Browse Articles

Spatiotemporal decoupling of floristic endemism in response to Qinghai–Tibet Plateau uplift

Novel insecticide resistance mutations associated with variable PBO synergy in Anopheles gambiae s.l. from the Democratic Republic of Congo

Correction to Supporting Information for Seigneur et al., Cbln2 and Cbln4 are expressed in distinct medial habenula-interpeduncular projections and contribute to different behavioral outputs

Light intensity and photoperiod interact to alter the phytonutrient profile and light-use efficiency of mizuna grown for the space diet

Retraction for Kundu et al., Selective neutralization of IL-12 p40 monomer induces death in prostate cancer cells via IL-12–IFN-γ

Democratizing cost-effective, agentic artificial intelligence to multilingual medical summarization through knowledge distillation

Abstract The increasing demand for multilingual capabilities in healthcare technology highlights the critical need for AI solutions capable of handling underrepresented languages, such as Arabic, in clinical documentation. Arabic’s unique linguistic complexities—morphological richness, syntactic variations, and diglossia—present significant challenges for foundational large language models (LLMs), especially in domain-specific tasks like medical summarization. This study introduces AraSum, a domain-specific AI agent built using a novel knowledge distillation framework that transforms large multilingual LLMs into lightweight, task-optimized small language models (SLMs). Leveraging a synthetic dataset of Arabic medical dialogues, AraSum demonstrates superior performance over JAIS-30B, a foundational Arabic LLM, across key evaluation metrics, including BLEU and ROUGE scores. AraSum also outperforms JAIS in Arabic-speaking evaluator assessments of accuracy, comprehensiveness, and clinical utility while maintaining comparable linguistic performance as measured by a modified PDQI-9 inventory. Beyond accuracy, AraSum achieves these results with significantly lower computational and environmental costs, demonstrating the feasibility of deploying resource-efficient AI models in low-resource settings for domain-specific tasks. This work underscores the potential of SLM-based agentic architectures for advancing multilingual healthcare, encouraging sustainable artificial intelligence, and fostering equity in access to care.

Tautomerism induces bending and twisting of biogenic crystals

Understanding and exploiting material flexibility through phenomena such as the bending and twisting of molecular crystals has been a subject of increased interest owing to the number of applications that benefit from these properties, such as optoelectronics, mechanophotonics, soft robotics, and smart sensors. Here, we report the growth of spontaneously bent and twisted ammonium urate crystals induced by the keto–enol tautomerism of the urate molecule. The major tautomer is native to biogenic crystals, whereas the minor tautomer functions as an effective crystal growth modifier to induce naturally bent and twisted ammonium urate crystals. We show that the degree of curvature can be tailored based on the judicious selection of growth conditions. A combination of state-of-the-art microscopy and spectroscopy techniques are used to characterize the origin of bending. Spatially resolved nano-electron diffraction and high-resolution electron microscopy of naturally bent crystals show nearly single crystallinity with local lattice deformations generated by a combination of screw and edge dislocations. These observations are consistent with photoinduced force microscopy and contact resonance atomic force microscopy, which confirmed spatially resolved changes in the intermolecular interactions and the mechanical properties throughout the cross-sectional and axial regions of bent crystals. A mechanism of bending involving the generation of regionally specific dislocations is proposed as an alternative to more commonly reported models. These findings highlight a unique characteristic of tautomeric crystals that may have broader implications for other biogenic materials.

Dynamic effects of COVID-19 vaccination on major acute cardiovascular events and mortality following SARS-CoV-2 infection in a target trial emulation study

Mutualism between degraders and nondegraders stabilizes the function of a natural biopolymer-degrading community

Natural biopolymer-degrading microbial communities drive carbon biogeochemical cycling. Within these communities, polymer degraders facilitate the growth of nondegraders by breaking down polymers through extracellular enzymes. However, the contributions of nondegraders to community dynamics, as well as the mechanisms that limit their access to degradation products, remain poorly understood. Here, we investigate EMSD5, a lignocellulose-degrading microbial community that efficiently converts corncob into isopropanol. We demonstrate that nondegraders, such as Escherichia coli , enable the growth of degraders (e.g., Lachnoclostridium sp. and Clostridium beijerinckii ) by creating anaerobic conditions and supplying biotin. Within such expanded niches, lignocellulose degradation proceeds sequentially, and the availability of breakdown products to E . coli is constrained by two interlinked processes. Specifically, Lachnoclostridium sp. produces oligosaccharides that are largely inaccessible to E . coli . A subset of these oligosaccharides is utilized by C . beijerinckii to produce monosaccharides that support E . coli growth, while glycosidase secretion by C . beijerinckii is reduced under coculture conditions. Building on these findings, we designed a synthetic consortium by coculturing C. beijerinckii with an engineered E . coli strain that expresses xylanase genes from an unculturable Lachnoclostridium . This consortium achieved isopropanol production from hemicellulose without requiring anaerobic conditions. Our findings reveal the niche-expanding role of nondegraders and the processes that constrain their access to degradation products, offering insights into maintaining stable cooperation in biopolymer-degrading communities and designing efficient consortia for biopolymer conversion.

A convolutional neural network-based deep learning approach for predicting surface chloride concentration of concrete in marine tidal zones

Abstract Chloride-induced corrosion is a major threat to the durability of reinforced concrete (RC) structures. This is especially critical in marine tidal zones, where surface chloride concentration (Cs) plays a key role in predicting chloride ingress using Fick’s second law. However, traditional assessment methods are time-consuming and impractical, necessitating advanced predictive models. This study developed a deep learning-based framework utilizing a convolutional neural network (CNN) trained on 284 samples with 11 critical features related to material composition and environmental conditions. The CNN’s performance was benchmarked against four machine learning (ML) models: stepwise linear regression (SLR), support vector machine (SVM), Gaussian process regression (GPR), and random forest (RF). Results demonstrated CNN’s superiority, achieving a coefficient of determination (R2) = 0.849 and a lower root mean square error (RMSE) = 0.18%, outperforming conventional models. Shapley additive explanation (SHAP) analysis revealed exposure time, water content, and fine aggregate as the most critical factors influencing Cs predictions. The findings highlighted the importance of material composition and environmental exposure in optimizing concrete mix designs to mitigate chloride ingress in tidal zones. This research can enhance durability assessment, proactive maintenance strategies, and service life estimation of RC structures in harsh marine environments. Furthermore, it can contribute to the sustainable development goals (SDGs) by promoting resilient infrastructure, sustainable construction practices, and improved climate adaptation strategies. By integrating deep learning in durability assessments, this study can provide a scalable, efficient solution for optimizing maintenance planning and reducing premature failures in coastal RC structures, ultimately extending their service life.

RACK1A positively regulates opening of the apical hook in <i>Arabidopsis thaliana</i> via suppression of its auxin response gradient

Apical hook development is an ideal model for studying differential growth in plants and is controlled by complex phytohormonal crosstalk, with auxin being the major player. Here, we identified a bioactive small molecule that decelerates apical hook opening in Arabidopsis thaliana . Our genetic studies suggest that this molecule enhances or maintains the auxin maximum found in the inner hook side and requires certain auxin signaling components to modulate apical hook opening. Using biochemical approaches, we then revealed the WD40 repeat scaffold protein RECEPTOR FOR ACTIVATED C KINASE 1A (RACK1A) as a direct target of this compound. We present data in support of RACK1A playing a positive role in apical hook opening by activating specific auxin signaling mechanisms and negatively regulating the differential auxin response gradient across the hook, thereby adjusting differential cell growth, an essential process for organ structure and function in plants.

The influence of physical activity on the mental health of high school students: the chain mediating effects of social support and self-esteem

Correction to Supporting Information for Gozashti et al., Horizontal transmission of functionally diverse transposons is a major source of new introns

Novel wolf-toothed forceps for single-piece intraocular lens scleral fixation in astigmatic aphakic eyes

A preclinical pig model of Angelman syndrome mirrors the early developmental trajectory of the human condition

Angelman syndrome is a neurodevelopmental disorder characterized by severe motor and cognitive deficits. It is caused by the loss of the maternally inherited allele of the imprinted ubiquitin-protein ligase E3A ( UBE3A ) gene. Rodent models of Angelman syndrome do not fully recapitulate all the symptoms associated with the condition and are limited as a preclinical model for therapeutic development. Here, we show that pigs ( Sus scrofa ) with a maternally inherited deletion of UBE3A ( UBE3A -/+ ) have altered postnatal behaviors, impaired vocalizations, reduced brain growth, motor incoordination, and ataxia. Neonatal UBE3A -/+ pigs exhibited several symptoms observed in infants with Angelman syndrome, including hypotonia, suckling deficits, and failure to thrive. Collectively, these findings are consistent with the pathophysiology and developmental trajectory observed in individuals with Angelman syndrome. We anticipate that this pig model will advance our understanding of the pathophysiology of Angelman syndrome and be used as a preclinical large animal model for therapeutic development.

Impact of an intersectoral universal workplace intervention on health related quality of life and wellbeing in a pragmatic cluster randomised trial

Abstract The intersectoral workplace intervention “health in work” (HIW), developed by the Norwegian healthcare service and labour and welfare administration, targets common musculoskeletal and mental health conditions by addressing both health and work environment factors. This study assessed the effectiveness of HIW on workers’ health-related quality of life (HRQoL) and subjective wellbeing (SWB) compared to standard inclusive work measures (IWM). A pragmatic cluster randomised controlled trial including 97 workplaces, randomized to either the HIW or IWM intervention over 12 months. HRQoL was measured using the EQ-5D-5L and the EQ-VAS, and SWB by using the satisfaction with life scale and a question on meaningful life. Measurements were taken at baseline, post-intervention period, and at a 12-month follow-up. EQ-5D-5L data were analysed using mixed-effects generalized linear models. No statistically significant difference-in-difference in HRQoL or SWB were found between the HIW and IWM groups at any time point. Participants in both groups reported high baseline levels of HRQoL and SWB. Although HIW did not yield significant improvements or detriments in HRQoL or SWB, this study contributes to addressing the knowledge gap regarding intersectoral collaboration in enhancing work and health. Further research is needed to assess broader outcomes such as healthcare utilisation and sick leave. Trial registration: The trial was prospectively registered with ClinicalTrials.gov on June 24, 2019, under the identifier NCT04000035.

Unprecedentedly high global forest disturbance due to fire in 2023 and 2024

Global forests provide key ecosystem services, from climate regulation to biodiversity habitat, but are under increasing pressure from the combined impacts of climate and land use change. Here, we show that forest disturbance due to fire is growing globally, with the most dramatic increases in intact forest landscapes, highlighting an existential threat to remaining high biomass, high biodiversity forests. The global annual area of forest disturbance due to fire for 2023 and 2024 was highest since the beginning of monitoring in 2001. Compared to 2002–2022 average annual forest disturbance due to fire, the 2023–2024 average was 2.2 times higher globally and 3 times higher in the Tropics. More than ¼ of all 2024 forest disturbance from fire occurred in tropical forests. We found a statistically significant increasing trend of forest disturbance due to fire from 2002 to 2024 in all climate domains except Subtropical. High forest, low deforestation tropical countries were not exempt, with Guyana and the Republic of the Congo experiencing record forest disturbance due to fire. Our results agree with recently estimated increases in global forest fire emissions and active fire detections. The unprecedented scale of fires in the world’s most remote forests is a potential harbinger of ecosystem tipping points. Protecting these remaining unfragmented high conservation value forests from this threat poses a daunting and as yet undeveloped policy and capacity challenge.

Goal-oriented autonomous decision-making for social robots via collaborative interactive inverse reinforcement learning approach

Modulation of COVID-19 incidence by environmental stressors is variant between pre-Omicron and Omicron periods

Abstract COVID-19 had a devastating impact on humanity. We investigated how residential air pollution (ozone (O3), nitrogen dioxide (NO2), fine particulate matter (PM2.5)) and meteorological factors (temperature (Temp), precipitation (Prec)) are associated with COVID-19 incidence in Baden-Württemberg (BW), Germany. We utilized data from the Copernicus Atmosphere Monitoring Service and the Copernicus Climate Change Service to model environmental exposure from 2020 to 2022 in postal code areas in BW. Health insurance data on SARS-CoV-2 infections were provided from the health insurance AOK BW on a quarterly level covering approximately 12 million person-years. We examined the spatiotemporal variability with a generalized additive model including various stressors, demographic factors, and area-wide data, offering a comprehensive analysis of the environmental stressor- COVI-10 incidence associations. In 2022, during the prevalence of the Omicron variant, the number of COVID-19 cases tripled compared to 2020. During the pre-Omicron period, COVID-19 incidence showed a positive association with PM2.5 (relative risk [RR] 2.41; 95% confidence interval [CI] (2.31, 2.52)), a negative association with Temp (RR 0.39 (0.32, 0.48)), and no clear or slight associations with O3, Prec, and NO2. During the Omicron period, there were either no clear or slight negative associations with Temp (RR 0.92 (0.74, 1.30)), PM2·5 (RR 0.70 (0.64, 0.79)), NO2, and Prec and a negative association with O3 (RR 0.46 (0.40, 0.53)). The analysis found clear links between environmental stressors and COVID-19 incidence, which strongly differed between pre-Omicron and Omicron periods. Consideration of environmental stressor concentration could be relevant in the management of the pandemic.

Exploring the nexus between hydroclimatic variability, population growth, land use land cover change, and long-term upper Nyong Basin River chemistry (Central Africa rainforest)