The role of environmental trace element toxicants on autism: A medical biogeochemistry perspective.

Since genetic factors alone cannot explain most cases of Autism, the environmental factors are worth investigating as they play an essential role in the development of some cases of Autism. This research is a review paper that aims to clarify the role of the macro elements (MEs), Trace elements (TEs) and ultra-trace elements (UTEs) on human health if they are greater or less than the normal range. Aluminium (Al), cadmium Cd), lead (Pb), chromium (Cr), zinc (Zn), copper (Cu), nickel (Ni), arsenic (As), mercury (Hg), manganese (Mn), and iron (Fe) have been reviewed. Exposure to toxicants has a chemical effect that may ultimately lead to autism spectrum disorder (ASD). The Cr, As and Al are found in high concentrations in the blood of an autistic child when compared to normal child reference values. The toxic metals, particularly aluminium, are primarily responsible for difficulties in socialization and language skills disabilities. Zinc and copper are important elements in regulating the gene expression of metallothioneins (MTs), and zinc deficiency may be a risk factor for ASD pathogenesis. Autistics frequently have zinc deficiency combined with copper excess; as part of the treatment protocol, it is critical to monitor zinc and copper levels in autistic people, particularly those with zinc deficiency. Zinc deficiency is linked to epileptic seizures, which are common in autistic patients. Higher serum manganese and copper significantly characterize people who have ASD. Autistic children have significantly decreased lead and cadmium in urine, whereas they have significantly higher urine Cr. A higher level of As and Hg was found in the ASD individual's blood.

Development of new computational machine learning models for longitudinal dispersion coefficient determination: case study of natural streams, United States.

Natural streams longitudinal dispersion coefficient (Kx) is an essential indicator for pollutants transport and its determination is very important. Kx is influenced by several parameters, including river hydraulic geometry, sediment properties, and other morphological characteristics, and thus its calculation is a highly complex engineering problem. In this research, three relatively explored machine learning (ML) models, including Random Forest (RF), Gradient Boosting Decision Tree (GTB), and XGboost-Grid, were proposed for the Kx determination. The modeling scheme on building the prediction matrix was adopted from the well-established literature. Several input combinations were tested for better predictability performance for the Kx. The modeling performance was tested based on the data division for the training and testing (70-30% and 80-20%). Based on the attained modeling results, XGboost-Grid reported the best prediction results over the training and testing phase compared to RF and GTB models. The development of the newly established machine learning model revealed an excellent computed-aided technology for the Kx simulation.

Prediction of sediment heavy metal at the Australian Bays using newly developed hybrid artificial intelligence models.

Hybrid artificial intelligence (AI) models are developed for sediment lead (Pb) prediction in two Bays (i.e., Bramble (BB) and Deception (DB)) stations, Australia. A feature selection (FS) algorithm called extreme gradient boosting (XGBoost) is proposed to abstract the correlated input parameters for the Pb prediction and validated against principal component of analysis (PCA), recursive feature elimination (RFE), and the genetic algorithm (GA). XGBoost model is applied using a grid search strategy (Grid-XGBoost) for predicting Pb and validated against the commonly used AI models, artificial neural network (ANN) and support vector machine (SVM). The input parameter selection approaches redimensioned the 21 parameters into 9-5 parameters without losing their learned information over the models' training phase. At the BB station, the mean absolute percentage error (MAPE) values (0.06, 0.32, 0.34, and 0.33) were achieved for the XGBoost-SVM, XGBoost-ANN, XGBoost-Grid-XGBoost, and Grid-XGBoost models, respectively. At the DB station, the lowest MAPE values, 0.25 and 0.24, were attained for the XGBoost-Grid-XGBoost and Grid-XGBoost models, respectively. Overall, the proposed hybrid AI models provided a reliable and robust computer aid technology for sediment Pb prediction that contribute to the best knowledge of environmental pollution monitoring and assessment.

Assessment of sulfurous springs in the west of Iraq for balneotherapy, drinking, irrigation and aquaculture purposes.

This research deals with the sulfurous spring waters flow along the course of the Euphrates River in western Iraq in the area extended between Haqlaniya and Hit within the Al-Anbar governorate. Eleven springs (3 in Haqlanya, 4 in Kubaysa and 4 in Hit) have been addressed for the purpose of water evaluation for balneology, drinking, irrigation and aquaculture (fish farming). In order to meet the objectives of this research, all springs were sampled and analyzed for the total dissolved solid, electrical conductivity, pH, temperature, major cations (Ca(2+), Mg(2+), Na(+) and K(+)), major anions (SO(4)(2-), Cl(-), HCO(3)(-) and CO(3)(2-)), minor anions (PO(4)(3-)and NO(3)(-)) as well as the trace elements that included Pb, Zn, Cd, Ni, Fe, Mn, Cu, Br, F, Ba, B, Sr, Al, As, Cr, Hg and Se. The International Standards of World Health Organization are used for assessing the water quality. The results revealed that the springs belong to the tepid springs of 27-30 °C and classified as hypothermal to the thermal springs. Lithochemistry and geochemical processes clearly affected the water chemistry. The hydrogeochemical processes are responsible for the element enrichment in water by the chemical dissolution of carbonate and gypsum and evaporation as well. The results of the study indicate the possibility of using spring water for therapeutic purposes, but not allowed for drinking and aquaculture (fish farming), except those free of H(2)S gas. On the other hand, it can be used for irrigation with risk. However, soil type as well as proper selection of plants should be taken into consideration.