A total of (25) stool samples were collected from children and adults (2- 4) years old suffering from diarrhea to isolate E. coli strains that produce heat-stable enterotoxin a (STa), and after performing microscopic examination, cultural characterization and biochemical identification only (11) isolates showed positive E. coli. STa activity was estimated by using suckling mouse assay (SMA) and from these (11) isolates only (5) showed STa activity and the one with the highest STa activity was selected for large scale production of STa, which was followed by partial purification using ion-exchange chromatography (normal phase) using DEAE sephadex A-50 column. After purification and determination of protein concentration by using the standard

The present study was conducted to estimate the antimicrobial activity and the potential biological control of the killer toxin produced byD. hanseniiDSMZ70238 against several pathogenic microorganisms. In this study, the effects of NaCl, pH, and temperature, killer toxin production, and antimicrobial activity were studied. The results showed that the optimum inhibitory effect of killer toxin was at 8% NaCl, and the diameters of clear zones were 20, 22, 22, 21, 14, and 13 mm forStaphylococcus aureus, Escherichia coli, Klebsiella pneumoniae, Streptococcus pyogenes, Candida albicans,andCandida neoformans, respectively. The largest inhibition zones were

The detection of fungi contaminating maize grain and the effect of four plant extracts Azadirachta indica, Eucalyptus globulus Glycyrrhiza glabra and Zingiber officinale on the growth of A. flavus and its ability to produce AflatoxinB1. The results showed that the incidence of Aspergillus spp., was 52.75% of the isolated fungi, of which 29.50% was due to Aspergillus flavus, followed by Penicillium spp., with an incidence of 21.06%, and then Fusarium spp., with a rate of 18.13%. The percentage of toxin-producing A. flavus isolates reached 70.8% out of 24 isolates. The results showed the effect of alcoholic plant extracts at a concentration of 10 mg/ml on the fungal growth activity of A. flavus, the alcoholic extract of neem leaves was superi

The aim of this study is to shed light on the importance of biofuels as an alternative to conventional energy, in addition to the importance of preserving agricultural crops, which are the main source of this fuel, to maintain food security, especially in developing countries. The increase in global oil prices, in addition to the fear of global warming, are among the main factors that draw the world’s attention to searching for alternative sources of traditional energy, which are sustainable on the one hand, and on the other hand reduce carbon emissions. Therefore, the volume of global investment in renewable energy in general, and in liquid biofuels and biomass in particular, has increased. Global fears emerged that the excessive convers

The aim of this study is to shed light on the importance of biofuels as an alternative to conventional energy, in addition to the importance of preserving agricultural crops, which are the main source of this fuel, to maintain food security, especially in developing countries. The increase in global oil prices, in addition to the fear of global warming, are among the main factors that draw the world’s attention to searching for alternative sources of traditional energy, which are sustainable on the one hand, and on the other hand reduce carbon emissions. Therefore, the volume of global investment in renewable energy in general, and in liquid biofuels and biomass in particular, has increased. Global fears emerged that the excessive

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class