Browse Articles

Random walk based snapshot clustering for detecting community dynamics in temporal networks

CLN3 disease disrupts very early postnatal hippocampal maturation

Microbial assisted zinc biofortification of wheat germplasm for the amelioration of zinc malnutrition

Evaluation of cytotoxicity and anti-inflammatory action of AH-Plus sealer with and without petasin, pachymic acid, curcumin & shilajit: an invitro-study

A novel approach to salinity monitoring using two dimensional hexagonal photonic crystals

Dissecting the relationship between heart failure and diabetic retinopathy severity in patients with diabetes and chronic kidney disease: an observational cohort study

A degradable form of polyoma small T antigen reveals the high specificity of TAZ in regulating gene expression

The study of DNA tumor viruses has revolutionized cancer biology, partly by virtue of the unique capabilities of viral oncoproteins to manipulate key proteins and pathways involved in tumorigenesis. We find a high affinity and selective binding of the polyoma small T antigen (PyST) with the transcription cofactor TAZ. By engineering a degradable version of PyST, we demonstrate that, when TAZ activity is modulated by PyST, a surprisingly small number of genes have altered expression and thus are candidate transcription targets of TAZ. Notably, knocking out TAZ, or its target genes CTGF or CYR61, abolishes the growth-promoting properties of PyST that are evident upon growth factor withdrawal. Therefore, by controlling the protein abundance of PyST and consequently TAZ activity, we find that TAZ is a transcriptional coactivator that can achieve important biological effects by acting on a limited number of gene targets.

Reply to Ju et al.: Mechanisms of deeper soil organic carbon loss

Network analysis of the intercorrelations between quality of life, trait mindfulness, and mental health among patients with breast cancer

Tandem ssDNA in neutrophil extracellular traps binds thrombin and regulates immunothrombosis

Neutrophils release neutrophil extracellular traps (NETs) to neutralize infections, a process that also contributes to immunothrombosis. While beneficial in localized infections, excessive NET formation can lead to widespread coagulopathy and organ failure. While the roles of NET-associated proteins such as histones in immunothrombosis are well characterized, NET-derived DNAs are much less known. To address this issue, we report herein the direct interaction between thrombin and DNA scaffolds and further, the identification of short tandem repeats of single-stranded (ATTCC) n in NETs that selectively bind thrombin, a crucial enzyme involved in both blood clot formation and immune response. We have also developed a strategy of selective targeting ss(ATTCC) n using antisense locked nucleic acids (LNAs), effectively disrupting NET–thrombin interactions. This finding reveals an unexplored role of single strand DNA (ssDNA) within NETs and provides a broad avenue for developing targeted therapeutic interventions for immunothrombosis-related disorders.

Deterioration mechanism and stochastic damage modeling of tunnel lining concrete in hydrothermal corrosive environments

Predictive models of influenza A virus lethal disease yield insights from ferret respiratory tract and brain tissues

Abstract Collection of systemic tissues from influenza A virus (IAV)-infected ferrets at a fixed timepoint post-inoculation represents a frequent component of risk assessment activities to assess the capacity of IAV to replicate systemically. However, few studies have evaluated how the frequency and magnitude of IAV replication at discrete tissues contribute to within-host phenotypic outcomes, limiting our ability to fully contextualize results from scheduled necropsy into risk assessment settings. Employing aggregated data from ferrets inoculated with > 100 unique IAV (both human- and avian-origin viruses, spanning H1, H2, H3, H5, H7, and H9 subtypes), we examined relationships between infectious virus detection in four discrete tissue types (nasal turbinate, lung, brain, and olfactory bulb [BnOB]) to clinical outcomes of IAV-inoculated ferrets, and the utility of including these discrete tissue data as features in machine learning (ML) models. We found that addition of viral tissue titer data maintained high performance metrics of a predictive lethality classification ML model with or without inclusion of serially-collected virological and clinical data. Interestingly, infectious virus in BnOB was detected at higher frequency and magnitude among IAV associated with high pathogenicity phenotypes in ferrets, more so than tissues from the respiratory tract; in agreement, BnOB was the highest relative ranked individual tissue specimen in predictive classification models. This study highlights the potential role of BnOB viral titers in assessing IAV pathogenicity in ferrets, and highlights the role ML approaches can contribute towards understanding the predictive benefit of in vivo-generated data in the context of pandemic risk assessment.

The global persistence of work from home

Work from home (WFH) surged worldwide during the COVID-19 pandemic, then partially receded as the pandemic subsided. Using our Global Survey of Working Arrangements covering dozens of countries, we find that average WFH rates among college-educated employees stabilized after 2022. The average number of WFH days per week is steady at roughly 1 d per week globally from 2023 through early 2025. Cross-country variation persists: WFH is about twice as common in advanced English-speaking economies as in much of Asia. These results show how the pandemic-driven shift to remote work has persisted and reached a new equilibrium with implications for urban economies, workforce flexibility, and future research on labor markets.

Humans self-organise balance control strategies on a dynamic platform

Abstract The human body continuously detects and predicts environmental disturbances and adaptively generates corrective responses to maintain standing balance. According to dynamical systems theory, these responses are self-organised, emerging naturally during human-environment interaction. Building on this, we introduce a model predictive controller (MPC) framework to simulate postural responses to environmental perturbations caused by a dynamic underfloor platform. The model uses a four-segment biomechanical system with sensory feedback and predicts the optimal response for maintaining balance, while accounting for biomechanical constraints, as the frequency of mechanical perturbation increases. The model findings, validated by the performance of nine young participants, provide evidence that indeed postural strategies emerge autonomously from the body’s dynamic interaction with the mechanical perturbation, without manual tuning. The emergent behaviour involves non-linear transitions from ankle to knee strategy, followed by transition in the relative motion between the centre of pressure and centre of mass as platform frequency increases. We demonstrate that effective models should include ankle, knee, and hip joint motion, with hip motion being less mechanically efficient in young people. The proposed framework also overcomes the limitations of traditional models which fail to capture the transitional dynamics and provides novel insights into the self-organising nature of postural responses.

SpecTf: Transformers enable data-driven imaging spectroscopy cloud detection

Current and upcoming generations of visible-shortwave infrared (VSWIR) imaging spectrometers promise unprecedented capacity to quantify Earth system processes across the globe. However, reliable cloud screening remains a fundamental challenge for these instruments, where traditional spatial and temporal approaches are limited by cloud variability and limited temporal coverage. The Spectroscopic Transformer (SpecTf) addresses these challenges with a spectroscopy-specific deep-learning architecture that performs cloud detection using only spectral information (no spatial or temporal data are required). By treating spectral measurements as sequences rather than image channels, SpecTf learns fundamental physical relationships without relying on spatial context. Our experiments demonstrate that SpecTf significantly outperforms the current baseline approach implemented for the Earth surface Mineral dust source InvesTigation (EMIT) instrument, and performs comparably with other machine learning methods with orders of magnitude fewer learned parameters. Critically, we demonstrate SpecTf’s inherent interpretability through its attention mechanism, revealing physically meaningful spectral features the model has learned. Finally, we present SpecTf’s potential for cross-instrument generalization by applying it to a different instrument on a different platform without modifications, opening the door to instrument-agnostic data-driven algorithms for future imaging spectroscopy tasks.

Selenium protected NMRI mice against methotrexate induced testicular injury

Detecting environmentally dependent developmental plasticity in fossilized individuals

The fossil record provides the most powerful evidence of large-scale biodiversity change on Earth, but it does so at coarse and often idiosyncratic temporal scales. One critical problem that arises concerns the evolutionary consequences of individual environmental experience. Individuals respond to their environment instantaneously, whereas the resolution of most fossil records aggregates multiple paleoenvironments over time scales beyond individual lifespans. Therefore, the presence of phenotypic plasticity in deep time and the extent of its influence on macroevolution remain poorly understood. Using coupled computed tomography and laser ablation inductively coupled plasma mass spectrometry protocols, we studied the environmental dependence of developmental trajectories across three sister species of macroperforate planktonic foraminifera. A foraminiferal shell preserves all stages of the individual’s ontogeny, as well as the environmental state experienced throughout its lifetime. Generalized additive mixed effect (GAMM) models show that somatic growth rates differ among the three Menardella species and that these are inversely correlated with calcification temperature, as reconstructed from Mg/Ca measurements through ontogeny. This environmental dependence varies among species: The thermal sensitivity of individual chamber-to-chamber growth rates of Menardella limbata and Menardella pertenuis is double that seen in Menardella exilis . In contrast, no such environmental signal was recovered for architectural shape traits. Our integrated approach is widely applicable and demonstrates that detecting developmental plasticity in the fossil record is feasible. Extrapolating these techniques in deep time promises to revolutionize our understanding of the ways in which environmentally associated trait variation drove the diversification of life on Earth.

Combined impact of semantic segmentation and quantitative structure modelling of Southern pine trees using terrestrial laser scanning

Building confidence in models for complex barrier systems for radionuclides

The modeling and simulation of the Cement–clay Interaction–Diffusion field (CI-D) experiment at the Mont Terri site in Switzerland presented here demonstrates that it is possible to capture the multiscale physical and chemical features of natural and engineered barrier systems for radionuclides. The simulations are successfully carried out with the newly developed CrunchODiTi high-performance computing software that accounts for multiple continua, including a continuum representing the electrical double layer (EDL) developed along negatively charged clay particles in clay rock. The simulation also accounts for both the complex three-dimensional (3D) geometry, expected as the norm in a geological waste repository, and the anisotropy of the geological formation. In addition, the high resolution of the model makes it possible to include “skin effects” developed at the interface between highly reactive materials, in this case between the high pH cement and the circumneutral but electrostatic Opalinus Clay. The successful history matching with the field experiment demonstrates that the distinct geochemical and physical properties of the cement and the Opalinus Clay in the CI-D experiment can be accounted for. Such analyses are essential for developing a defensible safety case for the underground storage of radioactive waste.

Multivariate analysis of energy and solar performance across Dubai: insights from MANOVA and cluster analysis

Abstract Solar energy adoption became a key component in achieving the UAE’s sustainability strategy, featuring the abundance of solar irradiance in the region that tends to reduce the dependence on carbon-based resources through solar energy integration. However, despite the UAE’s solar energy adoption efforts, there is a clear gap due to the limited statistical analysis to evaluate the performance of such solar implementation across Dubai’s diverse community areas for the different building types. This study aims to investigate the performance of energy consumption and solar generation under the Shams Dubai program, specifically across the residential, commercial, and industrial sectors within various communities in Dubai. The study analyzed 93 community areas using hierarchal clustering Analysis and Multivariate Analysis of Variance to group and compare energy patterns. The clustering analysis identified three clustered groups that differ in building types and area, influencing energy consumption and solar energy generated, causing energy and solar pattern disparities. The MANOVA results confirmed a statistically significant difference with a 95% confidence interval across the three clusters. Accordingly, this study offers actionable insights for utility companies and policymakers to prioritize large-scale solar projects in high-demand commercial areas while focusing on underperforming residential areas by conducting awareness and incentive campaigns to enhance solar adoption. The study’s results enable more informed resource allocation, support progress toward Dubai’s solar adoption targets, and aid in developing tailored data-driven decisions for energy efficiency improvements. By translating statistical insights into practical strategies, this research allows decision-makers to reduce carbon emissions, optimize solar energy, and achieve sustainability objectives based on Dubai’s rapid urbanization context.