Measles virus (MeV) remains a serious public health concern, necessitating the development of effective antivirals targeting the viral fusion (F) glycoprotein. This study employed a robust computational pipeline, including molecular docking, 1000 ns all-atom molecular dynamics (MD) simulations, and free energy landscape (FEL) analysis, to evaluate minor cannabinoids as novel inhibitors of the MeV F protein. Initial virtual screening identified Cannabichromenic acid (CBCA), Cannabichromevarin (CBCV), and Cannabiripsol (CBR) as high-affinity leads, with docking scores of - 8.5, - 8.2, and - 8.1 kcal/mol, respectively, outperforming the reference inhibitor AS-48 (- 7.6 kcal/mol). Post-MD binding free energy calculations (MM-GBSA) further confirmed the thermodynamic superiority of CBCV (ΔGbind = - 44.7 kcal/mol) and CBCA (ΔGbind = - 30.1 kcal/mol) over the reference. Dynamic analyses revealed that CBCV and CBCA effectively stabilize the F protein in its inactive prefusion conformation through a conformational locking mechanism. CBCV induced the most significant structural compaction (Rg = 2.4 nm) and displayed the sharpest global energy minimum (0.3 kcal/mol) in the FEL. Furthermore, ADMET profiling and ProTox-3.0 toxicity modeling identified CBCV as the most promising lead, possessing excellent drug-likeness, an inactive toxicity profile, and predicted blood-brain barrier permeability. This work establishes minor cannabinoids as novel scaffolds for anti-MeV drug development, positioning CBCV as a strong candidate for treating systemic and neurological complications of measles, such as Subacute Sclerosing Panencephalitis.
Impaired pupillary dynamics are a well-recognized feature of pseudoexfoliation syndrome (PXF), yet little is known about how cataract surgery influences postoperative iris function in these eyes. This study aimed to determine the longitudinal effects of cataract surgery on static pupil diameters and dilation velocity in eyes with pseudoexfoliation syndrome compared with age-matched controls. This longitudinal study included 166 eyes of 166 patients undergoing cataract surgery, comprising 91 eyes with pseudoexfoliation syndrome and 75 control eyes without pseudoexfoliation. Pupillary parameters were measured preoperatively and at six months postoperatively using automated pupillometry. Static pupil diameters were assessed under scotopic (0.04 lx), mesopic (4 lx), and photopic (40 lx) illumination conditions. Dynamic pupillary function was evaluated by measuring dilation velocity (DVel, mm/s) following a standardized light stimulus. Postoperative changes (Δ) were calculated as the difference between preoperative and postoperative measurements. Static pupil diameters remained stable in the PXF group across all illumination conditions (p > 0.05). In contrast, the control group demonstrated a significant reduction in scotopic pupil diameter after surgery (p = 0.008), while mesopic and photopic diameters remained unchanged. The most notable finding was observed in pupillary kinetics: dilation velocity significantly increased in the PXF group from 0.13 ± 0.04 mm/s to 0.17 ± 0.05 mm/s (p < 0.001), whereas no significant change was detected in the control group. Between-group comparison showed a significantly greater improvement in dilation velocity in PXF eyes (p < 0.001). Cataract morphology was not associated with postoperative pupillary changes. These findings suggest that cataract surgery may be associated with measurable changes in dynamic pupillary behavior in PXF eyes, particularly in dilation velocity, while static pupil diameter remains largely unchanged.
In this work, we evaluate the biomolecular dynamics behaviors when conventionally iterating between all-atom (AA) and coarse-grained (CG) molecular dynamics (MD) simulations over multiple cycles. We implemented the workflow to iterate between AA and CG in OpenMM, namely the iterative multiscale MD (iMMD) simulation workflow. In particular, we aim to identify practical applications for iterating between AA and CG simulations in a conventional manner without any constraints or model modifications. We evaluate the iMMD workflow on four representative systems, spanning folding of two soluble proteins and protein-protein as well as protein-lipid interactions of two membrane proteins. We observe that iteration between AA and CG representations could help the soluble proteins exit undesirable metastable states to fold, resulting from random protein structural distortions due to cycling. Consequently, the most reliable use of iterative AA and CG simulations appears to be to accelerating complex lipid mixing for membrane-bound protein systems rather than sampling protein conformational space. Our work explores the practical usages and limitations for iterative AA and CG simulations using readily available AA and CG force fields. The evaluated iMMD workflow in OpenMM is made available at https://github.com/lanl/iMMD.
Despite the wealth of data generated in the omics era to investigate molecular drivers, glioblastoma (GBM) remains one of the most incurable cancers with a poor median of survival. Here we unravelled the dynamic crosstalk between the endoplasmic reticulum and mitochondria, known as mitochondria-associated membranes (MAMs) and define how modulation of calcium fluxes and MAM structure influences GBM cell plasticity and metabolic flexibility. We identified ERO1α, whose expression is significantly associated with poor GBM patient survival, as a critical MAM protein that regulates MAM structure, dynamics and calcium-mediated functions. Our data demonstrate that ERO1α activity and expression promotes GBM aggressiveness in vitro and in vivo and enhances mitochondrial oxidative phosphorylation. By establishing a direct link between ERO1α-mediated MAM modulation and the antitumour effects of ERO1α inhibition, this work highlights a context-dependent, druggable vulnerability that can be exploited for GBM therapy.
Na⁺/H⁺ antiporters are vital for regulating intracellular pH and sodium ion levels across all domains of life. In Escherichia coli, NhaA is the principal Na⁺/H⁺ antiporter, exhibiting strong pH sensitivity and rapid turnover, yet the structural transitions underlying its activation and substrate recognition have remained obscure. Here, we use single-particle cryo-electron microscopy to determine the conformational ensemble of NhaA across a physiological pH range and in the presence of Na⁺, complemented by constant-pH molecular dynamics simulations. High-resolution structures of apo and Na⁺-bound NhaA reconstituted in lipid nanodiscs reveal progressive opening of the cytoplasmic funnel with increasing pH. We also visualize the previously unresolved N-terminal tail, which forms a dynamic plug at the cytoplasmic entrance under low-pH conditions and disengages at alkaline pH, coinciding with activation. The Na⁺-bound structure captures Na⁺ coordination at the ion-binding site, and simulations suggest potential roles for the conserved charged residues. Together, these findings illuminate how pH sensing, N-terminal gating, and substrate binding are structurally coordinated in NhaA, providing a framework for understanding Na⁺/H⁺ antiporter activation and regulation, and the basis for targeting clinical important antiporters.
Patients with lipopolysaccharide-responsive beige-like anchor protein (LRBA) deficiency typically suffer from severe B cell dysfunction. However, the underlying mechanisms remain incompletely understood. In this study, we identify non-muscle myosin IIA (NMIIA) as an interaction partner of LRBA in B cells, and uncover a role for LRBA in regulating actin cytoskeleton dynamics during B cell activation. LRBA-deficient B cells exhibit abnormal migration, impaired F-actin polymerization, and reduced B cell receptor signalling and polarization upon activation. In addition, LRBA deficiency severely disrupts immune synapse formation as evidenced by diminished central SMAC formation, reduced microtubule organizing center translocation and disrupted BCR and lysosome polarization. Consistent with these defects, internalization of the BCR-antigen complex is also impaired. Mechanistically, NMIIA activation, assessed by myosin light chain (MLC) phosphorylation, is reduced in LRBA-deficient cells. In addition, LRBA co-localizes with active NMIIA during both migration and immune synapse formation. Collectively, our findings establish LRBA as an important regulator of cytoskeleton dynamics during B cell activation, which may contribute to the defective humoral immunity observed in LRBA-deficient patients.
The bacterial flagellum is a protein-based rotary machine that drives bacterial motility. It comprises the bacterial flagellar motor (BFM), consisting of a stator which is anchored to the cell wall and a rotor in the cytoplasmic membrane, linked via the flagellar rod to the extracellular hook and filament. We observe passive rotational diffusion of six individual Escherichia coli flagella lacking torque-generating units via polarization microscopy of single gold nanorods attached to the hook, sampled at 250 kHz. Transitions across energy barriers of the 26-fold symmetric LP-ring/rod flagellar bearing exhibit highly non-Poissonian kinetics spanning four orders of magnitude in time scale. At sub-millisecond timescales we observe anomalous ultra-slow diffusion typically associated with disordered systems, despite the ordered crystalline atomic structure of the bearing revealed by cryo-Electron Microscopy. Over longer periods, we observe dynamic shifts in the preferred angular positions, indicating that the bearing's energy landscape evolves over time.
Total factor productivity (TFP) serves as a critical indicator for measuring enterprise efficiency and technological progress. However, existing prediction methods often fail to distinguish genuine causal mechanisms from spurious correlations while neglecting inter-enterprise network dependencies. This study proposes a Causal-Temporal Graph Convolutional Network (CT-GCN) that integrates causal inference techniques with temporal graph convolutional networks for dynamic TFP prediction and optimization. The framework employs the Levinsohn-Petrin method for TFP estimation, double machine learning for causal effect identification, and constructs enterprise relationship graphs capturing supply chain linkages, geographic proximity, and technological similarity. Using panel data from 12,847 Chinese manufacturing enterprises spanning 2008-2022, empirical results demonstrate that CT-GCN achieves substantial prediction improvements over baseline models, with RMSE reductions exceeding 19%. The causal analysis identifies R&D investment, digital transformation, and human capital as genuine productivity drivers, with significant treatment effect heterogeneity across industry sectors, firm sizes, and regions. An optimization decision mechanism translates these insights into differentiated strategic recommendations. This research contributes a novel methodology bridging causal reasoning and deep learning for economic forecasting applications.
Rapid environmental change has increased the need for predicting the long-term geospatial reliably. However, accurately modeling spatio-temporal geospatial dynamics remains challenging Because of the nonlinearities, complex spatial dependency, and external driving factors, it is difficult to predict. In this paper, a comprehensive benchmarking framework is proposed for the comparison of neighborhood-based, graph-based and attention-based spatiotemporal deep learning models, with the same preprocessing, training and testing procedure.. Long Short-Term Memory (LSTM) models with and without auxiliary variables are compared with hybrid Graph Attention Network-LSTM (GAT-LSTM) models and fully attention-based GAT-Temporal Attention models, with and without a feed-forward (MLP/FFN) block. All models are trained using a unified preprocessing and evaluation pipeline on annual satellite data in Network Common Data Form (NetCDF) from 2000 to 2023, with 2024 reserved as a fully unseen test dataset. Global pixel-wise measures such as R2, RMSE, MAE, MAPE, and correlation are used to evaluate model performance based on performance of vectors and alignment of predicted vectors and reference vectors. . Findings indicate that the LSTM-CA with auxiliary inputs (3 × 3 neighborhood) performs the best and most stable performance (R2 ≈ 0.95), highlighting the importance of the integrated Cellular Automata (CA) structure and auxiliary driving factors. The GAT-Temporal Attention model with an MLP block ranks second, while removing the MLP or using hybrid LSTM-GAT configurations lead to unstable or degraded performance. Index-wise analysis shows that vegetation and water-related indices are more predictable. The results indicate that strong temporal modeling of information combined with auxiliary information is more important than complexity of spatial attention. The main novelty of this paper is that it does not introduce a new model for a neural network, instead it proposes a comparative engineering experiment to assess the conditions where neighborhood-based temporal models could be superior to graph-attention models in geospatial long-range prediction applications.
Investigating early life growth dynamics is important for understanding the developmental origins of obesity. Basis splines (B-splines) provide excellent flexibility for modelling complex growth patterns, but they are prone to overfitting. Penalised B-splines (P-splines) extend B-splines by using a penalty to control their flexibility and avoid overfitting. Despite their advantages, P-splines remain underused in epidemiology, partly due to lack of guidance and accessible software. Our aim was to provide a guide on applying P-spline linear mixed effects models to analyse early life growth trajectories and extract key growth features. We outline the theoretical foundation and fitting procedures for P-splines and illustrate their use on repeated height, weight, and body mass index (BMI) measures up to age 10 years from a Southeast Asian birth cohort (n = 1014). P-splines linear mixed effects models were fitted by reformulating P-splines as mixed models with sparse matrices for efficient estimation. From the fitted trajectories, we estimated infant peak growth velocity, magnitude and timing of infant peak BMI and childhood rebound BMI, and examined their sex differences, intercorrelations, and associations with prenatal factors. Infant peak height velocity (means:.4.4 vs. 3.9 cm/month) and peak weight velocity (1121 vs. 890 grams/month) was higher in boys than girls. Infancy peak BMI (17.4 vs. 16.8 kg/m2), childhood rebound BMI (15.1 vs. 14.9 kg/m2), age at peak BMI (5.8 vs. 6.4 months), and age at rebound BMI (5.4 years) were comparable between sexes. Ages of peak and rebound BMI had a negligible correlation, higher maternal height was associated with higher peak growth velocity, higher maternal early-pregnancy weight was associated with higher and earlier rebound BMI, and higher birth weight was associated with higher and earlier peak BMI. P-splines simplify knot selection, making them a valuable approach for growth modelling. Software, code and datasets are provided to promote uptake of this method.
As technology-based learning environments increasingly employ automated feedback, understanding how learners process feedback in real time is essential. This study examined how automated cognitive and metacognitive failure feedback delivered by a humanoid robot affected performance and how effects were moderated by feedback characteristics and learner characteristics. Ninety adults (18-59 years, Mage = 29.53, 61 female, 27 male, 2 diverse) completed a learning task in three conditions: (1) fixed guidance condition with fixed-frequency and content-generic feedback, (2) basic-adaptive condition with frequency-adaptive but content-generic feedback, or (3) personalized-adaptive condition with frequency-adaptive and content-personalized feedback adjusting content to learners specific errors and prior steps. A three-level generalized path model (trials nested within time blocks within learners) was estimated to investigate effects of failure feedback on immediate task performance and cross-level moderation effects. Results showed that cognitive and metacognitive failure feedback increased the likelihood of a correct subsequent response across conditions. Relative to fixed guidance (condition 1), the implemented form of frequency-adaptive feedback (condition 2) did not show statistically significant moderation to these effects. Content-personalized feedback (condition 3) reduced effectiveness of cognitive failure feedback on immediate performance but improved overall performance as compared to content-generic feedback (condition 2). Across conditions, learners with higher cognitive ability benefited less, while those reporting higher momentary on-task boredom benefited more from cognitive feedback. These findings highlight that the effectiveness of automated failure feedback depends on both its design and learners' situational cognitive and emotional states, illustrating how a situational, temporally sensitive approach can help open the "black box" of feedback effectiveness.
The recycling of green crop residues is promoted for enhancing soil organic carbon sequestration and health, but can also stimulate emissions of nitrous oxide (N₂O) and ammonia (NH₃). Soil tillage can lead to different residue distributions which affect contact with decomposers and local gas and solute diffusion. This study aims to quantify N₂O, NH₃, and carbon dioxide emissions from N-rich red clover residue subjected to three different distributions in the soil-surface, layered, and mixed-across three contrasting agricultural soils. The incubations were performed under laboratory conditions for 50 days at 15 °C, with a water-filled pore space of 60%. Gas fluxes as well as soil ammonium and nitrate contents were measured. Results showed that N₂O emissions were strongly influenced by soil type and residue placement, with sandy loam producing the highest cumulative fluxes, particularly in the mixed (17.4 kg N ha⁻1) and layered (11.5 kg N ha⁻1) treatments. NH₃ volatilization occurred almost exclusively from surface residues, peaking at 10.2 kg N ha⁻1 in sandy loam soil. Emission for N2O were significantly higher than previously reported for residues. These findings highlight that both residue placement and soil properties critically determine gaseous emission patterns.
Communication between bacteriophages, particularly in biofilms, has long been studied. The recent discovery of the arbitrium lysis-lysogeny switch in Bacillus phages, similar to bacterial quorum sensing, has renewed interest in phage communication. This review examines the arbitrium system alongside other switching mechanisms, explores its role in pathogen-phage-host immune interactions, and proposes design principles for "smart" phage therapies.
Hospital fomites are major reservoirs of nosocomial pathogens, facilitating healthcare-associated infections, particularly in critical units. However, longitudinal studies investigating the persistence of pathogens on hospital surfaces and their antimicrobial resistance (AMR) patterns remain limited. This study examined the spatiotemporal distribution and antimicrobial resistance of bacterial pathogens isolated from fomite surfaces at Princess Marie Louise Children's Hospital (PML), Accra, Ghana. A 12-week longitudinal study (September 4-November 26, 2023) was conducted across four departments: Emergency, Neonatal Intensive Care, Malnutrition, and Outpatient. High-touch surfaces (including bed rails, incubators, infusion stands, blood pressure cuffs, thermometers, stethoscopes, etc.) were sampled weekly using sterile saline-moistened swabs. Samples were promptly transported in STGG medium on ice to the bacteriology laboratory of the University of Ghana Medical School's Department of Medical Microbiology, for standard culture and antimicrobial susceptibility testing using the Kirby-Bauer method. A total of 1,120 bacterial isolates belonging to 33 genera were recovered from 600 swab samples. Among Gram-positive bacteria, Staphylococcus epidermidis (97 isolates, 34.3%), Staphylococcus haemolyticus (42 isolates, 14.8%), and Staphylococcus aureus (34 isolates, 12.0%) predominated. The most frequently isolated Gram-negative bacteria were Acinetobacter baumannii (91 isolates, 18.2%), Klebsiella pneumoniae (54 isolates, 10.8%), and Escherichia coli (38 isolates, 7.6%). Contamination levels varied by surface type. Thermometers (N = 25 isolates), thermometer trays (N = 24), weighing tables (N = 24), and IV setting trays (N = 22) exhibited the highest contamination frequencies. Among individual items, Babe's Cot A recorded the highest contamination (N = 30), while Omicron thermometer B had the lowest (N = 18) within the top-ranked fomites.AMR rates were moderate to high across several key pathogens, with resistance ranging from 0-93% in Staphylococcus aureus, 66.7-91.7% in Enterococcus faecium, 18.9-90% in Enterococcus faecium, 29.6-88.9% in Escherichia coli, and 40-82% in Klebsiella pneumoniae. Overall, the evaluated hospital fomites were heavily contaminated with a diverse array of bacterial pathogens exhibiting substantial antimicrobial resistance, underscoring the need to strengthen infection prevention and control measures in healthcare settings.
Water oxidation, widely recognized as the kinetic bottleneck of artificial photosynthesis, limits solar fuel efficiency. Despite progress in elucidating reaction mechanisms and theoretical predictions, the dynamic spatial coupling of charge transfer, localized structural motifs and active-site evolution remains unresolved, particularly as identified under operando conditions, obscuring key mechanistic pathways. Here, by integrating operando electrochemical shell-isolated nanoparticle-enhanced Raman spectroscopy with nanoscale electrochemical reaction imaging, we spatially resolve the atomic-scale interplay between hole transfer dynamics and the evolution of water oxidation intermediates that dictates the reaction kinetics on faceted BiVO4 particles. We show that dynamic structural adaption, mediated by multihole accumulation, governs the bifurcation of water oxidation pathways. At low surface hole densities (<0.67 nm-2), both (110) and (010) facets operate under single-hole transfer limitations, stabilizing hydroperoxo and peroxo intermediates, with the (110) facet evolving higher activity. On reaching a critical hole density threshold, the (010) facet evolves to be catalytically superior, exhibiting third-order power-law kinetics driven by the dynamic hole accumulation within Bi-O-V core structures via peroxo intermediates, whereas the (110) facet shifts to accumulate dual oxidizing equivalents, facilitating favourable intramolecular O-O coupling with higher energy demands. This work reveals water oxidation catalysis from static site-centric models to dynamic systems that are governed by hole-mediated structural adaptability, providing design principles for tailoring photocharge-catalyst architectures with atomic-scale precision for solar fuel generation.
Microsoft Windows remains the dominant desktop operating system and therefore a frequent focus of digital forensic and incident response investigations. Windows Registry analysis is particularly valuable because it captures persistence mechanisms, execution traces, user activity, device usage, and system configuration changes that are often central to incident reconstruction. Nevertheless, modern investigations are challenged by the scale of Registry data, the fragmentation of evidence across hives and complementary sources, and the need to prioritise investigative actions under time pressure. This paper presents WinRegRL, a hybrid framework that combines a Markov Decision Process (MDP) solved by dynamic programming with bounded Reinforcement Learning (RL) refinement and Rule-based Artificial Intelligence (RB-AI) for automated Windows Registry and timeline-centred forensic analysis. Methodologically, the core planner is a finite-state dynamic-programming solver over an expert-specified model; reinforcement learning enters only as bounded, local tabular refinement for low-support state-action regions, so the framework is positioned as an MDP/dynamic-programming approach with bounded RL rather than as an end-to-end learned agent. The framework models the investigation process as a Markov Decision Process (MDP) with explicitly defined states, actions, transition dynamics, and reward design, and incorporates expert-derived policy graphs to initialise and refine the search strategy. We evaluate the framework on four heterogeneous forensic datasets spanning multiple Windows versions and incident scenarios, and we compare it against analyst-assisted baselines and controlled examiner-led workflows. Under the evaluation protocol adopted in this study, WinRegRL reduced investigation time by up to 68%, increased the number of adjudicated relevant artefacts identified by up to 35%, and achieved high artefact-level precision on the evaluated datasets. Rather than claiming universal superiority, we show that the proposed framework provides a reproducible and explainable decision-support mechanism that improves investigation efficiency while maintaining strong evidential coverage in the tested scenarios. These findings position WinRegRL as a promising decision-support framework for large-scale and time-critical Windows incident response.
Efficient patient management in hospitals requires adaptive decision-making under time-varying demand and dynamic service environments. This study proposes a heterogeneous medical patient queueing model that integrates reinforcement learning with stochastic queue dynamics to minimize overall patient waiting time. The model distinguishes between two categories of service providers (SPs): those attending first-time patients and those serving returning patients. Each category may differ in service rate but not in medical specialty. Patient arrivals follow a non-homogeneous Poisson process (NHPP) to capture realistic time-dependent flow variations. A Q-learning framework with a supervised ε-greedy policy is developed to determine optimal operational actions, such as adding or reallocating service providers, based on system state and event type. Separate Q-tables are maintained for arrival and departure events to account for differing cost and reward dynamics. Simulation results demonstrate that the proposed model significantly reduces total waiting time and system cost compared with conventional homogeneous queue models. This approach provides a data-driven mechanism for dynamic hospital queue management and can be extended to broader healthcare resource optimization scenarios.
Medical knowledge accumulation and clinical practice form a closed loop, yet enabling effective cooperation between the two elements, namely autonomously distilling updated knowledge from dynamic data to guide practice, remains challenging, especially in the emergency department (ED). To overcome this, we developed an autonomous AI agent that integrates established medical knowledge graphs with dynamic clinical data into a hybrid graph of over 800,000 nodes. Using large language models (LLMs) for knowledge extraction and semantic mapping, the system dynamically selects the most relevant graph to power specialized tools for ED recognition, prediction, and decision-making. The agent achieves average improvements over state-of-the-art baselines of 23.13% in ED triage, 13.05% in drug-drug interaction detection, 1.58% in readmission prediction, and 5.47% in medication recommendation, demonstrating superior performance across all task categories. This demonstrates an effective framework for synergizing established medical knowledge and dynamic clinical data in emergency care.
Landscape complexity shapes insect spatiotemporal population dynamics and trophic interactions. In this context, understanding seasonal and environmental patterns of potential resident natural enemies of novel polyphagous herbivores, such as the globally invasive fruit fly pest Drosophila suzukii, is fundamental for developing effective landscape-scale pest management strategies. Here, we investigated for the first time in the Andean Patagonian region of Argentina the spatiotemporal dynamics and parasitism of three resident parasitoids-Leptopilina heterotoma, Pachycrepoideus vindemiae, and Spalangia endius-with the potential to attack D. suzukii across heterogeneous landscape including fruit crops and surrounding natural habitats. All three parasitoid species were recorded across all environments, although their seasonal dynamics differed. L. heterotoma peaked in spring, P. vindemiae in summer, while S. endius showed a non-significant tendency toward higher activity in autumn. We further provide the first field-based evidence in the region that S. endius successfully attacks and completes development in D. suzukii, and that D. suzukii exploits wild fruit species as alternative hosts. These findings highlight the need for further research on the pupal parasitoid S. endius to assess its potential as a suitable candidate for the development of biological control strategies.