Advances in single-cell sequencing have enabled multi-omics profiling at unprecedented resolution; however, mass spectrometry-based single-cell proteomics (scMS) remains inherently destructive, precluding simultaneous transcriptomic capture. Unlike antibody-based methods such as CITE-seq, which permit paired profiling but are restricted to targeted protein panels, scMS provides unbiased, genome-scale coverage of the intracellular proteome yet necessitates post hoc integration of unpaired datasets. This diagonal integration challenge, where transcriptomes and proteomes are measured in separate cells lacking shared anchors, remains underserved by existing reviews, which focus predominantly on vertical integration strategies enabled by non-destructive assays.

Layered thermoelectric materials face intrinsic challenges in disentangling phonon and electron transport due to their anisotropic bonding networks. Here, we introduce a van der Waals gap engineering strategy that deliberately imposes out-of-plane stress on n-type bismuth telluride (Bi2Te3). Selective interlayer doping creates local charge imbalance, which in turn drives ripple-like lattice corrugations. These structural undulations mimic substrate-induced strain fields, renormalize phonon dispersion, and substantially reduce phonon velocity, thereby suppressing lattice thermal conductivity. The corrugation amplitude is far smaller than the electronic mean free path, ensuring negligible additional electron scattering. As a result, the material reaches a peak zT of 1.43 at 350 kelvin, while the fabricated module delivers a conversion efficiency of 7.5% under a 250-kelvin temperature gradient-both representing state-of-the-art performances for n-type Bi2Te3 systems. More broadly, this work establishes interlayer stress as a general strategy to manipulate phonons in van der Waals solids, providing previously unidentified design principles for high-efficiency thermoelectrics.

Charitable foundations play an important role in assisting vulnerable groups, narrowing the wealth gap, and addressing challenges such as urban-rural and regional development imbalances. Unlike other nonprofit organizations, fundraising effectiveness serves as a vital lifeline for the sustainable and stable development of charitable foundations. This study analyzed 54 Chinese charitable foundations and employed the fsQCA method to explore the factors influencing their fundraising effectiveness and their configuration effects. Research has found that: (1) digital transparency infrastructure and program activity intensity are key conditions for improving the fundraising effectiveness of charitable foundations; the level of professional management and political connections exert important impacts on enhancing the fundraising effectiveness of foundations, serving as important influencing conditions for the improvement of fundraising effectiveness. (2) There are two core paths for charitable foundations to achieve high fundraising benefits: one is a combination of strong program activity, robust digital transparency, and high political connections; the other is a combination of strong program activity, robust digital transparency, and high professional management. This study enriches and expands the theoretical connotation and research paradigm of charitable fundraising research and provides empirical evidence and policy guidance for promoting the sustainable and healthy development of charitable foundations.

In a cloud-edge-end collaborative system, data generated by terminal devices often contains users' sensitive information and is constantly generated and changing, leading to potential data privacy leaks in caches. Additionally, due to the inability to promptly capture these dynamic changes and the failure to consider the actual capabilities of nodes, caching strategies become outdated, resulting in reduced cache hit rates and cache imbalance issues. Therefore, this study proposes a cloud-edge-end collaborative intelligent caching method based on an incremental federated learning algorithm. First, the federated learning algorithm is used to aggregate data from terminal devices to the cloud, enabling collaborative data processing while protecting data privacy. Second, incremental learning methods are employed to continuously update terminal data, with the updated data aggregated to the cloud, thereby enabling real-time tracking of data trends and allowing cache strategies to rapidly adapt to dynamic changes in terminal data. Finally, considering the actual capabilities of nodes, the popularity of aggregated data and the weights of edge and terminal nodes are calculated. Data is cached in edge and terminal nodes in descending order of popularity and weight. When cache space is insufficient, data replacement is performed based on the importance of data within nodes, thereby completing intelligent data caching. Experimental results demonstrate that this method achieves good performance in data update aggregation, with high data caching balance and cache hit rates.

Lysine crotonylation (Kcr), as an emerging post-translational modification, plays a crucial role in core life activities such as chromatin dynamics and gene expression. To address the current limitations of Kcr site detection techniques, including high experimental costs, complex procedures, and high false-positive rates, as well as the poor generalization performance of existing computational models caused by limited training data and class imbalance, this study proposes an innovative intelligent recognition framework named MVFAN-Kcr. The system integrates multi-view feature fusion and attention mechanisms to synergistically enhance the prediction accuracy and robustness of Kcr site identification. Physicochemical property features are combined with global sequence semantic information derived from the ESM-2 protein language model to construct a fused feature representation that captures both local physicochemical information and global contextual information. To optimize computational efficiency, feature selection is performed using analysis of variance. To effectively address the problem of imbalanced data distribution, a stratified undersampling strategy based on the chi-square test is developed. In addition, a convolutional neural network combined with attention is designed to efficiently extract local sequence patterns and enhance the representation of key features. Under a rigorous evaluation framework based on five-fold cross-validation and an independent test set, MVFAN-Kcr demonstrates excellent predictive performance. The method significantly outperforms baseline approaches in terms of accuracy, achieving an ACC value of 79.18%, while the area under the ROC curve reaches 0.8618. SHAP analysis and gradient-based analysis reveal a strong dependence on pKa-related properties, charge characteristics, and locally enriched small side-chain residues, and indicate that the model can nonlinearly integrate high-order sequence semantic information for effective identification of Kcr sites. Overall, MVFAN-Kcr combines data balancing strategies, multi-view feature fusion, and attention mechanisms to achieve high accuracy, robustness, and interpretability, providing an effective tool for protein Kcr site prediction. The data and code are available on https://github.com/Lilyjoys/MVFAN-Kcr , and a free web platform is provided at http://www.mvfan-kcr.com .

The objective of this study is to investigate the effects of treadmill exercise on motor deficits in chronic 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine/probenecid (MPTP/p)-induced chronic Parkinson's disease (PD) mice and the underlying mechanisms. Healthy male C57BL/6 mice were randomly divided into the following groups: Control, PD model (PD), treadmill exercise (PD+Exe8), and treadmill exercise combined with the STING agonist DMXAA (PD+Exe8+DMXAA). Motor function was assessed using gait analysis, rota-rod test, wire hang test, pole test, and open field test. Fecal short-chain fatty acid (SCFA) levels were measured by gas chromatography-mass spectrometry (GC-MS). Colonic morphological changes were observed via H&E and PAS staining. Immunofluorescence staining was used to examine the colonic barrier and pathological protein accumulation. Western blot, qPCR, and ELISA were performed to assess immune responses, gut barrier integrity, blood-brain barrier (BBB) function, and inflammatory markers. (1) Chronic PD mice exhibited significant motor deficits, a decreased number of tyrosine hydroxylase (TH)-positive cells in the substantia nigra, and increased α-synuclein (α-syn) expression. (2) Treadmill exercise increased fecal valeric acid levels, ameliorated Th17/Treg immune imbalance in the colon and brain, and these beneficial effects were abolished by DMXAA. (3) Treadmill exercise improved gut barrier and BBB integrity, reversed colon pathology, and these improvements were blunted by DMXAA. (4) Treadmill exercise reduced inflammatory responses in both the colon and brain, and this anti-inflammatory effect was abolished by DMXAA. (5) DMXAA blunted the improvement in motor dysfunction induced by 8-week treadmill exercise. Treadmill exercise significantly improves motor dysfunction in mice with chronic PD, this effect is associated with STING-mediated SCFA valeric acid-Th17/Treg immune imbalance and impaired intestinal and blood-brain barriers.

Accurate and timely automatic detection of epileptic seizures is crucial for reducing the workload for visually inspecting long-term electroencephalogram (EEG) and improving diagnostic efficiency. However, this task remains a critical challenge owing to the complex and non-stationary dynamics of ictal EEG activities and the severe class imbalance in long-term EEG data. Therefore, we develop an end-to-end hybrid deep learning framework named CNN-SwT, aiming to detect seizures by integrating Convolutional Neural Network (CNN) and Swin Transformer (SwT). CNN-SwT leverages CNN to capture the spatiotemporal local features from multi-channel EEGs and SwT to further derive their global dependencies. Furthermore, supervised contrastive learning is adopted in place of the traditional cross-entropy loss to address the extreme data imbalance inherent in seizure detection. Through extensive evaluation over the publicly available CHB-MIT long-term EEG database and our SH-SDU database, CNN-SwT achieved an event-based sensitivity of 100% with an FDR of 0.36/h and an event-based sensitivity of 94.44% with an FDR of 0.78/h, respectively. These outstanding cross-database results underscore the robustness of the CNN-SwT model and its potential to advance clinical seizure detection.

Osteoporosis is a systemic skeletal disorder traditionally attributed to imbalances in bone metabolism. Increasing evidence suggests that the intestinal environment and chronic inflammation play important roles in itspathogenesis. The gut-bone-inflammation axis provides a novel perspective for understanding the interactions among intestinal barrier function, immune regulation, and bone homeostasis. This review aims to summarize current evidence regarding the role of intestinal barrier disruption in osteoporosis and to discuss its contribution within the framework of the gut-bone-inflammation axis. Relevant experimental and clinical studies from the existing literature were reviewed and synthesized, with a focus on intestinal barrier integrity, gut microbiota dysbiosis, inflammatory mediators, immune activation, and bone metabolism. Disruption of the intestinal barrier increases intestinal permeability, facilitating the translocation of microbial products and pro-inflammatory mediators into the systemic circulation. This process promotes chronic low-grade inflammation, enhances osteoclast differentiation and activity, suppresses osteoblast function, and disrupts the balance of bone remodeling. Gut microbiota imbalance, immune activation, and sustained inflammatory signaling collectively contribute to bone loss and structural deterioration in osteoporosis. Intestinal barrier dysfunction plays a pivotal role in osteoporosis through modulation of systemic inflammation and bone metabolism. Strategies aimed at preserving intestinal barrier integrity and regulating the gut microbiota may offer promising preventive and therapeutic approaches for the management of osteoporosis.

Seipin is a conformationally flexible, oligomeric scaffold that regulates cellular lipid homeostasis beyond lipid droplet (LD) biogenesis. Seipin senses local lipid composition and membrane features to direct metabolic flux toward specific pathways and organelles. Its ring adopts multiple conformations, influenced by cofactors such as the LD assembly factor 1 and adipogenin, as well as lipid ligands including phosphatidic acid, diacylglycerol, and triacylglycerol, conferring broad functional versatility. Although seipin is an ER-resident protein primarily enriched at ER-LD junctions, a fraction relocates to mitochondria-associated membranes under specific metabolic conditions, where it regulates lipid synthesis, turnover, and local Ca2+ levels, thereby facilitating interorganelle communication and maintaining metabolic stability. Seipin dysfunction disrupts this multinodal regulation, causing lipid imbalance, organelle abnormalities, and a range of metabolic and neuronal disorders. We propose a unified model in which seipin functions as a multistate proteolipid regulatory hub: a rheostat whose structure and interactome dynamically adjust to control lipid pathway decisions in response to metabolic signals across organelle contact networks.

Purpose To develop and evaluate a multiagent framework (ReclAIm) for automated monitoring, detection, and correction of performance decline in medical image classification models. Materials and Methods ReclAIm is a large language model-based multiagent system that operates through natural language interaction. A master agent coordinating three task-specific agents performed performance evaluation and triggered fine-tuning when substantial performance declines were detected. The fine-tuning workflow incorporated data augmentation, class imbalance handling, and a parameter-anchoring regularization strategy to limit catastrophic forgetting. The system was benchmarked using multiple imaging datasets, including brain MRI, chest CT, and chest radiography, partitioned into model development, inference (performance monitoring), and fine-tuning subsets (60%:20%:20%). Results ReclAIm successfully orchestrated training, evaluation, and performance monitoring across all datasets. Performance discrepancies between test and inference data were detected in 8 of 18 models, prompting fine-tuning workflows that reduced performance gaps. In cases with declines of up to 40.6% (cardiomegaly dataset, InceptionV3), fine-tuning restored performance metrics to within ± 2% of baseline values. Conclusion ReclAIm provides a prototype framework for automated monitoring and targeted fine-tuning of medical image classification models, with a natural language interface designed to support accessibility in research and potential clinical applications. © RSNA, 2026.

Purpose - This case report presents a pa- tient with recurrent cerebellitis attacks cha-racterized by acute cerebellar diffusion restriction due to a PRRT2 gene mutation, who demonstrated complete recovery.Case report - A 21-year-old female patient was admitted to our hospital with imbalance, vomiting, dizziness, and speech disorder. Neurological examination showed bilateral abnormal cerebellar tests. Diffusion restriction was seen in the right cerebellar hemisphere, and hyperintensity was seen in the same regions on the T2-Flair MRI sequence. The viral panel, culture, and biochemistry of cerebrospinal fluid (CSF) examinations were all within normal ranges. We diagnosed the patient with acute cerebellitis. A three-month corticosteroid treatment regimen was initiated after the first episode, and subsequent relapses were managed similarly. The patient's MRI results and clinical findings both fully improved. However, two more attacks were documented in the patient's history like the other attacks. The patient experienced recurring episodes and has a history of infantile epilepsy. Whole exome sequencing revealed compound he- te- rozygous pathogenic PRRT2 variants: he-terozygous c.649dupC (p. Arg217Profs*8) and a heterozygous 16p11.2 microdeletion encompassing PRRT2 gene.Conclusion - Biallelic pathogenic PRRT2 variants usually cause a severe phenotype compared to monoallelic variants. PRRT2 should be considered in cases of episodic movement disorders with reversible cerebellar diffusion restriction, as they may underlie recurring acute cerebellitis episodes. Longterm monitoring may be crucial for manage- ment.