Manufacturing camel cheese: the experience of Saudi Arabia

Unlike other dairy products, camel cheese processing is recent. Thanks to recombinant camel chymosin, camel cheese making is easier. In Saudi Arabia, camel cheese making was developed for last 4 years in the Camel project (FAO project). Our cheeses are made with fresh milk strictly controlled for their microbiological quality (coliform, total flora). The milk composition is known, all samples being analyzed with Milkoscan-FOSS FT1 ©. Different starters are used from Coquard © (France) according to the type of cheese expected. All along the process, the pH is checked (draining, moulding, pressing, salting). The composition of the whey is also determined. At the end of the process, after salting, dry matter and final pH are measured as well as the total yield. Cheese of Haloumi type are pasteurized in their whey and packaged under vacuum. Some cheeses are ripening in special cave for few weeks (Mozzarella, Feta) or months (Camembert, Saint- Paulin). The cheese yield is depending of the cheese type but occurs between 8 and 12%. Sensory tests are performed. These cheeses are available on the market. Different experiments are performed in order to test the effect of different starters. (Résumé d'auteur

Effect of Salvadora persica Linn root aqueous extract on oral epithelial dysplasia and oral cancer cell lines

Purpose: To evaluate the potential chemo-preventive and anti-oral cancer effects of Salvadora persica (S. persica) on oral epithelial dysplasia and oral squamous cell carcinoma cell lines. Methods: Aqueous S. persica root extracts were prepared at concentrations up to 15.75 mg/mL and applied to oral epithelial dysplasia (DOK), oral squamous cell carcinoma (PE/CA-PJ15), and periodontal ligament fibroblast (PDL) cell lines. The effect of the extract on cell survival and proliferation was determined using MTT assay, while its effect on apoptosis in DOK and PE/CA-PJ15 lines were investigated by measuring apoptotic index using Hoechst stain. Results: In DOK and PE/CA-PJ15 cell lines, cytotoxicity was significant at extract concentrations of 11.25, 13.50 and 15.75 mg/mL, while extract concentration of 13.50 mg/mL produced significant cytotoxic effects on PDL cell line (p < 0.05). The percentage of apoptotic cells significantly increased at extract concentration of 11.25 mg/mL for both DOK and PE/CA-PJ15 cell lines (p < 0.05). Conclusion: Significant cytotoxic effects of aqueous root extract of S. persica appeared at a lower concentration in oral epithelial dysplasia and oral cancer cell lines than in normal PDL cell line. These results suggest the potential of S. persica for preventing oral cancer

Using deep learning models for learning semantic text similarity of Arabic questions

Question-answering platforms serve millions of users seeking knowledge and solutions for their daily life problems. However, many knowledge seekers are facing the challenge to find the right answer among similar answered questions and writer’s responding to asked questions feel like they need to repeat answers many times for similar questions. This research aims at tackling the problem of learning the semantic text similarity among different asked questions by using deep learning. Three models are implemented to address the aforementioned problem: i) a supervised-machine learning model using XGBoost trained with pre-defined features, ii) an adapted Siamese-based deep learning recurrent architecture trained with pre-defined features, and iii) a Pre-trained deep bidirectional transformer based on BERT model. Proposed models were evaluated using a reference Arabic dataset from the mawdoo3.com company. Evaluation results show that the BERT-based model outperforms the other two models with an F1=92.99%, whereas the Siamese-based model comes in the second place with F1=89.048%, and finally, the XGBoost as a baseline model achieved the lowest result of F1=86.086%

A transfer learning with deep neural network approach for diabetic retinopathy classification

Diabetic retinopathy is an eye disease caused by high blood sugar and pressure which damages the blood vessels in the eye. Diabetic retinopathy is the root cause of more than 1% of the blindness worldwide. Early detection of this disease is crucial as it prevents it from progressing to a more severe level. However, the current machine learning-based approaches for detecting the severity level of diabetic retinopathy are either, i) rely on manually extracting features which makes an approach unpractical, or ii) trained on small dataset thus cannot be generalized. In this study, we propose a transfer learning-based approach for detecting the severity level of the diabetic retinopathy with high accuracy. Our model is a deep learning model based on global average pooling (GAP) technique with various pre-trained convolutional neural net- work (CNN) models. The experimental results of our approach, in which our best model achieved 82.4% quadratic weighted kappa (QWK), corroborate the ability of our model to detect the severity level of diabetic retinopathy efficiently

High-Quality Wavelets Features Extraction for Handwritten Arabic Numerals Recognition

Arabic handwritten digit recognition is the science of recognition and classification of handwritten Arabic digits. It has been a subject of research for many years with rich literature available on the subject.  Handwritten digits written by different people are not of the same size, thickness, style, position or orientation. Hence, many different challenges have to overcome for resolving the problem of handwritten digit recognition.  The variation in the digits is due to the writing styles of different people which can differ significantly.  Automatic handwritten digit recognition has wide application such as automatic processing of bank cheques, postal addresses, and tax forms. A typical handwritten digit recognition application consists of three main stages namely features extraction, features selection, and classification. One of the most important problems is feature extraction. In this paper, a novel feature extraction approach for off-line handwritten digit recognition is presented. Wavelets-based analysis of image data is carried out for feature extraction, and then classification is performed using various classifiers. To further reduce the size of training data-set, high entropy subbands are selected. To increase the recognition rate, individual subbands providing high classification accuracies are selected from the over-complete tree. The features extracted are also normalized to standardize the range of independent variables before providing them to the classifier. Classification is carried out using k-NN and SVMs. The results show that the quality of extracted features is high as almost equivalently high classification accuracies are acquired for both classifiers, i.e. k-NNs and SVMs

Evaluation the Non-Thermal Plasma Application Activity in AFB1 Detoxification

Contamination raw agricultural materials has been a food safety concern for human and animals. A non thermal plasma or cold plasma  is a novel antimicrobial intervention, that can be reduce the level of aflatoxin B1(AFB1) in complete cow's feed samples .AFB1 are carcinogenic compound produce primarily by two certain strain of Aspergillus include A. flavus and A. parasiticus , the  contamination of feed is arise for  animals health. Fifty samples from complete cow's feed were designated into imported 15 samples and local 35 samples , obtained randomly from different region in Baghdad from March 2014 to February 2015. Samples were tested for AFB1 by ELISA and HPLC technique and exposure to application cold plasma protocol to treatment of AFB1 contamination the samples in different time (5, 10 and 15) seconds at 3.5 cm between the plasma source and samples then tested by ELISA and HPLC. There are appears the best time successful in reducing levels of toxin at 10 sec. in local samples 3.12, 0.05 ng / g imported samples 1.21, 6.19 ng / g in HPLC and ELISA. In local and imported samples at 10 sec and that was 15 sec less time periods ability to reduce toxin level in local and imported samples, that indicated the length of exposure to NTP application is not necessary to reduce toxin level. According to the study we observed that the results from ELISA method were more sensitive, accuracy and simplicity when compared with results from HPLC technique. Keyword: Cold plasma, decontamination,AFB1, HPLC, ELISA

Transfer deep learning approach for detecting coronavirus disease in X-ray images

Currently, the whole world is fighting a very dangerous and infectious disease caused by the novel coronavirus, called COVID-19. The COVID-19 is rapidly spreading around the world due to its high infection rate. Therefore, early discovery of COVID-19 is crucial to better treat the infected person as well as to slow down the spread of this virus. However, the current solution for detecting COVID-19 cases including the PCR test, CT images, epidemiologically history, and clinical symptoms suffer from high false positive. To overcome this problem, we have developed a novel transfer deep learning approach for detecting COVID-19 based on x-ray images. Our approach helps medical staff in determining if a patient is normal, has COVID-19, or other pneumonia. Our approach relies on pre-trained models including Inception-V3, Xception, and MobileNet to perform two tasks: i) binary classification to determine if a person infected with COVID-19 or not and ii) a multi-task classification problem to distinguish normal, COVID-19, and pneumonia cases. Our experimental results on a large dataset show that the F1-score is 100% in the first task and 97.66 in the second task

ATTITUDE OF SAUDI CONSUMERS TOWARDS ONLINE SHOPPING WITH SPECIAL REFERENCE TO AL-HASSA REGION (KSA)

Abstract This research presents a study of measuring the attitude of Saudi consumers towards online shopping: Al-Hass

Performance of the CMS Cathode Strip Chambers with Cosmic Rays

The Cathode Strip Chambers (CSCs) constitute the primary muon tracking device in the CMS endcaps. Their performance has been evaluated using data taken during a cosmic ray run in fall 2008. Measured noise levels are low, with the number of noisy channels well below 1%. Coordinate resolution was measured for all types of chambers, and fall in the range 47 microns to 243 microns. The efficiencies for local charged track triggers, for hit and for segments reconstruction were measured, and are above 99%. The timing resolution per layer is approximately 5 ns

Emergency medicine in Oman: current status and future challenges

The Sultanate of Oman has a relatively young national health care system that could demonstrate its high performance at an international level. Emergency medicine as a specialty has developed rapidly in the country over the last decade. This has involved the parallel development of local emergency residency training, prehospital emergency care, and emergency nursing programs. This article reviews the progress of emergency care practice in this country from a general primary care system toward becoming an established specialty in hospital, prehospital, and private emergency care settings. It also describes aspects of undergraduate, postgraduate, and continuous emergency medicine education in the country. Further, a glimpse into academic emergency medicine and emergency nursing is provided. Since it describes a developing specialty, the article also attempts to address briefly major future challenges and their importance to the future development of the specialty in Oman