspots and developments in this field, offering robust support for future scientific inquiry. Methods Data were retrieved from the Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI) databases within the Web of Science Core Collection. The search was limited to articles published between January 1, 2019, and December 31, 2024. Tableau was used to analyze institutional distribution, Excel was employed to organize journal distribution data, and VOSviewer was utilized to examine author and keyword distributions. Latent Dirichlet Allocation (LDA) was applied to identify research topics and thematic clusters, with results visualized and interpreted using pyLDAvis. Results A total of 1206 articles were included in the analysis. During the COVID‐19 pandemic, the United Kingdom led in publication output in this research domain, contributing 327 articles. The University of Oxford was the most productive institution, publishing 111 articles. Among journals, BMJ Open published 83 relevant articles, which received 912 citations (excluding self‐citations) and achieved an H‐index of 13. The most prolific author was Mehrkar, A., with 25 publications. Thematic analysis revealed that research largely centered on the management of high‐risk groups and public health responses in primary care. Specifically, the topics “management of high‐risk groups in primary care” and “COVID‐19 prevention and public health response” accounted for 34.7% and 18.3% of the publications during the pandemic, respectively.

Lithium-sulfur batteries are recognized as a promising high-energy-density and low-cost energy storage devices. However, the sulfur cathode suffers from poor cycling stability resulting from the serious polysulfide shuttle. Herein, we develop a nitrogen-rich and highly porous carbon polyhedron for effectively hosting sulfur. The carbon host manifests an ultrahigh specific surface area of 3400 m 2  g −1 , a dominated micropore volume of 0.96 cm 3  g −1 , and a high-level nitrogen doping of 8.3 at.%. Such an intriguing structure could suppress the polysulfide shuttle via physical confinement by micropores and strong chemical adsorption by polar nitrogen species. Moreover, the electrically conductive carbon enables a substantially enhanced electrochemical kinetics. Consequently, the carbon/sulfur composite electrode delivers an ultralow fading rate of 0.033% per cycle at 2 C over 500 cycles and superior rate capability (483 mAh g −1 at a high 5 C rate). The present study demonstrates the potential use of nitrogen-rich porous carbon framework as an efficient polysulfide host for lithium-sulfur batteries.

Metallaphotocatalysis, integrating interlocked photocatalytic cycles and transition-metal catalysis, harmonizes the ground state and excited state reactivities, enabling cross-couplings under mild conditions and expanding the scope of accessible transformations. However, homogeneous dual metallaphotocatalysts often suffer from limitations such as low catalyst stability, high metal loading, and challenges in catalyst recycling. In this study, we engineered a class of nickel-incorporated pyridyl-quinoline-linked covalent organic frameworks (Ni-PQCOFs) serving as robust and efficient heterogeneous metallaphotocatalysts. These Ni-PQCOFs facilitate universal visible-light-driven C( sp 2 )-carbon and heteroatom (S, N, O, B, P, Se, and Cl) bond formations across a broad range of aromatic halides and nucleophiles, while maintaining low metal loading (1-2 mol%). The Ni-PQCOFs, featuring fully conjugated and tunable pyridyl-quinoline (PQ) motifs, exhibit exceptional (photo)chemical stability, broadened absorption wavelength range, and enhanced redox capability. Remarkably, these COF-based heterogeneous metallaphotocatalysts exhibited significantly enhanced catalytic efficiency compared to their homogeneous counterparts. The versatility and practicality of this photocatalytic system extend to diverse synthetic applications, including late-stage functionalization of complex molecules, sequential functionalizations, and decagram-scale synthesis assisted by an in-house-built high-speed circulation flow system. Moreover, the micrometer-sized Ni-PQCOF catalyst could be recycled over 10 times through direct filtration with minimal activity loss and negligible metal leaching. All these advantages establish Ni-PQCOFs as versatile, effective, and sustainable metallaphotocatalysts for cross-coupling reactions. Metallaphotocatalysis enables cross-couplings under mild conditions but homogeneous dual metallaphotocatalysts often suffer from low catalyst stability or high metal loading. Here, the authors report nickel-incorporated pyridyl-quinoline-linked covalent organic frameworks serving as robust and efficient heterogeneous metallaphotocatalysts for C( sp 2 ) cross coupling reactions.

Many studies have considered temperature trends at the global scale, but the literature is commonly associated with an overall increase in mean temperature in a defined past time period and hence lacking in in-depth analysis of the latent trends. For example, in addition to heterogeneity in mean and median values, daily temperature data often exhibit quasi-periodic heterogeneity in variance, which has largely been overlooked in climate research. To this end, we propose a joint model of quantile regression and variability. By accounting appropriately for the heterogeneity in these types of data, our analysis using Australian data reveals that daily maximum temperature is warming by ∼0.21°C per decade and daily minimum temperature by ∼0.13°C per decade. More interestingly, our modeling also shows nuanced patterns of change over space and time depending on location, season, and the percentiles of the temperature series.