Search Results (34)

Antibiotic resistance genes (ARGs) are regarded as a major threat to public health and ecological security globally. The Dongliao River Basin is a typical farming–pastoral ecotone in the northeast of China. It is of great practical significance to explore the pollution characteristics and sources of ARGs in the Dongliao River. In this study, the Dongliao River Basin was taken as the research object, and water samples were collected at five points in the wet season, the normal season and the dry season, and the qPCR technology was used to detect the ARGs, revealing its spatial and temporal distribution characteristics. The results show that the temporal difference in ARGs was mainly in the wet season, and the contribution rates of sulfonamides (SAs) and aminoglycosides (AMs)ARGs were the largest, with relative abundance reaching 13–27% and 7–37%. In the normal season, the contribution rate of AMs ARGs further increased to 26–37%, while the contribution rate of SAs and tetracyclines (TCs) ARGs also showed a high level, accounting for 12–20% and 11–16%. In dry season, the ARGs of AMs and TCs reached 29–43% and 16–22%. As far as the spatial distribution characteristics were concerned, the absolute abundance of ARGs reached the maximum value of 3.79 × 10⁶ copies/mL in the sampling section of Chengzishang during the wet season. In the normal season, the absolute abundance of ARGs was the largest at the sampling section of Heqing River, which was 2.62 × 10⁶ copies/mL; While in the dry season, the absolute abundance of ARGs reached the maximum at the sampling section of Sishuang Bridge, which was 5.30 × 10⁶ copies/mL. Furthermore, using principal component analysis–multiple linear regression (PCA–MLR) model, sul1, sul2, aadA2–03, aadA–01 genes with high absolute abundance was selected for source analysis, so as to reveal the source of ARGs pollution in Dongliao River. The results indicated that sulfonamide resistance genes (sul1, sul2) were primarily driven by nutrient salt contamination; aminoglycoside resistance genes (aadA2–03, aadA–01) exhibit sensitivity to temperature gradients, with significant proliferation during high–temperature seasons. This study provided a scientific basis for the prevention and control strategy of ARGs pollution in the Dongliao River Basin. Full article

In recent years, the rapid development of underground engineering projects has driven a significant increase in the variety and quantity of excavation equipment. The wear of excavation tools significantly increases construction costs and reduces construction efficiency. The wear rate of excavation tools is closely related to the abrasiveness of the rock. The Cerchar abrasivity index (CAI) is the most widely used index for estimating rock abrasiveness. The primary objective of this paper is to develop a novel prediction model for CAI, which is established based on the mechanical properties and petrographic parameters of rocks. These parameters include uniaxial compressive strength, Brazilian splitting strength, quartz content, equivalent quartz content, average quartz size, brittleness indices, rock abrasive index, and Schimazek’s F-abrasiveness. Correlation analysis was used to conduct a preliminary analysis between CAI and single-influence parameters. The results indicated that a single factor is not suitable for directly predicting CAI. In addition, multiple linear regression (MLR) and a non-linear algorithm, gene expression programming (GEP), were used to establish new prediction models for CAI. A statistical comparison was conducted between the prediction accuracy of the GEP-based model and the MLR-based model. In comparison to the MLR-based model, the GEP-based model demonstrates higher accuracy in predicting CAI. Full article

Background/Objectives: There is clear evidence for the higher prevalence of varicose veins (VVs) among women. In this regard, the research on sex differences affecting this condition is very important for sex-specific health care. We aimed to assess how male or female sex may contribute to the changes to gene expression profiles in the vein wall during varicose transformation. Methods: Paired varicose vein (VV) and non-varicose vein (NV) segments were harvested from patients with VVs after venous surgery. Processed RNAs from those samples were subjected to gene expression analysis by reverse transcription quantitative polymerase chain reaction (RT-qPCR) followed by further data analysis. Multiple linear regression (MLR) analysis was performed to identify and characterize relationships among multiple factors (relative mRNA levels of a gene in NV or VV or their ratio, as dependent variables) and sex (independent variable, used individually or in combination with other patient’s characteristics). For sex-specific gene regulation analysis, all potential binding sites for sex hormone receptors were identified in each gene’s regulatory region sequence. Results: Using the independent method and a replicative patient sample set, we validated our previous data on 23 genes’ differential expression in VVs and obtained insights on their sex-specific regulation. Sex (as an individual independent variable or in combination with other parameters—patient characteristics such as Age, BMI, CEAP class, Height, VVD manifestation and duration) was a moderate predictor (0.40 < R < 0.59; p (R) < 0.05) for the STK38L expression in VVs (with its higher mRNA level in NVs and VVs of women compared to men); sex was a strong predictor (0.6 < R < 0.79; p (R) < 0.05) for the TIMP1 expression in VVs (with its lower mRNA level in VVs of women compared to men); sex was a moderate predictor (0.40 < R < 0.59; p (R) < 0.05) for the EBF1 expression in NVs (with its lower mRNA level in NVs of women compared to men). Conclusions: Confirmed differential expression of the studied genes in VVs indicates their plausible participation in vein wall remodeling. Sex-specific expression in veins for the subset of those genes suggests their hormonal regulation as well as other mechanisms involved in VV pathogenesis. This work enriches our understanding of sex features for the development of VVs and may provide the foundation for future investigations and beneficial treatment options. Full article

Background/Objectives: Gestational diabetes mellitus (GDM) is characterized by a systemic inflammatory response and the expression of inflammatory factors in visceral adipose tissue (VAT). However, the association between these two inflammatory processes has not been fully elucidated. Therefore, this study aimed to (1) investigate whether whole blood counts, the neutrophil–lymphocyte ratio (NLR), the monocyte–lymphocyte ratio (MLR), serum adiponectin levels, and the mRNA expression of inflammatory genes (TLR2, TLR4, pro-inflammatory cytokines: IL-1β, IL-6, and TNF-α, anti-inflammatory cytokines: IL-1RA, IL-10, and adiponectin) in VAT are altered in women with GDM in comparison to pregnant women with normal glucose tolerance (NGT), and (2) determine the correlations between systemic and local VAT inflammation in all, GDM, and NGT women. Methods: Study of 50 GDM and 50 women with NGT with a cross-sectional design. Standard biochemical and hematological tests were conducted and relative mRNA expression in VAT was measured by RT-qPCR. Results: Women with GDM showed higher neutrophil, monocyte, NLR, MLR, and VAT TNF-α/IL-10 mRNA expression ratios while lymphocyte and eosinophil counts, serum adiponectin, and mRNA local VAT inflammatory markers such as TLR2, TLR4, IL-1β, IL-6, IL-1RA, and IL-10 were lower in women with GDM relative to women with NGT. Additionally, the circulating monocyte count were associated with TLR2 and TLR-4 VAT mRNA expression levels and eosinophils count were associated with IL-1β, IL-6, IL-10, and IL-1RA VAT expression levels in women with GDM. Conclusions: GDM is characterized by systemic inflammation, and some circulating immune cells, such as monocytes and eosinophils, are associated with the expression of inflammatory markers in VAT. Full article

Bacterial degradation is one important Microcystin (MC) removal method in the natural environment. The traditional MC-degrading pathway was proposed based on the functions of individual recombinant Mlr enzymes and the structures of the main MC-degrading products. However, the actual MC-degrading mechanism by Mlr enzymes in wild-type bacteria remains unclear. In this study, bioinformatic analysis, heterologous expression, and knockout mutation were performed to elaborate the MC-degrading mechanism by Mlr enzymes in Sphingopyxis sp. m6. The results showed that mlr gene cluster was initially acquired by horizontal gene transfer, followed by vertical inheritance within Alphaproteobacteria. Mlr enzymes exhibit distinct subcellular localizations and possess diverse conserved catalytic domains. The enzymatic cascade MlrA/MlrB/MlrC sequentially cleaves Microcystin-LR (MC-LR) via Adda-Arg, Ala-Leu, and Adda-Glu bonds, generating characteristic intermediates (linearized MC-LR, tetrapeptide, and Adda). Notably, recombinant MlrC demonstrated dual-targeting degrading capability (linearized MC-LR and tetrapeptide), while tetrapeptide specificity in endogenous processing of Sphingopyxis sp. m6. Marker-free knockout mutants of mlr genes were first constructed in MC-degrading bacteria, unveiling that mlrA was indispensable in initial MC cleavage, whereas mlrB/mlrC/mlrD displayed functional compensation through other enzymes with similar functions. This study promotes the mechanistic understanding of MC bacterial degradation and offers a theoretical basis for a bioremediation strategy targeting cyanotoxin pollution. Full article

Microcystins (MCs) are the most toxic and abundant cyanotoxins found in natural waters during harmful cyanobacterial blooms. These toxins pose a significant threat to plant, animal, and human health due to their toxicity. Degradation of MCs by MC-degrading bacteria is a promising method for controlling these toxins, demonstrating safety, high efficiency, and cost-effectiveness. In this study, we isolated potential MC-degrading bacteria (strains TA13, TA14, and TA19) from Lake Kasumigaura in Japan and found that they possess a high capacity for MC degradation. Based on 16S rRNA gene sequencing, all three isolated strains were identified as belonging to the Klebsiella species. These bacteria effectively degraded MC-RR, MC-YR, and MC-LR under various temperature and pH conditions within 10 h, with the highest degrading activity and degradation rate observed at 40 °C. Furthermore, the isolated strains efficiently degraded MCs not only under neutral pH conditions, but also in alkaline environments. Additionally, we detected the MC-degrading gene (mlrA) in all three isolated strains, marking the first report of the mlrA gene in Klebsiella species. The copy number of the mlrA gene in the strains increased after exposure to MCs. These findings indicate that strains TA13, TA14, and TA19 significantly contribute of MC bioremediation in Lake Kasumigaura during cyanobacterial blooms. Full article

As the global energy landscape evolves towards sustainability, the extensive usage of fossil fuels in electricity generation is progressively diminishing, while the contribution of renewable energy sources is steadily increasing. In this evolving scenario, the importance of load forecasting cannot be overstated in optimizing energy management and ensuring the efficient operation of industrial plants regardless of their scale. By accurately anticipating energy demand, industrial facilities can enhance efficiency, reduce costs, and facilitate the adoption of renewable energy technologies in the power grid. Recent studies have emphasized the pervasive utilization of machine learning-based algorithms in the field of electric load forecasting for industrial plants. Their capacity to analyze intricate patterns and enhance prediction accuracy renders them a favored option for enhancing energy management and operational efficiency. The present analysis revolves around the creation of short-term electric load forecasting models for a large industrial plant operating in Adana, Turkey. The integration of calendar, meteorological, and lagging electrical variables, along with machine learning-based algorithms, is employed to boost forecasting accuracy and optimize energy utilization. The ultimate objective of the present study is to conduct a thoroughgoing and detailed analysis of the statistical performance of the models and associated error metrics. The metrics employed include the R² and MAPE values. Full article

Understanding the pig immune function is crucial for disease-resistant breeding and potentially for human health research due to shared immune system features. Immune cell ratios, like monocyte/lymphocyte ratio (MLR) and neutrophil/lymphocyte ratio (NLR), offer a more comprehensive view of immune status compared to individual cell counts. However, research on pig immune cell ratios remains limited. This study investigated MLR and NLR in a Duroc × Erhualian F₂ resource population. Heritability analysis revealed high values (0.649 and 0.688 for MLR and NLR, respectively), suggesting a strong genetic component. Furthermore, we employed an ensemble-like GWAS (E-GWAS) strategy and functional annotation analysis to identify 11 MLR-associated and 6 NLR-associated candidate genes. These genes were significantly enriched in immune-related biological processes. These findings provide novel genetic markers and candidate genes associated with porcine immunity, thereby providing valuable insights for addressing biosecurity and animal welfare concerns in the pig industry. Full article

Moringa oleifera leaves are an inexpensive substitute for staple foods. Despite limited data, Moringa oleifera leaf protein (Mo-Pr) may be allergenic in BALB/c mice. In mouse models and allergic patients, dendritic cells (DCs) may be involved in food allergy. In addition, some allergens, including food allergens, can directly activate DCs and induce Th2 polarization. We investigated whether Mo-Pr can modulate the functional profile of murine bone marrow-derived dendritic cells (BMDCs) in vitro. BMDCs were obtained from mouse bone marrow cultured with granulocyte–macrophage colony-stimulating factor (GM-CSF) for 7 days and then treated with lipopolysaccharide (LPS) or Mo-Pr. BMDC phenotypes were evaluated via flow cytometry, cytokine production was assessed using ELISA, the expression of key genes was studied using qRT-PCR, the effects on T-cell differentiation were investigated using mixed lymphocyte reaction (MLR), and transcriptional changes in BMDCs were investigated using RNA-Seq. Mo-Pr-specific IgE was investigated in recipient serum after BMDC transfer. Mo-Pr treatment significantly induced BMDC maturation, increased the expression of CD80/86 and MHC II, resulted in the production of IL-12 and TNF-α, and induced T-cell differentiation. Mo-Pr treatment stimulated BMDCs’ expression of the Th2 promoters OX40L and TIM-4, induced the production of the Th2-type chemokines CCL22 and CCL17, and decreased the Th1/Th2 ratio in vitro. Healthy recipients of Mo-Pr-treated BMDCs produced Mo-Pr-specific IgE. Full article

Accurate estimation of shear wave velocity ($V_s$) is crucial for modeling hydrocarbon reservoirs. The $V_s$ values can be directly measured using the Dipole Shear Sonic Imager data; however, it is very expensive and requires specific technical considerations. To address this issue, researchers have developed different methods for $V_s$ prediction in underground rocks and soils. In this study, the well logging data of a wellbore in the Iranian Aboozar limestone oilfield were used for $V_s$ estimation. The $V_s$ values were estimated using five available empirical correlations, linear regression technique, and two machine learning algorithms including multivariate linear regression and gene expression programming. Those values were compared with the real $V_s$ data. Furthermore, three statistical indices including correlation coefficient ($R^2$), root mean square error ($RMSE$), and mean absolute error ($MAE$) were used to evaluate the effectiveness of the applied techniques. The mathematical correlation obtained by the GEP algorithm delivered the most accurate $V_s$ values with $R^2$ = 0.972, $RMSE$ = 0.000290, and $MAE$ = 0.000208. Compared to the available empirical correlations, the obtained correlation from the GEP approach uses multiple parameters to estimate the $V_s$, thereby leading to more precise predictions. The new correlation can be used to estimate the $V_s$ values in the Aboozar oilfield and other geologically similar reservoirs. Full article

To restrict the entry of polluting components into water bodies, particularly rivers, it is critical to undertake timely monitoring and make rapid choices. Traditional techniques of assessing water quality are typically costly and time-consuming. With the advent of remote sensing technologies and the availability of high-resolution satellite images in recent years, a significant opportunity for water quality monitoring has arisen. In this study, the water quality index (WQI) for the Hudson River has been estimated using Landsat 8 OLI-TIRS images and four Artificial Intelligence (AI) models, such as M5 Model Tree (MT), Multivariate Adaptive Regression Spline (MARS), Gene Expression Programming (GEP), and Evolutionary Polynomial Regression (EPR). In this way, 13 water quality parameters (WQPs) (i.e., Turbidity, Sulfate, Sodium, Potassium, Hardness, Fluoride, Dissolved Oxygen, Chloride, Arsenic, Alkalinity, pH, Nitrate, and Magnesium) were measured between 14 March 2021 and 16 June 2021 at a site near Poughkeepsie, New York. First, Multiple Linear Regression (MLR) models were created between these WQPs parameters and the spectral indices of Landsat 8 OLI-TIRS images, and then, the most correlated spectral indices were selected as input variables of AI models. With reference to the measured values of WQPs, the WQI was determined according to the Canadian Council of Ministers of the Environment (CCME) guidelines. After that, AI models were developed through the training and testing stages, and then estimated values of WQI were compared to the actual values. The results of the AI models’ performance showed that the MARS model had the best performance among the other AI models for monitoring WQI. The results demonstrated the high effectiveness and power of estimating WQI utilizing a combination of satellite images and artificial intelligence models. Full article

Macrolide (MLR) and fluoroquinolone (FQR) resistance in Mycoplasma genitalium (MG) has recently become a major problem worldwide. The available data on the prevalence of MLR and FQR in MG in Russia are limited. In this study, we aimed to evaluate the prevalence and pattern of mutations in 213 MG-positive urogenital swabs from patients in Moscow between March 2021 and March 2022. MLR- and FQR-associated mutations were searched in 23S rRNA as well as in the parC and gyrA genes using Sanger sequencing. The prevalence of MLR was 55/213 (26%), with A2059G and A2058G substitutions being the two most common variants (36/55, 65%, and 19/55, 35%, respectively). FQR detection showed 17% (37/213); two major variants were D84N (20/37, 54%) and S80I (12/37, 32.4%) and three minor variants were S80N (3/37, 8.1%), D84G (1/37, 2.7%), and D84Y (1/37, 2.7%). Fifteen of the fifty-five MLR cases (27%) simultaneously harbored FQR. This study revealed the high frequency of MLR and FQR. We conclude that the improvement of patient examination algorithms and therapeutic approaches should be combined with the routine monitoring of antibiotic resistance based on the sensitivity profiles presented. Such a complex approach will be essential for restraining the development of treatment resistance in MG. Full article

Contamination of water by microcystins is a global problem. These potent hepatotoxins demand constant monitoring and control methods in potable water. Promising approaches to reduce contamination risks have focused on natural microcystin biodegradation led by enzymes encoded by the mlrABCD genes. The first enzyme of this system (mlrA) linearizes microcystin structure, reducing toxicity and stability. Heterologous expression of mlrA in different microorganisms may enhance its production and activity, promote additional knowledge on the enzyme, and support feasible applications. In this context, we intended to express the mlrA gene from Sphingosinicella microcystinivorans B9 in an industrial Saccharomyces cerevisiae strain as an innovative biological alternative to degrade microcystins. The mlrA gene was codon-optimized for expression in yeast, and either expressed from a plasmid or through chromosomal integration at the URA3 locus. Recombinant and wild yeasts were cultivated in medium contaminated with microcystins, and the toxin content was analyzed during growth. Whereas no difference in microcystins content was observed in cultivation with the chromosomally integrated strain, the yeast strain hosting the mlrA expression plasmid reduced 83% of toxins within 120 h of cultivation. Our results show microcystinase A expressed by industrial yeast strains as a viable option for practical applications in water treatment. Full article

Microcystins are secondary metabolites produced by some cyanobacteria, a class of cyclic heptapeptide toxins that are stable in the environment. Microcystins can create a variety of adverse health effects in humans, animals, and plants through contaminated water. Effective methods to degrade them are required. Microorganisms are considered to be a promising method to degrade microcystins due to their high efficiency, low cost, and environmental friendliness. This review focuses on perspectives on the frontiers of microcystin biodegradation. It has been reported that bacteria and fungi play an important contribution to degradation. Analysis of the biodegradation mechanism and pathway is an important part of the research. Microcystin biodegradation has been extensively studied in the existing research. This review provides an overview of (1) pollution assessment strategies and hazards of microcystins in water bodies and (2) the important contributions of various bacteria and fungi in the biodegradation of microcystins and their degradation mechanisms, including mlr gene-induced (gene cluster expressing microcystinase) degradation. The application of biodegradable technology still needs development. Further, a robust regulatory oversight is required to monitor and minimize MC contamination. This review aims to provide more references regarding the detection and removal of microcystins in aqueous environments and to promote the application of biodegradation techniques for the purification of microcystin-contaminated water. Full article

In this study, the employment of the gene expression programming (GEP) technique in forecasting models on sustainable construction materials including mineral admixtures and civil engineering quantities (e.g., compressive strength), was investigated. Compared to the artificial neural networks (ANN) based formulations, which are often too complicated to be used, GEP-based derived models provide estimation equations that are reasonably simple and may be used for practical design purposes and even for hand calculations. Many popular models, such as best-fitted curves based on regression analyses, multi-linear regression (MLR), multinomial logistic regression (MNLR), and multinomial variate regression (MNVR), can also be used for construction materials properties modeling. However, due to the nonlinearity and complexity of the target properties, the models established using linear regression analyses may not reveal the precise behavior. Additionally, regression models lack generality, and this comes from the fact that some functions are defined for regression in classical regression techniques; while in the GEP approach, there is no predefined function to be considered, and it reproduces or omits various combinations of parameters to provide the formulation that fits the experimental outcomes. If the input parameters can be evaluated through simple laboratory or rapid measurements, and also a comprehensive experimental database is made available, the models can be constructed with optimal flexibility. Flexibility in choosing the complexity and fitness functions, such as RMSE, MAE, and MSE, might lead to better performance of the approach and well-capturing the governing pattern behind the material’s characteristics. There may be minor inaccuracies with this technique; however, the explicit mathematical expressions, which can be easily implemented in the design and analysis process, may cover the minor inaccuracies compared to ANN, support vector machine (SVM), and other intelligent approaches. Based on the presented study, sometimes it would be better to provide more than one GEP model and consider different combinations of input contributing variables to afford the possible initial feed for a more settled and comprehensive model. Mostly, GEP’s strengths as a superior machine learning technique in modeling the behavior of construction materials including mineral admixtures, leading to innovative solutions in civil engineering, have been presented. Full article