With the aim of reducing the radiologists' subjectivity and the high degree of inter-observer variability, Contentbased Image Retrieval (CBIR) systems have been proposed to provide visual comparisons of a given lesion to a collection of similar lesions of known pathology. In this paper, we present the effectiveness of shape features versus texture features for calculating lung nodules' similarity in Computed Tomography (CT) studies. In our study, we used eighty-five cases of thoracic CT data from the Lung Image Database Consortium (LIDC). To encode the shape information, we used the eight most commonly used shape features for pulmonary nodule detection and diagnosis by existent CAD systems. For the texture, we used co-occurrence, Gabor, and Markov features implemented in our previous CBIR work. Our preliminary results give low overall precision results for shape compared to texture, showing that shape features are not effective by themselves at capturing all the information we need to compare the lung nodules.

The Named Entity Recognition (NER) is an integrated task in many NLP applications such as machine translation, Information extraction and question answering. Arabic is one of the authorised spoken languages in the united nation. Currently, there is much Arabic information on the internet, so, nowadays the need for tools which process this information becomes significant. In this study, we have examined the impact of the conditional random field and the structured support vector machine in the task of Arabic NER. The structured support vector machine is the first time to be applied in the Arabic name entity recognition. Our proposed system has three stages: Preprocessing, extracting features and building model. We have used simple features like the bag of words in the [-1,1] window, the bag of part of speech tag in the [-1,1] window to enable our system to detect the multi-words entities. Also, we have tried to enhance the Stanford part of speech tagger to enhance the tagger output tags, which enabled our system to differentiate between the name entities from the nonentities. In addition, we have employed the binary features of: Is a person, is a prename, is a pre-location, is a location and is an organization. Our system has been trained and tested on part of ANER Crop. The results have proved that the conditional random field-based Arabic NER system outperforms the structured support vector machine-based Arabic NER using the same features set.

Background: Aging-related changes in liver alter both hepatic structure and function, thus, increasing the mortality rate in susceptible old patients. Metformin provide health benefits to elderly individuals when ingested in appropriate amounts and it is one of the physiological triggers of Peroxisome Proliferator-Activated Receptor-Gamma PPARγ) and Irisin. Aim: This study aimed to ( investigate the effect of metformin on hepatic aging induced by Dgalactose (D-gal) in rats, clarifying the role of PPARγ/ Irisin pathway in this process. Methods: 24 adult Wister albino male rats divided into 4 groups: group I (control group): rats received saline; ip, group II (D-gal group): rats received D-gal; ip, group III (Metformin group): rats received Metformin orally & group IV (Metformin + Dgal group): rats received D-gal with Metformin. At the end of experiment, the serum samples were taken for biochemical estimation of Alanine aminotransferase (ALT), Aspartate aminotransferase (AST), Albumin and hepatic tissue PPARγ and Irisin protein levels. Results: compared to control group, D-gal caused hepatic injury confirmed by a significant increase in ALT and AST with a significant decrease in Albumin. Metformin in group IV prevented these changes through increased hepatic PPARγ and Irisin. Conclusion: Metformin showed a protective effect against the D-gal induced hepatic aging through induction of hepatic PPARγ and Irisin.