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Intelligent evaluation and feedback mechanism based on student behavior data in the context of integrated education
Development of a PCA-based climatic similarity index to enhance weather file selection criteria for climate-based daylight modelling simulations in tropical climates
Adipocyte-derived CRAMP–neutrophil serine protease interaction axis regulates innate cutaneous defense against Staphylococcus aureus
Designing of a conserved subunit multiepitope vaccine candidate against Francisella tularensis Schu S4 using immunoinformatics
Automated real-time feeding control for microbial electrolysis cell-anaerobic digestion systems using finite state machine
Abstract This study investigates the use of biosensor-led control in Microbial Electrolysis Cell-Anaerobic Digestion (MEC-AD) systems to enhance operational stability. Traditional methods depend on human operators to interpret data and adjust processes, whereas this research employed a current threshold-based Finite State Machine (FSM) for automated control in lab-scale, single-chamber MEC-AD reactors operated continuously for four months. By monitoring current draw as an indirect electrochemical proxy for microbial substrate-utilisation activity, the study facilitated real-time control of feeding events based on current responses to organic loading. Results show that, under the tested lab-scale conditions, this method enabled adjustment of feed volume and timing in response to changes in system conditions and microbial activity. Using an FSM provided a structured framework that links current responses to feeding events and defined system states, enabling predictable management of the MEC-AD process. Using molasses as feedstock, the research demonstrates effectiveness across reactors with varied hydraulic retention times at lab scale, indicating potential for further investigation into scalability and automation. This approach offers a promising alternative for optimising the performance of continuous operation AD systems, ensuring better control and lower risk of overload failures.
Hyperlipidemia induces hippocampal inflammation and loss of vascularity and can be rescued by silencing RIPK1
Automated segmentation of neurons and spinal cord structures in immunofluorescence images using SpineDL
Abstract In this study, we present SpineDL, an open-source deep learning (DL) approach for neuron and anatomical structure segmentation of the spinal cord in fluorescence images immunostained with NeuN and DAPI, within the context of murine models of spinal cord injury (SCI). SpineDL comprises two main modules: SpineDL-Neuron, for instance-level identification of neuronal somas; and SpineDL-Structure, for semantic segmentation of key spinal cord structures including gray matter, white matter, ependyma, and damaged tissue. To train the models, we developed the SpineDL dataset, a curated collection of 161 confocal images of mouse spinal cord, manually annotated by SCI researchers and organized into specific subsets. Both models are based on the HRNetV2-W64 architecture and were trained using state-of-the-art data augmentation and optimization techniques, implemented within the BiaPy framework, following an iterative refinement process driven by quantitative evaluation, SCI researcher feedback, and systematic error analysis. Our results demonstrate that SpineDL achieves researcher-level performance in both structural segmentation and neuron identification tasks, showing high robustness across anatomical regions and injury conditions. Overall, this work provides a reproducible and extensible platform for quantitative analysis of neuron distribution in the naïve and injured spinal cord, supporting automation, standardization, and scalability of histopathological workflows in neuroscience research and preclinical studies and translational applications.
Geospatial assessment of land use transformation and potential ecological vulnerability in Saharsa District, India
Perchlorate supported anaerobic growth in Haloferax volcanii reveals a novel metabolic capability with implications for biosignature degradation
Abstract Haloferax volcanii ( H. volcanii) is a facultatively anaerobic model halophilic archaeon capable of anaerobic growth using nitrate, chlorate, fumarate, trimethylamine N-oxide (TMAO), and dimethyl sulfoxide (DMSO) as alternative electron acceptors. H. volcanii has been previously documented to tolerate high concentrations of perchlorate during aerobic respiration, but has not been previously documented to grow anaerobically using perchlorate as an alternative electron acceptor. Here, we document the novel metabolic capability of H. volcanii to grow anaerobically using perchlorate and show the initial preferred conditions with respect to NaCl concentration, pH, carbon sources, and perchlorate concentration. Additionally, we investigate changes in carotenoid composition during anaerobic growth on perchlorate with relevance for the search for signs of extinct and extant life on Mars. Our results show that NaCl concentrations of > 175 g/l are required to induce anaerobic growth on perchlorate. We show a preference for a pH of 7.0 and a combination of yeast extract and casamino acids as preferred carbon sources. Furthermore, we document anaerobic growth and perchlorate reduction in the presence of perchlorate concentrations (200 mM) that exceed the currently accepted limit for any organism (100 mM). Raman spectra of cultures grown anaerobically on perchlorate show significant decreases in the intensity of the carotenoid peaks corresponding to bacterioruberin at ~ 1505 cm -1 , ~ 1150 cm -1 , and ~ 1000 cm -1 , highlighting how extreme Martian conditions may cause biosignature degradation. Notably, we demonstrate the previously unreported ability of the model halophilic archaeon Haloferax volcanii to grow anaerobically using perchlorate and extend the known limits of biological perchlorate tolerance under anoxic conditions. The discovery that H. volcanii is capable of perchlorate reduction has potential implications for the development of biological strategies for perchlorate remediation and for the interpretation of potential biosignatures in perchlorate-rich environments, including those hypothesized to exist on Mars.
Tool life and surface quality in GTD-450 milling under dry, MQL, and nanofluid-MQL strategies
RCS-YOLOv8: an improved YOLOv8 for wind turbine blade defect detection
Deciphering glutamine metabolic reprogramming: a novel therapeutic target ALDH18A1 in triple-negative breast cancer
Neuron-specific expression of transmembrane protein 130 (TMEM130)
Isolating fast and slow flows in three-dimensional fluid dynamics
Influence of menstrual cycle on autonomic nervous system, muscular strength and mood states
Evidence of a limit to benefits from culling lionfish
Changes in obesity and waist circumference in children and parents during the COVID-19 pandemic
Comparative analysis of phytochemical traits, proximate composition, and metabolite diversity in Nigella sativa L. genotypes from India
Systematic optimization and characterization of bacterial depolymerization of poly(ethylene terephthalate) plastics
Abstract Biodepolymerization of poly(ethylene terephthalate) (PET) plastics using microorganisms has emerged as a promising and sustainable approach for mitigating pollution caused by PET waste. In this study, Glutamicibacter mysorens ASR14, a mesophilic bacterium isolated from Kodungaiyur dumpyard (Chennai, India), showed 27.6% PET biodepolymerization in terms of weight loss in 30 d. A customized screening of 20 trials was designed using JMP statistical software to evaluate the influence of various variables. Furthermore, a Central Composite Design (CCD) of Response Surface Methodology (RSM) was adopted and validated using four variables at five levels, with 25 trials, to correlate the relationship for enhanced PET biodepolymerization. A maximum PET weight loss of 75.6% was achieved in 60 d, representing a 2.73-fold improvement compared to that under unoptimized conditions. Enzymatic assays confirmed the involvement of esterase (5,690 U/mL) and lipase (962 U/mL) activities in accelerating the breakdown of PET. The analytical characterization techniques revealed significant surface erosion, reduction in crystallinity, and high yield of terephthalic acid (TPA), which also holds potential value for biorefinery applications. This work represents the first comprehensive report on process optimization for PET biodepolymerization using G. mysorens ASR14 as a whole-cell biocatalyst. The findings establish G. mysorens ASR14 as a promising candidate for developing scalable, green bioremediation strategies.