Search Results (507)

Leaves are the primary photosynthetic organs, and alterations in leaf color can affect photosynthesis and plant biomass. In an EMS-mutagenized SN9816 population, we identified two white-striped mutants, ws21-1 and ws21-2. Both mutants showed severely reduced pigment content, defective chloroplasts, and elevated reactive oxygen species. The ws21-2 allele caused a near-complete albino phenotype, while ws21-1 resulted in milder striping. Genetic mapping and cloning identified causal mutations in OsRNRS1, encoding the small subunit of ribonucleotide reductase. The G583R (ws21-1) and Y365F (ws21-2) mutations likely impair enzyme activity, disrupting the dNTP pool for plastid genome replication and causing aberrant chloroplast development. Correspondingly, the expression of genes for chlorophyll synthesis, photosynthesis, and ROS metabolism was altered. Our findings directly link nuclear-encoded nucleotide metabolism to chloroplast biogenesis and demonstrate that dNTP homeostasis is critical for maintaining photosynthetic capacity and redox balance in plants. Full article

Volatile organic compounds (VOCs) produced by Medicinal Aromatic Plants (MAPs) are bioactive signaling molecules that play key roles in plant defense, acting against pathogens and triggering resistance responses. Intercropping with VOC-emitting MAPs can therefore enhance disease resistance. This study investigated VOCs emitted by sage (Salvia officinalis) as potential resistance inducers in grapevine (Vitis vinifera) against Plasmopara viticola, the causal agent of downy mildew, under consociated growth conditions. Sage and grapevine plants were co-grown in an airtight box system for 24 or 48 h, after which grape leaves were inoculated with P. viticola. Disease assessments were integrated with grapevine leaf metabolic profiling to evaluate responses to VOC exposure and pathogen infection. Untargeted and targeted metabolomic analysis revealed that sage VOCs consistently reprogrammed grapevine secondary metabolism, without substantial differences between 24 and 48 h exposures. Lipids, phenylpropanoids, and terpenoids were markedly accumulated following VOC exposure and persisted following inoculation. Correspondingly, leaves pre-exposed to sage VOCs exhibited a significant reduction in disease susceptibility. Overall, our results suggest that exposure to sage VOCs induces signaling and metabolic reprogramming in grapevine. Further research should elucidate how grapevines perceive and integrate these signals, as well as the broader processes underlying MAP VOC-induced defense, and evaluate their translation into sustainable viticultural practices. Full article

Hand representation maps of the primate primary motor (M1) and somatosensory (SI) cortices exhibit plasticity, with their spatial extent modifiable through training. While activation and map enlargement during tapping tasks are well documented, the directionality of information flow between these regions remains unclear. We applied Information Imbalance Gain Causality (IIG) to examine the propagation and temporal dynamic of BOLD activity among Area 4 (precentral gyrus), Area 3a (fundus of the central sulcus), and SI areas (postcentral gyrus). Data were collected from both hemispheres of nine participants performing alternating right–left hand finger tapping inside a 1.5T fMRI scan. The results revealed strong information flow from both the precentral and postcentral gyri toward the sulcus during tapping task, with weaker bidirectional exchange between the gyri. When not engaged in tapping, both gyri communicated with each other and the sulcus. During active tapping, flow bypassed the sulcus, favoring a more direct postcentral to precentral way. Overtime, postcentral to sulcus influence strengthened during non task periods, but diminished during tapping. These findings suggest that M1, Area 3a, and SI areas form a dynamic network that supports rapid learning processing, where Area 3a of the sulcus may contribute to maintaining representational plasticity during complex tapping tasks. Full article

Heading date (HD) is a key adaptive trait determining wheat regional suitability, yield stability, and resilience to environmental stresses. We dissected the genetic architecture of heading date (HD) by phenotyping a doubled haploid (DH) population (178 lines, CASL7AS × ZNL12) across five environments and constructing a high-density genetic map with the wheat 55K SNP array. A total of 38 QTLs associated with HD were identified on 12 chromosomes, among which 10 were consistently detected across multiple environments. Two major stable loci, QHD.ZAFU.2B and QHD.ZAFU.4A, explained substantial phenotypic variation and were considered key regulators of heading time. Candidate gene analysis revealed Ppd-B1 (TraesCSU02G196100) as the causal gene for QHD.ZAFU.2B. Within QHD.ZAFU.4A, a zinc finger RNA-binding protein gene (TraesCS4A02G394400) exhibiting strong flag-leaf expression at the heading stage was identified as the most promising candidate. Notably, most favorable alleles were derived from ZNL12, highlighting its potential for breeding applications aimed at manipulating heading time. These results provide valuable genomic resources and molecular targets for marker-assisted selection aimed at optimizing flowering time and improving wheat adaptation. Full article

Background: Sarcoidosis is a heterogeneous, multisystem inflammatory disease with an unpredictable clinical course and limited prognostic markers. Increasing attention has focused on nutritional and metabolic factors—particularly obesity, body composition, and calcium–vitamin D metabolism—as potentially modifiable elements associated with disease development and clinical phenotype. However, the available literature remains fragmented and methodologically heterogeneous. Objective: To systematically map current evidence on the relationship between nutritional status and the development, clinical course, and prognosis of sarcoidosis, and to identify key gaps requiring further research. Methods: A scoping review was conducted in accordance with the Joanna Briggs Institute methodology and the PRISMA-ScR guidelines. PubMed, Scopus, Web of Science, Cochrane Library, EBSCO, and Google Scholar were searched for studies published between 2015 and 2025. Eligible studies included adult patients with sarcoidosis and addressed nutritional status broadly defined, encompassing anthropometric measures, body composition, immunonutritional indices, nutrition-related biomarkers, dietary factors, and supplementation practices. Due to substantial heterogeneity in exposure definitions and outcome measures, no quantitative synthesis or formal methodological quality appraisal was performed. Results: Eighteen studies, predominantly observational, were included. The most consistent findings concerned anthropometric parameters, with overweight and obesity showing the strongest association with an increased risk of sarcoidosis and, in selected studies, with reduced exercise capacity and greater disease burden. Evidence linking nutritional status to prognosis was indirect, while direct data on sarcoidosis-specific survival were lacking. Disturbances in calcium–vitamin D metabolism were frequent and clinically relevant, particularly in the context of supplementation-related hypercalcemia. Conclusions: Current evidence suggests that nutritional status—particularly excess body weight—and selected metabolic and immunonutritional factors are associated with sarcoidosis. However, given the largely observational nature of the available data and the lack of formal assessment of methodological quality, these results should be interpreted as association mapping and hypothesis generation rather than as evidence of causality. Well-designed prospective and interventional studies using standardized nutritional assessment tools and clinically relevant endpoints are needed to clarify the role of nutritional factors in sarcoidosis. Full article

Achieving sustainable development in the low-carbon transition requires securing critical raw materials (CRMs) while reducing environmental burdens and strengthening industrial resilience (SDGs 7, 9, 12, 13). This review synthesizes 2016–2025 evidence on how the European Union’s policy package—the Critical Raw Materials Act (CRMA), the Batteries Regulation, the Ecodesign for Sustainable Products Regulation (ESPR) with Digital Product Passports (DPPs), and the recast Waste Shipments Regulation (WSR)—shapes markets for secondary supply in battery-relevant metals such as lithium, cobalt, nickel, copper, aluminum, and rare earths. We apply a structured scoping review protocol to map the state of the art across policy instruments (EPR, ecodesign/DPP, recycled content mandates, recovery targets, shipment controls) and value chain stages (collection, preprocessing, refining, manufacturing). The analysis highlights benefits, including clearer investment signals, improved traceability, and emerging opportunities for industrial symbiosis, but also identifies drawbacks such as heterogeneous standards, compliance costs, and trade frictions. Evidence gaps remain, especially in causal ex post assessments, price pass-through, and interoperability of MRV/DPP systems. The paper contributes by (i) providing an integrative framework linking policy instruments, value chain stages, and investment signals for secondary CRM supply, and (ii) outlining a research agenda for rigorous ex post evaluation, improved MRV/DPP data architectures, and better alignment between EU trade rules, circularity, and a just energy transition. Full article

Background: Identifying causal genes underlying quantitative trait loci (QTLs) remains challenging due to small effect sizes and the prevalence of non-coding variants. Although multi-omics integration frameworks such as eQTL- and epigenomic-based approaches and TWAS have advanced gene prioritization, their application in poultry and livestock is often constrained by limited reference panels and tissue resources. This review introduces a cost-effective F₂-based integrative framework and compares it with existing multi-omics strategies. Methods: The proposed framework combines QTL remapping, transcriptome analysis, haplotype frequency comparison, association analysis, and conditional correlation analysis within a single workflow. Causal analysis and quantitative complementation tests using knockout birds are incorporated to identify causal genes. Results: By reusing the original F₂ population employed for QTL mapping, this approach enables hypothesis-independent gene prioritization without requiring additional fine-mapping crosses. Its effectiveness is demonstrated through comparison with conventional multi-omics methods, and the integration of causal analysis and quantitative complementation testing provides robust genetic evidence for pinpointing causal genes. Conclusions: This F₂-based framework efficiently prioritizes and verifies causal gene candidates directly within the mapping population, offering a cost-effective alternative to multi-omics approaches that require large-scale resources. It is broadly applicable to diverse chicken crosses and readily transferable to other small livestock species and model organisms. Full article

The widening digital divide within the Global Critical Mineral Resource Supply Chain (GCMRS) 4.0 creates significant barriers to cross-border governance and operational efficiency. To quantify and address this disparity, this study identifies 20 Critical Success Factors (CSFs) through expert interviews with 15 industry specialists in South Korea. A hybrid multi-criteria decision-making framework integrating Fuzzy DEMATEL, Analytic Network Process (ANP), and the Choquet integral is developed to map causal relationships and determine factor weights. The empirical results reveal a distinct ‘technology-first’ dependency. Specifically, Scalable Technical Solutions and Cloud Computing Access emerge as the primary driving forces with the highest global weights, while Digital Investment Subsidies serve as the central hub for resource allocation. Unlike generic governance models, this study provides a quantifiable decision-making basis for policymakers. It demonstrates that bridging the hard infrastructure gap is a prerequisite for the effectiveness of soft collaborative mechanisms in the critical mineral sector. Full article

Background/Objectives: Gut dysbiosis in Chronic Fatigue Syndrome (CFS) drives low-grade inflammation and shifts tryptophan metabolism toward neurotoxic pathways. The causal link between bacterial translocation, kynurenine pathway dysregulation, and symptom severity remains under-defined. We evaluated the impact of a high-concentration multi-strain probiotic on the “gut-kynurenine axis” and clinical status in CFS patients with co-morbid IBS-U and confirmed dysbiosis. Methods: Forty female patients with confirmed dysbiosis (GA-map™ Dysbiosis Index > 2) received the CDS22 formula (450 billion CFU/day) for 12 weeks. We compared urinary tryptophan metabolite profiles (LC-MS/MS), gut dysbiosis markers (3-indoxyl sulfate), and fatigue severity (FSS) against 40 age-matched healthy controls. Results: Baseline analysis revealed profound metabolic perturbations: elevated bacterial proteolytic markers (3-IS), substrate depletion (low tryptophan), and a neurotoxic signature (high quinolinic acid [QA], low kynurenic acid [KYNA]). Following the intervention, fatigue scores declined by 40.3%, with 97.5% of patients reaching the remission threshold (FSS < 36). Biochemically, 3-IS levels decreased to the range observed in healthy controls and attenuated xanthurenic acid levels. Although absolute QA concentrations remained elevated compared to controls, the neuroprotective KYNA/QA ratio increased significantly (+45%). Increased systemic tryptophan availability correlated directly with clinical symptom reduction (Spearman’s rho = −0.36, p = 0.024). Conclusions: The CDS22 formulation was associated with a restoration of intestinal eubiosis and functional tryptophan partitioning. Clinical remission coincides with a metabolic shift favoring neuroprotection (increased KYNA/QA ratio), validating the gut–kynurenine axis as a modifiable therapeutic target. Peripheral metabolic improvement relative to the healthy baseline appeared sufficient for symptom relief in this specific phenotype, despite incomplete clearance of neurotoxic metabolites. Full article

The acceleration of financial innovation and pro-crypto regulations in the digital asset space have spurred interest in cryptocurrencies among funds, and institutional and retail investors. Like any risky assets, investment in digital assets offers opportunities in terms of returns and challenges in terms of risk. However, unlike traditional assets, digital assets like cryptocurrencies are highly volatile. Accordingly, applying conventional single-criterion financial metrics for portfolio construction may not be sufficient as the method falls short in capturing the complex, multidimensional risk-return dynamics of innovative financial assets like cryptocurrencies. To address this gap, this study introduces a novel, integrated hybrid Multi-Criteria Decision-Making (MCDM) framework that provides a structured, transparent, and robust approach to cryptocurrency fund selection. The framework seamlessly integrates three well-established operations research methodologies: the Decision-Making Trial and Evaluation Laboratory (DEMATEL), the Analytic Network Process (ANP), and the Vlse Kriterijumsk Optimizacija I Kompromisno Resenje (VIKOR) algorithm. DEMATEL is utilized to map and analyze the intricate causal interdependencies among a comprehensive set of evaluation criteria, categorizing them into foundational “cause” factors and resultant “effect” factors. This causal structure informs the ANP model, which computes precise criterion weights while accounting for complex feedback and dependency relationships. Subsequently, the VIKOR algorithm is invoked to use these weights to rank cryptocurrency fund alternatives, delivering a compromise between optimizing group utility and minimizing individual regret. To illustrate the application and efficacy of the proposed method, a diverse set of 20 cryptocurrency funds is analyzed. From the analysis, it is shown that foundational criteria, such as “Fee (%)” and “Annualized Standard Deviation,” are the primary causal drivers of financial performance outcomes of funds. This proposed framework supports strategic capital allocation in a rapidly evolving domains of digital finance. Full article

A clear grasp of the interconnections among various drought types forms the foundation for effective drought mitigation policy-making. However, current research on the propagation of groundwater drought (GD) remains relatively limited. Therefore, this study employs a multi-source data approach, combining methods (such as Pearson correlation analysis, cross-convergence mapping systems, and Copula functions) to assess the characteristics and propagation patterns of meteorological (MD), agricultural (AD), and GD in the Yangtze River Basin (YRB). Findings demonstrated that (1) drought severity (mainly ranging from 3.25 to 6.49) and duration (mainly ranging from 2.6 to 5.4 months) in the upstream region (UR) of the YRB are relatively large. (2) A total of 79.92% of the regions showed a mutual feedback relationship between agricultural drought and groundwater drought. (3) The duration propagation threshold from MD to AD was relatively high in the source region (SR) (mainly ranging from 5.95 to 8.36) and the midstream region (MR) (mainly ranging from 5.68 to 7.39) under extreme drought conditions. The severity propagation threshold from AD to GD was relatively high in the MR (mainly ranging from 11.8 to 16.5) and the downstream region (DR) (mainly ranging from 14.5 to 20.2) under extreme drought conditions. This study is significant for the rational allocation of regional water resources and drought prevention policy formulation. Full article

This paper reports a design-rationale account of building and deploying a small ecosystem of AI-driven educational conversational agents with distinct pedagogical personas. Two strands target school contexts: (i) Talk to Bill, a historically grounded Shakespeare interlocutor intended to support close reading, contextual understanding, and interpretive dialogue; and (ii) Here to Help, a set of UK GCSE subject- and exam-board-specific tutors designed for formative practice in recognised question formats with feedback and iterative improvement. The third strand comprises six complementary assistants for an undergraduate Human–Computer Interaction (HCI) module, each bounded to a workflow-aligned role (e.g., empathise-stage coaching, study planning, course operations), with guardrails to privilege process quality over answer generation. We describe how persona differentiation is mapped to established learning, engagement, and motivation theories; how retrieval-augmented generation and provenance cues are used to reduce hallucination risk; and what early deployment observations suggest about orchestration, integration, and incentives. The contribution is a transferable, auditable rationale linking theory to concrete dialogue and UI moves for multi-persona tutoring ecosystems, rather than a claim of causal learning gains. Full article

This study addresses the global challenge of superficial soil contamination by heavy metals, focusing on differentiating natural geogenic sources from anthropogenic contributions in complex industrial–urban environments. We develop an integrated geostatistical and multivariate framework combining soil metal concentration analysis with AERMOD atmospheric dispersion modeling using a comparative multi-model machine learning approach (including Extreme Gradient Boosting, Random Forest, and Ridge Regression). Applied to the industrialized area of Taranto, Southern Italy, this approach incorporates spatial autocorrelation and multiple environmental predictors to identify contamination patterns and sources. The results reveal variable predictive accuracy across metals, with RF generally outperforming the other algorithms. The model achieved its highest performance for copper (R² = 0.58, RMSE = 25.82), Tin (R² = 0.53, RMSE = 5.95), and chromium, while showing instability for others. These disparities highlight the differential influence of remote sensing data on contamination mapping. The framework advances the quantitative assessment of soil pollution by linking atmospheric deposition and spatial processes with causal interpretability. Full article

Understanding the causal relationships between safety factors is essential for successful intervention in industries with intrinsically high-risk environments such as the construction industry. Therefore, the aim of this study is to employ the Interpretive Structural Modeling (ISM) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) techniques to analyze and map the interdependencies among various safety-related elements affecting construction safety. According to the results, resource allocation was shown to be the highest-level, most independent element in the analysis, highlighting its function as the primary facilitator of safety initiatives. This strategic commitment directly drives Management Commitment and Competence, which form the core organizational support structure. Mid-level elements that translate management intent into site-level practice include workers’ training, safety motivation, and communication structure. The frequency of safety observations, workers’ involvement in safety decisions, and subcontractor and procurement management—the immediate procedural controls—are then used to assess operational efficacy. Crucially, the most dependent factor was found to be Workers’ Compliance, indicating that frontline safety behavior is the result of efficient management at all higher levels. Therefore, in order to improve overall safety performance in construction, this research emphasizes the importance of improving resource provision and leadership commitment. The outputs of the current study provide an organized, evidence-based roadmap for selecting interventions. Full article

Machine Learning and Knowledge Extraction have evolved from algorithmic tools for pattern recognition into a unifying foundational scientific framework underpinning virtually all of today’s groundbreaking advances, enabling systematic discovery, interpretation and understanding across domains. This paper introduces a comprehensive research agenda that defines currently the future of innovation in Artificial Intelligence. We identify ten interrelated research frontiers that collectively map the transition from data-driven learning to knowledge-centric, trustworthy, and sustainable intelligence. These frontiers span the full spectrum of future AI research: from physics-informed and hybrid architectures that embed causality and domain knowledge, to multimodal and embedded intelligence that ground AI in real-world contexts; from interpretable and responsible design principles that ensure transparency and fairness, to safe and sustainable deployment in open-world environments. Together, these directions delineate a coherent roadmap toward AI systems that not only predict but also explain, reason, and collaborate. Future AI can be seen as a new member of your research lab, an active participant in knowledge creation, driven by interdisciplinary integration, global cooperation, ethical responsibility, and human oversight. By embedding principles of transparency, sustainability, and societal alignment from the outset, we envision AI as both a catalyst for scientific discovery and a cornerstone of responsible technological progress. Full article