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The widespread misuse of antibiotics has accelerated the emergence of multidrug-resistant bacterial strains, presenting a major threat to global public health. Bacteriophages (phages), owing to their host-specific lytic activity and self-replicating nature, have emerged as promising alternatives or adjuncts to conventional antibiotic therapies. In this study, a lytic phage targeting avian pathogenic Escherichia coli (APEC) was isolated from farm wastewater. The phage's morphological characteristics, host range, optimal multiplicity of infection (MOI), one-step growth curve, pH stability, thermal stability, chloroform sensitivity, and in vitro antibacterial activity were determined. Subsequently, the therapeutic efficacy of the phage was evaluated in a pigeon model. In this study, we isolated and characterized a lytic phage, designated vB_EcoM_P3322, from farm wastewater targeting APEC. Transmission electron microscopy classified vB_EcoM_P3322 within the Myoviridae family. The phage exhibited broad lytic activity against five Escherichia coliserotypes (O8:H10, O15:H18, O51:H20, O149:H20, and O166:H6). Optimal biological parameters included a multiplicity of infection (MOI) of 1, a latent period of 10 minutes, an 80-minute burst period, and a burst size of 252 PFUs/cell. vB_EcoM_P3322 maintained stable lytic activity across a pH range of 5-9 and temperatures from 4°C to 50°C, although it was sensitive to chloroform. In vitro, the phage effectively suppressed bacterial growth within 6 hours at MOIs of 0.1, 1, and 10. Whole-genome sequencing revealed a 151,674 bp double-stranded DNA genome encoding 279 predicted open reading frames. No virulence factors, toxin genes, antibiotic resistance genes, or lysogeny-related elements were identified, affirming its safety for therapeutic application. Phylogenetic analysis indicated 98.44% nucleotide identity (97% coverage) with phage vB_EcoM_Ro121c4YLVW (GenBank: NC_052654), suggesting a close evolutionary relationship. In a pigeon infection model, vB_EcoM_P3322 treatment significantly improved survival and reduced histopathological damage in the liver and spleen. Metagenomic analysis of duodenal contents revealed a marked reduction (P < 0.01) in E. coli abundance in the treatment group, indicating selective pathogen clearance and modulation of gut microbiota. In summary, vB_EcoM_P3322 displays broad-spectrum lytic activity, robust environmental stability, potent antibacterial efficacy both in vitro and in vivo, and a safe genomic profile. These attributes support its potential as a novel biocontrol agent for managing APEC infections in poultry farming.

Atherosclerosis is the major cause of cardiovascular diseases worldwide, and AIDS linked with chronic inflammation and immune activation, increases atherosclerosis risk. The application of bioinformatics and machine learning to identify hub genes for atherosclerosis and AIDS has yet to be reported. Thus, this study aims to identify the hub genes for atherosclerosis and AIDS. Gene expression profiles were downloaded from the Gene Expression Omnibus database. The Robust Multichip Average was performed for data preprocessing, and the limma package was used for screening differentially expressed genes. Enrichment analysis employed GO and KEGG, protein–protein interaction network was constructed. Hub genes were filtered using topological and machine learning algorithms and validated in external cohorts. Then immune infiltration and correlation analysis of hub genes were constructed. Nomogram, receiver operating curve, and single-sample gene set enrichment analysis were applied to evaluate hub genes. This study identified 48 intersecting genes. Enrichment analyses indicated that these genes are significantly enriched in viral response, inflammatory response, and cytokine signaling pathways. CCR5 and OAS1 were identified as common hub genes in atherosclerosis and AIDS for the first time, highlighting their roles in antiviral immunity, inflammation and immune infiltration. These findings contributed to understanding the shared pathogenesis of Atherosclerosis and AIDS and provided possible potential therapeutic targets for immunomodulatory therapy.

Objective The information age has transformed technologies across disciplines. Generative artificial intelligence (GenAI), as an emerging technology, has integrated into scientific research. Recent studies identify GenAI-related scientific research integrity concerns. Using Complex Adaptive Systems (CAS) theory, this research examines risk factors and preventive measures for each agent within the scientific research integrity management system during GenAI adoption, providing new perspectives for integrity management. Method This study applies CAS theory to analyze the scientific research integrity management system, identifying four core micro-level agents: researchers, research subjects, scientific research administrators, and academic publishing institutions. It examines macro-system complexity, agent adaptability, and the impact of agent interactions on the overall system. This framework enables analysis of GenAI’s effects on the research integrity management system. Results The scientific research integrity management system exhibits structural, hierarchical, and multidimensional complexities, with internal circulation of policy, funding, and information elements. In response to GenAI integration, four micro-level agents—researchers, research subjects, scientific research administrators, and academic publishing institutions—adapt their behaviors to systemic changes. Through these interactions, behavioral outcomes emerge at the macro level, driving evolution of the research integrity management system. Conclusions Risks of scientific misconduct permeate the entire research process and require urgent governance. This study recommends that scientific research administrators promptly define applicable boundaries for GenAI in research to guide researchers. Concurrently, they should collaborate with relevant departments to establish regulatory frameworks addressing potential GenAI-related misconduct. Academic publishing institutions must assume quality assurance responsibilities by strengthening review and disclosure protocols. Furthermore, research integrity considerations should be systematically integrated into GenAI’s technological development and refinement. Highlights ● Develops an analytical framework grounded in Complex Adaptive Systems (CAS) theory to map evolving interactions among researchers, research subjects, scientific research administrators, and academic publishing institutions within GenAI-integrated research ecosystems.  ● Identifies self-reinforcing dynamics between GenAI adoption and integrity governance, wherein adaptive rule adjustments by agents reshape system-wide integrity thresholds.  ● Proposes adaptive governance mechanisms that balance innovation safeguards with integrity guardrails, emphasizing context-sensitive policy calibration over universal solutions.

The study of the origin of asymmetries in mirror β decay is extremely important to understand the fundamental nuclear force and the nuclear structure. The experiment was performed at the National Laboratory of Heavy Ion Research Facility in Lanzhou (HIRFL) to measure the β-delayed γ rays of 26P by silicon array and Clover-type high-purity Germanium (HPGe) detectors. Combining with results from the β decay of 26P and its mirror nucleus 26Na, the mirror asymmetry parameter δ ( ≡ft+/ft−− 1) was determined to be 46(13)% for the transition feeding the first excited state in the daughter nucleus. Our independent results support the conclusion that the large mirror asymmetry is close to the proton halo structure in 26P.

Representations learned by self-supervised approaches are generally considered to possess sufficient generalizability and discriminability. However, we disclose a nontrivial mutual-exclusion relationship between these critical representation properties through an exploratory demonstration on self-supervised learning. State-of-the-art self-supervised methods tend to enhance either generalizability or discriminability but not both simultaneously. Thus, learning representations jointly possessing strong generalizability and discriminability presents a specific challenge for self-supervised learning. To this end, we revisit the learning paradigm of self-supervised learning from the perspective of evolutionary game theory (EGT) and outline the theoretical roadmap to achieve a desired trade-off between these representation properties. EGT performs well in analyzing the trade-off point in a two-player game by utilizing dynamic system modeling. However, the EGT analysis requires sufficient annotated data, which contradicts the principle of self-supervised learning, i.e., the EGT analysis cannot be conducted without the annotations of the specific target domain for self-supervised learning. Thus, to enhance the methodological generalization, we propose a novel self-supervised learning method that leverages advancements in reinforcement learning to jointly benefit from the general guidance of EGT and sequentially optimize the model to chase the consistent improvement of generalizability and discriminability for specific target domains during pre-training. Theoretically, we establish that the proposed method tightens the generalization error upper bound of self-supervised learning. Empirically, our method achieves state-of-the-art performance on various benchmarks.