Integrating machine learning models with cross-validation and bootstrapping for evaluating groundwater quality in Kanchanaburi province, Thailand.

Exploring the potential of new models for mapping groundwater quality presents a major challenge in water resource management, particularly in Kanchanaburi Province, Thailand, where groundwater faces contamination risks. This study aimed to explore the applicability of random forest (RF) and artificial neural networks (ANN) models to predict groundwater quality. Particularly, these two models were integrated into cross-validation (CV) and bootstrapping (B) techniques to build predictive models, including RF-CV, RF-B, ANN-CV, and ANN-B. Entropy groundwater quality index (EWQI) was converted to normalized EWQI which was then classified into five levels from very poor to very good. A total of twelve physicochemical parameters from 180 groundwater wells, including potassium, sodium, calcium, magnesium, chloride, sulfate, bicarbonate, nitrate, pH, electrical conductivity, total dissolved solids, and total hardness, were investigated to decipher groundwater quality in the eastern part of Kanchanaburi Province, Thailand. Our results indicated that groundwater quality in the study area was primarily polluted by calcium, magnesium, and bicarbonate and that the RF-CV model (RMSE = 0.06, R2 = 0.87, MAE = 0.04) outperformed the RF-B (RMSE = 0.07, R2 = 0.80, MAE = 0.04), ANN-CV (RMSE = 0.09, R2 = 0.70, MAE = 0.06), and ANN-B (RMSE = 0.10, R2 = 0.67, MAE = 0.06). Our findings highlight the superiority of the RF models over the ANN models based on the CV and B techniques. In addition, the role of groundwater parameters to the normalized EWQI in various machine learning models was found. The groundwater quality map created by the RF-CV model can be applied to orient groundwater use.

The risk assessment of arsenic contamination in the urbanized coastal aquifer of Rayong groundwater basin, Thailand using the machine learning approach.

The rapid expansion of urbanization has resulted in an insufficient of groundwater resource. In order to use groundwater more efficiently, a risk assessment of groundwater pollution should be proposed. The present study used machine learning with three algorithms consisting of Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) to locate risk areas of arsenic contamination in Rayong coastal aquifers, Thailand and selected the suitable model based on model performance and uncertainty for risk assessment. The parameters of 653 groundwater wells (Deep=236, Shallow=417) were selected based on the correlation of each hydrochemical parameters with arsenic concentration in deep and shallow aquifer environments. The models were validated with arsenic concentration collected from 27 well data in the field. The model's performance indicated that the RF algorithm has the highest performance as compared to those of SVM and ANN in both deep and shallow aquifers (Deep: AUC=0.72, Recall=0.61, F1 =0.69; Shallow: AUC=0.81, Recall=0.79, F1 =0.68). In addition, the uncertainty from the quantile regression of each model confirmed that the RF algorithm has the lowest uncertainty (Deep: PICP=0.20; Shallow: PICP=0.34). The result of the risk map obtained from the RF reveals that the deep aquifer, in the northern part of the Rayong basin has a higher risk for people to expose to As. In contrast, the shallow aquifer revealed that the southern part of the basin has a higher risk, which is also supported by the location of the landfill and industrial estates in the area. Therefore, health surveillance is important in monitoring the toxic effects on the residents who use groundwater from these contaminated wells. The outcome of this study can help policymakers in regions to manage the quality of groundwater resources and enhance the sustainable use of groundwater resources. The novelty process of this research can be used to further study other groundwater aquifers contaminated and increase the effectiveness of groundwater quality management.

Determination of conditioning factors for mapping nickel contamination susceptibility in groundwater in Kanchanaburi Province, Thailand, using random forest and maximum entropy.

Groundwater pollution from nickel (Ni) has been a severe concern in Kanchanaburi Province, Thailand. Recent assessments revealed that the Ni concentration in groundwater, particularly in urban areas, often exceeded the permissible limit. The challenge for groundwater agencies is therefore to delineate regions with high susceptibility to Ni contamination. In this study, a novel modeling approach was applied to a dataset of 117 groundwater samples collected from Kanchanaburi Province between April and July 2021. Twenty site-specific initial variables were considered as influencing factors to Ni contamination. The Random Forest (RF) algorithm with Recursive Feature Elimination (RFE) function was used to select the fourteen most influencing variables. These variables were then used as input features to train a ME model to delineate the Ni contamination susceptibility at a high confidence (Area Under the Curve (AUC) validation value of 0.845). Ten input variables of the altitude, geology, land use, slope, soil type, distance to industrial areas, distance to mining areas, electric conductivity, oxidation-reduction potential, and groundwater depth were discovered in the most explaining the variation of spatial Ni contamination at very high (95.47 km2) and high (86.65 km2) susceptibility. This study devises the novel machine learning approach to identify the conditioning factors and map Ni contamination susceptibility in the groundwater, which provides a baseline dataset and reliable methods for the development of a sustainable groundwater management strategy.

Design, synthesis, molecular docking study and molecular dynamics simulation of new coumarin-pyrimidine hybrid compounds having anticancer and antidiabetic activity.

Coumarin-pyrimidine hybrid compounds were synthesized by condensation reaction of α,ß-unsaturated ketones of 6-acetyl-5-hydroxy-4-methylcoumarin with guanidine. The reaction yields were of 42-62%. The antidiabetic and anticancer activities of these compounds were examined. These compounds displayed low toxicity to two cancer cell lines (including KB and HepG2 ones), but exhibited remarkably active against α-amylase with IC50 values of 102.32 ± 1.15 µM to 249.52 ± 1.14 µM and against α-glucosidase with IC50 values of 52.16 ± 1.12 µM to 184.52 ± 1.15 µM. Amongst these compounds, 6c was the best inhibitory activity against α-amylase, and 6f had the highest activity against α-glucosidase. The kinetics of inhibitor 6f was competitive α-glucosidase inhibitor property. ADMET predictions showed that almost all synthesized compounds exhibited drug-like activity. IFD and MD simulations were carried out on enzymes 4W93 and 5NN8 to elucidate inhibitory potential of 6c and 6f against tested enzymes. The binding free energy calculation by MM-GBSA approach showed that Coulomb, lipophilic and van der Waals energy terms are major contributors for the inhibitor binding. Molecular dynamics simulations in water solvent system were carried out for the 6f/5NN8 complex to elucidate the variability of active interactions between ligand 6f and active pockets of this enzyme.

Recent Electrokinetic and Microfluidic Strategies for Detection of Amyloid Beta Peptide Biomarkers: Towards Molecular Diagnosis of Alzheimer's Disease.

Among all neurodegenerative diseases, Alzheimer's Disease (AD) is the most prevalent worldwide, with a huge burden to the society and no efficient AD treatment so far. Continued efforts have been being made towards early and powerful diagnosis of AD, in the hope for a successful set of clinical trials and subsequently AD curative treatment. Towards this aim, detection and quantification of amyloid beta (Aß) peptides in cerebrospinal fluid (CSF) and other biofluids, which are established and validated biomarkers for AD, have drawn attention of the scientific community and industry over almost two decades. In this work, an overview on our major contributions over 15 years to develop different electrokinetic and microfluidic strategies for Aß peptides detection and quantification is reported. Accordingly, discussions and viewpoints on instrumental and methodological developments for microscale electrophoresis, microfluidic designs and immuno-enrichment / assays on magnetic beads in microchannels for tracing Aß peptides in CSF are given in this review.

Exploring the potential of ZnO-Ag@AgBr/SBA-15 Z-scheme heterostructure for efficient wastewater treatment: synthesis, characterization, and real-world applications.

This study reports on the synthesis and characterization of ZnO-Ag@AgBr/SBA-15 composites using natural halloysite clay from Yenbai Province, Vietnam, as a silica aluminum source. The synthesized materials demonstrated visible light absorption with a band gap energy range of 2.63-2.98 eV. The dual Z-scheme ZnO-Ag@AgBr/SBA-15 heterojunction exhibited superior catalytic performance compared to ZnO/SBA-15 and Ag@AgBr/SBA-15, owing to its improved electron transfer and reduced electron and hole recombination rate. In particular, the photocatalytic efficiency of ZnO-Ag@AgBr/SBA-15 was evaluated for the removal of harmful phenol red from wastewater under visible light irradiation. The photocatalytic process was optimized by varying the phenol red concentration, pH, and catalyst dosage, and showed that 98.8% of phenol red in 100 mL wastewater (pH = 5.5) can be removed using 40 mg of 20%ZnO-Ag@AgBr/SBA-15 within 120 min. Furthermore, the degradation pathway of phenol red was predicted using liquid chromatographic-mass spectrometry (LC-MS). Finally, the photocatalytic process was successfully tested using water samples collected from the four main domestic waste sources in Hanoi, including the To Lich River, the Hong River, the Hoan Kiem Lake, and the West Lake, demonstrating the high potential of the ZnO-Ag@AgBr/SBA-15 photocatalyst for phenol red degradation in real-world wastewater treatment applications.

Endertiins A-B, two lanostane triterpenoids from the fruit bodies of the mushoom Humphreya endertii.

Two new lanostane triterpnenoids named endertiins A and B (1 and 2) together with two known compounds as ganodecalone A (3) and ergosterol (4) have been successfully isolated from the cultivated fruit bodies of the mushroom Humphreya endertii Stey (Ganodermataceae). Their structures were elucidated by a combination of HR-MS and 2 D NMR spectroscopic analyses. In addition, endertiins A and B (1 and 2) were evaluated their cytotoxicity against two cancer cell lines, MCF7 (human breast carcinoma) and LU (human lung carcinoma). The result showed that endertiin A (1) could inhibit the growth of MCF-7 cells with its IC50 value of 71.16 ± 6.25 µg/ml.

Rapid decrease of malaria morbidity following the introduction of community-based monitoring in a rural area of central Vietnam.

BACKGROUND: Despite a successful control programme, malaria has not completely disappeared in Vietnam; it remains endemic in remote areas of central Vietnam, where standard control activities seem to be less effective. The evolution of malaria prevalence and incidence over two and half years in a rural area of central Vietnam, after the introduction of community-based monitoring of malaria cases, is presented. METHODS: After a complete census, six cross-sectional surveys and passive detection of malaria cases (by village and commune health workers using rapid diagnostic tests) were carried out between March 2004 and December 2006 in Ninh-Thuan province, in a population of about 10,000 individuals. The prevalence of malaria infection and the incidence of clinical cases were estimated. RESULTS: Malaria prevalence significantly decreased from 13.6% (281/2,068) in December 2004 to 4.0% (80/2,019) in December 2006. Plasmodium falciparum and Plasmodium vivax were the most common infections with few Plasmodium malariae mono-infections and some mixed infections. During the study period, malaria incidence decreased by more than 50%, from 25.7/1,000 population at risk in the second half of 2004 to 12.3/1,000 in the second half of 2006. The incidence showed seasonal variations, with a yearly peak between June and December, except in 2006 when the peak observed in the previous years did not occur. CONCLUSION: Over a 2.5-year follow-up period, malaria prevalence and incidence decreased by more than 70% and 50%, respectively. Possibly, this could be attributed to the setting up of a passive case detection system based on village health workers, indicating that a major impact on the malaria burden can be obtained whenever prompt diagnosis and adequate treatment are available.

Long-Lasting Insecticidal Hammocks for controlling forest malaria: a community-based trial in a rural area of central Vietnam.

BACKGROUND: In Vietnam, malaria remains a problem in some remote areas located along its international borders and in the central highlands, partly due to the bionomics of the local vector, mainly found in forested areas and less vulnerable to standard control measures. Long Lasting Insecticidal Hammocks (LLIH), a tailored and user-friendly tool for forest workers, may further contribute in reducing the malaria burden. Their effectiveness was tested in a large community-based intervention trial carried out in Ninh Thuan province in Central Vietnam. METHODS AND FINDINGS: Thirty villages (population 18,646) were assembled in 20 clusters (1,000 individuals per cluster) that were randomly allocated to either the intervention or control group (no LLIH) after stratification according to the pre-intervention P. falciparum antibody prevalence (<30%; > or =30%). LLIH were distributed to the intervention group in December 2004. For the following 2 years, the incidence of clinical malaria and the prevalence of infection were determined by passive case detection at community level and by bi-annual malariometric surveys. A 2-fold larger effect on malaria incidence in the intervention as compared to the control group was observed. Similarly, malaria prevalence decreased more substantially in the intervention (1.6-fold greater reduction) than in the control group. Both for incidence and prevalence, a stronger and earlier effect of the intervention was observed in the high endemicity stratum. The number of malaria cases and infections averted by the intervention overall was estimated at 10.5 per 1,000 persons and 5.6/100 individuals, respectively, for the last half of 2006. In the high endemicity stratum, the impact was much higher, i.e. 29/1000 malaria cases and 15.7 infections/100 individuals averted. CONCLUSIONS: LLIH reduced malaria incidence and prevalence in this remote and forested area of Central Vietnam. As the targets of the newly-launched Global Malaria Action Plan include the 75% reduction of the global malaria cases by 2015 and eventually the elimination/eradication of malaria in the long term, LLIH may represent an additional tool for reaching such objectives, particularly in high endemicity areas where standard control tools have a modest impact, such as in remote and forested areas of Southeast Asia and possibly South America. TRIAL REGISTRATION: ClinicalTrials.gov NCT00853281.