Search Results (562)

Variable selection (VS) is a critical step in developing predictive binary classification (BC) models. Many traditional methods for assessing the added value of a candidate variable provide global performance summaries and lack an interpretable graphical summary of results. To address this limitation, we developed the U-smile method, a residual-based, post hoc evaluation approach for assessing prediction improvements and worsening separately for events and non-events. The U-smile method produces three families of interpretable BA-RB-I coefficients at three levels of generality and a standardized graphical summary through U-smile and prediction improvement–worsening (PIW) plots, enabling transparent, interpretable, and explainable VS. Validated in balanced and imbalanced BC scenarios, the method proved robust to class imbalance and collinearity, and more sensitive than traditional metrics in detecting subtle but meaningful effects. Moreover, the method’s intuitive visual output (U-smile plot) facilitates the rapid communication of results to non-technical stakeholders, bridging the gap between data science and applied decision-making. The U-smile method supports both local and global evaluations and complements existing explainable machine learning (XML) and artificial intelligence (XAI) tools without overlapping in their functions. The U-smile method offers a transparency-enhancing and human-oriented approach for ethical and fair VS, making it highly suited for high-stakes domains, e.g., healthcare and public health. Full article

In familial Alzheimer’s disease (FAD), presenilin 1 (PSEN1) E280A cholinergic-like neurons (ChLNs) induce aberrant secretion of extracellular amyloid beta (eAβ). How PSEN1 E280A ChLNs-eAβ affects microglial activity is still unknown. We obtained induced microglia-like cells (iMG) from human peripheral blood cells (hPBCs) in a 15-day differentiation process to investigate the effect of bolus addition of Aβ42, PSEN1 E280A cholinergic-like neuron (ChLN)-derived culture supernatants, and PSEN1 E280A ChLNs on wild type (WT) iMG, PSEN1 E280A iMG, and sporadic Alzheimer’s disease (SAD) iMG. We found that WT iMG cells, when challenged with non-cellular (e.g., lipopolysaccharide, LPS) or cellular (e.g., Aβ42, PSEN1 E280A ChLN-derived culture supernatants) microenvironments, closely resemble primary human microglia in terms of morphology (resembling an “amoeboid-like phenotype”), expression of surface markers (Ionized calcium-binding adapter molecule 1, IBA-1; transmembrane protein 119, TMEM119), phagocytic ability (high pHrodo™ Red E. coli BioParticles™ phagocytic activity), immune metabolism (i.e., high generation of reactive oxygen species, ROS), increase in mitochondrial membrane potential (ΔΨm), response to ATP-induced transient intracellular Ca²⁺ influx, cell polarization (cluster of differentiation 68 (CD68)/CD206 ratio: M1 phenotype), cell migration activity according to the scratch wound assay, and especially in their inflammatory response (secretion of cytokine interleukin-6, IL-6; Tumor necrosis factor alpha, TNF-α). We also found that PSEN1 E280A and SAD iMG are physiologically unresponsive to ATP-induced Ca²⁺ influx, have reduced phagocytic activity, and diminished expression of Triggering Receptor Expressed on Myeloid Cells 2 (TREM2) protein, but when co-cultured with PSEN1 E280A ChLNs, iMG shows an increase in pro-inflammatory phenotype (M1) and secretes high levels of cytokines IL-6 and TNF-α. As a result, PSEN1 E280A and SAD iMG induce apoptosis in PSEN1 E280A ChLNs as evidenced by abnormal phosphorylation of protein TAU at residue T205 and cleaved caspase 3 (CC3). Taken together, these results suggest that PSEN1 E280A ChLNs initiate a vicious cycle between damaged neurons and M1 phenotype microglia, resulting in excessive ChLN death. Our findings provide a suitable platform for the exploration of novel therapeutic approaches for the fight against FAD. Full article

Gasification of municipal solid waste and other biogenic residues (e.g., biomass and biowaste) is increasingly recognized as a promising thermochemical pathway for converting non-recyclable fractions into valuable energy carriers, with applications in electricity generation, district heating, hydrogen production, and synthetic fuels. This paper provides a comprehensive analysis of major gasification technologies, including fixed bed, fluidized bed, entrained flow, plasma, supercritical water, microwave-assisted, high-temperature steam, and rotary kiln systems. Key aspects such as feedstock compatibility, operating parameters, technology readiness level, and integration within circular economy frameworks are critically evaluated. A comparative assessment of incineration and pyrolysis highlights the environmental and energetic advantages of gasification. The valorization pathways for main product (syngas) and by-products (syngas, ash, tar, and biochar) are also explored, emphasizing their reuse in environmental, agricultural, and industrial applications. Despite progress, large-scale adoption in Europe is constrained by economic, legislative, and technical barriers. Future research should prioritize scaling emerging systems, optimizing by-product recovery, and improving integration with carbon capture and circular energy infrastructures. Supported by recent European policy frameworks, gasification is positioned to play a key role in sustainable waste-to-energy strategies, biomass valorization, and the transition to a low-emission economy. Full article

Intravenous infusion is an important clinical medical intervention, and its safety is critical to patient recovery. To mitigate the elevated risk of complications (e.g., air embolism) arising from delayed response to infusion endpoints, this paper designs a flexible double pole capacitive (FPB) sensor, which includes a main pole plate, an adaptive pole plate, and a back shielding electrode. The sensor establishes a mapping between residual liquid volume in the infusion bottle and its equivalent capacitance, enabling a non-contact adaptive monitoring system. The system enables precise quantification of residual liquid levels, suppressing baseline drift induced by environmental temperature/humidity fluctuations and container variations via an adaptive algorithm, without requiring manual calibration, and overcomes the limitations of traditional rigid sensors when adapting to curved containers. Experimental results showed that the system achieved an overall sensitivity of 753.5 $\mathrm{f}\mathrm{F}/\mathrm{m}\mathrm{m}$, main pole plate linearity of 1.99%, and adaptive pole plate linearity of 0.53% across different test subjects, linearity of 0.53% across different test subjects, with liquid level resolution accuracy reaching 1 mm. These results validate the system’s ultra-high resolution (1 mm) and robust adaptability. Full article

Fasciola gigantica (F. gigantica) is a vital parasite that causes fasciolosis. Liver fluke infections affect livestock animals, and the Fasciola species (Fasciola spp.) vaccine has been tested for many types of these diseases. Currently, computer-based vaccine design represents an attractive alternative for constructing vaccines. Thus, this study aimed to design the epitopes of linear B-cells (BCL) and helper T lymphocytes (HTL) using an immunoinformatic approach and to investigate in silico and the mice’s immune response. A non-conserved host region, overlapping F. gigantica cathepsin B proteins (FgCatB), and the highest conserved residue percentages were the criteria used to construct epitopes. The GPGPG linker was used to link epitopes in the multi-epitope Fasciola gigantica cathepsin B (MeFgCatB) peptide. The MeFgCatB peptide has high antigenicity, non-allergenicity, non-toxicity, good solubility, and a high-quality structure. The molecular docking between the MeFgCatB peptide and Toll-like receptor 2 (TLR-2) was evaluated. The IgM, IgG1, and IgG2 levels were elevated in silico. In mice, the MeFgCatB peptide was synthesized and administered as an injection. The MeFgCatB-specific IgG1 and IgG2a levels were elevated after week 2, showing a predominance of IgG1. The rFgCatB1, rFgCatB2, and rFgCatB3 were detected using the MeFgCatB peptide-immunized sera. The MeFgCatB peptide-immunized sera were detected at approximately 28–34 kDa in the whole body. In addition, the MeFgCatB immunized sera can positively signal at the caecal epithelium in the NEJ, 4WKJ, and adult stages. In summary, the MeFgCatB peptide is able to induce mixed Th1/Th2 immune responses with Th2 dominating and to detect the native protein of F. gigantica. The MeFgCatB peptide should help against F. gigantica in future experiments. Full article

Pesticide residues pose risks to the environment and human health. Little is known about how tillage and straw management affect herbicide behavior in soil. This study investigated the effects of different tillage practices under varying straw incorporation scenarios on the degradation of five commonly used herbicides in a long-term experimental field located in the maize belt of Siping, Jilin Province. Post-harvest soil samples were analyzed for residual herbicide concentrations and basic soil physicochemical properties. A human health risk assessment was conducted, and a controlled incubation experiment was carried out to evaluate herbicide degradation dynamics under three management systems: straw incorporation with traditional rotary tillage (ST), straw incorporation with strip tillage (SS), and no-till without straw (CK). Residual concentrations of atrazine ranged from not detected (ND) to 21.10 μg/kg (mean: 5.28 μg/kg), while acetochlor showed the highest variability (2.29–120.61 μg/kg, mean: 25.26 μg/kg). Alachlor levels were much lower (ND–5.71 μg/kg, mean: 0.34 μg/kg), and neither nicosulfuron nor mesotrione was detected. Soil organic matter (17.6–20.89 g/kg) positively correlated with available potassium and acetochlor residues. Health risk assessments indicated negligible non-cancer risks for both adults and children via ingestion, dermal contact, and inhalation. The results demonstrate that tillage methods significantly influence herbicide degradation kinetics, thereby affecting environmental persistence and ecological risks. Integrating straw with ST or SS enhanced the dissipation of atrazine and mesotrione, suggesting their potential as effective residue mitigation strategies. This study highlights the importance of tailoring tillage and straw management practices to pesticide type for optimizing herbicide fate and promoting sustainable agroecosystem management. Full article

Circulating cell-free DNA (cfDNA) is an important biomarker for various cancer types, enabling a non-invasive testing approach. However, pre-analytical variables, including sample collection, tube type, processing conditions, and extraction methods, can significantly impact the yield, integrity, and overall quality of cfDNA. This study presents a comprehensive analytical validation of a magnetic bead-based, high-throughput cfDNA extraction system, with a focus on assessing its efficiency, reproducibility, and compatibility with downstream molecular applications. The validation was performed using a range of sample types: synthetic cfDNA spiked into DNA-free plasma, multi-analyte ctDNA plasma controls, Seraseq ctDNA reference material in a plasma-like matrix, extraction specificity controls, residual clinical specimen from patients, and samples from healthy individuals stored at room temperature or 4 °C for up to 48 h to assess stability. Extracted cfDNA was analyzed for concentration, percentage, and fragment size, using the Agilent TapeStation. Variant detection was evaluated using a next-generation sequencing (NGS) assay on the Seraseq ctDNA reference material. The results demonstrated high cfDNA recovery rates, consistent fragment size distribution (predominantly mononucleosomal and dinucleosomal), minimal genomic DNA (gDNA) contamination, and strong concordance between detected and expected variants in reference materials. The workflow also showed robust performance under different study parameters, variable sample conditions, including sample stability and integrity. Together, these findings confirm the efficiency and reliability of the evaluated cfDNA extraction system and underscore the importance of standardized pre-analytical workflows for the successful implementation of liquid biopsy for early cancer detection, therapeutic monitoring, and improved patient outcomes. Full article

Head and neck squamous cell carcinomas (HNSCCs) that develop from the mucosal epithelium in the oral cavity, pharynx, and larynx are a heterogeneous group of malignant tumors. A lack of appropriate screening and diagnostic methods leads to late diagnoses, with the majority of patients having locally advanced disease, which is associated with a high risk of local recurrence and a poor prognosis and is usually treated with combination therapies. Biomarkers for predicting the therapy response and risk of recurrence in HNSCC patients are urgently needed. Liquid biopsy, e.g., the profiling of circulating biomarkers in bodily fluids, is a promising approach with increasing utility in the early detection and diagnosis of cancer, monitoring cancer progression, patient stratification and treatment selection, detecting minimal residual disease (MRD), and predicting recurrence across different cancer types, including HNSCC. Among liquid biomarkers, circulating tumor DNA (ctDNA), which is based on detecting tumor-specific mutations, insertions/deletions, copy number alterations, and methylation, is the most promising transformative tool in cancer management and personalized cancer treatment. In this review, we provide an update of recent data on the role of non-viral ctDNA in the management of HNSCC patients. Accumulating data suggests the enormous potential of ctDNA profiling by serial sampling during and after definitive therapy in detecting MRD and predicting recurrence in HNSSC patients treated with a single treatment modality (surgery or radiotherapy) or with combination therapies, including immune-checkpoint-inhibitor-based immunotherapy. By incorporating the latest immunotherapy trials and organizing the data by the treatment modality, this review offers a novel perspective not found in previous surveys. Full article

Industrialization and modernization have significantly improved the quality of life but have also led to substantial pollution. Cost-effective technologies are urgently needed to mitigate emissions from major polluting sectors, such as the automotive and transport industries. In this study, we synthesized naturally derived, kapok-based porous carbon fibers (KP-PCFs) with hollow structures. We investigated their adsorption/desorption behavior for the greenhouse gas n-butane following ASTM D5228 standards. Scanning electron microscopy and X-ray diffraction analyses were conducted to examine changes in fiber diameter and crystalline structure under different activation times. The micropore properties of KP-PCFs were characterized using Brunauer–Emmett–Teller, t-plot, and non-localized density functional theory models based on N₂/77K adsorption isotherm data. The specific surface area and total pore volume ranged from 500 to 1100 m²/g and 0.24 to 0.60 cm³/g, respectively, while the micropore and mesopore volumes were 0.20–0.45 cm³/g and 0.04–0.15 cm³/g, respectively. With increasing activation time, the n-butane adsorption capacity improved from 62.2% to 73.5%, whereas retentivity (residual adsorbate) decreased from 6.0% to 1.3%. The adsorption/desorption rate was highly correlated with pore diameter: adsorption capacity was highest for diameters of 1.5–2.5 nm, while retentivity was greatest for diameters of 3.5–5.0 nm. Full article

The behaviors of hybrid and non-hybrid woven composite laminates with different stacking sequences under quasi-static indentation (QSI) and compression after indentation (CAI) were investigated in this paper. A comparative experimental and numerical study was conducted to find whether the hybridization exhibits better performance, and a focus was given to the mechanisms behind it. A C-scan ultrasonic imaging system and a digital microscope to assess the visibility of the damage and penetration resistance were employed for specimens after QSI. For CAI analysis, digital image correlation (DIC) was applied. Results show that glass–carbon hybrid woven laminates ([(±45)_g/(0,90)_c]_4s) exhibit 4.31% greater load bearing efficiency, 4.45% higher residual compressive strength, and 6.35% less indentation-induced damage area than the carbon–glass ([(±45)_c/(0,90)_g]_4s) hybrid woven laminates. These findings on different stacking sequences provide insights into surface layer behavior and interfacial failure in glass–carbon hybrid composites for designing surface-engineered laminates with improved resistance, energy absorption, and residual compressive strength. The results support the advancement of hybrid woven composite laminates and the development of durable, high-performance materials for structural applications. Full article

The processing of metallurgical slags is an urgent task, as they contain residual amounts of precious and non-ferrous metals such as gold, silver, copper and zinc. The efficiency of extraction of these metals directly depends on the granulometric composition of the processed material, which determines the need for its detailed analysis. The purpose of this study is to analyze the effect of the granulometric composition of slags on the efficiency of extraction of non-ferrous metals using the flotation method. For this purpose, studies were carried out, including granulometric analysis, chemical composition analysis and flotation tests using Na₂S, KAX and 3418A reagents. The analysis showed that the main part of the slag consisted of particles less than 3.36 mm, while the content of copper was 0.60%, zinc was 2.37%, gold was 0.1 g/t and silver was 7.2 g/t. Flotation experiments confirmed that the use of Na₂S and 3418A increased the recoverability of copper and zinc, and reducing the particle size to d80 <10 microns increased the efficiency of copper extraction by 7%. Thus, the optimization of flotation processes and the control of granulometric composition make it possible to increase the efficiency of metallurgical waste processing, reduce losses of valuable metals and reduce the environmental burden. Full article

The global population is rising at an alarming rate and is projected to reach 10 billion by 2050, necessitating a substantial increase in food production. However, the overuse of chemical pesticides, including antibacterial agents and synthetic fertilizers, poses a major threat to sustainable agriculture. This review examines the ecological and health impacts of antibacterial agents (e.g., streptomycin, oxytetracycline, etc.) in horticultural crops, focusing on their effects on non-target organisms such as beneficial microbes involved in plant growth promotion and resistance development. Certain agents (e.g., triclosan, sulfonamides, and fluoroquinolones) leach into water systems, degrading water quality, while others leave toxic residues in crops, leading to human health risks like dysbiosis and antibiotic resistance. To mitigate these hazards, sustainable alternatives such as integrated plant disease management (IPDM) and biotechnological solutions are essential. Advances in genetic engineering including resistance-conferring genes like EFR1/EFR2 (Arabidopsis), Bs2 (pepper), and Pto (tomato) help combat pathogens such as Ralstonia solanacearum and Xanthomonas campestris. Additionally, CRISPR-Cas9 enables precise genome editing to enhance inherent disease resistance in crops. Emerging strategies like biological control, plant-growth-promoting rhizobacteria (PGPRs), and nanotechnology further reduce dependency on chemical antibacterial agents. This review highlights the urgent need for sustainable disease management to safeguard ecosystem and human health while ensuring food security. Full article

Accurate assessment of biomass fuel properties is essential for quality control and fair market pricing, particularly when dealing with variable moisture content (MC) in agricultural residues. This study investigates the use of near-infrared (NIR) spectroscopy to predict the lower heating value (LHV) and ash content of sugarcane leaf pellets while minimizing the interference caused by moisture variability. Sixty-two samples were scanned using an NIR spectrometer over three week-long storage periods to get different MCs with the same sample. Additionally, variable selection methods such as a genetic algorithm (GA) and moisture-related wavelength exclusion were explored. The optimal model for LHV prediction was developed using GA-PLS regression (Method II), provided a coefficient of determination (R²) of 0.80, a root mean square error of calibration (RMSEc) of 595.80 J/g, and a ratio of performance to deviation (RPD) of 1.74, indicating fair predictive performance. The ash content model showed moderate accuracy, with a maximum R² of 0.61 and an RPD of 1.40. These findings suggest that the variables selected via GA in Method II were not relevant to MC; as Method II provided the best result, this indicates a low impact of MC, which may influence model construction in the future. Moreover, the findings also highlight the potential of NIR spectroscopy, combined with appropriate spectral preprocessing and wavelength optimization, as a rapid, non-destructive tool for evaluating biomass quality, enabling more precise control in bioenergy production and biomass trading. Full article

Recently, it has been proposed that plant melatonin receptors belong to the superfamily of G protein-coupled receptors (GPCRs). However, a detailed description of the phylogeny, protein structure, and binding properties of melatonin, which is still lacking, can help determine the signaling and function of this compound. Melatonin receptor homologs (PMTRs) were identified in 90 Viridiplantae sensu lato proteomes using profile Hidden Markov Models (HMM), which yielded 174 receptors across 87 species. Phylogenetic analysis revealed an expansion of PMTR sequences in angiosperms, which were grouped into three clades. Docking studies uncovered a conserved internal melatonin-binding site in PMTRs, which was analogous to the site in human MT1 receptors. Binding affinity simulations indicated this internal site exhibits stronger melatonin binding compared to a previously reported superficial pocket. Ligand–receptor interaction analysis and alanine scanning highlighted a major role of hydrophobic interactions, with hydrogen bonds contributing predominantly at the internal site, while non-interacting charged residues stabilize the binding pocket. Tunnel and ligand transport simulations suggested melatonin moves favorably through the internal cavity to access the binding site. Also, we presented for the first time details of these pockets in a non-model species, Capsicum chinense. Taken together, the structural analyses presented here illustrate opportunities and theoretical evidence for performing structure–function studies via mutations in specific residues within the proposed new melatonin-binding site in PMTRs, shedding light on their role in plant melatonin signaling. Full article

Sparse borehole sampling at contaminated sites results in sparse and unevenly distributed data on soil pollutants. Traditional interpolation methods may obscure local variations in soil contamination when applied to such sparse data, thus reducing the interpolation accuracy. We propose an adaptive graph convolutional network with spatial autocorrelation (ASI-GCN) model to overcome this challenge. The ASI-GCN model effectively constrains pollutant concentration transfer while capturing subtle spatial variations, improving soil pollution characterization accuracy. We tested our model at a coking plant using 215 soil samples from 15 boreholes, evaluating its robustness with three pollutants of varying volatility: arsenic (As, non-volatile), benzo(a)pyrene (BaP, semi-volatile), and benzene (Ben, volatile). Leave-one-out cross-validation demonstrates that the ASI-GCN_RC_G model (ASI-GCN with residual connections) achieves the highest prediction accuracy. Specifically, the R for As, BaP, and Ben are 0.728, 0.825, and 0.781, respectively, outperforming traditional models by 58.8% (vs. IDW), 45.82% (vs. OK), and 53.78% (vs. IDW). Meanwhile, their RMSE drop by 36.56% (vs. Bayesian_K), 38.02% (vs. Bayesian_K), and 35.96% (vs. IDW), further confirming the model’s superior precision. Beyond accuracy, Monte Carlo uncertainty analysis reveals that most predicted areas exhibit low uncertainty, with only a few high-pollution hotspots exhibiting relatively high uncertainty. Further analysis revealed the significant influence of pollutant volatility on vertical migration patterns. Non-volatile As was primarily distributed in the fill and silty sand layers, and semi-volatile BaP concentrated in the silty sand layer. At the same time, volatile Ben was predominantly found in the clay and fine sand layers. By integrating spatial autocorrelation with deep graph representation, ASI-GCN redefines sparse data 3D mapping, offering a transformative tool for precise environmental governance and human health assessment. Full article