Dietary components and changes cause shifts in the gastrointestinal microbial ecology that can play a role in animal health and a wide range of diseases. However, most information about the microbial populations in the gut of horses has not been quantitative. The objective of this study was to characterize the fecal bacterial and its prevalence in healthy horses and diarrheal one in a period from September 2010 to July 2013. Out of 100 Fecal samples of horses (from farms in Al-furusyia club) in Baghdad were examined for microbial differentiation founded eighty percent of the fecal samples isolated from healthy horses. The most common pathogen found were Streptococcus spp. (33.7%), Escherichia coli (20.9%), , and Staphylococcus aureus (9.2

The functional properties of the defatted powder and protein isolate of germinated mung bean seeds were studied and the estimation of the amino acids was carried out. The results showed a significant increase in the values of the amino acids leucine, lysine, phenylalanine and valine. The results of studying the functional properties of the protein isolate and defatted powder showed that there were significant differences between the sample The defatted and protein isolate, if the water absorption capacity of the defatted mung powder was 2.5% water/gm protein and the water absorption capacity of the protein isolate was 3%ml water/g protein, the fat binding capacity of the defatted powder was 0.3 ml fat/g protein and the isolate The ratio was

Between October and December 2018, 27wounds and burn swab specimens were collected by laboratories at Al-Yarmook hospital, and cultured on Mannitol salt agar. the isolate was subjected to Nd: YAG laser in different power (400mJ, 500mJ, 800mJ and 900mJ). In general the laser showed effect on bacterial growth that reach to complete killing, the statistical analysis showed that there is weak correlation between laser at 400mJ with killed percentage. While in 500mJ its exhibit complete correlation with killing percentage, this correlation was decreased with increasing in power to 800mJ and 900Mj.

Early and accurate detection of COVID-19 from chest computed tomography (CT) scans are becoming essential for effective clinical decision-making and disease control. This study is proposing a robust deep learning framework that integrates a convolutional self-attention network (CSAN), gamma correction for image enhancement, and a voting-based ensemble classifier to improving diagnostic performance. The model is being evaluated on a dataset of 2,271 CT images and is achieving an accuracy of 95.12%, sensitivity of 97.25%, specificity of 98.11%, F1-score of 96.46%, and area under the curve (AUC) of 0.977. Experimental results are demonstrating that the proposed method significantly surpasses baseline models, including standalone CSAN,

Intrusion Detection Systems (IDS) is the main defense mechanism deployed by the current networks to prevent cyber threats. Recurrent Neural Network (RNN) are also a novel IDS structure that replaces the conventional training and testing mechanism. The strategy encodes network traffic data as biological sequences using amino acid codons in such a fashion that the RNN is capable of effectively analyzing temporal and sequence data patterns. RNN architecture design adopts embedding layers to handle codon representations and Long Short-Term Memory (LSTM) layers to perform sequential data learning, which is followed by a fully connected network to perform classification functions, which preserve high feature extraction and classification