Search Results (12,063)

Background: Peanut is susceptible to iron (Fe) deficiency, particularly in calcareous soils. However, comparative studies on the adaptive mechanisms of different peanut cultivars to Fe deficiency remain limited. This study aimed to investigate the physiological and molecular responses of two distinct peanut cultivars to Fe deprivation and to identify the key traits contributing to differential Fe efficiency. Methods: Two peanut cultivars, LH11 and YZ9102, were cultivated under Fe-sufficient and Fe-deficient conditions, using both hydroponic and pot-based soil culture systems. Multiple parameters were assessed, including visual symptomology, biomass, tissue Fe concentration, active Fe in leaves, chlorophyll (Chl) content (SPAD value), net photosynthetic rate (Pn), Chl fluorescence (Fv/Fm), rhizosphere pH, root ferric chelate reductase (FCR) activity, and the relative expression of two Fe-acquisition-related genes (AhIRT1 and AhFRO1) via qRT-PCR. Results: Cultivar YZ9102 exhibited more severe Fe deficiency chlorosis symptoms, which also appeared earlier than in LH11, under both cultivation systems. Under Fe deficiency, YZ9102 showed significantly lower Chl content, Pn, and Fv/Fm compared to LH11. In contrast, LH11 demonstrated a greater capacity for rhizosphere acidification and maintained significantly higher root FCR activity under Fe-limited conditions. Gene expression analysis revealed that Fe deficiency induced the up-regulation of AhIRT1 and AhFRO1 in the roots of LH11, while their transcript levels were suppressed or unchanged in YZ9102. Conclusions: The peanut cultivar LH11 possesses superior tolerance to Fe deficiency compared to YZ9102. This enhanced tolerance is attributed to a synergistic combination of traits: the maintenance of photosynthetic performance, efficient rhizosphere acidification, heightened root Fe³⁺ reduction capacity, and the positive transcriptional regulation of key Fe uptake genes. These findings provide crucial insights for the selection and breeding of Fe-efficient peanut varieties for cultivation in Fe-deficient environments. Full article

Although neurological disease is common in people with human immunodeficiency virus (HIV) (PWH), the contributing factors and underlying inflammatory mechanisms remain challenging to identify. Extracellular vesicles (EVs) constitute a relatively uncharacterized modality of intercellular communication and bioactive cargo transport in the setting of viral infection and pathogenesis. EVs carry inflammatory mediators to areas of the periphery during antiretroviral therapy (ART) suppression but are understudied in the brain. Using a biologically relevant simian–human immunodeficiency chimeric virus with a clade D HIV envelope (SHIV.D)-infected rhesus macaque (RM) model of HIV persistence in the central nervous system (CNS), we investigate circulating EV populations and the protein cargo of myeloid-derived EVs during SHIV infection. Using EV flow cytometry to quantify specific EV subpopulations, we found a significant increase in TMEM119+ microglial EVs and CD171+ neuronal EVs in RM plasma during viremia and ART suppression. Using primary RM monocyte-derived macrophages (MDMs), we determined that MDMs increased EV production after SHIV infection. Whole proteomic analysis of these EVs demonstrated that myeloid EVs isolated from SHIV.D-infected MDMs carried significantly increased levels of neuropathogenic and inflammatory proteins. Altogether, these studies improve our understanding of the contribution of myeloid EVs to neurological disease during SHIV/HIV infection. Full article

The diffusion of intelligent construction technologies has improved construction efficiency and information integration, while also increasing the complexity and uncertainty of governance decisions in megaprojects. In particular, selecting an appropriate Engineering Transaction Mode (ETM) under intelligent construction involves multiple conflicting criteria, expert judgments, and loss-averse risk preferences, which are not fully captured by conventional multi-criteria decision-making methods. This study proposes a decision-making model that combines Pythagorean fuzzy sets (PFSs) and prospect theory to support ETM selection for megaprojects under intelligent construction. The model constructs an ETM evaluation system grounded in a systematic literature review and questionnaire evidence, encodes expert judgments using PFSs, determines expert and criterion weights via information-utility and fuzzy-entropy measures, and aggregates perceived gains and losses relative to positive and negative ideal solutions through prospect theory. A mega-pumping station project with four ETM alternatives is used for validation. Results indicate that “Self-management + Network-based integrated application + Consultant assistance” achieves the highest prospect value and is consistently ranked first; the same ordering is obtained using TOPSIS and a fuzzy comprehensive evaluation method, demonstrating robustness. The study contributes to theory by coupling hybrid fuzzy representation with loss-aversion-based behavioral aggregation for ETM governance under intelligent construction and provides practitioners with a transparent, replicable decision tool to support ETM selection in complex, uncertainty-laden megaprojects. Full article

Spatial accessibility to healthcare services is commonly determined by three core components: demand, supply, and traffic speed. Although understanding which factors contribute to accessibility changes can help prioritize interventions to enhance accessibility in underserved areas, limited research has examined the extent of their individual contributions. To better capture the local dynamics that shape healthcare accessibility, this study decomposes spatial accessibility to primary healthcare services using the chain substitution method (CSM), which quantifies the impact of each component by substituting them one by one. By examining how the order of factor substitution affects the relative impact of each factor on spatial accessibility, we analyzed the importance of substitution order in the CSM. This study found that the order of factor substitution plays a significant role in measuring the relative contribution of each factor. To mitigate the effects of substitution order, we proposed an averaging CSM that uses the average value across all possible substitution combinations. Based on the averaging CSM, our findings offer insight for healthcare policymakers and urban planners by clarifying how demand, supply, and traffic speed interact in determining accessibility, ultimately supporting targeted interventions in underserved areas. Full article

Soil erosion remains a critical global concern, yet long-term catchment-scale assessments that explicitly link historical land-use transitions with erosion responses remain limited. This study evaluates how ±240 years record of historical and projected land-use changes influence soil erosion in the Myjava Basin by integrating parcel-level land-use reconstructions from 1787 to 2030 into a distributed USLE-2D framework. R, K, and parcel-based C and P factors were temporally standardized, and LS was derived using an ensemble of four widely applied algorithms. A PCA was applied to quantify the relative contribution of RUSLE factors across time, and all analyses were performed within a reproducible geospatial modelling environment. The results indicated a long-term decline in total erosion of ±78% at the landscape scale and ±60% within arable land from the 19th century to the present, driven mainly by a major reduction in arable land (from ±62% to ±37%) and expansion of forest and shrub vegetation. Despite this decline, persistent hotspots remain concentrated on steep upland slopes with high LS (>10%), while agricultural parcels experienced erosion rates 10–20 times higher than the basin-wide mean across all periods. PCA shows that LS and rainfall erosivity dominate erosion variability (PC loadings ±0.78–0.84), while C and P factors increase in influence in recent and projected periods, contributing up to ±40% of total explained variance. These findings demonstrate that long-term land-use transitions have substantially reduced basin-scale erosion risk. Full article

China is the world’s leading producer of offshore mariculture, contributing more than 60 percent of global output. Yet the provincial distribution of mariculture space and its economic concentration are still not well described at a comparable national scale. This study draws on a publicly available nation-scale GIS dataset extracted from Landsat 8 imagery from 2018 to map offshore mariculture across nine coastal provinces and to quantify spatial inequality and specialization. The mapped offshore mariculture footprint totals 733,840 ha. The distribution is sharply uneven. Fujian alone reaches 183,025 ha, nearly thirty times the area of Hainan. The Gini coefficient is 0.412, and concentration ratios show that the top three provinces account for 64.0 percent of the total area, and the top five account for 84.5 percent. Location quotient results indicate strong specialization in Fujian, Jiangsu, and Hebei, while Hainan and Guangxi remain marginal. Cluster analysis further identifies three development modes: large-scale expansion, medium-scale and relatively balanced growth, and small-scale dispersed production. Overall, the pattern is consistent with resource endowment, agglomeration effects, and path dependence. The findings point to the need for improved coastal spatial planning, stronger interprovincial technology diffusion, and differentiated governance that balances efficiency with equity and environmental sustainability. Full article

New ventures operating in volatile and unpredictable environments must rely on rapid adaptation and decisive action, making improvisation a critical entrepreneurial capability. This study examines how improvisation enhances new venture performance by uncovering the psychological and learning-based mechanisms through which its effects unfold. Drawing on the Knowledge-Based View (KBV) and Social Learning Theory (SLT), the model proposes that improvisation strengthens entrepreneurial self-efficacy, enabling entrepreneurs to approach uncertainty with greater confidence and adaptive judgment. Using a two-wave survey of 322 startup founders in Turkey and analyses conducted through PROCESS and complementary SEM estimation, the findings show that improvisation significantly boosts both entrepreneurial self-efficacy and new venture performance. Entrepreneurial self-efficacy emerges as a key mediating mechanism, indicating that improvisational experiences help entrepreneurs develop mastery, reinforce capability beliefs, and translate spontaneous action into improved outcomes. The results further suggest that improvisational episodes provide immediate learning cues that enhance situational awareness and decision-making agility, deepening the psychological pathway that links spontaneous behavior to venture performance. Additionally, relative explorative learning significantly moderates the relationship between improvisation and entrepreneurial self-efficacy, demonstrating that entrepreneurs benefit more from improvisation when they actively pursue new knowledge, experiment with unfamiliar approaches, and challenge routine assumptions. This moderating role clarifies when improvisation produces its strongest effects, while the mediating mechanism explains how performance improvements materialize through confidence-building processes. By integrating these mechanisms into a unified explanation, the study advances understanding of the improvisation–performance relationship and highlights the importance of learning-oriented behavior in converting spontaneous action into sustained entrepreneurial advantage. The findings offer theoretical contributions and actionable insights for entrepreneurs seeking to strengthen adaptability, resilience, and competitiveness in fast-changing environments. Full article

We present an adaptive governance engine for blockchain-enabled Vehicular Ad Hoc Networks (VANETs) that regulates the latency–energy–coherence trade-off under rapid topology changes. The core contribution is an Ideal Information Cycle (an operational abstraction of information injection/validation) and a modular VANET Engine implemented as a real-time control loop in NS-3.35. At runtime, the Engine monitors normalized Shannon entropies—informational entropy S over active transactions and spatial entropy $H_{\mathrm{spatial}}$ over occupancy bins (both on $[0,1]$)—and adapts the consensus mode (latency-feasible PoW versus signature/quorum-based modes such as PoS/FBA) together with rigor parameters via calibrated policy maps. Governance is formulated as a constrained operational objective that trades per-block resource expenditure (radio + cryptography) against a Quality-of-Information (QoI) proxy derived from delay/error tiers, while maintaining timeliness and ledger-coherence pressure. Cryptographic cost is traced through counted operations, $E_{\mathrm{crypto}}=e_h{n}_{\mathrm{hash}}+e_{\mathrm{sig}}{n}_{\mathrm{sig}}$, and coherence is tracked using the LCP-normalized definition $D_{\mathrm{ledger}}\left(t\right)$ computed from the longest common prefix (LCP) length across nodes. We evaluate the framework under urban/highway mobility, scheduled partitions, and bounded adversarial stressors (Sybil identities and Byzantine proposers), using 600 s runs with 30 matched random seeds per configuration and 95% bias-corrected and accelerated (BCa) bootstrap confidence intervals. In high-disorder regimes ($S\gtrsim 0.8$), the Engine reduces total per-block energy (radio + cryptography) by more than 90% relative to a fixed-parameter PoW baseline tuned to the same agreement latency target. A consensus-first triggering policy further lowers agreement latency and improves throughput compared with broadcast-first baselines. In the emphasized urban setting under high mobility ($v=30$ m/s), the Engine keeps agreement/commit latency in the sub-100 ms range while maintaining finality typically within sub-150 ms ranges, bounds orphaning (≤10%), and reduces average ledger divergence below $0.07$ at high spatial disorder. The main evaluation is limited to $N\le 100$ vehicles under full PHY/MAC fidelity. PoW targets are intentionally latency-feasible and are not intended to provide cryptocurrency-grade majority-hash security; operational security assumptions and mode transition safeguards are discussed in the manuscript. Full article

Terpenes are major determinants of tea aroma, and terpene synthases (TPSs) catalyze key steps in terpenoid biosynthesis. To capture gene-family variation beyond a single reference, we performed a pan-genome–based analysis of TPS genes across nine Camellia genomes (three wild tea relatives and six cultivated Camellia sinensis accessions) and integrated pan-transcriptome profiling across eight tissues. We identified 381 TPS genes; wild species contained more TPSs than cultivated accessions (mean 58.3 vs. 34.3), suggesting a putative contraction. Phylogenetic analysis with Arabidopsis TPSs classified Camellia TPSs into five subfamilies, dominated by TPS-b (149) and TPS-a (140), whereas TPS-c was rare (8). Gene-structure and physicochemical analyses revealed marked subfamily divergence, with TPS-c showing highly conserved coding-region length. Orthology clustering assigned 355 TPSs to 19 orthogroups, including five core groups (190 genes, 53.5%) and 14 dispensable groups (165 genes, 46.5%); core/non-core status was significantly associated with subfamily composition. Tandem and proximal duplication contributed most to TPS expansion (29.4% and 29.1%), and all orthogroups exhibited copy-number variation, with pronounced lineage-specific expansions. Ka/Ks analyses indicated pervasive purifying selection (median 0.516) but heterogeneous constraints among subfamilies. Finally, cultivated tea showed higher TPS expression in most tissues, especially mature leaf and stem, and TPS-g displayed the broadest and strongest expression. Together, these results provide a pan-genome resource for Camellia TPSs and highlight how domestication, duplication, and CNV shape terpene-related genetic diversity. Full article

Fuel moisture content (FMC) estimation is essential for wildfire danger assessment and fire behavior modeling. This study quantifies the value of integrating satellite observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard Suomi-NPP and the Advanced Baseline Imager (ABI) aboard GOES-16 with High-Resolution Rapid Refresh (HRRR) numerical weather prediction data for hourly 10 h dead FMC estimation across the continental United States. We leverage the complementary characteristics of each system: VIIRS provides enhanced spatial resolution (375–750 m), while ABI contributes high temporal frequency observations (hourly). Using XGBoost machine learning models trained on 2020–2021 data, we systematically evaluate performance improvements stemming from incorporating satellite retrievals individually and in combination with HRRR meteorological variables through eight experimental configurations. Results demonstrate that while both satellite systems individually enhance prediction accuracy beyond HRRR-only models, their combination provides substantially greater improvements: 27% RMSE and MAE reduction and 46.7% increase in explained variance (R${}^2$) relative to HRRR baseline performance. Comprehensive seasonal analysis reveals consistent satellite data contributions across all seasons, with stable median performance throughout the year. Diurnal analysis across the complete 24 h cycle shows sustained improvements during all hours, not only during satellite overpass times, indicating effective integration of temporal information. Spatial analysis reveals improvements in western fire-prone regions where afternoon overpass timing aligns with peak fire danger conditions. Feature importance analysis using multiple explainable AI methods demonstrates that HRRR meteorological variables provide the fundamental physical framework for prediction, while satellite observations contribute fine-scale refinements that improve moisture estimates. The VIIRS lag-hour predictor successfully maintains observational value up to 72 h after acquisition, enabling flexible operational implementation. This research demonstrates the first systematic comparison of VIIRS versus ABI contributions to dead FMC estimation and establishes a framework for hourly, satellite-enhanced FMC products that support more accurate fire danger assessment and enhanced situational awareness for wildfire management operations. Full article

Background: Rowing performance in youth athletes is strongly influenced by anthropometric characteristics, body composition, and limb proportions; however, the combined contribution of these factors across developmental stages remains insufficiently understood. This study investigated the relationships between key anthropometric variables and ergometer performance in a multinational cohort of young rowers. Methods: A total of 194 athletes (48 females, 146 males) from ten countries participated. Based on age and sex, participants were categorized into junior female (JF), junior male (JM), adult female (AF), and adult male (AM) groups. Body height, body mass, body fat (F%), relative muscle mass (M%), limb lengths, and body surface area (BSA) were measured. Rowing performance was assessed via maximal 2000 m ergometer trials. Results: Males outperformed females across all age groups (p < 0.001). Performance showed strong positive correlations with body height (r = 0.673, p = 0.003), body mass (r = 0.724, p = 0.005), arm span (r = 0.681, p = 0.002), lower-limb length (r = 0.394, p = 0.004), relative muscle mass (39.9 ± 5.2%; r = 0.531, p < 0.001), and especially BSA (1.94 ± 0.19 m²; r = 0.739, p < 0.001). Relative body fat was negatively associated with performance (17.6 ± 6.9%; r = −0.465, p < 0.001). Conclusions: Findings indicate that rowing performance in youth athletes reflects multidimensional anthropometric configurations rather than isolated traits, characterized primarily by the combined contribution of body surface area, relative muscle mass, and segmental body dimensions. From a practical perspective, higher-performing athletes typically exhibited body surface area values approaching or exceeding ~1.90 m² and relative muscle mass above ~40%, suggesting these ranges as indicative reference benchmarks rather than fixed selection thresholds. Integrating anthropometric profiling with physiological assessment may enhance early talent identification and support individualized training strategies in competitive youth rowing. Full article

Alkane is a predominant wax component, whose production requires the aids of CER1 and CER3. In rice, OsCER1 and OsCER3 are present in multiple copies. Until now, the roles of these genes have been studied individually; however, a systematic comparison of their relative contributions to cuticular wax biosynthesis has not yet been carried out. Phylogenetic tree analysis revealed that CER1s and CER3s from different plants are classified into two subgroups. RT-qPCR analysis showed that these genes display distinct expression patterns, revealing their specific roles in wax production. Promoter prediction analysis showed that cis-elements responding to light, phytohormones and stress are enriched in the promoter region of OsCER1s and OsCER3s. These proteins are all localized in the endoplasmic reticulum. Further study showed that OsCER1s and OsCER3s are inclined to form a complex during the wax synthesis. Finally, the wax analysis of single mutants showed that among the examined genes, OsCER3a mutation greatly reduced the total wax amounts to 19.6% of wild-type plant with a decrease in most of wax components, whereas mutation of other genes including OsCER3b, OsCER3c, OsCER1a and OsCER1c slightly or barely affect wax production, suggesting that OsCER3a plays major roles in rice wax production whereas other proteins redundantly participate in the wax synthesis. Additionally, the wax increasing rates of Arabidopsis expressing OSCER1 are lower than those of overexpressing AtCER1. Taken together, our study identified the predominant genes involved in wax production, which will be useful for genetically engineering rice with enhanced stress tolerance. Full article

Yeast biosensors represent a promising biotechnological innovation for ensuring the safety and quality of fermented beverages such as beer, wine, and kombucha. These biosensors employ genetically engineered yeast strains to detect specific contaminants, spoilage organisms, or hazardous compounds during fermentation or the final product. By integrating synthetic biology tools, researchers have developed yeast strains that can sense and respond to the presence of heavy metals (e.g., lead or arsenic), mycotoxins, ethanol levels, or unwanted microbial metabolites. When a target compound is detected, the biosensor yeast activates a reporter system, such as fluorescence, color change, or electrical signal, providing a rapid, visible, and cost-effective means of monitoring safety parameters. These biosensors offer several advantages: they can operate in real time, are relatively low-cost compared to conventional chemical analysis methods, and can be integrated directly into the fermentation system. Furthermore, as Saccharomyces cerevisiae is generally recognized as safe (GRAS), its use as a sensing platform aligns well with existing practices in beverage production. Yeast biosensors are being investigated for the early detection of contamination by spoilage microbes, such as Brettanomyces and lactic acid bacteria. These contaminants can alter the flavor profile and shorten the product’s shelf life. By providing timely feedback, these biosensor systems allow producers to intervene early, thereby reducing waste and enhancing consumer safety. In this work, we review the development and application of yeast-based biosensors as potential safeguards in fermented beverage production, with the overarching goal of contributing to the manufacture of safer and higher-quality products. Nevertheless, despite their substantial conceptual promise and encouraging experimental results, yeast biosensors remain confined mainly to laboratory-scale studies. A clear gap persists between their demonstrated potential and widespread industrial implementation, underscoring the need for further research focused on robustness, scalability, and regulatory integration. Full article

Relative humidity (RH) is an important meteorological factor that affects both the climate system and human activities. However, the existing observational station data are insufficient to meet the requirements of regional scale research. Machine learning methods offer new avenues for high precision RH estimation, but the performance of different algorithms in complex geographical environments still needs to be thoroughly evaluated. Based on Chinese observational station data from 2011 to 2020, this study systematically evaluated the performance of three methods for estimating RH: the generalized linear mixed model (GLMM), random forest (RF) and the XGBoost algorithm. The results of ten-fold cross validation indicate that the two machine learning methods are significantly superior to the traditional GLMM. Among them, RF performed the best (the determinant coefficient (R²) = 0.73, root mean square error (RMSE) = 8.85%), followed by XGBoost (R² = 0.72, RMSE = 9.07%), while the GLMM performed relatively poorly (R² = 0.58, RMSE = 11.08%). The model performance shows significant spatial heterogeneity. All models exhibit high correlation but relatively large errors in the northern regions, while demonstrating low errors yet low correlation in the southern regions. Meanwhile, the model performance also shows significant seasonal variations, with the highest accuracy observed in the summer (June to September). Among all features, dew point temperature (T_d) aridity index (AI) and day of year (DOY) are the main contributing factors for RH estimation. This study confirms that the RF model provides the highest accuracy in RH estimation. Full article

Maintaining color constancy in polymer extrusion processes is a key difficulty in manufacturing applications, as fluctuations in processing parameters greatly influence pigment dispersion and the quality of the finished product. Preliminary historical data mining analysis was conducted in 2009. This work concentrates on Opaque PC Grade 5, which constituted 2.43% of the pigment; it contained 10 PPH of resin2 with a Melt Flow Index (MFI) of 6.5 g/10 min and 90 PPH of resin1. It also employs a fixed resin composition with an MFI of 25 g/10 min. This research identified the significant processing parameters (PPs) contributing to the lowest color deviation. Interactions between processing parameters, for the same color formulation, were analyzed using statistical methods under various processing conditions. A principle-driven General Trends (GT) diagnostic procedure was applied, wherein each parameter was individually varied across five levels while holding others constant. Particle size distribution (PSD) and colorimetric data (CIE Lab*) were systematically measured and analyzed. To complete this, correlations for the impact of temperature (Temp) on viscosity, particle characteristics, and color quality were studied by characterizing viscosity, Digital Optical Microscopy (DOM), and particle size distribution at various speeds. The samples were characterized for viscosity at three temperatures (230, 255, 280 °C) and particle size distribution at three speeds: 700, 750, 800 rpm. This study investigates particle processing features, such as screw speed and pigment size distribution. The average pigment diameter and the fraction of small particles were influenced by the speed of 700–775 rpm. At 700 rpm, the mean particle size was 2.4 µm, with 61.3% constituting particle numbers. The mean particle size diminished to 2 µm at 775 rpm; however, the particle count proportion escalated to 66% at 800 rpm. This research ultimately quantifies the relative influence of particle size on the reaction, resulting in a color value of 1.36. The mean particle size and particle counts are positively correlated; thus, reduced pigment size at increased speed influences color response and quality. The weighted contributions of the particles, 51.4% at 700 rpm and 48.6% at 800 rpm, substantiate the hypothesis. Further studies will broaden the GT analysis to encompass multi-parameter interactions through design experiments and will test the diagnostic assessment procedure across various polymer grades and colorants to create robust models of prediction for industrial growth. The global quality of mixing polycarbonate compounding constituents ensured consistent and smooth pigment dispersion, minimizing color streaks and resulting in a significant improvement in color matching for opaque grades. Full article