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Antioxidant antileishmanial cytotoxic and antimicrobial activities of a local plant Myrtus nivellei from Algeria Sahara

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Asian Pacific Journal of Tropical Biomedicine 2017年第8期7卷 702-707页

作者： Farah Ramdane rym essid Nadia Fares Dahmane El Ouassis Sana Aziz Mahfoud Hadj Mahammed Mohamed Didi Ould Hadj Ferid Limam Biogeochemistry Laboratory in Desert Environments Kasdi Merbah University Faculty of Natural and Life Sciences El Oued University Laboratory of Bioactive Substances Biotechnology Center of Borj-Cedria Technopole National Institute of Forest Research Protection Laboratory of Ecosystem in Arid and Semi Arid Zone Kasdi Merbah University

To study the phytochemical constituents and in vitro biological activities of hydromethanolic extract and fractions from Algerian Sahara Myrtus nivellei (M. nivellei) collected in Hoggar region and to identify the active fraction that can act as an alternative of commonly used antibiotics and as antileishmanial or antioxidant *** screening of M. nivellei aerial parts was realised according to the literature. Extract was firstly prepared by using aqueous methanol then fractionated with ethyl acetate and butanol solvents. Total phenolics, tannis and flavonoids, of the hydromethanolic extract and their fractions were determined by Folin-Ciocalteu method as gallic acid equivalents and by aluminium chloride as rutin equivalent respectively. Extract and fractions were tested for their antimicrobial and antiparasital activities against standard bacteria using agar diffusion method and two kinds of leishmania visceral and cutaneous. The antioxidant activities were realized using phosphomolybdenum, FRAP and DPPH *** phytochemical screening exhibited the presence of flavonoids, tannins, saponins, and alkaloids. The experimental results showed that plant extract and fractions were high in phenolic compounds and exhibited an important role as antioxidant, antimicrobial and had a moderate antileishmanial *** observations lead us toward more studies in this field, so that we can get more benefits from our local Algerian medicinal plants.

关键词： Phenolic compounds Flavonoids Antioxidant activity Antimicrobial activity Antileishmanial activity Myrtus nivellei

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Towards an Intelligent Predictive Model for Analyzing Spatio-Temporal Satellite Image Based on Hidden Markov Chain

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Advances in Remote Sensing 2013年第3期2卷 247-257页

作者： Houcine essid Imed Riadh Farah Vincent Barra Research Laboratory in Computer Integrated Documentation and Arabized-Documentiel Geniuses and Software National School of Computer Science Campus Universitaire de Manouba Tunis Tunisie

Nowadays remote sensing is an important technique for observing Earth surface applied to different areas such as, land use, urban planning, remote monitoring, real time deformation of the soil that can be associated with earthquakes or landslides, the variations in thickness of the glaciers, the measurement of volume changes in the case of volcanic eruptions, deforestation, etc. To follow the evolution of these phenomena and to predict their future states, many approaches have been proposed. However, these approaches do not respond completely to the specialists who process yet more commonly the data extracted from the images in their studies to predict the future. In this paper, we propose an innovative methodology based on hidden Markov models (HMM). Our approach exploits temporal series of satellite images in order to predict spatio-temporal phenomena. It uses HMM for representing and making prediction concerning any objects in a satellite image. The first step builds a set of feature vectors gathering the available information. The next step uses a Baum-Welch learning algorithm on these vectors for detecting state changes. Finally, the system interprets these changes to make predictions. The performance of our approach is evaluated by tests of space-time interpretation of events conducted over two study sites, using different time series of SPOT images and application to the change in vegetation with LANDSAT images.

关键词： Satellite Image Remote Sensing Hidden Markov Model Change Detection Image Processing

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