Integrating deep learning and regression models for accurate prediction of groundwater fluoride contamination in old city in Bitlis province, Eastern Anatolia Region, Türkiye.

Groundwater resources in Bitlis province and its surroundings in Türkiye's Eastern Anatolia Region are pivotal for drinking water, yet they face a significant threat from fluoride contamination, compounded by the region's volcanic rock structure. To address this concern, fluoride levels were meticulously measured at 30 points in June 2019 dry period and September 2019 rainy period. Despite the accuracy of present measurement techniques, their time-consuming nature renders them economically unviable. Therefore, this study aims to assess the distribution of probable geogenic contamination of groundwater and develop a robust prediction model by analyzing the relationship between predictive variables and target contaminants. In this pursuit, various machine learning techniques and regression models, including Linear Regression, Random Forest, Decision Tree, K-Neighbors, and XGBoost, as well as deep learning models such as ANN, DNN, CNN, and LSTM, were employed. Elements such as aluminum (Al), boron (B), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), phosphorus (Pb), lead (Pb), and zinc (Zn) were utilized as features to predict fluoride levels. The SelectKbest feature selection method was used to improve the accuracy of the prediction model. This method identifies important features in the dataset for different values of k and increases model efficiency. The models were able to produce more accurate predictions by selecting the most important variables. The findings highlight the superior performance of the XGBoost regressor and CNN in predicting groundwater quality, with XGBoost consistently outperforming other models, exhibiting the lowest values for evaluation metrics like mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE) across different k values. For instance, when considering all features, XGBoost attained an MSE of 0.07, an MAE of 0.22, an RMSE of 0.27, a MAPE of 9.25%, and an NSE of 0.75. Conversely, the Decision Tree regressor consistently displayed inferior performance, with its maximum MSE reaching 0.11 (k = 5) and maximum RMSE of 0.33 (k = 5). Furthermore, feature selection analysis revealed the consistent significance of boron (B) and cadmium (Cd) across all datasets, underscoring their pivotal roles in groundwater contamination. Notably, in the machine learning framework evaluation, the XGBoost regressor excelled in modeling both the "all" and "rainy season" datasets, while the convolutional neural network (CNN) outperformed in the "dry season" dataset. This study emphasizes the potential of XGBoost regressor and CNN for accurate groundwater quality prediction and recommends their utilization, while acknowledging the limitations of the Decision Tree Regressor.

Allergenicity and structural properties of new Cor a 1 isoallergens from hazel identified in different plant tissues.

The hazel allergen Cor a 1 is a PR-10 protein, closely related to the major birch pollen allergen Bet v 1. Hazel allergies are caused by cross-reactive IgE antibodies originally directed against Bet v 1. Despite the importance of PR-10 proteins in allergy development, their function and localization in the plant remain largely elusive. Therefore, the presence of Cor a 1 mRNA and proteins was investigated in different tissues, i.e., the female flower, immature and mature nuts, catkins, and pollen. Four yet unknown Cor a 1 isoallergens, i.e., Cor a 1.0501-1.0801, and one new Cor a 1.03 variant were discovered and characterized. Depending on the isoallergen, the occurrence and level of mRNA expression varied in different tissues, suggesting different functions. Interestingly, Cor a 1.04 previously thought to be only present in nuts, was also detected in catkins and pollen. The corresponding Cor a 1 genes were expressed in Escherichia coli. The purified proteins were analysed by CD and NMR spectroscopy. Immunoblots and ELISAs to determine their allergenic potential showed that the new proteins reacted positively with sera from patients allergic to birch, hazel and elder pollen and were recognized as novel isoallergens/variants by the WHO/IUIS Allergen Nomenclature Sub-Committee.

Evaluation of pollution indicators and transported sediment content in agricultural drainage waters for reuse as irrigation, Harran Plain, Turkey.

The aim of this study is to determine the variation of certain pollution indicators and the level of sediment carried within drainage channel discharge waters following irrigation of fields on the Harfran Plain during the irrigation season. In this context, water samples were taken from 27 stations in total, including 26 drainage channels and 1 irrigation water channel (reference point), for 6 months between May 2020 and October 2020, and determined parameters were measured. Areal distribution maps were prepared with the ArcGIS program in order to better visually present the pollution level across the plain with the obtained data. The monthly changes of the analysis parameters and the significance level of the differences between the stations were determined using ANOVA analysis and the correlations between the measured parameters were determined using the Pearson correlation matrix SPSS package program. Evaluated on the basis of these areal distribution maps, it can be seen that the agricultural drainage waters of the Harran Plain are not suitable for irrigation in terms of five pollution indicators (pH, conductivity (EC), turbidity (Turb), sodium (Na+), and nitrate (NO3-)). Some sampling points in terms of pH (D9, R), conductivity (D10, D20), turbidity (all points) and Na + (as sodium adsorption rate) (D20) are in the "high usage restriction" category. In terms of conductivity, five points (D12, D13, D14, D15, D18); all points in terms of bicarbonate (HCO3-); and three points (D10, D12, D18) in terms of Na+ (as SAR) are classified as "low-medium usage restriction". According to the One-way ANOVA test performed to determine the differences between the points, there is a significant difference (p < 0.05) between the sampling points for all the following values: EC, dissolved oxygen (DO), Turb, Na+, HCO3-, chloride (Cl-), sulphate (SO42-), NO3-, pH, and transported sediment (TS), within the 95% confidence interval. In the test performed to determine the differences between the months, at the 95% confidence interval there is a significant difference (p < 0.05) between the months for parameters such as water temperature (WT), pH, DO, Turb, HCO3-, NO3-, and TS. There is a strong positive correlation between EC and Na+, Cl-, and NO3- (r = 0.785-0.915) and Turb and TS (r = 0.725). With regard to sustainable agricultural practice for the plain, it is thought that the obtained results will contribute to administrative decision-making at a variety of management levels.

A BODIPY-based ICT probe for ratiometric and chromo-fluorogenic detection of hazardous oxalyl chloride.

Herein, a novel long wavelength monostyryl BODIPY derivative (BDZ-1) has been synthesized by rational design and used to detect oxalyl chloride, a highly reactive and harmful chemical to humans and other living organisms, with high sensitivity and selectivity. The purple solution of BDZ-1 changed to pink color immediately upon the addition of oxalyl chloride and the weak red fluorescence changed to strong orange fluorescence simultaneously. In addition, the practicability of BDZ-1 was further explored by using a smartphone application, allowing the sensitive and selective on-site detection of oxalyl chloride.

Multivalent Presentation of Cationic Peptides on Supramolecular Nanofibers for Antimicrobial Activity.

Noncovalent and electrostatic interactions facilitate the formation of complex networks through molecular self-assembly in biomolecules such as proteins and glycosaminoglycans. Self-assembling peptide amphiphiles (PA) are a group of molecules that can form nanofibrous structures and may contain bioactive epitopes to interact specifically with target molecules. Here, we report the presentation of cationic peptide sequences on supramolecular nanofibers formed by self-assembling peptide amphiphiles for cooperative enhanced antibacterial activity. Antibacterial properties of self-assembled peptide nanofibers were significantly higher than soluble peptide molecules with identical amino acid sequences, suggesting that the tandem presentation of bioactive epitopes is important for designing new materials for bactericidal activity. In addition, bacteria were observed to accumulate more rapidly on peptide nanofibers compared to soluble peptides, which may further enhance antibacterial activity by increasing the number of peptide molecules interacting with the bacterial membrane. The cationic peptide amphiphile nanofibers were observed to attach to bacterial membranes and disrupt their integrity. These results demonstrate that short cationic peptides show a significant improvement in antibacterial activity when presented in the nanofiber form.

Treatment of tar burns: two case reports.

Hot tar burns are still a challenging clinical form because the removal of tar is very difficult for the emergency physician and there is no specified appropriate agent for the removal of tar. In this study, two patients with hot tar burns who were treated with diesel, sunflower oil and mayonnaise are presented.