Combined analysis of cecal microbiota and metabolomics reveals the intervention mechanism of Dayuan Yin in acute lung injury.

The ancient Chinese medicinal formula, Dayuan Yin (DYY), has a long history of use in treating respiratory ailments and is shown to be effective in treating acute infectious diseases. This study aims to explore how DYY may impact intestinal flora and metabolites induced by acute lung injury (ALI). ALI rats were induced with lipopolysaccharide (LPS) to serve as models for assessing the anti-ALI efficacy of DYY through multiple lung injury indices. Changes in intestinal microflora were assessed via 16SrRNA gene sequencing, while cecum contents were analyzed using non-targeted metabonomics. Differential metabolites were identified through data analysis, and correlations between metabolites, microbiota, and inflammatory markers were examined using Pearson's correlation analysis. DYY demonstrated a significant improvement in LPS-induced lung injury and altered the composition of intestinal microorganisms, and especially reduced the potential harmful bacteria and enriched the beneficial bacteria. At the gate level, DYY exhibited a significant impact on the abundance of Bacteroidota and Firmicutes in ALI rats, as well as on the regulation of genera such as Ruminococcus, Lactobacillus, and Romboutsia. Additionally, cecal metabonomics analysis revealed that DYY effectively modulated the abnormal expression of 12 key metabolic biomarkers in ALI rats, thereby promoting intestinal homeostasis through pathways such as purine metabolism. Furthermore, Pearson's analysis indicated a strong correlation between the dysregulation of intestinal microbiota, differential metabolites, and inflammation. These findings preliminarily confirm that ALI is closely related to cecal microbial and metabolic disorders, and DYY can play a protective role by regulating this imbalance, which provides a new understanding of the multi-system linkage mechanism of DYY improving ALI.

Prediction of Wear Rate of Glass-Filled PTFE Composites Based on Machine Learning Approaches.

Wear is induced when two surfaces are in relative motion. The wear phenomenon is mostly data-driven and affected by various parameters such as load, sliding velocity, sliding distance, interface temperature, surface roughness, etc. Hence, it is difficult to predict the wear rate of interacting surfaces from fundamental physics principles. The machine learning (ML) approach has not only made it possible to establish the relation between the operating parameters and wear but also helps in predicting the behavior of the material in polymer tribological applications. In this study, an attempt is made to apply different machine learning algorithms to the experimental data for the prediction of the specific wear rate of glass-filled PTFE (Polytetrafluoroethylene) composite. Orthogonal array L25 is used for experimentation for evaluating the specific wear rate of glass-filled PTFE with variations in the operating parameters such as applied load, sliding velocity, and sliding distance. The experimental data are analysed using ML algorithms such as linear regression (LR), gradient boosting (GB), and random forest (RF). The R2 value is obtained as 0.91, 0.97, and 0.94 for LR, GB, and RF, respectively. The R2 value of the GB model is the highest among the models, close to 1.0, indicating an almost perfect fit on the experimental data. Pearson's correlation analysis reveals that load and sliding distance have a considerable impact on specific wear rate as compared to sliding velocity.

Molecular insights into the hedgehog signaling pathway correlated non-coding RNAs in acute lymphoblastic leukemia, a bioinformatics study.

BACKGROUND: Acute lymphoblastic leukemia (ALL) is a common hematologic cancer with unique incidence and prognosis patterns in people of all ages. Recent molecular biology advances have illuminated ALL's complex molecular pathways, notably the Hedgehog (Hh) signaling system and non-coding RNAs (ncRNAs). This work aimed to unravel the molecular complexities of the link between Hh signaling and ALL by concentrating on long non-coding RNAs (lncRNAs) and their interactions with significant Hh pathway genes. METHODS: To analyze differentially expressed lncRNAs and genes in ALL, microarray data from the Gene Expression Omnibus (GEO) was reanalyzed using a systems biology approach. Hh signaling pathway-related genes were identified and their relationship with differentially expressed long non-coding RNAs (DElncRNAs) was analyzed using Pearson's correlation analysis. A regulatory network was built by identifying miRNAs that target Hh signaling pathway-related mRNAs. RESULTS: 193 DEGs and 226 DElncRNAs were found between ALL and normal bone marrow samples. Notably, DEGs associated with the Hh signaling pathway were correlated to 26 DElncRNAs. Later studies showed interesting links between DElncRNAs and biological processes and pathways, including drug resistance, immune system control, and carcinogenic characteristics. DEGs associated with the Hh signaling pathway have miRNAs in common with miRNAs already known to be involved in ALL, including miR-155-5p, and miR-211, highlighting the complexity of the regulatory landscape in this disease. CONCLUSION: The complex connections between Hh signaling, lncRNAs, and miRNAs in ALL have been unveiled in this study, indicating that DElncRNAs linked to Hh signaling pathway genes could potentially serve as therapeutic targets and diagnostic biomarkers for ALL.

Identification of the regulatory roles of water qualities on the spatio-temporal dynamics of microbiota communities in the water and fish guts in the Heilongjiang River.

The Heilongjiang River is one of the largest rivers in the cool temperate zone and has an abundant fish source. To date, the microbiota community in water samples and fish guts from the Heilongjiang River is still unclear. In the present study, water samples and fish guts were collected from four locations of the Heilongjiang River during both the dry season and the wet season to analyze the spatio-temporal dynamics of microbiota communities in the water environment and fish guts through 16s ribosome RNA sequencing. The water qualities showed seasonal changes in which the pH value, dissolved oxygen, and total dissolved solids were generally higher during the dry season, and the water temperature was higher during the wet season. RDA indicated that higher pH values, dissolved oxygen, and total dissolved solids promoted the formation of microbiota communities in the water samples of the dry season, while higher water temperature positively regulated the formation of microbiota communities in the water samples of the wet season. LEFSe identified five biomarkers with the most abundant difference at the genus level, of which TM7a was upregulated in the water samples of the dry season, and SM1A02, Rheinheimera, Gemmatimonas, and Vogesella were upregulated in the water samples of the wet season. Pearson analysis revealed that higher pH values and dissolved oxygen positively regulated the formation of TM7a and negatively regulated the formation of SM1A02, Rheinheimera, Gemmatimonas, and Vogesella (p < 0.05), while higher water temperature had the opposite regulatory roles in the formation of these biomarkers. The relative abundance of microbiota diversity in fish guts varies greatly between different fish species, even if the fishes were collected from the same water source, indicating that dietary habits and fish species may be key factors, affecting the formation and construction of microbiome community in fish gut. P. glenii, P. lagowskii, G. cynocephalus, and L. waleckii were the main fish resources, which were collected and identified from at least six sample points. RDA indicated that the microbiota in the water environment regulated the formation of microbiota community in the guts of G. cynocephalus and L. waleckii and had limited regulated effects on P. glenii and P. lagowskii. The present study identified the regulatory effects of water qualities on the formation of microbiota communities in the water samples and fish guts, providing valuable evidence for the protection of fish resources in the Heilongjiang River.

"A quantitative analysis on the adoption of soil, water, and forest conservation technologies in the upper Gelana watershed, Northeast Ethiopian highlands".

Globally, the degradation of soil, water, and forests has had a significant impact on both livelihoods and the environment. This issue is particularly severe in developing countries, including Ethiopia. Despite extensive efforts to implement conservation measures for soil, water, and forests in the highlands of Ethiopia, there has been a lack of thorough evaluation and documentation regarding the adoption of these practices by rural households. It is crucial to have scientific and up-to-date information at various spatial scales in order to effectively monitor existing practices, scale up successful initiatives, and promote sustainable regional development. Therefore, this paper focuses on analyzing the adoption of soil, water, and forest conservation activities by households in the upper Gelana watershed, South Wollo zone, Amhara Regional State of Ethiopia. The field data collection for this study took place from January to March 2022, from 150 rural household heads. Data analysis was carried out using SPSS software version 23. Descriptive statistics, Pearson bivariate correlation, and multinomial logistic regression were used. The survey findings revealed that 69 % of the respondents had implemented various soil, water, and forest conservation measures at different stages. The Pearson correlation results indicated a positive relationship between the adoption of soil, water and forest conservation practices. The multinomial logistic regression analysis has revealed that age, gender, access to credit, and access to extension services, significantly influenced the households' decision behaviour to adopt soil conservation practices. Age, access to extension service, and access to water resource were significant predictors of adoption of water conservation practices; whereas age, educational status, and access to extension service were significant predictors of adoption of forest conservation practices. This study underscores the significance of institutional factors in driving the adoption of technology in the research area. It further recommends policies that prioritize the dissemination of information on effective strategies, improvement of access to extension services, water resources, and credit facilities to promote sustainable watershed management. This study is exceptional in its innovative approach, which explores the convergence of these vital conservation domains within the distinct setting of the upper Gelana watershed. Studying the adoption of these technologies is crucial for informing policy-making and designing effective interventions that promote sustainable watershed practices. In this case, the Ministry of Agriculture, and development agents should scale up the adoption of these practices and take remedial actions for those not yet adopted.

Combined transcriptome and microbiome analysis reveals the thyrotoxic effects of PM2.5 in female rats.

Pervasive environmental pollutants, specifically particulate matter (PM2.5), possess the potential to disrupt homeostasis of female thyroid hormone (TH). However, the precise mechanism underlying this effect remains unclear. In this study, we established a model of PM2.5-induced thyroid damage in female rats through intratracheal instillation and employed histopathological and molecular biological methods to observe the toxic effects of PM2.5 on the thyroid gland. Transcriptome gene analysis and 16S rRNA sequencing were utilized to investigate the impact of PM2.5 exposure on the female rat thyroid gland. Furthermore, based on the PM2.5-induced toxic model in female rats, we evaluated its effects on intestinal microbiota, TH levels, and indicators of thyroid function. The findings revealed that PM2.5 exposure induced histopathological damage to thyroid tissue by disrupting thyroid hormone levels (total T3 [TT3], (P < 0.05); total T4 [TT4], (P < 0.05); and thyrotropin hormone [TSH], (P < 0.05)) and functional indices (urine iodine [UI], P > 0.05), thus further inducing histopathological injuries. Transcriptome analysis identified differentially expressed genes (DEGs), primarily concentrated in interleukin 17 (IL-17), forkhead box O (FOXO), and other signaling pathways. Furthermore, exposure to PM2.5 altered the composition and abundance of intestinal microbes. Transcriptome and microbiome analyses demonstrated a correlation between the DEGs within these pathways and the flora present in the intestines. Moreover, 16 S rRNA gene sequencing analysis or DEGs combined with thyroid function analysis revealed that exposure to PM2.5 significantly induced thyroid hormone imbalance. We further identified key DEGs involved in thyroid function-relevant pathways, which were validated using molecular biology methods for clinical applications. In conclusion, the homeostasis of the "gut-thyroid" axis may serve as the underlying mechanism for PM2.5-induced thyrotoxicity in female rats.

Neighborhood structure-guided brain functional networks estimation for mild cognitive impairment identification.

The adoption and growth of functional magnetic resonance imaging (fMRI) technology, especially through the use of Pearson's correlation (PC) for constructing brain functional networks (BFN), has significantly advanced brain disease diagnostics by uncovering the brain's operational mechanisms and offering biomarkers for early detection. However, the PC always tends to make for a dense BFN, which violates the biological prior. Therefore, in practice, researchers use hard-threshold to remove weak connection edges or introduce l 1-norm as a regularization term to obtain sparse BFNs. However, these approaches neglect the spatial neighborhood information between regions of interest (ROIs), and ROI with closer distances has higher connectivity prospects than ROI with farther distances due to the principle of simple wiring costs in resent studies. Thus, we propose a neighborhood structure-guided BFN estimation method in this article. In detail, we figure the ROIs' Euclidean distances and sort them. Then, we apply the K-nearest neighbor (KNN) to find out the top K neighbors closest to the current ROIs, where each ROI's K neighbors are independent of each other. We establish the connection relationship between the ROIs and these K neighbors and construct the global topology adjacency matrix according to the binary network. Connect ROI nodes with k nearest neighbors using edges to generate an adjacency graph, forming an adjacency matrix. Based on adjacency matrix, PC calculates the correlation coefficient between ROIs connected by edges, and generates the BFN. With the purpose of evaluating the performance of the introduced method, we utilize the estimated BFN for distinguishing individuals with mild cognitive impairment (MCI) from the healthy ones. Experimental outcomes imply this method attains better classification performance than the baselines. Additionally, we compared it with the most commonly used time series methods in deep learning. Results of the performance of K-nearest neighbor-Pearson's correlation (K-PC) has some advantage over deep learning.

Development of a Predictive Model for Carbon Dioxide Corrosion Rate and Severity Based on Machine Learning Algorithms.

Carbon dioxide corrosion is a pervasive issue in pipelines and the petroleum industry, posing substantial risks to equipment safety and longevity. Accurate prediction of corrosion rates and severity is essential for effective material selection and equipment maintenance. This paper begins by addressing the limitations of traditional corrosion prediction methods and explores the application of machine learning algorithms in CO2 corrosion prediction. Conventional models often fail to capture the complex interactions among multiple factors, resulting in suboptimal prediction accuracy, limited adaptability, and poor generalization. To overcome these limitations, this study systematically organized and analyzed the data, performed a correlation analysis of the data features, and examined the factors influencing corrosion. Subsequently, prediction models were developed using six algorithms: Random Forest (RF), K-Nearest Neighbors (KNN), Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), XGBoost, and LightGBM. The results revealed that SVM exhibited the lowest performance on both training and test sets, while RF achieved the best results with R2 values of 0.92 for the training set and 0.88 for the test set. In the classification of corrosion severity, RF, LightGBM, SVM, and KNN were utilized, with RF demonstrating superior performance, achieving an accuracy of 99% and an F1-score of 0.99. This study highlights that machine learning algorithms, particularly Random Forest, offer substantial potential for predicting and classifying CO2 corrosion. These algorithms provide innovative approaches and valuable insights for practical applications, enhancing predictive accuracy and operational efficiency in corrosion management.

Ecological and human health risk associated with heavy metals (HMs) contaminant sourced from petroleum refinery oily sludge.

The environmental and human health risk of heavy metals (HMs) in petroleum based oily sludge (OS) varies depending upon the source of origin of the crude oil and treatment processes practiced at the refineries. Consequently, the present study explores the potential risk associated with HMs of OS obtained from different refinery sites to the environment and human health. The results showed that HMs (Cu, Ni, Zn, Mn) present in OS surpasses the permissible limit of WHO guidelines except for Cr. Additionally, the Igeo value (grade 3-6), Ef (2.48-121.4), PLI (5.12-22.65), Cd (32.48-204.76) and PERI (grade 1-5) confirmed the high level of HMs contamination into the OS and its risk to the environment. Besides, the hazard index (HI) and the total carcinogenic risk (TCR) for HMs show substantial risk to both adult and children health. Likewise, the G-mean enzyme index and potential soil enzyme risk index (PSERI) of the OS showed a high risk to soil biological properties. Furthermore, statistical analysis confirmed the heterogeneity in properties of the OS and its potential impact on the soil ecosystem arising from different sites. Finally, the study unveils a novel perspective on the environmental and human health consequences associated with the OS.

The adipokine profile in the plasma and anterior pituitary of pigs during the estrous cycle.

Adipokines play crucial roles in both reproductive and energy metabolic processes. This study aimed to compare the hormonal plasma profile of adiponectin, apelin, vaspin, chemerin, resistin, visfatin, and adipolin, and the expression of their receptors in the anterior pituitary (AP) between normal-weight Large White (LW) and fat Meishan (MS) pigs during different phases of the estrous cycle. We measured adipokine levels in the plasma and assessed their gene expression in the AP. We used Pearson's correlation analysis to examine potential links between adipokines levels, their receptors, and metabolic parameters (body weight; backfat thickness) and reproductive parameters (pituitary weight; age at puberty; levels of gonadotropins, steroid hormones; and gene expression of gonadotropin-releasing hormone receptor and gonadotropins in AP). The plasma levels of the evaluated adipokines fluctuated with phase and breed, except for visfatin and adipolin. Moreover, adipokine expression in AP varied significantly between breeds and estrous cycle phases, except for resistin receptor CAP1. Notably, we observed a positive correlation between plasma levels of adiponectin and its transcript in the AP only in MS pigs. Apelin gene expression correlated negatively with its receptor in MS, while we observed a breed-dependent correlation between chemerin gene expression and its receptor CMKLR1. We identified significant positive or negative correlations between adipokines or their receptor levels in plasma and AP as well as metabolic or reproductive parameters, depending on the breed. In conclusion, we have demonstrated breed-specific and estrous cycle-dependent regulation of adipokines in AP, underscoring their potential impact on metabolic and reproductive processes in swine.

Optimizing short-term in-class process evaluation: analyzing the effectiveness of teaching interventions in pharmaceutical education using repeated measures analysis.

BACKGROUND: The assessment of the effectiveness of teaching interventions in enhancing students' understanding of the Pharmaceutical Care Network Europe (PCNE) Classification System is crucial in pharmaceutical education. This is especially true in regions like China, where the integration of the PCNE system into undergraduate teaching is limited, despite its recognized benefits in addressing drug-related problems in clinical pharmacy practice. Therefore, this study aimed to evaluate the effectiveness of teaching interventions in improving students' understanding of the PCNE Classification System in pharmaceutical education. METHODS: Undergraduate pharmacy students participated in a series of sessions focused on the PCNE system, including lectures (t1), case analyses (t2), and practical implementation (t3). The levels of understanding were evaluated using time-course questionnaires. Initially, paired samples t-Tests were used to compare understanding levels between different time points. Subsequently, Repeated Measures Analysis (RMA) was employed. Pearson correlation analysis was conducted to examine the relationship between understanding levels and the usability and likelihood of using the PCNE system, as reported in the questionnaires. RESULTS: The paired samples t-Tests indicated insignificant differences between t2 and t3, suggesting limited improvement following the practical implementation of the PCNE system. However, RMA revealed significant time effects on understanding levels in effective respondents and the focused subgroup without prior experience (random intercept models: all p < 0.001; random slope models: all p < 0.001). These results confirmed the effectiveness of all three teaching interventions. Pearson correlation analysis demonstrated significant positive correlations between understanding levels and the usability and likelihood of using the PCNE system at all examined time points. This finding highlighted the reliability of the understanding levels reported in the questionnaires. The homework scores were used as external calibration standards, providing robust external validation of the questionnaire's validity. CONCLUSION: The implementation of RMA provided robust evidence of the positive impact of time on understanding levels. This affirmed the effectiveness of all teaching interventions in enhancing students' comprehension of the PCNE Classification System. By utilizing RMA, potential errors inherent in common statistical methods, such as t-Tests, were mitigated. This ensured a more comprehensive and accurate assessment of the effectiveness of the teaching interventions.

Addressing the Gap: Racial Disparities and Public Health Strategies in the Epidemiology of Gastrointestinal Stromal Tumors.

Background Gastrointestinal stromal tumors (GISTs) represent the most common mesenchymal neoplasms of the gastrointestinal tract, arising from the interstitial cells of Cajal. These tumors bridge the nervous system and muscular layers of the gastrointestinal tract, playing a crucial role in the digestive process. The incidence of GISTs demonstrates notable variations across different racial and ethnic groups, underscoring the need for in-depth analysis to understand the interplay of genetic, environmental, and socioeconomic factors behind these disparities. Linear regression analysis is a pivotal statistical tool in such epidemiological studies, offering insights into the temporal dynamics of disease incidence and the impact of public health interventions. Methodology This investigation employed a detailed dataset from 2009 to 2020, documenting GIST incidences across Asian, African American, Hispanic, and White populations. A meticulous preprocessing routine prepared the dataset for analysis, which involved data cleaning, normalization of racial terminologies, and aggregation by year and race. Linear regression models and Pearson correlation coefficients were applied to analyze trends and correlations in GIST incidences across the different racial groups, emphasizing an understanding of temporal patterns and racial disparities in disease incidence. Results The study analyzed GIST cases among four racial groups, revealing a male predominance (53.19%) and an even distribution of cases across racial categories: Whites (27.66%), Hispanics (25.53%), African Americans (24.47%), and Asians (22.34%). Hypertension was the most common comorbidity (32.98%), followed by heart failure (28.72%). The linear regression analysis for Asians showed a decreasing trend in GIST incidences with a slope of -0.576, an R-squared value of 0.717, and a non-significant p-value of 0.153. A significant increasing trend was observed for Whites, with a slope of 0.581, an R-squared value of 0.971, and a p-value of 0.002. African Americans exhibited a moderate positive slope of 0.277 with an R-squared value of 0.470 and a p-value of 0.201, indicating a non-significant increase. Hispanics showed negligible change over time with a slope of -0.095, an R-squared value of 0.009, and a p-value of 0.879, suggesting no significant trend. Conclusions This study examines GIST incidences across racial groups, revealing significant disparities. Whites show an increasing trend (p = 0.002), while Asians display a decreasing trend (p = 0.153), with stable rates in African Americans and Hispanics. Such disparities suggest a complex interplay of genetics, environment, and socioeconomic factors, highlighting the need for targeted research and interventions that address these differences and the systemic inequalities influencing GIST outcomes.

Mitigation of ammonia volatilization from organic and inorganic nitrogen sources applied to soil using water hyacinth biochars.

The recent global population explosion has increased people's food demand. To meet this demand, huge amounts of nitrogen (N) fertilizer have been applied in the worldwide. However, ammonia (NH3) volatilization is one of the primary factors of N loss from soil after N application causing decrease crop N utilization efficiency and productivity. Incubation experiments were conducted on an acidic clayey soil with two different N sources (urea and anaerobic digestion effluent; ADE), two differently-produced biochars, and three biochar application rates (0%, 0.25%, and 1.0% w/w). Ammonia volatilization was lower from urea (14.0-23.5 mg N kg-1) and ADE (11.3-21.0 mg N kg-1) with biochar application than those without biochar (40.1 and 26.2 mg N kg-1 from urea and ADE alone, respectively). Biochar application significantly mitigated volatilization and reduction percentages for urea and ADE were 40%-64% and 18%-55%, respectively. 1.0% biochar application mitigated volatilization significantly compared to 0.25% application regardless of N source and biochar types. Possible mechanism for volatilization mitigation for urea and ADE were increased N immobilization by soil microorganisms and accelerated net nitrification rate due to increased soil nitrifying bacteria, respectively. Overall, our results clarified different mechanisms for N volatilization mitigation from different (inorganic vs. organic) N sources with biochar application.

Evolution and health risk of indicator microorganisms in landscape water replenished by reclaimed water.

As an important means to solve water shortage, reclaimed water has been widely used for landscape water supply. However, with the emergence of large-scale epidemic diseases such as SARS, avian influenza and COVID-19 in recent years, people are increasingly concerned about the public health safety of reclaimed water discharged into landscape water, especially the pathogenic microorganisms in it. In this study, the water quality and microorganisms of the Old Summer Palace, a landscape water body with reclaimed water as the only replenishment water source, were tracked through long-term dynamic monitoring. And the health risks of indicator microorganisms were analyzed using Quantitative Microbial Risk Assessment (QMRA). It was found that the concentration of indicator microorganisms Enterococcus (ENT), Escherichia coli (EC) and Fecal coliform (FC) generally showed an upward trend along the direction of water flow and increased by more than 0.6 log at the end of the flow. The concentrations of indicator microorganisms were higher in summer and autumn than those in spring. And there was a positive correlation between the concentration of indicator microorganisms and COD. Further research suggested that increased concentration of indicator microorganisms also led to increased health risks, which were more than 30% higher in other areas of the park than the water inlet area and required special attention. In addition, (water) surface operation exposure pathway had much higher health risks than other pathways and people in related occupations were advised to take precautions to reduce the risks.

Gradient synchronization for multivariate functional data, with application to brain connectivity.

Quantifying the association between components of multivariate random curves is of general interest and is a ubiquitous and basic problem that can be addressed with functional data analysis. An important application is the problem of assessing functional connectivity based on functional magnetic resonance imaging (fMRI), where one aims to determine the similarity of fMRI time courses that are recorded on anatomically separated brain regions. In the functional brain connectivity literature, the static temporal Pearson correlation has been the prevailing measure for functional connectivity. However, recent research has revealed temporally changing patterns of functional connectivity, leading to the study of dynamic functional connectivity. This motivates new similarity measures for pairs of random curves that reflect the dynamic features of functional similarity. Specifically, we introduce gradient synchronization measures in a general setting. These similarity measures are based on the concordance and discordance of the gradients between paired smooth random functions. Asymptotic normality of the proposed estimates is obtained under regularity conditions. We illustrate the proposed synchronization measures via simulations and an application to resting-state fMRI signals from the Alzheimer's Disease Neuroimaging Initiative and they are found to improve discrimination between subjects with different disease status.

GIS based geostatistical modelling and trends analysis of groundwater quality for suitable uses in Dhaka division.

Preserving the quality of groundwater has become Bangladesh's primary challenge in recent years. This study explores temporal trend variations in groundwater quality on a broader scale across 18 stations within the Dhaka division over 35 years. The data set encompasses an analysis of 15 distinct water quality parameters. Modified Mann-Kendal, Sens Slope and Mann-Kendal tests were performed to determine the trend's variation and slope. In addition, the spatial-temporal changes in the quality of groundwater are studied through Geographic Information System (GIS) mapping and Piper diagram was applied to identify the unique hydrochemical properties. This is the first study conducted on this area using various trends analysis and no in-depth study is available highlighting the trends analysis of groundwater quality on a larger magnitude. In contrast, the correlation matrix reveals a high association between Mg2+ and SO42-, Na+ and Cl- that affects salinity and overall hardness at the majority of sites. The Piper diagram also demonstrates that the groundwater in Madaripur Sadar has major salinity issues. The analysis reveals a distinctive dominance of bicarbonate (HCO3-) ions across all sampling stations, with (HCO3-) equivalent fractions consistently ranging from 0.70 to 0.99 which can cause a significant impact on groundwater uses. This extensive analysis of long-term groundwater quality trends in the Dhaka Division enables researchers to comprehend the overall transition of groundwater quality for hardness related complications in future. Moreover, it can be a baseline study considering the valuable implications and future steps for sustainable water resource management in this region.

Pesticide occurrence and distribution in fogwater collected at four sites at Strasbourg metropolitan between 2015 and 2021.

The presence of pesticides in fogwater plays a major role in accumulating relatively substantial levels of trace compounds due to their unique physico-chemical characteristics. The radiation wintertime fog in Alsace has been studied in the past few years (between 2015 and 2021) at four sites (Geispolsheim, Erstein, Strasbourg, and Cronenbourg). Fog samples are extracted using the liquid-liquid extraction (LLE) performed on the XTR Chromabond cartridge coupled with gas/liquid chromatography-tandem mass spectrometry (GC-MS/MS and LC-MS/MS). The samples are found to be contaminated by 25 semi- and non-volatile currently-used and previously-banned pesticides (like procymidone) and 16 organochlorine pesticides (OCPs) at notable levels and high detection frequency (DF). The analysis also reveals that Cronenbourg is the most contaminated site (31.5 ± 3.0 µg. L-1), followed by Erstein (23.1 ± 17.0 µg. L-1), Strasbourg (23.0 ± 3.5 µg. L-1), and Geispolsheim (22.8 ± 7.7 µg. L-1). Pearson and principal component analyses (PCA) prove the simultaneous application of fungicides, insecticides, and herbicides, and their atmospheric transport, mainly through west-southern air currents, from highly impacted sites to near-by urban and less impact sites (Strasbourg and Cronenbourg). The levels of OCPs are found at lower concentrations at all sites than other pesticides, of which dichlorodiphenyltrichloroethane (DDT) and its metabolites have the highest contribution (27%), while hexachlorobenzene (HCB) has the least contribution (3%). Ratio analysis indicates the historical emission of DDTs, whereas a recent and local input of lindane and endosulfan has been observed.

The feedstock anatomical properties determine biochar adsorption capacities: A study using woody bamboos (Bambuseae) and methylene blue as a model molecule.

Feedstock characteristics impact biochar physicochemical properties, and reproducible biochar properties are essential for any potential application. However, in most articles, feedstock aspects (i.e., taxonomic name of the species, part of the plant, and phenological phase) are scarcely reported. This research aimed at studying the effect of species and phenological stage of the feedstock on the properties of the derived biochars and, thus, adsorption capacities in water treatment. In this study, we analysed the anatomical characteristics of three different woody bamboo species [Guadua chacoensis (GC), Phyllostachys aurea (PA), and Bambusa tuldoides (BT)] in culms harvested at two different phenological phases (young and mature), and statistically correlated them with the characteristics of the six derived biochars, including their adsorption performance in aqueous media. Sclerenchyma fibres and parenchyma cells diameter and cell-wall width significantly differed among species. Additionally, sclerenchyma fibres and parenchyma cell-wall width as well as sclerenchyma fibre cell diameters are dependent on the phenological phase of the culms. Consequently, differences in biochar characteristics (i.e., yield and average pore diameter) were also observed, leading to differential methylene blue (MB) adsorption capacities between individuals at different phenological phases. MB adsorption capacities were higher for biochar produced from young culms compared to those obtained from matures ones (i.e., GC: 628.66 vs. 507.79; BT: 537.45 vs. 477.53; PA: 477.52 vs. 462.82 mg/g), which had smaller cell wall widths leading to a lower percentage of biochar yield. The feedstock anatomical properties determined biochar characteristics which modulated adsorption capacities.

Quantitative Determination of Minerals and Toxic Elements Content in Tropical and Subtropical Fruits by Microwave-Assisted Digestion and ICP-OES.

In the presented study, 15 tropical and subtropical fruits were studied for their mineral composition ranging from trace to major elements by ICP-OES after microwave digestion. The moisture amounts were assigned to be between 21.90 (tamarind) and 95.66% (pepino). The differences between the macroelement quantities of the fruits were established to be statistically significant (p<0.01). P and K quantities of fruits were displayed to be between 53.40 (pepino) and 927.74 mg/kg (tamarind) to 720.27 (pepino) and 13441.12 mg/kg (tamarind), respectively. While Ca quantities of fruits vary between 123.71 (pineapple) and 1519.76 mg/kg (blood orange), Mg quantities of fruits were established to be between 78.66 (pepino) and 875.02 mg/kg (tamarind). In general, the lowest macroelement quantities were determined in pepino fruit, but the highest P and K contents were determined in Gooseberry and Tamarind fruits, respectively. The microelement amounts of the fruits were established to be at very low levels compared to the macroelement contents. In general, the most abundant element in fruits was Fe, followed by Zn, Cu, Mn and B in decreasing order. In general, heavy metal quantities of fruits were detected at very low levels (except As and Ba). As and Ba quantities of fruits were assigned to be between 0.972 µg/g (mandarin) and 5.86 (kiwi) to 0.103 (pineapple) and 4.08 (avocado), respectively. As with macro and microelements, results regarding heavy metal concentrations varied depending on fruit types.

Analysis of the Steelmaking Process via Data Mining and Pearson Correlation.

The continuous improvement of the steelmaking process is a critical issue for steelmakers. In the production of Ca-treated Al-killed steel, the Ca and S contents are controlled for successful inclusion modification treatment. In this study, a machine learning technique was used to build a decision tree classifier and thus identify the process variables that most influence the desired Ca and S contents at the end of ladle furnace refining. The attribute of the root node of the decision tree was correlated with process variables via the Pearson formalism. Thus, the attribute of the root node corresponded to the sulfur distribution coefficient at the end of the refining process, and its value allowed for the discrimination of satisfactory heats from unsatisfactory heats. The variables with higher correlation with the sulfur distribution coefficient were the content of sulfur in both steel and slag at the end of the refining process, as well as the Si content at that stage of the process. As secondary variables, the Si content and the basicity of the slag at the end of the refining process were correlated with the S content in the steel and slag, respectively, at that stage. The analysis showed that the conditions of steel and slag at the beginning of the refining process and the efficient S removal during the refining process are crucial for reaching desired Ca and S contents.