Search

Soil testing is a unique tool for finding the available soil reaction (pH), organic carbon, and nutrients status of the soil. It helps to select the suitable crops concerning available pH and soil nutrients level to increase crop production. In this current approach, the soil test prediction is used to differentiate several soil features like soil fertility indices of available pH, organic carbon, electrical conductivity, macro nutrients, and micro nutrients. The Classification and prediction of the soil parameters lead to reduce the artificial fertilizer inputs, increasing crop yield, improves soil health and crop growth and increase profitability. These problems are solved by using fast learning and classification techniques known as machine learning (ML) classifier techniques such as random forest, Gaussian naïve Bayes, logistic Regression, decision tree, k-nearest neighbour and support vector machine. After the analysis decision tree classifier attains the maximum performance to solve all problems which goes above 80% followed by other classifiers.

AbstractAlzheimer’s Disease is a complex progressive neurodegenerative disorder characterized by neurofibrillary tangles and senile plaques in various parts of the brain particularly cerebral cortex affecting memory and cognition. Nuclear receptors such as Peroxisome proliferator-activated receptor γ [PPAR-γ] is reported to have a role in lipid and glucose homeostasis in the brain, reduces the synthesis of Aβ (beta-amyloid plaques) and also regulates mitochondrial biogenesis and inhibit the neuro-inflammation, which contributes for the improvement in the cognitive function in AD. Hence PPAR-γ is one of the newer targets for the researchers to understand the pathology of AD and to evolve the novel strategy to retard/reverse the progression of AD. PPAR-γ agonists such as Rosiglitazone and Pioglitazone have shown promising results in AD by decreasing neuro-inflammation and restoring glucose dysmetabolism leading to a reduction in neuronal deterioration. These agonists possess poor blood-brain permeability and are poor candidates for clinical use in AD. Therefore, search, design, and development for new PPAR- γ agonists with improved BBB penetration ability are imperative. The present work deals with the use of computational tools and techniques such as molecular docking, molecular dynamics to discover PPAR-γ agonists from the unexplored Seaweed Metabolite Database and predicts it’s toxicological and physiochemical profile, thereby saving time and resources. Out of 1,110 seaweed compounds, the hit molecule BS052 displayed a strong binding affinity towards PPAR-γ, which possessed better lipid solubility indicating the potential to be considered as a PPAR-γ agonist, which may be useful in the management of AD.Communicated by Ramaswamy H. Sarma.