Browse Articles

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.

Weighted analysis of the association between cannabis smoking and chronic pain intensity among hemodialysis patients

Contrasting land use systems regulate active and passive soil carbon pools and the carbon management index across soil depths

Abstract Understanding how soil carbon pools respond to contrasting land use systems is essential for evaluating soil functioning and land sustainability. Here, we examined the response of total organic carbon (TOC), oxidizable carbon fractions, microbial biomass carbon (MBC), soil organic carbon (SOC) stocks, and the carbon management index (CMI) across four contrasting land use types (forest, orchard, cropland, and abandoned land) at two soil depths (0–10 and 10–20 cm) in a sub-humid watershed. Oxidizable carbon fractions were grouped into active pools (very labile + labile) and passive pools (less labile + non-labile). Forest soils showed the highest TOC and MBC, whereas conversion to cropland and abandoned land reduced surface TOC by 36.0% and 47.4%, respectively, and MBC declined markedly under non-forest uses. Surface SOC stocks also decreased by 30.8% in cropland and 41.3% in abandoned land relative to forest. Active carbon pools declined substantially in the 0–10 cm layer, with reductions of 50.0% in cropland and 45.2% in abandoned land, while passive fractions accounted for a greater proportion of total SOC under these land uses, indicating a shift toward more stable carbon forms. CMI values further highlighted relative differences in SOC status among land uses: at 0–10 cm, CMI was 85.05 in orchard, 56.18 in abandoned land, and 44.16 in cropland, while at 10–20 cm the corresponding values were 77.25, 40.28, and 63.99, respectively. Integrating SOC fractionation with CMI provides a useful comparative framework for detecting relative changes in soil carbon status across contrasting land use systems.

Single station seismic observations enable high resolution localization of tectonic tremor sources using a vision transformer

Abstract Precisely locating tectonic tremor events is essential for understanding subduction zone dynamics. However, the lack of impulsive phase arrivals typically necessitates multi-station observations, posing a fundamental challenge for high-resolution monitoring using limited seismic records. Here, we show that single-station three-component wavefields can encode sufficient information to constrain tremor source locations using TremorViT, a vision transformer-based framework designed to estimate three-dimensional coordinates together with aleatoric uncertainties. Applied to the Nankai subduction zone, TremorViT achieves a mean epicentral error of 4.8 km using a single station, which improves to 2.5 km when independent station-level estimates are integrated. Using this approach, we localized 68,101 tremor events between January and September 2016, corresponding to approximately 1135.0 h of cumulative tremor activity. These metrics are substantially larger than those reported in existing network-based catalogs. This discrepancy likely reflects differences in detection sensitivity, event definition, and temporal windowing, even as the model preserves previously documented spatiotemporal patterns. This framework enables high-resolution tremor detection and offers a more granular view of tectonic activity, including potential signals associated with geodetically undetected slow slip events.

Machine learning prediction of customer satisfaction in fitness centres

Abstract Customer satisfaction in fitness centres is critical for fostering loyalty, higher spending, cross-buying, and positive recommendations. This study seeks to develop a predictive model of gym users’ satisfaction, identify its main determinants, and optimise predictive accuracy through machine learning techniques. Data from 10,368 users across five Spanish fitness centre chains were analysed. Five machine learning algorithms were applied: decision tree, random forest, logistic regression, gradient boosting, and Naïve Bayes. Model performance was evaluated using AUC, sensitivity, specificity, F-measure, Cohen’s Kappa, and overall accuracy. The random forest model showed the highest accuracy (AUC = 0.954, sensitivity = 0.933, specificity = 0.825, F-measure = 0.91, Cohen’s Kappa = 0.767, overall accuracy = 0.889). The most influential factors for satisfaction were the overall environment of the centre, employee trustworthiness, staff quality, and management of waiting times. This study extends prior research by applying machine learning algorithms to explain customer satisfaction in fitness centres, positioning satisfaction as the primary predictive outcome and providing interpretable insights into how environmental and service-related factors shape satisfaction beyond traditional retention-focused approaches.

Waterbird guilds function as dynamic cross-ecosystem energy vectors in unique soda pan model systems

Abstract Migratory animals play a key role in coupling ecosystems through the redistribution of energy, to date, energy transport by functional guilds has not been comprehensively quantified. Here, I present a generalized, own guild-based framework that extends established waterbird nutrient-cycling guild concepts into the energy flow across ecosystems, enabling the quantification of energy import, export and net balance mediated by waterbirds. Using long-term (1986–2017) waterbird census data from two internationally important (Ramsar-sites) intermittent soda pans in Central Europe, as simplified unique model systems. I estimated avian energy fluxes by integrating guild-specific foraging behaviour, habitat use, metabolic demand and residence time. Waterbird guilds were classified as net importers, importer–exporters or net exporters of energy based on Boros’s method. Estimated annual energy fluxes varied across several orders of magnitude, reflecting pronounced interannual variability in the studied ecosystem. Net energy import ranged widely from 574,712 to 57,022,011 kJ/ha/yr, while net energy export from 361,206 to 16,171,538 kJ/ha/yr. The net energy import consistently and significantly exceeded net energy export. Consequently, the average community-level energy balance was significantly positive (+ 51.3) regarding waterbird-mediated nutrient transport. The functional asymmetry between guilds ensures that energy import remains dominant over export regardless of species richness, fundamentally defining the ecosystem’s role as a significant energy sink in the studied system. Temporal analyses revealed significant dynamics in net energy import, whereas variation in open water area did not predict exactly energy balance. I partly reject the hypothesis that net energy import responds predictably to long-term trends in open water area, but partly confirm as it is rather depends on guild-structure dynamics. These results demonstrate that cross-ecosystem energy transport by waterbirds is governed primarily by functional guild composition and behaviour rather than habitat extent alone. The proposed framework provides a scalable tool for integrating avian-mediated energy transport into ecosystem energetics and spatial food-web theory.