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Genetic Association of the Renin-Angiotensin-Aldosterone System with hypertension among the Malays and their adaptation to climate change

Hypertension (HT) is a ‘by-product’ to the forces of natural selection against environmental drift and salt availability, therefore contributed to differential HT susceptibility. This study provides further supporting evidence through: (i) associating three salt-sensitive related candidate gene variants, to the susceptibility of HT among the Malays from Peninsular Malaysia with a detail genotype-phenotype evaluation; (ii) comparing the blood pressure and the frequency spectrums of these variants across global populations; (iii) correlating them with the geographical coordinates and BP of the respective populations, and evaluating the presence of local adaptation in these candidate variants. We tested the genetic association of six SNPs underlying CYP11B2 , AGT and ADRB2 in 918 normotensives and hypertensives Malays, men and women. CYP11B2 and ADRB2 were associated with elevated BP in males and females, respectively. Evaluation of these gene variations across 38 populations residing in different latitudinal clines revealed strong correlations between CYP11B2 , AGT and latitudinal coordinates; whilst ADRB2 to a weaker extent. Tajima’s D analyses suggested a non-neutral evolution on CYP11B2 , which suggested a modest putative signals of local adaptation. In summary, we complement the notion that effective pharmacogenetic marker(s) to predict responsiveness of anti-HT medication requires comprehensive characterization of population genetics and HT phenotypes.

Redox Conductivity in Covalent Organic Frameworks

Investigating antibody cross-reactivity and transmission dynamics of alphaviruses and flaviviruses using a multiplex serological assay

Data compression of Bridge Resilience Control: Algorithm and case analysis

Bridge inspection and structural health monitoring represent the primary approaches to managing bridge resilience. Data acquired through inspection and monitoring activities provides an effective technical basis for the systematic implementation of bridge resilience control strategies. Yet, uninterrupted monitoring and diverse inspection campaigns have yielded an enormous volume of data, which directly imposes comprehensive and stringent challenges on data storage, transmission and processing. Consequently, data compression has become a research priority in the field of bridge resilience control. However, existing data compression algorithms are all general-purpose data processing techniques, which decouple the intrinsic physical relevance between monitoring data and bridge structural behaviors. To tackle this limitation, this study integrates domain knowledge, the time-series characteristics of bridge monitoring data, and bridge deterioration models into the design of a novel data compression algorithm. This approach addresses the issue of indiscriminate data compression inherent to conventional algorithms, thereby enabling efficient data compression while preserving critical bridge structural state information. By incorporating domain knowledge, the proposed method transforms raw monitoring data into data information with engineering attributes. based on these attributes, a set of interrelated monitoring data is further converted into a small subset of key data that is directly applicable to bridge resilience control practice. Leveraging the steady-state variation law of bridge operational performance, the dynamic structural characteristics of bridges are extracted from time-series monitoring data, which correspondingly reduces the storage demand of time-series datasets. For data sampling intervals interrupted by various types of system faults, a sparse data supplementation method is proposed. After data supplementation, the complete dataset is further refined by utilizing the inherent time-series characteristics of the monitoring data, which not only ensures data integrity but also further reduces the overall data volume. Simulation analyses demonstrate that the domain knowledge-based compression method achieves a data compression ratio of 75%. Moreover, the comprehensive compression ratio exceeds 92% after the synergistic processing of time-series feature extraction and sparse data supplementation, with a data fidelity rate of 95%. These performance metrics indicate that the proposed method can reduce the data storage costs and transmission bandwidth consumption associated with bridge resilience control by 75% to 92%. Meanwhile, the 95% feature retention accuracy satisfies the engineering precision requirements for bridge resilience control assessments, which effectively reconciles the inherent contradiction between data compression efficiency and structural evaluation accuracy.

Decoupling Cyanide Activation from C–C Bond Formation in Ni-Catalyzed Cyanation of Strained Ketones Using Benzonitriles

Achieving high-performance room-temperature organic ferromagnetic semiconductor films via topochemical reduction

Abstract The development of high-performance organic ferromagnetic semiconductors has been hampered by the intrinsic coupling of radical formation and structural organization during synthesis, which makes it difficult to achieve long-range magnetic coupling in highly conjugated systems. Here, we report an effective topochemical reduction strategy that decouples radical formation from structural organization, enabling unprecedented control over intermolecular arrangements in organic ferromagnetic materials. Using perylene diimide as a model system, this approach preserves the highly ordered structure of thermally evaporated precursor films during reduction, resulting in a shortened π-π stacking distance of 3.26 Å and exceptional long-range molecular order. The resulting films exhibit remarkable room-temperature ferromagnetism, as evidenced by X-ray magnetic circular dichroism, with a saturation magnetization of 10.5 emu g⁻ 1 —nearly an order of magnitude higher than conventional organic magnetic materials—while retaining semiconducting properties. Generality of this strategy has also been demonstrated in naphthalene-based systems, underscoring its broad applicability. Theoretical calculations reveal that this enhanced performance originates from optimized ferromagnetic coupling between adjacent radicals through controlled twisted stacking configurations. This work provides a practical route to high-performance ferromagnetic semiconductors.

NSUN4-mediated m5C RNA methylation protects retinal cells against excitotoxic injury via the SHH signaling pathway

Glaucoma, a leading global cause of blindness, is characterized by progressive retinal neuronal loss. NOP2/Sun RNA methyltransferase 4 (NSUN4), a writer of 5-methylcytosine (m5C) RNA modifications, has established roles in methylation and mitoribosome assembly, yet its function in retinal cell survival remains unexplored. In this study, integrated methylated RNA immunoprecipitation sequencing (MeRIP-seq) and RNA-seq analysis in an NMDA-induced retinal injury model revealed widespread mRNA hypomethylation enriched in the Sonic Hedgehog (SHH) signaling pathway, accompanied by significant downregulation of Nsun4 . To investigate the underlying mechanisms, we utilized the R28 retinal cell line, a widely accepted model for studying retinal neuroprotection. In glutamate-stimulated R28 cells, NSUN4 overexpression mitigated excitotoxic injury, attenuating Ca² ⁺ overload, mitochondrial dysfunction, and apoptosis. Mechanistically, NSUN4 enhanced m5C methylation on key SHH pathway transcripts ( Shh, Gli1, and Gli2 ). Crucially, the neuroprotective effect of NSUN4 was abolished upon pharmacological inhibition of the SHH pathway using Vismodegib, confirming that pathway activation is essential for NSUN4-mediated protection. Clinically, NSUN4 levels were significantly reduced in the aqueous humor of patients with primary open-angle glaucoma compared to controls. Together, these findings establish NSUN4 as an m5C-dependent activator of the SHH pathway that protects retinal cells against excitotoxic injury, nominating it as a novel candidate for glaucoma neuroprotection.

Cooperative Aldehyde Chemistry Maps an Orthogonal Lysine Reactivity Landscape

Octahedral-rigidity-engineered linear dielectrics for harsh-temperature energy storage capacitors

Fiscal decentralization, local government innovation preference, and enterprise technological innovation: Evidence from China

Fiscal decentralization influences the allocation of attention and effort by local governments across multiple tasks, consequently impacting enterprise technological innovation within their jurisdictions. This represents a significant institutional constraint on corporate innovation. Employing a sample of A-share listed companies that regularly published annual financial reports from 2015 to 2023, this study constructs a moderated mediation model within a three-tier “central government – local government – enterprise” principal-agent framework. It is the first to uncover the intrinsic mechanism and property-right heterogeneity through which fiscal decentralization affects the technological innovation of microeconomic entities. The empirical findings reveal: (1) Overall, fiscal decentralization exerts a significant inhibitory effect on enterprise technological innovation, providing micro-level empirical support for the “decentralization inhibition theory”. (2) Local government preference for innovation serves as a mediator between fiscal decentralization and enterprise technological innovation. Fiscal decentralization indirectly influences enterprise innovation by shaping local governments’ innovation priorities. (3) The mediating effect of local government innovation preference exhibits property-right dependence. Fiscal expenditures on science and technology and innovation incentive policies by local governments trigger arbitrage behavior in private enterprises, creating a “crowding-out effect” on their technological innovation. As “special market entities,” state-owned enterprises (SOEs) combine the advantages of both market and governmental economic coordination mechanisms. Fiscal decentralization, by influencing local government innovation preference, effectively promotes technological innovation in SOEs. These empirical results offer policy implications for accelerating the formation of new institutional frameworks compatible with new quality productive forces, particularly concerning central-local relations, government-enterprise relations, and the functional positioning of SOEs.

Diastereo- and Enantioselective Construction of Vicinal Stereocenters through Tandem Electrochemical Dearomatization and Allylic Alkylation

Network pharmacological insight into traditional bone healing practices of Sikkim, India

Ethnopharmacological relevance Sikkim is a mountainous state situated in the Eastern Himalaya region of India, which constitutes an area with rich cultural diversity, having different traditional healthcare practices and rituals. Traditional formulations for treating bone fractures are prevalent in rural areas of Sikkim. Aim The present study was designed to document and analyse the traditional knowledge, practices, and medicinal plants used by traditional healers of Sikkim for the treatment of Bone fractures. And network pharmacological perspectives on the bone-mending properties of the medicinal plants used by the traditional healers of Sikkim. Method Semi-structured questionnaires, semi-structured interviews, and guided field walks were used in this explorative study for four years in all six districts of Sikkim, India. The quantity indices frequency of citation (FoC) and Relative Frequency of Citation (RFC) are used to authenticate the most important medicinal plant species. Further, to examine the intricate relationships among drugs, targets, and diseases, we conducted network pharmacological annotations using several advanced tools, including Swiss Target Prediction, STRING, STITCH, DAVID, GeneCodis, and SwissADME. Results The study documented 18 distinct traditional polyherbal formulations that incorporate 32 medicinal plant species native to Sikkim and are utilised for the therapeutic management of bone fractures. Notably, several plant species identified in this investigation, particularly those exhibiting high Frequency of Citation (FoC) values, represent promising candidates for further pharmacological evaluation targeting osteoregenerative properties. Additionally, four plant species, Urtica parviflora Roxb . , Saurauia napaulensis DC., Rubus calycinus Wall. ex D.Don, and Schima wallichii (DC.) Korth., employed by traditional healers in this study, warrant prioritised phytochemical investigation due to their limited scientific exploration in existing literature. The network pharmacological annotations revealed several pathways that are directly or indirectly affecting bone development, biomineralisation, calcium signalling, endochondral ossification with skeletal dysplasia, RANK signalling, RUNX2 regulation, and Vitamin D-sensitive Ca signalling. Conclusion This study systematically documents traditional treatments for bone fractures in Sikkim, highlighting 32 medicinal plants with therapeutic potential. The findings of this study will provide baseline data to address an immediate need to preserve and scientifically validate (in vivo and in vitro) indigenous ethnomedicinal knowledge. Furthermore, provide valuable insights into the development of safe and effective lead compounds by considering the biological processes, molecular functions, and cellular components involved in bone mending from natural formulations.

Machine Learning Optimization of Laser Ablation in Liquid for the Green and Low-Cost Synthesis of Clean Gold Nanoparticles

Bayesian hierarchical models for multivariate mixed responses with repeated measures: A case study in arterial occlusive disease

Modeling repeated measures of arterial occlusive diseases, such as peripheral artery disease (PAD), using data with mixed-type outcomes poses unique challenges due to complex dependency structures and diverse distributional assumptions. This study proposes a comprehensive Bayesian hierarchical modeling framework for the simultaneous analysis of binary and continuous outcomes observed repeatedly within individuals. We focus on the methodological comparison of three major Markov Chain Monte Carlo (MCMC) Bayesian computational methods—Metropolis-Hastings, Gibbs sampling, and Hamiltonian Monte Carlo (HMC) that are suitable for hierarchical models without random effects, as well as those with random intercepts and slopes, by utilizing arterial occlusive disease (AOD) data that includes repeated leg measurements on 16 patients with a total of 256 observations. We evaluate model performance across multiple criteria, including the widely applicable information criterion (WAIC), Leave-one-out information criteria (LOO-IC), K-fold cross-validation (K = 10), the Bayesian information criterion (DIC), and the BIC information complexity (ICOMP). Our results reveal that the full random effects model estimated via HMC performed better and achieved higher predictive accuracy across the considered information criteria for this small-sample, historical dataset used for modeling applications. This work emphasizes the importance of model selection strategies in hierarchical Bayesian analysis and highlights the advantages of employing modern MCMC techniques in medical applications. However, we realize that these findings may depend on the precise priors and parameterizations used and may not apply to all small-sample hierarchical datasets. Thus, expanding this model to larger, contemporary datasets will improve its generalizability and clinical relevance.

Proton-Coupled Electron Transfer in the Catalytic Hydrogenation of Hydroxyacetone: A pH-Induced Switch from Potential Pinning to Kinetic Coupling

Integrating behavioral, social, and technological factors in cryptocurrency investment decisions

Cryptocurrency markets are characterized by high volatility and behavioral biases. Although several studies have examined psychological, social, and technological factors separately, few have comprehensively integrated them within a unified framework based on behavioral finance. This study addresses this gap by examining the interplay among digital financial literacy, impulsivity, social media financial influencers, fintech self-efficacy, and attitude toward investment in shaping investment decisions in Vietnam’s cryptocurrency market. Data were collected from 505 individual investors, and partial least squares structural equation modeling was employed to test the proposed framework. The results indicate that digital financial literacy fosters positive attitudes toward investment and directly supports informed investment decision-making. However, contrary to conventional expectations, digital financial literacy is positively associated with impulsivity-related investment behavior, suggesting that higher perceived digital competence may foster overconfidence and an illusion of control in highly volatile cryptocurrency markets. Social media financial influencers significantly shape investors’ attitudes and also exert a direct influence on investment decisions, highlighting both attitudinal and behavioral pathways. Furthermore, fintech self-efficacy moderates the relationship between attitudes and investment decisions by reducing investors’ reliance on attitudinal cues when making investment choices. The findings highlight the importance of distinguishing rapid informed decision-making from affect-driven impulsivity and emphasize the role of overconfidence and perceived digital literacy in shaping investor behavior. Practically, the results call for targeted digital financial education and regulatory oversight of online financial content to promote informed and sustainable investment practices.

Socioeconomic and contextual correlates of suicidal ideation among Indonesian adults: Evidence from a multilevel analysis of the 2018 National Health Survey

Suicide is a major public health concern and a leading cause of premature death, particularly in low- and middle-income countries. In Indonesia, the true burden is likely underestimated due to stigma and underreporting. Evidence on suicidal ideation, an important precursor to suicide, remains limited. This study aims to identify individual and community correlates of persistent suicidal ideation among Indonesian adults using nationally representative data. A cross-sectional analysis was conducted using data from the 2018 Indonesia National Health Survey covering adults aged 18 years and older. District-level indicators were obtained from the 2018 Village Potential Statistics and regional economic data from the National Bureau of Statistics. Multivariable and multilevel logistic regression models identified individual and contextual factors associated with suicidal ideation, accounting for individuals nested within districts. Among 636,285 adults (mean age 42.5 years; 47.4% female), 0.87% reported persistent suicidal ideation in the past month. Higher community social capital (OR = 0.94; 95% CI 0.88–1.00) and district GDP (OR = 0.91; 95% CI 0.86–0.97) were associated with lower odds, while social deprivation increased risk (OR = 1.13; 95% CI 1.06–1.21). Lower odds of suicidal ideation were observed among women (OR = 0.56), married (OR = 0.65) or widowed individuals (OR = 0.77), those with higher education attainments (OR = 0.32 for university graduates), and resident of Java (OR = 0.61). Divorce (OR = 1.28), older age (OR = 1.83 for ages 65–74), chronic illness (OR = 1.56 for heart disease), and poor self-rated health (OR = 3.38) were linked to higher risk. Strengthening community social capital and reducing social deprivation are vital to prevent suicidal ideation in Indonesia. Interventions should address socioeconomic inequalities and improve access to health and social support, especially among older adults and those with chronic illness. Promoting inclusive economic growth and community resilience may help mitigate the underlying stressors contributing to suicidal thoughts.

Direct CO ₂ Reduction to CO with an Fe ₄ S ₄ -Based Coordination Polymer

Towards eco-friendly apple farming: Real-time codling moth monitoring using improved YOLOv10 and IoT integration

Pest-related crop losses pose a critical threat to food security and sustainable agriculture, especially in apple orchards where the codling moth (Cydia pomonella) is a major concern. This study introduces an advanced pest monitoring system that integrates an improved YOLOv10-m deep learning model with Internet of Things (IoT) technology, designed specifically for real-time detection of codling moths. The system operates on a low-power Raspberry Pi platform, making it accessible and cost-effective for widespread field deployment. By enabling precise, geolocated, and real-time monitoring of pest populations, the system facilitates the rational and timely application of pesticides—only when and where they are truly needed. This not only enhances the effectiveness of pest control but also significantly reduces excessive chemical usage, thereby minimizing harmful residues in the environment and promoting better human health outcomes. Comparative evaluation against YOLO versions 5–12 confirms the superior balance of accuracy, confidence stability, and computational efficiency of the proposed model. Aligned with the principles of Integrated Pest Management (IPM), this approach promotes eco-friendly and health-conscious farming practices. Ultimately, the study demonstrates the potential of combining AI and IoT technologies to revolutionize pest management, contributing to a more sustainable and responsible agricultural ecosystem.

Structure and Chemistry of Flat and Stepped Rh Surfaces during NO Dissociation near 1 mbar