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This study quantitatively assesses the risk of water shortage (WSR) in the Baiyangdian area due to the Inter-Basin Water Transfer (IBWT) project, focusing on the impact of water transfer on regional water security. The actual evapotranspiration (ETa) is calculated, and the correlation simulation using Archimedes’ Copula function is implemented in Python 3.7.1, with optimization using the sum of squares of deviations (OLS) and the AIC criterion. The joint distribution model between ETa and three water supply scenarios is constructed. Key findings include (1) ETa increased by 27.3% after water transfer, far exceeding the slight increase in water supply before the transfer; (2) various Archimedean Copulas effectively capture the dependence and joint probability distribution between water supply and ETa; (3) water shortage risk increased after water transfer, with rainfall and upstream water unable to alleviate the problem in Baiyangdian; and (4) cross-basin water transfer reduced risk, with a reduction of 8.90% in the total probability of three key water resource scheduling combinations. This study establishes a Copula-based framework for water shortage risk assessment, providing a scientific basis for water allocation strategies in ecologically sensitive areas affected by human activities.

BST2/Tetherin is a restriction factor with broad antiviral activity against enveloped viruses, including coronaviruses. Specifically, BST2 traps nascent particles to membrane compartments, preventing their release and spread. In turn, viruses have evolved multiple mechanisms to counteract BST2. Here, we examined the interactions between BST2 and SARS-CoV-2. Our study shows that BST2 reduces SARS-CoV-2 virion release. However, the virus uses the Spike (S) protein to downregulate BST2. This requires a physical interaction between S and BST2, which routes BST2 for lysosomal degradation in a Clathtin- and ubiquitination-dependent manner. By surveying different SARS-CoV-2 variants of concern (Alpha-Omicron), we found that Omicron is more efficient at counteracting BST2, and that mutations in S account for its enhanced anti-BST2 activity. Mapping analyses revealed that several surfaces in the extracellular region of BST2 are required for an interaction with the Spike, and that the Omicron variant has changed its patterns of association with BST2 to improve its counteraction. Therefore, our study suggests that, besides enhancing receptor binding and evasion of neutralizing antibodies, mutations accumulated in the Spike afford more efficient counteraction of BST2, which highlights that BST2 antagonism is important for SARS-CoV-2 infectivity and spread.

In the past decade, human activity recognition (HAR) has grown in popularity due to its applications in security and entertainment. As recent years have witnessed the emergence of health care and exoskeleton robotics which make use of wearable suits, human–machine interaction based on action recognition performs an important role in multimedia applications. Considering the limitations of the application scenario, the surface electromyography (sEMG) signal stands out in many wearable data collection devices for HAR. That is because: (1) timely feedback; (2) no damage to the human body; and (3) the wide range of recognizable actions. However, existing public datasets of sEMG contained relatively few activities, and several large-scale datasets only collected the action of the hand. In addition, the processing of sEMG signals is a new field with no effective evaluation system for it. To tackle these problems, we establish a novel dataset for HAR on lower-limb sEMG named “HAR-sEMG,” using 6 sEMG signal sensors attached to the left leg. A benchmark summarizing experiments with many combinations of existing high-dimensional signal processing algorithms-based manifold learning on our dataset is also provided for a performance analysis.

Hand gesture recognition is pivotal in facilitating human–machine interaction within the Internet of Things. Nevertheless, it encounters challenges, including labeling expenses and robustness. To tackle these issues, we propose a semi-supervised learning framework guided by pseudo-label consistency. This framework utilizes a dual-branch structure with a mean-teacher network. Within this setup, a global and locally guided self-supervised learning encoder acts as a feature extractor in a teacher–student network to efficiently extract features, maximizing data utilization to enhance feature representation. Additionally, we introduce a pseudo-label Consistency-Guided Mean-Teacher model, where simulated noise is incorporated to generate newly unlabeled samples for the teacher model before advancing to the subsequent stage. By enforcing consistency constraints between the outputs of the teacher and student models, we alleviate accuracy degradation resulting from individual differences and interference from other body parts, thereby bolstering the network’s robustness. Ultimately, the teacher model undergoes refinement through exponential moving averages to achieve stable weights. We evaluate our semi-supervised method on two publicly available hand gesture datasets and compare it with several state-of-the-art fully-supervised algorithms. The results demonstrate the robustness of our method, achieving an accuracy rate exceeding 99% across both datasets.

Rail surface defects typically serve as early indicators of railway malfunctions, which may compromise the quality and corrosion resistance of rails, thereby endangering the safe operation of trains. The timely detection of defects is essential to ensure the safe operation of railways. To improve the classification accuracy of rail surface defect detection, this paper proposes a rail surface defects detection algorithm based on MobileNet-YOLOv7. By integrating lightweight deep learning algorithms into the engineering application of rail surface defect detection, a MobileNetV3 lightweight network is used as the backbone network for YOLOv7 to enhance both speed and accuracy in complex defect extraction. Subsequently, the efficient intersection over union (EIOU) loss function is utilized as the positional loss function to bolster system resilience. Finally, the k-means++ clustering algorithm is applied to obtain new anchor boxes. The experimental results demonstrate the effectiveness of the proposed method, achieving superior detection accuracy compared with traditional algorithms.

Accurate extraction of dynamically evolving haul roads in open-pit mines from very-high-resolution (VHR) satellite imagery remains a critical challenge due to domain gaps between urban and mining environments, prohibitive annotation costs, and morphological irregularities. This paper introduces OMRoadNet, an unsupervised domain adaptation (UDA) framework for open-pit mine road extraction, which synergizes self-training, attention-based feature disentanglement, and morphology-aware augmentation to address these challenges. The framework employs a cyclic GAN (generative adversarial network) architecture with bidirectional translation pathways, integrating pseudo-label refinement through confidence thresholds and geometric rules (eight-neighborhood connectivity and adaptive kernel resizing) to resolve domain shifts. A novel exponential moving average unit (EMAU) enhances feature robustness by adaptively weighting historical states, while morphology-aware augmentation simulates variable road widths and spectral noise. Evaluations on cross-domain datasets demonstrate state-of-the-art performance with 92.16% precision, 80.77% F1-score, and 67.75% IoU (intersection over union), outperforming baseline models by 4.3% in precision and reducing annotation dependency by 94.6%. By reducing per-kilometer operational costs by 78% relative to LiDAR (Light Detection and Ranging) alternatives, OMRoadNet establishes a practical solution for intelligent mining infrastructure mapping, bridging the critical gap between structured urban datasets and unstructured mining environments.

Sterile‐α and Toll/interleukin 1 receptor (TIR) motif‐containing protein 1 (SARM1), a key intracellular molecule that plays numerous important biological functions in the nervous system, has attracted much attention. Recent studies have shown that SARM1 plays a key role in nerve injury, degeneration, and neurodegenerative diseases. Therefore, understanding the role of SARM1 in the central nervous system (CNS) will enhance our knowledge of the pathogenesis of CNS diseases and aid in the development of new therapeutic strategies. This review will explore the biological functions of SARM1 in the nervous system and its potential roles in nerve injury and disease, thus providing new directions for future research and treatment. Sterile‐α and Toll/interleukin 1 receptor (TIR) motif‐containing protein 1 (SARM1) is a pivotal molecule that has garnered extensive attention in neuroscience. As an intracellular molecule, SARM1 possesses various crucial biological functions in the nervous system. Recent studies have demonstrated that SARM1 plays a pivotal role in nerve injury, degenerative diseases, and neurodegenerative diseases. Exploring the role of SARM1 in the central nervous system (CNS) allows us to gain a deeper understanding of the pathogenesis of CNS diseases and explore novel therapeutic strategies. In this study, we will investigate the biological functions of SARM1 in the nervous system and its potential implications in nerve injury and related diseases, thereby offering novel perspectives for future research and treatment.

Influenza B virus (IBV) causes significant seasonal disease burden, and frequent antigenic drift limits the effectiveness of conventional vaccines. To address this, we designed mosaic haemagglutinin (HA) and neuraminidase (NA) antigens to maximize T-cell epitope coverage and incorporated them into a nasal live attenuated influenza vaccine (LAIV) platform. Using B/Victoria lineage sequences (2009-2021), mosaic HA/NA candidates were generated via an iterative genetic algorithm. BALB/c mice were randomized into PBS, conventional inactivated influenza vaccine (IIV), conventional LAIV, and mosaic-LAIV (MoBV) groups, and immunized intranasally on days 0 and 14. Immune responses were evaluated by haemagglutination inhibition (HAI), microneutralization (MN), neuraminidase inhibition (NAI), mucosal IgA by ELISA, and T-cell profiling by flow cytometry. Protective efficacy was assessed by challenge with multiple IBV strains. MoBV-induced higher cross-reactive antibody responses (HAI, MN, NAI) and markedly increased mucosal IgA, which persisted for 100 days, alongside enhanced T-cell responses. Upon challenge, MoBV improved survival to 25-100% compared with controls. These results demonstrate that MoBV elicits broad systemic and mucosal immunity, providing robust cross-lineage protection against diverse IBV strains and supporting the development of a universal influenza B vaccine.

Background Autophagy plays an important role as a cellular defense mechanism against intracellular pathogens, like viruses. Specifically, autophagy orchestrates the recruitment of specialized cargo, including viral components needed for replication, for lysosomal degradation. In addition to this primary role, the cleavage of viral structures facilitates their association with pattern recognition receptors and MHC-I/II complexes, which assists in the modulation of innate and adaptive immune responses against these pathogens. Importantly, whereas autophagy restricts the replicative capacity of human immunodeficiency virus type 1 (HIV-1), this virus has evolved the gene nef to circumvent this process through the inhibition of early and late stages of the autophagy cascade. Despite recent advances, many details of the mutual antagonism between HIV-1 and autophagy still remain unknown. Here, we uncover the genetic determinants that drive the autophagy-mediated restriction of HIV-1 as well as the counteraction imposed by Nef. Additionally, we also examine the implications of autophagy antagonism in HIV-1 infectivity. Results We found that sustained activation of autophagy potently inhibits HIV-1 replication through the degradation of HIV-1 Gag, and that this effect is more prominent for nef -deficient viruses. Gag re-localizes to autophagosomes where it interacts with the autophagosome markers LC3 and SQSTM1. Importantly, autophagy-mediated recognition and recruitment of Gag requires the myristoylation and ubiquitination of this virus protein, two post-translational modifications that are essential for Gag’s central role in virion assembly and budding. We also identified residues T 48 and A 49 in HIV-1 NL4-3 Nef as responsible for impairing the early stages of autophagy. Finally, a survey of pandemic HIV-1 transmitted/founder viruses revealed that these isolates are highly resistant to autophagy restriction. Conclusions This study provides evidence that autophagy antagonism is important for virus replication and suggests that the ability of Nef to counteract autophagy may have played an important role in mucosal transmission. Hence, disabling Nef in combination with the pharmacological manipulation of autophagy represents a promising strategy to prevent HIV spread.

SERINC5 is a restriction factor that becomes incorporated into nascent retroviral particles, impairing their ability to infect target cells. In turn, retroviruses have evolved countermeasures against SERINC5. For instance, the primate lentiviruses (HIV and SIV) use Nef, Moloney Murine Leukemia Virus (MLV) uses GlycoGag, and Equine Infectious Anemia Virus (EIAV) uses S2 to remove SERINC5 from the plasma membrane, preventing its incorporation into progeny virions. Recent studies have shown that SERINC5 also restricts other viruses, such as Hepatitis B Virus (HBV) and Classical Swine Fever Virus (CSFV), although through a different mechanism, suggesting that SERINC5 can interfere with multiple stages of the virus life cycle. To investigate whether SERINC5 can also impact other steps of the replication cycle of HIV, the effects of SERINC5 on viral transcripts, proteins, and virus progeny size were studied. Here, we report that SERINC5 causes significant defects in HIV gene expression, which impacts virion production. While the underlying mechanism is still unknown, we found that the restriction occurs at the transcriptional level and similarly affects plasmid and non-integrated proviral DNA (ectopic or non-self-DNA). However, SERINC5 causes no defects in the expression of viral RNA, host genes, or proviral DNA that is integrated in the cellular genome. Hence, our findings reveal that SERINC5’s actions in host defense extend beyond blocking virus entry.