A rapid and sensitive method for analysis of amino acid hydrolysates of nigella sativa L seed has been developed using O-phthaldialehyde(OPA ) as a pre-column derivatizing agent. OPA reagents in the presence of mercaptoethanol react rapidly with primary amino acids ( less than 60 sec.) to form isindole derivatives which easily separated with good selectivity on ODS column. Resolution of amino acid derivatives is carried out with a methanol gradient in 0.01 maqueous sodium acetate. pH 7.1 . The quantitation of amino acid derivatives is reproducible within an average relative deviation of + 1.4% the linearity for most amino acids were more than 0.9993 with detection limit of 0.2 ppm. 15 amino acid were detected in the analysis of

Background: Normal Left Ventricular systolic function is present in nearly 50% of patients with congestive heart failure, the majority of such patients have systemic hypertension. Recent studies have demonstrated Left Ventricular dyssynchrony among patients with heart failure and normal systolic function. The co-existence between Left Ventricular dyssynchrony and hypertension with normal systolic function (with no clinical evidence of heart failure) is less well understood.

Objective:

To assess the Left Ventricular dyssynchrony among hypertensive patients with normal systolic function by using Tissue doppler imaging.To find out the associations between the LV dyssynchrony and other global

 Data Driven Requirement Engineering (DDRE) represents a vision for a shift from the static traditional methods of doing requirements engineering to dynamic data-driven user-centered methods. Data available and the increasingly complex requirements of system software whose functions can adapt to changing needs to gain the trust of its users, an approach is needed in a continuous software engineering process. This need drives the emergence of new challenges in the discipline of requirements engineering to meet the required changes. The problem in this study was the method in data discrepancies which resulted in the needs elicitation process being hampered and in the end software development found discrepancies and could not meet the need

Alzheimer's disease (AD) increasingly affects the elderly and is a major killer of those 65 and over. Different deep-learning methods are used for automatic diagnosis, yet they have some limitations. Deep Learning is one of the modern methods that were used to detect and classify a medical image because of the ability of deep Learning to extract the features of images automatically. However, there are still limitations to using deep learning to accurately classify medical images because extracting the fine edges of medical images is sometimes considered difficult, and some distortion in the images. Therefore, this research aims to develop A Computer-Aided Brain Diagnosis (CABD) system that can tell if a brain scan exhibits indications of

Deep Learning Techniques For Skull Stripping of Brain MR Images

The need for image compression is always renewed because of its importance in reducing the volume of data; which in turn will be stored in less space and transferred more quickly though the communication channels.
In this paper a low cost color image lossy color image compression is introduced. The RGB image data is transformed to YUV color space, then the chromatic bands U & V are down-sampled using dissemination step. The bi-orthogonal wavelet transform is used to decompose each color sub band, separately. Then, the Discrete Cosine Transform (DCT) is used to encode the Low-Low (LL) sub band. The other wavelet sub bands are coded using scalar Quantization. Also, the quad tree coding process was applied on the outcomes of DCT and

Texture synthesis using genetic algorithms is one way; proposed in the previous research, to synthesis texture in a fast and easy way. In genetic texture synthesis algorithms ,the chromosome consist of random blocks selected manually by the user .However ,this method of selection is highly dependent on the experience of user .Hence, wrong selection of blocks will greatly affect the synthesized texture result. In this paper a new method is suggested for selecting the blocks automatically without the participation of user .The results show that this method of selection eliminates some blending caused from the previous manual method of selection.

Ultrasound imaging is often preferred over other medical imaging modalities because it is non-invasive, non-ionizing, and low-cost. However, the main weakness of medical ultrasound image is the poor quality of images, due to presence of speckle noise and blurring. Speckle is characteristic phenomenon in ultrasound images, which can be described as random multiplicative noise that occurrence is often undesirable, since it affects the tasks of human interpretation and diagnosis. Blurring is a form of bandwidth reduction of an ideal image owing to the imperfect image formation process. Image denoising involves processing of the image data to produce a visually high quality image. The denoising algorithms may be classified into two categorie

Printed Arabic document image retrieval is a very important and needed system for many companies, governments and various users. In this paper, a printed Arabic document images retrieval system based on spotting the header words of official Arabic documents is proposed. The proposed system uses an efficient segmentation, preprocessing methods and an accurate proposed feature extraction method in order to prepare the document for classification process. Besides that, Support Vector Machine (SVM) is used for classification. The experiments show the system achieved best results of accuracy that is 96.8% by using polynomial kernel of SVM classifier.

People may believe that tissue of normal brain and brain with benign tumor
have the same statistical descriptive measurements that are significantly different
from the of brain with malignant tumor. Thirty brain tumor images were collected
from thirty patients with different complains (10 normal brain images, 10 images
with benign brain tumor and 10 images with malignant brain tumor). Pixel
intensities are significantly different for all three types of images and the F-test was
measured and found equal to 25.55 with p-value less than 0.0001. The means of
standard deviations and coefficients of variation showed that pixel intensities from
normal and benign tumors images are almost have the same behavior whereas the