Browse Articles

Development and validation of a food frequency questionnaire for caffeine intake and dietary assessment in Iranian adolescents

Deep learning-based first-trimester down syndrome risk prediction in East Asian pregnant women using simplified clinical indicators

Reconfigurable hybrid beamforming for 6G wireless systems across quasi-far-field and strong near-field regimes

Synthetic data to boost under-represented patients and create virtual trial cohorts: the RATE-AF case study

Abstract Clinical trials are essential for medical progress, but in certain circumstances can be constrained by recruitment costs, ethical challenges, and limited diversity in participant representation, reducing the generalisability of findings across all subgroups. In these cases, the development of digital approaches that complement traditional trials could be of particular value. We present a Bayesian network framework for synthetic data generation in clinical trials, designed to (1) boost the representation of small and under-represented subgroups and (2) generate virtual patient cohorts that replicate full trial populations with high fidelity. The framework combines probabilistic modelling with conditional synthetic data generation and is evaluated using data from the RAte control Therapy Evaluation in permanent Atrial Fibrillation (RATE-AF) randomised controlled trial, a study that compared two treatments for rate control (digoxin versus bisoprolol, a beta-blocker) in patients with atrial fibrillation and symptoms of heart failure. The framework was first assessed in a controlled boosting experiment, designed to recover simulated subgroup under-representation within the original cohort, and then extended to an exploratory boosting scenario to examine hypothetical increases in subgroup representation, before being applied to replicate the full trial population. Across these settings, it preserved statistical fidelity and reproduced the analytical results observed in the real data, boosting under-represented subgroups where sufficient data are available, whilst acknowledging limitations of boosting under extreme small-sample scenarios. This study positions synthetic data as a potential digital pathway that, with further development, could be used to support real-world clinical trials where recruitment of some population subgroups may be challenging.

Simulation-based state and health co-estimation for battery–supercapacitor hybrid energy storage systems

Diatomaceous earth as physical control agent for root-knot nematode

Abstract This study evaluated diatomaceous earth (DE), a naturally occurring mineral composed primarily of amorphous silicon dioxide (SiO₂), as a physical control agent against the root-knot nematode Meloidogyne javanica infecting eggplant ( Solanum melongena ). Under laboratory conditions, DE was tested against second-stage juveniles (J2) at different concentrations. Among the concentrations of 0.01, 0.05, 0.1, 0.3, 0.5, 1.0, and 2.0 g/L, mortality rates of 100% were observed at concentrations ranging from 0.05 to 2.0 g/L, while 85% mortality was recorded at the lowest concentration (0.01 g/L). In greenhouse conditions, DE was applied at concentrations of 0.05, 0.5, 1.0, and 2.0 g/L using three application methods: root dipping and foliar spray at inoculation time, and soil drench at three distinct time points relative to nematode inoculation. All treatments significantly ( P  ≤ 0.05) reduced nematode infection parameters in a concentration-dependent manner. Root dipping at 2.0 g/L resulted in reductions of 92% (galls), 93% (egg masses), and 92% (adult females). Soil drench applied at the time of inoculation produced reductions of 86%, 85%, and 85% for the same parameters, respectively; while foliar spray resulted in reductions of 73%, 68%, and 73%. Among the application methods, soil drench consistently produced the greatest suppression of J2 populations. In addition to nematode control, DE application was associated with increased plant growth parameters. Soil drench applied one week after inoculation increased root dry weight by 178% at 0.5 g/L and by 185% at 2.0 g/L. These results indicate that DE has potential as a physical control agent against M. javanica infecting eggplant and may warrant further investigation for integration into nematode management strategies.

DASTNet-X: an explainable dual-attention based spatio-temporal network for arrhythmia classification

A DCN+CSF1+ fibroblast subpopulation drives pathological fibrosis in ARDS by exploiting the TGF-β signaling axis

Asymmetric dual pathway fusion for histology driven spatial transcriptomic prediction

A numerical simulation study on dual-mineral carbonate rocks incorporating thermo-hydro-chemical (THC) coupling

A multiplicative additive bias variational framework for accurate and interpretable brain MRI segmentation in cloud-based medical imaging systems

Abstract Accurate brain magnetic resonance imaging (MRI) segmentation remains challenging due to intensity inhomogeneity, acquisition-related bias fields, and ambiguous tissue boundaries. To address these challenges, a Multiplicative–Additive Bias Single-Function Dual-Level-Set (MAB-SFDLS) model is introduced within a Software-as-a-Service (SaaS)-based medical image analysis framework. The model incorporates both multiplicative and additive bias components into a unified variational energy formulation and employs a single level-set function with dual thresholds to achieve stable multi-region segmentation with smooth and continuous boundaries. The method was evaluated on the MRBrainS18 dataset, achieving Dice scores of 0.95 for white matter and 0.86 for gray matter, with a boundary deviation of 2.20 mm measured using HD95. Compared with the classical level-set formulation, notable improvements were observed in both overlap accuracy and boundary precision. The approach also demonstrated competitive performance against state-of-the-art deep learning models, including nnU-Net and U-Mamba, while maintaining lower computational requirements. Statistical analysis confirmed that the improvements were significant (p < 0.05). To enhance interpretability and practical applicability, the segmentation framework is integrated with a browser-based 3D visualization module that supports synchronized surface and volume rendering, as well as interactive region-of-interest exploration. This framework provides a practical, interpretable, computationally efficient, and scalable approach to robust brain MRI segmentation in a cloud-based medical imaging environment. The proposed model code and SaaS platform prototype are publicly available at https://doi.org/10.5281/zenodo.20797546 .

The role of a confining bulk phase in modulating activity and reorganization within viscous membranes

Abstract Active molecular motors coordinate their action to reorganize on fluid membranes to drive faster or more efficient cellular transport. The resulting membrane hydrodynamics have been shown to enhance in-plane transport and guide self-assembly. In this work, we examine the qualitative differences that arise in the collective motion of active, force-free inclusions as a result of ‘confining’ the extent of the 3D fluid phase adjacent to the membrane. A thin film underlying the membrane screens hydrodynamic interactions and modifies the effective Saffman-Delbrück length. We characterize resulting features in cluster formation and lipid-field reorganization using large-scale simulations. We also analytically investigate the stability of typical aggregate structures seen in simulations, demonstrating the role of hydrodynamic interactions modulated by the extent of bulk phase in driving aggregation and reorganization in the plane of the membrane. Put together, we reveal new mechanisms of driving lipid transport and coordinated motion by modulating collective activity within membranes.

A novel geometric approach to surface area estimation in forested mountain terrain using multi-source elevation and canopy height data

Nanosilica increases tolerance to Cu toxicity in soybean seeds by reducing Cu uptake and improving nitrogen use efficiency

The robustness of repetition-based illusory truth and certainty effects in social media contexts

Abstract In the past, cognitive illusions, such as the repetition-based illusory truth and certainty effects, were primarily studied in experimental settings that were far removed from real-world conditions. However, information channels – particularly social media – provide competing cues (e.g., likes) that may influence such cognitive phenomena. In the present study ( N  = 165), participants were assigned to one of two groups that viewed Instagram-like posts either with visible like counts (experimental condition) or without them (control condition). In addition, within the experimental group ( N  = 82), the number of likes was systematically manipulated. Results showed no moderating effect of like visibility on the repetition-based illusory truth or certainty effects. However, especially when repetition-based cues were absent, low (compared to high) like counts substantially reduced both subjective truth and confidence (experimental condition), highlighting the dominant role of information repetition in shaping perceived truth and confidence in truth evaluations.

A privacy-preserving federated learning approach with multilevel differential privacy for lung cancer detection

Study on the friction distribution characteristics of directional long-hole drill pipe systems

Epacadostat and Olaparib synergistically inhibit pancreatic cancer growth through downregulation HR-associated proteins

No association of childhood maltreatment experiences and attention to facial emotions in men

Abstract A recent study investigated the relationship between childhood trauma and attention to emotions as a function of type and severity of maltreatment in a sample of women. It was found that high levels of physical and emotional abuse severity were associated with a diminished attentional preference for happy faces. Given that men exhibit distinct patterns of childhood maltreatment and often show lower accuracy and attention toward facial emotions compared to women, investigating these relationships specifically in male populations is warranted. In the present study, we examined the relationship between childhood trauma and attention to emotions in a sample of men. Gaze behavior of 98 men with a history of childhood trauma was analyzed in a free-viewing task. Pairs of faces with an emotional (happy, surprised, angry, disgusted, fearful, and sad) and a neutral face were shown for 5 s. An attentional bias score was calculated by subtracting the total fixation duration on the neutral face from that on the emotional face. To assess childhood maltreatment experiences, the Childhood Trauma Questionnaire was employed. Linear mixed models were used to investigate research questions. The attentional bias scores exhibited high reliability. For all emotion categories, participants looked longer at the emotional compared to the neutral face. The highest attentional preference was found for happy faces. Results indicated neither a significant main effect of childhood maltreatment type, nor a significant interaction effect between childhood maltreatment type and emotion category. Similarly, no main effect or interaction involving overall childhood maltreatment severity was found. The present findings reveal no significant relationship between childhood experiences of maltreatment (including emotional, sexual, and physical abuse, as well as emotional neglect) and visual attention to facial emotions in men. Our data do not support the hypothesis that early life trauma may lead to alterations in attentional processing of emotional facial expressions in men.

Reducing computational complexity in nonlinear power system state estimation via ANN-assisted linear Kalman filtering

Abstract This paper proposes a computationally efficient dynamic state estimation algorithm for nonlinear power networks using synchronized phasor measurements and a combination of an artificial neural network and a linear Kalman filter. The methodology involves using both the predicting abilities of the artificial neural network (ANN) and the iterative correction properties of the linear Kalman filter to estimate system states under dynamic operating conditions. Instead of using the nonlinear estimation approaches requiring the repeated computation of Jacobians and matrices, the presented approach significantly reduces the computational complexity while maintaining accurate estimation. The ANN is first trained on PMU measurement data and generates predictive state values for the system, which are corrected using the linear Kalman filter to remove the effect of noise and increase resistance to measurement corruption and malicious attacks. Numerical simulation experiments carried out on the IEEE 6-bus test network have shown that the hybrid state estimator provides more accurate results in terms of reduced RMSE and faster convergence when compared to the conventional state estimation methods.