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Maritime accidents frequently lead to severe property damage and casualties, and an accurate and reliable risk prediction model is necessary to help maritime stakeholders assess the current risk situation. Therefore, the present study proposes a hybrid methodology to develop an explainable prediction model for maritime accident types. Based on the advantages of selective ensemble learning method, this study pioneers to introduce a two-stage model selection method, aiming to enhance the predictive accuracy and stability of the model. Then, SHAP (Shapley Additive Explanations) method is integrated to identify effective mapping associations of seafarers’ unsafe acts and their risk factors with the prediction results. The results demonstrate that the model developed achieves good prediction performance with an accuracy of 87.50 % and an F1-score of 84.98 %, which benefits stakeholders in assessing the type of maritime accident in advance, so as to make proactive intervention measures.

Seafarers’ unsafe acts as the direct causes of maritime accidents are considered to be the result of the interaction between complex and dynamic influencing factors. Identifying the risk evolution characteristics and paths of seafarers’ unsafe acts has always been a challenge in maritime safety management. For this purpose, the present study introduces association rule technique into complex network to develop a directed weighted interaction network of seafarers’ unsafe acts and their influencing factors. Through global network topology analysis and local network community detection, the risk evolution characteristics of seafarers’ unsafe acts in maritime accidents are analyzed from a multidimensional perspective. The results indicate that the developed network has small-world characteristics, and the top 10 critical nodes all belong to seafarers’ unsafe acts, of which failure to make proper sound and light signals achieves the highest PageRank value. Results from this study would help maritime stakeholders to understand the evolution mechanism of seafarers’ unsafe acts and develop safety management strategies for interrupting the risk propagation of seafarers’ unsafe acts.

Objective To evaluate the clinical significance of biomarkers of cartilage synthesis and degradation for early diagnosis and prognosis of knee osteoarthritis (OA). Methods Two hundred and twenty-seven knee OA outpatients and inpatients in Shenmu Hospital were collected as the knee OA group, while 178 healthy physical examiners as the control group during the same period. Serum pro-collagen ⅡA N-teminal peptide (PⅡANP),cartilage oligomeric matrix protein (COMP), matrix metalloproteinase (MMP-3), and urinary cross-linked C-telopeptide (CTX-Ⅱ)were measured by enzyme-linked immunosorbent assay. The degree of knee OA involvement and quality of life were assessed by antero-posterior and lateral radiographs of both knees and the SF-36 brief health questionnaire, respectively. Results 1)Compared with the control group, the serum PⅡANP level decreased and serum COMP, MMP-3 as well as urinary CTX-Ⅱ increased in the knee OA group (P＜0.05). 2)Serum PⅡANP was lower (P＜0.05), serum COMP, MMP-3(P＜0.05) and urinary CTX-Ⅱ levels were higher in mid-late stage knee OA group than early knee OA group and control group. 3)Serum PⅡANP levels were inversely correlated with K-L grade (P＜0.05), serum COMP and MMP-3 levels were positively correlated with K-L grade (P＜0.05), and serum COMP level was positively correlated with age (P＜0.05). Serum PⅡANP level was negatively correlated with mental health dimension in the SF-36 scale (P＜0.05); serum COMP level was negatively correlated with physical function dimension (P＜0.05) but positively correlated with mental health dimension (P＜0.05). Conclusions The expression of PⅡANP, COMP, MMP-3 and CTX-Ⅱ is abnormal in knee OA and related to the progress of the severity of the disease, so is a potential reference index for the diagnosis and prognosis of knee OA.

Regulatory T cells (Tregs) are instrumental in maintaining immune tolerance and preventing destructive autoimmunity, but how heterogeneous Treg populations are established remains largely unknown. Here, we show that Zfp335 deletion in Tregs failed to differentiate into effector Tregs (eTregs) and lose Treg-suppressive function and that KO mice exhibited early-onset lethal autoimmune inflammation with unrestricted activation of conventional T cells. Single-cell RNASeq analyses revealed that Zfp335-deficient Tregs lacked a eTreg population and showed dramatic accumulation of a dysfunctional Treg subset. Mechanistically, Zfp335-deficient Tregs displayed reduced oxidative phosphorylation and dysfunctional mitochondrial activity. Further studies revealed that Zfp335 controlled eTreg differentiation by regulating fatty acid oxidation (FAO) through direct targeting of the FAO enzyme Hadha. Importantly, we demonstrate a positive correlation between ZNF335 and HADHA expression in human eTregs. Our findings reveal that Zfp335 controls FAO-driven eTreg differentiation to establish immune tolerance., Introduction Regulatory T cells (Tregs) expressing forkhead box P3 (Foxp3) play a key role in the establishment and maintenance of peripheral immune tolerance through the prevention and suppression of autoimmunity [...]

Understanding the spatial heterogeneity of bird community distribution within urban greenways is crucial for optimizing ecological functions and supporting urban biodiversity. While ecological corridors enhance connectivity and biodiversity, specific mechanisms by which landscape composition and configuration influence bird diversity remain unclear. This study examines bird community distribution along the Qingshan Lake Greenway in Hangzhou during autumn and winter, using 17 observation points across wetlands, forests, and mixed habitats. The key findings indicate that wetlands have significantly higher species richness compared to forests and mixed habitats, underscoring wetlands' critical role in supporting diverse bird communities. Greenways primarily facilitate short-distance bird movement with limited permeability between habitats. Additionally, greenway effectiveness in enhancing bird diversity depends heavily on design and structural characteristics. This research highlights the necessity of incorporating microhabitat design and diverse habitat configurations in greenway planning to enhance ecological connectivity and biodiversity. It provides essential insights for urban planners and ecologists, emphasizing detailed landscape composition and configuration analyses. Future research should include year-round studies and advanced ecological monitoring technologies to validate and expand these findings, ultimately contributing to more effective urban biodiversity conservation and sustainable development strategies. [ABSTRACT FROM AUTHOR]

Breast cancer (BC) significantly contributes to cancer-related mortality in women, underscoring the criticality of early detection for optimal patient outcomes. Mammography is a key tool for identifying and diagnosing breast abnormalities; however, accurately distinguishing malignant mass lesions remains challenging. To address this issue, we propose a novel deep learning approach for BC screening utilizing mammography images. Our proposed model comprises three distinct stages: data collection from established benchmark sources, image segmentation employing an Atrous Convolution-based Attentive and Adaptive Trans-Res-UNet (ACA-ATRUNet) architecture, and BC identification via an Atrous Convolution-based Attentive and Adaptive Multi-scale DenseNet (ACA-AMDN) model. The hyperparameters within the ACA-ATRUNet and ACA-AMDN models are optimized using the Modified Mussel Length-based Eurasian Oystercatcher Optimization (MML-EOO) algorithm. The performance is evaluated using a variety of metrics, and a comparative analysis against conventional methods is presented. Our experimental results reveal that the proposed BC detection framework attains superior precision rates in early disease detection, demonstrating its potential to enhance mammography-based screening methodologies. [ABSTRACT FROM AUTHOR]

Objective: To assess the effects of Yi Qi Jie Du Formula (YQJDF) combined with salinomycin (SAL) on nasopharyngeal carcinoma stem cells (NPC-SCs) and investigate the underlying molecular mechanisms. Methods: Cell counting methods, the CCK-8 assay, transwell migration assay and JC-1 staining, were used to observe the effects of the combination on the proliferation, migration and apoptosis of NPC stem cells, respectively. Western blot was used to detect the levels of protein in NPC-SCs. Results: YQJDF combined with SAL had a synergistic effect on the inhibition of proliferation and migration and induction of NPC-SC apoptosis. Mechanistically, YQJDF combined with SAL synergistically upregulated the levels of apoptotic proteins, including cleaved Caspase-3, cleaved Caspase-7 and cleaved Caspase-9. Moreover, YQJDF combined with SAL synergistically decreased the levels of CD44, p-c-Src, Ras, p-PKCδ, p-MEK, p-c-Raf, p-ERK1/2 and p-AKT proteins. Conclusions: The combination of YQJDF and SAL has a synergistic effect on the inhibition of NPC-SC proliferation and migration and induction of apoptosis, which may be closely related to the downregulation of the CD44/Ras signaling pathway.

It is necessary to develop an effective knee prosthesis to recover the lost mobility of amputees. Variable-damping knee prostheses utilizing hydraulic dampers offer several advantages, but current researches are limited in approaching healthy knee behavior. To improve the gait symmetry, this study proposed a variable-damping knee prosthesis with a novel hydraulic damper using neural network predictive control (NNPC) scheme during swing phase. The external fan valve structure of the hydraulic damper can not only realize independent and continuous adjustment of flexion and extension damping by a single motor, but also can effectively avoid the damping adjustment failure caused by excessive load. NNPC was proposed as a controller to control the novel hydraulic damper during swing phase. The online simulation is carried out based on MATLAB utilizing the workbench composed of knee prosthesis prototype and self-built gait simulation and evaluation platform, so as to preliminarily verify the online feasibility and effectiveness of the algorithm at different speeds. The offline gait symmetry experiments are designed to more intuitively and effectively compare the performance of NNPC with the fuzzy logical control proposed in previous work. The results show that NNPC improves the gait symmetry from the fuzzy logical control at different walking speeds as the values of symmetry indices are significantly decrease in the range of 5.13% to 33.96%. These results indicate that the proposed variable-damping knee prosthesis can make a good performance on improving the approximation of healthy gait characteristics and meet the fundamental requirements of walking at various walking speeds.