Space-time variation of ciliates related to environmental factors in 15 nearshore stations of the Gulf of Gabes

Diversity and structure of ciliate communities in the Gulf of Gabes (Tunisia) were investigated based on a survey of 15 nearshore stations along 237 Km, by monthly sampling over a 1-year. Ciliated protozoa were identified to genus and/or species level and enumerated. Statistic tools were used to explain the ciliates assemblage. High ciliates species richness from 133 taxa was recorded, including new records of 76 species. This study showed a longitudinal distribution of ciliate communities, which are organized in northern stations (from Tabia to Harbor of Gabes) and southern stations (from Zarrat to Jabiat Haj Ali). The number of taxa increased significantly in northern stations but decreased in the southern. This distribution was mainly influenced by the salinity and phytoplankton abundance. Ciliate taxa were grouped into fives size-classes: 15-30 µm, 30-50 µm, 50-100 µm, 100-200 µm and >200 µm. In terms of abundance, most abundant size groups were small ciliates (15-30 μm) accounted from 15 to 79 %, while the greatest biomass contribution came from the 50-100 μm size classes. We thus conclude high diversity of ciliates communities that showed a geographical distribution influenced by abiotic and biotic factors along the coast of Gulf of Gabes

Distribution of microplankton in relation to environmental variables during summer stratification in the Gulf of Hammamet, Tunisia

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Influence of the sulfate content of the exopolysaccharides from Porphyridium sordidum on their elicitor activities on date palm vitroplants

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What are the factors leading to the success of small planktonic copepods in the Gulf of Gabes, Tunisia ?

An oceanographic cruise conducted during June 2008 in the Gulf of Gabes revealed the existence of different water masses; the Modified Atlantic Waters (MAW) circulated in the upper 100m in the offshore area, the Mixed Mediterranean Water (MMW) was confined to the inshore region and the Ionian Water (IW) was in deep offshore water. The thermal stratification was indicated by the vertical profiles of temperature generated from a coast-offshore section. Phosphorus limitation was induced by the thermal stratification as shown by the high N/P ratio. Heterotrophic and mixotrophic dinoflagellates were the major contributors to total phytoplankton biomass. Ciliates were less abundant and dominated by tintinnids. Small planktonic copepods (1.45mm) contributed to 93.64% of total copepod abundance in the inshore area as a result of the high density of Oithona similis, Oithona nana, Clausocalanus furcatus and Euterpina acutifrons in this area characterized by warm and salty MMW. In fact, small copepods were significantly correlated to both temperature and salinity. Small copepod fraction prevailed also in the MAW contributing to 71.05% of total copepod abundance as a result of the dominance of O. nana and C. furcatus. Nonetheless, the large copepod Nannocalanus minor was more adapted to the deep IW where it contributed to 44.05% of total copepod abundance. Invasive species were encountered in the offshore region intruded by the Atlantic waters. The Atlantic copepods were scarce and less abundant reflecting the weakening of the Atlantic flow in the eastern basin of the Mediterranean

Handling noise in textual image resolution enhancement using online and offline learned dictionaries

International audienceThe resolution enhancement of textual images poses a significant challenge mainly in the presence of noise. The inherent difficulties are twofold. First is the reconstruction of an upscaled version of the input low-resolution image without amplifying the effect of noise. Second is the achievement of an improved visual image quality and a better OCR accuracy. Classically, the issue is addressed by the application of a denoising step used as a preprocessing or a post-processing to the magnification process. Starting by a denoising process could be more promising to avoid any magnified artifacts while proceeding otherwise. However, the state of the art underlines the limitations of denoising approaches faced with the low spatial resolution of textual images. Recently, sparse coding has attracted increasing interest due to its effectiveness in different reconstruction tasks. This study proves that the application of an efficient sparse coding-based denoising process followed by the magnification process can achieve good restoration results even if the input image is highly noisy. The main specificities of the proposed sparse coding-based framework are: (1) cascading denoising and magnification of each image patch, (2) the use of sparsity stemmed from the non-local self-similarity given in textual images and (3) the use of dual dictionary learning involving both online and offline dictionaries that are selected adaptively for each local region of the input degraded image to recover its corresponding noise-free high-resolution version. Extensive experiments on synthetic and real low-resolution noisy textual images are carried out to validate visually and quantitatively the effectiveness of the proposed system. Promising results, in terms of image visual quality as well as character recognition rates, are achieved when compared it with the state-of-the-art approaches