Explore the vast range of titles available.

In patients with traumatic brain injury (TBI), calcium channel blockers (CCBs) may be used in the early phase to manage hypertension. However, these medications may exacerbate secondary brain injury via hypotension and cerebral vasodilation, blunting Cushing's reflex. We hypothesized that preoperative CCB use does not improve outcomes. A retrospective study (2017–2023) at a Level 1 Trauma Center includes adults undergoing craniotomy or craniectomy within 6 ​h of arrival. Patients were grouped by CCB exposure. Outcomes included mortality, Glasgow Outcome Scale (GOS), ICU days, hospital length of stay, and discharge location. Of 105 patients, 33 received CCBs. Groups were similar in injury severity and initial GCS. Preoperative CCB was linked to significantly higher mortality (46.9 ​% vs. 23.3 ​%, p ​= ​0.029) without significant GOS differences (p ​= ​0.135). Other outcomes showed no significant differences. Preoperative CCB use in TBI patients may be associated with higher mortality without improving outcomes. •TBI patients receiving preoperative CCBs showed significantly higher mortality.•Preoperative CCB use in TBI linked to non-significant change in functional outcomes.•Findings support cautious BP management and reevaluation of CCB use in TBI.

This study investigates the hydrogen‒oxygen crossover behavior induced by membrane perforation in alkaline electrolyzers, assessing explosion risks during operation. Perforation defects were simulated using an experimental platform with transparent end plates to analyze the effects of perforation diameter, position, and operating power on gas mixing. Results demonstrate that membrane perforation significantly increases HTO (Hydrogen to Oxygen), with severity dependent on perforation size and proximity to flow inlets/outlets. Maximum hydrogen-oxygen crossover occurs at high-flow-velocity zones due to flow field heterogeneity and gas accumulation. In addition, Lower operating power intensifies HTO, while higher load dilutes HTO through increased oxygen output. Based on the experimental data, a predictive regression model was developed, capable of forecasting HTO under different perforation conditions with high accuracy (R 2  = 0.9499). The novelty lies in quantifying the correlation between diaphragm perforation and risk in alkaline electrolyzer, and providing reference for early fault detection and preventive maintenance.

The widespread adoption of electronic health records has created new opportunities for translational clinical research, yet this promise remains constrained by fragmented data across privacy-siloed institutions and substantial heterogeneity in local coding practices. While privacy-preserving collaborative learning allows institutions to work together without sharing patient-level data, it does not address inconsistencies in how clinical concepts are represented across sites. We introduce a graph-based framework that addresses this gap by treating data harmonization as a scalable representation learning problem. Rather than relying on fixed standards or manual mappings, the framework integrates institution-specific summary statistics from health records, curated biomedical knowledge graphs, and semantic information derived from large language models to learn a shared semantic space. This joint learning approach aligns diverse, site-specific vocabularies while preserving patient privacy. Evaluated across seven institutions and two languages, the framework provides a robust, data-centric foundation for training and deploying clinical models across heterogeneous healthcare systems. Authors present a framework that harmonizes electronic health record data across hospitals by integrating medical knowledge, large language models, and graph learning. It enables cross-institutional analysis without sharing patient-level data.

The preparation was carried out by liquid-phase reduction method with AgNO 3 as the oxidizing agent, ascorbic acid (C 6 H 8 O 6 ) as the reducing agent, and polyvinyl pyrrolidone (PVPK30) as the dispersing agent. The effects of silver nitrate concentration, ascorbic acid concentration, PVPK30 concentration, solution PH value before and after the reaction, and the temperature of reaction on average particle size (D50), dispersity, and particle size uniformity of silver particles were investigated by orthogonal experiment L18 (3 6 ) with six factors and three levels. The spheroid-like silver particles with good dispersity of 400-600nm and the spheroid-like silver particles with suitable particle size uniformity of 80-100nm were successfully prepared by optimal experiments. The results of the orthogonal experiment show that the molar ratio of the reducing agent to the oxidizing agent plays a decisive role in the type of grain growth. The crystal nucleus will coagulate and grow at a higher molar ratio (⩾1.05). With the increase in molar ratio, the nucleation rate is higher, the grain growth rate is faster, and the size of silver particles is smaller.

The adoption of EHRs has expanded opportunities to leverage data-driven algorithms in clinical care and research. A major bottleneck in effectively conducting multi-institutional EHR studies is the data heterogeneity across systems with numerous codes that either do not exist or represent different clinical concepts across institutions. The need for data privacy further limits the feasibility of including multi-institutional patient-level data required to study similarities and differences across patient subgroups. To address these challenges, we developed the GAME algorithm. Tested and validated across 7 institutions and 2 languages, GAME integrates data in several levels: (1) at the institutional level with knowledge graphs to establish relationships between codes and existing knowledge sources, providing the medical context for standard codes and their relationship to each other; (2) between institutions, leveraging language models to determine the relationships between institution-specific codes with established standard codes; and (3) quantifying the strength of the relationships between codes using a graph attention network. Jointly trained embeddings are created using transfer and federated learning to preserve data privacy. In this study, we demonstrate the applicability of GAME in selecting relevant features as inputs for AI-driven algorithms in a range of conditions, e.g., heart failure, rheumatoid arthritis. We then highlight the application of GAME harmonized multi-institutional EHR data in a study of Alzheimer's disease outcomes and suicide risk among patients with mental health disorders, without sharing patient-level data outside individual institutions.

Recently, LLM agents have made rapid progress in improving their programming capabilities. However, existing benchmarks lack the ability to automatically evaluate from users' perspective, and also lack the explainability of the results of LLM agents' code generation capabilities. Thus, we introduce ProjectEval, a new benchmark for LLM agents project-level code generation's automated evaluation by simulating user interaction. ProjectEval is constructed by LLM with human reviewing. It has three different level inputs of natural languages or code skeletons. ProjectEval can evaluate the generated projects by user interaction simulation for execution, and by code similarity through existing objective indicators. Through ProjectEval, we find that systematic engineering project code, overall understanding of the project and comprehensive analysis capability are the keys for LLM agents to achieve practical projects. Our findings and benchmark provide valuable insights for developing more effective programming agents that can be deployed in future real-world production.

The future of World Models depends not only on scaling model capability, but also on scaling practicality and inference efficiency. High-frame-rate inference enables responsive perception, planning, and control in real-world autonomous systems. To this end, we present MoWorld, a cost-effective yet high-performance Flash World Model with an end-to-end framework spanning data generation, pre-training, distillation, and efficient inference, enabling up to 50 FPS real-time interaction with cinematic visual quality without the need of high-end GPUs. To enable large-scale real-world deployment, MoWorld jointly optimizes model capability and cost throughout the entire development pipeline. Specifically, unlike existing approaches that primarily rely on large-scale video corpora, MoWorld is built upon a scalable 3D-native data engine accumulated from our large-scale 3D vision and generative modeling pipeline, enabling the efficient construction of geometrically consistent training data across diverse real-world and synthetic environments. Based on this foundation, a curriculum cross-frame pre-training strategy for stable and scalable World Model learning, an efficient denoising-step distillation algorithm to reduce diffusion training cost, and a mixed-precision parallel inference framework for low-cost real-time deployment. MoWorld is the first real-time interactive World Model built on the Neural Processing Unit (NPU) and can achieves up to 50 FPS in such the devices, enabling practical and efficient deployment at scale. Comprehensive evaluations demonstrate that MoWorld achieves leading performance; notably, its average inference cost is only 30\\%-50\\% of that of existing World Models, providing a practical foundation for large-scale real-world applications of World Models. We also demonstrate diverse applications of MoWorld.

The widespread adoption of electronic health records has created new opportunities for translational clinical research, yet this promise remains constrained by fragmented data across privacy-siloed institutions and substantial heterogeneity in local coding practices. While privacy-preserving collaborative learning allows institutions to work together without sharing patient-level data, it does not address inconsistencies in how clinical concepts are represented across sites. We introduce a graph-based framework that addresses this gap by treating data harmonization as a scalable representation learning problem. Rather than relying on fixed standards or manual mappings, the framework integrates institution-specific summary statistics from health records, curated biomedical knowledge graphs, and semantic information derived from large language models to learn a shared semantic space. This joint learning approach aligns diverse, site-specific vocabularies while preserving patient privacy. Evaluated across seven institutions and two languages, the framework provides a robust, data-centric foundation for training and deploying clinical models across heterogeneous healthcare systems.

The adoption of EHRs has expanded opportunities to leverage data-driven algorithms in clinical care and research. A major bottleneck in effectively conducting multi-institutional EHR studies is the data heterogeneity across systems with numerous codes that either do not exist or represent different clinical concepts across institutions. The need for data privacy further limits the feasibility of including multi-institutional patient-level data required to study similarities and differences across patient subgroups. To address these challenges, we developed the GAME algorithm. Tested and validated across 7 institutions and 2 languages, GAME integrates data in several levels: (1) at the institutional level with knowledge graphs to establish relationships between codes and existing knowledge sources, providing the medical context for standard codes and their relationship to each other; (2) between institutions, leveraging language models to determine the relationships between institution-specific codes with established standard codes; and (3) quantifying the strength of the relationships between codes using a graph attention network. Jointly trained embeddings are created using transfer and federated learning to preserve data privacy. In this study, we demonstrate the applicability of GAME in selecting relevant features as inputs for AI-driven algorithms in a range of conditions, e.g., heart failure, rheumatoid arthritis. We then highlight the application of GAME harmonized multi-institutional EHR data in a study of Alzheimer's disease outcomes and suicide risk among patients with mental health disorders, without sharing patient-level data outside individual institutions.

We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum real-world intelligence. The flagship M2 contains 229.9B total parameters with only 9.8B activated per token. Designed end-to-end for agentic deployment, the M2 series rests on three components: (i) agent-driven data pipelines producing large-scale, verifiable trajectories across agentic coding and agentic cowork, each grounded in an executable workspace and an artifact-aligned reward; (ii) Forge, a scalable agent-native RL system that adapts to long-horizon agent trajectories, paired with windowed-FIFO scheduling, prefix-tree merging, inference optimization, and a clean training-inference-agent decoupling that supports both white-box and black-box agents; (iii) the latest M2.7 checkpoint takes an early step toward self-evolution -- autonomously debugging training runs and modifying its own scaffold. Across M2 through M2.7, this combination translates a mini-activation footprint into frontier-tier performance on agentic coding, deep search, office-task, and reasoning benchmarks.