Search Results (13,642)

The rare edible and medicinal mushroom Floccularia luteovirens faces challenges from limited wild resources and low triterpenoid yield in submerged fermentation. To address this, we systematically optimized the fermentation medium using one-factor-at-a-time experiments combined with Response Surface Methodology (RSM). Wheat flour, peptone, and KH₂PO₄ were identified as the optimal carbon, nitrogen, and inorganic salt sources, respectively. Subsequently, we developed and validated distinct, highly predictive mathematical models for intracellular (R² = 0.9989) and extracellular (R² = 0.9984) triterpenoid production. This yielded two optimized media: one designed to maximize intracellular accumulation (29.71 g/L wheat flour, 2.03 g/L peptone, 1.02 g/L KH₂PO₄), achieving a yield of 18.83 mg/g, and another tailored for high extracellular secretion (30.28 g/L wheat flour, 2.08 g/L peptone, 1.05 g/L KH₂PO₄), achieving a titer of 0.63 g/L. The experimental results for both targets closely matched the model predictions. Thus, this study not only significantly enhanced overall triterpenoid production but also delineated nutrient-specific strategies for targeting different product locales. The findings provide a reliable technical and theoretical foundation for the scalable and sustainable production of these bioactive compounds. Full article

The use of adsorbents based on naturally occurring materials to eliminate antibiotics from industrial effluents has attracted remarkable interest owing to the abundance of raw materials and the sustainability of this method. The ciprofloxacin (CIP) removal capacity of a previously synthesized antimicrobial hydrogel based on carboxymethyl chitosan (CMCs)/xanthan gum (XG) was investigated for the first time in this study. CMCs and XG were blended in an equivalent-weight ratio and crosslinked using trimellitic anhydride isothiocyanate (TAI) to synthesize an eco-friendly, low-cost hydrogel, which was characterized using FTIR, SEM, and XRD analyses. The pseudo-second-order model fitted the experimental data well: the experimental q_e (49.59 mg g⁻¹) was close to the theoretical value (51.81 mg g⁻¹). The Langmuir isotherm best fitted the adsorption results (R² = 0.999), with a maximum adsorption capacity of 147.06 mg g⁻¹. The thermodynamic results indicate that adsorption is spontaneous, favorable, and exothermic in nature. The percentages of desorption obtained were 95.72, 94.34, 89.52, 88, and 86.28% after five consecutive cycles. Thus, this hydrogel possesses potential for further testing and application in wastewater remediation. Full article

Optimizing calcium metabolism is crucial for skeletal development and overall productivity in growing ruminants. Twenty-four Sunite lambs were randomly assigned to four groups and fed 0, 0.6, 1.2, or 2.4 g/(d·head) of γ-PGA for 60 days. Growth performance, serum parameters, duodenal morphology and calcium transporter expression, bone microarchitecture, and duodenal microbiota were analyzed. Supplementation with 1.2 g/(d·head) of γ-PGA (the M group) yielded optimal results, significantly improving final body weight and size. It enhanced duodenal health, evidenced by increased villus height, crypt depth, and microvilli density. Crucially, this dose significantly upregulated the expression of key duodenal calcium transporters (TRPV5/6, CaBPD9k, PMCA, VDR, claudin-12) and altered systemic calcium-regulating hormones (elevated calcitriol, PTH, FGF23). Bone micro-CT analysis revealed changes in trabecular architecture indicative of active remodeling. 16S rRNA sequencing and weighted OTU co-expression network analysis (WOCNA) revealed that γ-PGA reshaped the duodenal microbiota and identified core microbial modules strongly associated with host phenotypes. Genera such as [Eubacterium]_ruminantium_group, Fusicatenibacter, and Prevotella emerged as central hubs. In conclusion, dietary γ-PGA at 1.2 g/(d·head) enhances calcium absorption and bone metabolism in lambs through a coordinated modulation of intestinal integrity and calcium transport, systemic endocrine responses, and the duodenal microbial community, with specific microbiota identified as potential key mediators associated with these effects. Full article

Deciphering the coupling mechanisms between post-harvest precursor shaping and roasting thermochemistry is pivotal for precise coffee flavor modulation. This study aimed to investigate the regulation mechanisms of in vitro biomimetic fermentation (BF) on the precursor-roasting reaction network. Integrated multi-omics characterization and sensory evaluation reveal that the BF protocol achieves targeted substrate enrichment, notably amplifying free amino acids—particularly leucine and phenylalanine—by 1.89-fold while accumulating lactate and succinate buffering salt systems. This reconfiguration constructs a matrix with superior thermal buffering capacity (ΔpH 0.17), which optimizes the thermal reaction kinetic window during roasting. Consequently, BF drives a 3.08-fold surge in esterification flux, significantly increasing the abundance of key fruity markers such as ethyl acetate and ethyl isovalerate. It also enhances the diversity of Maillard products, specifically elevating nutty-associated alkylpyrazines (e.g., 2,3,5-trimethylpyrazine). Concurrently, BF improves the thermal stability of bioactive compounds, including 5-caffeoylquinic acid (5-CQA) and trigonelline. Multi-scale molecular dynamics and quantum chemical calculations elucidate that BF-derived organic acid–salt complexes exert a ‘pseudo-catalytic effect,’ lowering activation free energy barriers for critical aroma-generating reactions by approximately 8.5 kcal/mol. This study demonstrates high sensory predictability (predictive model R² = 0.98) and provides a quantitative theoretical framework to advance coffee processing from empirical observation to rational flavor design. Full article

Rainfall variability strongly governs vegetation dynamics in the Semi-Arid Tropics (SAT) of Sub-Saharan Africa (SSA). Yet the impacts of heavy rainfall are less well quantified than those of drought. This study proposes a modified heavy rainfall index (mR95pT) to enable robust comparison of extreme rainfall signals across seasons and regions. The index mitigates the strong seasonal background signal inherent to constant-threshold approaches and highlights episodic heavy rainfall events more clearly. Using CHIRPS precipitation (1981–2022, to derive long-term climatological means) and MODIS NDVI (2003–2022) aggregated to 0.05° and 16-day intervals, we computed the cumulative precipitation, the original ETCCDI-based index (R95pT), and mR95pT across three subregions (Sahel, Southern Africa, and Eastern Africa) and examined event-scale detectability. mR95pT reduced spurious concentration around climatological wet-season peaks and more clearly captured episodic events (e.g., cyclone-related extremes). The vegetation stress (VS) responses were quantified based on the Vegetation Condition Index (VCI) and a probabilistic framework conditioned on background wetness (SPI-3) and heavy rainfall intensity (mR95pT). Under near-normal wetness (SPI-3 ≈ 0), the baseline VS probability was 18% in Eastern Africa and 13% in the other regions. Conditioning on heavy rainfall increased VS probability (relative to the SPI-3 ≈ 0 baseline) by +0.8 to +38% (Eastern Africa), +0.6 to +24% (Southern Africa), and +11 to +39% (Sahel), with the additional effect diminishing under very wet conditions. Overall, mR95pT and the proposed probabilistic framework provide a scalable pathway to monitor both drought- and heavy-rain-related vegetation risks over data-sparse semi-arid regions. Full article

This paper investigates the fundamental mechanisms of technological change in complex systems by analyzing how the evolution of embedded subsystems dictates the trajectory and sets the tempo of a host technology. Building on the theoretical framework of technological parasitism, the study conceptualizes host systems having a modular architecture—such as smartphones—as evolving through dynamic, coevolutionary interactions with their constituent subsystems. These relations gradually shift from parasitic reliance to mutualistic and ultimately symbiotic interactions. Central to this research is the concept of subsystems as pacemakers. Methodologically, this research employs a longitudinal, mixed-methods approach, combining an 18-year case study of the iPhone (2007–2025) with time-series regression and log–log hedonic pricing models. Key findings are: (a) Temporal precedence: Advances in subsystems (e.g., Bluetooth protocols) consistently precede host releases. The integration lag has contracted from three years to one, signaling an acceleration in symbiotic coupling and highlighting Bluetooth as a systemic pacemaker whose evolutionary tempo anticipates shifts in the wider smartphone architecture. (b) Differential evolutionary pressure in technological host systems: While camera resolution exhibited the highest exponential growth (+16.73%), it remained a secondary driver of systemic evolution. (c) Economic pacemakers: Hedonic analysis identifies battery life as the dominant evolutionary predictor (standardized beta = 0.77). With an elasticity of approximately 0.30, a 1% gain in battery performance correlates to a 0.3% increase in nominal price, whereas display and camera resolution exert significantly less influence on the system’s valuation and trajectory. These findings reveal that subsystems evolve—and exert influence—at different speeds and with different degrees of systemic leverage. Overall, the proposed theory shows that subsystem evolution functions as a leading indicator of forthcoming host–system transitions. By identifying which subsystems act as temporal pacemakers, this research contributes new design rules for forecasting technological generations and optimizing R&D strategies in complex, multi-component innovations. Hence, the study demonstrates that mastering complex innovation requires a granular understanding of the asynchronous rhythms between a host technology and its constitutive parts. Full article

Background/Objectives: Current musculoskeletal health assessment expanded beyond bone mineral density (BMD) at central DXA to include, for instance, trabecular bone score (TBS) and emergent biomarkers, such as adipokines and myokines (e.g., irisin) assays. A current gap in their application is reflected in limited research regarding adrenal tumors, especially non-functional adrenal tumors/mild autonomous cortisol secretion (NFATs/MACS). To assess this current gap, we aimed to explore beyond BMD, specifically, TBS and circulating irisin, in relation to the adrenal status in NFATs/MACS. Methods: This is a prospective, cross-sectional, single-center, exploratory study, conducted between October 2024 and December 2025. Results: A total of 81 menopausal women were included (mean age of 63.26 ± 8.82 years, 15.86 ± 9.5 years since menopause, average BMI of 30.69 ± 5.76 kg/sqcm. Out of them, 33.33% had NFATs/MCAS (group AI) and 66.67% were controls (group C), with similar age, years since menopause, and BMI. The prevalence of type 2 diabetes was 66.67% versus 68.52% (p = 0.865). TBS correlated with lumbar BMD/T-score (N = 33), while age and lumbar BMD were independent TBS predictors (N = 81), but not type 2 diabetes nor NFAs/MCAS. TBS correlated with the five-year age groups (r = −0.273, p = 0.003). Irisin correlated with osteocalcin (r = −0.252, p = 0.007), P1NP (r = −0.187, p = 0.049) and CrossLaps (r = −0.209, p = 0.026) in tumor-free controls. In the AI group, a higher irisin was associated with a higher second-day cortisol after 1 mg DST (r = 0.11, p = 0.584) and a lower ACTH (r = −0.716, p < 0.001). The rate of low TBS (based on 1.350 cutoffs) was 48.15% versus 38.89% in group AI versus C. In the AI group, patients with low TBS had lower osteocalcin, P1NP, and CrossLaps than those with normal TBS, with a similar rate of type 2 diabetes (which might reduce the bone turnover markers) and MACS-positive prevalence (between 25 and 28%). Conclusions: The median glycated hemoglobin A1c (5.78% versus 5.93%, p = 0.94) and median HOMA-IR (1.53 versus 1.42, p = 0.948) suggest a certain level of glucose control, which might not be reflected in severely damaged bone microarchitecture, as shown by TBS. Irisin may be one of the additional factors in these tumors reflecting the hormonal burden. Irisin was statistically significantly elevated with the increase in BMI groups. To our best awareness, this is the first synchronous analysis of TBS and irisin levels in this type of tumor to address the bone status in relation to the glucose profile and adrenal panel. Noting this is an exploratory, hypothesis-generating study, further research will highlight the true value of TBS and irisin for practitioners in the adrenal field, including multi-layered models of bone status prediction. Full article

Peony seed meal (PSM), a protein-rich by-product of oil extraction from Paeonia ostii, represents an underutilized resource with significant potential for functional ingredient development. In this study, an integrated strategy combining artificial neural network (ANN)-guided hydrolysis, peptidomics, molecular simulation, and cellular validation was employed to identify antioxidant peptides from PSM. Neutrase was selected as the optimal protease, and hydrolysis conditions were optimized using a backpropagation ANN model (R = 0.9935), yielding a hydrolysate with strong radical-scavenging activity (DPPH^• IC₅₀ = 0.30 mg/mL; ABTS^•+ IC₅₀ = 0.07 mg/mL). LC–MS/MS identified 364 peptides, predominantly low-molecular-weight sequences. In silico screening highlighted four candidates (FRF, WQFR, FEFR, and RWL) with favorable binding toward ABTS^•+, DPPH^•, and Keap1. Molecular docking and 100 ns molecular dynamics simulations confirmed stable peptide–Keap1 interactions, particularly for FRF. Cellular assays demonstrated that FRF and RWL significantly protected HepG2 cells against H₂O₂-induced oxidative damage by restoring antioxidant enzyme activities (SOD, CAT, and GSH-Px). Collectively, this study establishes a systematic workflow for discovering plant-derived antioxidant peptides and supports the sustainable valorization of PSM as a functional food ingredient. Full article

Rapid urbanization has significantly exacerbated the Urban Heat Island (UHI) effect in high-density megacities, driven by the intensifying competition between built-up morphology and natural cooling infrastructure. Current research, however, often fails to accurately predict land surface temperatures (LST) because traditional models frequently overlook the complex spatial dependencies and neighborhood spillover effects inherent in urban environments. Existing studies often ignore the spatial dependence of heat transfer. This study proposes an explainable machine learning framework incorporating spatial lag variables to capture the thermal spillover from adjacent neighborhood context—such as green space cooling diffusion or built-up heat accumulation—which is frequently treated as noise in traditional models. Taking Shenzhen as a case study, we integrated multi-source data (Landsat 8, building vectors, DEM) and developed an XGBoost regression model (R² = 0.806) augmented with SHAP (Shapley Additive exPlanations) to quantify the contributions of local and contextual features. The results revealed that: (1) Non-linear Thresholds: Vegetation cooling exhibits a saturation effect, with the highest marginal benefit observed in the NDVI range of 0.2–0.4, while building warming effects converge at extremely high densities due to mutual shading; (2) Neighborhood Spillovers: Spatial interaction analysis confirms significant cool island synergy (where clustered green spaces provide amplified cooling) and heat island agglomeration effects—e.g., green spaces surrounded by high ecological backgrounds provide amplified cooling benefits; (3) Spatial Antagonism: A novel Interaction Balance Index (IBI) based on game-theoretic SHAP contributions was constructed to map the source-sink competition patterns, identifying distinct heat-dominated (West) and cool-dominated (East) zones. Unlike traditional area-weighted source-sink landscape metrics, IBI enables a pixel-level additive decomposition of warming and cooling factors, quantifying the net thermal outcome of local morphology and neighborhood spillover. By explicitly encoding spatial context into non-linear modeling, this study provides a more mechanistically robust understanding of urban thermal environments. The identified thresholds and dominant driver maps offer precise, spatially differentiated guidance for urban climate-adaptive planning and ecological restoration. Full article

This study presents the development and validation of a consistent rainfall database for Maranhão State, Brazil, covering historical records from 1987 to 2023 obtained from 100 rainfall stations (90 from ANA and 10 from INMET). A total of 314 missing records across 74 stations were corrected using the Regional Weighting method, restricted to stations within the same Homogeneous Precipitation Region (HPR). The consistency of the reconstructed series was verified using the Double Mass method, which yielded coefficients of determination (R²) above 0.97 for all stations, confirming the robustness of the procedure. Statistical analyses with the Mann–Kendall test and Sen’s Slope estimator did not identify significant long-term trends, although weak positive slopes were detected in some regions (e.g., HPR3: +9.98 mm/year; HPR6: +3.70 mm/year), while HPR10 showed a negative slope (−0.99 mm/year). The novelty of this work lies in consolidating the first homogeneous and validated rainfall database for Maranhão, providing a reliable foundation for assessing regional climate variability. The results provide a solid foundation for future applications, including drought monitoring, agricultural planning, water resource management, and adaptation strategies under climate change scenarios. Full article

Vertical farming requires precise environmental control, yet long-term multivariable analyses linking environmental dynamics and crop growth remain limited. This study analyzes a two-year operational dataset from a commercial vertical farm in South Korea to evaluate the suitability of advanced artificial intelligence models for harvest yield prediction. Conventional machine learning models and recent deep learning architectures were systematically benchmarked under identical conditions. Among them, the patch-based Transformer model achieved the highest predictive accuracy (R² = 0.942; RMSE = 5.81 g per plant). The variable-importance analysis revealed that daily light integral and CO₂ concentration were the dominant drivers of harvest yield variability, jointly accounting for more than 76% of total contribution. These findings demonstrate the effectiveness of Transformer-based architectures for long-term multivariate time series modeling and provide actionable insights for the data-driven optimization of vertical farming systems. Full article

Camellia reticulata is a valuable woody species prized for both its ornamental and oil-producing qualities. This study focused on four qualitative traits and nine quantitative traits of the fruits collected from nine wild populations and 30 cultivated varieties of C. reticulata. Multivariate statistical methods were employed to analyze the variation patterns of these fruits among populations and varieties, aiming to provide a scientific basis for the resource utilization and genetic improvement of this species. The results showed that the pericarp color clustered into two series: an orange-yellow (red) series (found in eight populations and all 30 cultivars) and a yellow-green series (unique to the Heiniu Mountain I population). The a* value was identified as the key indicator for distinguishing between these two color-series. The fruit shape was predominantly spherical, the seed shape was mostly hemispherical, and the seed coat color was primarily brown. Significant differences (p < 0.05) were observed among the nine quantitative phenotypic traits. Fruit weight exhibited the greatest variation (ranging from 28.499 g to 149.068 g), with particularly prominent differences among populations (Fengqing I was the heaviest at 149.068 g, while Yongping I was the lightest at 28.499 g). The coefficients of variation (CV) for phenotypic traits within populations ranged from 17.209% to 60.803% (mean 31.655%), and within varieties from 13.951% to 72.911% (mean 35.290%). Based on CV weights, seed weight showed the largest variation amplitude (21.342%) among populations, while seed number showed the largest variation amplitude (22.956%) among varieties. Correlation analysis revealed that all nine traits exhibited highly significant correlations across different populations and cultivars. Principal component analysis (PCA) indicated that the eigenvalues of the first two principal components were both greater than 1.00, with cumulative contribution rates reaching 73.570% for populations and 76.064% for cultivars, respectively. Cluster analysis grouped the studied materials into three clusters. The comprehensive evaluation identified the cultivar ‘Lichan’ as having the optimal performance (F = 2.410). Box plots revealed greater dispersion in seed number and pericarp thickness within wild populations, while cultivated varieties showed a wider distribution in locule number and fruit transverse diameter. Frequency distribution analysis demonstrated that all traits followed a normal distribution (R² = 0.673~0.999). Among them, fresh seed weight and fruit transverse diameter displayed obvious skewness. Furthermore, the variation in seed number was significantly higher in wild populations than in cultivars. This study reveals rich phenotypic variation in fruit traits between wild populations and cultivated groups of C. reticulata, with fruit size and seed number identified as key traits. These findings provide an important basis for the subsequent selection of hybrid parents and breeding of high-yield, high-oil varieties. Full article

Background: Microgravity exposure causes muscle atrophy and bone density loss in astronauts. Traditional motion analysis provides estimations of external kinematics and muscle activation, but cannot resolve internal load. OpenSim closes this gap by applying musculoskeletal modeling to estimate internal joint mechanics. Methods: In this study, we aimed to develop an OpenSim workflow to estimate joint angles and moments using datasets from two publicly available gait studies: the Politecnico di Milano study (Dataset 1), which includes level-floor walking, walking on heels, walking on toes, and step-down-from-stairs tasks, and Maclean et al.’s walking study in reduced gravities (Dataset 2), which includes four simulated gravity levels (1.0 G, 0.76 G, 0.54 G, and 0.31 G). Marker and ground reaction force (GRF) data, along with participants’ mass, were used to prepare the first three steps of OpenSim’s workflow, including scaling, inverse kinematics (IK), and inverse dynamics (ID). Scripts using MATLAB R2025a (The MathWorks, Inc., Natick, MA, USA) were created to store, normalize, and compare OpenSim outputs with reference data on the right leg. Pearson’s correlation coefficient (PCC) was used to quantify agreement between OpenSim-derived joint angles and moments and the reference data, and root mean square error (RMSE) was used to characterize accuracy. Results: Hip and knee angles showed excellent correlation across both datasets (PCC > 0.974). Ankle angles were more variable, particularly in Dataset 1 (PCC = 0.833; RMSE = 19.797°) compared to Dataset 2 (PCC = 0.995; RMSE = 8.73°). Joint moment correlations were strong for hip and knee (PCC > 0.85), though ankle moments in Dataset 1 exhibited lower correlation (PCC = 0.677) and higher error (0.30 Nm/kg) compared to the high accuracy observed across all joints in Dataset 2. Discussion: We speculate that the lower PCC values and higher RMSE observed for ankle dorsi/plantar flexion angle and moment in Dataset 1 are mainly attributable to differences in shank segment frame definitions between the OpenSim model and the human body model used in Dataset 1. Higher ankle angle RMSEs in Dataset 2 may be due to lower weights assigned to ankle markers in the scaling and IK setup files, resulting in different ankle joint center definitions. Conclusion: In the future, we plan to improve this OpenSim workflow by including additional participants and datasets collected in simulated reduced-gravity environments and by implementing a residual reduction algorithm (RRA) and computed muscle control (CMC) to enable muscle activation estimation. Full article

Topological optimization has established itself as an efficient tool for the design of highly complex structures and the rational use of materials, especially in problems involving multiple constraints and conflicting objectives. This work presents a new multi-material topological optimization approach based on the ESO smoothing method (SESO), formulated as a multi-objective optimization problem in a MATLAB R2021a environment. The multi-objective formulation simultaneously considers the minimization of the maximum von Mises equivalent stress (or minimum principal stress) and the maximum displacement, which are fundamental criteria for structural engineering design. The proposed methodology also incorporates a reliability analysis using the First-Order Reliability Method (FORM), modeling uncertainties associated with the applied force, volume fraction, and modulus of elasticity through normal and lognormal probability distributions, with a target reliability index of ${\beta }_{target}=3.0$. The consistency of the reliability analysis was evaluated using Monte Carlo simulations, validating the reliability indices obtained via FORM. The approach was applied to two classical three-dimensional numerical examples: a cantilever beam under base and center loads and an MBB beam, considering two widely used engineering materials, steel and concrete. The results indicate improved multi-material distribution in the design domain and greater structural robustness against unfavorable loading planes, variations in the modulus of elasticity, and volume constraints imposed by FORM. Furthermore, the minimum yield stress of steel (${\sigma }_y^{min}$) and the compressive strength of concrete ($f_{ck}^{min}$) were calibrated, representing the minimum material strengths required to resist the maximum von Mises stress in steel and the minimum principal stress (${\sigma }_3$) in concrete, ensuring the target reliability index is achieved. This method, thus, highlights the integration of SESO with multi-material, multi-objective, and reliability-based optimization as a consistent, robust, and practically relevant strategy with potential for future applications in structural engineering projects. Full article

Moringa oleifera is widely recognized for its pharmacological properties and has recently attracted interest for its potential anticancer effects. In this study, we investigated the in vitro activity of Moringa oleifera leaf extract on the human prostate cancer PC3 cell line, focusing on the insulin-like growth factor 1 receptor (IGF1R) signaling pathway, a central regulator of prostate cancer progression. PC3 cells were treated with Moringa oleifera extract, IGF-1, the IGF1R inhibitor NVP-AEW541, and their combinations. Cell migration, apoptosis, cell cycle distribution, gene expression, and protein regulation were evaluated using scratch assays, flow cytometry, RT-PCR, and Western blotting. Under our experimental conditions, Moringa oleifera extract was associated with reduced IGF1R expression and phosphorylation, together with decreased activation of downstream ERK/MAPK and AKT signaling pathways. These changes were accompanied by increased apoptosis, G0/G1 cell cycle accumulation, and reduced migratory capacity of PC3 cells. In addition, Moringa oleifera modulated the expression of genes involved in epithelial–mesenchymal transition, tumor progression, and extracellular matrix remodeling, suppressing pro-invasive markers while enhancing anti-metastatic factors. The extract also reduced the expression of bone metastasis–associated markers, including osteocalcin and alkaline phosphatase. Overall, these findings indicate that Moringa oleifera exposure is associated with modulation of IGF1R-related signaling and cellular programs relevant to aggressive prostate cancer. Further studies will be required to determine pharmacological feasibility and translational relevance. Full article