Search Results (3,934)

Background/Objectives: Previous studies suggest that nutrient deficiencies can alter immune responses in animals. However, the impact of micronutrients on autoimmune diseases like type 1 diabetes (T1D) in humans remains unclear since the described associations are based on observational data and they cannot establish causality. This study aims to examine the causal relationship between various micronutrients and T1D using Mendelian randomization (MR). Methods: We performed a two-sample MR analysis using genetic variants from genome-wide association studies (GWASs) of 17 micronutrients as instrumental variables (IVs). We analyzed T1D GWAS datasets of European (18,942 cases/520,580controls), multi-ancestry (25,717 cases/583,311 controls), Latin American/Hispanic (2295 cases/55,134 controls), African American/Afro-Caribbean (6451 cases/109,410 controls), and East Asian (1219 cases/132,032 controls) ancestries. We applied the inverse variance weighted (IVW) method in our main analysis, and additional MR estimators (MR-Egger, weighted median, weighted mode, MR-PRESSO) to address pleiotropy, and the Steiger test to test directionality in sensitivity analyses. Results: Following Bonferroni correction (p < 0.05/17), we found positive association between potassium levels and T1D risk (OR = 1.098, 95% CI [1.075, 1.122] p = 5.5 × 10⁻¹⁸) in the multi-ancestry analysis. Zinc, vitamin B12, retinol, and alpha tocopherol showed nominal associations. Vitamin C, D, K1, B6, beta- and gamma-tocopherol, magnesium, iron, copper, selenium, carotene, and folate showed no significant effects on T1D risk. For the multi-ancestry analysis, we had sufficient power to detect ORs for T1D larger than 1.065. Conclusions: Higher serum potassium levels were associated with increased T1D risk in our MR study, though supporting observational evidence is currently limited. Other micronutrients are unlikely to have large effects on T1D. Full article

Background/Objectives: Self-perceptions in childhood shape motivation, behavior, and well-being; however, their relationship to motor competence and physical fitness remains unclear. We tested whether physical fitness mediates the association between motor competence and domain-specific self-perceptions in middle childhood. Methods: In a school-based sample of 100 ten-year-olds (59 girls, 41 boys; 3 exclusions ≤ 5th MABC-2 percentile), children completed MABC-2 (motor competence), EUROFIT (physical fitness), and SPPC (self-perceptions). Principal component analysis of the nine EUROFIT tests yielded two factors: Motor Fitness (agility/endurance/flexibility/muscular endurance) and Strength/Size (handgrip and BMI). Parallel mediation models (MABC-2 → [Motor Fitness, Strength/Size] → SPPC) were estimated with maximum likelihood and 5000 bias-corrected bootstrap resamples. Benjamini–Hochberg FDR (q = 0.05) was applied within each path family across the six SPPC domains. Results: In baseline models (no covariates), Motor Fitness → Athletic Competence was significant after FDR (β = 0.263, p = 0.003, FDR p = 0.018). Associations with Scholastic (β = 0.217, p = 0.039, FDR p = 0.090) and Social (β = 0.212, p = 0.046, FDR p = 0.090) were positive but did not meet the FDR threshold. Strength/Size showed no associations with any SPPC domain. Direct effects from MABC-2 to SPPC were non-significant. Indirect effects via Motor Fitness were minor and not supported after FDR (e.g., Athletic: β = 0.067, p = 0.106, 95% CI [0.007, 0.174], FDR p = 0.251). In BMIz-adjusted sensitivity models, Motor Fitness remained significantly related to Athletic (β = 0.285, p = 0.008, FDR p = 0.035), Scholastic (β = 0.252, p = 0.018, FDR p = 0.035), and Social (β = 0.257, p = 0.015, FDR p = 0.035); MABC-2 → Motor Fitness was β = 0.235, p = 0.020. Some paths reached unadjusted significance but were not significant after FDR correction (all FDR p-values = 0.120 for indirect effects). Conclusions: Functional Motor Fitness, but not Strength/Size, showed small-to-moderate, domain-specific links with children’s Athletic (and, when adjusting for adiposity, Scholastic/Social) self-perceptions; mediated effects were small and not FDR-supported. Findings highlight the salience of visible, functional performances (e.g., agility/endurance tasks) for children’s self-views and support PE approaches that foster diverse motor skills and motor fitness. Because the study is cross-sectional and BMI-adjusted analyses are presented as robustness checks, caution should be exercised when interpreting the results causally. Full article

Acute stress impairs executive functions, and these higher-order cognitive processes are often positively associated with intelligence. Even though intelligence is generally stable over time, performance in an intelligence test can be influenced by a variety of factors, including psychological processes like motivation or attention. For instance, test anxiety has been shown to correlate with individual differences in intelligence test performance, and theoretical accounts exist for causality in both directions. However, the potential impact of acute stress before or during an intelligence test remains elusive. Here, in a research context, we investigated the effects of test anxiety and acute stress as well as their interaction on performance in the short version of the Intelligence Structure Test 2000 in its German version (I-S-T 2000 R). Forty male participants completed two sessions scheduled 28 days apart, with the order counterbalanced across participants. In both sessions, participants underwent either the socially evaluated cold-pressor test (SECPT) or a non-stressful control procedure, followed by administration of I-S-T 2000 R (parallelized versions on both days). The SECPT is a widely used laboratory paradigm that elicits a stress response through the combination of psychosocial and physical components. Trait test anxiety scores were obtained via the German Test Anxiety Inventory (TAI-G). Stress induction was successful as indicated by physiological and subjective markers, including salivary cortisol concentrations. We applied linear mixed models to investigate the effects of acute stress (elicited by our stress manipulation) and test anxiety on the intelligence quotient (IQ). The analysis revealed that neither factor had a significant effect, nor was there a significant interaction between them. Consistent with these findings, Bayesian analyses provided evidence supporting the absence of these effects. Notably, IQ scores increased significantly from the first to the second testing day. These results suggest that neither test anxiety nor stress is significantly impacting intelligence test performance. However, improvements due to repeated testing call for caution, both in scientific and clinical settings. Full article

Digital transformation and sustainable orientation have become key drivers of economic development within the European Union. This study investigates how progress in digitalization and sustainable orientation influences economic outcomes. To address this objective, we apply a combination of techniques, including factor analysis to reduce dimensionality and identify underlying structures, generalized linear models to estimate causal connections and cluster analysis to group countries with similar profiles. The findings highlight strong complementarities between digital transformation and sustainable development in nurturing higher levels of economic outcome, with digital readiness amplifying the effects of sustainable development practices. Moreover, cluster analysis methods reveal significant asymmetries among EU countries, underlining persistent regional disparities in the pace of digital and sustainable transitions. The study concludes that a systems-based approach to managing the twin transition is essential for promoting convergence, competitiveness, and resilience in the EU economic system. Full article

Background: G-quadruplexes (G4s) are non-canonical higher-order nucleic acid structures that form at guanine-rich motifs, with features spanning both secondary and tertiary structural levels. These dynamic structures play pivotal roles in diverse cellular processes. Endogenous G4s (eG4s) function through their dynamically formed structures, prompting the hypothesis that their thermostability, as a key structural property, may critically influence their functionality. This study investigates the relationship between G4 stability and other functional genomic signals within eG4 regions and examines its broader impact on chromatin organization and transcriptional regulation. Methods: We developed a mapping strategy to associate in vitro-derived thermostability metrics and multi-omics functional signals with eG4 regions. A stability-centric analytical framework combining correlation analysis and causal inference using the Bayesian networks was applied to decipher causal relationships between G4 stability and the other related signals. We further analyzed the association between the stability of transcription start site (TSS)-proximal eG4s and the biological functions of their downstream genes. Results: Our analyses demonstrate that G4 thermostability exerts causal effects on epigenetic states and transcription factor binding, thereby influencing chromatin and transcription regulation. We further show distinct network architectures for G4-binding versus non-binding transcription factors. Additionally, we find that TSS-proximal eG4s are enriched in genes involved in core proliferation and stress-response pathways, suggesting that eG4s may serve as regulatory elements facilitating rapid stress responses through genome-wide coordination. Conclusions: These findings establish thermostability—though measured in vitro—as an intrinsic property that shapes eG4 functionality. Our study not only provides novel insights into the functional relevance of G4 thermostability but also introduces a generalizable framework for high-throughput G4 data interpretation, significantly advancing the functional decoding of eG4s across biological contexts. Full article

Mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer’s disease (AD), comprises three potential trajectories: reversion, stability, or progression. Accurate prediction of these trajectories is crucial for disease modeling and early intervention. We propose a novel analytical framework that integrates a healthy control–AD difference template (HAD) with a large-scale Granger causality algorithm based on long short-term memory networks (LSTM-lsGC) to construct effective connectivity (EC) networks. By applying principal component analysis for dimensionality reduction, modeling dynamic sequences with LSTM, and estimating EC matrices through Granger causality, the framework captures both symmetrical and asymmetrical connectivity, providing a refined characterization of the network alterations underlying MCI progression and reversion. Leveraging graph-theoretical features, our method achieved an MCI subtype classification accuracy of 84.92% (AUC = 0.84) across three subgroups and 90.86% when distinguishing rMCI from pMCI. Moreover, key brain regions, including the precentral gyrus, hippocampus, and cerebellum, were identified as being associated with MCI progression. Overall, by developing a symmetry-aware effective connectivity framework that simultaneously investigates both MCI progression and reversion, this study bridges a critical gap and offers a promising tool for early detection and dynamic disease characterization. Full article

The genus Agave (family Asparagaceae) represents the second-most important group of plants in Mexico. Several fungal species have been identified as causal agents of leaf spot disease affecting Agave salmiana and A. lechuguilla, producing necrotic lesions that compromise plant health and productivity. Pathogenicity experiments were conducted under greenhouse conditions, field tests were performed, and in vitro antagonism using Trichoderma asperellum strain JEAB02 against selected pathogenic isolates was evaluated. Phylogenetic analysis of genomic DNA fragments allowed the identification of 26 fungal isolates belonging to Curvularia lunata, C. verruculosa, Bipolaris zeae, Alternaria alternata, Fusarium lactis, Epicoccum sorghinum, Myrmaecium rubricosum, Penicillium diversum, and Aspergillus oryzae. In pathogenicity assays under greenhouse conditions on A. salmiana and A. lechuguilla, treatments T5–T12 exhibited statistically similar levels of disease severity (33.10–37.29%), caused mainly by C. verruculosa, A. alternata, B. zeae, and F. lactis. In field tests, Agave plants inoculated with the selected pathogenic fungi (T4, T5, T7, T8, T10, and T11) showed 21.07–36.73% leaf damage after 75 days. The antagonistic effect of T. asperellum JEAB02 caused complete (100%) growth inhibition of the pathogenic isolate JCPN27 and inhibition levels from 99.81 to 99.98% for isolates JCPN18, JCPN24, JCPN28, JCPN29, JCPN31, and JCPN33, demonstrating its high potential as a biological control agent. Full article

This study investigates how short-term naturalistic immersion shapes the development of evaluative narrative competence in Japanese junior high school students learning English as a foreign language. While prior second language acquisition (SLA) research has established the benefits of input-rich environments, little is known about how implicit learning during brief immersion experiences supports higher-order storytelling skills. To address this gap, we analyzed students’ performance on a standardized problem-solving task and a storytelling task before and after a one-month homestay abroad. Results showed significant post-immersion gains in narrative complexity, with longer stories, greater use of causal and evaluative devices, and increased diversity of expression. Regression analysis revealed that the age of first English exposure strongly predicted outcomes: early starters demonstrated broader and more sophisticated use of evaluative strategies than later starters. These findings suggest that short-term immersion can substantially enhance narrative competence, particularly for learners with early exposure, while highlighting the need for tailored pedagogical interventions to help later starters capitalize on implicit learning opportunities. Full article

In tightly coupled Industrial Control Systems (ICS), abnormal disturbances often propagate throughout the process, triggering a large number of time-correlated alarms that exceed the handling capacity of the operator. Consequently, a key challenge is how to leverage the directional and temporal characteristics of disturbance propagation to alleviate alarm overload. This paper proposes a delay-sensitive causal inference approach for industrial alarm analysis to address this problem. On the one hand, time delay estimation is introduced to precisely align the responses of two sensor sequences to disturbances, thereby improving the accuracy of causal relationship identification in the temporal domain. On the other hand, a multi-scale subgraph fusion strategy is designed to address the inconsistency in causal strength caused by disturbances of varying intensities. By integrating significant causal subgraphs from multiple scenarios into a unified graph, the method reveals the overall causal structure among alarm variables and provides guidance for alarm mitigation. To validate the proposed method, a case study is conducted on the Tennessee Eastman Process. The results demonstrate that the approach identifies causal relationships more accurately and reasonably and can effectively reduce the number of alarms by up to 51.6%. Full article

The issue of modeling the personal innovativeness of project team members is determined in this study. Findings from prior research on social capital associated with innovations and innovative activities reveal that social capital factors such as trust, social networks and connections, and social values determine a person’s attitude to innovations. Different connections involved in bridging (external) and bonding (internal) social capital can create conflict between project team members in different ways. To stimulate innovation in a conflict environment, a specially configured conflict management system is required that is capable of regulating the strength and intensity of the relationship between project team members. This paper analyzes the relationship between three constructs—innovativeness, social capital, and conflict. The existence of these latent constructs, which are formed by observable indicators of employees, is proven using confirmatory factor analysis (CFA). The construct of innovativeness depends on indicators such as creativity, risk propensity, and strategicity. Social capital includes observable indicators such as trust, social networks and connections, and social norms and values. Conflict consists of observable indicators of conflict between tasks, processes, and relationships. Using structural equation modeling (SEM), the causal relationship between social capital and innovativeness is substantiated with the mediating role of conflict in project groups between its participants—innovators and adaptors. The developed sociodynamic model for measuring conflict between innovators and adapters examines the required values of the controlled parameters of intra-group and inter-group connections between innovators and adapters in order to achieve equilibrium conflict dynamics, resulting in cooperation between them. This study was conducted using data from a survey of employees of a research organization. All model constructs were tested on a sample of employees as a whole, as well as for groups of innovators and adaptors separately. Full article

Offline reinforcement learning suffers from critical vulnerability to data corruption from sensor noise or adversarial attacks. Recent research has achieved a lot by downweighting corrupted samples and fixing the corrupted data, while data corruption induces feature entanglement that undermines policy robustness. Existing methods fail to identify causal features behind performance degradation caused by corruption. To analyze causal relationships in corrupted data, we propose a method, Robust Causal Feature Disentanglement(RCFD). Our method introduces a learnable causal feature disentanglement mechanism specifically designed for reinforcement learning scenarios, integrating the CausalVAE framework to disentangle causal features governing environmental dynamics from corruption-sensitive non-causal features. Theoretically, this disentanglement confers a robustness advantage under data corruption conditions. Concurrently, causality-preserving perturbation training injects Gaussian noise solely into non-causal features to generate counterfactual samples and is enhanced by dual-path feature alignment and contrastive learning for representation invariance. A dynamic graph diagnostic module further employs graph convolutional attention networks to model spatiotemporal relationships and identify corrupted edges through structural consistency analysis, enabling precise data repair. The results exhibit highly robust performance across D4rl benchmarks under diverse data corruption conditions. This confirms that causal feature invariance helps bridge distributional gaps, promoting reliable deployment in complex real-world settings. Full article

Background/Objectives: X-linked intellectual disability (XLID) is a highly heterogeneous disorder accounting for ~10% of all males with ID. Next-generation sequencing (NGS) has revolutionized the discovery of causal XLID genes and variants; however, many cases remain unresolved. We present a four-generation syndromic XLID family with multiple males exhibiting variable degree of ID, focal dystonia and epilepsy. Methods: Extensive cytogenetic and targeted genetic testing was initially performed, followed by whole-exome sequencing (WES) and short-read whole-genome sequencing (WGS). Apart from the routine NGS analysis pipelines, sequencing data was revisited by focusing on poorly covered/mapped regions on chromosome X (chrX), to potentially reveal unidentified clinically relevant variants. Candidate variant validation and family segregation analysis were performed with Sanger sequencing. Results: All initial diagnostic testing was negative. Subsequently, 300 previously reported “dark” chrX coding DNA sequences, overlapping 97 genes, were cross-checked against 29 chrX genes highly associated (p < 0.05) with ID and focal dystonia, according to Phenomizer. Manual inspection of the existing NGS data in two low-coverage regions, chrX:25013469-25013696 and chrX:111744737-111744820 (hg38), revealed a recurrent pathogenic ARX variant NM_139058.3:c.441_464dup p.(Ala148_Ala155dup) (ARXdup24) associated with non-syndromic or syndromic XLID, including Partington syndrome. Sanger sequencing confirmed ARXdup24 in all affected males, with carrier status in their unaffected mothers, and absence in other unaffected relatives. Conclusions: After several years of diagnostic odyssey, the pathogenic ARXdup24 variant was unmasked, supporting a genotype–phenotype correlation in the first Partington syndrome family in Cyprus. This study highlights that re-examining underrepresented genomic regions and using phenotype-driven tools can provide critical diagnostic insights in unresolved XLID cases. Full article

In recent years, with the development of distributed photovoltaic (PV) systems, their impact on power grids has become increasingly significant. However, the complexity of meteorological variations makes the prediction of distributed PV power challenging and often ineffective. This study proposes a short-term power forecasting method for distributed photovoltaics that can identify seasonal characteristics matching weather types, enabling a deeper analysis of complex meteorological changes. First, historical power data is decomposed seasonally using the adaptive noise complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Next, each component is reconstructed based on a characteristic similarity approach, and a two-stage feature selection process is applied to identify the most relevant features for reconstruction, addressing the issue of nonlinear variable selection. A CNN-LSTM-KAN model with multi-dimensional spatial representation is then proposed to model different weather types obtained by the K-shape clustering method, enabling the segmentation of weather processes. Finally, the proposed method is applied to a case study of distributed PV users in a certain province for short-term power prediction. The results indicate that, compared to traditional methods, the average RMSE decreases by 8.93%, the average MAE decreases by 4.82%, and the R² increases by 9.17%, demonstrating the effectiveness of the proposed method. Full article

Pakistan’s fisheries sector is vital for livelihoods, exports, and food security, yet growth has been constrained by weak infrastructure, limited compliance with sanitary standards, and underinvestment. The China–Pakistan Economic Corridor (CPEC) has been promoted as a driver of trade facilitation, but its actual effect on fisheries exports remains unclear. This study analyzes export performance to five leading Asian markets—China, Thailand, Vietnam, Saudi Arabia, and Japan—over 2005–2024 using Interrupted Time Series (ITS) and Difference-in-Differences (DiD) models. Results show that overall fisheries exports averaged 1.25 million metric tons (USD 728.7 million) annually, with Asia absorbing 59% of trade. ITS results show that after 2015, there are considerable structural discontinuities in export paths, mainly for China (coefficient = −1.42, p < 0.001) and Thailand (0.95, p = 0.071). DiD analysis confirmed that CPEC had a statistically significant positive impact: the treatment × post-2015 effect was 0.55 (p = 0.050), showing that exports to China and Thailand grew disproportionately compared with control markets (Malaysia, Indonesia). Importantly, value growth outpaced volume growth, suggesting early evidence of value-chain upgrading. By contrast, Vietnam and Saudi Arabia showed contraction, and Japan remained stable with weak significance (−1.16, p = 0.088). These results provide the first causal evidence that CPEC’s operational phase altered Pakistan’s fisheries export dynamics, though benefits remain uneven. The conclusions indicate the necessity to invest specifically in cold chains, certification, and aquaculture to generate corridor-led benefits in sustainable trade, food security, and long-term sectoral resiliency. Full article

Causal discovery in time series data presents a significant computational challenge. Standard algorithms are often prohibitively expensive for datasets with many variables or samples. This study introduces and validates a heuristic approximation of the VarLiNGAM algorithm to address this scalability problem. The standard VarLiNGAM method relies on an iterative refinement procedure for causal ordering that is computationally expensive. Our heuristic modifies this procedure by omitting the iterative refinement. This change permits a one-time precomputation of all necessary statistical values. The algorithmic modification reduces the time complexity of VarLiNGAM from $O\left(m^{3}n\right)$ to $O(m^{2}n+m^3)$ while keeping the space complexity at $O\left(m^2\right)$, where m is the number of variables and n is the number of samples. While an approximation, our approach retains VarLiNGAM’s essential structure and empirical reliability. On large-scale financial data with up to 400 variables, our algorithm achieves up to a 13.36× speedup over the standard implementation and an approximate 4.5× speedup over a GPU-accelerated version. Evaluations across medical time series analysis, IT service monitoring, and finance demonstrate the heuristic’s robustness and practical scalability. This work offers a validated balance between computational efficiency and discovery quality, making large-scale causal analysis feasible on personal computers. Full article