An experiment was carried out by using post in kalar horticulture Station/Sulaimania province on soil taked from once region sields during growing season of 2008-2009. The objective was to study adding increasing levels of urea fertilizer which is (0.0, 0.20, 0.40, 0.80) gm/Pot and superphosphate fertilizer which is (0.0, 0.24, 0.48) gm/pot in some chemical properties of grain for wheat IPA 95. This experiment was carried out by completely randomized design (CR D) with three replications. Results in dictated of clear increase in all the studied characteristics (concentration for each nitrogen, phosphorus and potassium and carbohydrate percentage with increasing levels of fertilizers).

The seed propagation is the predominant method of Echinacea propagation, which has been criticized for its time-consuming control over the separation factor and the uncertainty of pathogen-free plants produced by this method. The technology of tissue culture has provided multiple opportunities for the production of secondary metabolites continuously without being restricted to a specific season, due to the possibility of controlling the environmental conditions and the components of the nutrient medium needed by the plant. This study was conducted to investigate the effects of salicylic acid as elicitor and tyrosine as precursor on propagation and some secondary compounds production in coneflower in vitro. The result showed the superiori

This work includes preparing of some new derivatives of 1,2,4-Triazoles and oxazoles from the reaction between 3,5-dimethyl phenol and chloroethyl acetate in the presence of potassium carbonate and dry acetone to obtain the ester ethyl 3,5- dimethyl phenoxy acetate , then converted this ester to Thiosemicarbazone and Semicarbazone. Then ,the Triazoles were prepared from the cyclization of these Thiosemcarbazones and Semicarbazones in alkaline media( potassium hydroxide solution) . The prepared compounds were characterized by spectral methods FT-IR ,1H-NMR and measurement of some of its physical properties and evaluation of the biological activity for some of them.

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.

Lymphoma is a cancer arising from B or T lymphocytes that are central immune system ‎components. It is one of the three most common cancers encountered in the canine; ‎lymphoma affects middle-aged to older dogs and usually stems from lymphatic tissues, ‎such as lymph nodes, lymphoid tissue, or spleen. Despite the advance in the management of ‎canine lymphoma, a better understanding of the subtype and tumor aggressiveness is still ‎crucial for improved clinical diagnosis to differentiate malignancy from hyperplastic ‎conditions and to improve decision-making around treating and what treatment type to use. ‎This study aimed to evaluate a potential novel biomarker related to iron metabolism,

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

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