No abstract

With the evolution of social media platforms, the Internet is used as a source for obtaining news about current events. Recently, Twitter has become one of the most popular social media platforms that allows public users to share the news. The platform is growing rapidly especially among young people who may be influenced by the information from anonymous sources. Therefore, predicting the credibility of news in Twitter becomes a necessity especially in the case of emergencies. This paper introduces a classification model based on supervised machine learning techniques and word-based N-gram analysis to classify Twitter messages automatically into credible and not credible. Five different supervised classification techniques are applied and compared namely: Linear Support Vector Machines (LSVM), Logistic Regression (LR), Random Forests (RF), Naïve Bayes (NB) and K-Nearest Neighbors (KNN). The research investigates two feature representations (TF and TF-IDF) and different word N-gram ranges. For model training and testing, 10-fold cross validation is performed on two datasets in different languages (English and Arabic). The best performance is achieved using a combination of both unigrams and bigrams, LSVM as a classifier and TF-IDF as a feature extraction technique. The proposed model achieves 84.9% Accuracy, 86.6% Precision, 91.9% Recall, and 89% F-Measure on the English dataset. Regarding the Arabic dataset, the model achieves 73.2% Accuracy, 76.4% Precision, 80.7% Recall, and 78.5% F-Measure. The obtained results indicate that word N-gram features are more relevant for the credibility prediction compared with content and source-based features, also compared with character N-gram features. Experiments also show that the proposed model achieved an improvement when compared to two models existing in the literature.

Abstract:This study was carried out to investigate that the effect of grape seed oil (GSO) on Hypercholesterolemia in rats.Thirty-five rats were divided into 5 groups, group 1 was given the basal diet as a negative control group (-ve), group 2 was given the basal diet with GSO instead of soybean oil as a negative control for GSO. Groups (3, 4 and 5) were fed on high cholesterol diet (HCD) (1% cholesterol powder and 0.5% bile salt for 8 weeks), Group 3 was +ve control, group 4 and 5 were treatment groups which received GSO 2% and 4% daily with HCD. The results of this study indicated that the GSO caused an improvement in the blood lipids especially 4% GSO. The 4% GSO decreased serum TC, TG, LDL-c and VLDL significantly (P ˂ 0.05), and caused a significant increase in HDL-C level. Also, 2% GSO decreased TG, LDL-C and VLDL significantly (P ˂ 0.05), whereas the level of HDL-C showed significant increases. Subsequently, GSO enhanced the lipid ratios: atherogenic coefficient (AC), cardiac risk ratio (CRR), LDL-c to HDL-c ratio and atherogenic index of plasma (AIP). Moreover, the serum, liver function (AST, ALT and ALP) levels also, enhanced in the GSO groups. However, 4% GSO led to significant decrease in serum MDA and elevated serum GST. Otherwise, histopathological examination showed enhanced in the heart and aorta of rats compared with the +ve control group. This study indicates that GSO effective in lowering total cholesterol, triglyceride and LDL-c and increasing HDL-c. Therefore, GSO have hypocholesterolemic effect and might be effective to protect against the risk of CVD.

Textual entailment recognition is one of the recent challenges of the Natural Language Processing (NLP) domain. Deep learning strategies are used in the work of text entailment instead of traditional Machine learning or raw coding to achieve new enhanced results. Textual entailment is also used in the substantial applications of NLP such as summarization, machine translation, sentiment analysis, and information verification. Text entailment is more precise than traditional Natural Language Processing techniques in extracting emotions from text because the sentiment of any text can be clarified by textual entailment. For this purpose, when combining a textual entailment with deep learning, they can hugely showed an improvement in performance accuracy and aid in new applications such as depression detection. This paper lists and describes applications of natural language processing regarding textual entailment. Various applications and approaches are discussed. Moreover, datasets, algorithms, resources, and performance evaluation for each model is included. Also, it compares textual entailment application models according to the method used, the result for each model, and the pros and cons of each model.

The present study was conducted in order to examine the protective effect of lemon grass (Cymbopogon citratus) water extract (LGWE) against nephrotoxicity induced by cisplatin of male Albino rats. Thirty five adult male Albino rats weighing between 120-140g were randomly separated into five different groups (7rats each). Groupl was a normal control group (-ve), fed on basal diet. Group 2 was the positive control group (+ve) fed on basal diet for 6 weeks and then injected intraperitoneally (i.p.) with a single dose of cisplatin 5mg/kg of body weight. Groups 3, 4 and 5 fed the same as group2 and received 5, 7.5 and 10% lemon grass water extract, respectively, for 6 weeks and then injected intraperitoneally (i.p.) with the same dose of cisplatin. Five days later all rats in all groups were sacrificed and the blood was collected for biochemical and histopathological investigations. Cisplatin treatment caused significantly increase in serum malondialdehyde, uric acid, blood urea nitrogen and creatinine as well as alanine aminotransferase, aspartate aminotransferase and alkaline phosphatase (p<0.05) in +ve control group compared to-ve control group. Rats which were fed LGWE (groups 3, 4 and 5) showed marked reduction in the same biochemical investigations compared to +ve control group. Reduced glutathione (GSH), serum sodium and potassium mean values were decreased in +ve control group compared to-ve control rats. Feeding LGWE in groups 3, 4 and 5 showed a rise in the same biochemical parameters compared to +ve control group. 2, 2-Dipheny1-1-picrylhydrazyl (DPPH), half maximal inhibitory concentration (ICH) and total phenolic content of lemon grass was assayed. Parallel to the above mentioned changes, cisplatin treatment enhances renal damage as evidenced by sharp impairment of kidney function corresponds to biochemical parameters and histopathological findings. Additionally, feeding LGWE caused gradually histopathological improvement in renal tissues in groups 3, 4 and 5. These results of this present study indicated that aqueous extracts of Cymbopogon citratus has antinephrotoxic properties against cisplatin induced renal oxidative damage in rats which might be ascribed to its antioxidant and free radical scavenging property. According to these above results, it is recommended to conduct further studies on the use of LGWE and possible protection of human beings against nephrotoxicity.