Search Results (3,624)

Copper ions serve as essential cofactors for many enzymes but exhibit toxicity at elevated concentrations. In Gram-negative bacteria, the Cop system, typically encoded by copABCD, plays a crucial role in maintaining copper homeostasis and detoxification. The chromosome of Pseudomonas putida harbors two copAB clusters but lacks copCD, along with two copR-copS clusters that encode the cognate two-component system. Here, the roles of these Cop components in countering copper toxicity were studied. We found that copAB2 was essential for full resistance to Cu²⁺ in P. putida, while copAB1 made only a minor contribution, partially due to its low expression. The two-component systems CopRS1 and CopRS2 both played significant regulatory roles in copper resistance. Although they could compensate for the absence of each other to mediate copper resistance, they exhibited distinct regulatory effects. CopR1 bound to all four cop promoters and activated their transcription under copper stress. In contrast, though CopR2 bound to the same sites as CopR1 in each cop promoter, it significantly activated only copAB2 and copRS2 expression. Its competitive binding at the copAB1 and copRS1 promoters likely impeded CopR1-mediated activation of these genes. Overall, this study reveals the distinct contributions of the two Cop systems to copper resistance and their regulatory interplay in P. putida. Full article

In the era of digital transformation and data-driven decision-making, big data analytics capability (BDAC) is crucial for firms to enhance innovation and sustainable competitive advantage in highly dynamic markets. Grounded in dynamic capability theory, this study used a moderated mediation model to explore the impact of BDAC on innovativeness. Empirical analysis was conducted by using survey data from 270 enterprises to test the hypotheses. The results reveal that BDAC significantly and positively influences innovativeness, and sustained innovation mediates this relationship. Moreover, organizational slack positively moderates the effect of BDAC on innovativeness, both the direct effect and indirect effect. These findings provide theoretical support and practical implications for understanding how BDAC enhances firm innovativeness. Full article

As an important branch of lanthanide-based complexes, clusters show unique properties in magnetocaloric effect (MCE) and single-molecule magnets (SMMs) using O/N ligands, while research on heavy p-block elements (e.g., S atom) with larger atomic radii and more diffuse p valence orbitals as coordinating atoms remains relatively scarce. Herein, using the sulfur-containing ligand of 2-pyridinethiol 1-oxide (HL), we successfully synthesized a series of hourglass-like Ln₉ clusters [Ln₉(L)₁₇(μ₃-OH)₉(μ₄-OH)]·nH₂O (1: Ln = Gd, n = 3; 2: Ln = Tb, n = 3; 3: Ln = Dy, n = 1). Magnetic data analysis reveals that cluster 1 shows a significant MCE, with the entropy change (−ΔS_m) reaching a maximum of 34.41 J kg⁻¹ K⁻¹ at 2 K under ΔH = 7 T. Cluster 3, meanwhile, exhibits distinct frequency- and temperature-dependent behavior, indicating its SMM characteristics. Interestingly, despite possessing the highest molar mass among reported Gd₉ clusters with MCE, 1 exhibits a competitive −ΔS_m value, highlighting the critical role of sulfur-containing ligand on the structure and even exchange interactions. This work offers new insights into synthesizing high-performance MCE materials and understanding magneto-structural relationships. Full article

The BCR-ABL1 fusion protein is a critical therapeutic target in Chronic Myeloid Leukemia (CML). Current monotherapy approaches involve types of inhibitors that can be categorized into ATP competitive inhibitors and allosteric inhibitors. However, resistance mutations in the tyrosine kinase domain of BCR-ABL1 have limited the effectiveness of these drugs. Research indicates that dual inhibition of BCR-ABL1 by combining these two types of inhibitors effectively addresses the issue of drug resistance as there are no overlapping resistance mechanisms. However, the underlying reasons for the observed synergistic effects have not yet been thoroughly elucidated. In this study, we employed molecular dynamics simulation to observe the synergistic interactions of BCR-ABL1 by the allosteric inhibitor asciminib and ATP competitive inhibitors nilotinib and ponatinib. Our study reveals that when asciminib binds to BCR-ABL1, nilotinib and ponatinib exhibit more substantial binding stability compared to monotherapy. At the atomic level, we have elucidated the reasons for the enhanced binding affinity of nilotinib and ponatinib when using a co-inhibition therapy. Our study reveals the allosteric communication pathway between asciminib and ponatinib, providing more detailed insights into the effectiveness of combination therapy. These findings provide valuable insights into combination therapies, aiding in the rational use of medications and guiding the design of novel inhibitors Full article

Understanding the integration of newly recorded species into forest ecosystems is essential for evaluating their ecological impacts on native wildlife diversity. In this study, we examined the spatial and temporal niche dynamics of three sympatric squirrel species within the Labagoumen nature reserve, a temperate forest located in northern China. Particular emphasis was placed on the recently documented Eurasian red squirrel (Sciurus vulgaris) and its potential interactions with two native species: Père David’s rock squirrel (Sciurotamias davidianus) and the Siberian chipmunk (Tamias sibiricus). Using camera trapping data from 91 sites (2019–2024), we examined habitat use, activity rhythms, and niche overlap under contrasting levels of human disturbance. A total of 3419 independent effective photos of squirrels were recorded. S. vulgaris showed a broader spatial distribution and a higher relative abundance index (RAI) in the tourist area, while native species were more abundant in the non-tourist area. All three species showed similar annual activity patterns based on the monthly relative abundance index (MRAI), although native species exhibited an additional activity peak in June–July. Temporal niche overlap (C_ih) and the coefficient of overlap (Δ) between S. vulgaris and native species increased during the tourist season, suggesting synchronized activity under high disturbance. In contrast, lower overlap in the non-tourist season indicated stronger temporal partitioning. The daily activity rhythm of S. vulgaris remained stable, while native species displayed more variability, especially in non-tourist areas. S. vulgaris also exhibited a significantly broader spatial niche breadth (B_i), suggesting greater habitat exploitation and adaptability. Non-metric multidimensional scaling (NMDS) revealed no significant spatial segregation among the three species, indicating successful integration of S. vulgaris into the local community. Our findings emphasize the competitive advantage of S. vulgaris and demonstrate how human activities can restructure forest small mammal assemblages by altering spatiotemporal niche partitioning. We recommend long-term ecological monitoring to assess species diversity changes and guide adaptive conservation strategies. Full article

The pharmaceutical contamination of water, especially by widely used drugs, presents important environmental and health concerns due to the inefficiency of conventional treatment methods. The present study proposes a sustainable solution using biochar (Bch) obtained from tomato waste, functionalized with Fe₃O₄ and MnO₂ nanoparticles, for the removal of paracetamol from aqueous solutions. The composite materials were synthesized, characterized, and evaluated under varying conditions, including pH, temperature, contact time, initial drug concentration, and adsorbent dose. The materials exhibited porous structures with wide pore size distributions. Optimal removal efficiency was achieved for 30 mg L⁻¹ paracetamol concentration, pH 2, 25 °C, 0.3 g L⁻¹ adsorbent dose, and 20 min contact time. The Freundlich isotherm provided the best fit for the adsorption data. Kinetic studies revealed that the pseudo-second-order model best described the adsorption process. Thermodynamic parameters indicated that the process was spontaneous, feasible, and exothermic. Compared with similar materials derived from agricultural waste, the tomato waste-based composites demonstrated competitive adsorption capacities. These findings suggest that Bch-HCl/MnO₂ and Bch-HCl/Fe₃O₄/MnO₂ are promising, cost-effective adsorbents for mitigating pharmaceutical pollutants in wastewater. Full article

This study examines the impact of environmental, social, and governance (ESG) practices on green innovation and financial performance among 174 publicly listed firms across ASEAN countries over the period from 2019 to 2023. Utilizing an unbalanced panel dataset of firms from key ASEAN economies, the analysis employs panel regression techniques. Green innovation performance is measured through innovation disclosures related to environmental technologies, while financial success is assessed via return on assets (ROA) and Tobin’s Q. The findings reveal that environmental and governance disclosure scores positively influence green innovation, whereas social scores exert a more immediate impact on financial performance. Moreover, green innovation is found to partially mediate the relationship between overall ESG practices and long-term market valuation. These results highlight the strategic role of ESG transparency in enhancing innovation-driven competitiveness, responsible business conduct, and sustainable employment across Southeast Asian markets. Implications are discussed for corporate managers, policymakers, and socially responsible investors. The study reinforces the case for ESG-aligned strategy as a pathway to both innovation, inclusive economic growth, and long-term competitiveness in ASEAN markets. Full article

3,4-dihydroxybenzenesulfonyl-functionalized polyethyleneimine (PS), a novel polymeric chelator, was synthesized by conjugating 3,4-dihydroxybenzenesulfonyl (CAM) groups with branched polyethyleneimine (BPEI, MW = 600 Da) via N-acylation. PS demonstrated a high uranium adsorption capacity of 78.08% at a concentration of 4 mg/mL, accompanied by significant selectivity over competing ions such as Ca²⁺, Zn²⁺, and Cu²⁺. Notably, in competitive adsorption experiments, PS exhibited a uranium adsorption rate of 59.49%, which was 3.95 times higher than that of calcium (15.06%) in the Ca²⁺ system. Cytotoxicity assays revealed enhanced biocompatibility (IC₅₀ = 86.98 μg/mL), surpassing CaNa₃-DTPA 3.7-fold. In a uranium exposure model (200 μg/mL), PS significantly improved cell survival rates and reduced intracellular uranium levels by 77.37% (immediate administration) and 64.18% (delayed administration). These findings establish PS as a potent and safe polymeric chelator for uranium decorporation, offering a promising strategy for mitigating the hazards of radioactive materials. Full article

Aim: This scoping review examines the application of artificial intelligence (AI) in sports biomechanics, with a focus on enhancing performance and preventing injuries. The review addresses key research questions, including primary AI methods, their effectiveness in improving athletic performance, their potential for injury prediction, sport-specific applications, strategies for translating knowledge, ethical considerations, and remaining research gaps. Following the PRISMA-ScR guidelines, a comprehensive literature search was conducted across five databases (PubMed/MEDLINE, Web of Science, IEEE Xplore, Scopus, and SPORTDiscus), encompassing studies published between January 2015 and December 2024. After screening 3248 articles, 73 studies met the inclusion criteria (Cohen’s kappa = 0.84). Data were collected on AI techniques, biomechanical parameters, performance metrics, and implementation details. Results revealed a shift from traditional statistical models to advanced machine learning methods. Based on moderate-quality evidence from 12 studies, convolutional neural networks reached 94% agreement with international experts in technique assessment. Computer vision demonstrated accuracy within 15 mm compared to marker-based systems (6 studies, moderate quality). AI-driven training plans showed 25% accuracy improvements (4 studies, limited evidence). Random forest models predicted hamstring injuries with 85% accuracy (3 studies, moderate quality). Learning management systems enhanced knowledge transfer, raising coaches’ understanding by 45% and athlete adherence by 3.4 times. Implementing integrated AI systems resulted in a 23% reduction in reinjury rates. However, significant challenges remain, including standardizing data, improving model interpretability, validating models in real-world settings, and integrating them into coaching routines. In summary, incorporating AI into sports biomechanics marks a groundbreaking advancement, providing analytical capabilities that surpass traditional techniques. Future research should focus on creating explainable AI, applying rigorous validation methods, handling data ethically, and ensuring equitable access to promote the widespread and responsible use of AI across all levels of competitive sports. Full article

The innovation capability of seed enterprises reflects their core competitiveness and serves as a vital foundation for sustainable agricultural development and modernization. Therefore, evaluating this capability is of great importance. However, existing evaluation methods primarily focus on internal enterprise attributes, overlooking the complex inter-enterprise relationships and lacking sufficient feature fusion capabilities to capture latent information. To address these limitations, this paper proposes a Multi-Channel Graph Convolutional Network (MGCN) model that integrates enterprise attributes with three types of relational graphs. The model adopts a multi-channel architecture for feature extraction and employs a gated attention mechanism for cross-graph feature fusion, jointly considering node features and relation information to improve prediction accuracy. Experimental results demonstrate that MGCN achieves an average accuracy of 83.59% under five-fold cross-validation, outperforming several mainstream models such as Random Forest and traditional GCN. Case studies further reveal that MGCN not only captures key features of individual enterprises but also leverages features and label distribution from neighboring enterprises, facilitating more context-aware classification decisions. In conclusion, the MGCN model provides an effective method for the intelligent evaluation of innovation capability in seed enterprises and supports the formulation of sustainable strategic plans at both the national and enterprise level. Full article

The inherent complexity of modern supply chains obscures significant hidden CO₂ and Water Pollution Equivalent (WPE) emissions, presenting mounting challenges for integrated environmental governance. While prior research has largely treated carbon and water pollution metabolic systems in isolation, this study addresses the critical gap in understanding their bidirectional interactions under socioeconomic dynamics. We develop a novel Three-Dimensional Evaluation Method for the Metabolic Interaction System of Industrial CO₂ and Water Pollution (TDE-ISCW). This framework integrates Environmental Input–Output Analysis and Ecological Network Analysis to: (1) identify key industrial sectors and utility relationships within individual CO₂ and WPE systems; (2) quantify the mutual disturbance responses between the CO₂ and WPE metabolic systems through changes in sectoral emissions/output, inter-sectoral relationships, and sector–system linkages; and (3) propose optimized industrial restructuring strategies for synergistic pollution and carbon reduction. Applied to the highly industrialized Yangtze River Economic Belt, key findings reveal: (i) substantial upstream dependency, exemplified by Advanced Equipment Manufacturing’s 95.7% indirect CO₂ emissions; (ii) distinct key sectors for CO₂ (e.g., MOO, FTO, MNM) and WPE (e.g., MPM, OTH, FTO) reduction based on competitive relationships; and (iii) complex trade-offs, where emission reductions in one system (e.g., CO₂ via FTO restructuring) can trigger heterogeneous responses in the other (e.g., altered WPE influence or downstream CO₂/economic shifts). The TDE-ISCW framework provides actionable insights for designing coordinated, adaptive emission reduction policies that account for cascading cross-system effects, ultimately supporting regional industrial upgrading and resource efficiency goals. Future research should incorporate temporal dynamics and full industrial–metabolic cycles. Full article

Norovirus–bacterial interactions influence viral replication and immune responses, yet the molecular details that mediate binding of these viruses to commensal bacteria are unknown. Studies with other enteric viruses have revealed that LPS and other lipid/carbohydrate structures facilitate virus–bacterial interactions, and it has also been shown that human noroviruses (HuNoVs) can interact with histo-blood group antigen (HBGA)-like compounds on the surface of bacterial cells. Based on these findings, this study hypothesized that carbohydrate-based compounds were the ligands that facilitated binding of both human and murine noroviruses (MNV) to bacteria. Using glycan microarrays, competitive inhibition assays, and a panel of bacterial mutants, the project assessed the influence of specific glycans on viral attachment to bacteria. Protein-based interactions were also examined. The results supported previous work which demonstrated that HuNoVs strongly bind HBGA-like glycans, while MNV displayed distinct binding to other glycans including aminoglycosides and fucosylated structures. Ultimately, this work demonstrates that HuNoVs have more limited binding requirements for bacterial attachment compared to MNV, and the MNV binding to bacteria may involve both specific structures as well as electrostatic interactions. Given the importance of commensal bacteria during viral infection, defining the molecular mechanisms that mediate virus–bacteria interactions is critical for understanding infection dynamics and may be useful in the development of disease therapeutics and novel technologies for viral detection from food and environmental sources. Full article

High-throughput transcriptomic analyses have identified numerous candidate miRNA–mRNA and long non-coding RNA (lncRNA) regulatory networks in teleosts, but most remain without systematic functional validation or mechanistic definition. Here, by interrogating miRNA–lncRNA networks in rainbow trout (Oncorhynchus mykiss) gonads, we define their roles in meiotic progression and gonadal development. From preliminary screening, we identified lncRNA74687 as a central node and characterised its function. Subcellular localisation showed predominant nuclear enrichment of lncRNA74687 in gonadal cells. Dual-luciferase assays confirmed miR-15a-5p targeting of Cyclin E (CCNE1) and lncRNA74687. Functional studies showed that concurrent overexpression of lncRNA74687 and inhibition of miR-15a-5p synergistically increased the CCNE1 protein to maximal levels. 5-ethynyl-2′-deoxyuridine (EdU) assays showed that knockdown of lncRNA74687 and CCNE1 in rainbow trout gonadal (RTG-2) cells reduced proliferation by 36.4% and 41.2%, respectively (p < 0.05). Immunofluorescence indicated that lncRNA74687 increased Synaptonemal Complex Protein 1 (SYCP1) signalling 6.93-fold in gonadal cells via CCNE1. In vivo, lncRNA74687 knockdown increased miR-15a-5p expression 6.34-fold relative to the wild-type controls (p < 0.01). Transcriptomic profiling revealed broad downregulation of meiosis-related genes in lncRNA74687-deficient gonads, with the strongest reduction in mstrg1 expression, indicating a key role of lncRNA74687 in germ-cell meiotic progression. Together, these data show that lncRNA74687 enhances CCNE1 mRNA and the CCNE1 protein in rainbow trout by competitively binding miR-15a-5p. This lncRNA74687–miR-15a-5p–CCNE1 axis regulates gonadal cell proliferation and meiotic gene expression during gonadal development. Full article

Dictyophora indusiata cultivation is severely impeded by premature hyphal regression. This study elucidates the spatiotemporal dynamics of mycelial regression and associated microbial succession in both substrate and soil matrices across progressive regression stages (CK: normal growth; S1: initial recession; S2: advanced recession; S3: complete recession). Microscopic analysis revealed preferential mycelial regression in the substrate, preceding soil regression by 1–2 stages. High-throughput sequencing demonstrated significant fungal community restructuring, characterized by a sharp decline in Phallus abundance (substrate: 99.7% → 7.0%; soil: 78.3% → 5.5%) and concomitant explosive proliferation of Trichoderma (substrate: 0% → 45.2%; soil: 0.1% → 55.3%). Soil fungal communities exhibited a higher richness (Chao1, p < 0.05) and stability, attributed to functional redundancy (e.g., Aspergillus, Conocybe) and physical protection by organic–mineral complexes. Conversely, substrate bacterial diversity dominated, driven by organic matter availability (e.g., the Burkholderia–Caballeronia–Paraburkholderia complex surged to 59%) and optimized porosity. Niche analysis confirmed intensified competition in post-regression soil (niche differentiation) versus substrate niche contraction under Trichoderma dominance. Critically, Trichoderma overgrowing was mechanistically linked to (1) nutrient competition via activated hydrolases (e.g., Chit42) and (2) pathogenic activity (e.g., T. koningii causing rot). We propose ecological control strategies: application of antagonistic Bacillus subtilis (reducing Trichoderma by 63%), substrate C/N ratio modulation via soybean meal amendment, and Sphingomonas–biochar soil remediation. This work provides the first integrated microbial niche model for D. indusiata regression, establishing a foundation for sustainable cultivation. Full article

Driven by climate change and human activities, the expansion of highly invasive moso bamboo (Phyllostachys edulis) into coniferous forests induces a serious ecological imbalance. Its rapidly spreading underground roots significantly alter soil structure, yet the mechanisms by which this expansion affects soil detachment capacity (Dc), a key soil erosion parameter, remain unclear. While bamboo expansion modifies soil physicochemical properties and root characteristics, influencing Dc and, consequently, soil erosion resistance, the underlying mechanisms, particularly stage-specific variations, are not thoroughly understood. In this study, we examined Japanese white pine (Pinus parviflora Siebold & Zucc.) forest (CF), moso bamboo–Japanese white pine mixed forest (MF), and moso bamboo forest (BF) as representative stages of bamboo expansion. By integrating laboratory-controlled measurements of soil physicochemical properties and root traits with field-based flume experiments, we comprehensively investigate the effects of moso bamboo expansion into CF on soil detachment capacity. The results of the study can be summarized as follows: (1) Expansion of moso bamboo significantly changed soil physicochemical properties and root characteristics. Soil bulk density was the highest in the MF (1.13 g·cm⁻³), followed by the CF (1.08 g·cm⁻³) and BF (1.03 g·cm⁻³); non-capillary porosity increased significantly with expansion (CF 0.03% to MF 0.10%); and although the stability of aggregates (MWD) increased by 24.5% from the CF to MF, root mass density (RMD) in the MF (0.0048 g·cm⁻³) was much higher than that in the CF (0.0009 g·cm⁻³). This intense root competition between forest types, combined with increased macroporosity development, compromised overall soil structural integrity. This weakening may lead to a looser soil structure during the transition phase, thereby increasing erosion risk. (2) There were significant stage differences in Dc: it was significantly higher in the MF (0.034 kg·m⁻²·s⁻¹) than in the CF (0.023 kg·m⁻²·s⁻¹) and BF (0.018 kg·m⁻²·s⁻¹), which revealed that the MF was an erosion-sensitive stage. (3) Our Partial Least Squares Structural Equation Modeling (PLS-SEM) results revealed that soil physicochemical properties (soil moisture content and soil total nitrogen) dominated Dc changes through direct effects (total effect −0.547); in comparison, root properties indirectly affected Dc by modulating soil structure (indirect effect: −0.339). The results of this study reveal the dynamics and mechanisms of Dc changes during bamboo expansion, and for the first time, we identify a distinct Dc peak during the mixed forest transition phase. These findings provide a scientific basis for moso bamboo forest management, soil erosion risk assessment, and optimization of soil and water conservation strategies. Full article