Search Results (19,950)

The aim of this study was to investigate the potential for co-bioleaching of ground printed circuit boards (PCBs) and flotation tailings using a single-stage biohydrometallurgical process. The ground PCB sample was a finely divided waste product from industrial shredding, which was collected using an air filtration system. The flotation tailings sample was mainly composed of pyrite (49%), quartz (29%), gypsum (8%), feldspar (8%), and chlorite (6%). The experiment was carried out in laboratory-scale reactors at 35 °C with constant aeration and a flotation tailings pulp density of 5% (solid-to-liquid ratio). In a control reactor, only flotation tailings were leached. In an experimental reactor, both flotation tailings and ground PCBs were leached simultaneously. The experiment was conducted in two stages. In the first stage, the experiment was carried out in a batch mode. The second stage involved two reactors operating continuously in cascade. During the experiment, we monitored the dynamics of several key parameters as a function of PCB concentration, including pH, redox potential, the concentrations of Fe³⁺ and Fe²⁺ ions, and the number of microbial cells. The 16S rRNA gene analysis revealed that the presence of PCBs had a significant effect on the composition of the microbial community. The concentration of PCB was gradually increased in order to examine the limits of the process and optimize potential economic benefits. The increase was done in 3 stages: 5 g/L in the first stage, from 5 to 12 g/L in the second stage, and up to 35.5 g/L in the third stage. However, this increase had a negative effect on the pyrite oxidation rate and the effectiveness of PCB bioleaching in continuous mode. The bioleaching efficiency of copper from printed circuit boards (PCBs) was above 70% in batch mode and above 80% in continuous mode at PCB concentrations up to 12 g per liter. Copper recovery decreased to around 53.1–61.6% as the PCB concentration continued to increase. The nickel leaching efficiency in batch mode was 46.3 ± 4.8%. In continuous mode, the nickel recovery decreased as the PCB concentration increased, reaching 48.53% in the first stage, then declining to 37.62% in the second stage and finally dropping to 27.06% in the third stage, depending on the higher concentration of PCB. Full article

The accumulation of copper smelting slags generated by non-ferrous metallurgy represents both an environmental challenge and a potential source of technogenic raw materials for value-added products. In this study, the feasibility of producing magnesia–quartz proppants from secondary copper smelting slag formed after the pyrometallurgical extraction of iron and zinc was investigated. The slag, primarily composed of oxides of the SiO₂–CaO–Al₂O₃–MgO system, was processed by centrifugal melt granulation to obtain spherical granules suitable for proppant applications. The initial granules exhibited an amorphous glassy structure and insufficient mechanical strength, with up to 70% of particles destroyed under a pressure of 34.5 MPa. Controlled heat treatment within the temperature range of 300–1000 °C induced crystallization of silicate and aluminosilicate phases, leading to a significant improvement in mechanical performance. Optimal properties were achieved after holding at 800 °C for 60 min, where the fraction of crushed granules decreased to 10%, meeting the requirements of GOST R 54571-2011. The influence of MgO content on microstructure and strength was also examined. Increasing the MgO concentration from 5 to 16 wt.% resulted in grain refinement and improved crushing resistance, reducing the fraction of destroyed granules to 3%. To enhance chemical durability, a phenol–formaldehyde protective coating was applied, decreasing proppant solubility in a hydrochloric–hydrofluoric acid mixture from 19% to 2%. These results demonstrate that secondary copper smelting slag can serve as a promising raw material for producing standard-compliant proppants while contributing to the efficient utilization of metallurgical waste. Full article

Dredged Fill Soil, as a primary foundation material in reclamation projects, exhibits complex physical and mechanical properties, characterized by a high plasticity index, high water content, low density, high compressibility, large void ratio, and low bearing capacity. Its creep behavior is highly sensitive to temperature changes. This study systematically investigates the temperature-dependent creep behavior of reclaimed soil from Humen Port through laboratory experiments, theoretical modeling, and experimental validation. Triaxial creep tests conducted at different temperatures (5 °C, 15 °C, 25 °C, 35 °C) show that increasing temperature significantly exacerbates creep deformation: under undrained conditions, creep strain at 35 °C is nearly 300% higher than at 5 °C, while drainage reduces the strain by approximately 29.3%. Based on these results, a Burgers-type creep constitutive model considering temperature effects is developed, incorporating the impact of temperature on viscosity and elastic modulus. The model’s predictions show good agreement with the experimental results (15 °C: R² = 0.9788; 35 °C: R² = 0.9890), confirming the model’s validity. The research findings provide theoretical and practical references for the long-term stability evaluation and engineering design of reclaimed foundations in complex marine environments. Full article

Aiming at the poor robustness of maintenance schemes in industrial equipment maintenance projects, which arises from uncertain factors including fault degree, maintenance time, and resource availability, this paper proposes a synergistic cost-risk optimization method that integrates Distributionally Robust Optimization (DRO) and Conditional Value-at-Risk (CVaR). First, the paper analyzes the uncertainty characteristics of such projects and constructs a distribution ambiguity set based on the Wasserstein distance to depict unknown probability distributions. Second, a two-stage DRO-CVaR optimization model is established: the first stage formulates a pre-optimization scheme to minimize maintenance costs, and the second stage introduces CVaR for extreme risk measurement, thus achieving optimal decision-making under the worst-case scenario. Finally, a nested Column-and-Constraint Generation (C&CG) algorithm is designed to solve the proposed model. A numerical example is conducted for verification, and results show that compared with traditional stochastic programming and pure DRO methods, the proposed method reduces the total cost by 10.4%, the worst-case scenario loss by 28.9%, and the CVaR value by 32.0%. It thus exhibits superior economic efficiency and risk resistance in uncertain environments. Full article

Mycoplasma genitalium is a significant sexually transmitted pathogen, and its clinical management is increasingly complicated by the global distribution of mutations associated with macrolide and fluoroquinolone resistance. To characterize the molecular resistance landscape in a routine diagnostic setting, we retrospectively analyzed residual clinical specimens collected during routine sexually transmitted infection testing between January and December 2024. Among 374,021 specimens screened, we included 4019 M. genitalium-positive samples containing sufficient residual material. Using multiplex polymerase chain reaction assays, we detected mutations associated with macrolide and fluoroquinolone resistance in the 23S rRNA and parC genes, respectively. Frequent substitutions included A2059G and A2058G in the 23S rRNA gene (1253 samples) and substitutions at positions 248 and 259 in the parC gene (1306 samples). Mutations in the predefined 23S rRNA and/or parC targets were identified in approximately 44% of the analyzed samples, with distinct patterns of mutation distribution and co-occurrence. Although phenotypic susceptibility and clinical outcomes were not assessed, this large-scale, assay-based analysis provides a comprehensive overview of resistance-associated mutation patterns in M. genitalium derived from routine diagnostics, supporting molecular surveillance for monitoring antimicrobial resistance trends. Full article

In situ stope leaching is an economically and environmentally friendly metal recovery method suitable for low-grade copper ores, with the internal temperature of the deposit typically ranging from 30 to 45 °C. The fragmented ore with a specific particle size distribution formed after blasting constitutes a complex pore structure, which provides channels for acid solution infiltration and chemical reactions, directly affecting leaching efficiency. To reveal the spatiotemporal heterogeneity of pore structure evolution during leaching at the microscopic level and its fundamental impact on macroscopic permeability and leaching rate, leaching experiments were conducted using acid leaching methods based on ore particle models with different size distributions. Computed Tomography (CT) scanning technology and Avizo 2023 software were employed to scan and reconstruct three-dimensional physical models, enabling quantitative calculation and analysis of the evolutionary patterns of pore structure parameters. These results were then correlated with the measured leaching rate evolution. The findings indicate that both the connectivity and overall volumetric porosity of the stope models for Sample 1 (2–20 mm, uniformly graded) and Sample 2 (0–20 mm, high fine particle content) continuously decreased during leaching, with a more pronounced decline in the lower regions, particularly for Sample 2. The pore-throat sizes of both models increased with leaching time, and after 45 days of leaching, the average pore radius of the two granular ore samples increased by 16.75% and 9.21%, respectively. The leaching rate showed a high correlation with the effective reaction area (R² = 0.93). During the 0–15-day period, a sharp decline in the effective reaction area led to a rapid decrease in leaching efficiency. Sample 1 exhibited a longer effective leaching duration, achieving a leaching rate of 61%, significantly higher than that of Sample 2. Full article

Background: Mas-related G-protein-coupled receptor b4 (Mrgprb4)-lineage neurons in the peripheral nervous system are a type of C fibers in hairy skin. Our prior work demonstrated that these neurons respond to both noxious and innocuous mechanical and thermal stimuli. Ablating them eliminates the pleasant sensation elicited by gentle pressure on a mouse’s nape. However, their potential role in mitigating pain and pain-related negative emotions in response to somatic stimuli remains unclear. Methods: A CFA-induced chronic pain and anxiety comorbidity model was established in C57BL/6J mice. In vivo calcium imaging of dorsal root ganglia (DRG) neurons in Mrgprb4-GCaMP6s transgenic mice characterized neuronal responses to transcutaneous electrical nerve stimulation (TENS) at the Zusanli (ST36) acupoint. Optogenetic activation (Mrgprb4-ChR2 mice) and viral ablation of Mrgprb4-lineage neurons were employed to evaluate their role in mediating TENS effects on mechanical pain thresholds and anxiety-like behaviors. Results: In vivo calcium imaging revealed that 0.5 mA TENS preferentially activated Mrgprb4-lineage neurons compared to 2.0 mA TENS. In CFA model mice, 0.5 mA TENS at ST36 significantly increased mechanical pain thresholds and reduced anxiety-like behaviors in the open-field test. Optogenetic activation of Mrgprb4-lineage neurons at ST36 replicated these analgesic and anxiolytic effects, demonstrating the sufficiency of these neurons for therapeutic outcomes. Conversely, viral ablation of L3–L5 Mrgprb4-lineage neurons substantially attenuated the therapeutic effects of 0.5 mA TENS for both pain relief and anxiety reduction, indicating their necessity in mediating TENS efficacy. Conclusions: Mrgprb4-lineage neurons serve as critical peripheral mediators of TENS-induced analgesia and anxiolysis. These findings identify a specific neuronal population underlying the therapeutic effects of somatic stimulation at ST36, providing mechanistic insights that may guide optimization of TENS parameters for treating chronic pain and comorbid anxiety in clinical settings. Full article

Background/Objectives: Carbapenem-resistant Acinetobacter baumannii poses a critical threat due to its ability to acquire multiple resistance mechanisms and persist under antibiotic pressure. This study aimed to elucidate the molecular basis of imipenem resistance in clinical A. baumannii isolates by integrating phenotypic, molecular, transcriptional, and clonal analyses. Methods: Eleven A. baumannii isolates identified by MALDI-TOF MS (matrix-assisted laser desorption ionization time-of-flight mass spectrometry) were investigated. Antimicrobial susceptibility to imipenem and meropenem was assessed, followed by polymerase chain reaction (PCR) detection of Ade efflux pump, outer membrane porin, and OXA-type carbapenemase genes. Transcriptional responses to sub-inhibitory imipenem exposure were evaluated using quantitative real-time PCR, and clonal relatedness was assessed by arbitrarily primed PCR. Results: All isolates were carbapenem-resistant, with bla_OXA-23 detected in all isolates and bla_OXA-24 absent in one isolate. Transcriptional analysis revealed isolate-specific responses to imipenem exposure. Among Ade efflux pump components, only adeR exhibited expression changes, displaying either downregulation or upregulation depending on the isolate, whereas adeA, adeB, adeC, and adeS transcripts were not detected under the tested conditions. Outer membrane porin genes showed heterogeneous regulation, with ompA and carO downregulated, while some isolates showed increased expression. Expression of oprD varied among isolates, and omp33–36 transcripts were detected in a single isolate and were reduced after exposure. Clonal analysis identified nine distinct genotypes, indicating genetic diversity and the absence of clonal dominance. Conclusions: These findings highlight the multifactorial and heterogeneous nature of carbapenem resistance in A. baumannii, emphasizing the interplay between regulatory efflux mechanisms, porin modulation, and carbapenemase carriage. Full article

Regarding the precipitation behavior of Cu particles in steel, conventional studies have primarily focused on isothermal precipitation, which has limitations in characterizing precipitation kinetics under variable temperature conditions. For this purpose, in the present study, the Fe-3%Si-Cu alloy was selected as a model system to systematically investigate the regulation of Cu particle precipitation behavior and associated strengthening effects in a ferrite matrix during continuous heating—a process path that better aligns with practical conditions. The results indicate that, during the continuous heating process, an increase in the heating rate from 10 °C/h to 600 °C/h leads to a significant rise in the peak temperature, from 490.2 °C to 609.7 °C, while the time required to reach the peak temperature decreases substantially, from approximately 9.1 h to 19.6 min. Through TEM microstructure analysis and characterization, it is evident that rapid heating at 500 °C/h significantly promotes the high-density nucleation of B2 and 9R-Cu metastable phases while effectively suppressing particle coarsening. This results in a finely dispersed nano-Cu precipitate phase with an average particle size of 8.21 nm and a number density of 30.35 × 10¹⁰ cm⁻². Under the rapid heating condition of 500 °C/h, the precipitation strengthening contribution of Cu particles reaches 501.86 MPa, significantly higher than the 451.02 MPa observed under the slow heating condition of 50 °C/h. This study, from the perspective of the coupling effect between thermodynamics (driven by undercooling) and kinetics (governed by diffusion), elucidates the kinetic behavior of Cu particle precipitation during continuous heating. It provides a novel fundamental and strengthening theory in the field of ferrite metallurgy for copper-enriched electrical steels and related engineering steels, offering significant insights for further understanding the role of copper in ferrite-based steels. Full article

This work investigates the potential of wollastonite-based brushite cement (WBC) for the stabilization and solidification of radioactive waste contaminated by ⁹⁰Sr. This phosphate binder was formed by the reaction of wollastonite (CaSiO₃) with a phosphoric acid solution containing borax and metallic cations (Al³⁺, Zn²⁺). Two cement pastes were investigated: a commercial binder (WBC-C) and an optimized formulation (WBC-O), produced using a zinc-free mixing solution with a higher aluminum content than that of WBC-C. Mineralogical characterizations using XRD, TGA, XRF, SEM-EDX, and Raman spectroscopy showed that both materials mainly contained amorphous hydrated silica and calcium aluminophosphate, along with crystalline brushite, residual wollastonite, and quartz. The stability of WBC-C under γ-irradiation was evaluated up to a dose of 1 MGy. The only observable effect was water radiolysis, leading to dihydrogen production at yields comparable to Portland cement matrices and geopolymers. Strontium leaching, assessed using the ANSI/ANS-16.1-2003 (R2008) procedure, followed a two-stage release mechanism combining surface wash-off and diffusion. The apparent diffusion coefficient D_a of Sr in WBC-C was markedly lower than typical values reported for Portland cement matrices. WBC-O exhibited enhanced Sr retention, possibly due to its higher aluminum content, which refines mesopores and reduces diffusion pathways accessible to Sr. WBC binders therefore appear to be promising candidates for strontium immobilization. Full article

Impatiens hawkeri W. Bull (I. hawkeri) is popular among consumers due to its diverse flower colors and year-round blooming. However, changes in ecological conditions, cultivation methods, and planting scale have led to increased disease incidence and diversity, particularly the widespread and destructive leaf spot disease. Currently, studies addressing the pathogen species and its biological characteristics remain limited. In this study, a highly pathogenic strain (IH-4) was selected from previously isolated fungi associated with leaf spot in I. hawkeri. Its taxonomic status was confirmed using upright fluorescence microscope analysis, internal transcribed spacer (ITS)/large subunit (LSU)/RNA polymerase II second largest subunit (rpb2)/β-tubulin (tub2) rRNA gene sequencing, and phylogenetic tree construction. Additionally, the biological characteristics of the pathogen and its sensitivity to 8 chemical fungicides were assessed. Strain IH-4 was identified as Ectophoma multirostrata (E. multirostrata) through combined morphological and molecular approaches. Optimal growth conditions included a temperature of 25 °C, a pH of 7, Potato Dextrose Agar (PDA) medium, fructose as the optimal carbon source, and urea as the optimal nitrogen source, with the fastest growth observed under a semi-light photoperiod (12 h light/12 h dark). Fungicide sensitivity assays indicated that 25% azoxystrobin exhibited the lowest half-maximal effective concentration (EC₅₀, 0.0724 μg/mL) and the steepest virulence regression slope (1.7), demonstrating the strongest inhibitory activity and highest sensitivity. Microscopic observations revealed that IH-4 hyphae penetrate I. hawkeri leaf tissues via stomata, colonize internally, and consequently cause host damage. This study provides a theoretical foundation for the timely and effective management of leaf spot disease in I. hawkeri. Full article

Background: Artificial intelligence (AI) shows promising results in lymphoma detection, prediction, and classification. However, translating these findings into practice requires a rigorous assessment of potential biases, clinical utility, and further validation of research models. Objective: The goal of this study was to summarize existing studies on artificial intelligence models for the histopathological detection of lymphoma. Design: This study adhered to the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. A systematic search was conducted across three major databases (Scopus, PubMed, Web of Science) for English-language articles and reviews published between 2016 and 2025. Seven precise search queries were applied to identify relevant publications, accounting for variations in study modality, algorithmic architectures, and disease-specific terminology. Results: The search identified 612 records, of which 36 articles met the inclusion criteria. These studies presented 36 AI models, comprising 30 diagnostic and six prognostic applications, with Convolutional Neural Networks (CNNs) being the predominant architecture. Regarding data sources, 83% (30/36) of datasets utilized Hematoxylin and Eosin (H&E)-stained images, while the remainder relied on diverse modalities, including IHC-stained slides, bone marrow smears, and other tissue preparations. Studies predominantly utilized retrospective, private cohorts with sample sizes typically ranging from 50 to 400 patients; only a minority leveraged open-access repositories (e.g., Kaggle, TCGA). The primary application was slide-level multi-class classification, distinguishing between specific lymphoma subtypes and non-neoplastic controls. Beyond diagnosis, a subset of studies explored advanced prognostic tasks, such as predicting chemotherapy response and disease progression (e.g., in CLL), as well as automated biomarker quantification (c-MYC, BCL2, PD-L1). Reported diagnostic performance was generally high, with accuracy ranging from 60% to 100% (clustering around 90%) and AUC values spanning 0.70 to 0.99 (predominantly > 0.90). Conclusions: While AI models demonstrate high diagnostic accuracy, their translation into practice is limited by unstandardized protocols, morphological complexity, and the “black box” nature of algorithms. Critical issues regarding data provenance, image noise, and lack of representativeness raise risks of systematic bias, hence the need for rigorous validation in diverse clinical environments. Full article

Short-chain acyl-CoA dehydrogenase (SCAD) is a critical enzyme in mitochondrial fatty acid β-oxidation, catalyzing the initial dehydrogenation of short-chain acyl-CoAs. Mutations in the ACADS gene cause SCAD deficiency (SCADD), a disorder with remarkably heterogeneous clinical presentation. However, the molecular mechanisms underlying substrate specificity and the pathogenicity of most ACADS variants remain poorly understood. Here, we present high-resolution cryo-EM structures of human SCAD in complex with its physiological substrate butyryl-CoA (C₄) and the longer substrate hexanoyl-CoA (C₆). The butyryl-CoA-bound structure at 2.1 Å resolution details a pre-catalytic geometry ideal for hydride transfer, with Glu392 positioned as the catalytic base. We systematically characterized nineteen disease-associated mutations, which we classify into three functional categories: those disrupting FAD binding, those impairing substrate binding, and those compromising protein folding and stability. In addition, using the W177R mutant as a representative model, we demonstrate that folding-defective mutations provoke protein aggregation, leading to proteotoxicity, oxidative stress, and apoptosis, revealing a pathogenic mechanism beyond mere catalytic loss. In brief, our integrated findings elucidate the structural determinants of substrate specificity and catalytic mechanism in SCAD, and provide mechanistic insights into the functional impairments caused by mutations linked to SCADD. Full article

Given that freeze–thaw damage of prestressed concrete significantly threatens structural service life and that existing conventional simulation techniques fail to capture prestress time series, this paper proposes a deep learning prediction model based on the Transformer model. The model integrates a multi-head self-attention mechanism and positional encoding to effectively capture long-range dependencies in prestressed time series. It enhances temporal modeling capability through a 128-dimensional high-dimensional feature space (chosen to balance representation capacity and computational efficiency for the dataset scale) and a 4-layer encoder stacking structure. A dataset was constructed using time-series data from three prestressed concrete components subjected to 50 freeze–thaw cycles. The F-a component was used as the training set, while F-b and F-c served as the testing sets. During the training phase, a Noam learning rate scheduler, gradient clipping, and an early stopping strategy were employed. The results indicate that the training strategy enables the loss function to converge quickly without overfitting, demonstrating good generalization performance. The prediction model performs well on the F-a and F-c datasets, with determination coefficients (R²) of 0.8404 and 0.8425, and corresponding Mean Absolute Error (MAE) of 61.71 MPa and 57.41 MPa, respectively. It can accurately track the periodic variation trend of prestress, demonstrating the model’s effectiveness in prestress prediction. This model provides a new technical tool for the health monitoring and performance prediction of prestressed concrete structures in freeze–thaw environments. Full article

Implantable artificial kidneys represent a promising alternative for patients with end-stage renal disease (ESRD), aiming to overcome the limitations of conventional dialysis through the integration of microfluidic and electrokinetic technologies. In this study, we present a sawtooth electrode microfluidic chamber that achieves blood cell separation via negative dielectrophoresis at a record-low operating voltage of 1.4 V, representing a fivefold reduction compared with rectangular electrode designs and supporting potential integration into implantable artificial kidney systems. A microfluidic chip incorporating an asymmetric sawtooth electrode geometry was developed to enhance local electric field gradients while reducing power consumption. Device performance was investigated using COMSOL Multiphysics simulations. Response Surface Methodology (RSM) based on a Box–Behnken design was employed to optimize the number of teeth per unit length (N), sawtooth height (H), and applied voltage (V), while excitation frequency was fixed at 1 MHz and flow velocity was maintained constant at 0.1 µL·min⁻¹. Statistical analysis was conducted using analysis of variance (ANOVA) in Minitab (Version 27; Minitab, LLC, State College, PA, USA, 2024) . The optimization model showed strong predictive capability (R² = 95.8%) and identified applied voltage (59.45% contribution) and sawtooth height (33%) as the dominant factors affecting separation efficiency, with a significant H × V interaction (p = 0.023). Comprehensive voltage-response mapping over the range of 0.8–4.0 V revealed four operational regimes, including a previously unreported high-voltage failure zone above 2.8 V, where electrothermal flow and electroporation degrade performance. Under physiological conductivity conditions, the optimized design maintained a separation efficiency of 78.3% at 1.4 V with a tip temperature rise of only 1.2 °C, while full recovery of performance was achieved at 2.2 V. Cell-specific separation efficiencies reached 97.3% for white blood cells, 95.8% for red blood cells, and 84.7% for platelets, reducing the downstream cellular load by 92.6%. These findings demonstrate that the proposed low-voltage, high-efficiency separation platform has strong potential as a cellular pre-filtration module in implantable artificial kidney systems and other lab-on-chip biomedical devices. Full article