Search Results (937)

Background/Objectives: Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a debilitating complex disease with an elusive etiology, lacking objective diagnostic biomarkers. This study leverages advanced Automated Machine Learning (AutoML) to analyze plasma metabolomic and lipidomic profiles for the purpose of ME/CFS detection. Methods: We utilized a publicly available dataset comprising 888 metabolic features from 106 ME/CFS patients and 91 matched controls. Three AutoML frameworks—TPOT, Auto-Sklearn, and H2O AutoML—were benchmarked under identical time constraints. Univariate ROC and PLS-DA analyses with cross-validation, permutation testing, and VIP-based feature selection were applied to standardized, log-transformed omics data to identify significant discriminatory metabolites/lipids and assess their intercorrelations. Results: TPOT significantly outperformed its counterparts, achieving an area under the curve (AUC) of 92.1%, accuracy of 87.3%, sensitivity of 85.8%, and specificity of 89.0%. The PLS-DA model revealed a moderate but statistically significant discrimination between ME/CFS and controls. Explainable artificial intelligence (XAI) via SHAP analysis of the optimal TPOT model identified key metabolites implicating dysregulated pathways in mitochondrial energy metabolism (succinic acid, pyruvic acid, leucine), chronic inflammation (prostaglandin D₂, 11,12-EET), gut–brain axis communication (glycocholic acid), and cell membrane integrity (pc(35:2)a). Conclusions: Our results demonstrate that TPOT-derived models not only provide a highly accurate and robust diagnostic tool but also yield biologically interpretable insights into the pathophysiology of ME/CFS, highlighting its potential for clinical decision support and elucidating novel therapeutic targets. Full article

In this study, the exhaust particulates of a diesel engine burning methanol/F-T diesel blends were collected. The nanostructure and oxidation reactivity of the particulates were explored using the Brunauer–Emmett–Teller (BET) method, high-resolution transmission electron microscope (HRTEM), and thermogravimetric analysis (TGA), and the relationship between them was assessed via the partial least squares (PLS) and variable importance in the projection (PLS-VIP). The results showed that particulates from methanol/F-T diesel combustion were aggregates composed of several primary particles, and the distribution range of particulate half pore width ($R$) was 8~76 nm. As the methanol mixture ratio increased, the mean $R$ of particulates decreased, and the particulates′ total pore volume ($V_p$), specific surface area ($S_{BET}$), and the fractal dimension ($D_f$) increased. Compared with F-T diesel, methanol/F-T diesel blends particulates showed more disordered structure with a smaller diameter ($d_p$) of primary particles, a shorter fringe length ($L_a$), a wider separation distance ($d$), and a larger tortuosity ($T_f$). With increasing the methanol mixture ratio, it was also found that the amount of soluble organic fraction (SOF) of particulates increased, while oxidation characteristic temperature and the apparent activation energy ($E_a$) reduced. The correlation coefficients of $E_a$ with $T_f$ and $D_f$ were 0.99 and 0.98, respectively, by the linear fitting, illustrating that they showed the strongest linear relationship with the reactivity among the discussed nanostructure parameters. The VIP values of $D_f$, $T_f$, $V_p$, and d, with $E_a$ obtained by the PLS and PLS-VIP, were greater than 1, indicating that they were the chief factors influencing particulate reactivity. Full article

Background/Objectives: Long-term survival among liver transplant (LT) recipients who live beyond one year has remained relatively stable over recent decades. However, reducing long-term morbidity is increasingly important, and metabolomics may enable risk-based, personalized immunosuppression. We aimed to evaluate and compare the serum metabolomic profiles of LT recipients treated with tacrolimus (TAC) versus sirolimus (SIR), to elucidate metabolic pathways associated with these regimens. Methods: Targeted metabolomic profiling of 894 metabolites was conducted on serum samples from 128 LT recipients using the Biocrates MxP^® Quant 500 kit. Data were analyzed with MetaboAnalyst 6.0, and multivariate analysis was performed using Partial Least Squares-Discriminant Analysis (PLS-DA). Metabolites with Variable Importance in Projection (VIP) scores > 1.5 underwent pathway enrichment in OmicsNet, incorporating Gene Ontology annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG)-based network analysis. Results: Eighty-seven metabolites were significantly altered between groups. Phosphatidylcholines (PCs) and ceramides were elevated in TAC-treated patients, while di- and triacylglycerols were higher in the SIR group. Pathway enrichment implicated lipid metabolism, particularly glycerophospholipid, ether lipid, and sphingolipid pathways. Network analysis identified enriched modules related to metabolic regulation and immune response. Conclusions: Divergent metabolomic profiles distinguish TAC- and SIR-treated recipients, suggesting regimen-specific impacts on lipid metabolism with potential relevance to post-transplant complications. Full article

This study systematically examined the effects of Tai Chi exercise on bone health in menopausal women, with subgroup analyses of potential moderators. A systematic search was conducted across nine databases (PubMed, Web of Science, Cochrane Library, EBSCO-Medline, EBSCO-Sportdiscus, Embase, CNKI, VIP and Wanfang Data) on June 1 and updated on 14 September 2025 to identify controlled trials evaluating perimenopausal or postmenopausal women. A three-level meta-analysis was performed to pool effect estimates, reported as standardized mean differences (SMDs), with heterogeneity further explored through subgroup analyses. Across 16 studies involving 1091 participants aged 49–64 years, Tai Chi interventions led to significant improvements in bone health. Training protocols ranged from 6 to 104 weeks, with sessions lasting 30 to 90 min. Bone mineral density (BMD) improved significantly at the femoral neck (SMD = 0.50), greater trochanter (SMD = 0.61), and lumbar spine L2–L4 (SMD = 0.81), with stronger effects observed in perimenopausal women. Bone mineral content (BMC) also increased significantly in menopausal women (SMD = 1.63, I² = 91.46%), although heterogeneity was substantial, and no significant differences were detected in subgroup moderators. In contrast, no significant effects were found for bone mineral metabolism (p = 0.38) or bone turnover markers (p = 0.25). According to GRADE assessments, the certainty of evidence ranged from low to moderate across these outcomes. In conclusion, while Tai Chi has been shown to improve BMD and BMC in menopausal women, the relatively high heterogeneity observed for BMC necessitates cautious interpretation of these particular outcomes. In contrast, no statistically significant effects were observed on bone mineral metabolism (BMM) and bone turnover markers (BTMs). Notably, given the significant differences observed between perimenopausal and postmenopausal women, future well-designed studies that stratify participants by menopausal status and possess adequate statistical power are needed to further explore the potential differential effects of Tai Chi on bone health. Full article

The Zhunuo porphyry Cu deposit (2.9 Mt Cu @ 0.48%) in the Gangdese belt, southern Xizang, represents a key Miocene post-collisional system. This study integrates textural, major-, and trace-element analyses of pyrite from distinct alteration zones to unravel its hydrothermal evolution and metal precipitation mechanisms. Our study identifies four distinct pyrite types (Py1-Py4) that record sequential hydrothermal stages: main-stage Py2-Py3 formed at 354 ± 48 to 372 ± 43 °C (based on Se thermometry), corresponding to A and B vein formation, respectively, and late-stage Py4 crystallized at 231 ± 30 °C, coinciding with D-vein development. LA-ICP-MS data revealed pyrite contains diverse trace elements with concentrations mostly below 1000 ppm, showing distinct distribution patterns among different pyrite types (Py1-Py4). Elemental correlations revealed coupled behaviors (e.g., Au-As, Zn-Cd positive correlations; Mo-Sc negative correlation). Tellurium variability (7–82 ppm) records dynamic fO₂ fluctuations during system cooling. A comparative analysis of pyrite from the regional deposits (Xiongcun, Tiegelongnan, Bada, and Xiquheqiao) highlighted discriminative geochemical signatures: Zhunuo pyrite was enriched in Co-Bi-Ag-Pb (galena inclusions); Tiegelongnan exhibited the highest Cu but low Au-As; Xiquheqiao had the highest Au-As coupling; and Bada showed epithermal-type As enrichment. Partial Least Squares Discriminant Analysis (PLS-DA) identified Cu, As, and Bi as key discriminators for deposit types (VIP > 0.8), with post-collisional systems (Zhunuo and Xiquheqiao) showing intermediate Cu-Bi and elevated As versus arc-related deposits. This study establishes pyrite trace-element proxies (e.g., Se/Te, Co/Ni, and As-Bi-Pb) for reconstructing hydrothermal fluid evolution and proposes mineral-chemical indicators (Cu-As-Bi) to distinguish porphyry-epithermal systems in the Qinghai-Tibet Plateau. The results underscore pyrite’s utility in decoding metallogenic processes and exploration targeting in collisional settings. Full article

Sesame seed oil is a bioactive-rich lipid source, notable for lignans, tocopherols, and unsaturated fatty acids that underpin its antioxidant and cardioprotective properties. This study optimized two innovative, non-thermal extraction techniques—pulsed electric field (PEF) and ultrasound bath-assisted extraction (UBAE)—to maximize yield and preserve oil quality for functional food applications. A blocked definitive screening design combined with response surface methodology modeled the effects of energy power (X₁, 60–100%), liquid-to-solid ratio (X₂, 10–20 mL/g), and extraction time (X₃, 10–30 min) on fat content, DPPH antiradical activity, and oxidative stability indices (Conjugated Dienes, CDs/Conjugated Trienes, CTs). UBAE achieved the highest fat yield—59.0% at low energy (60%), high X₂ (20 mL/g), and short X₃ (10 min)—while PEF maximized DPPH to 36.0 μmol TEAC/kg oil at high energy (100%), moderate X₂ (17 mL/g), and short X₃ (10 min). CDs were minimized to 19.78 mmol/kg (UBAE, 60%, 10 mL/g, 10 min) and CTs to 3.34 mmol/kg (UBAE, 60%, 12 mL/g, 10 min). Partial least squares analysis identified X₂ and X₃ as the most influential variables (VIP > 0.8), with energy–time interplay (X₁ × X₃) being critical for antioxidant capacity. Compared to cold-pressing and Soxhlet extraction, PEF and cold-pressing retained higher antioxidant activity (~19 μmol TEAC/kg) and oxidative stability (TBARS ≤ 0.30 mmol MDAE/kg), while Soxhlet—though yielding 55.65% fat—showed the poorest quality profile (Totox value > 560). Both non-thermal techniques can deliver bioactive-rich sesame oil with lower oxidative degradation, supporting their application in functional foods aimed at improving dietary antioxidant intake and mitigating lipid oxidation burden. PEF at high energy/short time and UBAE at low energy/short time present complementary, scalable options for producing high-value edible oils aligned with human health priorities. As a limitation, we did not directly quantify lignans or tocopherols in this study, and future work will address their measurement and bioaccessibility. Full article

Hyperspectral remote sensing provides a powerful tool for crop nutrient monitoring and precision fertilization, yet its application is hindered by high-dimensional redundancy and inter-band collinearity. This study aimed to improve maize nitrogen estimation by constructing three types of two-dimensional full-band spectral indices—Difference Index (DI), Simple Ratio Index (SRI), and Normalized Difference Index (NDI)—combined with spectral preprocessing methods (raw spectra (RAW), first-order derivative (FD), and second-order derivative (SD)). To optimize feature selection, three strategies were evaluated: Grey Relational Analysis (GRA), Pearson Correlation Coefficient (PCC), and Variable Importance in Projection (VIP). These indices were then integrated into machine learning models, including Backpropagation Neural Network (BP), Random Forest (RF), and Support Vector Regression (SVR). Results revealed that spectral index optimization substantially enhanced model performance. NDI consistently demonstrated robustness, achieving the highest grey relational degree (0.9077) under second-derivative preprocessing and improving BP model predictions. PCC-selected features showed superior adaptability in the RF model, yielding the highest test accuracy under raw spectral input (R² = 0.769, RMSE = 0.0018). VIP proved most effective for SVR, with the optimal SD–VIP–SVR combination attaining the best predictive performance (test R² = 0.7593, RMSE = 0.0024). Compared with full-spectrum input, spectral index optimization effectively reduced collinearity and overfitting, improving both reliability and generalization. Spectral index optimization significantly improved inversion accuracy. Among the tested pipelines, RAW-PCC-RF demonstrated robust stability across datasets, while SD-VIP-SVR achieved the highest overall validation accuracy (R² = 0.7593, RMSE = 0.0024). These results highlight the complementary roles of stability and accuracy in defining the optimal pipeline for maize nitrogen inversion. This study highlights the pivotal role of spectral index optimization in hyperspectral inversion of maize nitrogen content. The proposed framework provides a reliable methodological basis for non-destructive nitrogen monitoring, with broad implications for precision agriculture and sustainable nutrient management. Full article

Background: Cardiomyopathy is a major cause of heart failure. Ischemic cardiomyopathy (IC) and non-ischemic cardiomyopathy (NIC) have distinct pathophysiological mechanisms. Carnitine plays a critical role in transporting long-chain fatty acids into mitochondria for β-oxidation. Disruptions in carnitine and acylcarnitine homeostasis have been implicated in cardiomyopathy; however, comparative profiling between IC and NIC remains limited. Methods: Serum samples were obtained from 40 IC patients, 40 NIC patients, and 40 age- and sex-matched controls. Free carnitine and 27 acylcarnitine species were quantified using LC–MS/MS. Multivariate analyses (PCA, PLS-DA), univariate statistics (ANOVA with Tukey’s HSD), and ROC curve analyses were performed to identify discriminatory metabolites and assess their diagnostic performance. Results: Compared with controls, IC patients exhibited reduced levels of short- and medium-chain acylcarnitines (C2, C4DC, C6, C8, C10, and C14), whereas NIC patients showed elevations in medium- and long-chain species (C6DC and C16). Heatmaps demonstrated clear group clustering. PCA and PLS-DA revealed partial separation, with C2, C6DC, and C16 emerging as the most influential metabolites (highest VIP scores). ROC analysis indicated modest diagnostic performance, with AUC values ranging from 0.623 to 0.635. Conclusions: IC and NIC are characterized by distinct alterations in serum carnitine profiles, reflecting differential metabolic remodeling. These findings may clarify disease mechanisms and highlight potential metabolic biomarkers or therapeutic targets. Acylcarnitine profiling could support differential diagnosis and personalized management in cardiomyopathy. Full article

Background: Soccer’s varied physical demands require meticulous load monitoring, which is now being advanced by combining GPS for external metrics and NMR-based metabolomics for internal metabolic profiling. This study aimed to investigate how player position influences the metabolomic profile (as a marker of internal load) under known match effort (external load). Methods: This was a longitudinal observational descriptive study involving 12 professional soccer players from the U-20 São Paulo Football Club, enrolled in the 2022 São Paulo State Under-20 Football Championship. Players were monitored across six matches during the season, culminating in a total of 49 individual match observations from those players (4-2-3-1 formation: Central Defenders [CD], n = 9; Full Backs [FB], n = 9; Central Midfielders [CM], n = 14; Wide Midfielders [WM], n = 12; Forwards [F], n = 5). Internal load was assessed via urinary metabolomics, with urine samples collected 24 h post-match. A non-targeted, global metabolomics approach was employed using nuclear magnetic resonance (NMR) spectroscopy. External load was monitored using GPS tracking devices. Multivariate analyses included partial least squares discriminant analysis (PLS-DA), and heat maps. Results: Metabolomic analysis identified 38 metabolites with a Variable Importance in Projection (VIP) score > 1.0, revealing perturbations in carbohydrate metabolism and the tricarboxylic acid (TCA) cycle, amino acid and peptide metabolism, pyrimidine metabolism, and ketone body pathways, and effectively discriminating post-match recovery metabolic profiles. External load metrics varied significantly by player position: CMs covered greater distances below 20 km/h (8702.93 ± 1271.89 m), exhibited higher relative distance (114.29 ± 7.67 m/min), total distance (9193.21 ± 1261.35 m), and player load (945.71 ± 135.82 a.u.); CDs achieved higher peak speeds (31.78 ± 1.20 m/s); and WMs performed greater sprint distances (168.11 ± 91.69 m). Metabolomic profiles indicated that CMs showed stronger associations with markers of muscle damage and inflammation, whereas CDs and WMs were more closely linked to energy metabolism and oxidative stress. Conclusions: These results highlight the importance of a personalized approach to training load monitoring and recovery strategies, considering the distinct physiological and metabolic demands associated with each player position. Full article

Pomegranate (Punica granatum L.), valued for its health benefits and distinctive flavor, derives its characteristic aroma from volatile organic compounds (VOCs) that vary significantly with geographical origin. In this study, VOCs in pomegranates from six Chinese geographical regions were characterized using an electronic nose (E-nose), an electronic tongue (E-tongue), headspace gas chromatography–ion mobility spectrometry (HS-GC-IMS), and headspace solid-phase microextraction–gas chromatography–mass spectrometry (HS-SPME-GC-MS). To elucidate geographical variations in odor, taste, and volatile profiles, a comprehensive multivariate statistical analysis integrating principal component analysis (PCA), hierarchical cluster analysis, orthogonal partial least squares-discriminant analysis (OPLS-DA), and variable importance in projection (VIP) was employed. The results demonstrated that the E-nose and E-tongue effectively distinguished pomegranate by geographical origin, with aroma contributing more significantly than taste to regional differentiation. A total of 46 and 58 VOCs were identified using HS-GC-IMS and HS-SPME-GC-MS, respectively, with different characteristic volatile compounds in pomegranate from various origins, and alkenes, esters, and alcohols were the primary contributors to regional variations. Notably, OPLS-DA revealed that HS-GC-IMS exhibited superior discriminatory power in separating pomegranates of different geographical origins, with HY and HL displaying closely related odor profiles while the other samples showed the most pronounced odor differences, but these findings contrasted with HS-SPME-GC-MS results. Additionally, the VIP method and the relative odor activity value (ROAV) further identified six and eight key aroma compounds based on HS-GC-IMS and HS-SPME-GC-MS data; in particular, hexanal, nonanal, β-pinene, 3-hydroxybutan-2-one, and β-ocimene were identified as key aroma compounds in pomegranate as potential regional markers. These findings highlight VOC profiles as potential geographical origin markers, supporting origin traceability and quality control in the pomegranate industry. Full article

Establishing refuges is a primary strategy for managing resistance in target pests against Bt maize. The larval feeding and dispersal, and adult oviposition behaviors of Spodoptera exigua (Hübner) on Bt and non-Bt maize plants are critical factors in determining optimal refuge configurations. This study employed laboratory and field experiments to evaluate the larval feeding and dispersal behaviors, as well as the oviposition preferences of S. exigua moths, on Bt (Cry1Ab + Vip3Aa19) and non-Bt maize plants. Results showed that as time of the choice test increased, the larval selection rate on Bt maize leaves declined progressively, with all instars (1st–5th) preferring to feed on non-Bt maize. After 48 h, the selection rates of larvae for non-Bt and Bt maize were 40.63–66.25% and 9.38–33.75%, respectively. Female moths exhibited no significant oviposition preference between Bt and non-Bt plants under undamaged conditions; however, when non-Bt maize was infested by the larvae, females preferentially oviposited on Bt maize plants (73.55%). Under the seed-mixture refuge pattern in field conditions, increasing the proportion of non-Bt maize significantly enhanced larval dispersal distances and facilitated larval transit damage between Bt and non-Bt plants. Our research clarifies the behavioral patterns of S. exigua on Bt and non-Bt maize, provides a scientific basis for optimizing refuge strategy to delay the development of resistance. Full article

As a wine-producing region in China, Xinjiang’s ecological conditions endow grapes with distinctive flavor potential. However, systematic research on volatile compounds in wines from Vitis amurensis Rupr. varieties in this region remains limited. Therefore, wines from four Xinjiang Vitis amurensis varieties (‘Shuanghong’, ‘Zuoyouhong’, ‘Xuelanhong’, and ‘Beibinghong’) were analyzed using high-performance liquid chromatography (HPLC), headspace gas chromatography–ion mobility spectrometry (HS-GC-IMS), electronic nose (E-nose), odor activity value (OAV) calculation, and multivariate analysis. Physicochemical parameters, organic acids, volatile organic compounds (VOCs), and OAVs were determined. Results showed significant differences in physicochemical properties among the varieties, potentially correlating with wine mouthfeel. Beibinghong wine contained the highest total VOC concentration. Among 64 identified VOCs, 37 had OAVs ≥ 1. Multivariate analysis identified 14 key differential volatile compounds (VIP ≥ 1, p < 0.05) responsible for flavor differences between varieties, with each variety exhibiting distinct key compounds. E-nose analysis effectively distinguished the aroma profiles of the four wines. This study elucidates the chemical and volatile compound characteristics of wines from Xinjiang Vitis amurensis varieties, providing a theoretical foundation for research on their flavor profiles. It also aids in selecting Vitis amurensis varieties for cultivation and supports the development of distinctive regional wines in Xinjiang. Full article

The goal of the current study is to introduce a new class of proximal-point mappings that are associated with a new class of ${\mathcal{B}}^l$-co-monotone mappings that are being defined. The ${\mathcal{B}}^l$-co-monotone mapping is the sum of co-coercive and symmetric monotone mappings and an extension of the $C_n$-monotone mapping. The investigation is further discussed, along with its application, which involves a variational inclusion problem (VIP) in Banach spaces. Moreover, the study proposes an iterative algorithm and systematically investigates the convergence characteristics of its generated sequences. For the purpose of illustrating our findings, a simplified numerical example is created to show the convergence graph by using the MATLAB 2015a. Full article

Volatile organic compounds (VOCs) are critical indicators of the metabolic status of whole-plant maize silage (WPMS). However, the impact of inoculating various strains of fermentation agents on VOC changes has not been systematically explored. This study aimed to determine how inoculation with Lactiplantibacillus plantarum and Lentilactobacillus buchneri modulates the VOC profile and odor of WPMS after 90 days. VOCs were extracted by headspace solid-phase microextraction and analyzed by gas chromatography-mass spectrometry (HS-SPME-GC-MS). Key VOCs were screened using the variable importance in projection (VIP) and substantiated by relative odor activity values (rOAV) and odor descriptions. A total of 82 compounds were identified, including 22 esters, 19 alcohols, 3 acids, 9 aldehydes, 2 ethers, 6 hydrocarbons, 4 ketones, 10 phenols, and 8 terpenoids. L. plantarum enhanced green/fruity odors while strain L. buchneri significantly reduced undesirable phenolic and aldehydic compounds. Six key VOCs influencing the odor of WPMS were selected: 4-ethyl-2-methoxyphenol and benzaldehyde, which contribute smoky, bacon, and bitter almond aromas, and (E)-3-hexen-1-ol, benzyl alcohol, (E, E)-2,4-heptadienal and methyl salicylate, which impart green, fruity, and nutty aromas. These findings highlight the effects and contributions of various strain additives on VOCs in WPMS, providing new theoretical insights for regulating the flavor profile of WPMS. Full article

A rapid targeted screening method for 22 compounds, including flavonoids, glycosides, and phenolics, in Dendrobium officinale was developed using UHPLC–MS/MS, demonstrating good linear correlation coefficients, precision, repeatability, and stability. D. officinale from the Guangnan and Maguan regions can be effectively classified into two distinct categories using PCA. In addition, OPLS-DA discriminant analysis enables clear separation between groups, with samples forming well-defined clusters. The 22 chemical components provide valuable origin-related information for D. officinale. The compounds with VIP values of >1 included eriodictyol, vanillic acid, protocatechuic acid, gentisic acid, and naringenin. The difference in naringenin content between D. officinale from the two production areas was minimal. By contrast, eriodictyol and vanillic acid were relatively abundant in D. officinale from Guangnan, while gentisic acid and protocatechuic acid were more prevalent in D. officinale from Maguan. The pathways with higher Kyoto Encyclopedia of Genes and Genomes enrichment were primarily associated with lipid metabolism and atherosclerosis, fluid shear stress and atherosclerosis, and nonalcoholic fatty liver disease. These findings suggest that D. officinale exhibits promising lipid-balancing properties and potential cardiovascular health benefits. Seven machine learning algorithms—Random Forest, XGBoost, Support Vector Machine, k-Nearest Neighbor, Backpropagation Neural Network, Random Tree, and CatBoost—demonstrated superior accuracy and precision in distinguishing D. officinale from the Guangnan and Maguan regions. The key compounds with higher weights—vanillic acid, chrysoeriol, trigonelline, isoquercitrin, gallic acid, 4-hydroxybenzaldehyde, eriodictyol, sweroside, apigenin, and homoeriodictyol—play a crucial role in model construction and the identification of D. officinale from the Guangnan and Maguan regions. The quantification of 22 compounds using UHPLC–MS/MS, combined with PCA, OPLS-DA, and machine learning, enables effective discrimination of D. officinale from these two Yunnan production areas. Full article