3D Spatial Learning for Adsorption Energy Prediction in Multi-Temporal Solution Systems: The MTSS Data Set and a GCN-Based Network.

Existing methods for adsorption energy prediction primarily focus on individual molecules or static molecular pairs, lacking the capabilities to model the diverse spatial configurations found in complex solution systems. While traditional data sets are static, dynamic systems explore a vast conformational space over time. This paper introduces the Multi-Temporal Solution System (MTSS) data set containing 500,000 temporally resolved configurations (3D atomic coordinates + adsorption energy labels) across five solvents.

Major depressive disorder detection via temporal-frequency-spatial transformer with sub-domain knowledge alignment using EEG.

Major Depressive Disorder (MDD) is a common mental illness that seriously jeopardizes the physical and mental health of patients. Accurate detection of MDD is crucial for treatment. Currently, there are significant differences in the EEG signals of each MDD patient, leading to lower accuracy of cross-subject MDD detection.

All-in-one medical image-to-image translation.

The growing availability of public multi-domain medical image datasets enables training omnipotent image-to-image (I2I) translation models. However, integrating diverse protocols poses challenges in domain encoding and scalability. Therefore, we propose the "every domain all at once" I2I (EVA-I2I) translation model using DICOM-tag-informed contrastive language-image pre-training (DCLIP).

Diagnosis and Subtyping of Autoimmune Encephalitis Using an Attention-Based Multi-Instance Learning Model: A Multi-Center F-FDG PET Study.

Background: The aim was to develop an attention-based model using F-fluorodeoxyglucose (F-FDG) PET imaging to differentiate autoimmune encephalitis (AE) patients from controls and to discriminate among different AE subtypes.

Methods: This multi-center retrospective study enrolled 390 participants: 222 definite AE patients (comprising four subtypes: LGI1-AE, NMDAR-AE, GABAB-AE, GAD65-AE), 122 age- and sex-matched healthy controls, and 33 age- and sex-matched antibody-negative AE patients along with 13 age- and sex-matched viral encephalitis patients, both serving as disease controls. An attention-based multi-instance learning (MIL) model was trained using data from one hospital and underwent external validation with data from other institutions.

Cross-sectoral synergy governance programme for antimicrobial resistance control in China using a 'One Health' approach: study protocol for a mixed-methods study.

Introduction: Antimicrobial resistance (AMR) is a critical global public health concern, particularly acute in rural China. Counties, which cover extensive rural regions, face major challenges in AMR governance and thus require priority attention. Yet, AMR governance efforts across sectors are fragmented, with notable gaps in translating policy objectives into sustainable, practical governance measures.

Research on the optimization method of inventory management of important spare parts of intercity railway.

As cities grow, intercity railways are becoming increasingly popular for short trips between neighboring areas. These railways cater well to commuters and travelers, making reliable and cost-effective maintenance crucial. Timely access to spare parts is essential for ensuring the smooth operation of intercity railways.

Service-Based Architecture for 6G RAN: A Cloud Native Platform That Provides Everything as a Service.

The 5G network's commercialization has revealed challenges in providing customized and personalized deployment and services for diverse vertical industrial use cases, leading to high cost, low resource efficiency and management efficiency, and long time to market. Although the 5G core network (CN) has adopted a service-based architecture (SBA) to enhance agility and elasticity, the radio access network (RAN) keeps the traditional integrated and rigid architecture and suffers the difficulties of customizing and personalizing the functions and capabilities. Open RAN attempted to introduce cloudification, openness, and intelligence to RAN but faced limitations due to 5G RAN specifications.

Early detection of Parkinson's disease: Machine learning-based prediction of UPDRS Part III scores in patients using smartphone assessments.

BackgroundDetecting motor symptoms in Parkinson's disease (PD) at home, especially in the prodromal, is crucial for disease-modifying therapies.ObjectiveTo evaluate the effectiveness of machine learning models using smartphone-based assessments in predicting motor symptoms in untreated PD.MethodsUsing a clinical trial in early patients with PD, the PDAssist smartphone application and machine learning models were investigated for eight motor tasks: resting tremor, postural tremor, finger tapping, facial expressions, rigidity, speech, walking, and pronation/supination to predict motor symptoms of PD as comparing with UPDRS Part III scores.

Identifying patterns of high intraoperative blood pressure variability in noncardiac surgery using explainable machine learning: a retrospective cohort study.

Background: High intraoperative blood pressure variability (HIBPV) is significantly associated with postoperative adverse complications. However, practical tools to characterize perioperative factors associated with HIBPV remain limited. This study aimed to develop explainable supervised machine learning (ML) models to classify patients with HIBPV and to identify structural perioperative patterns associated with HIBPV through model interpretation.

Multiomics Reveals the Mechanism of Adaptation to Combined Hypersaline, Alkaline, and Elevated Temperature Environments.

The halophilic alkalithermophile grows optimally at the combined extremes of 3.3-3.9 M Na, pH 9.

Single Inspiratory Chest CT-based Generative Deep Learning Models to Evaluate Functional Small Airways Disease.

Purpose To develop a deep learning model that uses a single inspiratory chest CT scan to perform parametric response mapping (PRM) and predict functional small airways disease (fSAD). Materials and Methods In this retrospective study, predictive and generative deep learning models for PRM using inspiratory chest CT were developed using a model development dataset with fivefold cross-validation, with PRM derived from paired respiratory CT as the reference standard. Voxelwise metrics, including sensitivity, area under the receiver operating characteristic curve (AUC), and structural similarity index measure, were used to evaluate model performance in predicting PRM and generating expiratory CT images.

Utilizing GCN-Based Deep Learning for Road Extraction from Remote Sensing Images.

The technology of road extraction serves as a crucial foundation for urban intelligent renewal and green sustainable development. Its outcomes can optimize transportation network planning, reduce resource waste, and enhance urban resilience. Deep learning-based approaches have demonstrated outstanding performance in road extraction, particularly excelling in complex scenarios.

Wheat Straw Biochar Amendment Increases Salinity Stress Tolerance in Alfalfa Seedlings by Modulating Physiological and Biochemical Responses.

Salinity stress is a major environmental challenge that adversely impacts the physiological and biochemical processes of pasture, consequently resulting in reduced yields and compromised quality. Biochar amendment has recently emerged as a promising strategy to alleviate the deleterious effects of salinity stress. However, the interactive influences of salinity stress and wheat straw biochar on the physiological, biochemical, and growth characteristics of alfalfa ( L.

TCMP-300: A Comprehensive Traditional Chinese Medicinal Plant Dataset for Plant Recognition.

Traditional Chinese Medicinal Plants (TCMPs) are often used to prevent and treat diseases for the human body. Since various medicinal plants have different therapeutic effects, plant recognition has become an important topic. Traditional identification of medicinal plants mainly relies on human experts, which does not meet the increased requirements in clinical practice.

Multi-objective operation optimization method of microgrid considering the influence of electric vehicle.

In view of the negative impact on the stable operation of the system caused by the disorderly charging of large-scale electric vehicles connected to the microgrid, an optimization method for the operation of microgrid considering the impact of electric vehicles is proposed. Based on the traditional microgrid, a grid-connected microgrid system with electric vehicles is designed, and the system is studied. Based on Monte Carlo simulation method, the load model of disorderly charging and orderly charging and discharging of electric vehicles is constructed.

A near-threshold memristive computing-in-memory engine for edge intelligence.

Memristive computing-in-memory and near-threshold computing are two unconventional computing paradigms that can potentially enhance the energy efficiency and real-time performance of edge devices. However, their scalability faces challenges, primarily due to process variation. Here, we report a 1-Mb, 16-macro near-threshold memristive computing-in-memory engine.

Deep generalizable prediction of RNA secondary structure via base pair motif energy.

Deep learning methods have demonstrated great performance for RNA secondary structure prediction. However, generalizability is a common unsolved issue on unseen out-of-distribution RNA families, which hinders further improvement of the accuracy and robustness of deep learning methods. Here we construct a base pair motif library that enumerates the complete space of the locally adjacent three-neighbor base pair and records the thermodynamic energy of corresponding base pair motifs through de novo modeling of tertiary structures, and we further develop a deep learning approach for RNA secondary structure prediction, named BPfold, which learns relationship between RNA sequence and the energy map of base pair motif.

Automated ejection fraction and risk stratification in cardiomyopathy patients with diverse LV geometry using 2D echocardiography.

Cardiomyopathy often alters left ventricular geometry (LVG), impairing cardiac function. We developed a deep learning (DL) model to estimate left ventricular ejection fraction (LVEF) from echocardiographic images while accounting for LVG variability and assessed prognostic factors across LVG subtypes. For all patients with cardiomyopathy, we computed LV volume on apical two- and four-chamber views processed with novel DeepLabV3+ algorithm and calculate EF using Simpson's method.

Development and validation of a deep learning algorithm for discriminating glioma recurrence from radiation necrosis on MRI.

Purpose: Accurate differentiation between glioma recurrence and radiation necrosis is critical for the management of patients suspected of glioma recurrence following radiation therapy. This study aims to develop a deep learning-based methodology for automated discrimination between glioma recurrence and radiation necrosis using routine magnetic resonance imaging (MRI) scans.

Method: We retrospectively investigated 234 patients who underwent radiotherapy after glioma resection and presented with suspected recurrent lesions during follow-up MRI examinations.

Foundation models and intelligent decision-making: Progress, challenges, and perspectives.

Intelligent decision-making (IDM) is a cornerstone of artificial intelligence (AI) designed to automate or augment decision processes. Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make effective and adaptive choices and decompose complex tasks into manageable steps, such as AI agents and high-level reinforcement learning. Recent advances in multimodal foundation-based approaches unify diverse input modalities-such as vision, language, and sensory data-into a cohesive decision-making process.

FoodSky: A food-oriented large language model that can pass the chef and dietetic examinations.

Food is the cornerstone of both survival and social life. With the increasing complexity of global dietary cultures, there is a growing demand for food intelligence to enable tasks like recipe recommendations and diet-disease correlation discovery. To address this, we introduce the food-oriented large language model (LLM) FoodSky, which offers fine-grained perception and reasoning on food data.

Transformer-based prototype network for Chinese nested named entity recognition.

Nested named entity recognition (NNER), a subtask of named entity recognition (NER), aims to recognize more types of entities and complex nested relationships, presenting challenges for real-world applications. Traditional methods, such as sequence labeling, struggle with the task because of the hierarchical nature of these relationships. Although NNER methods have been extensively studied in various languages, research on Chinese NNER (CNNER) remains limited, despite the complexity added by ambiguous word boundaries and flexible word usage in Chinese.

High-accuracy prediction of mutations in nine genes in lung adenocarcinoma via two-stage multi-instance learning on large-scale whole-slide images.

Background: Lung cancer is widely recognized as a prevalent malignant neoplasm. Traditional genetic testing methods face limitations such as high costs and lengthy procedures. The prediction of clinically relevant genetic mutations via histopathological images could facilitate the expedited identification of genetic mutations in clinical settings.