Search Results (1,040)

NAC (NAM, ATAF1/2 and CUC2) transcription factors constitute one of the largest plant-specific transcription factor families and play pivotal roles in plant growth, development, and responses to environmental stresses. Systematic characterization of NAC genes is essential for understanding regulatory networks underlying key agronomic and adaptive traits. As a conservation-priority woody species with distinctive biological and horticultural value, Sinojackia xylocarpa Hu lacks comprehensive knowledge of its NAC repertoire, and elucidating its NAC family will facilitate functional studies related to development and environmental adaptation. Based on whole-genome data of S. xylocarpa, we conducted a systematic survey and characterization of the NAC transcription factor family. In total, 115 SxyNAC genes encoding the conserved NAC domain were identified, and their loci were unevenly distributed across 12 chromosomes. Analyses of gene-duplication modes and collinearity indicated that whole-genome/segmental duplication events were the major driving force for the expansion of this family. Phylogenetic relationships, gene structures, and conserved motifs classified the SxyNAC members into 15 subfamilies, revealing a highly conserved N-terminal NAC domain and a markedly diversified C-terminal regulatory region with pronounced member- and lineage-specific differences. Promoter cis-element prediction showed extensive enrichment of light-responsive, phytohormone-responsive, and stress-related elements, suggesting that SxyNAC genes may participate in coordinated regulation of multiple environmental cues and endogenous hormone pathways. Transcriptome data from six fruit developmental stages, together with qRT-PCR validation of ten representative genes, demonstrated diverse temporal and tissue-specific expression patterns during fruit development and close associations with fruit growth regulation. Overall, our findings establish a framework for exploring the evolutionary trajectories and functional diversification of NAC genes in S. xylocarpa, and they offer a valuable resource for NAC-family research and conservation-focused functional genomics in other rare or threatened plant species. Full article

Innovation is the core driving force behind high-quality development. This study uses a sample of Chinese A-share non-financial listed companies from 2011 to 2024. It empirically examines the impact of digital–green synergy on corporate innovation resilience. We find that digital–green synergy (DG) significantly enhances firm innovation resilience. The baseline regression coefficient is 0.031 (p < 0.01). This conclusion remains robust after addressing endogeneity and conducting various robustness checks. Mechanism tests show that digital–green synergy enhances innovation resilience by improving firms’ absorptive capacity, attracting capital market attention, and cultivating both resource and organizational synergy. Heterogeneity analyses reveal that the impact of this dual transformation depends on firms’ specific characteristics and their internal and external environments. This research provides micro-level evidence on the value-creation mechanisms of dual transformation synergy. The findings offer significant insights for supporting corporate innovation systems in navigating uncertainty and achieving high-quality, sustainable development. Full article

This paper estimates how corporate innovation investment responds to climate policy uncertainty using panel data with 3197 listed firms from 2010 to 2022 in China. The findings show that climate policy uncertainty positively contributes to corporate innovation investment, and this result continues to hold after controlling for endogeneity and conducting a series of robustness tests. Furthermore, we find that stringent government environmental regulation serves as a potential mechanism, compelling firms to adopt cleaner production and increase their investment in innovation. Additionally, this positive relationship is stronger for firms with higher government subsidies and disappears for firms with a higher allocation of fixed assets. We also find that firms with fewer connections to the government are more sensitive to climate policy uncertainty and they tend to increase their investment in innovation to mitigate the uncertainty. Furthermore, when firms invest more in innovation during periods of high policy uncertainty, their long-term performance and firm value are likely to improve. This study sheds light on the importance and influence of climate policy uncertainty on corporate innovation investment in China. Full article

Urinary microRNAs (miRNAs) are promising noninvasive biomarkers for cancer detection, but their clinical utility is reduced by inconsistent normalization strategies, reducing reproducibility and comparability across studies. In this study, we assessed the stability of miR-20a as an endogenous normalizer for urinary miRNA profiling in clear cell renal cell carcinoma (ccRCC) while standardizing the pre-analytical phase using a urine stabilizing solution. Ninety-nine urine samples were analyzed: 47 from healthy individuals, 30 from ccRCC patients pre-surgery, and 22 post-operative patients. Six candidate miRNAs—miR-20a, miR-15b, miR-16, miR-15a, miR-210-3p, and miR-let-7b—were quantified via RT-qPCR. Stability analysis with RefFinder, integrating multiple algorithms (geNorm, normFinder, BestKeeper, and ΔCt methods), identified miR-20a as the most stable among the six candidates. Raw Ct values of miR-20a were normally distributed (Shapiro–Wilk test, p > 0.05), with no significant intergroup differences (one-way ANOVA, F(2.96) = 2.324, p = 0.103) and minimal intragroup variability (CV% 4.98–6.38). MiR-20a expression remained stable across different tumor staging, grading, and urine storage durations. These findings confirm miR-20a as a robust endogenous normalizer for urinary miRNA analyses and support the feasibility of developing reproducible urinary liquid biopsy workflows for ccRCC, even in settings where immediate sample processing is not feasible. Full article

Background/Objectives: Differentiating Cushing’s disease (CD) from adrenocorticotropic hormone (ACTH)-independent Cushing’s syndrome (AICS) remains challenging in patients with equivocal ACTH levels. While dynamic testing is frequently required, baseline hormonal measurements may offer a simpler diagnostic approach. We aim to evaluate the diagnostic value of baseline plasma ACTH, cortisol, and dehydroepiandrosterone sulfate (DHEAS) levels and their derived ratios for differentiation between ACTH-dependent and ACTH-independent Cushing’s syndrome, and to propose a diagnostic algorithm based on these parameters. Methods: This retrospective single-centre study included adult patients with endogenous Cushing’s syndrome aged 18–75 years who were followed at our institution. Patients with ectopic/paraneoplastic Cushing’s syndrome were excluded. The AICS group comprised overt adrenal CS and mild autonomous cortisol secretion cases. Morning baseline plasma ACTH (pg/mL), serum cortisol (µg/dL), and serum DHEAS (µg/dL) levels were measured and ratios calculated: cortisol-to-ACTH ratio (CAR), DHEAS-to-cortisol ratio (DCR), and CAR-to-DHEAS ratio (CAR/D). ROC analysis assessed diagnostic performance with age and sex adjustments. Results: A total of 100 patients were included, comprising 43 patients with CD and 57 with AICS. Plasma ACTH demonstrated high diagnostic accuracy for identifying CD with a cut-off of ≥14.65 pg/mL (sensitivity 100%, specificity 98.25%, AUC 0.998). Serum DHEAS showed strong discriminative power with a cut-off of ≥67.15 µg/dL (sensitivity 88.37%, specificity 91.23%, AUC 0.925), achieving high discriminative power after age–sex adjustment at ≥85.59 µg/dL (sensitivity 100%, specificity 100%, AUC 0.999). CAR showed good performance in identifying CD with a cut-off of ≤0.75 µg/dL per pg/mL (sensitivity 93.02%, specificity 98.25%, AUC 0.980). CAR/D demonstrated high diagnostic power with a cut-off of ≤1.54 (sensitivity 95.35%, specificity 98.25%, AUC 0.974), improving after age–sex adjustment to ≤2.36 (sensitivity 97.87%, specificity 96.23%, AUC 0.992). Conclusions: Baseline plasma ACTH, serum cortisol, and serum DHEAS measurements, along with derived ratios—especially CAR and CAR/D—provide highly accurate differentiation between ACTH-dependent and ACTH-independent Cushing’s syndrome. These widely available measurements may reduce dependence on dynamic testing and improve diagnostic accuracy in patients with equivocal findings. Full article

In this VUCA era, investigating the impact of supply chain concentration on enterprise resilience holds significant theoretical and practical value. Using panel data from Chinese A-share listed companies (2012–2024), we find that high supply chain concentration significantly undermines enterprise resilience, and the conclusion remains robust after a series of robustness tests and endogeneity treatments. Mechanism analysis shows that financing constraints, innovation capability, and risk-taking act as important mediating channels. Furthermore, nonlinear analysis identifies structural dual-threshold effects associated with industry competition intensity and business environment quality, suggesting that the adverse effect of supply chain concentration on enterprise resilience varies across different threshold intervals. Heterogeneity analysis further shows that this negative impact is more pronounced in enterprises with weak internal management, low levels of digitalization, or excessive ESG greenwashing, as well as in external contexts such as less-developed regions, low regional data factorization levels, or non-high-tech industries. This study provides micro-level empirical evidence for understanding the strategic trade-off between supply chain structure and enterprise resilience and provides a reference for policy makers to improve the resilience and security of industrial chains. Full article

Electricity demand forecasting is essential for optimizing energy management and planning in microgrids and institutional contexts. The purpose of this article is to demonstrate how flexibility characterization can serve as a structural foundation for prediction, providing a contextualized framework that surpasses the limitations of traditional approaches. Representative trajectories (A–D), derived from entropy and variability metrics, were consolidated from historical user data and used as the basis for modeling. Two complementary approaches were implemented: ARIMA models, which capture endogenous dynamics, and ARX models, which extend this capacity by incorporating exogenous cyclical variables (hour, day of the week, month) and lagged predictors. A systematic grid search was conducted to identify optimal parameter configurations, followed by validation through rolling forecasts with a 24-h horizon, relevant for operators of microgrids, institutional managers, and energy planners. Performance was evaluated using MAE, RMSE, MAPE, and SMAPE, ensuring comparability across trajectories. Results show that ARIMA consistently achieved lower error rates in stable trajectories (A and C), with SMAPE values around 2.0%, while ARX provided substantial improvements in irregular ones (B and C), reducing SMAPE from 3.7–5.9% to approximately 2.2–2.6%. In highly irregular profiles (D), all models converged to similar accuracy (SMAPE ≈ 9.0%). When applied to individual users, predictive errors varied more widely depending on trajectory assignment: stable users showed SMAPE values around 3–4%, while irregular users exhibited much higher errors, exceeding 18–21%. Unlike conventional methods that treat characterization and prediction as separate processes, this study integrates both into a unified framework, enabling forecasts to capture stability, cyclicity, and adaptability. The methodology was further applied to individual users by assigning them to representative trajectories and adjusting predictions through baseline scaling. Overall, the findings demonstrate that embedding forecasts within characterized trajectories transforms prediction into a contextualized analysis of flexibility, enabling accurate short-term forecasts and supporting practical applications in energy planning, demand management, and economic dispatch. The framework has been designed to support electricity demand forecasting across multiple contexts, from microgrids and institutional systems to larger territorial and national scales. Through contextual calibration, the methodology ensures adaptability and broader relevance for energy forecasting and demand-side management. Full article

This study explored Entities’ Performance as an explained function of Human Resource Costs (HRC) to further justify recognition of the Labour Costs proxies in the Statement of Financial Position (SOFP). This has been investigated to provide robust empirical evidence from the Nigerian Exchange Group (NGX) to spur the International Accounting Standard Board (IASB) to release an Exposure Draft (ED) for public discussion and have a standard to recognize proxies of HRC as assets in the SOFP. To provide grounds for inclusion of HRC in the SOFP by the IASB, unlike most other empirical studies reviewed, which deployed limited methods and years of time series data, this study expanded the scope and methods using Pooled Cross-Sectional (PCS) time series data of 27 quoted companies from 1992 to 2023 in the NGX. While most studies employed inefficient Ordinary Least Squares (OLS), this current study progressed from Descriptive Statistics to OLS, Pooled OLS, and Rodman’s Xtabond2 Generalized Method of Moments (GMM) to resolve the conundrums of endogeneity, reversed causality, and stationarity common to unbalanced PCS time series data. The results revealed from the GMM showed that LSW (18.40), positive, and LTD (−22.63), inverse, and Wald ^2 = 66.35 with p-value (0.002), obviously validated the strong joint significance of the regressors on ROA (performance) of 27 sampled firms in the NGX. It is recommended that IASB align with the momentum from the output of research from academia by issuing standards to recognize HRC as assets in the SOFP. Full article

Chronic exposure to even low levels of pesticides is a serious public health issue, mainly due to the role of oxidative stress in damaging the liver and promoting cancer. This has driven interest in finding natural, plant-based antioxidants that can counteract this kind of chemical injury. In this study, we tested whether a methanol extract from the leaves of Pteropyrum scoparium (PSE) could protect the liver against oxidative harm caused by four common pesticides: acetochlor, deltamethrin, thiamethoxam, and rotenone. Chemical analysis showed that the extract contains high levels of phenolics (345.1 ± 7.6 mg GAE/g) and flavonoids (17.3 ± 1.3 mg CAE/g). GC–MS profiling revealed a diverse set of compounds, including fat-soluble antioxidants like squalene, α-tocopherol, and γ-sitosterol, and water-soluble phenolics like pyrogallol and catechol, suggesting PSE is equipped with a multi-layered antioxidant defence. In the animal experiment, rats were given each pesticide for 30 days, with or without PSE. All four pesticides caused clear oxidative stress in the liver: glutathione (GSH), total antioxidant capacity (TAC), antioxidant enzymes activities dropped, while markers of lipid damage (MDA) and free radical activity (DPPH) rose. Co-administration of PSE significantly restored GSH, TAC and antioxidant enzymes levels and reduced MDA and residual DPPH values compared to pesticide-only groups; these parameters were statistically comparable to the controls (p > 0.05), indicating a substantial recovery of hepatic redox balance. Histopathological examination of liver tissues confirmed these findings, as pesticide treatment caused visible liver injury; deltamethrin and thiamethoxam led to congestion in central veins, while rotenone and acetochlor triggered clusters of inflammatory Kupffer cells. In animals that also received PSE, liver structure remained largely normal, with much less congestion and inflammation. These results show that the combination of antioxidant constituents in PSE might contribute to hepatoprotection through redox modulation and preservation of endogenous antioxidant balance, as suggested by the observed biochemical and histological improvements. Full article

Fermentation is a type of biological process conducted domestically or commercially to preserve foods and beverages, produce alcohol, add nutritional value and improve aroma and flavor. The natural fermentation of flour in water to obtain a leaven for baking, lately scrutinized in the laboratory with the application of metagenomic methods, has been ubiquitous since the dawn of civilization. Commercially, single culture or defined mixtures of microorganisms are used for their predictability, but regularly fed two-domain microorganism cultures are favored in less industrialized and domestic operations. Fungi principally produce the carbon dioxide responsible for leavening. The bacteria produce acid in the bread commonly known as sourdough for its aroma and flavor. A leaven made by fermentation using flour and water can be stored while it is dormant. We studied a mature culture that is fed twenty-fold with water and flour by incubating it for 24 h, sampling it regularly for pH measurements, and plating it. The colonies were suspended for micrography and DNA extraction for PCR and Sanger sequencing. The metagenomic DNAs were analyzed for bacterial and fungal composition. The proportions of the plant and microbial DNA endogenous to the flour decline rapidly, and the predominant bacteria and fungi in mature leaven propagate, without overlap between the respective microbiomes. Full article

Intra-urban commuting plays a fundamental role in shaping urban spatial structure and daily mobility patterns. Existing studies have largely explained commuting flows using attribute-based or distance-centred approaches. Such approaches overlook the interdependent and relational nature of commuting within complex urban systems. This study constructs a subdistrict-level commuting network using anonymised mobile phone signalling data from Hangzhou, China, and a valued exponential random graph model (valued ERGM) to examine how commuting flows are generated through the interaction of network self-organization, local job-housing conditions, and multi-dimensional proximity. The results reveal strong endogenous dependence exemplified by reciprocal commuting ties. Employment agglomeration and public rental housing provision are associated with stronger integration of subdistricts within the commuting network, while high housing prices and certain residential amenities are associated with reduced inter-subdistrict commuting. Beyond geographic distance, metro connectivity, administrative affiliation, and social interaction are significantly associated with commuting flows. This study advances a relational explanation of intra-urban commuting and demonstrates the methodological value of valued ERGMs for analysing weighted urban flow networks. The findings have implications for integrated transport, housing, and governance strategies, particularly transit-oriented development, cross-jurisdictional coordination, and the strategic siting of affordable housing, aimed at promoting more locally embedded and sustainable urban mobility. Full article

Comprehensively integrating eco-farms and rural tourism represents a crucial pathway for advancing rural revitalization and sustainable development; however, existing research has pre-dominantly focused on the net effects of individual factors, failing to reveal the underlying complexity of multiple co-occurring factors and their interactive logics. With the aim of addressing this theoretical gap, we employ a configurational approach that integrates Necessity Condition Analysis (NCA) with fuzzy set qualitative comparative analysis (fsQCA), and data was collected from 1041 Chinese ecological farms (ecological farm operators) using a structured questionnaire, to systematically explore the integrated complex configurational driving logic. Our findings reveal that no single necessary condition independently causes high-level integration. The fsQCA results further reveal that high-level integration is attainable via two distinct, yet equivalent pathways. First, the “Endogenous–Technological–Economic Synergistic Drive Model” emphasizes the intrinsic development needs of business entities, requiring extensive synergy with external technological empowerment and the regional economic environment; second, the “re-source–market–integration linkage-driven” pathway leverages unique resource endowments and achieves value transformation through efficient resource integration capabilities, guided by clear market demand. Both pathways exhibit functional substitutability among their conditions, demonstrating strategic systemic flexibility. Additionally, in the analysis of non-high-integration configurations, we draw upon structural hole theory to categorize systemic failures caused by missing key connections or factor misalignment. Full article

Under China’s rural revitalization and agricultural modernization strategies, digital village construction overcomes resource limits to drive transformation. Using 2013–2022 provincial panel data and a case study of Lin’an, Hangzhou, this study reveals how digital villages boost high-quality agriculture. The empirical results show they significantly enhance agricultural total factor productivity via three paths: IoT-driven precision production, blockchain-enabled green value addition, and e-commerce direct sales demonstrate more pronounced effectiveness in major grain-producing regions and those characterized by balanced production and sales. Simultaneously, this study employs the instrumental variable (TI) approach to address endogeneity from reverse causality and omitted variables. Mechanism testing reveals agricultural technological innovation exerts a significant 77.5% mediating effect. Finally, digital rural construction exhibits a non-linear threshold (0.3082); surpassing it triggers a gradual slowdown in growth with decreasing marginal returns. The Lin’an case validates the empirical results while revealing structural barriers, including industrial chain penetration gaps, data silos, and factor supply constraints, leading to the formulation of targeted optimization strategies. The practical contribution of this study is the proposal of a “data-value-technology” closed loop: public brands like “Tianmu Mountain Treasures” channel premiums into R&D funds, creating a self-sustaining mechanism. The findings indicate that digital villages drive high-quality agriculture primarily through direct effects, powered by full-chain tech coordination, institutional reform, and inclusive factor supply. Finally, this study proposes a coordinated governance framework encompassing “technical synergy, institutional innovation, and factor optimization,” providing theoretical support and strategic references for optimizing the pathways of regional agricultural digital transformation. Full article

Technology upgrades are a central lever for sustainability, yet many optimization models primarily account for use-phase emissions and treat embodied impacts and technological change exogenously. We propose a multi-period mixed-integer optimization framework that couples upgrade timing, technology choice, and operations with a life-cycle assessment (LCA) structure. The model (i) separates use-phase and embodied impacts at the transition level, (ii) supports time-weighted valuation of impacts through a flexible weighting sequence (time value of carbon), and (iii) incorporates endogenous learning-by-doing that can reduce both investment costs and embodied impacts of future upgrades. We derive an exact Benders (L-shaped) decomposition that separates discrete upgrade dynamics from a linear operating subproblem. Computational experiments illustrate model behavior and report runtimes under an outer-loop implementation with open-source solvers, highlighting that decomposition becomes most beneficial when extensions substantially enlarge the dispatch layer (e.g., scenario expansion). Experiments also show that ignoring embodied impacts can mis-rank upgrade schedules and even violate life-cycle caps, that stronger time-weighting pushes upgrades earlier, and that learning can make staged upgrades economically preferable. Full article

We propose Adaptive Inference, a portfolio management framework extending Active Inference to non-stationary financial environments. The framework integrates inference, control, and execution under endogenous uncertainty, modeling investment decisions as coupled dynamics of belief updating, preference encoding, and action selection rather than optimization over fixed objectives. In this approach, portfolio behavior is governed by the expected free energy (EFE) minimization, showing that classical valuation models emerge as limiting cases when epistemic components vanish. Using train–test evaluation on the ARKK Innovation ETF (2015–2025), we identify a Passivity Paradox: frozen belief transfer outperforms naive adaptive learning. A Professional Agent achieves a Sharpe ratio of 0.39 while its adaptive counterpart degrades to $-0.28$, reflecting belief contamination when learning from policy-dependent signals. Crucially, the architecture is not designed to generate alpha but to perform endogenous risk management that mitigates overtrading under regime ambiguity and distributional shift. Adaptive Inference Agents maintain long exposure most of the time while tactically reducing positions during high-entropy periods, implementing uncertainty-aware passive investing. All agents reduce realized volatility relative to ARKK Buy-and-Hold (43.0% annualized). Cross-asset validation on the S&P 500 ETF (SPY) shows that inference-guided risk shaping achieves a positive Entropic Sharpe Ratio (ESR), defined as excess return per unit of informational work, thereby quantifying the economic value of information under thermodynamic constraints on inference. Full article