Browse Articles

Novel universal domain-centric method for protein classification

Explainable multi agent reinforcement learning framework for secure and adaptive communication in UAV swarm based fanets

Association between the geriatric nutritional risk index and all-cause mortality in patients with acute pancreatitis in the intensive care unit: a retrospective cohort study

Sleep quality prediction in basketball athletes using a deep learning framework with an attention mechanism based on multimodal data

Analysis of construction impact and safety evaluation of metro shield tunnel under-crossing existing bridge pile foundation

Human umbilical cord mesenchymal stem cells alleviate hypoxic-ischemia-induced white-matter injury in neonatal rats by regulating polarization of microglia

Scalable and accurate rare-variant association tests for whole genome sequencing time-to-event analysis in large biobanks

Whole genome sequencing (WGS) studies in large biobanks provide an unprecedented opportunity to study the rare-variant (RV) effects on the natural history of human diseases by analyzing censored time-to-event (TTE) phenotypes, such as age at disease diagnosis, disease progression, and lifespan. Unlike existing methods developed for continuous and categorical phenotypes, rare-variant association tests (RVATs) for TTE phenotypes in large biobanks face several major challenges, including heavy censoring, cryptic relatedness, and population structure. We introduce GATE-STAAR (Genetic Analysis of Time-to-Event phenotypes via the variant-Set Test for Association using Annotation infoRmation), a powerful and computationally efficient frailty model framework for RVATs of TTE phenotypes in large biobanks. GATE-STAAR accounts for high censoring rates, cryptic relatedness, and population structure in large biobanks, while incorporating multifaceted variant functional annotations to improve power and result interpretability. We propose a rare-variant saddlepoint approximation method to effectively address heavy censoring in WGS TTE analysis. We demonstrate through extensive simulations that GATE-STAAR is powerful while maintaining proper control of type I error rates. We apply GATE-STAAR to analyze the WGS data of approximately 400,000 UK Biobank participants of white British ancestry across a variety of TTE phenotypes, and validate the findings using participants of European ancestry from the All of Us Research Program. These analyses uncover RV associations with age at diagnosis of a range of diseases.

Influence of synthetic derivatives of cytokinin and auxin on yield and quality of rainy season guava (Psidium guajava L.) cv. Shweta

Electrified release of pure CO ₂ from postcapture liquid: A two-stage system lowers the total energy cost

Electrified carbon capture and release holds promise in carbon management; but it is constrained by high energy demand. Here, we studied two candidate systems for electrochemical CO 2 release from a direct air capture (DAC) postcapture liquid: The first, a hydrogen loop cell, is electricity-efficient, but its evolved CO 2 is mixed with H 2 , introducing a 3 to 4 GJ/tonCO 2 additional energy separation cost. The second system, a solid-state metal oxide redox couple (proton sponge), avoids the gas separation challenge but is stable only in the bicarbonate and not the highly alkaline regime. These considerations led us to examine a two-stage system: An efficient hydrogen loop would first downshift the pH from 13.5 to 9; and a second metal oxide would be used to release CO 2 from bicarbonate. We report an electrified process that provides the release of a pure CO 2 stream from a post-DAC liquid with a measured total energy of ~4.5 GJ/tonCO 2 : 2.4 GJ from the H 2 looping stage and 2.1 GJ from the MnO 2 -based stage—substantially lower than the >=10 GJ/tonCO 2 required by pH-swing methods such as bipolar membrane electrodialysis. We conclude with a generalized analysis of how staged pH downshifting reduces the overall Nernst voltage penalty and facilitates energy-efficient CO 2 release.

Identifying neuromuscular and mental fatigue in elite youth table tennis players using machine learning

Human hippocampal theta–gamma coupling coordinates sequential planning during navigation

Human behavior often relies on executing a specific sequence of actions to achieve a desired outcome. However, the neural mechanisms underlying the dynamic construction and maintenance of such sequences during goal-directed behavior are not yet clear. Empirical and theoretical studies of working memory function suggest that sequential information may be encoded in neural circuits by bursts of gamma activity occurring at consecutive theta phases. Here, we asked whether a similar coding scheme might support sequential planning during goal-directed navigation. Using noninvasive magnetoencephalography and an abstract navigation task, we found that hippocampal theta power during both planning and subsequent navigation decreased with proximity to the current goal, only during accurate navigation. At the same time, theta–gamma phase–amplitude coupling (PAC) increased with goal proximity, consistent with sequences of upcoming locations being represented by gamma bursts occurring at successive theta phases. Importantly, entorhinal high gamma and hippocampal low gamma dominated while traversing novel and previously experienced paths, respectively, consistent with previous rodent studies. These findings suggest that hippocampal theta–gamma PAC flexibly and dynamically coordinates sequences of actions during goal-directed behavior across mammalian species, using different gamma bands for mnemonic and prospective planning.

Multi-omics analysis of NEDD1 in hepatocellular carcinoma: biological function, prognostic value, and clinical significance

Ecological inheritance facilitates the coexistence of environmental helpers and free riders

Many traits influence fitness indirectly by modifying shared environments that are transmitted across generations, a process known as ecological inheritance. Here, we investigate how variation in traits to improve common resources emerges when locally modified environments show ecological inheritance. Using eco-evolutionary modeling, we reveal that ecological inheritance, when combined with limited dispersal, can facilitate the coexistence of two types with opposite ecological legacies: environmental helpers, who improve the local environment for the future at a personal cost, and environmental free riders, who benefit without contributing to the detriment of future generations, particularly when interactions among helpers generate diminishing returns. This polymorphism generates lasting spatial heterogeneity in environmental quality and, consequently, in survival and reproduction—particularly under isolation-by-distance, where it creates stable clusters of high- and low-quality habitats across an otherwise homogeneous landscape. These findings reveal how ecological inheritance and spatial structure interact to stabilize polymorphism, potentially driving long-term behavioral, ecological, and fitness variation across diverse biological systems.

Hybrid fuzzy machine learning models optimized with meta-heuristics for accurate EEG-based neurological assessment

Permeability of silty clay and its prediction model based on NMR T2 double cutoff value

A structural disorder function linking local symmetry breaking to plastic indicators and strength in amorphous solids

Establishing intrinsic structure–property relationships in amorphous solids remains a central challenge in materials science because the absence of long-range order obscures universal structural descriptors. Here, we introduce a structural disorder function, S d ( r ), as a physically interpretable and quantitative metric for atomic-scale disorder in amorphous systems. S d ( r ) is formulated as the magnitude of the normalized vector sum from a reference atom to its neighbors within different radial shells, thereby capturing local symmetry breaking analogous in concept to the Burgers vector in crystals. Molecular dynamics simulations across diverse amorphous alloys and glasses, together with colloidal-glass experiments, demonstrate that S d ( r ) correlates meaningfully (correlation coefficient > 0.68) with key particle-scale plastic properties, including vibrational mean-square displacement, flexibility volume, atomic stiffness, and vibrational frequency. Liquid-like regions consistently exhibit higher S d ( r ) values than solid-like ones, revealing its ability to distinguish mechanical heterogeneity. When averaged over the field, S d monotonically increases with cooling rate and exhibits a universal negative linear relationship with shear strength, τ p = A – B S d , quantitatively linking structural disorder to macroscopic strength. These results establish S d ( r ) as a simple, dimensionless, and broadly applicable descriptor that unifies atomic configuration, processing history, and mechanical response in disordered materials, providing a physics-based framework for the rational design of amorphous solids.

Physical characterization of cotoneaster seeds to improve sorting efficiency

The theory of epidemics with altruism

Social distancing can mitigate the spread of diseases in humans and animals. Social distancing allows susceptible individuals to protect themselves (and others) but confers no personal benefit for infected individuals if recovery provides immunity. However, individuals are likely to be at least weakly altruistic and may be interested in protecting others when infected. A strongly altruistic population where individuals value others as equal to themselves would be expected to self-isolate when infected. This would strongly suppress the disease, avoid Herd Immunity, and vastly improve outcomes. Still, little is known about how weaker altruism affects behavior during epidemics. Here we show using game theory that even extremely weakly altruistic individuals, valuing their own lives equivalent to roughly 100,000 others, can rationally achieve almost identical outcomes. Individuals self-isolate in order to avoid setting off chains of infections that they would perceive as costly to them even at such small altruism. Our results are robust to a moderate fraction of asymptomatic cases or completely selfish individuals. The resulting behavior, while emerging from a complex optimization problem, is simple enough that it could have evolved as a behavioral response in social animals, as well as being easy to communicate and understand for humans.

A principal component entropy metric for assessing global synchronicity in EEG signals

Abstract Neuronal oscillations and their inter-areal synchronisation are fundamental for brain function and cognitive processes. While electrophysiological recordings, such as electroencephalography (EEG), provide invaluable insights, existing quantitative methodologies for assessing neuronal synchrony in EEG often focus on pairwise interactions, thereby limiting a comprehensive understanding of global network coordination. This study proposes principal component (PC)-entropy, a novel multichannel synchronisation metric designed to quantify the global degree of synchrony within brain signals. PC-entropy is a hybrid measure derived from Principal Component Analysis and Shannon entropy, specifically by applying normalised entropy to the eigenvalues obtained from data covariance. This approach effectively translates the distribution of variance across principal components into a synchrony measure, ranging from 0 (perfect synchrony) to 1 (complete desynchronisation), and is notably robust to variations in the number of recording channels. We validated PC-entropy using synthetic data from the Kuramoto model, including non-isofrequency signals, demonstrating its efficacy in assessing synchronisation. PC-entropy was then applied to three human EEG datasets, demonstrating its utility in detecting neural synchrony changes during sleep, differentiating nocturnal frontal lobe epilepsy (NFLE) patients from controls, reflecting consciousness levels in coma patients, and distinguishing arithmetic task performance. PC-entropy offers a valuable and sensitive tool for assessing global brain synchrony. It provides a new dimension for understanding functional connectivity and various physiological states, extending beyond the limitations of pairwise analyses and conventional spectral approaches.

Microbial metabolite oxindole curbs acute lung injury by suppressing CXCL13

The gut–lung axis is involved in acute lung injury (ALI) and its fatal sequela, acute respiratory distress syndrome (ARDS), yet the molecular mechanisms governing this crosstalk remain poorly defined. Untargeted metabolomics of plasma revealed significant dysregulation of tryptophan metabolism in ARDS patients compared to healthy controls. Murine dietary interventions demonstrated that high tryptophan intake alleviated ALI severity, whereas deficiency exacerbated injury, with protection being gut microbiota dependent. 16S ribosomal RNA (16S rRNA) gene sequencing revealed marked depletion of a functionally central bacterium Lactobacillus johnsonii ( L. johnsonii ) during ALI. Supplementation with L. johnsonii or its encapsulated form attenuated ALI, but this required dietary tryptophan sufficiency. Mechanistically, L. johnsonii converts tryptophan into oxindole, which enters pulmonary macrophages, promotes the aryl hydrocarbon receptor-RelA binding, and thereby suppresses RelA-mediated transcriptional activation of C-X-C motif chemokine 13 (CXCL13). Both genetic ablation and pharmacological inhibition of CXCL13 ameliorated ALI symptoms. Importantly, oxindole and CXCL13 levels correlated with ARDS severity in patients, suggesting their clinical relevance. Collectively, these findings define a protective microbiota-dependent gut–lung axis in ALI/ARDS that is mediated by dietary tryptophan-derived oxindole, which acts at least partially through CXCL13 suppression to underscore targetable diet–microbe–metabolite therapeutic paradigms.