Search Results (13,397)

Biosensors based on electrolyte-gated organic field-effect transistors (EGOFETs) have attracted considerable attention due to their advantages, including low cost, inherent signal amplification, and low-voltage operation. A critical step influencing sensing performance is the integration of specific receptors onto the device surface. Among various strategies, the covalent immobilization of biorecognition elements onto gold surfaces via thiol chemistry is one of the most widely used approaches. In this study, we report the optimization of a mixed self-assembled monolayer (SAM) composed of 11-mercaptoundecanoic acid (11-MUA) and 3-mercaptopropionic acid (3-MPA) for label-free detection of human IgG using EGOFETs. The quality of the SAM was systematically modulated by varying the total concentration from 10 to 400 mM and characterized using X-ray Photoelectron Spectroscopy (XPS), Electrochemical Impedance Spectroscopy (EIS), Cyclic Voltammetry (CV), and Atomic Force Microscopy (AFM). The results revealed that a concentration of 50 mM yielded a densely packed and well-ordered monolayer. After covalent immobilization of anti-IgG antibodies via 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride/N-hydroxysuccinimide (EDC/NHS) chemistry and subsequent blocking with ethanolamine and bovine serum albumin (BSA), the functionalized gate electrodes were integrated into poly(3-hexylthiophene) (P3HT)-based EGOFETs. Electrical measurements demonstrated that EGOFET biosensors functionalized with the 50 mM SAM achieved optimal sensing performance. The devices exhibited a highly linear response (R² = 0.998) over a wide concentration range from 1 fM to 10 nM, with a LOD of 2.82 fM, and showed excellent selectivity against non-target immunoglobulins A and M (IgA and IgM). This SAM concentration optimization strategy provides a versatile approach for engineering high-performance EGOFET biosensors, with potential applicability to a broad range of disease biomarkers. Full article

Human serum albumin (HSA) is the most abundant protein in plasma, and the redox state of circulating HSA has been used as a biomarker of systemic oxidative stress (OS) for decades. While informative, many traditional biomarkers of OS measure short-lived or downstream products of oxidative damage that offer limited perspectives on the dynamic and integrated processes that govern systemic redox biology within human populations. By moving beyond single-analyte damage markers and towards coordinated patterns of protein modifications, HSA-Cys³⁴ adductomics offers a systems-level approach that simultaneously captures change in multiple layers of OS. Because of its high abundance in plasma and HSA’s unique and highly reactive single free thiol (Cys³⁴), HSA-Cys³⁴ serves as an ideal sentinel target for monitoring reactions with reactive oxygen species (ROS), reactive nitrogen species (RNS), and electrophilic species produced by endogenous metabolism and responses to exogenous chemical exposures. The reaction of HSA with ROS, RNS, and reactive electrophiles yields a diverse array of protein modifications, including direct oxidation products (sulfenic, sulfinic, and sulfonic acid), low molecular weight thiol-disulfide exchange, and lipid peroxidation (LPO)-derived reactive aldehydes. With a mean residence time of about a month, these accumulated adducts serve as an integrated picture of oxidative and electrophilic stress that together function as a molecular record of systemic redox physiology. Previous studies using high-resolution mass spectrometry-based adductomics have enabled global untargeted analysis of HSA-Cys³⁴ modifications, yielding an expansive inventory of novel redox signatures of environmental stressors and disease states. In this paper we review the chemistry and biology underlying OS-related modifications of HSA-Cys³⁴ and highlight the important role of HSA-Cys³⁴ adducts as integrative biomarkers of OS at the interface of molecular biology, exposure assessment, and public health research. Full article

The autophagy regulator ATG7 helps maintain cellular homeostasis and has been suggested to modulate aspects of antiviral immune responses. In Drosophila, ATG7-dependent autophagy contributes to host resistance to vesicular stomatitis virus (VSV), a negative-strand RNA virus of family Rhabdoviridae that is widely used for studying viral biology and developing vaccines and virotherapy. However, the role of ATG7 in mammalian cells, especially non-immune cell types, remains unclear. Herein, we systematically examined the impact of ATG7 on VSV infection using CRISPR-edited cell lines derived from murine embryonic fibroblast (MEF), HeLa, and Huh7.5 cells, in relation to its effect on the expression of antiviral interferon-stimulated genes (ISGs). We found that ATG7 deficiency blocked basal as well as VSV-induced LC3B lipidation, concomitant with moderate reductions in progeny virus yields, while the reconstitution of ATG7 reversed the phenotypes. Mechanistically, ATG7 did not affect viral entry but rather was associated with moderate upregulation of VSV RNA replication. Intriguingly, ATG7 inhibited baseline ISG expression, and this correlated with its pro-VSV effect in all three cell types, while its suppression of innate immune responses elicited post-VSV infection did not. Altogether, these data provide new insights into the role of ATG7 in regulating VSV replication and innate immunity and have implications for developing VSV-based prophylaxis/therapeutics. Full article

Background: Major Depressive Disorder (MDD) is increasingly recognized as involving psychomotor slowing and impaired cortical timing. Objective vibrotactile assessments can quantify sensory and cognitive integration, potentially identifying mechanistic biomarkers of depression. Objective: To determine whether tactile performance metrics from the Brain Gauge system differentiate individuals with depression from healthy controls and to identify the most predictive domains using cross-validated modeling. Methods: Eighty-two adults (43 with depression, 39 controls) completed the Brain Gauge battery assessing reaction time (RT), RT variability, amplitude and duration discrimination, temporal order judgment, accuracy, and cortical plasticity. Results: After FDR correction, participants with depression showed significantly slower and more variable tactile responses (FDR-adjusted p < 0.05). Speed and RT variability remained independent predictors (OR = 4.14; OR = 0.015), yielding an AUC = 0.86 (sensitivity = 0.87; specificity = 0.77). These findings suggest reduced cortical stability and efficiency in depression. Conclusions: Tactile timing measures—particularly Speed and RT variability—objectively capture psychomotor and temporal instability in MDD. Cross-validated logistic modeling supports their potential as non-invasive digital biomarkers for depression phenotyping and monitoring. These findings suggest tactile timing instability as a clinically relevant neurofunctional dimension of major depressive disorder, with potential applications in psychiatric phenotyping, objective symptom monitoring, and future precision-guided treatment strategies. Full article

This study presents a comprehensive multi-objective optimization of sewage wastewater treatment using bio-based coagulants, guided by the Grey Wolf Optimizer (GWO) and its multi-objective variant (MOGWO). Experimental coagulation data, employing Citrullus lanatus and Cucumis melo as natural coagulants, were modeled using multivariate regression techniques, yielding high coefficients of determination (R² > 0.95) across key water quality parameters. The optimization process targeted maximal reductions in turbidity, total suspended solids (TSS), biochemical oxygen demand (BOD), and chemical oxygen demand (COD) through strategic manipulation of pH and coagulant dosage. The single-objective GWO achieved significant outcomes, including a 96.68% turbidity reduction at pH 5 and 50 mg/L dosage. The MOGWO algorithm identified Pareto-optimal solutions, such as a 94.2% turbidity reduction at pH 5 and 72 mg/L dosage, and a balanced BOD reduction of 52.7% at pH 7. The predictive models indicated that optimal treatment conditions could reduce chemical usage by up to 90% compared to conventional coagulants, resulting in potential cost savings of up to 30%. Moreover, the algorithms demonstrated rapid convergence, averaging 200 iterations, highlighting their computational efficiency and robustness. These findings illustrate that integrating bio-based coagulants with advanced optimization techniques can achieve high treatment efficiency while reducing chemical inputs, thus directly supporting environmental sustainability by minimizing sludge and secondary pollution. In this situation, the wastewater treatment plant will focus on resource-recovery systems with less or no waste at the end of the treatment process. This approach aligns with circular economy principles by promoting eco-friendly, cost-effective wastewater treatment solutions suitable for resource-limited settings. The study offers a forward-looking pathway for environmentally responsible wastewater management practices that significantly reduce chemical dependency and contribute to pollution mitigation efforts. Full article

Drought stress poses a significant challenge to food security in sub-Saharan Africa, particularly for smallholder farmers in dryland systems. Bambara groundnut (Vigna subterranea (L.) Verdc.), an underutilised legume with inherent drought tolerance, remains underexplored in terms of its root system traits. This greenhouse study investigated the early root and shoot responses of six Bambara groundnut genotypes under well-watered (100% field capacity) and water-stressed (50% field capacity) conditions using rhizotron-based phenotyping. Significant genotypic differences (p < 0.01) were observed in root traits such as root system depth (RSD: 11.0–19.9 cm), root system width (RSW: 6.96–12.2 cm), and root dry mass (RDM: 0.42–1.27 g). The ARC genotype exhibited a strong drought-avoidance strategy, increasing RSD from 12.2 to 19.9 cm and RDM from 0.42 to 1.16 g under stress. The Tiga Nicuru DIP-C-F7471 genotype showed adaptive plasticity, maintaining deeper roots (11.0–14.5 cm), high convex hull area (CHA), and root–shoot ratio (RSR) values, despite a reduction in RDM, suggesting a resource-conserving strategy. Principal Component Analysis (PCA) captured 93.6% of the total variability among genotypes. Root traits, particularly total root length (TRL), convex hull area (CHA), root system width (RSW), and root dry mass (RDM), were the main contributors to genotype differentiation. Strong positive correlations (r = 0.88–0.97) between root and shoot traits suggest that genotypes with more developed root systems also supported greater shoot growth, highlighting the coordinated response of above- and below-ground traits under drought stress. These findings provide valuable targets for breeding and highlight the value of rhizotron-based screening for root trait selection. Future field validation and full-season studies are recommended to confirm their relevance for improving yield stability in dryland agriculture. Full article

Food waste is commonly valorized through aerobic composting, yet the responses of water-soluble organic carbon (WSOC) across molecular-weight (MW) fractions remain insufficiently resolved. This study aimed to quantify how distinct composting strategies regulate WSOC MW distribution and compositional evolution and identify the key physicochemical drivers. Food waste was treated by 30-day conventional composting (CK), 15-day phased inoculation (JJ; 2% (w/w) antioxidative consortium dominated by Bacillus/Pseudomonas followed by 2% (w/w) thermophilic cellulolytic consortium enriched in Geobacillus/Paenibacillus when the temperature reached 50 °C), and 24-h rapid thermophilic composting (RC; 2% (w/w) inoculation with a 24-h moist-heat pretreatment). RC yielded a small molecular weight organic carbon (SMOC)-rich product with low aromaticity, with MW < 5 kDa accounting for 68.21% (MW < 500 Da: 28.50%). JJ preferentially enriched more oxidized, fulvic-like/carboxyl-rich organics, increasing the fulvic-like contribution from 15.97% to 35.40% and raising the HMOC/SMOC to 2.72:1. CK showed the strongest humification, with MW > 5 kDa reaching 65.56% and humic-like Region V increasing from 26.25% to 66.36%. pH was the primary predictor of MW (day 6: CK 3.9; JJ 4.9; final ~8.8), while temperature jointly governed humic-like formation in RC. Full article

Alpha-mangostin (AM), a xanthone from Garcinia mangostana, has shown promising nephroprotective properties, but its mechanisms in acute kidney injury (AKI) remain incompletely defined. In this study, we applied an integrative network pharmacology pipeline combined with molecular docking to clarify AM’s multi-target mechanisms in AKI. We identified 128 predicted AM targets and intersected them with AKI-related genes, yielding 122 shared targets. Protein–protein interaction analysis identified ten hub genes—TNF, AKT1, IL6, SRC, CTNNB1, HSP90AA1, NFKB1, HIF1A, PPARG, and PTGS2—implicating inflammatory, hypoxia, and cell-survival pathways. KEGG enrichment highlighted HIF-1 signaling, PI3K–Akt signaling, chemokine signaling, AGE–RAGE signaling, and pathways related to cellular senescence and oxidative stress, while GO terms emphasized responses to chemical/oxygen-containing compounds, kinase activity, signal transduction, and apoptosis. Molecular docking against the ten hub proteins showed favorable binding energies across multiple targets. The strongest predicted affinities were observed for PTGS2 (−11.13 kcal/mol), TNF (−9.74 kcal/mol), and AKT1 (−9.48 kcal/mol). Docking positioned AM within the COX-2 catalytic pocket, engaging key catalytic and hydrophobic residues similar to known inhibitors. MD simulation interaction analysis confirmed that AM maintained stable contacts with key human PTGS2 residues, characterized by dominant hydrogen bonds and water-bridge interactions with SER353, TYR355, ARG513, and SER530, along with consistent hydrophobic contacts, and persistent interactions sustained throughout the 200 ns trajectory. Collectively, these results suggest that AM modulates interconnected inflammatory, hypoxic, and survival pathways relevant to AKI, acting as a multi-target ligand with notable interaction involving COX-2, TNF, and AKT1. Further experimental validation and formulation strategies to improve bioavailability are recommended for the advancement of AM toward therapeutic evaluation in AKI. Full article

Load-redistribution false-data-injection (LR-FDI) attacks can degrade power-system operation by reshaping the perceived nodal demand pattern, thereby inducing congestion-aware redispatch and economic inefficiency while preserving the net system load. Prior LR-FDI studies commonly adopt bilevel/Stackelberg formulations with a continuous attack vector and an embedded operator response; however, these formulations often (i) do not represent explicit compromised-load selection, (ii) become computationally restrictive when combinatorial target sets are considered, and (iii) offer limited transparency for structured, stage-wise attack planning. This paper proposes a sequential two-stage attacker–operator framework for LR-FDI vulnerability assessment that integrates sparse load compromise decisions with screening-regularized attack synthesis and post-attack operational evaluation. In Stage-1, a mixed-integer nonlinear program identifies economically influential load buses via binary selection and determines admissible perturbation magnitudes under total-load conservation and proportional shift bounds. To confine the attacker-side search region and avoid economically exaggerated solutions, a screening-derived conservative operating-cost ceiling is first estimated through a parametric load-sensitivity analysis and then used to regularize the attack-synthesis step. In Stage-2, the system operator’s corrective redispatch is evaluated by solving an active-power-oriented economic dispatch model with nonlinear network-consistent assessment of operational outcomes. Using the IEEE 24-bus RTS, results show that the hourly operating-cost deviation reaches ≈0.2% in the most adverse feasible cases, and the cumulative daily impact approaches ≈5% only under selectively realizable compromised-load patterns, accompanied by a nearly 80% increase in total active-power transmission losses relative to the base case. Overall, the framework yields a practically grounded quantification of conditionally severe economic and network stress under coordinated LR-FDI scenarios and provides actionable insight for prioritizing vulnerable load locations for protection and monitoring. Full article

Place-based approaches have gained significant attention as a means of addressing entrenched disadvantage through collaborative, locally responsive service delivery, yet implementation has yielded mixed results and the systemic factors that facilitate or impede inter-organisational collaboration remain inadequately understood. This study applied participatory systems mapping as part of a systemic inquiry to identify leverage points for place-based integrated service delivery in the City of Casey, an outer-metropolitan municipality in Melbourne, Australia. Twenty-one representatives from the Casey Futures Partnership engaged in group model building workshops, co-producing a causal loop diagram containing 33 factors and 104 directional connections. The resulting map was analysed using a blended analytical approach combining network metrics with the Action Scales Model. Funding availability and criteria emerged as the most central factor within the system, while belief-level factors, including territorial behaviour and resource and collaboration mindset, were found to be substantially shaped by upstream structural conditions. Factors combining network influence with deeper system positioning and amenability to local action included awareness of community needs and priorities, trust and willingness to collaborate from funders, inter-organisational communication, and advocacy effectiveness. The findings support multi-level place-based approaches that address underlying beliefs and structural conditions alongside operational improvements. Full article

Background/Objectives: Ulcerative colitis (UC) involves inflammatory response, oxidative stress, changes in metabolites, and the gut microbiota. Jingangteng capsule (JGTC) has been utilized clinically for the treatment of inflammatory diseases for many years. However, the efficacy of JGTC in ameliorating UC remains unclear, and the underlying mechanisms have not yet been elucidated. This study aims to investigate the effect and mechanism of JGTC on UC. Methods: The chemical compositions of JGTC were examined using ultra-high-performance liquid chromatography with quadrupole time-of-fight mass spectrometry. The anti-UC effect of JGTC was evaluated by assessing the disease activity index (DAI), colon length, intestinal barrier recovery, and inflammatory factors in a dextran sulfate sodium (DSS)-induced colitis model. Mechanisms were investigated through fecal 16S rDNA sequencing, metabolomics analysis, enzyme-linked immunosorbent assay (ELISA), Western blotting, and network pharmacology analysis. Results: JGTC significantly reduced the DAI scores in UC mice, increased their body weight and colon length (p < 0.001), repairing damaged intestinal tissue. It decreased the levels of inflammatory cytokines TNF-α, IL-6, IL-1β, and LPS (p < 0.01, p < 0.001), alleviating intestinal inflammation. It also raised the expression of tight junction proteins ZO-1, Claudin-1, and Occludin (p < 0.05, p < 0.001), thereby enhancing intestinal barrier function. Fecal metabolomic analysis revealed that the favorable alterations in amino acid and lipid metabolites were more pronounced. Heat maps showed strong correlations between pharmacological indicators and gut microbiota, as well as between the main differential metabolites and gut microbial communities. UPLC-QTOF-MS detection yielded 33 components of JGTC, and network pharmacology analysis based on these components predicted pathways of action of JGTC in UC. Functional pathways closely associated with significantly differential metabolites and metabolic pathways were also investigated. The PI3K-AKT-mTOR pathway was one of them, which is consistent with the conclusions drawn from network pharmacology. JGTC significantly modulated key factors in this pathway, inhibiting the expression of PI3K, Akt, PDK1, and mTOR, while augmenting the expression of PTEN (p < 0.05, p < 0.01, p < 0.001). It also mitigated the levels of related oxidative stress factors MDA, MPO, and D-LA, and raised SOD levels (p < 0.01, p < 0.001). Conclusions: JGTC improved the excessive inflammatory response in UC by regulating intestinal flora and metabolic disorders, affecting the PI3K-AKT-mTOR signaling pathway, restoring intestinal tissue damage and intestinal barrier, and inhibiting inflammatory and oxidative stress factors. Full article

Water deficit is a major abiotic constraint limiting peanut (Arachis hypogaea L.) production. This study evaluated the combined effects of filter cake, foliar application of an amino acid-based biostimulant, microbial consortium inoculation, on peanut growth, physiology, and yield under water scarcity conditions. Treatments were arranged in a split-plot design with four replicates, where filter cake (0 and 5 t ha⁻¹) was assigned to main plots, amino acid application to subplots (0.25 and 0.50 L ha⁻¹), and microbial consortium to sub-subplots (100 and 200 mL m⁻²). At 50 days after sowing, plant growth parameters, relative chlorophyll content, and aboveground biomass were assessed, while yield components and seed yield were determined at harvest. Results indicated that the combined treatment with 5 t ha⁻¹ filter cake, 0.50 L ha⁻¹ amino acids, and 200 mL m⁻² microbial consortium, consistently produced the highest main stem length (increase of 40%), aboveground biomass accumulation (increase of 41%), number of matured pods per plant (increase of 38%), seed mass per plant (increase of 87%), and final seed yield (increase of 86%) compared to the lowest-input treatment (F₀A_0.25M₁₀₀) under water-limited conditions. These findings indicate that the integrated fertilization can improve phenological, physiological, and yield responses and represents a sustainable approach to improve peanut resilience and productivity under water scarcity. Full article

Pulsed polarization of Au|YSZ gas sensors is examined to clarify the mechanism of NO_x detection under dynamic operation and to disentangle catalytic surface effects from electrochemical relaxation. Using gold electrodes with substantially lower catalytic activity than platinum explicitly enables this mechanistic separation. During pulsed polarization, periodic voltage pulses are followed by self-discharge under open-circuit conditions, and the response is measured based on the self-discharge rate. NO₂ consistently accelerates the self-discharge from the beginning, whereas NO slows the relaxation predominantly at later times. CO and H₂ produce similar delaying effects, and C₃H₆ shows no measurable influence under the tested conditions. Decreasing ambient O₂ slows the discharge and amplifies the NO₂ effect, which indicates that oxygen supply and surface exchange at the triple-phase boundary are rate determining. A Pt-containing catalytic overlayer drives local NO/NO₂ interconversion toward equilibrium so that both gases yield to an accelerated self-discharge. These findings support a mechanistic picture in which NO₂ provides effective oxygen equivalents that accelerate discharge, whereas NO, CO, and H₂ consume oxygen and slow down discharge. Overall, this establishes a materials-based approach for distinguishing between NO and NO₂ and evaluating the underlying mechanism during pulsed polarization. Full article

Heilongjiang Province is China’s largest commercial grain-producing base, meaning that understanding the stability and climatic sensitivity of its major crops are essential for national food security. Using statistical and meteorological data from 2004 to 2023, this study systematically examines the impacts of climate change on cropping structure, yield dynamics, and production stability. The results show that over two decades the total grain crops-sown area and the yield per unit area increased by 79.4% and 38.4%, respectively. The cropping pattern shifted from a diversified structure to a maize-soybean-rice dominated pattern, while the wheat area declined by 92.2%. Additionally, mean and extreme yield fluctuations decreased by 52.3% and 42%, respectively. Rice exhibited the highest yield stability, whereas maize and soybeans experienced marked reductions in interannual variability. Spatial analysis identified Harbin and Daqing as hotspots for yield stability risk, characterized by higher yield standard deviations relative to other cities in the province. Climate elasticity analysis revealed that soybeans and rice were sensitive to warming, while wheat responded positively to increased rainfall. Overall, Heilongjiang’s grain production system has expanded and become more stable at the provincial scale, but it remains vulnerable to emerging climatic risks. Strengthening climate adaptation through crop-specific management, varietal improvement, and field water regulation is vital for enhancing system resilience and sustaining food production in cold-region agroecosystems. Full article

Accurate monitoring of crop phenology is essential for precision agriculture and yield forecasting. However, satellite-derived time series often suffer from inherent noise, such as residual atmospheric effects and mixed pixels, as well as a frequent lack of ground-truth data in agriculture. In response, this study proposes an algorithm to define the type of spectral signatures for the principal phenological stages of crops, using them as the foundation for training supervised machine learning classification models. The algorithm was developed using Fuzzy C-Means (FCM) clustering to identify the spectral signature reference groups in winter wheat across the Burgos region (Spain) during the 2020 and 2021 growing seasons. To enhance cluster independence and biological coherence, a multi-step filtering process was implemented, including spectral purity (membership degree, SAM, and SAMder) and temporal coherence filters. The filtered and labeled dataset (80% original Burgos dataset) was used to train supervised classification models (KNN and XGBoost). The models’ reliability was verified through three wheat tests (remaining 20%), labeled using other clustering techniques, and an independent barley dataset from diverse geographic locations (Valladolid and Soria). The filtering process significantly improved cluster stability by removing outliers and transition spectral signatures. The supervised models demonstrated exceptional performance; the KNN model slightly outperformed XGB, achieving a mean Accuracy of 0.977, a Kappa of 0.967, and an F1-score of 0.977 in the wheat external test. Furthermore, the model showed, when applied to barley, that its phenological spectral signatures are equivalent in shape to those of wheat, with an Accuracy of 0.965 and an F1-score of 0.974. In addition, it was verified that the type spectral signatures remain the same regardless of the location. This study presents a robust classification tool capable of labeling four key phenological stages (tillering, stem elongation, ripening, and senescence) without ground truth. By effectively removing inherent satellite noise, the proposed methodology produces organized, cleaned datasets. This structured foundation is critical for future research integrating spectral signatures with harvester data to develop high-precision yield prediction models. Full article