Browse Articles

Thermodynamics and selection of the plasminogen activator inhibitor-1 latency transition

Reconstruction of magnetic structures and material parameters with convolutional neural network and bias field-constrained micromagnetic relaxation

Cognitive load and visual attention assessment using physiological eye tracking measures in multimedia learning

Effective multimedia content design can boost performance, capture visual attention, and optimize cognitive load. The current study employs eye-tracking technology to establish metrics to measure cognitive load, analyze visual attention allocation, and evaluate learners’ performance in English language learning. The study focuses on creating and comparing two different multimedia presentations. The differentiation between them lies in their adherence to or deviation from Mayer’s educational multimedia design principles: coherence, signaling, and spatial contiguity. participants were randomly assigned to two groups. The first group viewed with principles version, while the second group viewed without principles version, during which their eye movement data were collected. Subsequently, both groups participated in a recall test and completed the NASA-TLX questionnaire. The research establishes connections between specific eye-tracking parameters, subjective cognitive load scores, and recall test results through regression models and analyzes fixation distributions. The study also delves into microsaccades rate and changes in pupil size, each analyzed within times of interest. The study’s findings indicate that the examined metrics can significantly help distinguish between the two conditions: principles and no principles. These metrics are pertinent for assessing individuals’ cognitive load and visual attention and serve as beneficial indicators for gauging the efficacy of the designed multimedia content.

The advent of confocal laser scanning microscopy in biological research

Structural and spectroscopic resolution of the NADPH redox state in the STEAP2 cytosolic oxidoreductase domain

Quantitative assessment of the standard method for measuring steam composition in surface sterilisation

Outside Back Cover: Iron (IV) Formation and the pH‐Dependent Kinetics of the Fenton Reaction (Angew. Chem. Int. Ed. 49/2025)

Hematocrit in the first 2 hours of life and short-term outcomes in very preterm infants: A secondary analysis from a prospective cohort study

Background Preterm birth, particularly very preterm birth (before 32 weeks of gestation), is a leading cause of neonatal morbidity and mortality. The early neonatal period is critical for preterm infants, with hematocrit levels serving as a important physiological indicator. We aimed to assess the relationship hematocrit in the first 2 hours of life and Short Outcomes in very preterm infants. Methods The research was a prospective cohort study completed by Dongli Song et al. We acquired data from the DATA DRYAD website and utilized exclusively for secondary analysis. From January 2008 to April 2014, data were gathered prospectively from eligible infants at the Santa Clara Valley Medical Center. The main outcomes included any Intraventricular Hemorrhage (IVH), any Retinopathy of Prematurity (ROP), Necrotizing Enterocolitis (NEC), chronic lung disease (CLD), and late-onset sepsis (LOS). Secondary outcomes were any intubation and any transfusion. We used multivariable logistic regression analyses to calculate adjusted odd ratio (OR) with 95% CI. Results This study included 312 patients in total. Hematocrit in the first 2 hours of life, considered as a continuous variable, was significantly associated with short-term outcomes in univariate analyses (P < 0.05). After adjusting for GA, BW, and sex, only any ROP, any intubation, and any transfusion were statistically significant. With adjustments for multiple factors, the odds ratios for any ROP and any transfusion in infants whose Hematocrit was 45 or more in the first two hours of life, compared to those with an HCT less than 45 were 0.43 (95% CI, 0.19 ~ 0.97, p = 0.043) and 0.29 (95% CI, 0.12 ~ 0.7, p = 0.006). Conclusions Our study shows that higher HCT in the first 2 hours of life was statistically significant association with decreased ROP and blood transfusion in very preterm infants. Further clinical trials are necessary to confirm and validate this association.

The receptor tyrosine kinase ErbB2/HER2 governs CDK4 inhibitor sensitivity, timing, and irreversibility of the G1/S transition

Tobacco use disorder is associated with increased risk of cardiomyopathy in a population-based study from Taiwan

Using pre-training and interaction modeling for ancestry-specific disease prediction using multiomics data from the UK Biobank

Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals, underscoring a critical gap in genetic research. Prediction models trained primarily on European ancestry often fail to generalize to diverse populations, leading to reduced accuracy and potential health disparities. Here, we assess whether incorporating interaction modeling and pretraining into disease prediction models can improve performance. We evaluated the performance of Group-LASSO INTERaction-NET (glinternet) and pretrained lasso in disease prediction focusing on diverse ancestries in the UK Biobank. Models were trained on multiomic data from White British and other ancestries and validated in a cohort of more than 96,000 individuals for 8 diseases. Of the 96 trained models, we report 16 with statistically significant incremental predictive performance in terms of ROC-AUC scores ( p -value < 0.05 ), found for diabetes, arthritis, gall stones, cystitis, asthma, and osteoarthritis. Our findings suggest that interaction terms and pre-training can modestly improve prediction accuracy, but these effects are not consistent across all diseases. Our code is available at ( https://github.com/rivas-lab/AncestryOmicsUKB ).

RhoE downregulation leads to enhanced cholesterol biosynthesis and sorafenib resistance in hepatocellular carcinoma

Assessment of indoor radon exposure in kermanshah’s educational facilities, and its determinants, health risks, and mitigation strategies

The quantile time–frequency connectedness of economic policy uncertainty between China and the G7 countries

Most of the existing studies on the connectedness among economic policy uncertainties (EPUs) usually neglect the quantile and frequency domain perspectives. To address this limitation, this paper proposes a quantile time–frequency connectedness model to analyze the connectedness among EPUs by combining the quantile and frequency domain dimensions. First, the quantile-vector autoregressive model (QVAR(p)) is estimated and converted into the quantile-vector moving average representation (QVMA( ∞ )). Next, the generalized prediction error variance decomposition (GFEVD) is computed, from which various types of time-domain connectedness metrics are calculated. Finally, the spectral decomposition method is used to compute frequency-domain connectedness metrics and establish a link between time- and frequency-domain metrics. The empirical results of this paper, based on the sample data of China and G7 countries, reveal several important findings. The EPU of the United States acts as a net transmitter of shocks in both the short and long term, whereas China functions as a net receiver of shocks. The total connectedness index (TCI) demonstrates significant heterogeneity, with its dynamics primarily driven by short-term rather than long-term components. Additionally, connectedness shows substantial improvement under extreme conditions.

Towards a unified framework for the function of endoplasmic reticulum exit sites

Loss of O-GlcNAcylation in cardiac myocytes triggers the integrated stress response, contributing to heart failure

Propagation of solitary waves for hydrodynamical nonlinear complex model in a fractional derivative setting

Crafting Immune‐Cancer Cell Crosstalk via Modular Multilayer Logic DNA Nanoadaptors

Abstract Synthetic molecular recognition tools offer a promising approach to reconfigure cell‐cell interactions, but engineering user‐defined logic circuits to control intercellular communication in heterogeneous microenvironments endures as a critical barrier. Herein, we present a multilayer logic DNA nanoadaptor (Magic‐NA) to define immune‐cancer cell crosstalk by a customized logic operation of multiple variables, including cell surface antigens and microenvironmental characteristics. The modular Magic‐NA consists of a DNA di‐tetrahedral scaffold featuring three ports at each terminal. These ports can be interfaced with a library of plug‐and‐play logic connectors specifically activated by molecular triggers and targeted to desired cells, enabling programmable design of both logic circuits and inputs. We demonstrated an AND‐AND logic network in Magic‐NA that guides natural killer (NK) cells to target cells for selective cytotoxicity, based on tumor microenvironment (TME) acidic pH, and cellular antigens PTK7 and MUC1. Furthermore, leveraging the multi‐port design, we implemented an OR‐AND logic gate system to achieve on‐demand cell targeting and broad‐spectrum cell killing. This nanoplatform provides a paradigm for manipulating cellular interactions through Boolean integration of cell‐intrinsic markers and microenvironmental cues, advancing the development of precision cancer immunotherapy.

Training path of big data management and application talents based on BERTopic-TOPSIS model

This study examines the discrepancy between big data talent training and industry demand. The study analyzed 85 training programs and over 10,000 job postings from two job boards in China (51job and Zhaopin). Using content analysis, social network analysis, and the BERTopic-TOPSIS model, it mined implicit information from training programs and labeled key competencies in job descriptions. A key finding was a significant supply-demand misalignment: while “data application ability” was a stated goal in 52% of programs, only 11% of graduation requirements specified concrete, measurable skills to achieve it. The study identified three primary employment pathways for big data management and application majors: data management, data analysis, and data platform development. Institutions such as Peking University and Hefei University of Technology were identified as best practices. The study then delineated a cultivation path for the major by integrating the characteristics of these employment pathways, and optimised general knowledge and compulsory courses, core courses, graduation requirements, and the cultivation objectives of the major.

Allosteric regulation of prostaglandin endoperoxide H2 synthases