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Metagenomic next-generation sequencing (mNGS) is a transformative approach in the diagnosis of infectious diseases, utilizing unbiased high-throughput sequencing to directly detect and characterize microbial genomes from clinical samples. This review comprehensively outlines the fundamental principles, sequencing workflow, and platforms utilized in mNGS technology. The methodological backbone involves shotgun sequencing of total nucleic acids extracted from diverse sample types, enabling simultaneous detection of bacteria, viruses, fungi, and parasites without prior knowledge of the infectious agent. Key advantages of mNGS include its capability to identify rare, novel, or unculturable pathogens, providing a more comprehensive view of microbial communities compared to traditional culture-based methods. Despite these strengths, challenges such as data analysis complexity, high cost, and the need for optimized sample preparation protocols remain significant hurdles. The application of mNGS across various systemic infections highlights its clinical utility. Case studies discussed in this review illustrate its efficacy in diagnosing respiratory tract infections, bloodstream infections, central nervous system infections, gastrointestinal infections, and others. By rapidly identifying pathogens and their genomic characteristics, mNGS facilitates timely and targeted therapeutic interventions, thereby improving patient outcomes and infection control measures. Looking ahead, the future of mNGS in infectious disease diagnostics appears promising. Advances in bioinformatics tools and sequencing technologies are anticipated to streamline data analysis, enhance sensitivity and specificity, and reduce turnaround times. Integration with clinical decision support systems promises to further optimize mNGS utilization in routine clinical practice. In conclusion, mNGS represents a paradigm shift in the field of infectious disease diagnostics, offering unparalleled insights into microbial diversity and pathogenesis. While challenges persist, ongoing technological advancements hold immense potential to consolidate mNGS as a pivotal tool in the armamentarium of modern medicine, empowering clinicians with precise, rapid, and comprehensive pathogen detection capabilities.

Nonalcoholic fatty liver disease (NAFLD) and its inflammatory and often progressive subtype nonalcoholic steatohepatitis (NASH), have emerged as significant contributors to hepatic morbidity worldwide. The pathophysiology of NAFLD/NASH is multifaceted, variable, and remains incompletely understood. The pivotal role of liver-resident and recruited macrophages in the pathogenesis of NAFLD and NASH is widely acknowledged as a crucial factor in innate immunity. The remarkable plasticity of macrophages enables them to assume diverse activation and polarization states, dictated by their immunometabolism microenvironment and functional requirements. Recent studies in the field of immunometabolism have elucidated that alterations in the metabolic profile of macrophages can profoundly influence their activation state and functionality, thereby influencing various pathological processes. This review primarily focuses on elucidating the polarization and activation states of macrophages, highlighting the correlation between their metabolic characteristics and the transition from pro-inflammatory to anti-inflammatory phenotypes. Additionally, we explore the potential of targeting macrophage metabolism as a promising therapeutic approach for the management of NAFLD/NASH.

Succinate serves as an essential circulating metabolite within the tricarboxylic acid (TCA) cycle and functions as a substrate for succinate dehydrogenase (SDH), thereby contributing to energy production in fundamental mitochondrial metabolic pathways. Aberrant changes in succinate concentrations have been associated with pathological states, including chronic inflammation, ischemia/reperfusion (IR) injury, and cancer, resulting from the exaggerated response of specific immune cells, thereby rendering it a central area of investigation. Recent studies have elucidated the pivotal involvement of succinate and SDH in immunity beyond metabolic processes, particularly in the context of cancer. Current scientific endeavors are concentrated on comprehending the functional repercussions of metabolic modifications, specifically pertaining to succinate and SDH, in immune cells operating within a hypoxic milieu. The efficacy of targeting succinate and SDH alterations to manipulate immune cell functions in hypoxia-related diseases have been demonstrated. Consequently, a comprehensive understanding of succinate’s role in metabolism and the regulation of SDH is crucial for effectively targeting succinate and SDH as therapeutic interventions to influence the progression of specific diseases. This review provides a succinct overview of the latest advancements in comprehending the emerging functions of succinate and SDH in metabolic processes. Furthermore, it explores the involvement of succinate, an intermediary of the TCA cycle, in chronic inflammation, IR injury, and cancer, with particular emphasis on the mechanisms underlying succinate accumulation. This review critically assesses the potential of modulating succinate accumulation and metabolism within the hypoxic milieu as a means to combat various diseases. It explores potential targets for therapeutic interventions by focusing on succinate metabolism and the regulation of SDH in hypoxia-related disorders.

Traffic flow forecasting is crucial for improving urban traffic management and reducing resource consumption. Accurate traffic conditions prediction requires capturing the complex spatial-temporal dependencies inherent in traffic data. Traditional spatial-temporal graph modeling methods often rely on fixed road network structures, failing to account for the dynamic spatial correlations that vary over time. To address this, we propose a Transformer-Enhanced Adaptive Graph Convolutional Network (TEA-GCN) that alternately learns temporal and spatial correlations in traffic data layer-by-layer. Specifically, we design an adaptive graph convolutional module to dynamically capture implicit road dependencies at different time levels and a local-global temporal attention module to simultaneously capture long-term and short-term temporal dependencies. Experimental results on two public traffic datasets demonstrate the effectiveness of the proposed model compared to other state-of-the-art traffic flow prediction methods.

The composition, structure, and functionalities of prolamins from highland barley were investigated. These parameters were compared with those of the commonly applied prolamins (zein). There are more charged and hydrophilic amino acids in highland barely prolamins than zein. The molecular weight of highland barely prolamins was between 30 and 63 kDa, which was larger than that of zein (20 and 24 kDa). The main secondary structure of highland barely prolamins was β-turn helices, while α-helical structures were the main secondary structure in zein. The water holding capacity, thermal stability, emulsifying capacity, and stability of prolamins from highland barley were significantly higher than in zein, while the opposite results were observed for oil absorption capacity between the two. The diameter of fibers prepared using highland barely prolamins was almost six times that of zein, while highland barely prolamins formed ribbon structures instead of fibers. Therefore, the results provide guidance for applications of prolamins from highland barley.

The increasing integration of Artificial Intelligence (AI) within enterprises is generates significant technostress among employees, potentially influencing their intention to adopt AI. However, existing research on the psychological effects of this phenomenon remains inconclusive. Drawing on the Affective Events Theory (AET) and the Challenge-Hindrance Stressor Framework (CHSF), the current study aims to explore the \"black box\" between challenge and hindrance technology stressors and employees' intention to adopt AI, as well as the boundary conditions of this mediation relationship. The study employs a quantitative approach and utilizes three-wave data. Data were collected through the snowball sampling technique and a structured questionnaire survey. The sample comprises employees from 11 distinct organizations located in Guangdong Province, China. We received 301 valid questionnaires, representing an overall response rate of 75%. The theoretical model was tested through confirmatory factor analysis and regression analyses using Mplus and the Process macro for SPSS. The results indicate that positive affect mediates the positive relationship between challenge technology stressors and AI adoption intention, whereas AI anxiety mediates the negative relationship between hindrance technology stressors and AI adoption intention. Furthermore, the results reveal that technical self-efficacy moderates the effects of challenge and hindrance technology stressors on affective reactions and the indirect effects of challenge and hindrance technology stressors on AI adoption intention through positive affect and AI anxiety, respectively. Overall, our study suggests that AI-driven challenge technology stressors positively impact AI adoption intention through the cultivation of positive affect, while hindrance technology stressors impede AI adoption intention by triggering AI anxiety. Additionally, technical self-efficacy emerges as a crucial moderator in shaping these relationships. This research has the potential to make a meaningful contribution to the literature on AI adoption intention, deepening our holistic understanding of the influential mechanisms involved. Furthermore, the study affirms the applicability and relevance of Affective Events Theory (AET) and the Challenge-Hindrance Stressor Framework (CHSF). In practical terms, the research provides actionable insights for organizations to effectively manage employees' AI adoption intention.

BackgroundObservational studies have demonstrated an association between primary sclerosing cholangitis (PSC) and thyroid dysfunction (TD). However, the causal relationship between PSC and TD remains uncertain. The purpose of this study is to investigate the causal associations and specific direction between these two conditions. Gaining insight into the potential causal relationship between PSC and TD is valuable for elucidating the pathogenesis of PSC and for devising innovative approaches for the prevention and treatment of PSC and its associated complications.MethodsWe conducted a bidirectional two-sample Mendelian randomization (MR) analysis to investigate the causal association between PSC and TD, such as autoimmune thyroid disease (AITD), thyroid cancer (TC), thyroid stimulating hormone (TSH), thyrotropin-releasing hormone (TRH), among others. PSC was the exposure variable, while TD was the outcome variable. To identify suitable instrumental variables (IVs), we utilized genome-wide association study (GWAS) datasets to select potential candidate single-nucleotide polymorphisms (SNPs). The primary statistical approach employed was the inverse-variance weighted (IVW) method, which was complemented by a series of sensitivity analyses to assess the robustness of the results by estimating heterogeneity and pleiotropy.ResultsWe found that the causal associations between genetically predicted PSC and Graves’ disease (GD), hyperthyroidism (IVW OR=1.230, 95%CI: 1.089–1.389, P=0.001; IVW OR=1.001, 95%CI: 1.000-1.002, P=0.000) were statistically significant. The reverse MR analysis indicated that genetic susceptibility to hyperthyroidism (P=0.000) and hypothyroidism (p=0.028) might be the risk of PSC. There was no statistically significant causal association observed between PSC and other TD (IVW P>0.05), with the exception of GD, hyperthyroidism, and hypothyroidism as determined through bidirectional two-sample analysis. To ensure the reliability of our findings, additional sensitivity analyses were conducted, including the leave-one-out (LOO) test, heterogeneity test, and pleiotropic test.ConclusionIn this study, we conducted an investigation into the causal association between PSC and TD. Our findings indicate that PSC significantly elevates the susceptibility to GD and hyperthyroidism from a statistical perspective. These results shed light on the etiology of PSC and have implications for the management of patients with PSC.

With the popularity of Android, malware detection and family classification have also become a research focus. Many excellent methods have been proposed by previous authors, but static and dynamic analyses inevitably require complex processes. A hybrid analysis method for detecting Android malware and classifying malware families is presented in this paper, and is partially optimized for multiple-feature data. For static analysis, we use permissions and intent as static features and use three feature selection methods to form a subset of three candidate features. Compared with various models, including k-nearest neighbors and random forest, random forest is the best, with a detection rate of 95.04%, while the chi-square test is the best feature selection method. After using feature selection to explore the critical static features contained in this dataset, we analyzed a subset of important features to gain more insight into the malware. In a dynamic analysis based on network traffic, unlike those that focus on a one-way flow of traffic and work on HTTP protocols and transport layer protocols, we focused on sessions and retained protocol layers. The Res7LSTM model is then used to further classify the malicious and partially benign samples detected in the static detection. The experimental results show that our approach can not only work with fewer static features and guarantee sufficient accuracy, but also improve the detection rate of Android malware family classification from 71.48% in previous work to 99% when cutting the traffic in terms of the sessions and protocols of all layers.

Drowning constitutes the leading cause of injury-related fatalities among adolescents. In swimming pool environments, traditional manual surveillance exhibits limitations, while existing technologies suffer from poor adaptability of wearable devices. Vision models based on YOLO still face challenges in edge deployment efficiency, robustness in complex water conditions, and multi-scale object detection. To address these issues, we propose YOLO11-LiB, a drowning object detection model based on YOLO11n, featuring three key enhancements. First, we design the Lightweight Feature Extraction Module (LGCBlock), which integrates the Lightweight Attention Encoding Block (LAE) and effectively combines Ghost Convolution (GhostConv) with dynamic convolution (DynamicConv). This optimizes the downsampling structure and the C3k2 module in the YOLO11n backbone network, significantly reducing model parameters and computational complexity. Second, we introduce the Cross-Channel Position-aware Spatial Attention Inverted Residual with Spatial–Channel Separate Attention module (C2PSAiSCSA) into the backbone. This module embeds the Spatial–Channel Separate Attention (SCSA) mechanism within the Inverted Residual Mobile Block (iRMB) framework, enabling more comprehensive and efficient feature extraction. Finally, we redesign the neck structure as the Bidirectional Feature Fusion Network (BiFF-Net), which integrates the Bidirectional Feature Pyramid Network (BiFPN) and Frequency-Aware Feature Fusion (FreqFusion). The enhanced YOLO11-LiB model was validated against mainstream algorithms through comparative experiments, and ablation studies were conducted. Experimental results demonstrate that YOLO11-LiB achieves a drowning class mean average precision (DmAP50) of 94.1%, with merely 2.02 M parameters and a model size of 4.25 MB. This represents an effective balance between accuracy and efficiency, providing a high-performance solution for real-time drowning detection in swimming pool scenarios.

Based on published literature and typical profiles from the Nankai University source library, a total of 3326 chemical profiles of the main primary sources of ambient particulate matter (PM) across China from 1987 to 2017 are investigated and reviewed to trace the evolution of their main components and identify the main influencing factors concerning their evolution. In general, the source chemical profiles are varied with respect to their sources and are influenced by different sampling methods. The most complicated profiles are likely attributed to coal combustion (CC) and industrial emissions (IE). The profiles of vehicle emissions (VE) are dominated by organic carbon (OC) and elemental carbon (EC), and vary due to the changing standards of sulfur and additives in gasoline and diesel as well as the sampling methods used. In addition to the sampling methods used, the profiles of biomass burning (BB) and cooking emissions (CE) are also impacted by the different biofuel categories and cooking types, respectively. The variations of the chemical profiles of different sources, and the homogeneity of the subtype source profiles within the same source category are examined using uncertainty analysis and cluster analysis. As a result, a relatively large variation is found in the source profiles of CC, VE, IE, and BB, indicating that these sources urgently require the establishment of local profiles due to their high uncertainties. The results presented highlight the need for further investigation of more specific markers (e.g., isotopes, organic compounds, and gaseous precursors), in addition to routinely measured components, in order to properly discriminate sources. Although the chemical profiles of the main sources have been previously reported in the literature, it should be noted that some of these chemical profiles are currently out of date and need to be updated immediately. Additionally, in the future, specific focus should be placed on the source profile subtypes, especially with respect to local IE in China.