Search Results (1,739)

Handgrip strength (HGS) is widely used as a biomarker of muscle function and overall health in older adults. However, conventional analyses based on peak force values may overlook relevant temporal features of the HGS curve. This cross-sectional study proposes a novel methodological approach that examines the shape and variability of HGS(t) curves recorded from community-dwelling older adults. Functional principal component analysis (FPCA) was applied to assess the consistency of individual trials and the representativeness of mean curves. Statistical non-parametric mapping (SnPM) was then used to identify time regions showing significant differences between groups. Complementary analyses of discrete and derivative parameters, together with non-parametric comparisons based on the Hodges–Lehmann estimator and corresponding 95% confidence intervals, were conducted to quantify effect sizes. FPCA revealed high within-participant consistency, supporting the use of mean curves for group-level comparisons. SPM analyses indicated significant differences in the early force development phase. Importantly, this approach shows that sex differences are attributable to magnitude effects, with men generating higher forces and faster early rates of force development, and not to differences in the neuromuscular strategy of force production. Traditional discrete parameters partly captured these patterns but failed to reflect the full temporal dynamics. This methodological approach to the HGS curve may provide further insights into neuromuscular control mechanisms that cannot be truly captured by the minimalistic HGS discrete parameters. Full article

Soybean (Glycine max (L.) Merr.) is a globally important legume crop valued as a major source of plant-based protein and edible oil. Understanding the transcriptional programs underlying tissue-specific development is essential for improving seed quality and agronomic performance. Here, we present an integrative transcriptomic analysis of soybean based on 12 samples representing key seed developmental stages—including globular, heart, cotyledon, embryo, dry seed, mid-mature, and late-mature—and vegetative and reproductive tissues, including leaf, root, stem, flower bud, and seedling at 6 days after imbibition (6 DAI). Following data preprocessing and filtering, 54,880 genes were retained for downstream analysis. Principal component analysis revealed clear separation between seed and non-seed tissues, indicating that tissue identity is the dominant driver of transcriptomic variation. Analysis of the top 100 most variable genes further highlighted distinct expression modules associated with seed maturation and vegetative growth. Differential expression analysis identified 9785 genes exhibiting significant expression differences between seed and non-seed tissues, including 1139 upregulated and 8646 downregulated genes under relaxed statistical thresholds. Functional characterization of seed-upregulated genes revealed enrichment of biological processes related to storage metabolism, embryo development, and stress protection mechanisms associated with desiccation tolerance. In addition, co-expression network and correlation analyses demonstrated strong transcriptional coherence among seed tissues and distinct clustering of vegetative organs. Together, these results provide a comprehensive systems-level overview of transcriptional organization across soybean tissues and identify candidate gene sets relevant to seed biology, functional genomics, and crop improvement. Full article

Wuweiganlu (WGL) is a traditional formulation widely applied in the treatment of rheumatoid arthritis (RA), yet the identity of its bioactive constituents remains inadequately defined. In this study, ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) and untargeted serum metabolomics were employed to characterize the active components of WGL. Fraxin was identified as a principal compound from WGL. To investigate its therapeutic mechanism in RA, a series of in silico and experimental approaches were conducted. Network pharmacology analysis and RNA sequencing identified heat shock protein family member 8 (HSPA8) as a potential molecular target of Fraxin, which was further validated by molecular docking studies. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated that Fraxin exerts its effects primarily by modulating cell apoptosis through the PI3K signaling pathway. In vitro experiments demonstrated that Fraxin significantly reduced inflammatory responses and downregulated HSPA8 expression in lipopolysaccharide (LPS)-stimulated fibroblast-like synoviocytes (FLs) and macrophages. In vivo, Fraxin administration markedly reduced paw swelling, alleviated bone deformities, and improved bone volume fraction (BV/TV) in male IL1RA-deficient mice exhibiting spontaneous arthritis. Histological analysis confirmed that Fraxin attenuated joint inflammation by modulating the inflammatory microenvironment. Additionally, Fraxin inhibited synovial hyperplasia by regulating mitochondrial membrane potential collapse in FLs. Functional assays revealed that this regulation occurred via the inhibition of HSPA8/PI3K/AKT signaling axis, thereby suppressing aberrant FLS proliferation and contributing to the attenuation of RA progression. Full article

This study addresses a critical gap in the operationalization of sustainability frameworks at the project level by developing and validating an empirically grounded measurement instrument for assessing Environmental, Social, and Governance (ESG) compliance in manufacturing industry projects. While the United Nations Sustainable Development Goals (SDGs) articulate sustainability aspirations at the national and global level, and ESG frameworks capture organizational-level sustainability performance, no validated instrument exists for measuring ESG integration at the project level where sustainability commitments are ultimately operationalized. Drawing on the theoretical foundations of sustainable project management, stakeholder theory, and the ESG governance literature, the authors developed a 30-item survey instrument capturing six conceptual dimensions of ESG-aligned project performance. Data were collected from 2231 project sponsors and decision-makers in North American goods manufacturing firms classified under NAICS codes 31–33, which collectively encompass the entire manufacturing sector in North America. Through a sequential analytical approach employing principal component analysis (PCA) for initial item reduction, exploratory factor analysis (EFA) for dimensionality assessment, and structural equation modelling (SEM) for confirmatory validation, a parsimonious two-factor model emerged with excellent fit indices (CFI = 0.99, TLI = 0.98, RMSEA = 0.052, SRMR < 0.035). The first factor captures ESG planning activities undertaken during project initiation and planning phases, while the second factor represents ESG monitoring and controlling functions during project execution. The reduction from six theoretical dimensions to two empirical factors reflects lifecycle governance theory, where planning-phase governance and execution-phase control emerge as functionally distinct but correlated constructs. The validated instrument offers practical utility for project managers, organizational sustainability officers, and policy-makers seeking standardized benchmarks for ESG compliance at the operational project level. The validated instrument and complete survey are shared for replication and testing across different industries and countries. Full article

Maize production in semi–arid irrigated systems depends on soil fertility and an active rhizosphere. We hypothesized that vermiculite enriched with humic substances (HS) or Chlorella vulgaris (CV) would outperform vermiculite alone by improving soil fertility, maize nutrition, and rhizosphere-associated microbial indicators. A field experiment was conducted in southern Kazakhstan under medium–loam sierozem using a randomized block design with three replicates and seven treatments: control, vermiculite at 1 and 2 t ha⁻¹, vermiculite + HS at 1 and 2 t ha⁻¹, and vermiculite + CV at 1 and 2 t ha⁻¹. Amendments were incorporated before sowing, and soil, plant, and microbial measurements were taken before sowing, at V6–V8, and after harvest. Compared with the control, all amendments improved early maize growth, leaf area development, biomass accumulation, and nutrient status, and increased grain yield. The strongest response was obtained with vermiculite + HS at 2 t ha⁻¹, which increased grain yield from 6.48 to 10.24 t ha⁻¹ (+58%). Microbial indicators differed between bulk soil and the rhizosphere, while Pearson correlation and PCA revealed coordinated soil–plant–microbe responses and productivity–linked variables across treatments. Taken together, these results indicate that enriched vermiculite, especially HS–enriched vermiculite at 2 t ha⁻¹, is a promising amendment for improving maize productivity and rhizosphere functioning in semi–arid irrigated systems. Full article

Assessing crop water status during the reproductive development of winter wheat is challenging because soil–plant–atmosphere interactions are strongly influenced by soil physical conditions, and measured soil water content (SWC) does not necessarily reflect plant-accessible water. This study applied an integrated, process-based multisensor approach to evaluate functional crop water status and its relationship to grain yield, combining hyperspectral canopy reflectance, atmospheric observations, in situ SWC, and pedological characterization. Five winter wheat cultivars were monitored at two contrasting pedoclimatic sites in continental Croatia during the 2022/2023 growing season. Hyperspectral canopy reflectance (350–2500 nm) was measured at reproductive stages (BBCH 61–83), and seventeen vegetation indices describing canopy water status, structure, pigments, and senescence were derived. Principal component analysis (PCA) identified location as the dominant source of spectral variability, while cultivar effects were secondary. Although atmospheric conditions were broadly comparable, the sites differed markedly in soil physical properties, resulting in contrasting soil water–air regimes. Despite consistently higher volumetric SWC at one site, hyperspectral indicators revealed lower canopy water status, reduced canopy structure, earlier senescence, and lower grain yield across all cultivars. Water-sensitive indices exploiting near-infrared (700–1300 nm) and shortwave infrared (1300–2400 nm) bands (NDWI, NDMI, NMDI, MSI) consistently indicated greater physiological stress. Conversely, the site with lower SWC but more favorable soil physical conditions exhibited higher values of water- and structure-related indices and achieved higher grain yield, with a mean increase of 669 kg ha⁻¹. The results demonstrate that hyperspectral canopy reflectance captures yield-relevant water stress that cannot be inferred from soil moisture alone, highlighting the importance of multisensor integration for interpreting soil–plant–atmosphere interactions under heterogeneous soil conditions. Full article

7-ketocholesterol (7-KC) is a bioactive oxysterol generated under oxidative stress and may contribute to bone marrow niche reprogramming in acute myeloid leukemia (AML), thereby promoting stress tolerance and therapeutic resistance Bone marrow mesenchymal stromal cells (MSCs) from healthy donors and AML patients were exposed to subtoxic 7-KC concentrations for 24 h. We evaluated the ABC transporters involved in lipid transport, multidrug resistance and membrane microdomain remodeling; Hedgehog pathway proteins; stress–survival signaling; redox balance by glutathione measurements, and mitochondrial function and dynamics, including membrane potential and gene expression of mitochondrial fission and fusion regulators. Results were integrated using principal component analysis (PCA), heatmaps, and correlation-based networks. Multivariate analyses revealed an integrated, lineage-dependent response. Healthy donor MSCs showed greater plasticity of the efflux and microdomain axis and higher oxidative and mitochondrial vulnerability at high 7-KC doses. AML-MSCs exhibited a basal preconditioned state phenotype and preferentially routed the response toward Hedgehog and stress–survival modules, accompanied by glutathione expansion and adaptive mitochondrial remodeling. 7-KC acts as a broad modulator of several MSC functions, linking sterol homeostasis to Hedgehog signaling, stress–survival pathways, redox balance, and mitochondrial remodeling, potentially supporting a pro-survival, more therapy-tolerant leukemic niche. Full article

Uranium mining generates complex multi-element contamination that affects interconnected ecosystem components, posing long-term ecological and sanitary risks; this review places these impacts in a broad environmental context and aims to synthesize current knowledge on the distribution, migration, and accumulation of uranium and associated heavy metals in water, soil, and plants. A structured analysis of international peer-reviewed literature was conducted, focusing on documented pathways of metal release from tailings and waste dumps, geochemical controls on mobility, and biological uptake by vegetation. The reviewed studies consistently show that tailings and disturbed ore-bearing strata act as persistent sources of uranium and heavy metals (e.g., Cd, Pb, Cr, Ni, Zn, Mn, As), which migrate through infiltration, acid mine drainage, and atmospheric dispersion, leading to elevated concentrations in surface and groundwater and long-term accumulation in soils. Soils function as the principal sink controlling metal bioavailability, while vegetation reflects the bioavailable fraction and exhibits pronounced species-specific accumulation patterns. These processes establish an active “soil–water–plant” transfer chain that facilitates entry of contaminants into food webs. The synthesis indicates that combined uranium and heavy metal contamination represents a sustained ecological and public health concern in uranium-mining regions and underscores the need for integrated monitoring of soils, waters, and vegetation, along with quantitative risk assessment and scientifically grounded remediation strategies. Full article

This study investigates how financial technology (FinTech) contributes to economic growth, focusing on whether it acts primarily as a mediator or a moderator within the finance–growth nexus. A composite FinTech index is constructed using Principal Component Analysis based on cross-country data for 2021, and the analysis distinguishes between High-Income and Non-High-Income economies following the World Bank classification. The results show that in developing and emerging economies, FinTech mainly serves as a mediator, helping to close structural gaps in financial intermediation and expanding access to financial services. In High-Income countries, by contrast, FinTech acts as a moderator, enhancing innovation and efficiency in mature financial systems. When financial inclusion is disaggregated by gender, the findings reveal additional nuances. FinTech fosters growth through inclusion for both men and women, but its effects are stronger for male account ownership in developing economies and more balanced in High-Income contexts. In general, the study contributes to the literature by developing a multidimensional FinTech index, clarifying its dual mediating and moderating functions, and introducing a gender-sensitive perspective that highlights the uneven distribution of FinTech’s growth benefits between income levels and genders. Full article

Strawberry aroma and flavor are key determinants of consumer acceptance and market value, yet their relationship with physico-chemical and functional traits remains complex and cultivar-dependent. This study aimed to characterize the volatile profile, quality parameters, antioxidant capacity, microbial load, and sensory attributes of three strawberry cultivars (‘Rossetta’, ‘Melissa’, and ‘Gioelita’) grown in soilless culture systems and harvested at the commercial ripening stage. ‘Melissa’ showed significantly higher total soluble solids (8.65 °Brix) than ‘Rossetta’ (7.78 °Brix) and ‘Gioelita’ (7.47 °Brix), while titratable acidity was highest in ‘Gioelita’ (4.97 mg CA/L). Regarding phytochemical traits, ‘Melissa’ exhibited the greatest total polyphenol, flavonoid, and antioxidant capacity values, followed by ‘Rossetta’ and ‘Gioelita’. Sixty-four volatile organic compounds (VOCs) were identified, semi-quantified, and combined with physico-chemical and sensory data related to odor and taste perception. Principal component analysis was applied to evaluate cultivar discrimination and identify the key discriminatory volatiles. The results revealed clear separation among cultivars based on their compositional and sensory profiles. ‘Rossetta’ was characterized by a higher abundance of esters, lactones, and mesifuran and received the highest sensory scores for sweetness and overall flavor, consistent with its elevated anthocyanin content. ‘Gioelita’ was associated with key esters contributing to strawberry flavor and with higher titratable acidity and perceived acidity. ‘Melissa’ showed a balanced volatile composition, higher antioxidant capacity, and greater phenolic content but also had higher microbial counts. Overall, the integration of chemical and sensory analyses provided useful insights into cultivar-specific quality traits relevant for breeding and production strategies. Full article

Bilateral idiopathic carpal tunnel syndrome (CTS) is a neuromuscular condition involving the compression of the median nerve at both wrists, leading to pain, neurological symptoms, and loss of function. This paper proposes a robust machine-learning framework for a randomized crossover clinical trial comparing two physiotherapeutic treatment regimens: stretching followed by myofascial mobilization (S/M) and the reverse sequence (M/S). Instead of making inferences about the superiority of one treatment over another, the treatment regimen serves as a structured analytical label for investigating predictive separability, feature representation, and model stability within a controlled experimental setting. The clinical dataset of 73 patients underwent rigorous preprocessing, including strength feature aggregation and principal component analysis (PCA). Various classifiers were evaluated, with CatBoost achieving an ROC-AUC of 0.985 and a test accuracy of 96.5%, while Random Forest demonstrated strong adversarial robustness with an adversarial accuracy of 96.83%. To assess robustness, clinically constrained perturbations were introduced into the PCA feature space, simulating realistic input variability. The findings indicate that ensemble learning algorithms can capture structured patterns in crossover clinical datasets and remain stable under low-magnitude adversarial perturbations. The study underscores the importance of robustness evaluation and interpretability when applying machine learning models to biomedical data, particularly in small and well-structured clinical cohorts. Full article

Although evidence exists for android-induced spontaneous facial mimicry, the timing and temporal precedence (Granger causality) of this effect remain uncertain. We used the Facial Action Coding System (FACS) to analyze simultaneous dyadic facial video recordings of participants observing android Nikola’s negative (frowning) and positive (smiling) dynamic facial expressions. Principal component analysis of Nikola’s expressions indicated that, in addition to the action units (AUs) 04 (brow lowerer) and 12 (lip-corner puller), AUs 25 (lips part) and 26 (jaw drop) contributed significantly to Nikola’s facial expressions. Cross-correlation analysis revealed AU04 mimicry of negative expressions and AU12 mimicry of positive expressions from 400 ms onwards. AU25 and AU26 mimicry occurred faster, starting at around 200 ms. Multilevel vector autoregression incorporated the android and participant AUs and quantified the temporal evolution of the Granger causality for the first time. In addition to paired android–human AU04, 12, 25, and 26 effects, significant Granger causality was found between different android–human AU combinations, such as from android AU04 to participant AU25 in the negative condition, and from android AU25 to participant AU12 in the positive condition. These results suggest that the spontaneous facial responses to Nikola’s expressions involved not only motor copying, but also higher-level goal emulation and motor planning in the mirror mechanism, supporting the reliability of the social function of android facial expressions. Cross-correlation and Granger causality analysis can be valuable when further investigating behavioral matching in real-life contexts. Full article

This study examines the first corporate disclosures issued under the IFRS Sustainability Standards, with full alignment to IFRS S2, using natural language processing and text mining techniques, and contributes evidence to an underexplored phase of sustainability reporting research. Focusing on an emerging market setting, the analysis covers the 2024 reports of 18 firms included in the Borsa Istanbul Sustainability 25 Index. The reports are evaluated through readability metrics (Flesch–Kincaid, Gunning Fog, and SMOG), conceptual concentration measures (TF–IDF), semantic proximity analysis (Cosine Similarity), and network-based methods. The findings indicate a strong degree of technical discipline and standard adherence in the first year of implementation, alongside a pronounced barrier to linguistic accessibility. Average Gunning Fog and Flesch–Kincaid scores of 18.94 and 14.90 suggest that meaningful interpretation of these disclosures requires advanced academic proficiency. The observed technical density reflects the detailed and standard-driven structure of IFRS-based sustainability reporting and points to a persistent tension between technical precision and interpretability, consistent with the Managerial Obfuscation perspective (H1). High levels of semantic overlap further indicate that, under conditions of reporting uncertainty, firms rely heavily on established disclosure patterns, reinforcing professional convergence through both coercive (regulatory alignment) and mimetic (uncertainty-driven emulation) isomorphism (H2). In contrast, distinct narrative configurations identified through principal component and network analyses are evaluated as potential credibility-enhancing signals within the framework of Signaling Theory (H3). Overall, IFRS Sustainability Standards reporting functions in emerging markets as a learning-oriented and strategically relevant disclosure mechanism that may potentially mitigate information asymmetry through its linguistic properties. Full article

We propose a robust method for outlier detection in functional data analysis. This approach uses the robust Minimum Covariance Determinant estimator to compute the Mahalanobis distance applied to functional principal component scores. The main contribution of this research is the detection of outlier curves using the robust covariance matrix of functional principal components, in contrast to existing methods that use principal components on the discrete dataset. The proposed method is practical because it considers the entire functional form of the data, through their functional principal components, providing a comprehensive analysis that can detect anomalies across the entire functional range. A simulation study compares this approach with existing methods to evaluate their performance, followed by applications to El Niño Sea Surface Temperature data and SCImago Journal Rank data. The results show that the proposed method provides greater accuracy, demonstrating its effectiveness in detecting outlier curves. Full article

The giant panda holds a critical position in global biodiversity conservation, yet the ongoing fragmentation of its habitat poses a severe threat to the long-term viability of its survival. This study focused on the giant panda habitat in the Xiangling region and systematically analyzed the mechanisms through which human socioeconomic activities drive habitat fragmentation. The analysis was based on data from 2000 to 2023, encompassing land use, population density, transportation networks, mining activities, and nighttime light emissions, utilizing a methodology that integrated Principal Component Analysis, the Moving Window method, trend analysis, and the Geodetector model. The findings reveal the following: First, the degree of habitat fragmentation has intensified over time with significant spatial heterogeneity, exhibiting a pattern of “low fragmentation in the core areas and high fragmentation in the periphery,” where areas of very high fragmentation have expanded markedly along the habitat edges. Second, the trend in fragmentation demonstrates an overall improvement in the core zones, particularly within the Giant Panda National Park, where over 70% of the area shows reduced fragmentation; conversely, nearly 30% of the peripheral areas continue to degrade. Third, the driving factors of habitat fragmentation exhibit bi-factor enhancement and nonlinear enhancement effects, with land use identified as the dominant factor. The study recommends enhancing the overall connectivity and ecological functionality of the habitat through measures such as refining land-use planning, constructing ecological corridors, implementing hierarchical management, and promoting community co-management. Full article