Explore the vast range of titles available.

While sign language combines sequential hand motions with concurrent non-manual cues (e.g., mouth shapes and head tilts), current recognition systems lack multimodal annotation methods capable of capturing their hierarchical semantics. To bridge this gap, we propose GSP-MC, the first method integrating generative large language models into sign language recognition. It leverages retrieval-augmented generation with domain-specific large language models and expert-validated corpora to produce precise multipart descriptions. A dual-encoder architecture bidirectionally aligns hierarchical skeleton features with multi-level text descriptions (global, synonym, part) through probabilistic matching. The approach combines global and part-level losses with KL divergence optimization, ensuring robust alignment across relevant text-skeleton pairs while capturing sign semantics and detailed dynamics. Experiments demonstrate state-of-the-art performance, achieving 97.1% accuracy on the Chinese SLR500 (surpassing SSRL’s 96.9%) and 97.07% on the Turkish AUTSL (exceeding SML’s 96.85%), confirming cross-lingual potential for inclusive communication technologies.

Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammation and joint destruction. Its pathogenesis is closely related to the imbalance of glycolipid metabolism. This article reviews the pathophysiological mechanisms of glycolipid metabolism in the RA pathogenesis, focusing on the physiological mechanisms of glucose and lipid metabolism as well as the characteristics of glycolipid metabolism imbalance and their interactions in RA. Moreover, this study highlights the relationship between specific glycolipid metabolism markers and disease activity, as well as the innovative targets and intervention strategies of glycolipid metabolism modulation in the RA treatment. Studies show that RA patients have over-activated glycolytic pathways and disrupted lipid metabolism. These metabolic changes drive the inflammatory response and joint destruction and are also strongly associated with disease activity. Through a deeper understanding of the key nodes and regulatory mechanisms of glycolipid metabolism in RA, this article might provide new ideas for the precise diagnosis and treatment of RA.

Virtual simulation (VS) is a developing education approach with the recreation of reality using digital technology. The teaching effectiveness of VSs compared to mannequins and real persons (RPs) has never been investigated in medical and nursing education. This study aims to compare VSs and mannequins or RPs in improving the following clinical competencies: knowledge, procedural skills, clinical reasoning, and communication skills. Following Cochrane methodology, a meta-analysis was conducted on the effectiveness of VSs in pre- and postregistration medical or nursing participants. The Cochrane Library, PubMed, Embase, and Educational Resource Information Centre databases were searched to identify English-written randomized controlled trials up to August 2024. Two authors independently selected studies, extracted data, and assessed the risk of bias. All pooled estimates were based on random-effects models and assessed by trial sequential analyses. Leave-one-out, subgroup, and univariate meta-regression analyses were performed to explore sources of heterogeneity. A total of 27 studies with 1480 participants were included. Overall, there were no significant differences between VSs and mannequins or RPs in improving knowledge (standard mean difference [SMD]=0.08; 95% CI -0.30 to 0.47; I =67%; P=.002), procedural skills (SMD=-0.12; 95% CI -0.47 to 0.23; I =75%; P<.001), clinical reasoning (SMD=0.29; 95% CI -0.26 to 0.85; I =88%; P<.001), and communication skills (SMD=-0.02; 95% CI: -0.62 to 0.58; I =86%; P<.001). Trial sequential analysis for clinical reasoning indicated an insufficient sample size for a definitive judgment. For procedural skills, subgroup analyses showed that VSs were less effective among nursing participants (SMD=-0.55; 95% CI -1.07 to -0.03; I =69%; P=.04). Univariate meta-regression detected a positive effect of publication year (β=.09; P=.02) on communication skill scores. Given favorable cost-utility plus high flexibility regarding time and space, VSs are viable alternatives to traditional face-to-face learning modalities. The comparative effectiveness of VSs deserves to be followed up with the emergence of new technology. In addition, further investigation of VSs with different design features will provide novel insights to drive education reform. PROSPERO CRD42023466622; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=466622.

[This corrects the article DOI: 10.3389/fimmu.2025.1633752.].

Evidence from the early months of the COVID-19 pandemic in the U.S. indicated that the virus had vastly different effects across races, with black Americans faring worse on dimensions including illness, hospitalization and death. New data suggests that our understanding of the pandemic's racial inequities must be revised given the closing of the gap between black and white COVID-related mortality. Initial explanations for inequality in COVID-related outcomes concentrated on static factors-e.g., geography, urbanicity, segregation or age-structures-that are insufficient on their own to explain observed time-varying patterns in inequality. Drawing from a literature suggesting the relevance of political factors in explaining pandemic outcomes, we highlight the importance of political polarization-the partisan divide in pandemic-related policies and beliefs-that varies over time and across geographic units. Specifically, we investigate the role of polarization through two political factors, public opinion and state-level public health policies, using fine-grained data on disparities in public concern over COVID and in state containment/health policies to understand the changing pattern of inequality in mortality. We show that (1) apparent decreases in inequality are driven by increasing total deaths-mostly among white Americans-rather than decreasing mortality among black Americans (2) containment policies are associated with decreasing inequality, likely resulting from lower relative mortality among Blacks (3) as the partisan disparity in Americans who were \"unconcerned\" about COVID increased, racial inequality in COVID mortality decreased, generating the appearance of greater equality consistent with a \"race to the bottom'' explanation as overall deaths increased and substantively swamping the effects of containment policies.

Traditional statistical models for debris-flow susceptibility often overlook critical triggering mechanisms and geotechnical parameters. To address this, we propose an innovative framework that couples the Maximum Entropy (MaxEnt) statistical model with the TRIGRS physical model, which simulates transient rainfall infiltration and grid-based regional slope stability. Focusing on seven towns in Beichuan County, China, we integrated thirteen environmental factors, geotechnical parameters, and historical hazard records to build a dual-driven “statistical–physical” evaluation framework. Our methodology consists of three steps: (1) Use TRIGRS to compute rainfall-induced safety factors (FS) and identify unstable zones (FS < 1), which serve as the positive-sample database for MaxEnt; (2) Employ the MaxEnt model—using the TRIGRS-derived positive samples and historical debris-flow factors—to predict the spatial distribution of susceptibility; (3) Integrate both outputs spatially in GIS using dynamic weighting. Validation shows that the hybrid model improves prediction accuracy by 21% compared to MaxEnt alone (AUC = 0.845). Its susceptibility map corrects 34.7% of the overpredicted areas from the statistical model and enlarges stable zones by 1.8 times. Additionally, to determine the optimal weighting between machine learning and the physical model, we tested three weight combinations and found that a 0.55:0.45 ratio (MaxEnt: TRIGRS) yields the best performance. Using an independent validation set from another study area, we correctly identified 83.6% of the historical debris-flow events in Changtan, demonstrating the framework’s ability to integrate geostatistical patterns with geomechanical processes. This coupled framework offers a paradigm for multi‐hazard chain assessment in complex terrain and can be directly applied to debris-flow early warning and regional disaster mitigation planning.

Evidence from the early months of the COVID-19 pandemic in the U.S. indicated that the virus had vastly different effects across races, with black Americans faring worse on dimensions including illness, hospitalization and death. New data suggests that our understanding of the pandemic’s racial inequities must be revised given the closing of the gap between black and white COVID-related mortality. Initial explanations for inequality in COVID-related outcomes concentrated on static factors—e.g., geography, urbanicity, segregation or age-structures—that are insufficient on their own to explain observed time-varying patterns in inequality. Drawing from a literature suggesting the relevance of political factors in explaining pandemic outcomes, we highlight the importance of political polarization—the partisan divide in pandemic-related policies and beliefs—that varies over time and across geographic units. Specifically, we investigate the role of polarization through two political factors, public opinion and state-level public health policies, using fine-grained data on disparities in public concern over COVID and in state containment/health policies to understand the changing pattern of inequality in mortality. We show that (1) apparent decreases in inequality are driven by increasing total deaths—mostly among white Americans—rather than decreasing mortality among black Americans (2) containment policies are associated with decreasing inequality, likely resulting from lower relative mortality among Blacks (3) as the partisan disparity in Americans who were “unconcerned” about COVID increased, racial inequality in COVID mortality decreased, generating the appearance of greater equality consistent with a “race to the bottom’’ explanation as overall deaths increased and substantively swamping the effects of containment policies.

Debris flow risk assessment can provide some reference for debris flow prevention and control projects. In risk assessment, researchers often only focus on the impact of objective or subjective indicators. For this purpose, this paper proposed a weight calculation method based on t-distribution and linear programming optimization algorithm (LPOA). Taking 72 mudslides in Beichuan County as an example, this paper used analytic hierarchy process (AHP), entropy weight method (EWM) and variation coefficient method (VCM) to obtain the initial weights. Based on the initial weights, weight intervals with different confidence levels were obtained by t-distribution. Subsequently, the final weights were obtained by LOPA in the 90% confidence interval. Finally, the final weights were used to calculate the risk score for each debris flow, thus delineating the level of risk for each debris flow. The results showed that this paper’s method can avoid overemphasizing the importance of a particular indicator compared to EWM and VCM. In contrast, EWM and VCM ignored the effect of debris flow frequency on debris flow risk. The assessment results showed that the 72 debris flows in Beichuan County were mainly dominated by moderate and light risks. Of these, there were 8 high risk debris flows, 24 medium risk debris flows, and 40 light risk debris flows. The excellent triggering conditions provide favorable conditions for the formation of high-risk debris flows. Slightly and moderate risk debris flows are mainly located on both sides of highways and rivers, still posing a minor threat to Beichuan County. The proposed fusion weighting method effectively avoids the limitations of single weight calculating method. Through comparison and data analysis, the rationality of the proposed method is verified, which can provide some reference for combination weighting method and debris flow risk assessment.

Accumulation landslides are prone to occur during the continuous infiltration of heavy rainfall, which seriously threatens the lives and property safety of local residents. In this paper, based on the Green-Ampt (GA) infiltration model, a new slope rainfall infiltration function is derived by combining the effect of air resistance and lateral seepage of saturated zone. Considering that when the soil layer continues to infiltrate after the saturation zone is formed, the air involvement cannot be discharged in time, which delays the infiltration process. Therefore, the influence of air resistance factor in soil pores is added. According to the infiltration characteristics of finite long slope, the lateral seepage of saturated zone is introduced, which makes up for the deficiency that GA model is only applicable to infinite long slope. Finally, based on the seepage characteristics of the previous analysis, the overall shear strength criterion is used to evaluate the stability of the slope. The results show that the safety factor decreases slowly with the increase of size and is inversely correlated with the slope angle and initial moisture content. The time of infiltration at the same depth increases with the increase of size and slope angle, and is inversely correlated with the initial moisture content, but is less affected by rainfall intensity. By comparing with the results of experimental data and other methods, the results of the proposed method are more consistent with the experimental results than other methods.

We read with great interest the report on the new anti-obesity potential in mice models of reversible monoamine oxidase B inhibitors by Moonsun et al., as opposed to the lack of such effects observed with irreversible MAO-B inhibitors (iMAO-Bi). Our research aimed to explore the potential anti-obesity effects of iMAO-Bi in patients with Parkinson’s disease (PD). This retrospective study included 37 PD in-patients from 2018 to 2023. Patients who took iMAO-Bi were assigned to the iMAO-Bi group, and those who never took iMAO-Bi were assigned to the control. The major outcomes were changes in body weight and body mass index (BMI) during follow-up. A subgroup analysis was conducted to compare the anti-obesity effect between the short-term and long-term administrations of the iMAO-Bi group. The results showed a slight yet insignificant trend of bodyweight loss among the iMAO-Bi group of PD patients. Subgroup analysis showed that short-term treatment of iMAO-Bi (less than six months) led to reductions in BMI and body weight, while the long-term treatment of iMAO-Bi displayed a slight increase in BMI and body weight. The results suggested that short-term administration of iMAO-Bi may have potential weight-loss effects. Further studies with larger sample sizes are needed to evaluate the weight-loss effect of iMAO-Bi.