Microbiological quality of sous and tamarind, traditional drinks consumed in Jordan

This study was conducted to evaluate the microbiological quality of sous (a drink prepared by extracting dried roots of Glycyrrhiza glabra) and tamarind (a drink prepared by infusing Tamarindus indica dried pulp), traditional drinks consumed in Jordan. Twenty-one samples of sous and 44 samples of tamarind were collected from the local market in Amman, Jordan. Water is the major component of the drinks. Sous drink is characterized by having an alkaline pH (range, 6.6 to 9.9; mean, 8.6), whereas tamarind drink has an acidic pH (range, 1.8 to 3.7; mean, 2.8). The drinks are not processed for safety before serving, and at some vendors drinks are not properly refrigerated. The mean counts for aerobic bacteria, lactic acid bacteria, and yeasts in sous drink samples were 5.9, 5.0, and 3.8 log CFU/ml, respectively; those in tamarind drink samples were 4.0, <1, and 5.8 log CFU/ml, respectively. The lactic acid bacteria isolated were Enterococcus raffinosus, Enterococcus hirae, Enterococcus durans, Lactobacillus acidophilus, and Lactobacillus buchneri. The yeast isolates in sous drink were from the genera Candida, Filobasidium, Hanseniaspora, Lodderomyces, Pichia, and Williopsis, and those in tamarind drink were from Arthroascus, Brettanomyces, Candida, Debaromyces, Filobasidiella, Hanseniaspora, Klavispora, Lodderomyces, Pichia, Saccharomycodes, Trichosporon, and Zygosaccharomyces. Enterobacteriaceae were detected in two sous samples and were identified as Enterobacter sakazakii and Erwinia sp., and in two tamarind samples and were identified as Citrobacter freundii and Klebsiella pneumoniae. Salmonella was detected in one sous and one tamarind sample. Pseudomonas aeruginosa was detected in only one sous sample. These findings highlight the importance of application of hygienic practices throughout preparation and vending of drinks, starting with raw ingredients and continuing through preparation, storage, display, and serving.     

An Improved Deep Structure for Accurately Brain Tumor Recognition

Brain neoplasms are recognized with a biopsy, which is not commonly done before decisive brain surgery. By using Convolutional Neural Networks (CNNs) and textural features, the process of diagnosing brain tumors by radiologists would be a noninvasive procedure. This paper proposes a features fusion model that can distinguish between no tumor and brain tumor types via a novel deep learning structure. The proposed model extracts Gray Level Co-occurrence Matrix (GLCM) textural features from MRI brain tumor images. Moreover, a deep neural network (DNN) model has been proposed to select the most salient features from the GLCM. Moreover, it manipulates the extraction of the additional high levels of salient features from a proposed CNN model. Finally, a fusion process has been utilized between these two types of features to form the input layer of additional proposed DNN model which is responsible for the recognition process. Two common datasets have been applied and tested, Br35H and FigShare datasets. The first dataset contains binary labels, while the second one splits the brain tumor into four classes; glioma, meningioma, pituitary, and no cancer. Moreover, several performance metrics have been evaluated from both datasets, including, accuracy, sensitivity, specificity, F-score, and training time. Experimental results show that the proposed methodology has achieved superior performance compared with the current state of art studies. The proposed system has achieved about 98.22% accuracy value in the case of the Br35H dataset however, an accuracy of 98.01% has been achieved in the case of the FigShare dataset.     

Structure,morphology, thermal,and sorption characteristics of epoxidized natural rubber conjugated spent coffee via one-pot synthesis

The present work highlights the preparation of the epoxidized natural rubber conjugated spent coffee biocomposites (ENR-g-SC) via one-pot synthesis to control petroleum oil spills. The structural determination of the spent coffee grafted epoxidized natural rubber (ENR-g-SC) was confirmed through FTIR and ¹H-NMR spectroscopic analyses. The thermal performance, tensile tests, and morphological properties of the synthesized (ENR-g-SC) biocomposites were performed. The data revealed that ENR-g-SC biocomposite with 20 phr of spent coffee (SC) exhibited the highest tensile properties due to maximal chemical linkages of spent coffee and epoxide groups in ENR. The epoxidized natural rubber conjugated spent coffee copolymers were evaluated as oil sorbers for oil absorbency applications in chloroform, toluene, and 10% crude petroleum diluted with toluene. The data revealed that the oil absorbency increased slightly with chloroform or toluene instead of 10% crude oil diluted with toluene. Furthermore, swelling and network parameters including the maximum oil absorbency (Q_max), swelling rate constant (k), polymer-solvent interaction (χ), effective crosslink density (ύe), equilibrium modulus of elasticity (G_T), and average molecular weight between crosslinks (M_c) and theoretical crosslink density (ύ_t) were determined, and correlated to the efficiency of the synthesized epoxidized natural rubber conjugated spent coffee.