The current study performed in order to detect and quantify epicatechin in two tea samples of Camellia sinensis (black and green tea) by thin layer chromatography (TLC) and high performance liquid chromatography (HPLC). Extraction of epicatechin from black and green tea was done by using two different methods: maceration (cold extraction method) and decoction (hot extraction method). Qualitative and quantitative determinations of epicatechin in two tea samples were investigated. Epicatechin identification was made by utilizing preliminary chemical tests and TLC. This identification was also boosted by HPLC and then quantified epicatechin in all ethyl acetate fractions of two tea samples. This research revealed the existence of epica

Background/Objectives: The purpose of this study was to classify Alzheimer’s disease (AD) patients from Normal Control (NC) patients using Magnetic Resonance Imaging (MRI). Methods/Statistical analysis: The performance evolution is carried out for 346 MR images from Alzheimer's Neuroimaging Initiative (ADNI) dataset. The classifier Deep Belief Network (DBN) is used for the function of classification. The network is trained using a sample training set, and the weights produced are then used to check the system's recognition capability. Findings: As a result, this paper presented a novel method of automated classification system for AD determination. The suggested method offers good performance of the experiments carried out show that the

The Ligand 6,6--(1,2-benzenediazo) bis (3-aminobenzoicacid) derived from o-phenylenediamine and 3-aminobenzoicacid was synthesized. The prepared ligand was identified by Microelemental Analysis, 1HNMR, FT-IR and UV-Vis spectroscopic techniques. Treatment of the ligand with the following metal ions (CoII, NiII, CuII and ZnII ) in aqueous ethanol with a 1:1 M:L ratio and at optimum pH. Characterization of these compounds has been done on the basis of elemental analysis, electronic data, FT-IR and UV-Vis, as well as magnetic susceptibility and conductivity measurements. The nature of the complexes formed were studied following the mole ratio and continuous variation methods, Beer's law obeyed over a concentration range (1×10-4 - 3×10-4 M). H

New complexes of the type [ML2(H2O)2] ,[FeL2(H2O)Cl] and [VOL2] were M=Co(II),Ni(II) and Cu(II) ,L=4-(2-methyl-4-oxoquinazoline-3(4H)-yl) benzoic acid were synthesized and characterized by element analysis, magnetic susceptibility ,molar conductance ,FT-IR and UV-visible. The studies indicate that the L acts as doubly monodentate bridge for metal ions and form mononuclear complexes. The complexes are found to be octahedral except V(IV) complex is square pyrimde shape . The structural geometries of compounds were also suggested in gas phase by theoretical treatments, using Hyper chem-6 program for the molecular mechanics and semi-empirical calculations, addition heat of formation(?Hf ?) and binding energy (?Eb)for the free ligan

This study objective is to identify the visual pollution in Karrada district main streets as an example of main streets in Baghdad, the public opinion about each pollutants, solutions to reduce and eliminate the pollution were suggested as well. In order to accomplish this objective different methods were used, 16 pollutants were selected, pictures of each pollutants were taken and a questioner were distributed randomly for 270 people to evaluate the public opinion with statistical methods. Garbage, their disposal and storage areas took the first two places as the highest offensive pollutants. The people showed that they find long lines of vehicles, debris and generators appearance ranked third, fourth and fifth respectively .This resear

Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD