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.

Updates to the CASP Infrastructure in 2024.

CASP (critical assessment of structure prediction) conducts community experiments to determine the state of the art in calculating macromolecular structures. The CASP data management system is continually evolving to address the changing needs of the experiments. For CASP16, we expanded the infrastructure to enable data handling of newly introduced categories and fully support pilot categories introduced in CASP15.

Graph-based feature learning methods for subject-dependent and subject-independent motor imagery EEG decoding.

Unlabelled: The significant intra-individual variability and inter-individual differences in scalp electroencephalogram (EEG) make it difficult to learn task-distinguishable features, posing a challenge for motor imagery brain-computer interfaces. Current feature learning methods often produce an incomplete feature space, struggling to accommodate these variations and differences. Additionally, the weak discriminative nature of this feature space results in diminished EEG classification performance.

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.

Ultrack: pushing the limits of cell tracking across biological scales.

Tracking live cells across two-dimensional, three-dimensional (3D) and multichannel time-lapse recordings is crucial for understanding tissue-scale biological processes. Despite advancements in imaging technology, accurately tracking cells remains challenging, particularly in complex and crowded tissues where cell segmentation is often ambiguous. We present Ultrack, a versatile and scalable cell tracking method that tackles this challenge by considering candidate segmentations derived from multiple algorithms and parameter sets.

Machine Learning-based Prediction of Temporal Velocity-Informatics (TVI) Variables for Accelerated Characterization of Intracranial Aneurysms' Rupture Status.

Temporal velocity-informatics (TVI) is a novel technique utilizing spatial analysis of time-resolved 3D velocity fields to quantify flow disturbance in vascular aneurysms. Although it can improve the characterization of intracranial aneurysms' (IA) rupture status, calculation of time-resolved 3D velocity fields using computational fluid dynamics (CFD) simulations limits its clinical translation. This study aims to test the feasibility of using IA's geometrical information in conjunction with machine learning (ML)-based regression methods to predict TVI parameters.

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).

Molecular logic gates based on programmable self-assembly of DNA origami triangles for the detection of nucleic acid molecules.

The development of programmable DNA origami architectures with combinatorial complexity remains a critical challenge in molecular nanotechnology. This study develops a programmable nucleic acid detection platform by integrating DNA origami nanostructures with molecular logic gates, advancing the field of dynamic molecular computation. Triangular DNA origami modules, designed with edge-specific hybridization sites, successfully emulate Boolean logic operations (YES, AND, and OR gates) to achieve target-driven hierarchical self-assembly.

Commercial wrist-worn wearable devices for older adults: a scoping review.

Purpose: This study aims to review the scientific literature on commercial smart wrist-worn devices for monitoring health and well-being in older adults. Searches were conducted in four electronic databases: PubMed, Scopus, Web of Science and IEEE Xplore. The included studies are original, peer-reviewed, published in English, and involved older adults aged 60 years and older who used commercial smart wrist-worn devices (such as smart bands and smartwatches).

A special machine for solving NP-complete problems.

A specialized computer named as the Electronic Probe Computer (EPC) has been developed to address large-scale NP-complete problems. The EPC employs a hybrid serial/parallel computational model, structured around four main subsystems: a converting system, an input/output system, and an operating system. The converting system is a software component that transforms the target problem into the graph coloring problem, while the operating system is designed to solve these graph coloring challenges.

Assessing Effective Engagement with Digital Behavior Change Intervention: The Case Study of the CAPABLE Project.

Digital Behavior Change Interventions (DBCIs) can support the development of new health behaviors. Evaluating their effectiveness is crucial for improving them and understanding the factors that contribute to their success. Building on the CAncer PAtients Better Life Experience (CAPABLE) project, this study proposes both a conceptual framework mapping the relationships between patient motivation, engagement, and outcomes, and a set of testable hypotheses to evaluate these relationships.

ProProtein: A platform for fully automated identification of 3D structure fluctuations in MD simulation trajectories.

The main goal of Molecular Dynamics (MD) is a simulation of a physical system motions in a fixed time period. This technique allows users to observe the dynamic evolution of the system but requires advanced force fields and is computationally intensive. Furthermore, finding desirable features in the results obtained is usually a time-consuming task.

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.

Salvador Urban Network Transportation (SUNT): A Landmark Spatiotemporal Dataset for Public Transportation.

Efficient public transportation management is essential for the development of large urban centers, providing several benefits such as comprehensive coverage of population mobility, reduction of transport costs, better control of traffic congestion, and significant reduction of environmental impact limiting gas emissions and pollution. Realizing these benefits requires a deeply understanding the population and transit patterns and the adoption of approaches to model multiple relations and characteristics efficiently. This work addresses these challenges by providing a novel dataset that includes various public transportation components from three different systems: regular buses, subway, and BRT (Bus Rapid Transit).

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.

Evidence Generation for a Host-Response Biosignature of Respiratory Disease.

Background: In just twenty years, three dangerous human coronaviruses-SARS-CoV, MERS-CoV, and SARS-CoV-2 have exposed critical gaps in early detection of emerging viral threats. Current diagnostics remain pathogen-focused, often missing the earliest phase of infection. A virus-agnostic, host-based diagnostic capable of detecting responses to viral intrusion is urgently needed.

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.

Multi-graph Graph matching for coronary artery semantic labeling in invasive coronary angiograms.

Coronary artery disease (CAD) stands as the leading cause of death worldwide, and invasive coronary angiography (ICA) remains the gold standard for assessing vascular anatomical information. However, deep learning-based methods encounter challenges in generating semantic labels for arterial segments, primarily due to the morphological similarity between arterial branches and varying anatomy of arterial system between different projection view angles and patients. To address this challenge, we model the vascular tree as a graph and propose a multi-graph matching (MGM) algorithm for coronary artery semantic labeling.

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.