The Bélinga Iron Ore Deposit (~2.8 Ga), NE-Gabon: Reactualization and New Interpretations on Crests

The Bélinga iron ore deposit is the biggest iron ore situated in NEGabon. Very little is known about that iron ore and available data are sometimes incompatible. We revisited documents that evoke the deposit to reactualize and propose new interpretations. The Bélinga iron ore deposit is composed of thirteen (13) N-S mineralized crests that underwent 3 tectonic events, folding (D1), fracture (D2) and folding (D3). The ore is subdivided into four main categories which are blue and yellow ores, hematitic phyllites and enriched itabirites, with variable amounts of canga. The estimated reserves at Bélinga are ~384 Mt of high grade ore (Fe > 60% and P < 0.09%), and more than 1 Gt by considering an iron ore with Fe > 50% and P < 0.18%

The Yin-Yang of the Green Fluorescent Protein:Impact on Saccharomyces cerevisiae stress resistance

International audienceAlthough fluorescent proteins are widely used as biomarkers (Yin), no study focuses on their influence on the microbial stress response. Here, the Green Fluorescent Protein (GFP) was fused to two proteins of interest in Saccharomyces cerevisiae. Pab1p and Sur7p, respectively involved in stress granules structure and in Can1 membrane domains. These were chosen since questions remain regarding the understanding of the behavior of S. cerevisiae facing different heat kinetics or oxidative stresses. The main results showed that Pab1p-GFP fluorescent mutant displayed a higher resistance than that of the wild type under a heat shock. Moreover, fluorescent mutants exposed to oxidative stresses displayed changes in the cultivability compared to the wild type strain. In silico approaches showed that the presence of the GFP did not influence the structure and so the functionality of the tagged proteins meaning that changes in yeast resistance were certainly related to GFP ROS-scavenging ability (Yang)

Méthodes robustes en traitement d'image pour la détection et la caractérisation d'objets compacts : application à la biologie

In the field of microbiology, many experiments are based on a fine observation of microorganisms. Because of their interest in the development of modern agri-food processes, it is important to study their development and survival rate under specific environmental conditions such as osmotic or thermal stress. Microscopic imaging is one of the most used tools for observing microorganisms. The manual interpretation of acquired images raises problems of subjectivity, cost and reproducibility. This thesis proposes the development of standardized image analysis tools allowing the interpretation of images at two scales:- At the scale of the observation slide: the use of specific counting slides (Malassez) allows, from the counting of the cells present in the zone of interest of the slide, to deduce the cell concentration of a solution of Saccharomyces cerevisiae subjected to osmotic stress. The tools developed allow for the identification and characterization of this area of interest (grid) and precise counting of the cells.- At the cell scale: a mutant strain of Saccharomyces cerevisiae allows for the observation in fluorescence the Pab1p-GFP protein involved in the formation of intracellular ribo-nucleoprotein aggregates consecutive to thermal stress. The tools developed allows for obtaining a statistical view of the development of these aggregates by automating the estimation of their number for a very large number of cells.Dans le domaine de la microbiologie, de nombreuses expériences se basent sur une fine observation des micro-organismes. De par leur intérêt dans le développement de procédés agroalimentaires modernes, il est important d’étudier leur développement et leur taux de survie dans des conditions environnementales spécifiques telles que des stress osmotiques ou thermiques. L’imagerie microscopique est un des outils les plus utilisés pour observer les micro-organismes. L’interprétation manuelle des images acquises pose des problèmes de subjectivité, de coût et reproductibilité. Cette thèse propose le développement d’outils d’analyse d’image standardisés permettant l’interprétation des images à deux échelles :- A l’échelle de la lame d’observations : l’utilisation de lames de comptage spécifiques (Malassez) permet, à partir du comptage des cellules présente dans la zone d’intérêt de la lame, de déduire la concentration cellulaire d’une solution de Saccharomyces cerevisiae soumises à un stress osmotique. Les outils développés permettent l’identification et la caractérisation de cette zone d’intérêt (grille) puis le comptage précis des cellules.- A l’échelle de la cellule : une souche mutante de Saccharomyces cerevisiae permet d’observer en fluorescence la protéine Pab1p-GFP impliquée dans la formation d’agrégats ribo-nucléoprotéiques intracellulaires consécutifs à un stress thermique. Les outils développés permettent d’obtenir une vue statistique du développement de ces agrégats grâce à l’automatisation de l’estimation de leur nombre pour un très grand nombre de cellules

Robust image analysis methods for the detection and the characterization of compact objects : application to biology

Dans le domaine de la microbiologie, de nombreuses expériences se basent sur une fine observation des micro-organismes. De par leur intérêt dans le développement de procédés agroalimentaires modernes, il est important d’étudier leur développement et leur taux de survie dans des conditions environnementales spécifiques telles que des stress osmotiques ou thermiques. L’imagerie microscopique est un des outils les plus utilisés pour observer les micro-organismes. L’interprétation manuelle des images acquises pose des problèmes de subjectivité, de coût et reproductibilité. Cette thèse propose le développement d’outils d’analyse d’image standardisés permettant l’interprétation des images à deux échelles :- A l’échelle de la lame d’observations : l’utilisation de lames de comptage spécifiques (Malassez) permet, à partir du comptage des cellules présente dans la zone d’intérêt de la lame, de déduire la concentration cellulaire d’une solution de Saccharomyces cerevisiae soumises à un stress osmotique. Les outils développés permettent l’identification et la caractérisation de cette zone d’intérêt (grille) puis le comptage précis des cellules.- A l’échelle de la cellule : une souche mutante de Saccharomyces cerevisiae permet d’observer en fluorescence la protéine Pab1p-GFP impliquée dans la formation d’agrégats ribo-nucléoprotéiques intracellulaires consécutifs à un stress thermique. Les outils développés permettent d’obtenir une vue statistique du développement de ces agrégats grâce à l’automatisation de l’estimation de leur nombre pour un très grand nombre de cellules.In the field of microbiology, many experiments are based on a fine observation of microorganisms. Because of their interest in the development of modern agri-food processes, it is important to study their development and survival rate under specific environmental conditions such as osmotic or thermal stress. Microscopic imaging is one of the most used tools for observing microorganisms. The manual interpretation of acquired images raises problems of subjectivity, cost and reproducibility. This thesis proposes the development of standardized image analysis tools allowing the interpretation of images at two scales:- At the scale of the observation slide: the use of specific counting slides (Malassez) allows, from the counting of the cells present in the zone of interest of the slide, to deduce the cell concentration of a solution of Saccharomyces cerevisiae subjected to osmotic stress. The tools developed allow for the identification and characterization of this area of interest (grid) and precise counting of the cells.- At the cell scale: a mutant strain of Saccharomyces cerevisiae allows for the observation in fluorescence the Pab1p-GFP protein involved in the formation of intracellular ribo-nucleoprotein aggregates consecutive to thermal stress. The tools developed allows for obtaining a statistical view of the development of these aggregates by automating the estimation of their number for a very large number of cells

Automatic Biological Cell Counting Using a Modified Gradient Hough Transform

International audienceWe present a computational method for pseudo-circular object detection and quantitative characterization in digital images, using the gradient accumulation matrix as a basic tool. This Gradient Accumulation Transform (GAT) was first introduced in 1992 by Kierkegaard and recently used by Kaytanli & Valentine. In the present article, we modify the approach by using the phase coding studied by Cicconet, and by adding a local contributor list (LCL) as well as a used contributor matrix (UCM), which allow for accurate peak detection and exploitation. These changes help make the GAT algorithm a robust and precise method to automatically detect pseudo-circular objects in a microscopic image. We then present an application of the method to cell counting in microbiological images

Reliable Detection and Smart Deletion of Malassez Counting Chamber Grid in Microscopic White Light Images for Microbiological Applications

International audienceIn biology, hemocytometers such as Malassez slides are widely used and are effective tools for counting cells manually. In a previous work, a robust algorithm was developed for grid extraction in Malassez slide images. This algorithm was evaluated on a set of 135 images and grids were accurately detected in most cases, but there remained failures for the most difficult images. In this work, we present an optimization of this algorithm that allows for 100% grid detection and a 25% improvement in grid positioning accuracy. These improvements make the algorithm fully reliable for grid detection. This optimization also allows complete erasing of the grid without altering the cells, which eases their segmentation

A Robust Generic Method for Grid Detection in White Light Microscopy Malassez Blade Images in the Context of Cell Counting

International audienceIn biology, cell counting is a primary measurement and it is usually performed manually using hemocytometers such as Malassez blades. This work is tedious and can be automated using image processing. An algorithm based on Fourier transform filtering and the Hough transform was developed for Malassez blade grid extraction. This facilitates cell segmentation and counting within the grid. For the present work, a set of 137 images with high variability was processed. Grids were accurately detected in 98% of these images

Texture, Color and Frequential Proxy-Detection Image Processing for Crop Characterization in a Context of Precision Agriculture

The concept of precision agriculture consists to spatially manage crop management practices according to in-field variability. This concept is principally dedicated to variable-rate application of inputs such as nitrogen, seeds and phytosanitary products, allowing for a better yield management and reduction on the use of pesticides, herbicides … In this general context, the development of ICT techniques has allowed relevant progresses for Leaf Area Index (LAI) (Richardson et al., 2009), crop density (Saeys et al., 2009), stress (Zygielbaum et al., 2009) … Most of the tools used for Precision Farming utilizes optical and/or imaging sensors and dedicated treatments, in real time or not, and eventually combined to 3D plant growth modeling or disease development (Fournier et al., 2003 ; Robert et al., 2008). To evaluate yields or to better define the appropriated periods for the spraying or fertilizer input, to detect crop, weeds, diseases …, the remote sensing imaging devices are often used to complete or replace embedded sensors onboard the agricultural machinery (Aparicio et al., 2000). Even if these tools provide sufficient accurate information, they get some drawbacks compared to “proxy-detection” optical sensors: resolution, easy-to-use tools, accessibility, cost, temporality, precision of the measurement … The use of specific image acquisition systems coupled to reliable image processing should allow for a reduction of working time, a lower work hardness and a reduction of the bias of the measurement according to the operator, or a better spatial sampling due to the rapidity of the image acquisition (instead of the use of remote sensing). The early evaluation of yield could allow farmers, for example, to adjust cultivation practices (e.g., last nitrogen (N) input), to organize harvest and storage logistics. The optimization of late N application could lead to significant improvements for the environment, one of the most important concerns that precision agriculture aims to address. We propose in this chapter to explore the proxy-detection domain by focusing first on the development of robust image acquisition systems, and secondly on the use of image processing for different applications tied on one hand to wheat crop characterization, such as the detection and counting of wheat ears per m² (in a context of yield prediction) and the weed detection, and on the other hand to the evolution of seed development/germination performance of chicory achenes. Results of the different processing are presented in the last part just before a conclusion

Automatic Counting of Intra-Cellular Ribonucleo-Protein Aggregates in Saccharomyces cerevisiae Using a Textural Approach

International audienceIn the context of microbiology, recent studies show the importance of ribonucleo-protein aggregates (RNPs) for the understanding of mechanisms involved in cell responses to specific environmental conditions. The assembly and disassembly of aggregates is a dynamic process, the characterization of the stage of their evolution can be performed by the evaluation of their number. The aim of this study is to propose a method to automatically determine the count of RNPs. We show that the determination of a precise count is an issue by itself and hence, we propose three textural approaches: a classical point of view using Haralick features, a frequency point of view with generalized Fourier descriptors, and a structural point of view with Zernike moment descriptors (ZMD). These parameters are then used as inputs for a supervised classification in order to determine the most relevant. An experiment using a specific Saccharomyces cerevisiae strain presenting a fusion between a protein found in RNPs (PAB1) and the green fluorescent protein was performed to benchmark this approach. The fluorescence was observed with two-photon fluorescence microscopy. Results show that the textural approach, by mixing ZMD with Haralick features, allows for the characterization of the number of RNPs