Search Results (349)

Microfiltration membranes are widely used in drinking water treatment due to their high efficiency. However, long-term operation of polymeric membranes may lead to deterioration of hydraulic properties as a result of fouling and material aging. This study aims to determine the impact of long-term aging on hydraulic permeability and separation properties, and to determine the lifespan of microfiltration polyvinylidene fluoride (PVDF) membranes. The practical and industrial novelty of this study lies in providing an authentic, 11-year operational baseline for a full-scale microfiltration system treating highly variable surface water. The study evaluates membranes installed in a full-scale plant in Jarosław (Poland), treating surface water from the San River. The system includes 120 PVDF capillary modules (0.1 μm). After 11 years, the membranes maintained very high separation efficiency, ensuring almost complete removal of turbidity and microorganisms. However, membrane resistance increased nearly threefold, while permeability decreased by about 86%. Maintaining capacity required a gradual increase in transmembrane pressure. The permeability loss exceeded the commonly accepted replacement threshold of 70%, suggesting that membrane replacement after more than a decade of operation is technically and economically justified. Full article

Background: Viral vectors are indispensable tools in gene therapy and neural circuit mapping, offering promising therapeutic strategies for diverse genetic diseases and advancing neuroscience research. To achieve high transduction efficiency while mitigating impurity-induced immunogenicity, the development of viral vectors with improved purity and quality is essential. However, this critical requirement is often unmet by conventional purification methods such as ultracentrifugation, which are time-consuming and frequently result in limited product purity. The pseudorabies virus (PRV) is extensively employed as a viral tool for mapping neural circuits, where improved purity contributes to enhanced accuracy of neural tracing. PRV531 is a retrograde trans-synaptic tracer modified from the PRV Bartha strain, specifically designed to facilitate the precise visualization of hierarchical neural networks. Methods: In this study, we developed a method for the concentration and purification of PRV531 by integrating hollow fiber ultrafiltration (HF) with Capto^TM Core 700 (CC700) chromatography. Initially, to concentrate the viral supernatant, a 500 kDa HF membrane was employed, maintaining a feed flow rate of 80 mL/min, a shear rate ranging from 2000 to 6000 s⁻¹, and a transmembrane pressure (TMP) between 0.5 and 1 bar. Following concentration, the virus underwent purification through CC700 chromatography, operating at linear flow rates ranging from 100 to 300 cm/h. Results: Sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) revealed distinct bands consistent with the expected sizes of major PRV structural proteins, each with molecular weights ranging from 25 kDa to 150 kDa, concurrently demonstrating a substantial reduction in host cell proteins (HCPs) contamination. The purified PRV531 achieved a high final infectious titer of 3.55 × 10⁹ PFU/mL, with an overall functional virus recovery of 8.88% from the crude supernatant to the final product. Conclusion: These data demonstrate that TFF combined with CC700 resin can efficiently purify retrograde trans-synaptic PRV tracer. Furthermore, this approach provides a promising strategy for purifying other viral-based tracers that traditionally rely on conventional centrifugation methods. Full article

Background: Cystic fibrosis (CF) is a systemic genetic disorder characterized by chronic inflammation, hypoxia, and metabolic imbalance that may affect neural and microvascular structures. While previous studies have evaluated superficial optic nerve head (ONH) parameters in CF, deep ONH structures, particularly the lamina cribrosa (LC), remain insufficiently explored. This study aimed to assess both superficial and deep ONH morphology in adults with CF using swept-source optical coherence tomography (SS-OCT). Methods: This observational case–control study included 34 CF individuals and 34 age- and sex-matched healthy controls. CF patients were examined at baseline and after 12–18 months of cystic fibrosis transmembrane conductance regulator (CFTR) modulator therapy. All participants underwent comprehensive ophthalmic examination and SS-OCT imaging. Assessed parameters included peripapillary retinal nerve fiber layer (RNFL) thickness, lamina cribrosa thickness (LCT), and lamina cribrosa depth (LCD). Between-group comparisons were performed using ANCOVA adjusted for axial length (AL) and IOP. Results: After adjustment for AL and IOP, total RNFL thickness did not differ significantly between the CF group and controls (F(1,92) = 0.363, p = 0.548). However, CF patients demonstrated significantly reduced central LCT (170.2 µm [95% CI 162.2–178.2] vs. 214.6 µm [95% CI 208.6–220.7]; F(1,91) = 72.205, p < 0.001) without markedly altered LCD (425.5 µm [95% CI 390.4–454.2] vs. 354.7 µm [95% CI 329.6–379.8]; F(1,94) = 10.090, p = 0.099) compared with controls. Intraocular pressure was also higher in CF patients (17.50 mmHg [95% CI 16.60–18.41] vs. 15.87 mmHg [95% CI 15.19–16.55]; F(1,83) = 7.660, p = 0.007). Longitudinally, total RNFL thickness decreased from 106.5 µm (IQR 15.25) to 102.0 µm (IQR 15.5) following therapy (z = 3.488, p < 0.001), while LCT (p = 0.364) and LCD (p = 0.660) remained stable. Conclusions: CF is associated with significant alterations in deep ONH structures, characterized by thinner LC, independent of ocular biometry. In contrast, superficial RNFL differences appear to be largely influenced by AL. LC parameters, particularly LCT, may represent potential structural markers of systemic involvement in CF, pending further validation. Full article

Wastewater membrane bioreactors (MBRs) have become an important advanced treatment technology due to their ability to produce high-quality effluent suitable for discharge and water reuse. However, their broader and more sustainable application remains constrained by membrane fouling, elevated energy demand, and the operational complexity of coupled biological and membrane separation processes. This comprehensive review critically evaluates the growing application of machine learning (ML), explainable artificial intelligence (XAI), and digital twin (DT) technologies in MBR systems. Published studies on fouling prediction, energy optimization, effluent quality estimation, and intelligent operational support are critically evaluated, with explicit attention to model performance, dataset limitations, and generalizability. The reviewed literature shows that ML models, particularly ensemble methods, support vector machines, and deep learning approaches, have demonstrated strong potential for predicting major MBR performance indicators, including transmembrane pressure, permeate flux, fouling resistance, and selected effluent-quality variables. In parallel, XAI methods such as SHAP, LIME, and Anchors are increasingly being used to enhance model transparency and to reveal the dominant factors controlling process performance. Digital twin frameworks further extend this potential by enabling the integration of mechanistic understanding, online sensor data, data-driven prediction, and interpretable decision support within real-time operational platforms. Nevertheless, several barriers continue to hinder practical implementation, including the limited number of full-scale studies, the scarcity of openly accessible and standardized datasets, insufficient consideration of uncertainty and model drift, and the early-stage maturity of DT deployment in operational plants. The evidence reviewed suggests that integrating ML, XAI, and DT can substantially improve the reliability, interpretability, and operational efficiency of MBR systems. Future research should therefore focus on full-scale validation, the development of benchmark datasets, uncertainty-aware modeling, and practical deployment strategies for interpretable intelligent MBR management. Full article

Antimicrobial resistance genes threaten the effective treatment of infectious diseases, underscoring the importance of their control in line with the EU One Health policy. Wastewater treatment plants are recognized hotspots for antimicrobial resistance. We assessed whether multi-channel mixed-matrix membranes (MCMMMs)—polyethersulfone (PES)/polyvinylpyrrolidone (PVP) ultrafiltration membranes with embedded activated carbon—can concurrently reduce microorganisms and extracellular DNA in wastewater effluent, building on prior reports of micropollutant removal. We evaluated the performance of MCMMMs in removing Escherichia coli and Saccharomyces cerevisiae as model organisms, as well as colony-forming units (CFUs) from wastewater effluent at a transmembrane pressure of 1 bar with a filtration area of 66 cm² over 1 h. DNA was extracted from wastewater effluent following filtration and analyzed to assess changes in microbial community composition. MCMMMs achieved log₁₀ reductions of 5.47 ± 0.42 (Escherichia coli), 5.99 ± 0.46 (Saccharomyces cerevisiae), and 2.79 ± 0.31 (wastewater CFU); reductions by pure PES/PVP membranes were comparable: higher for Escherichia coli and wastewater CFUs, lower for Saccharomyces cerevisiae. Amplicon sequencing showed altered relative abundances in wastewater effluent. Collectively, these findings demonstrate the potential of MCMMMs to simultaneously remove microorganisms, extracellular DNA, and micropollutants, highlighting their suitability for water treatment applications within the One Health framework. Full article

The oil and gas industry is increasingly challenged by the global transition toward renewable energy systems aimed at reducing carbon emissions. Nevertheless, opportunities remain to mitigate the environmental impacts associated with ongoing oil and gas operations. One of the major environmental challenges in this sector is the extensive use and treatment of water. Membrane-based separation has emerged as an effective technology for oil–water separation due to its ability to overcome limitations associated with conventional treatment methods. This study aims to build a CFD model to investigates the influence of operational hydrodynamic conditions on membrane separation, including transmembrane pressure 202, 101, 50, 10 kPa, crossflow velocity 0.08 m/s, 0.116 m/s, 0.33 m/s, 0.66 m/s, and oil droplet diameter 1, 5, 10, 50, 100 µm, on membrane performance in addition to different oil concentrations 1%, 2%, 4%, 8% using Eulerian-Eulerian multiphase model. This is done by experimentally extracting the membrane water resistance, which is found to be 6.46 × 10¹⁰ (1/m) and using it as an input to the numerical model. The results indicate that permeate flux is primarily governed by transmembrane pressure, in agreement with Darcy’s law, while fouling development along the membrane length is mainly influenced by crossflow velocity and oil droplet size. Where it was found that for large droplets 100 µm and 50 µm the buoyancy forces were large enough to lift the oil droplets away from the membrane at velocities 0.08, 0.16 and 0.33 m/s while smaller droplets remained at the membrane surface In addition, backward diffusion, which has been emphasized in previous studies, was found to play a comparatively minor role in the present numerical analysis. Full article

Sulfamethoxazole (SMX) is one of the most frequently detected antibiotics in aquatic environments and is difficult to remove by conventional biological treatment because of its persistence, potential toxicity to microbial communities, and associated risk of antibiotic resistance selection. Aerobic granular sludge membrane bioreactors (AGMBRs), which combine the compact and stratified structure of aerobic granular sludge with membrane-based solid–liquid separation, have emerged as a promising platform for SMX-contaminated wastewater treatment because they provide high biomass retention, decoupled sludge retention time (SRT) and hydraulic retention time (HRT), and stable effluent quality. This review systematically summarizes recent advances in AGMBRs for SMX removal, with emphasis on how operating parameters (e.g., dissolved oxygen, hydraulic retention time, organic loading rate, C/N ratio, and sludge retention time) and membrane-related factors (e.g., membrane flux, aeration-induced shear, membrane type, and pore size) affect treatment performance and process stability. The main SMX attenuation pathways in AGMBRs are discussed from three perspectives: sorption and partitioning within granules and extracellular polymeric substances (EPSs), microbial biodegradation and co-metabolism, and membrane retention that prolongs effective contact time and shapes microbial ecology. Particular attention is given to the dual role of EPS and soluble microbial products (SMPs), which contribute to granule stability and SMX tolerance but also accelerate membrane fouling through cake-layer formation, pore blocking, and transmembrane pressure increase. Current challenges include incomplete understanding of transformation products, ARG- and MGE-related risks, long-term fouling–biodegradation interactions, and the lack of pilot-scale validation. Future research should therefore focus on mechanism clarification, integrated control of removal and fouling, energy-efficient operation, and scale-up of AGMBRs for practical antibiotic wastewater treatment. Full article

The tomato leaf miner, Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae), poses a significant threat to global tomato production. However, environmentally sustainable management strategies for this pest, as well as its mechanisms of insecticide resistance, remain insufficiently understood. Broflanilide, a novel meta-diamide compound, can bind specifically to the transmembrane domain of the RDL subunit, causing prolonged opening of the chloride channel, disruption of neurotransmission, and ultimately insect paralysis and death. This study employed the leaf immersion method to conduct bioassays on the second-instar larvae of T. absoluta to evaluate physiological responses to sublethal concentrations of the novel amide insecticide broflanilide. Subsequently, high-throughput transcriptome sequencing was performed to investigate changes in gene expression and metabolic pathways. Bioassay results determined the larval sublethal concentrations of broflanilide to be 0.136 mg/L (LC₁₀) and 0.210 mg/L (LC₃₀). Sublethal exposure significantly prolonged the larval period, reduced pupal weight, and inhibited fecundity of female adults. Transcriptomic and qPCR analyses revealed that, compared with the control (CK), expression of the vitellogenin gene Vg decreased by 15.99% and 30.27% under LC₁₀ and LC₃₀ treatments, respectively, while its receptor gene VgR decreased by 11.56% and 24.49%. Similarly, expression of chitin synthase genes chs1 and chs2 declined by 13.56% and 30.17% (chs1), and 7.85% and 19.45% (chs2), respectively. Gene expression analysis elucidated how sublethal insecticides treatment impact larval development and fecundity. Furthermore, the study revealed upregulation of cytochrome P450-mediated detoxification pathways and Toll/Imd immune signaling pathways under broflanilide stress, indicating activation of a coordinated defense response in T. absoluta. Sublethal broflanilide exposure modulated larval gene expression to balance growth, development, and stress adaptation. Such exposure exerts selective pressure on susceptible populations, potentially driving adaptive shifts in detoxification metabolism and contributing to the development of field resistance. These findings advance our understanding of the sublethal effects of novel insecticides and provide valuable insights for insecticide deployment strategies and resistance management. Full article

Transmembrane pressure (TMP) is a central state variable in haemodialysis (HD) and haemodiafiltration (HDF), governing ultrafiltration dynamics, convective transport, and membrane performance. Although dialysis devices specify high maximum allowable pressure limits derived from in vitro testing and mechanical safety margins, the effective operating pressure space encountered under routine clinical conditions remains insufficiently quantified from a systems engineering perspective. In this study, aggregated real-world minimum–maximum TMP intervals collected from four geographically distributed dialysis centres were used to anchor a model-based characterisation of operational pressure ranges. To enable reproducible modelling and numerical exploration, Gaussian-based synthetic datasets were constructed from empirically observed pressure intervals while incorporating physiological and operational constraints. Across all centres, HD exhibited stable and narrowly distributed TMP values (typically 20–60 mmHg), whereas HDF operated within higher but well-defined pressure regimes (approximately 120–260 mmHg). Values above 300 mmHg were rare, and pressures exceeding 400 mmHg were not observed under routine conditions. Statistical tail modelling, extreme value theory, and unsupervised anomaly detection consistently identified such extreme pressures as structurally incompatible with the learned operational state space. These results provide quantitative engineering bounds for TMP that may be directly integrated into reduced-order models, control design, and digital twin development for dialysis systems. By constraining modelling environments to empirically supported pressure regimes, the proposed framework enhances numerical stability, prevents non-physical extrapolation, and supports physiologically realistic data-driven applications in biomedical engineering. Full article

This study employs an integrated modeling approach to elucidate the mechanisms of nitrate ion transport through nanofiltration (NF) and reverse osmosis (RO) membranes. The investigation first applied models from irreversible thermodynamics, specifically the Kedem–Katchalsky and Spiegler–Kedem models, to describe convective/diffusive contributions and the impact of the initial nitrate concentration (50–150 mg/L) on phenomenological parameters (reflection coefficient σ, and solute permeability P_s). The results revealed a marked sensitivity of NF membranes to the initial nitrate concentration, in contrast to the stable performance of RO membranes. To deepen this analysis, Response Surface Methodology (RSM) was used as a robust statistical tool to systematically model and quantify the synergistic effects of the initial concentration and other key operational parameters, transmembrane pressure (TMP) and recovery rate (Y) on NF performance. The results highlight the complementarity between transport modelling and statistical approaches for analysing nitrate rejection and permeate flux. The proposed approach provides useful insight into NF membrane-specific behaviour and relative sensitivity to operating conditions, within the scope and limitations of the studied membrane and experimental configurations. Full article

Membrane fouling remains a major obstacle to the performance of the reverse osmosis (RO) desalination processes. Artificial intelligence (AI) is now a promising approach for the reliable modeling of these complex systems. This study evaluates three modeling techniques—multiple linear regression (MLR), artificial neural networks (ANNs), and support vector regression (SVR)—for predicting transmembrane pressure (TMP) at the Boujdour desalination plant, based on five input parameters: temperature, turbidity, pH, conductivity, and feedflow. The analysis is based on an original dataset of 195 daily measurements, and due to the absence of timestamps, the study focuses on state-to-TMP prediction rather than chronological forecasting, with no temporal generalization claimed. Approximately 2000 augmented training samples generated using a conservative SMOGN approach were used for model development, while performance evaluation relied exclusively on 39 independent real test observations. Two modeling strategies were adopted: (i) a minimalist approach based on significant variables identified by an ordinary least squares (OLS) model (pH and conductivity), and (ii) a multivariate approach integrating all parameters to capture non-linear interactions. A rigorous validation framework was put in place to avoid information leakage and ensure the robustness and generalizability of the models. Performance was evaluated using R², RMSE, and MAE metrics, supplemented by robustness and significance analyses including bootstrap confidence intervals, paired statistical comparisons, and interpretability analyses based on permutation importance, partial dependence plots (PDPs), and individual conditional expectation (ICE) curves. The results indicate that the SVR model achieves the best average predictive accuracy among the tested models, albeit with moderate explanatory power. Full article

The demand for effective downstream processing of adeno-associated virus (AAV) is increasing as gene therapies advance toward broader clinical applications. Robust, efficient, and scalable ultrafiltration and diafiltration (UF|DF) operations are essential for generating high-quality AAV preparations, with tangential flow filtration (TFF) serving as a critical unit operation for vector concentration, impurity reduction, and buffer exchange while maintaining viral functionality. Development of TFF processes requires careful consideration of membrane characteristics—including chemistry, pore size or channel architecture—as these parameters directly influence vector retention, fouling behavior, and overall process efficiency. Equally important is the optimization of critical process parameters such as recirculation rate, transmembrane pressure (TMP), and total processing time, all of which govern hydrodynamic performance and product quality. This study assessed two Sartocon^® Hydrosart^® TFF cassette architectures—ECO-Screen and E-Screen—for the ultrafiltration and diafiltration of AAV8 clarified lysate. Through flux characterization and controlled small-scale evaluations, cassette-specific operating regions were defined. Both configurations supported high viral genome retention; however, the E-Screen geometry achieved faster processing and superior removal of host–cell protein and DNA contaminants, whereas the ECO-Screen format allowed for efficient operation under reduced pump rates and, therefore, lower shear conditions. Reproducibility assessments demonstrated minimal run-to-run variability, confirming the robustness of the optimized operating parameters. A 10-fold scale-up further validated the linearity and predictability of the UF|DF process, with consistent impurity-reduction profiles and only modest deviations in viral recovery. Collectively, these findings provide a quantitative basis for rational cassette selection in AAV purification workflows and establish a scalable, scientifically grounded UF|DF framework applicable across development and manufacturing scales. Full article

The MERS-CoV (Middle East respiratory syndrome coronavirus) is a zoonotic virus with a high mortality rate and a lack of antiviral drugs, underscoring the need for effective therapeutic methods. Viral entry depends on interactions between viral surface proteins and human receptors, with Dipeptidyl Peptidase-4 (DPP4), a transmembrane glycoprotein, acting as the receptor for MERS-CoV. We employed Molecular Dynamics (MD) Simulations to identify critical interface residues under a high-performance computing (HPC) workflow for accelerated results. Target residue pairs were identified through analysis of salt bridge and hydrogen bond occupancy. The stability of these residues was confirmed through three independent MD Simulations at human body temperature and constant pressure. Additionally, binding affinity predictions were calculated to determine the interaction strength between the virus and human receptors. Applying the scientific threshold criteria, we narrowed our results to seven key interaction pairs; two of the identified pairs (Asp510-Arg317, and Arg511-Asp393) are consistent with findings published in previous research studies, and five novel interactions are proposed for future experimental studies with our active collaborators in Pharmacology. The results provide a molecular basis for targeted mutation-based experiments and support the rational design of structure-based inhibitors aimed at disrupting the MERS-CoV-DPP4 complex, thereby facilitating the translation of computational findings into antiviral drug discovery. Full article

Lagerstroemia suprareticulata, an endemic ornamental species in limestone karst ecosystems of Guangxi—a global biodiversity hotspot—holds significant ecological value. However, habitat degradation and anthropogenic pressures have driven this species to the brink of extinction, leading to its classification as Endangered (EN) on the China Biodiversity Red List. To address this crisis, we conducted whole-genome resequencing to generate single-nucleotide polymorphisms (SNPs) for comprehensive analyses of genetic diversity, population structure, demographic history, and adaptive variation. Our results reveal four distinct genetic clusters in L. suprareticulata, all of which share a history of population expansion followed by contraction. Maximum entropy modeling (MaxEnt) projects a severe contraction in the range under high-carbon-emission scenarios. Selective sweep analysis identified genomic regions under positive selection, including those associated with protein homeostasis, metabolism, signal transduction, and developmental regulation. Genotype–environment association (GEA) analysis further identified adaptive SNPs linked to temperature and precipitation, which were enriched in genes regulating transmembrane transport, stress response, and the immune system. Additionally, risk of non-adaptedness (RONA) analysis identified high-risk populations. By integrating genomic data with advanced analytical approaches, this study enhances our understanding of the adaptive mechanisms of L. suprareticulata to limestone karst habitats and provides critical insights for its conservation. Full article

It is promising to in situ retrofit the activated sludge process with a membrane bioreactor (MBR) to increase treatment capacity and improve effluent quality in a textile dyeing wastewater treatment plant (WWTP). Membrane selection among commercial products for real engineering applications is critical for this specific wastewater, and little information is available in the literature. This study systematically evaluated the application potential of two flat-sheet microfiltration membranes made of polyvinylidene fluoride (PVDF) and polyether sulfone (PES) in pilot-scale MBRs for in situ retrofitting textile dyeing WWTP. During the four stages with different loads, both membranes achieved nearly the same effluent quality and rejection performance. Both membranes showed little trans-membrane pressure (TMP) increase at an average flux of 15 L/(m²·h) with sub-critical flux characteristics, and showed a sharp TMP increase with super-critical flux characteristics observed at an average flux of 18/22.5 L/(m²·h). After 74 d of filtration, at an average sludge concentration of 12,000 g/L, the PVDF membrane showed less variation in pore size distribution and bubble point pressure, while the PES membrane showed less change in permeability and contact angle. Both membranes met general MBR requirements due to the minimizing pristine effects of both membranes by this specific wastewater matrix. The PVDF membrane showed better anti-fouling capability, especially during high-/over-load stages, and thus was suggested for MBR retrofit, with a sustainable membrane flux below 18 L/(m²·h). Full article