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.

Molecular Epidemiology and Characterization of Plasmid-Encoded β-Lactamases Produced by Tunisian Clinical Isolates of Salmonella enterica Serotype Mbandaka Resistant to Broad-Spectrum Cephalosporins

Abstract: We studied 31 clinical isolates of Salmonella enterica serotype Mbandaka resistant to broad-spectrum cephalosporins and recovered in Tunisia over a 5-year period. The transferability of this resistance was demonstrated by conjugation experiments. Thirty of the 31 isolates were positive in the double-disk synergy test. By isoelectric focusing analysis, all of the isolates were found to produce a band of β-lactamase activity with a pI of 5.9. Three of these isolates produced an additional band with a pI of 7.6. PCR and DNA sequencing identified these β-lactamases as TEM-4 and SHV-2a, respectively. The remaining isolate, highly resistant to ceftazidime but susceptible to cefepime, produced a β-lactamase that focused at pI 7.8. No synergy was detected by the double-disk synergy test. Sequence analysis of the bla gene amplified by PCR showed that the plasmid-mediated AmpC-type enzyme was ACC-1a. Fingerprinting analysis by repetitive-element PCR and enterobacterial repeat intergenic consensus-PCR suggested that 29 of the 31 Salmonella serotype Mbandaka isolates belonged to the same clonal population.

Elderly fall detection based on multi-stream deep convolutional networks

Abstract: Fall is the biggest threat to seniors, with significant emotional, physical and financial implications. It is the major cause of serious injuries, disabilities, hospitalizations and even death especially for elderly people living alone. Timely detection could provide immediate medical service to the injured and avoid its harmful consequences. Great number of vision-based techniques has been proposed by installing cameras in several everyday environments. Recently, deep learning has revolutionized these techniques, mostly using convolutional neural networks (CNNs). In this paper, we propose weighted multi-stream deep convolutional neural networks that exploit the rich multimodal data provided by RGB-D cameras. Our method detects automatically fall events and sends a help request to the caregivers. Our contribution is three-fold. We build a new architecture composed of four separate CNN streams, one for each modality. The first modality is based on a single combined RGB and depth image to encode static appearance information. RGB image is used to capture color and texture and depth image deals with illumination variations. In contrast of the first feature that lacks the contextual information about previous and next frames, the second modality characterizes the human shape variations. After background subtraction and person recognition, human silhouette is extracted and stacked to define history of binary motion HBMI. The last two modalities are used to more discriminate the motion information. Stacked amplitude and oriented flow are used in addition to stacked optical flow field to describe respectively the velocity, the direction and the motion displacements. The main motivation behind the use of these multimodal data is to combine complementary information such as motion, shape, RGB and depth appearance to achieve more accurate detection than using only one modality. Our second contribution is the combination of the four streams to generate the final decision for fall detection. We evaluate early and late fusion strategies and we have defined the weight of each modality based on its overall system performance. Weighted score fusion is finally adopted based on our experiments. In the third contribution, transfer learning and data augmentation are applied to increase the amount of training data, avoid over fitting and improve the accuracy. Experiments have been conducted on publicly available standard datasets and demonstrate the effectiveness of the proposed method compared to existing methods.

Antimicrobial, antioxidant, cytotoxic and anticholinesterase activities of water-soluble polysaccharides extracted from microalgae Isochrysis galbana and Nannochloropsis oculata

Abstract: The present work is carried out to evaluate potential applications of aqueous extracts of two microalgae Isochrysis galbana (PEA) and Nannochloropsis oculata (PEB) containing mainly polysaccharides. The monosaccharide composition of microalgal extracts was determined. GC-MS analyses after derivatization show that glucose is the major compound in both microalgae PEA (56.88%) and PEB (68.23%). Mannitol (38.8%) and inositol (20.32%) are respectively the second major compounds in PEA and PEB. Silylation of monosaccharides allows the determination of sorbitol that attained 3.38% in PEB. The determination of antioxidant, antimicrobial and cytotoxic properties were also analyzed. Antioxidant activity was evaluated from the DPPH scavenging activity. PEA and PEB show a concentration dependent DPPH·radical scavenging activity. At concentration of 10 mg/mL, both PEA and PEB exhibit an antioxidant activity of 41.45 % and 59.07 %, respectively. PEB and PEA are able to inhibit the growth of Gram-negative bacteria, Gram-positive bacteria and three Candida species. Cytotoxic activity was evaluated on human HeLa cervical cancer cells. HeLa cell proliferation was totally inhibited after treatment with PEA and PEB (1 mg/mL) and the inhibition was dose dependent (from 0.031 to 1 mg/mL). Their anticholinesterase activity was also investigated against butyrylcholinesterase enzymes. These polysac­charides possess interesting antimicrobial, anticancer and anticholinesterase activities that could represent an additional value for these microalgal products.