Search Results (104)

This study investigates the biodeterioration of mosaic surfaces in a semi-confined archaeological environment along the Phlegraean coast (Baiae, Italy), focusing on the interaction between salt efflorescence and phototrophic biofilms. A multi-analytical approach was employed, integrating in situ observations with ex situ analyses, including SEM/EDS, FTIR spectroscopy, and metabarcoding (16S and 18S rRNA), to characterize both abiotic and biotic alteration patterns. Results highlight subtle traces of spatial differentiation: samples from the more exposed sector showed a more consistent colonization by halotolerant and halophilic taxa, particularly among Halobacteria and Rubrobacter, along with abundant sodium, chloride, and sulfate signals suggestive of active salt crystallization. Protected areas exhibit a comparable presence of salts with less diverse halophilic communities that vary along a vertical gradient of light exposure. The integration of chemical and biological data supports a model in which salt stress and biofilm development are co-dependent and synergistic in driving surface degradation. These findings emphasize the need for context-specific conservation strategies that account for the combined action of environmental salinity and microbial communities on historical materials. Full article

Urbanization imposes complex environmental gradients that threaten plant diversity and urban ecosystem integrity. Understanding the multifactorial drivers that govern species distribution in urban contexts is essential for biodiversity conservation and sustainable landscape planning. This study addresses this challenge by examining the environmental determinants of urban flora in a rapidly developing city. We integrated data from 397 floristic sampling sites and 13 environmental monitoring locations across Düzce, Türkiye. A multidimensional suite of environmental predictors—including microclimatic variables (soil temperature, moisture, light), edaphic properties (pH, EC (Electrical Conductivity), texture, carbonate content), precipitation chemistry (pH and major ions), macroclimatic parameters (CHELSA bioclimatic variables), and spatial metrics (elevation, proximity to urban and natural features)—was analyzed using nonlinear regression models and machine learning algorithms (RF (Random Forest), XGBoost, and SVR (Support Vector Regression)). Shannon diversity exhibited strong variation across land cover types, with the highest values in broad-leaved forests and pastures (>3.0) and lowest in construction and mining zones (<2.3). Species richness and evenness followed similar spatial trends. Evenness peaked in semi-natural habitats such as agricultural and riparian areas (~0.85). Random Forest outperformed other models in predictive accuracy. Elevation was the most influential predictor of Shannon diversity, while proximity to riparian zones best explained richness and evenness. Chloride concentrations in rainfall were also linked to species composition. When the models were recalibrated using only native species, they exhibited consistent patterns and maintained high predictive performance (Shannon R² ≈ 0.937474; Richness R² ≈ 0.855305; Evenness R² ≈ 0.631796). Full article

The Solana aquifer is located in the South-East of the Iberian Peninsula and forms part of the Villena-Benejama groundwater body. It is a limestone and dolomite aquifer that has historically been considered overexploited due to intensive agriculture and urban use. Despite this, the quality of the water has remained stable over time. This study analyses the spatial and temporal variability within the aquifer and identifies the controlling processes. Chemical analyses were conducted on samples taken from 26 wells in July 2024 and February 2025. The results reveal a predominant calcium carbonate facies with minimal seasonal variation. However, sulphate-chloride water was found in the South-Western sector, which is associated with the dissolution of evaporitic materials from the Triassic Keuper. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) identified two processes: a salinity gradient linked to lithology, and a second process related to bicarbonates and nitrates, indicating potential nitrate inputs in the eastern half of the aquifer. HCA differentiates four clusters: one highly mineralised group located in the south-western sector near Triassic outcrops, two intermediate groups with slight differences in composition and distribution, and a fourth group with the lowest mineralisation located on the Southern flank of the Solana range. Full article

The mineralization of groundwater within the Żary pericline exhibits a broad range, from 0.2 to 0.3 g/L up to 401 g/L, with the majority classified as brines. These waters are predominantly chloride-rich, characterized by variable concentrations of cations such as Na⁺, K⁺, Ca²⁺, and Mg²⁺. Their chemical composition varies by geological formation: Na-Cl and Mg-Cl types dominate in the Triassic strata, while more complex mixtures are observed in the Zechstein and Rotliegend formations. Brine formation and evolution are primarily influenced by evaporation and ion exchange processes, particularly Na⁺/Ca²⁺ exchange. These brines represent residual evaporative fluids that migrate through the subsurface during sediment compaction and tectonic deformation. The observed variability in mineral content suggests the occurrence of hydrochemical inversion within the geological layers. Groundwater temperatures range from 20 °C to 55 °C at depths between 490 and 1525 meters below ground level. The geothermal gradient spans from 3.55 °C/100 m to 4 °C/100 m, with the highest values recorded in the western and northwestern sectors of the pericline. These thermal conditions indicate promising potential for geothermal energy development in the region. Full article

To investigate the effects of micro-nanoplastics (MNPs) on spinach seed germination and sprout growth, this study employed polyvinyl chloride micro-nanoplastics (PVC-MNPs) as the treatment factor. Six concentration gradients were established under two cultivation conditions—hydroponic and soil. Two spinach cultivars grown in different seasons—the winter cultivar cv. xinbofeit and the autumn cultivar cv. connaught—were evaluated for germination characteristics, sprout morphology, and antioxidant capacity. Results indicated that low to moderate PVC-MNP concentration (1–100 mg/L in hydroponics or 0.1–1.0% in soil) moderately promoted seed germination and seedling growth, with cv. Xinbofeit exhibiting stronger stress tolerance. Conversely, high concentrations (200 mg/L in hydroponic or 2.0% in soil) inhibited germination and root development in both cultivars and induced oxidative stress responses. Principal component analysis identified germination rate, superoxide dismutase (SOD), and peroxidase (POD) activities as key response indicators. Significant inter-cultivar differences and cultivation method dependencies were observed: cv. xinbofeit showed higher sensitivity to elevated PVC-MNPs level, whereas cv. connaught demonstrated greater overall stress resistance. This study demonstrates that micro-nanoplastics exert a dual effect on spinach seed germination and sprout growth, with low to moderate concentrations promoting growth, while high concentrations inhibit development and induce oxidative stress. Moreover, significant differences in response were observed among different cultivars, highlighting the complex risks of micro-nanoplastics in agricultural ecosystems and their cultivar-dependent impacts. Full article

Background: Acute Kidney Injury (AKI) is a pivotal concern in neurocritical care, impacting patient survival and quality of life. This study harnesses machine learning (ML) techniques to predict the occurrence of AKI in patients receiving hyperosmolar therapy, aiming to optimize patient outcomes in neurocritical settings. Methods: We conducted a retrospective cohort study of 4886 patients who underwent hyperosmolar therapy in the neurosurgical intensive care unit (ICU). Comparative predictive analyses were carried out using advanced ML algorithms—eXtreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Random Forest (RF)—against standard multivariate logistic regression. Predictive performance was assessed using an 8:2 training-testing data split, with model fine-tuning through cross-validation. Results: The RF with KNN imputation showed slightly better performance than other approaches in predicting AKI. When applied to an independent test set, it achieved a sensitivity of 79% (95% CI: 70–87%) and specificity of 85% (95% CI: 82–88%), with an overall accuracy of 84% (95% CI: 81–87%) and AUROC of 0.86 (95% CI: 0.82–0.91). The multivariate logistic regression analysis, while informative, showed less predictive strength compared to the ML models. Delta chloride levels and serum osmolality proved to be the most influential predictors, with additional significant variables including pH, age, bicarbonate, and the osmolar gap. Conclusions: The prominence of delta chloride and serum osmolality among the predictive variables underscores its potential as a biomarker for AKI risk in this patient population. Full article

Atmospheric corrosion significantly impacts infrastructure worldwide, with traditional assessment methods being time-intensive and costly. This study developed a comprehensive machine learning framework for predicting atmospheric corrosion rates using environmental and material parameters. Three regression models (Linear Regression, Random Forest, and Gradient Boosting) were trained on a scientifically informed synthetic dataset incorporating established corrosion principles from ISO 9223 standards and peer-reviewed literature. The Gradient Boosting model achieved superior performance with cross-validated R² = 0.835 ± 0.024 and RMSE = 98.99 ± 16.62 μm/year, significantly outperforming the Random Forest (p < 0.001) and Linear Regression approaches. Feature importance analysis revealed the copper content (30%), exposure time (20%), and chloride deposition (15%) as primary predictors, consistent with the established principles of corrosion science. Model diagnostics demonstrated excellent predictive accuracy (R² = 0.863) with normally distributed residuals and homoscedastic variance patterns. This methodology provides a systematic framework for ML-based corrosion prediction, with significant implications for protective coating design, material selection, and infrastructure risk assessment, pending comprehensive experimental validation. Full article

This study evaluated the effects of different plant growth regulators and their concentration gradients on the agronomic traits, seed yield, and yield components of Medicago sativa L. cv. “Xinmu No. 5” alfalfa. This experiment comprised 10 treatments, including 98% mepiquat chloride (200, 250, and 300 mg/L), 5% prohexadione-calcium (150, 250, and 350 mg/L), and 5% uniconazole (50, 100, and 150 mg/L), each at three concentration levels, along with a distilled water control (CK). The results show that the 98% mepiquat chloride treatment (MCT3) significantly reduced plant height (by 22%) and internode length (by 28.3%), while increasing stem diameter, branch number, and seed yield. Plant height and internode length exhibited a significant positive correlation, and both were highly significantly negatively correlated (p < 0.01) with seed yield components, indicating that controlling vegetative growth can enhance seed yield. Principal component analysis (extracting four principal components with a cumulative contribution rate of 80.8%) further confirmed that the 98% mepiquat chloride treatment MCT3 (300 mg/L) was the most effective treatment for improving seed yield of alfalfa in arid regions. Full article

This paper addresses the issues of insufficient expansion force, low early strength (1-day compressive strength < 1.5 MPa), and poor toughness (flexural strength < 0.8 MPa) in traditional chemical foamed cement used for road grouting repair. By combining single-factor gradient experiments with microscopic mechanism analysis, the study systematically investigates the performance modulation mechanisms of controlled-release foamed cement using additives such as heavy calcium powder (0–20%), calcium chloride (0.2–1.2%), latex powder (0.2–1.2%), and polypropylene fiber (0.2–0.8%). The study innovatively employs a titanium silicate coupling agent coating technique (with the coating agent amounting to 25% of the catalyst’s mass) to delay foaming by 40 s. Scanning electron microscopy (SEM) and pore structure analysis reveal the microscopic essence of material performance optimization. Full article

The slurry aluminizing process is widely employed to enhance the oxidation and corrosion resistance of nickel-based superalloys used in high-temperature environments such as gas turbines and aerospace engines. This study investigates the effects of the concentration of Al vapors in the reactor chamber and the initial slurry layer thickness on the microstructure, chemical composition, and phase composition of aluminide coatings. Coatings were manufactured on Ni-based superalloy substrates using CrAl powders as an aluminum source and chloride- and fluoride-based activator salts. The effect of the initial thickness of the slurry layer was studied by varying the amount of deposited slurry in terms of mg_slurry/cm²_sample (with constant mg_slurry/cm³_chamber). The microstructure and phase composition of the produced aluminide coatings were evaluated by SEM, EDS, and XRD analysis. Slurry thickness can affect concentration gradients during diffusion, and the best results were obtained with an initial slurry amount of 100 mg_slurry/cm²_sample. The effect of the Al vapor phase in the reaction chamber was then investigated by varying the mg_slurry/cm³_chamber ratio while keeping the slurry layer thickness constant at 100 mg_slurry/cm²_sample. This parameter influences the amount of Al at the substrate surface before the onset of solid-state diffusion, and the best results were obtained for a 6.50 mg_slurry/cm³_chamber ratio with the formation of 80 µm coatings (excluding the interdiffusion zone) with a β-NiAl phase throughout the thickness. To validate process flexibility, the same parameters were successfully applied to produce platinum-modified aluminides with a bi-phasic ζ-PtAl2 and β-(Ni,Pt)Al microstructure. Full article

The GABA_A receptors, through a short-term interaction with a mediator, induce hyperpolarization of the membrane potential (V_m) via the passive influx of chloride ions (Cl⁻) into neurons. The massive (or intense) activation of the GABA_ARs by the agonist could potentially lead to depolarization/excitation of the V_m. Although the ionic mechanisms of GABA_A-mediated depolarization remain incompletely understood, a combination of the outward chloride current and the inward bicarbonate current and the resulting pH shift are the main reasons for this event. The GABA_A responses are determined by the ionic gradients—neuronal pH/bicarbonate homeostasis is maintained by carbonic anhydrase and electroneutral/electrogenic bicarbonate transporters and the chloride level is maintained by secondary active cation–chloride cotransporters. Massive activation can also induce the rundown effect of the receptor function. This rundown effect partly involves phosphorylation, Ca²⁺ and the processes of receptor desensitization. In addition, by various methods (including fluorescence and optical genetic methods), it has been shown that massive activation of GABA_ARs during pathophysiological activity is also associated with an increase in [Cl⁻]_i and a decline in the pH and ATP levels in neurons. Although the relationship between the neuronal changes induced by massive activation of GABAergic signaling and the risk of developing neurodegenerative disease has been extensively studied, the molecular determinants of this process remain somewhat mysterious. The aim of this review is to summarize the data on the relationship between the massive activation of inhibitory signaling and the ionic changes in neurons. The potential role of receptor dysfunction during massive activation and the resulting ionic and metabolic disruption in neurons during the manifestation of network/seizure activity will be considered. Full article

This study presents a quantitative electrophysiological method to directly measure the passive transport of ammonium ions through bacterial outer membrane porins. Using a zero-current reversal potential assay in planar lipid bilayers under defined bi-ionic gradients, this study evaluates the permeability of ammonium salts through two general diffusion porins: Omp-Pst2 from Providencia stuartii and OmpF from Escherichia coli. Under matched ionic conditions, Omp-Pst2 exhibited significantly higher ammonium flux—approximately 6000 ions per second per monomer at a 1 µM gradient—compared to ~4000 ions per second for OmpF. Importantly, the identity of the accompanying anion (chloride vs. sulfate) modulated both the ion selectivity and flux rate, highlighting the influence of counterion interactions on porin-mediated transport. These findings underscore how structural differences between porins—such as pore geometry and charge distribution—govern ion permeability. The method applied here provides a robust framework for quantifying nutrient flux at the single-channel level and offers novel insights into how Gram-negative bacteria may adapt their membrane transport mechanisms under nitrogen-limited conditions. This work not only enhances our understanding of outer membrane permeability to small ions like ammonium, but also has implications for antimicrobial strategy development and biotechnological applications in nitrogen assimilation. Full article

Atmospheric corrosion, especially in coastal environments, presents a major challenge for the long-term durability of metallic and concrete infrastructure due to chloride deposition from marine aerosols. With a significant portion of the global population residing in coastal zones—often associated with intense industrial activity—there is growing demand for accurate and early corrosion prediction methods. Traditional standards for assessing atmospheric corrosivity depend on long-term empirical data, limiting their usefulness during the design stage of infrastructure projects. To address this limitation, this study develops predictive models using machine-learning techniques, namely gradient boosting, support vector machine, and neural networks, to estimate chloride deposition levels based on easily accessible climatic and geographical parameters. Our models were trained on a comprehensive dataset that included variables such as land coverage, wind speed, and orientation. Among the models tested, tree-based algorithms, particularly gradient boosting, provided the highest prediction accuracy (F1 score: 0.8673). This approach not only highlights the most influential environmental variables driving chloride deposition but also offers a scalable and cost-effective solution to support corrosion monitoring and structural life assessment in coastal infrastructure. Full article

A VOC-contaminated shallow aquifer in an industrial site was investigated to evaluate its potential for natural attenuation. The shallow groundwater aquifer beneath the industrial site has been contaminated by dissolved volatile organic compounds (VOCs) such as trichloroethylene (TCE), cis-1,2-dichloroethylene (cis-DCE), and vinyl chloride (VC) for more than three decades. Monitoring and investigation were implemented during 2011–2024, aiming to propose future groundwater aquifer management strategies. This study included groundwater borehole investigation, well installation monitoring, hydraulic head measurements, slug tests, groundwater samplings, and microbial analyses. Microbial investigations identified the predominant group of microorganisms of Proteobacteria, indicating biodegradation potential, as demonstrated by the presence of cis-DCE and VC. BIOSCREEN was used to evaluate the process of natural attenuation, incorporating site-specific parameters. A two-layer groundwater flow model was developed using MODFLOW with hydraulic conductivities obtained from slug tests. The site has an average hydraulic head of 259.6 m amsl with a hydraulic gradient of 0.026, resulting in an average groundwater flow velocity of 11 m/y. Hydraulic conductivities were estimated during model calibration using the PEST pilot point technique. A reactive transport model, RT3D, was used to simulate dissolved TCE transport over 30 years, which can undergo sorption as well as biodegradation. Model calibration demonstrated a satisfactory fit between observed and simulated groundwater heads with a root mean square error of 0.08 m and a correlation coefficient (r) between measured and simulated heads of 0.81, confirming the validity of the hydraulic conductivity distribution. The TCE plume continuously degraded and gradually migrated southward, generating a cis-DCE plume. The concentrations in both plumes decreased toward the end of the simulation period at Source 1 (located upstream), while BIOSCREEN results confirmed ongoing natural attenuation primarily by biodegradation. The integrated MODFLOW-RT3D-BIOSCREEN approach effectively evaluated VOC attenuation and plume migration. However, future remediation strategies should consider enhanced bioremediation to accelerate contaminant degradation at Source 2 and ensure long-term groundwater quality. Full article

Estrogens are potent hormones involved in numerous physiological and pathological processes. Their typically low concentrations in biological samples necessitate highly sensitive analytical methods for accurate quantification. This study presents a high-performance liquid chromatography with fluorescence detection (HPLC-FLD) method for quantifying estradiol and its metabolites in blood serum and saliva. Analytes were extracted using solid-phase microextraction with a divinylbenzene sorbent and methanol as the desorption agent. FLD was performed after the derivatization of the analytes with dansyl chloride. Separation was achieved on a Poroshell 120 EC-C18 column (2.1 × 100 mm, 2.7 µm) at 50 °C using water with 0.1% formic acid and methanol as the mobile phase at 0.5 mL/min. A gradient elution increased the methanol concentration from 76% to 100% over 0–8 min, then it returned to 76% at 8.1 min and was held until 11 min had passed. Detection was at λ_EX 350 nm and λ_EM 530 nm. Good linearity was observed for estradiol, 2-hydroxyestradiol, and 2-methoxyestradiol (10–300 ng/mL; R² = 0.9893–0.9995). The LOQ for all analytes was 10 ng/mL. Solid-phase microextraction (SPME) offered advantages over liquid–liquid extraction. The method is suitable for quantifying estrogens in the 10 ng/mL–1 µg/mL range. Full article