Search Results (677)

Salt-affected soil constrains maize establishment, and rhizosphere and non-rhizosphere responses to soil biochar remain difficult to establish. We evaluated soil physical and nitrogen properties, qPCR marker genes, 16S and ITS communities, and molecular profiles of dissolved organic matter (DOM) in a 90-day randomized complete block pot experiment (four biochar rates; five blocks; 20 pots). Control maize did not survive; control pots therefore yielded only non-rhizosphere soil, whereas rhizosphere and non-rhizosphere samples from biochar pots were paired. Block-adjusted comparisons showed higher water content and porosity and lower bulk density in all biochar non-rhizosphere groups than in the control (Holm-adjusted p < 0.05). Biochar-associated shifts in mineral-N partitioning and selected DNA-level marker-gene abundances accompanied design-aware community differences for NR treatment, R dose and pooled compartment (16S R² = 0.360, 0.435 and 0.218; ITS R² = 0.720, 0.712 and 0.304; all p < 0.001); fungal differences also included heterogeneous dispersion. D90 FT-ICR MS profiles described relative DOM molecular variation, but cross-layer residual associations did not survive false-discovery rate correction. Thus, biochar-associated physical and nitrogen changes coincided with microbial and relative DOM restructuring under the tested pot conditions. Because control survival was confounded with treatment, these are observed-group associations rather than pure causal biochar effects. Full article

Real-time event detection from social media has become increasingly important for emergency response, public safety, and situational awareness. However, accurately identifying emerging events from Thai social media remains challenging because Thai is a low-resource language without explicit word boundaries, while social media text is often characterized by informal writing, spelling variations, and noisy user-generated content. This paper proposes an entity-centric real-time event detection framework for Thai social media that employs a Multi-Granularity Thai Character Cluster (TCC)-Aware Named Entity Recognition (NER) model as its core information extraction component. The proposed NER architecture integrates contextual word embeddings, character-level representations, Thai Character Cluster features, and Part-of-Speech embeddings through an attention-based feature fusion mechanism to improve entity recognition under noisy conditions. Recognized entities are subsequently used as semantic anchors for event construction, clustering, temporal trend analysis, event ranking, and alert generation within a unified streaming framework. Event discovery combines Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Exponential Moving Average (EMA)-based temporal analysis to identify emerging events in real time. Experiments conducted on a large-scale Thai social media corpus demonstrate that the proposed model achieves an F1-score of 94.14% for named entity recognition and 92.2% for downstream event detection, outperforming representative baseline methods. Additional ablation studies, qualitative error analysis, and statistical significance tests confirm the effectiveness of the proposed multi-granularity representation. These results demonstrate that the proposed framework provides an effective solution for real-time event monitoring in low-resource language environments. Full article

This article presents and analyses an interview with Cristóbal Balenciaga published in ¡Hola! in 1954, previously absent from the academic corpus on the Spanish couturier. The interview constitutes a significant discovery that substantially expands the documentation available on the designer, who has traditionally been described as reluctant to engage with the media. The study situates the interview within a dual context: first, the business and geopolitical environment of postwar haute couture, shaped by tensions between Paris, Spain, and the American market; and second, the editorial context of ¡Hola! during the 1950s, marked by its rapid expansion and transnational circulation. Through a qualitative analysis combining historically grounded approaches based on primary sources, studies on the journalistic interview genre, and close textual reading, the article examines the narrative strategies employed by journalist José A. Irurozqui, the strategic choice of publication, and the content of Balenciaga’s statements. The findings demonstrate that the prominence given to the designer’s first-person voice, the journalist’s narrative framing, and ¡Hola!’s editorial positioning all contributed to consolidating a public image of the couturier and of his brands in Paris and Spain. Full article

Background: Peganum harmala L. stem ethanolic extract (PHL) has been scarcely evaluated as a ruminant feed component. We evaluated its effects on serum inflammatory and antioxidant biomarkers and duodenal and hepatic molecular responses in Hu sheep. Methods: Twelve male Hu sheep, approximately 120 days old, were randomly assigned to a control group or a PHL treatment group (n = 6 per group). After a 7-day adaptation period, the PHL group received 3 g/head/day of PHL (total flavonoid content: 65.7 mg rutin equivalents (RE)/g; approximately 197.1 mg RE/head/day) for 50 days. Serum markers, duodenal untargeted liquid chromatography–mass spectrometry (LC–MS) metabolomics, liver RNA sequencing, and alternative splicing were analyzed. Results: Serum interleukin-6 and malondialdehyde were lower, whereas interleukin-4 and glutathione peroxidase were higher, in the PHL group than in the control group (p < 0.05). Using variable importance in projection ≥ 1, fold change ≥ 1.2 or ≤0.83, and unadjusted p < 0.05 as screening criteria, 479 LC–MS features met these nominal screening criteria, of which 81 had database annotations. Following Benjamini–Hochberg correction, only two features remained significant (q < 0.05); however, only one was successfully annotated, rendering the pathway analysis exploratory. Liver RNA sequencing identified 279 differentially expressed genes (absolute log₂FC ≥ 1 and q ≤ 0.001) and 104 skipped-exon events (false discovery rate < 0.05). Functional enrichment analysis highlighted annotations related to bile secretion, ATP-binding cassette transport, nutrient metabolism, and glutathione metabolism. Conclusions: PHL supplementation was associated with selective changes in serum inflammatory and antioxidant biomarkers, accompanied by hepatic molecular responses. The duodenal metabolomic findings remain exploratory, and dose–response studies incorporating alkaloid quantification and targeted molecular validation are required before practical application can be considered. Full article

Plant genomes exhibit extensive structural diversity generated by large-scale genomic alterations, collectively known as structural variations (SVs). Unlike single nucleotide polymorphisms (SNPs) and small insertions/deletions (indels), SVs can reshape genome architecture through changes in sequence content, gene dosage, regulatory landscapes, and chromosome organization. Recent advances in long-read sequencing (LRS), pan-genome construction, and multi-omics technologies have greatly expanded our ability to identify and interpret SVs across plant species. In this review, we summarize recent progress in understanding the formation mechanisms, classification, and functional consequences of plant SVs. We discuss major sources of SV generation, including transposable element activity, non-allelic homologous recombination (NAHR), horizontal gene transfer (HGT), and genome restructuring following polyploidization. We further highlight how LRS and graph-based pan-genomes overcome limitations of traditional linear reference genomes and enable more comprehensive characterization of genetic diversity. Beyond variant discovery, we emphasize the importance of integrating genomic, transcriptomic, epigenomic, proteomic, metabolomic, and spatial omics datasets to decipher how SVs influence gene regulation and complex agronomic traits. We also discuss current challenges, including repetitive genomes, polyploidy, computational complexity, and translation of SV knowledge into practical breeding applications. Together, these advances establish SV-centered genomics as a critical framework for understanding plant genome evolution and accelerating precision crop improvement. Full article

Design studio education requires sustained attention and prolonged occupancy of a shared workspace, making environmental opportunities for brief recovery potentially relevant to students’ well-being. This exploratory study used affordance theory as an interpretive lens to examine the environmental modifications students proposed from their assigned desk positions and the extent to which sketch content was associated with academic year, studio location, and desk-level window view access. The dataset comprised 71 structured sketches, geometric view metrics for 85 workstations that were linked to the 71 sketch participants for inferential analyses, and a nonmatched lighting survey administered to the same cohorts one year later (n = 57). Sketches were coded into 17 modification categories. Water and Collaborative/Social Space were retained descriptively but excluded from inferential interpretation because of quasi-complete separation. Fifteen ordinary logistic regression models were evaluated with Benjamini–Hochberg false-discovery-rate correction, together with a complementary specification omitting studio location. Plants (73.2%), natural shapes/patterns (52.1%), and electric light quality (52.1%) were the most frequent modifications. In the primary models, Technology (q = 0.033) and Plants (q = 0.047) retained FDR-supported omnibus fit; only the academic-year coefficient for Technology retained FDR support (OR = 0.17, 95% CI [0.06, 0.50], q = 0.021). The study identifies environmental preferences and hypotheses for future intervention in restorative learning environments research. Full article

This systematic literature review examines the application of artificial intelligence (AI) and natural language processing (NLP) techniques in intelligent business document processing. The study systematically analyses 46 peer-reviewed articles published between 2014 and 2025 and indexed in the Scopus database. The reviewed literature was grouped into six core NLP-based analytical tasks: semantic search, question answering, summarisation, text data integration and matching, event extraction, and business process management. The findings show that AI- and NLP-based methods have significantly improved the automation, retrieval, interpretation, and structuring of business documents. Semantic search methods enhance information retrieval by moving beyond keyword matching, while question-answering systems and summarisation techniques support automated knowledge discovery and content reduction. Deep learning and transformer-based models have also improved entity matching, event extraction, and predictive business process monitoring. However, the review identifies several persistent limitations, including the continued dominance of extractive approaches, limited adoption of abstractive summarisation, insufficient integration of knowledge graphs, fragmented system development, limited enterprise-scale validation, and a lack of reusable code and shared resources. The findings further indicate that large language models (LLMs), particularly when combined with prompt engineering, retrieval-augmented generation, knowledge graphs, and agent-based architectures, offer promising opportunities to address these gaps. Overall, this review highlights both the progress and remaining challenges in developing scalable, explainable, and domain-adaptable AI-driven systems for intelligent business document processing. Full article

The swimming crab, Portunus trituberculatus, holds significant economic value in aquaculture, and marker-assisted selection (MAS) offers a powerful strategy to accelerate genetic improvement of growth-related traits. In this study, we performed an association analysis between 40 SSR markers and growth traits in 244 P. trituberculatus individuals. High genetic diversity was observed, with mean Shannon’s diversity index (SI) = 1.970, expected heterozygosity (He) = 0.773, and polymorphism information content (PIC) = 0.747. Linkage disequilibrium (LD) analysis revealed that all 485 marker pairs were in linkage equilibrium (r² < 0.33), validating the suitability of these markers for association mapping. A total of eight SSR markers were identified as significantly associated with growth traits (p < 0.05, FDR < 0.05). Notably, marker PrMa04 was associated with six traits (FCW, CW, CL, MLC, BH, BW), suggesting pleiotropic effects. Marker TRAN20 explained the highest phenotypic variance (PVE = 17.06%) for CL. Sequence analysis revealed that PrMa01, PrMa05, and ZL06 are located near genes encoding zinc finger protein, receptor-type tyrosine-protein phosphatase, and mucin-2, respectively, which are known to be involved in growth and development. The eight candidate markers identified in the discovery population were not replicated in an independent cohort after FDR correction, suggesting that their effects may be population-specific. These findings provide valuable SSR markers and genetic insights for MAS programs in P. trituberculatus, while also highlighting the critical importance of cross-population validation before routine application. Full article

A set of “stylized facts” about state-backed influence operations now circulates across journalism, policy, and the peer-reviewed literature: that they are monolithic troll armies; that they win by weaponizing moral-emotional language; that they manufacture their own virality; that they learn and optimize against feedback; and that they have become indistinguishable from ordinary users. Most rest on single-campaign studies, uncontrolled comparisons, and large-sample significance reported without baselines or multiple-comparison control, and are rarely re-tested. We assemble complete, government-attributed archives of seven state campaigns (25,076,853 tweets from 9071 accounts) with a matched organic-user baseline for five of them, and re-test all five claims under one protocol: pre-registration, Benjamini–Hochberg false-discovery control, permutation nulls, a future-reception placebo, and a takedown-snapshot decomposition. Scoped to these campaigns, every claim weakens or reverses: the operations are narratively segregated, thinly staffed production desks, not a unified army; an organic moral-contagion law fails to replicate in any of them, and a meaningless placebo predicts engagement as well; internal amplification supplies only $0.10$–$5.31\%$ of top-percentile reach; the remainder is captured from an external audience whose composition—genuine organic uptake versus coordination the archive cannot see—is structurally unobservable in takedown data, a limit we state as part of the finding; behavior is scripted, with rare apparent feedback mean-reverting toward baseline; and, while their per-account language has drifted off the 2016 “troll” fingerprint, they still coordinate 7–$70\times$ more tightly than matched real users—a regularity a frozen re-test reproduces, with the language-drift and segregation patterns, across twelve further country-groups. The five verdicts do not carry equal evidentiary weight: three rest on matched-baseline contrasts, one on a placebo-gated temporal design, and one (the moral-emotional claim) on a placebo-anchored test alone, a hierarchy this paper makes explicit. The corrected picture is coherent: an industrial content factory siloed in production but coordinated in execution, whose reach it does not internally manufacture. The detectable signature has migrated from language to coordination: per-account content fingerprints age out, while cross-account coordination remains the durable, cross-national marker. Because these operations run scripts rather than optimize against feedback, an adversary that genuinely optimized—now feasible with large language models—would look measurably different from the operations studied here: a forward warning, not a present finding. The recurring lesson is methodological: on corpora of confirmed manipulation, baseline-free significance reconstructs the analyst’s expectations, and platform and policy decisions rest on those beliefs. Full article

Background and Objectives: Psychiatric symptoms and multidimensional impulsivity may be associated with the clinical and functional heterogeneity of substance use disorders (SUDs), but evidence from Saudi Arabian treatment populations remains limited. This study examined concurrent associations with a study-specific aggregate of Addiction Severity Index (ASI-5) interviewer severity ratings. Materials and Methods: This cross-sectional study included 204 adults receiving inpatient detoxification or residential rehabilitation services in Najran, Saudi Arabia. The seven ASI interviewer ratings were summed as an aggregate score. Psychiatric symptoms were assessed using the Brief Psychiatric Rating Scale (BPRS), and impulsiveness using the Barratt Impulsiveness Scale-11 (BIS-11). Analyses included age- and sex-adjusted analyses of covariance, false-discovery-rate correction, bootstrap Spearman correlations, an a priori multivariable model with HC3 robust confidence intervals, and sensitivity analyses addressing content overlap, multicollinearity, influential observations, functional form, and the bounded outcome distribution. Results: The mean age was 34.4 ± 7.3 years; 78.9% were male. The aggregate score was 26.33 ± 4.15. It correlated strongly with BPRS total (ρ = 0.874, 95% CI 0.827–0.908) and moderately with BIS-11 total (ρ = 0.590, 95% CI 0.494–0.669). In the primary model (adjusted R² = 0.864), age, BPRS total, motor impulsiveness, and non-planning impulsiveness were conditional correlates of greater aggregate burden; male sex was inversely associated, and polysubstance use was not. BPRS remained associated after exclusion of the ASI psychiatric domain. Separate impulsivity-domain and BIS-11 total models, 5000-resample bootstrap analysis, median regression, and fractional-logit analysis retained the principal psychiatric and impulsivity directions. The polysubstance coefficient varied across sensitivity models. Conclusions: Psychiatric symptom burden and selected impulsivity dimensions were concurrently associated with higher study-specific aggregate ASI interviewer severity scores. Integrated psychiatric and impulsivity assessment may help characterize patients with greater concurrent multidomain clinical burden; the aggregate score should not be interpreted as a validated global severity or prediction measure. Full article

Route discovery in ad hoc on-demand distance vector (AODV)-based mobile ad hoc sensor networks relies on route request (RREQ) dissemination, which improves reachability, but generates redundant rebroadcasts, channel contention, delay, and energy waste. Fixed probabilistic rebroadcasting mitigates broadcast storms, but its performance depends on a manually selected forwarding probability applied uniformly across different local redundancy conditions. This work proposes the Thompson-sampling probabilistic AODV (TSP-AODV), a lightweight adaptive extension, in which each intermediate node selects among a small set of forwarding probability arms using a local duplicate-pressure context and delayed route reply (RREP) feedback. The reward function uses the local observation of a corresponding RREP as delayed feedback while penalising high forwarding probability in locally redundant contexts. TSP-AODV requires no additional control packets, no topology exchange, and only a small number of local Beta belief distribution parameters per node. Evaluated against AODV, fixed probabilistic rebroadcasting, counter-based suppression, and dynamic probabilistic-counter suppression over 1600 simulation runs spanning four node densities and four mobility levels, TSP-AODV achieves the lowest normalised routing overhead and the highest RREQ suppression ratio, while no statistically significant PDR difference relative to the fixed probabilistic baseline was observed under the tested conditions. End-to-end delay is also reduced significantly relative to fixed probabilistic rebroadcasting. The learned belief behaviour confirms context-dependent adaptation, with the dominant forwarding-probability arm decreasing as duplicate pressure increases. An additional 800-run sensitivity analysis characterises the PDR–NRO–delay trade-off across the tested penalty coefficients and feedback-window durations in two representative scenarios. These results are limited to the evaluated parameter grid and do not establish scenario-independent parameter robustness. Full article

Osteoporosis and functional decline are major health concerns among postmenopausal women. Identifying factors associated with exercise responsiveness may improve understanding of interindividual variability in exercise adaptations and support the development of more individualized intervention strategies. This study investigated whether baseline body composition, bone health, and functional fitness characteristics were associated with exercise responsiveness in postmenopausal women. Eighty women participated in a 30-week supervised multicomponent exercise program and were classified as responders or non-responders according to changes in functional performance following the intervention. Body composition and bone health were assessed by dual-energy X-ray absorptiometry (DXA), and functional fitness was evaluated using the Senior Fitness Test battery. Unadjusted analyses suggested small differences in baseline Z-score (p = 0.038) and upper-body flexibility assessed by the Back Scratch test (p = 0.030), while total bone mineral content showed a trend toward significance (p = 0.056). However, none of these differences remained statistically significant after Benjamini–Hochberg false discovery rate correction. As a complementary analysis, multiple linear regression adjusted for baseline performance identified baseline body mass (p = 0.007), total bone mineral content (p = 0.018), and upper-body flexibility (p = 0.020) as factors independently associated with post-intervention functional performance. Exploratory machine learning analyses demonstrated limited ability to discriminate responders from non-responders using baseline characteristics alone, although Random Forest variable importance highlighted similar variables to those identified in the regression analysis. Overall, these findings suggest that baseline musculoskeletal characteristics alone are insufficient to accurately classify exercise responsiveness and that larger studies incorporating additional clinical, behavioural, physiological, and biological variables are needed to improve individualized exercise prescription. Full article

Stock markets are complex adaptive systems whose feature dependencies evolve over time, and identifying which signals genuinely drive returns is fundamentally a problem of information. Traditional feature selection methods and linear models rely on statistical correlations and overlook causality, which weakens their predictive performance in non-linear, evolving markets. This paper develops an information-theoretic approach to causal feature selection based on Markov Blanket discovery. We apply the Iterative Parent–Child-based search of Markov Blanket (IPCMB), whose conditional-independence tests are conditional mutual information measures, to recover the Markov Blanket of next-month returns—the minimal feature set that carries all Shannon information about the target. We then pair it with a Classification and Regression Tree (CART), whose impurity-based splitting admits an information-gain interpretation, to predict the direction of Chinese A-share returns non-linearly. Using data on 2760 stocks, IPCMB selects 13 causal features from 72 candidates, and CART forecasts whether the next month’s return is positive. The empirical results show an accuracy of 58.0%, 7.7 percentage points above the all-features CART benchmark, and a 13-month cumulative return 35.88% higher than that of the CSI 300 index. The findings indicate that selecting features according to their causal information content and combining them with interpretable tree-based prediction can support more reliable investment decisions in emerging markets. Full article

Background/Objectives: Post-COVID follow-up may provide an opportunity to reassess persistent and modifiable cardiometabolic risk. We characterized six-month cardiometabolic and vascular trajectories after COVID-19 hospitalization and explored associations with routinely documented dietary counseling and omega-3 recommendations. Methods: This single-center retrospective longitudinal cohort included 238 adults hospitalized with COVID-19 between March 2020 and December 2024. Patients were classified according to recorded post-discharge management as usual care (n = 64), dietary management (n = 60), omega-3 supplementation (n = 59), or combined management (n = 55). Outcomes were assessed at the index visit and at visits labeled approximately three and six months later. Longitudinal changes were estimated using generalized estimating equations; six-month associations were evaluated using baseline-adjusted ANCOVA with robust standard errors and Benjamini–Hochberg correction. Results: Mean age was 54.0 ± 10.6 years, 54.6% were women, 82.4% had hypertension, 40.8% had diabetes, and mean body mass index was 33.8 ± 4.6 kg/m². At six months, endpoint-specific data were available for 182 participants for blood pressure, total cholesterol, triglycerides, and HDL-C; 180 for waist circumference; 179 for SCORE; 177 for LDL-C and bilateral carotid IMT analyses; 173 for HOMA-IR; and 164 for IL-6. Across the cohort, systolic and diastolic blood pressure decreased by 7.91 and 4.75 mmHg; several lipid, anthropometric, and insulin-resistance markers also improved. No omnibus group-by-time interaction was significant. After false-discovery-rate correction, only higher six-month HDL-C in the dietary and combined-management groups versus usual care remained statistically significant; all other between-group findings were nonsignificant after correction. Conclusions: Several cardiometabolic risk markers improved during follow-up, but no recorded management category showed consistent trajectory-level superiority. Because intervention content and adherence, time-updated medication use, acute COVID-19 severity, and lifestyle changes were not captured, the comparative findings are exploratory and noncausal. The isolated HDL-C associations should not be interpreted as evidence of cardiovascular benefit. No cardiac biomarkers, echocardiographic measures, incident heart failure, or cardiovascular events were assessed. Full article

The Amaryllidaceae family is a rich source of structurally diverse alkaloids with recognized neuroactive and antiparasitic properties. This study provides the first phytochemical and biological characterization of Pyrolirion boliviense and wild Stenomesson miniatum from Bolivia. Alkaloid extracts from bulbs and leaves were analysed by GC–MS and evaluated for acetylcholinesterase (AChE), butyrylcholinesterase (BuChE), and Trypanosoma cruzi inhibitory activities. Thirty-two Amaryllidaceae alkaloids were identified, with P. boliviense exhibiting greater alkaloid diversity (25 compounds) and S. miniatum a higher total alkaloid content (227.86 vs. 138.92 μg Gal/100 mg DW). P. boliviense bulb extracts showed the strongest cholinesterase inhibition (AChE IC₅₀ = 6.07 ± 0.47 μg·mL⁻¹; BuChE IC₅₀ = 30.93 ± 1.17 μg·mL⁻¹), whereas S. miniatum extracts displayed weaker AChE inhibition and no detectable BuChE activity. In anti-T. cruzi assays, bulb extracts were the most active, with S. miniatum showing an IC₅₀ of 0.90 ± 0.15 μg·mL⁻¹ (SI = 20.12) and selective anti-amastigote activity (IC₅₀ = 1.42 ± 0.66 μg·mL⁻¹; SI = 12.77). These findings identify Bolivian Andean Amaryllidaceae as promising sources of bioactive alkaloids with potential applications for Alzheimer’s disease and Chagas disease drug discovery. Full article