Browse Articles

Compositional Phase Control in High-Entropy Alloy Electrocatalysts

Heterogeneous graph neural networks reveal molecular mechanisms of folate deficiency in placental insufficiency through multiomics integration

A numerical flow experiment for assessing the risk of rupture in anterior communicating artery aneurysms in relation to aneurysm projection

UAV photogrammetry and lidar integration for high-fidelity 3D campus mapping at KFUPM

Optimizing just-in-time adaptive interventions for interpersonal distress: mechanisms, prediction, and the challenge of engagement

Abstract Common mental health disorders (CMD) feature fluctuating emotional and interpersonal symptoms inadequately addressed by traditional weekly therapies. Ecological momentary interventions offer potential for timely support, yet their mechanisms and optimal delivery contexts remain unclear. This secondary analysis of a randomized trial ( N  = 77) compared mindfulness and mentalization micro-interventions triggered by personalized symptom thresholds. We examined dynamic symptom networks, proximal effectiveness, engagement predictors, and distress forecasting in adults with CMD. Dynamic networks revealed stable communities (interpersonal threat, social connection, affective states) with mood as a key bridge. No significant proximal intervention effects were observed. Non-engagement was significantly predicted by high stress (OR = 1.21), elevated mood (OR = 1.22), and perceived criticism (OR = 1.22). Conversely, cumulative symptom triggers (OR = 0.69) and social contact (OR = 0.83) facilitated engagement. The dynamic prediction model achieved fair performance (AUC = 0.66) for next-beep distress. Beyond autoregressive effects, perceived criticism (OR: 1.12) and paradoxically perceived support predicted future distress (OR = 1.14), while warmth was protective (OR = 0.87). Micro-interventions operate through stable networks and may yield cumulative rather than immediate benefits. High stress and criticism impede intervention use despite high need highlighting the necessity for context-sensitive, low-friction adaptive designs to align clinical need with receptivity.

Adequacy of pain management and its predictors following cesarean section: a longitudinal data analysis using a generalized estimating equation model

Optimizing the dissection of small-diameter pulmonary vessels using vessel-sealing systems

16-Step Scalable Chemoenzymatic Synthesis of Tetrodotoxin

Testing sensorimotor timing across age and music experience in a real-world environment

Plasma vitamin profiles and their associations with metabolic health and mental wellbeing in midlife Asian women

Evaluation of commercial kits and purification approaches for DNA extraction from atmospheric samples for 3rd generation sequencing without amplification

Abstract We present a DNA extraction protocol for atmospheric bioaerosol samples collected on glass-fiber filters widely used in air quality monitoring. The protocol produces high-quality molecules suitable for third-generation sequencing and other applications. The initial protocol was developed and applied in a Bioaerosol campaign performed in Finland and Lithuania in 2021 using low-volume air samplers, which posed stringent requirements to the method sensitivity. The protocol included a phenol–chloroform step for DNA purification, thus involving aggressive reagents; it was also quite time consuming and laborious. The present study advances this protocol to exclude the use of hazardous chemicals by using the SPRI paramagnetic bead technology for DNA purification and compares it to several commercial extraction methods. Despite trailing in efficiency to the initial method, the new development proved to be more efficient than several column-based commercial kits. The updated protocol was effective for a relatively high mass ratio of biological material to filter material: 70 nanograms of potential DNA on the filter to one milligram of filter fiber, as detected with the initial phenol–chloroform-based method. However, the new approach was not effective for a mass ratio lower than 15 nanograms of potential DNA per milligram of the filter material. The applicability of the new protocol for preparation of samples for the 3rd generation sequencing was confirmed by subsequent processing of the samples with the Oxford Nanopore (ONT) GridION sequencer.

Antibacterial activity and cytotoxicity of tricalcium silicate-based cements with different antibacterial additives

Intravenous high mobility group box 1 fragment improves cardiac function, fibrosis, and coronary flow in porcine ischemic cardiomyopathy model

Embigin is involved in the regulation of early mouse kidney development

Abstract Embigin (Gp70) is a transmembrane glycoprotein that serves as an ancillary protein for monocarboxylate transporters and functions as a fibronectin receptor. In mice, embigin is associated with the regulation of stem and progenitor cells as well as embryonic development. Our study demonstrates that embigin has a prominent role in early mouse kidney development. We found that during early kidney morphogenesis, embigin protein is present in the ureteric bud (UB) and differentiating nephron precursors. Notably, the absence of embigin retards UB branching. In the E13.5 Emb −/− kidneys, we observed a downregulation of genes linked to nephron development, including those involved in podocyte development. However, by E17.5, we found no significant transcriptional or morphological differences, suggesting a transient delay in the Emb −/− kidneys. Furthermore, reanalysis of mouse embryonic single-cell RNA sequencing data revealed that embigin is expressed in renal primordial cells as early as E8.75. Additionally, in embigin knockdown mouse epithelial cells, we noted a downregulation of genes central to kidney development and function, including Pappa2 , Acta2 , and Tagln , which are also downregulated in the E13.5 Emb −/− kidneys. Overall, our findings indicate that embigin plays a significant role in mouse early development by supporting the functions of tissue-specific stem cells.

Behavioral responses of captive-bred post-hatchling and juvenile sea turtles to different colors of single-use plastic film

A comprehensive evaluation framework for consumer-grade EEG devices: signal quality, robustness, and usability

Interpretable machine learning rationalizes carbonic anhydrase inhibition via conformal and counterfactual prediction

A pilot study on protocol consistency and graph metric reproducibility in microstructure-weighted connectomes

Abstract Microstructure-weighted connectomes incorporate diffusion parameters into structural networks, offering a rich characterisation of brain connectivity. While these biologically-informed connectomes have shown sensitivity to pathology-related alterations (for example in multiple sclerosis), their reproducibility remains largely unexplored. In this study, we evaluated the consistency of connectomes weighted with tensor and Bingham-NODDI parameters, employing a four-shell acquisition protocol to ensure accurate fibre reconstruction. Phantom and in vivo (N=4) data were acquired to assess temporal, inter-site and inter-protocol reproducibility of weighting parameters and inter-site stability of graph metrics. High reproducibility was observed for fractional anisotropy (FA), mean diffusivity (MD), and intra-neurite (INVF) and intra-cellular (ICVF) volume fractions, with coefficients of variation (CVs) below 5% and negligible Bland-Altman biases. Orientation dispersion index and $$\beta$$ concentration parameter showed CVs above 5% and were excluded from connectome construction. Graph metrics extracted from FA-, MD- and INVF-weighted connectomes exhibited good consistency, except for modularity. Extra-cellular volume fraction (ECVF)-weighted connectomes showed poor reproducibility (CV>5%, intraclass correlation coefficient <0.5). These preliminary findings demonstrate the reliability of microstructure-weighted connectomes, identifying the weighting strategies and graph metrics with the highest reproducibility. This supports the use of network metrics derived from weighted connectomes as potential biomarkers of altered brain connectivity in neurological disorders.

rhinotypeR enables reproducible rhinovirus genotype assignment from VP4/2 sequences

Topological signatures of collective dynamics and turbulent-like energy cascades in apolar active granular matter

Active matter refers to a broad class of nonequilibrium systems where energy is continuously injected at the level of individual “particles.” These systems exhibit emergent collective behaviors that have no direct thermal-equilibrium counterpart. Their scale ranges from micrometer-sized swarms of bacteria to meter-scale human crowds. In recent years, the role of topology and self-propelled topological defects in active systems has garnered significant attention, particularly in polar and nematic active matter. Building on these ideas, we investigate emergent collective dynamics in apolar active granular fluids. Using isotropic granular vibrators as a model experimental system of apolar active Ornstein–Uhlenbeck particles in a dry environment, we uncover a distinctive three-stage time evolution arising from the intricate interplay between activity and inelastic interactions. By analyzing the statistics, spatial correlations, and dynamics of vortex-like topological defects in the displacement vector field, we demonstrate their ability to describe this intrinsic collective motion. Furthermore, associated to these topological defects, we reveal the onset of a turbulent-like inverse energy cascade, where kinetic energy transfers across different length scales over time. As the system evolves, the power scaling of the energy transfer increases with the duration of observation. Our findings show that topological concepts can be extended to the nonequilibrium dynamics of apolar active matter, revealing a direct link between microscopic topological processes and emergent large-scale behaviors in active granular fluids that lack both a well-defined direction of motion and an intrinsic axis of orientation at the particle scale.