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We aimed to verify the impact of functional remediation (FR) on serum brain-derived neurotrophic factor (BDNF) and tyrosine kinase receptor B (TrkB) levels, to explore the biomechanism of FR intervention in patients with euthymic bipolar disorder (BD). This is a randomized controlled, 12-week intervention study with participants randomized into the FR group (n=39) and the treatment as usual group (TAU, n=42) at the 1∶1 ratio. 17-Hamilton Depression Rating Scale-17 (HDRS-17), Young Mania Rating Scale (YMRS), and Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery (MCCB) were used to assess affective symptoms and cognitive functioning both at baseline and week 12, respectively. Meanwhile, we collected blood samples (10 milliliters) from all participants for determination of serum BDNF/ TrkB levels both at baseline and week 12. After baseline assessment, all participants received FR or TAU treatments, respectively. Our results showed significant decreasing in HDRS-17 and YMRS scores, increasing in serum BDNF and TrkB levels in both groups over 12 weeks (all s<0.05). There were no group differences in the HDRS-17 and YMRS scores (all s>0.05), but the FR group showed greater increasing in serum BDNF and TrkB levels than those in the TAU group (all s<0.05). In terms of cognition, the change in serum BDNF levels was negatively correlated with changes in Mazes test, and the improved TrKB levels were associated with improved Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) in the FR group (all s<0.05). The changes in serum BDNF and TrkB levels may be implicated in the mechanisms underlying FR intervention in euthymic patients with BD. A longer follow-up period than 12 weeks and set up healthy controls may make the results more convincing, and the sample size of this study is still insufficient.

Major depressive disorder (MDD) is associated with worse cognitive functioning. We aim to examine the association between baseline cognitive functioning and the reduction rate in HDRS-17 total scores and to highlight the predictors of the reduction rate in HDRS-17 total scores in MDD with first-episode, drug-naïve (FED) patients. Ninety FED patients were recruited consecutively and evaluated using the 17-item Hamilton Depression Rating Scale (HDRS-17), the 14-item Hamilton Anxiety Scale (HAMA-14), the Functioning Assessment Short Test (FAST) and the MATRICS Consensus Cognitive Battery (MCCB) at baseline and again at week 8. Eighty-four FED patients completed the study. Comparison showed that response group had significantly higher T scores in TMT-A, BACS-SC, WMS-III, BVMT-R, MSCEI and CPT-IP, but showed significantly lower scores in FAST total scores including autonomy, occupational functioning, cognitive functioning, interpersonal relationship than non- response group (all < 0.05). Partial correlation analysis also found that the reduction rate in HDRS-17 total scores could be negatively associated with autonomy, cognitive functioning and interpersonal relationship domains as well as total FAST scores, also was further positively associated with T-scores of BACS-SC, CPT-IP and MSCEI in MCCB, even when accounting for potential confounders. Furthermore, the levels of cognitive function domain, autonomy domain in FAST, and BACS-SC, CPT-IP in MCCB may predict the reduction rate in HDRS-17 total scores in FED patients (all < 0.05). Our findings underscore significant correlations between baseline functioning and the reduction rate in HDRS-17 total scores in FED patients. Moreover, better baseline cognitive function, autonomy, speed of processing and attention/vigilance are more likely to predict patients' response to antidepressant treatment, indicating pre-treatment better cognitive functioning may be predictors to treatment response in FED.

Surface reconstruction generates real active species in electrochemical conditions; rational regulating reconstruction in a targeted manner is the key for constructing highly active catalyst. Herein, we use the high-valence Mo modulated orthorhombic Pr 3 Ir 1− x Mo x O 7 as model to activate lattice oxygen and cations, achieving directional and accelerated surface reconstruction to produce self-terminated Ir‒O bri ‒Mo (O bri represents the bridge oxygen) active species that is highly active for acidic water oxidation. The doped Mo not only contributes to accelerated surface reconstruction due to optimized Ir‒O covalency and more prone dissolution of Pr, but also affords the improved durability resulted from Mo-buffered charge compensation, thereby preventing fierce Ir dissolution and excessive lattice oxygen loss. As such, Ir‒O bri ‒Mo species could be directionally generated, in which the strong Brønsted acidity of O bri induced by remaining Mo assists with the facilitated deprotonation of oxo intermediates, following bridging-oxygen-assisted deprotonation pathway. Consequently, the optimal catalyst exhibits the best activity with an overpotential of 259 mV to reach 10 mA cm geo −2 , 50 mV lower than undoped counterpart, and shows improved stability for over 200 h. This work provides a strategy of directional surface reconstruction to constructing strong Brønsted acid sites in IrO x species, demonstrating the perspective of targeted electrocatalyst fabrication under in situ realistic reaction conditions. Regulating surface reconstruction is an important way to construct highly active catalyst for acidic water oxidation. Here the authors report high-valence Mo accelerates surface reconstruction of orthorhombic Pr3Ir1−xMoxO7 by activating lattice oxygen and cations, forming highly active and self-terminated Ir‒Obri‒Mo species with strong Brønsted acidity for acidic oxygen evolution reaction.

Unsorted retired batteries with varied cathode materials hinder the adoption of direct recycling due to their cathode-specific nature. The surge in retired batteries necessitates precise sorting for effective direct recycling, but challenges arise from varying operational histories, diverse manufacturers, and data privacy concerns of recycling collaborators (data owners). Here we show, from a unique dataset of 130 lithium-ion batteries spanning 5 cathode materials and 7 manufacturers, a federated machine learning approach can classify these retired batteries without relying on past operational data, safeguarding the data privacy of recycling collaborators. By utilizing the features extracted from the end-of-life charge-discharge cycle, our model exhibits 1% and 3% cathode sorting errors under homogeneous and heterogeneous battery recycling settings respectively, attributed to our innovative Wasserstein-distance voting strategy. Economically, the proposed method underscores the value of precise battery sorting for a prosperous and sustainable recycling industry. This study heralds a new paradigm of using privacy-sensitive data from diverse sources, facilitating collaborative and privacy-respecting decision-making for distributed systems. Unsorted retired batteries pose recycling challenges due to diverse cathodes. Here, the authors propose a privacy-preserving machine learning system that enables accurate sorting with minimal data, important for a sustainable battery recycling industry.

To address the challenges of age and gender recognition in uncontrolled scenarios with facial absence or severe occlusion, this paper proposes a Spatial Correlation Guided Cross Scale Feature Fusion Network (SCGNet). The proposed method specifically tackles the limitations of existing approaches that heavily rely on facial features, which become unreliable under partial/complete occlusion scenarios. The method integrates multi-granularity semantic features through a Cross-Scale Combination (CSC) module, enhances local detail representation using a Local Feature Guided Fusion (LFGF) module, and designs a Spatial Correlation Composition Analysis (SCCA) module based on Getis-Ord Gi* statistics for feature reorganization, effectively resolving interference from non-informative regions. The SCCA module introduces a novel bipartite grouping mechanism that leverages hotspot detection to preserve discriminative body features when facial cues are unavailable. Comprehensive experiments demonstrate that SCGNet achieves state-of-the-art performance with minimum Mean Absolute Error (MAE) 4.01% for age estimation on IMDB-Clean (2.9% improvement over VOLO-D1) and highest gender classification accuracy on IMDB-Clean, UTKFace, and Lagenda datasets, showing improvements in cross-scene adaptability compared to VOLO and MiVOLO models respectively. Notably, the method maintains gender discrimination accuracy under complete facial occlusion scenarios, validating the effectiveness of spatial correlation modeling for non-facial feature reasoning, maintaining 97.32% gender accuracy even with complete facial occlusion on Lagenda dataset. The proposed architecture shows 73.30% CS@5 for age prediction in cross-domain testing, demonstrating superior cross-scene adaptability compared to VOLO (69.72%) and MiVOLO (71.27%). Ablation studies confirm the individual contributions of CSC, LFGF, and SCCA modules. This research provides new insights for robust identity analysis in human-computer interaction and intelligent security applications.

Background Insulin resistance (IR) is an important pathologic component in the occurrence and development of cardiovascular disease (CVD). The estimated glucose disposal rate (eGDR) is a measure of glucose handling capacity, that has demonstrated utility as a reliable marker of IR. The study aimed to determine the predictive utility of IR assessed by eGDR for CVD risk. Methods This nationwide prospective cohort study utilized data of 6416 participants from the China Health and Retirement Longitudinal Study (CHARLS) who were free of CVD but had complete data on eGDR at baseline. The Boruta algorithm was performed for feature selection. Multivariate Cox proportional hazards regression models and restricted cubic spline (RCS) analysis were conducted to examine the associations between eGDR and CVD, and the results were expressed with hazard ratio (HR) and 95% confidence interval (CI) values. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, Hosmer–Lemeshow test, net reclassification improvement (NRI), and decision curve analysis (DCA) were employed to evaluate the clinical efficacy of eGDR in identifying CVD. Subgroup analysis was performed to explore the potential association of with CVD in different populations. Results During a median follow-up of 106.5 months, 1339 (20.87%) incident CVD cases, including 1025 (15.96%) heart disease and 439 (6.84%) stroke, were recorded from CHARLS. The RCS curves demonstrated a significant and linear relationship between eGDR and all endpoints (all P for nonlinear > 0.05). After multivariate adjustment, the lower eGDR levels were found to be significantly associated with a greater prevalence of CVD. Compared to the lowest quartile, the highest eGDR quartile was associated with a decreased risk of CVD (HR 0.686, 95% CI 0.545–0.862). When assessed as a continuous variable, individuals with a unit increasement in eGDR was related to a 21.2% (HR 0.788, 95% CI 0.669–0.929) lower risk of CVD, a 18.3% (HR 0.817, 95% CI 0.678–0.985) decreased risk of heart disease, and 39.5% (HR 0.705, 95% CI 0.539–0.923) lower risk of stroke. The eGDR had an excellent predictive performance according to the results of ROC (AUC = 0.712) and χ 2 likelihood ratio test (χ 2  = 4.876, P  = 0.771). NRI and DCA analysis also suggested the improvement from eGDR to identify prevalent CVD and the favorable clinical efficacy of the multivariate model. Subgroup analysis revealed that the trend in incident CVD risk were broadly consistent with the main results across subgroups. Conclusion A lower level of eGDR was found to be associated with increased risk of incident CVD, suggesting that eGDR may serve as a promising and preferable predictor for CVD. Graphical Abstract Research insights What is currently known about this topic? Insulin resistance (IR) constitutes a critical pathophysiological mechanism that significantly contributes to the pathogenesis and progression of cardiovascular disease (CVD). What is the key research question? What is the relationship between IR assessed by estimated glucose disposal rate (eGDR) and the incident CVD risk in China? What is new? The study demonstrated a significant association between reduced eGDR levels and elevated CVD risk in the Chinese population. How might this study influence clinical practice? Our findings underscore the critical need to expand cardiovascular risk stratification paradigms beyond conventional parameters.

Since service robots serving as salespersons are expected to be deployed efficiently and sustainably in retail environments, this paper explores the impacts of their interaction cues on customer experiences within small-scale self-service shops. The corresponding customer experiences are discussed in terms of fluency, comfort and likability. We analyzed customers’ shopping behaviors and designed fourteen body gestures for the robots, giving them the ability to select appropriate movements for different stages in shopping. Two experimental scenarios with and without robots were designed. For the scenario involving robots, eight cases with distinct interaction cues were implemented. Participants were recruited to measure their experiences, and statistical methods including repeated-measures ANOVA, regression analysis, etc., were used to analyze the data. The results indicate that robots solely reliant on voice interaction are unable to significantly enhance the fluency, comfort and likability effects experienced by customers. Combining a robot’s voice with the ability to imitate a human salesperson’s body movements is a feasible way to truly improve these customer experiences, and a robot’s body movements can positively influence these customer experiences in human–robot interactions (HRIs) while the use of colored light cannot. We also compiled design strategies for robot interaction cues from the perspectives of cost and controllable design. Furthermore, the relationships between fluency, comfort and likability were discussed, thereby providing meaningful insights for HRIs aimed at enhancing customer experiences.

Evaluation of landslide susceptibility along highways is critical for risk management in engineering development, construction, and operation and maintenance. The research target is the S211 Highway in Lanping County, Nujiang Prefecture, Yunnan Province, with its buffer zone extending 10 km as the research area. Eight evaluation factors are selected for the study, including slope, slope aspect, vegetation coverage, distance from the water system, rock group, rainfall, distance from the fault, and elevation. The findings of the susceptibility evaluation were classified into five categories, and the susceptibility grades of landslide disasters in the study area were evaluated using the information value and logistic regression coupling model. The accuracy of the coupling model was evaluated by the ROC curve and AUC value. The deformation rate in the study area was estimated by processing 28 Sentinel-1 A satellite images captured from January to December 2019 using the SBAS-InSAR technology and was used to optimize the landslide susceptibility grade. The results show that the extremely high and high-risk areas of the information value-logistic regression coupling model account for 28.33% of the total area of the study area, which constitutes nearly 83.82% of the historical landslide disaster sites, mainly occupying areas along highways with low vegetation coverage and within 2000 m from rivers. The AUC values in the accuracy verification reach 0.843, indicating that the evaluation model can accurately predict the landslide susceptibility. The vulnerability grade of landslide geological disaster in the entire evaluation unit is significantly increased by optimizing the result of the surface deformation obtained by SBAS-InSAR technology. A total of 79,587 grid cells were added to the extremely high susceptibility level region. This technique may optimize the evaluation results of landslide hazard susceptibility and provide decision support for disaster prevention and maintenance along highways.

Autophagy‒endoplasmic reticulum (ER) stress axis dysregulation is linked to myocardial ischemia‒reperfusion injury (MIRI), which counteracts the benefits of acute myocardial infarction (AMI) reperfusion therapy. Qingre Huoxue decoction (QRHX) improves the short- and long-term prognosis of AMI after percutaneous coronary intervention and alleviates myocardial injury in AMI rats by stimulating autophagy via the PI3K/Akt pathway. We aimed to further explore the efficacy of QRHX in treating MIRI and its regulatory relationship with FAM134B-mediated ER-phagy. Rats were administered different concentrations of QRHX for 2 weeks, and then MIRI was induced. Ultra-performance liquid chromatography‒tandem mass spectrometry (UPLC‒MS) was used to examine the levels of the main pharmacological metabolites of the serum of rats treated with QRHX. H9c2 cells were pretreated with QRHX-mediating serum (QRHX-MS) for 24 h before being exposed to hypoxia/reoxygenation (H/R). The mechanisms underlying the effects of QRHX-MS were further studied via rescue experiments involving FAM134B knockdown. The myocardial infarct size, cardiac function, morphology and the expression of apoptosis-, autophagy-, and ER stress-related proteins and genes were assessed. The colocalization of autophagosomes with lysosomes and the localization of proteins involved in ER-phagy or autophagic flux was examined. QRHX decreased the myocardial infarct size and oxidative stress, improved cardiac function and alleviated morphological changes in a dose-dependent manner in MIRI rats by promoting autophagic flux to inhibit ER stress and ER stress-related apoptosis, which was related to FAM134B-mediated ER-phagy, as revealed by autophagy analysis. UPLC‒MS analysis of QRHX-MS revealed 20 major active metabolites of QRHX-MS, including baicalin, cryptotanshinone, 3,4-dihydroxybenzaldehyde and caffeic acid. QRHX-MS attenuated H/R-induced cardiomyocyte injury and apoptosis by increasing autophagic flux to suppress ER stress and ER stress-related apoptotic protein and gene expression. When autophagic flux was inhibited or FAM134B was knocked down in H9c2 cells followed by QRHX-MS pretreatment, the protective effect of QRHX was partially reversed. QRHX alleviates myocardial injury, apoptosis and infarct size expansion in MIRI by regulating the autophagy‒ER stress axis via FAM134B-mediated ER-phagy.

The emotional expression of body movement, which is an aspect of emotional communication between humans, has not been considered enough in the field of human–robot interactions (HRIs). This paper explores human perceptions of the emotional expressions of humanoid robot body movements to study the emotional design of the bodily expressions of robots and the characteristics of the human perception of these emotional body movements. Six categories of emotional behaviors, including happiness, anger, sadness, surprise, fear, and disgust, were designed by imitating human emotional body movements, and they were implemented on a Yanshee robot. A total of 135 participants were recruited for questionnaires and eye-tracking measurements. Statistical methods, including K-means clustering, repeated analysis of variance (ANOVA), Friedman’s ANOVA, and Spearman’s correlation test, were used to analyze the data. According to the statistical results of emotional categories, intensities, and arousals perceived by humans, a guide to grading the designed robot’s bodily expressions of emotion is created. By combining this guide with certain objective analyses, such as fixation and trajectory of eye movements, the characteristics of human perception, including the perceived differences between happiness and negative emotions and the trends of eye movements for different emotional categories, are described. This study not only illustrates subjective and objective evidence that humans can perceive robot bodily expressions of emotions through only vision but also provides helpful guidance for designing appropriate emotional bodily expressions in HRIs.