Tunis University

About: Tunis University is a education organization based out in Tunis, Tunisia. It is known for research contribution in the topics: Population & Thin film. The organization has 11745 authors who have published 15400 publications receiving 154900 citations. The organization is also known as: University of Tunis & UT.

Deep feature learning for soft tissue sarcoma classification in MR images via transfer learning

Abstract: Medical image analysis is motivated by deep learning emergence and computation power increase. Meanwhile, relevant deep features can significantly enhance learnable expert and intelligent systems performance and reduce diagnosis time and arduousness. This paper presents a deep learning-based radiomics framework for aided diagnosis of soft tissue sarcomas of the extremities. MR Images with histologically confirmed Liposarcoma (LPS) and Leiomyosarcomas (LMS) have been retrieved from the Cancer Imaging Archives database and pre-processed to recuperate ROIs from MR scans with delineated tumors. This study investigates the significance and impact of medical image fusion on deep feature learning based on transfer learning from the natural domain to the medical domain. Towards this end, we propose to fuse T1 with T2FS or STIR modalities using type-2 fuzzy sets in the non-subsampled shearlet domain. Being decomposed, low-frequency sub-images were selected using local energy and type-2 fuzzy entropy, while high frequencies were selected according to the maximum of the absolute value. Experimental results indicated that the proposed fusion framework outperformed the state-of-the-art fuzzy logic-based fusion techniques in terms of entropy and mutual information. Accordingly, we fine-tuned the pre-trained AlexNet deep convolutional neural network (CNN) with stochastic gradient descent (SGD). First, with the pre-processed dataset, and second with the fused images. As a result, the average classification accuracy using the augmented training data by image rotation and flipping was 97.17% with the raw data and 98.28% with the fused images, which highlighted the usefulness of complementary information for deep feature learning. One crucial concern was to investigate the depth of knowledge transferability. We incrementally fine-tuned the pre-trained CNN to assess the required level that achieves performance improvements in STS classification. Through layer-wise fine-tuning, our study further confirms the potential of middle and deep layers in performance improvement. Moreover, the transferability was concluded better than random weights. With the encouragement of classification results, our aided diagnosis framework may be in the pipeline to assist radiologists in classifying LPS and LMS.

Biomarkers of Amyotrophic Lateral Sclerosis: Current Status and Interest of Oxysterols and Phytosterols.

Abstract: Amyotrophic lateral sclerosis (ALS) is a non-demyelinating neurodegenerative disease in adults with motor disorders. Two forms exist: a sporadic form (90% of cases) and a family form due to mutations in more than 20 genes including the Superoxide dismutase 1, TAR DNA Binding Protein, Fused in Sarcoma, chromosome 9 open reading frame 72 and VAPB genes. The mechanisms associated with this pathology are beginning to be known: oxidative stress, glutamate excitotoxicity, protein aggregation, reticulum endoplasmic stress, neuroinflammation, alteration of RNA metabolism. In various neurodegenerative diseases, such as Alzheimer's disease or multiple sclerosis, the involvement of lipids is increasingly suggested based on lipid metabolism modifications. With regard to ALS, research has also focused on the possible involvement of lipids. Lipid involvement was suggested for clinical arguments where changes in cholesterol and LDL/HDL levels were reported with, however, differences in positivity between studies. Since lipids are involved in the membrane structure and certain signaling pathways, it may be considered to look for oxysterols, mainly 25-hydroxycholesterol and its metabolites involved in immune response, or phytosterols to find suitable biomarkers for this pathology.

Glutathione and phytochelatin contents in tomato plants exposed to cadmium

Abstract: The effect of cadmium on growth and contents of glutathione (GSH) and phytochelatins (PCs) were investigated in roots and leaves of tomato plants (Lycopersicon esculentum Mill cv 63/5 F1) The accumulation of Cd increased with external Cd concentrations and was considerably higher in roots than in leaves Dry mass production decreased under Cd treatment especially in leaves In both roots and leaves, exposure to Cd caused an appreciable decline in GSH contents and increase in PCs synthesis proportional to Cd concentrations in the growth medium At the same Cd concentration, PCs production was higher in roots than in leaves The implication of glutathione in PC synthesis was strongly suggested by the use of buthionine sulfoximine (BSO) The major fraction of Cd accumulated by tomato roots was in the form of a Cd-PCs complex