Human Microbiome-Based Prediction of Health Effects of Foods via Machine Learning.

Food absorption is dependent on the activities of internal microorganisms. When exploring food functionality, considering the food compounds and their metabolites produced by microbial metabolism is crucial. In this study, we developed a machine learning method to predict food functionalities using microorganism and metabolic data.

Federated modality-specific encoders and partially personalized fusion decoder for multimodal brain tumor segmentation.

Most existing federated learning (FL) methods for medical image analysis only considered intramodal heterogeneity, limiting their applicability to multimodal imaging applications. In practice, some FL participants may possess only a subset of the complete imaging modalities, posing intermodal heterogeneity as a challenge to effectively training a global model on all participants' data. Meanwhile, each participant expects a personalized model tailored to its local data characteristics in FL.

Aerodynamic Effects of a Blended Multi-Winglet on an Airliner in Subsonic and Transonic Regimes.

This study investigates the aerodynamic performance of a blended multi-winglet configuration installed on the wingtip of a transonic commercial aircraft, focusing on both subsonic and transonic regimes. Conventional single winglets are typically optimized to reduce induced drag during cruise, but multi-winglets have the potential to enhance lift during takeoff and landing. However, their effectiveness in transonic conditions remains insufficiently explored.

Synergizing a knowledge graph and large language model for relay catalysis pathway recommendation.

Relay catalysis integrates multiple catalytic reactions to efficiently transform intermediates and enhance conversion and selectivity. However, designing these pathways and multifunctional catalysts is often lengthy and costly, heavily relying on in-depth literature analysis by experienced researchers. To address this, we developed an approach that combines a knowledge graph (KG) and large language models (LLMs) to automatically recommend multistep catalytic reaction pathways.

Development of an electronic system for self/proxy symptom tracking alongside children with cancer and their caregivers: a co-design exemplar.

Purpose: Symptom tracking systems are essential for identifying symptoms in patients, prompting timely clinical intervention to reduce suffering and improve outcomes for children with cancer. Electronic patient-reported outcome tools have been designed to assess the presence and severity of symptoms and notify clinicians of the patient's condition, facilitating quick and effective symptom management. Utilizing co-design methods allows end user input in the tool development process.

Epidemiology of pathologic myopia in UK adults with high myopia.

Aims: To conduct the first cross-sectional epidemiological investigation of pathologic myopia (PM) in UK adults with high myopia.

Methods: Fundus photographs of 3024 highly myopic eyes (spherical equivalent refraction (SER) ≤-5.00D) from 2000 randomly sampled adults (aged 40-70 years) in the UK Biobank were double graded by an ophthalmic reading centre using the Meta-analysis for Pathologic Myopia framework.

The contribution of other effective area-based conservation measures (OECMs) to protecting global biodiversity.

Nations recently agreed to set aside 30% of the planet by 2030 as conservation areas (the "30 × 30" goal) necessitating major expansions, not just of traditional protected areas like national parks, but also of 'other effective area-based conservation measures' (OECMs) - areas that provide de facto benefits to biodiversity despite conservation not being the primary management objective. But evidence for whether OECMs achieve positive biodiversity outcomes remains critically needed. Here we quantify how OECMs contribute to biodiversity conservation in the three high-biodiversity countries in which they have been extensively trialed.

Social inference brain networks in autistic adults during movie-viewing: functional specialization and heterogeneity.

Background: Difficulty in social inferences is a core feature in autism spectrum disorders (ASD). On the behavioral level, it remains unclear whether reasoning about others' mental states (Theory of Mind, ToM) and empathic responses to others' physical states may be similarly or differentially affected in autism. On the neural level, these inferences typically engage distinct brain networks (ToM versus Pain networks), but their functional specialization remains not well understood in autism.

Longitudinal prevalence and co-carriage of pathogens associated with nursing home acquired pneumonia in three long-term care facilities.

Nursing home acquired pneumonia (NHAP), and its subset - aspiration-associated pneumonia, is a leading cause of morbidity and mortality among residents in long-term care facilities (LTCFs). Understanding colonization dynamics of respiratory pathogens in LTCF residents is essential for effective infection control. This study examines the longitudinal trends in prevalence, persistence, bacterial load, and co-colonization patterns of five respiratory pathogens in three LTCFs in Phoenix, Arizona.

A spiking network model of the cerebellum for predicting movements with diverse complex spikes.

Smooth and coordinated motor control is believed to be achieved through prediction by forward models in the cerebellum, which generate predicted movements from motor commands. These models are acquired via supervised learning, where instruction signals, originating from the inferior olive and represented as complex spikes (CSs) in Purkinje cells, guide learning. Previous studies show that CSs represent a wide variety of motor- and nonmotor-related activities, but how this diversity contributes to forward model acquisition remains unclear.

GastritisMIL: An interpretable deep learning model for the comprehensive histological assessment of chronic gastritis.

The comprehensive histological assessment of chronic gastritis is imperative for guiding endoscopic follow-up strategies and surveillance of early-stage gastric cancer, yet rapid and objective assessment remains challenging in clinical workflows. We propose a powerful deep learning model (GastritisMIL) to effectively identify pathological alterations on H&E-stained biopsy slides, thereby expediting pathologists' evaluation and improving decision-making regarding follow-up intervals. We have trained and tested GastritisMIL by using retrospective data from 2,744 patients and evaluated discriminative performance across three medical centers (467 patients).

Time-varying synergy/redundancy dominance in the human cerebral cortex.

Recent work has emphasized the ubiquity of higher-order interactions in brain function. These interactions can be characterized as being either redundancy or synergy-dominated by applying tools from multivariate information theory. Though recent work has shown the importance of both synergistic and redundant interactions to brain function, their dynamic structure is still unknown.

SIRT6 Is a Key Regulator of Pancreatic β-Cell Survival and Function During Aging.

Pancreatic β-cell function declines with age, but the underlying mechanism is poorly understood. In this study, we attempted to address how to reverse β-cell aging. Our data showed that sirtuin 6 (SIRT6) overexpression can reduce age-associated DNA damage, cell death, and functional decline in β-cells.

Datasets of Bird Species Composition in a Land Reclamation Area of Lake Kahokugata, Central Japan, in Relation to Various Farmland Types.

Agricultural intensification and land reclamation have transformed natural wetlands into farmland across East Asia, which has been a threat to bird diversity, particularly wetland and grassland specialists. Despite extensive research in warm temperate and tropical rice-growing regions, bird communities in snow-rich agricultural wetland landscapes remain poorly studied. Here we present a dataset describing bird assemblages in a heterogeneous agricultural landscape surrounding Lake Kahokugata, located in a snow-rich region on the Sea of Japan side of central Japan.

Massive extended streamers feed high-mass young stars.

Stars are born in a variety of environments that determine how they gather gas to achieve their final masses. It is generally believed that disks are ubiquitous around protostars as a result of angular momentum conservation and are natural places to grow planets. As such, they are proposed to be the last link in the inflow chain from the molecular cloud to the star.

TrimNN: characterizing cellular community motifs for studying multicellular topological organization in complex tissues.

The spatial organization of cells plays a pivotal role in shaping tissue functions and phenotypes in various biological systems and diseased microenvironments. However, the topological principles governing interactions among cell types within spatial patterns remain poorly understood. Here, we present the triangulation cellular community motif neural network (TrimNN), a graph-based deep learning framework designed to identify conserved spatial cell organization patterns, termed cellular community (CC) motifs, from spatial transcriptomics and proteomics data.

The effect of hydration and dynamics on the mass density of single proteins.

The density of a protein molecule is a key property within a variety of experimental techniques. We present a computational method for determining protein mass density that explicitly incorporates hydration effects. Our approach uses molecular dynamics simulations to quantify the volume of solvent excluded by a protein.

Efficient inference of rankings from multibody comparisons.

Many of the existing approaches to assess and predict the performance of players, teams, or products in competitive contests rely on the assumption that comparisons occur between pairs of such entities. There are, however, several real contests where more than two entities are part of each comparison, e.g.

Cycle-configuration descriptors: a novel graph-theoretic approach to enhancing molecular inference.

Inference of molecules with desired activities/properties is one of the key and challenging issues in cheminformatics and bioinformatics. For that purpose, our research group has recently developed a state-of-the-art framework mol-infer for molecular inference. This framework first constructs a prediction function for a fixed property using machine learning models, which is then simulated by mixed-integer linear programming to infer desired molecules.

Development of an Exciton Model of Light-Harvesting Antenna LH2 Considering Charge Transfer States and Their Fluctuations.

The light-harvesting complex 2 (LH2) of the purple bacteria absorbs sunlight and transfers energy to the RC with high efficiency. To achieve a microscopic understanding of the energy transfer process, theoretical analysis of the excited states of the bacteriochlorophyll pigment aggregates is effective. In particular, not only the locally excited (LE) states of pigments but also the charge transfer (CT) states between pigments play a significant role in the excited states of the B850 ring, where the pigments are in close proximity to each other.

Temporal relationship between dancer's body movements and music beats in classical ballet.

Musicality is one of the most essential aspects of dance performance: Dancers control their bodies to the music, and audiences appreciate dances beautifully harmonized with music. The present study explores the physical reality of the dance musicality from the perspective of synchronization between music beats and body movements. Specifically, we investigated the temporal relationship between dancer's body movements and metronome beats when a dancer performed four basic classical ballet movements (i.

Exploring the potential of generative artificial intelligence in medical image synthesis: opportunities, challenges, and future directions.

Generative artificial intelligence has emerged as a transformative force in medical imaging since 2022, enabling the creation of derivative synthetic datasets that closely resemble real-world data. This Viewpoint examines key aspects of synthetic data, focusing on its advancements, applications, and challenges in medical imaging. Various generative artificial intelligence image generation paradigms, such as physics-informed and statistical models, and their potential to augment and diversify medical research resources are explored.

A software ecosystem for brain tractometry processing, analysis, and insight.

Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) to assess physical properties of brain connections. Here, we present an integrative ecosystem of software that performs all steps of tractometry: post-processing of dMRI data, delineation of major white matter pathways, and modeling of the tissue properties within them. This ecosystem also provides a set of interoperable and extensible tools for visualization and interpretation of the results that extract insights from these measurements.

Plasma proteomic analysis identifies proteins and pathways related to Alzheimer's risk.

Introduction: We investigated associations of plasma proteins with blood-based amyloid/tau/neurodegeneration/inflammation (A/T/N/I) biomarkers for Alzheimer's disease (AD).

Methods: Plasma proteomics and clinical data from the Indiana AD Research Center (N = 498) were used. Association analysis of plasma proteins with blood A/T/N/I biomarkers as well as diagnosis was performed, followed by replication in an independent cohort (N = 323), network analysis, pathway enrichment, and machine learning classification to identify proteins and pathways related to AD risk.