INTRODUCTION: Late‐life depression symptoms are implicated in dementia. We examined how cumulative burden, duration, trajectory, and variability of depression symptoms are associated with incident dementia. METHODS: A prospective cohort analysis of 23,305 dementia‐free adults from four population studies (English Longitudinal Study of Ageing [ELSA], Health and Retirement Study [HRS], Survey of Health, Ageing and Retirement in Europe [SHARE], China Health and Retirement Longitudinal Study [CHARLS]) with repeated Centre for Epidemiologic Studies of Depression scale (CES‐D)/Euro‐Depression scale (EURO‐D) assessments across three waves and a pooled median follow‐up of ≈10.8 years. Exposures included CumSD/cumulative average depression symptom score (CumADS), high‐symptom exposure duration, visit‐to‐visit variability (Standard deviation [SD], coefficient of variation [CV], variation independent of the mean [VIM]), and time‐course patterns. Associations were analyzed using multivariable‐adjusted Cox regression. RESULTS: Each 1‐unit increase in cumulative score was associated with a 3%–8% higher dementia hazard across cohorts. Highest versus lowest cumulative quartiles showed markedly elevated risk. Sustained high exposure for 4 years conferred ≈2.7–3.9× greater risk. Higher variability and worsening trajectories were also linked to higher incidence. Associations were robust across subgroups. DISCUSSION: Persistent and unstable depression symptoms independently predict higher dementia risk, supporting longitudinal mood monitoring and sustained management. Highlights: Multi‐cohort study of 23,305 adults (ELSA, HRS, SHARE, CHARLS). Cumulative depression burden shows a dose–response with dementia risk. Highest versus lowest quartile: dementia hazard up to 18× (HRS). Sustained high symptoms (4 years) linked to ≈2.7–3.9× greater risk. Visit‐to‐visit variability independently associates with higher dementia risk.

Landslide dams, formed by natural disasters or human activities, pose significant challenges for lifespan prediction, which is crucial for effective water conservancy management and disaster prevention. This study proposes a hybrid CNN–Transformer model optimized using the Improved Black-Winged Kite Algorithm (IBKA) aimed at improving the accuracy of landslide dam lifespan prediction by combining local feature extraction with global dependency modeling. The model integrates CNN’s local feature extraction with Transformer’s global modeling capabilities, effectively capturing the nonlinear dynamics of key parameters affecting landslide dam lifespan. The IBKA ensures optimal parameter tuning, which enhances the model’s adaptability and generalization, especially when dealing with small-sample datasets. Experiments utilizing multi-source heterogeneous datasets compare the proposed model with traditional machine learning and deep-learning approaches, including LightGBM, MLP, SVR, CNN–Transformer, and BKA–CNN–Transformer. The results show that the IBKA–CNN–Transformer achieves R² values of 0.99 on training data and 0.98 on testing data, surpassing the baseline methods. Moreover, SHapley Additive exPlanations analysis quantifies the influence of critical features such as dam length, reservoir capacity, and upstream catchment area on lifespan prediction, improving model interpretability. This approach not only provides scientific insights for risk assessment and decision making in landslide dam management but also demonstrates the potential of deep learning and optimization algorithms in broader geological disaster management applications.

Selective autophagy is a critical cellular process in eukaryotic cells, characterized by the targeted degradation of specific organelles and proteins. This

Accurate delineation of lung lesions in computed tomography (CT) scans is critical for diagnosis, staging, and treatment planning, yet remains a challenging task. While foundation models like the Segment Anything Model (SAM) excel in natural images, they often falter in medical imaging due to low contrast, ambiguous boundaries, and a lack of 3D context. To address these limitations, we propose StructSAM, a structure-aware prompt adaptation framework designed for robust volumetric segmentation, with a primary focus on lung cancer. StructSAM injects anatomical priors into the prompt pathway, employs a 3D inter-slice aggregator for volumetric consistency, and leverages PEFT for scalability. Experiments on the LIDC-IDRI dataset demonstrate that StructSAM achieves state-of-the-art accuracy on lung nodule segmentation, outperforming both classical architectures and SAM-based adaptations. Crucially, extended cross-organ evaluations on KiTS19 and MSD Pancreas datasets reveal that StructSAM effectively generalizes to other anatomical structures, highlighting its robustness to domain shifts. These findings suggest that embedding structural priors into foundation models is a promising strategy toward generic, clinically reliable, and efficient medical image segmentation.

To solve the problem of limited generalization ability that is widely existing in lightweight models used for leaf disease detection, this paper puts forward a lightweight detection model named CE-Fusion Botanic, which is based on the adaptive control of local–global information fusion. Therefore, this model includes a globally guided dynamic gating fusion mechanism that dynamically adjusts fusion weights between local features, such as spot lesions, and global semantic features, such as symptoms of systemic infection, thus realizing adaptive perception of the dual characteristics of plant diseases. Hence, the local information extraction branch combines an improved MobileNetV3-Small structure and a CBAM attention mechanism, while the global information extraction branch uses a lightweight Vision Transformer (ViT) design called EffiViT. Comprehensive contrast experiments were carried out by using seven mainstream lightweight models on the PlantVillage tomato disease subset, the full-category PlantVillage leaf disease dataset, and the Grapevine leaf disease dataset. Models were divided into large-scale, medium-scale, and small-scale groups according to the number of parameters. The results show that CE-Fusion Botanic is significantly better than comparative methods in both detection accuracy and generalization performance, and at the same time, it keeps a lightweight profile, which demonstrates superior cross-dataset adaptation capabilities.

Myocardial ischemia remains one of the principal causes of mortality and morbidity worldwide, necessitating novel approaches to facilitate early diagnosis and subsequent risk evaluation following reperfusion. Although advancements in wearables capable of ECG (electrocardiogram) monitoring have been initiated, these devices have encountered barriers due to limited capacities to encapsulate the temporally complex nature of ischemic events, notably in risk stratifying reperfusion injury. In this paper, we describe a framework that leverages bionic, wearable ECG sensor technologies along with multimodal large language models using a coherent temporal modeling effort to address the intertwining of fine-grained temporal dependencies, heterogeneous biomedical modalities, and interpretable risk stratification. Our temporally hierarchical fusion transformer utilizes a cross-granularity attention mechanism to model intrabeat, interbeat, and long-term dependencies all simultaneously. The validation of our system was carried out using 4 datasets across n = 108,778 patients, 17,173 of whom were ischemia-positive cases (4,627 from PTB-XL, 5,243 from MIMIC-IV, 6,891 from CODE-15%, and 412 in the wearable cohort). The area under receiver operating characteristic curve (AUROC) for the model for ischemia was 0.947, and the C-index for post-reperfusion risk stratification was 0.923, with a relative AUROC improvement of 4.8% to 9.5% over the best baseline in each dataset. Importantly, we achieved an average lead time of 18.4 min prior to the ischemic event to allow the clinician to enact interventions. Ultimately, this research demonstrates a prototype of an intelligent cardiovascular care monitoring system that couples advanced sensing with clinical decision support.