Some feline intestinal parasites such as Toxocara, Giardia, and Cryptosporidium can spread to humans through feces. Therefore, it is important to prevent exposure of family members by screening cat fecal samples twice per year for potential treatment regimes. This study was initiated to compare and identify gastrointestinal parasites of domestic and stray cats (Felis domesticus) in Baghdad City, Iraq. Parasite eggs and oocysts were identified under light microscopy by applying standard laboratory techniques (flotation and sedimentation). Overall, 59 of 121 (48.7%) fecal samples were positive for intestinal parasites. The rate of infection by Toxoplasma gondii was (3.31%), Isospora spp. (6.61%), Cryptosporidium spp. (31.4%), and Toxocara spp

To identify the fungi associated with water hyacinth (Eichhornia crassipes [Mart.] Solms), an aquatic weed, which presents in Tigris river from Baghdad south ward. Five regions from middle and south of Iraq (Al-Noumanya, Saeid Bin-Jubier, Al-Azizia, Al-Reyfay and Al-Hay) were selected for this study. Twelve fungal species were isolated. Alternaria alternata, Acremonium sp and Cladsporium herbarum, were the most frequently species (91.66 % ,50 % and 25 %) respectively. The fungi Alternaria alternata, Acremonium sp. and Phoma eupyrena were more aggressive to water hyacinth as (91.66%,83,33%, and 75%) in pathogenicity test.

Iridoid glycosides are a group of naturally occurring chemical compounds. They are a large family of compounds biosynthesized by plants, they often have pharmacological effects. The aim of this study is to isolate and identified iridoid glycoside in a newly studied, cultivated in Iraq named Gardenis jasminoides. The medicinal importance of iridoid glycoside, on one hand and absence of phytochemical investigation on leaves of Gardenia on the other hand, acquired this study its importance. Many compounds were isolated from leaves plant part one of these compounds was identified by different chemical analysis like: melting point (MP), thin layer chromatography (TLC), Fourier transforms infrared spectra (FTIR) and high performance l

This work deals with thermal cracking of heavy vacuum gas oil which produced from the top of vacuum distillation unit at Al- DURA refinery, by continuous process. An experimental laboratory plant scale was constructed in laboratories of chemical engineering department, Al-Nahrain University and Baghdad University. The thermal cracking process was carried out at temperature ranges between 460-560^oC and atmospheric pressure with liquid hourly space velocity (LHSV) equal to 15hr^-1.The liquid product from thermal cracking unit was distilled by atmospheric distillation device according to ASTM D-86 in order to achieve two fractions, below 220^oC as a gasoline fraction and above 220^oC as light cycle o

A pulsed (TEA-0O2) laser was used to dissociate molecules of silane ethylene (C2I-14) and ammonia (NH3) gases, through collision assisted multiple photon dissociation (MPD) to deposit(SiC i_xNx) thin films, where the X-values are 0, 0.13 and 0.33, on glass substrate at T,----648 K. deposition rate of (0.416-0.833) nm/pulse and thickness of (500-1000)nm .Fourier transform infrared spectrometry (FT-IR) was used to study the nature of the chemical bonds that exist in the films. Results revealed that these films contain complex networks of the atomic (Si, C, and N), other a quantity of atomic hydrogen and chemical bonds such as (Si-N, C-N, C-14 and N-H).Absorbance and Transmittance spectra in the wavelength range (400-1100) nm were used to stud

Echocardiography is a widely used imaging technique to examine various cardiac functions, especially to detect the left ventricular wall motion abnormality. Unfortunately the quality of echocardiograph images and complexities of underlying motion captured, makes it difficult for an in-experienced physicians/ radiologist to describe the motion abnormalities in a crisp way, leading to possible errors in diagnosis. In this study, we present a method to analyze left ventricular wall motion, by using optical flow to estimate velocities of the left ventricular wall segments and find relation between these segments motion. The proposed method will be able to present real clinical help to verify the left ventricular wall motion diagnosis.

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with