Search Results (555)

This paper presents a critical and comprehensive review of the application of mining waste, specifically waste rock and tailings, in asphalt pavements, with the aim of synthesizing performance outcomes and identifying key research gaps. A systematic literature search yielded a final dataset of 41 peer-reviewed articles for detailed analysis. Bibliometric analysis indicates a notable upward trend in annual publications, reflecting growing academic and practical interest in this field. Performance-based evaluations demonstrate that mining wastes, particularly iron and copper tailings, have the potential to enhance the high-temperature performance (i.e., rutting resistance) of asphalt binders and mixtures when utilized as fillers or aggregates. However, their effects on fatigue life, low-temperature cracking, and moisture susceptibility are inconsistent, largely influenced by the physicochemical properties and dosage of the specific waste material. Despite promising results, critical knowledge gaps remain, particularly in relation to long-term durability, comprehensive environmental and economic Life-Cycle Assessments (LCA), and the inherent variability of waste materials. This review underscores the substantial potential of mining wastes as sustainable alternatives to conventional pavement materials, while emphasizing the need for further multidisciplinary research to support their broader implementation. Full article

Background: Peptides are a class of molecules that can be presented as good antimicrobials and with mechanisms that avoid resistance, and the design of peptides with good activity can be complex and laborious. The study of their quantitative structure–activity relationships through machine learning algorithms can shed light on a rational and effective design. Methods: Information on the antimicrobial activity of peptides was collected, and their structures were characterized by molecular descriptors generation to design regression and classification models based on machine learning algorithms. The contribution of each descriptor in the generated models was evaluated by determining its relative importance and, finally, the antimicrobial activity of new peptides was estimated. Results: A structured database of antimicrobial peptides and their descriptors was obtained, with which 56 machine learning models were generated. Random Forest-based models showed better performance, and of these, regression models showed variable performance (R² = 0.339–0.574), while classification models showed good performance (MCC = 0.662–0.755 and ACC = 0.831–0.877). Those models based on bacterial groups showed better performance than those based on the entire dataset. The properties of the new peptides generated are related to important descriptors that encode physicochemical properties such as lower molecular weight, higher charge, propensity to form alpha-helical structures, lower hydrophobicity, and higher frequency of amino acids such as lysine and serine. Conclusions: Machine learning models allowed to establish the structure–activity relationships of antimicrobial peptides. Classification models performed better than regression models. These models allowed us to make predictions and new peptides with high antimicrobial potential were proposed. Full article

This study investigates the heavy metal contamination in urban and peri-urban soils of Thessaloniki, Greece, over a two-year period (2023–2024). A total of 208 composite soil samples were systematically collected from 52 sites representing diverse land uses, including high-traffic roadsides, industrial zones, residential neighborhoods, parks, and mixed-use areas, with sampling conducted both after the wet (winter) and dry (summer) seasons. Soil physicochemical properties (pH, electrical conductivity, texture, organic matter, and calcium carbonate content) were analyzed alongside the concentrations of heavy metals such as Cd, Co, Cr, Cu, Mn, Ni, Pb, and Zn. A pollution assessment employed the Geoaccumulation Index (I_geo), Contamination Factor (Cf), Pollution Load Index (PLI), and Potential Ecological Risk Index (RI), revealing variable contamination levels across the city, with certain hotspots exhibiting a considerable to very high ecological risk. Multivariate statistical analyses (PCA and HCA) identified distinct anthropogenic and geogenic sources of heavy metals. Health risk assessments, based on USEPA models, evaluated non-carcinogenic and carcinogenic risks for both adults and children via ingestion and dermal contact pathways. The results indicate that while most sites present low to moderate health risks, specific locations, particularly near major transport and industrial areas, pose elevated risks, especially for children. The findings underscore the need for targeted monitoring and remediation strategies to mitigate the ecological and human health risks associated with urban soil pollution in Thessaloniki. Full article

Following hygiene standards in milk production is essential for making high-quality sour cream, especially when using traditional methods that rely on raw milk. The aim of this study was to determine the physicochemical, microbiological, and sensory quality of artisanal sour cream samples collected from major marketplaces in the wider Zagreb area. On average, the samples contained 27.99% milk fat, 3.30% protein, 34.29% dry matter, 6.51% fat-free dry matter and 3.00% lactose, with considerable variability observed across all components. Microbiological analysis revealed the presence of Staphylococcus aureus in 35.30% of the samples, Enterobacteriaceae in 76.47%, Escherichia coli in 94.11%, Bacillus spp. in 23.53%, and yeasts in 100% of the samples. Listeria monocytogenes and Salmonella spp. were not detected. The sensory analysis of the textural properties showed significant variability in firmness, adhesiveness, viscosity, creaminess, and fizziness. Samples with higher milk fat and dry matter content were rated better for creaminess, viscosity and mouth firmness. Flavour assessments, particularly for cream and diacetyl notes, also varied widely among samples. These findings highlight the complexity of sour cream’s sensory attributes and the significant influence of ingredient composition and processing techniques on appearance, aroma, texture, taste, and flavour. Principal component analysis (PCA) with Varimax rotation simplified the data structure and identified key dimensions of quality variation. Principal component analysis (PCA) revealed that the first principal component (PC1) effectively discriminated the cream samples based on sensory attractiveness and indicators of spoilage and highlighted the association between off-flavour and microbial contamination with inferior characteristics. The second principal component (PC2) captured the differences in physicochemical characteristics and showed a gradient from richer, creamier samples with higher fat content to those with lower acidity and higher freshness. Full article

This study aimed to develop a fermented mango-based beverage using Lactiplantibacillus plantarum strains Lp6 and Lp32, focusing on enhancing its functional properties, ensuring microbiological safety, improving nutritional value, and achieving sensory acceptability. A central composite design (CCD) was employed to assess the effects of two factors (fermentation time and inoculum concentration) on several response variables: viable cell concentration (CC), total phenolic compounds (TPCs), total flavonoid compounds (TFCs), and concentrations of L-lactic acid and D-lactic acid. The optimized formulation was achieved using L. plantarum Lp6, with an inoculum concentration of 9.89 Log (7.76 × 10⁹) CFU/mL and a fermentation time of 20.47 h. Under these conditions, the beverage reached the highest values for CC, TPC, TF, and L-lactic acid while minimizing the production of D-lactic acid. Following optimization, the fermented beverage underwent further characterization, including physicochemical analysis, microbiological evaluation, proximate composition analysis, and sensory evaluation. The final product exhibited a viable cell count of 13.01 Log (10.23 × 10¹²) CFU/mL, demonstrated functional potential, complied with microbiological safety standards, and showed adequate nutritional content. Sensory analysis revealed high consumer acceptability, attributed to its distinctive mango aroma and flavor. These findings highlight the potential of this fermented mango-based beverage as a novel functional food with promising market appeal. Full article

Potassium fertilizer management is critical for achieving high yields of Korla fragrant pear, yet current practices often overlook or misuse potassium inputs. In this study, a two-year field experiment (2023–2024) was conducted with 7- to 8-year-old pear trees using four potassium levels (0, 75, 150, and 225 kg/hm²). Metagenomic sequencing was employed to assess the effects on soil microbial communities, sulfur cycle functional genes, and fruit yield. Potassium treatments significantly altered soil physicochemical properties, the abundance of sulfur cycle functional genes, and fruit yield (p < 0.05). Increasing application rates significantly elevated soil-available potassium and organic matter while reducing pH (p < 0.05). Although alpha diversity was unaffected, NMDS analysis revealed differences in microbial community composition under different treatments. Functional gene analysis showed a significant decreasing trend in betB abundance, a peak in hpsO under K150, and variable patterns for soxX and metX across treatments (p < 0.05). All potassium applications significantly increased yield relative to CK, with K150 achieving the highest yield (p < 0.05). PLS-PM analysis indicated significant positive associations between potassium rate, nutrient availability, microbial abundance, sulfur cycling, and yield, and a significant negative association with pH (p < 0.05). These results provide a foundation for optimizing potassium fertilizer strategies in Korla fragrant pear orchards. It is recommended that future studies combine metagenomic and metatranscriptomic approaches to further elucidate the mechanisms linking potassium-driven microbial functional changes to improvements in fruit quality. Full article

Background: Annual influenza vaccine updates target viral drift, but immune responses may be biased by original antigenic sin (OAS). Few studies have explored this across closely related strains. This study examines how OAS shapes responses to sequential influenza variants in the context of seasonal vaccination. Methods: We conducted a prospective, longitudinal study to assess the humoral immune response to the 2023–2024 seasonal influenza vaccine containing the A/Victoria/4897/2022 (H1N1) strain. Bioinformatic analyses compared the hemagglutinin (HA) sequences of A/Victoria/4897/2022 and the antigenically related A/Victoria/2570/2019 strain. B-cell epitopes were mapped with BepiPred-3.0 and BepiBlast, and their physicochemical properties analyzed via accessibility, β-turns, flexibility, and hydrophilicity. Antibody responses were measured pre- and 28 days post-Vaxigrip Tetra vaccination in young (18–35) and older (>65) adults, stratified by cytomegalovirus (CMV) serostatus. HA sequences showed >97% identity, with variations mainly in the globular head. Predicted B-cell epitopes overlapped variable sites, suggesting possible immune escape. Despite having been vaccinated against the 2022 strain, serology showed higher antibody titers against the 2019 HA strain in all participants. This pattern suggests a potential antigen imprinting effect, though confirmation awaits further analysis. Age groups differed: older adults showed greater variability, while younger CMV+ individuals tended toward stronger 2019 HA responses. Conclusions: These findings suggest a complex interplay of factors shaping immune responses, though the imprinting effect and the potential role of CMV warrant further exploration in larger, more focused studies. Full article

Rare earth elements (REEs) are emerging soil contaminants due to increasing fertilizer use, mining activities, and technological applications. However, few studies have assessed their concentrations in soils or associated environmental risks. Here, we evaluate the influence of land cover types (Eucalyptus plantation, forest, and pasture), parent material, and soil physicochemical properties (predictor variables) on REE content in the Brazilian Atlantic Forest and measure pseudo-total REE content using inductively coupled plasma mass spectrometry (ICP-MS). Differences in REE content across land cover types, parent materials, and soil properties were assessed using similarity and variance analyses (ANOSIM, ANOVA, and Kruskal–Wallis) followed by post hoc tests (Tukey HSD and Dunn’s). We used model selection based on the Akaike criterion (ΔAICc < 2) to determine the influence of predictor variables on REE content. Our results showed that parent materials (igneous and metamorphic rocks) were the best predictors, yielding plausible models (Adj R² ≥ 0.3) for Y, δEu, and La_N/Sa_N. In contrast, Ca:Mg alone provided a plausible model (Adj R² = 0.15) for δCe anomalies, while clay content (Adj R² = 0.11) influenced the Sa_N/Yb_N ratio, though soil properties had weaker effects than parent materials. However, we found no evidence that Eucalyptus plantations or pastures under non-intensive management increase REE content in Brazilian Atlantic Forest soils. Full article

Carbonaceous sorbents were prepared from Chlorella vulgaris via hydrothermal carbonization (200 °C and 250 °C) and slow pyrolysis (300–500 °C) to assess their effectiveness in removing the herbicide metribuzin from water. The biomass was cultivated under controlled laboratory conditions, allowing for consistent feedstock quality and traceability throughout processing. Using a single microalgal feedstock for both thermal methods enabled a direct comparison of hydrochar and pyrochar properties and performance, eliminating variability associated with different feedstocks and allowing for a clearer assessment of the influence of thermal conversion pathways. While previous studies have examined algae-derived biochars for heavy metal adsorption, comprehensive comparisons targeting organic micropollutants, such as metribuzin, remain scarce. Moreover, few works have combined kinetic and isotherm modeling to evaluate the underlying adsorption mechanisms of both hydrochars and pyrochars produced from the same algal biomass. Therefore, the materials investigated in the present work were characterized using a combination of standard physicochemical and structural techniques (FTIR, SEM, BET, pH, ash content, and TOC). The kinetics of sorption were also studied. The results show better agreement with the pseudo-second-order model, consistent with chemisorption, except for the hydrochar produced at 250 °C, where physisorption provided a more accurate fit. Freundlich isotherms better described the equilibrium data, indicating heterogeneous adsorption. The hydrochar obtained at 200 °C reached the highest adsorption capacity, attributed to its intact cell structure and abundance of surface functional groups. The pyrochar produced at 500 °C exhibited the highest surface area (44.3 m²/g) but a lower affinity for metribuzin due to the loss of polar functionalities during pyrolysis. This study presents a novel use of Chlorella vulgaris-derived carbon materials for metribuzin removal without chemical activation, which offers practical benefits, including simplified production, lower costs, and reduced chemical waste. The findings contribute to expanding the applicability of algae-based sorbents in water treatments, particularly where low-cost, energy-efficient materials are needed. This approach also supports the integration of carbon sequestration and wastewater remediation within a circular resource framework. Full article

Background/Objectives: Tofacitinib is a Janus kinase 1 and 3 inhibitor that was developed to treat rheumatoid arthritis. Accordingly, this study aimed to predict plasma tofacitinib concentrations and pharmacokinetic parameters in patients with renal failure through physiologically based pharmacokinetic (PBPK) simulations. Methods: PK-Sim and Simcyp simulators were used, as well as conventional Dedrick plot analysis, employing a single animal extrapolation method. The predictions were compared with previously published data. Results: PBPK simulations of tofacitinib in patients with renal failure closely matched the observed plasma concentration profiles and pharmacokinetic results, including the area under the plasma concentration–time curve (AUC), maximum plasma concentration (C_max), and time to reach C_max (T_max). The ratios of the simulated to observed plasma concentrations and pharmacokinetic parameters for tofacitinib were within a 0.5–2.0-fold error range. Although the results from the Dedrick plot were reasonably good, they were less accurate than those of the PBPK simulations. This was because the Dedrick plot relied solely on preclinical plasma concentration data without incorporating drug physicochemical properties, in vitro data, or physiological and pathophysiological variables. Conclusions: The findings suggest that PBPK simulations using single-species extrapolation effectively provide preliminary estimates of plasma tofacitinib concentration profiles and pharmacokinetic parameters in humans under specific conditions, including renal failure. Furthermore, the results provide a foundation for adjusting tofacitinib dosage and dosing schedules to maintain effective plasma concentrations by considering the pathophysiological characteristics of patients according to their specific diseases. Full article

Pesticide residues pose risks to the environment and human health. Little is known about how tillage and straw management affect herbicide behavior in soil. This study investigated the effects of different tillage practices under varying straw incorporation scenarios on the degradation of five commonly used herbicides in a long-term experimental field located in the maize belt of Siping, Jilin Province. Post-harvest soil samples were analyzed for residual herbicide concentrations and basic soil physicochemical properties. A human health risk assessment was conducted, and a controlled incubation experiment was carried out to evaluate herbicide degradation dynamics under three management systems: straw incorporation with traditional rotary tillage (ST), straw incorporation with strip tillage (SS), and no-till without straw (CK). Residual concentrations of atrazine ranged from not detected (ND) to 21.10 μg/kg (mean: 5.28 μg/kg), while acetochlor showed the highest variability (2.29–120.61 μg/kg, mean: 25.26 μg/kg). Alachlor levels were much lower (ND–5.71 μg/kg, mean: 0.34 μg/kg), and neither nicosulfuron nor mesotrione was detected. Soil organic matter (17.6–20.89 g/kg) positively correlated with available potassium and acetochlor residues. Health risk assessments indicated negligible non-cancer risks for both adults and children via ingestion, dermal contact, and inhalation. The results demonstrate that tillage methods significantly influence herbicide degradation kinetics, thereby affecting environmental persistence and ecological risks. Integrating straw with ST or SS enhanced the dissipation of atrazine and mesotrione, suggesting their potential as effective residue mitigation strategies. This study highlights the importance of tailoring tillage and straw management practices to pesticide type for optimizing herbicide fate and promoting sustainable agroecosystem management. Full article

The disposal of olive mill wastewater (OMWW) poses significant environmental challenges due to its high content of phytotoxic and pollutant compounds. This study aims to explore the chemical composition of OMWW derived from various olive varieties (Buža, Buža puntoža, Istarska bjelica, Leccino, and Rosinjola) and assess its antifungal potential against phytopathogenic fungi from the Botryosphaeriaceae family. OMWW samples were analyzed for their physicochemical properties, phenolic composition via LC-MS/MS, and antifungal activity against Botryosphaeria dothidea (Moug. ex Fr.) Ces. & De Not., Diplodia mutila (Fr.) Fr., D. seriata De Not., Dothiorella iberica A.J.L. Phillips, J. Luque & A. Alves, Do. sarmentorum (Fr.) A.J.L. Phillips, Alves & Luque, and Neofusicoccum parvum (Pennycook & Samuels) Crous, Slippers & A.J.L. Phillips. Antifungal efficacy was tested at varying concentrations, alongside the phenolic compounds hydroxytyrosol and vanillic acid. Antifungal activity varied across fungal species and OMWW concentrations. Lower OMWW concentrations inhibited mycelial growth in some pathogens, while higher concentrations often had a stimulatory effect. Among the OMWW treatments, Leccino and Buža showed the most significant antifungal activity against species from the Botryosphaeriaceae family. The results demonstrated significant variability in OMWW composition, with Istarska bjelica exhibiting the highest concentrations of phenolic compounds, sugars, dry matter, and carbon and nitrogen content. The results also highlight the impact of acidification on the phenolic profile of OMWW. Treatment with HCl significantly altered the concentration of individual phenolic compounds, either enhancing their release or contributing to their degradation. Among the two compounds, vanillic acid showed greater efficacy than hydroxytyrosol. In addition, microorganisms isolated from OMWW, including Bacillus velezensis Ruiz-Garcia et al., Rhodotorula mucilaginosa (A. Jörg.) F.C. Harrison, Nakazawaea molendiniolei (N. Cadez, B. Turchetti & G. Peter) C. P. Kurtzman & C. J. Robnett, and Penicillium crustosum Thom, demonstrated antagonistic potential against fungal pathogens, with B. velezensis showing the strongest inhibitory effect. The greatest antagonistic effect against fungi was observed with the species Do. Iberica. The findings highlight the potential of OMWW as a sustainable alternative to chemical fungicides, simultaneously contributing to the management of waste and protection of plants through circular economy principles. Full article

Propylene production through the CO₂-assisted oxidative dehydrogenation of propane (CO₂-ODP) is an effective route able to address the ever-increasing demand for propylene and simultaneously utilize CO₂. In this study, a series of alumina-supported gallium oxide catalysts of variable Ga₂O₃ loading was synthesized, characterized, and evaluated with respect to their activity for the CO₂-ODP reaction. It was found that both the catalysts’ physicochemical characteristics and performance were strongly affected by the amount of Ga₂O₃ dispersed on Al₂O₃. Surface basicity was maximized for the sample containing 20 wt.% Ga₂O₃, whereas surface acidity was monotonically increased with increasing Ga₂O₃ loading. A volcano-type correlation was found between catalytic performance and acid/base properties, according to which propane conversion and propylene yield exhibited optimum values for intermediate surface basicity and acidity, which both correspond to the sample containing 30 wt.% Ga₂O₃. The dispersion of a suitable amount of Ga₂O₃ on the Al₂O₃ surface not only enhances the conversion of propane to propylene but also suppresses the formation of side products (C₂H₄, CH₄, and C₂H₆) at temperatures of practical interest. The 30%Ga₂O₃-Al₂O₃ catalyst exhibited very good stability at 550 °C, where byproduct formation and carbon deposition were limited. Mechanistic studies indicated that the reaction proceeds through a two-step oxidative route with the participation of CO₂ in the abstraction of H₂, originating from propane dehydrogenation, through the reverse water–gas reaction (RWGS) reaction, shifting the thermodynamic equilibrium towards propylene generation. Full article

Potentially toxic elements such as cadmium (Cd) in agricultural soils represent a global concern due to their toxicity and potential accumulation in the food chain. However, our understanding of cadmium’s complex sources and the mechanisms controlling its spatial distribution across diverse edaphic and geological contexts remains limited, particularly in underexplored agricultural regions. Our study aimed to assess the total accumulated Cd content in soils under avocado cultivation and its association with edaphic, geochemical, and geomorphological variables. To this end, we considered the total concentrations of other metals and explored their associations to gain a better understanding of Cd’s spatial distribution. We analyzed 26 physicochemical properties, the total concentrations of 22 elements (including heavy and trace metals such as As, Ba, Cr, Cu, Hg, Ni, Pb, Sb, Se, Sr, Tl, V, and Zn and major elements such as Al, Ca, Fe, K, Mg, and Na), and six geospatial variables in 410 soil samples collected from various avocado-growing regions in Peru in order to identity potential associations that could help explain the spatial patterns of Cd. For data analysis, we applied (1) univariate statistics (skewness, kurtosis); (2) multivariate methods such as Spearman correlations and principal component analysis (PCA); (3) spatial modeling using the Geodetector tool; and (4) non-parametric testing (Kruskal–Wallis test with Dunn’s post hoc test). Our results indicated (1) the presence of hotspots with Cd concentrations exceeding 3 mg·kg⁻¹, displaying a leptokurtic distribution (skewness = 7.3); (2) dominant accumulation mechanisms involving co-adsorption and cation competition (Na⁺, Ca²⁺), as well as geogenic co-accumulation with Zn and Pb; and (3) significantly higher Cd concentrations in Leptosols derived from Cretaceous intermediate igneous rocks (diorites/tonalites), averaging 1.33 mg kg⁻¹ compared to 0.20 mg·kg⁻¹ in alluvial soils (p < 0.0001). The factors with the greatest explanatory power (q > 15%, Geodetector) were the Zn content, parent material, geological age, and soil taxonomic classification. These findings provide edaphogenetic insights that can inform soil cadmium (Cd) management strategies, including recommendations to avoid establishing new plantations in areas with a high risk of Cd accumulation. Such approaches can enhance the efficiency of mitigation programs and reduce the risks to export markets. Full article

The study aimed to determine the physical and physicochemical properties of soybean grains using NIR spectroscopy coupled with multivariate data analysis. The experiment was carried out in two stages: first, individual characterization of defects and healthy grains; then, analyses of samples classified into different types (type I, type II, basic standard, and out of type). The centesimal composition of the grains (crude protein, lipids, water content, crude fiber, starch, and ash) was determined by NIR spectroscopy, and the data were analyzed by ANOVA, Scott-Knott test, principal component analysis (PCA), k-means clustering, and Pearson correlation. The results showed significant variations between defects and commercial types in all the variables evaluated (p < 0.05), with an emphasis on germinated grains (higher protein content) and broken grains (higher fiber content). The PCA explained 66.6% of the total variance in the defect sets and 52.2% of the types, allowing the formation of groups defined by the clustering algorithms. Pearson correlations indicated important interactions between the chemical variables, such as the negative correlation between protein and crude fiber (r = −0.73) and between lipids and water content (r = −0.66). It is concluded that the NIR method combined with multivariate modeling allows for the rapid assessment of soybean grain quality in real time, optimizing, reducing waste in, and increasing the efficiency of post-harvest processes. Full article