L'imagerie par spectrométrie de masse MALDI est une technologie actuellement en plein essor. Elle permet d'obtenir rapidement en une seule étape d'acquisition la répartition moléculaire de différents composés (en particulier protéines) présents dans un tissu ; en s'affranchissant des étapes longues et coûteuses en échantillons que sont les extractions et séparations habituellement nécessaires par des techniques plus classiques. <br />Cependant, afin d'augmenter encore la potentialité de cette technologie, des développements restent encore à effectuer. Les recherches menées ont donc plus particulièrement portées sur ces développements. <br />En particulier, la recherche et l'étude de nouvelles matrices plus adaptées à l'analyse directe de tissu en MALDI sont particulièrement importantes. Dans ce contexte, certaines matrices ioniques se sont révélées particulièrement adaptées aux tissus en permettant d'obtenir une plus grande intensité du signal, un plus grand nombre de composés détectés, de bonnes performances en mode négatif, une grande homogénéité de cristallisation, une grande stabilité sous vide et une faible ablation de matériel consécutivement à l'irradiation laser. Dans un autre aspect, le traitement préalable des tissus permet également une amélioration de la qualité spectrale et des performances d'études structurales en mode MS/MS. Se sont révélés particulièrement intéressants les traitements des tissus aux solvants organiques et les digestions enzymatiques et en particulier pour les tissus conservés en blocs de paraffine après fixation. <br />D'autre part l'étude de la répartition des ARNm au sein des tissus est un développement crucial afin d'obtenir des images de colocalisation transcriptome/protéome. Est proposé dans ce travail un nouveau concept permettant de réaliser ces images, basé sur une analyse indirecte des ARNm, au travers de l'utilisation d'un groupement photoclivable relié à un peptide marqueur de séquence connue qui sera détecté. <br />Enfin, l'ensemble de ces développements trouve de nombreuses applications dans le domaine de la biologie et notamment dans le cadre de pathologies tel que le cancer de l'ovaire.

Indiana University-Purdue University Indianapolis (IUPUI) / The literature corresponding to disordered proteins has been on a rise. As the number of publications increase, the time and effort needed to manually identify the relevant publications and protein information to add to centralized repository (called DisProt) is becoming arduous and critical. Existing search facilities on PubMed can retrieve a seemingly large number of publications based on keywords and does not have any support for ranking them based on the probability of the protein names mentioned in a given abstract being added to DisProt. This thesis explores a novel system of using disorder predictors and context based dictionary methods to quickly identify publications on disordered proteins from the PubMed database. NLProt, which is built around Support Vector Machines, is used to identify protein names and PONDR-FIT which is an Artificial Neural Network based meta- predictor is used for identifying protein disorder. The work done in this thesis is of immediate significance in identifying disordered protein names. We have tested the new system on 100 abstracts from DisProt [these abstracts were found to be relevant to disordered proteins and were added to DisProt manually by the annotators.] This system had an accuracy of 87% on this test set. We then took another 100 recently added abstracts from PubMed and ran our algorithm on them. This time it had an accuracy of 68%. We suggested improvements to increase the accuracy and believe that this system can be applied for identifying disordered proteins from literature.

Indiana University-Purdue University Indianapolis (IUPUI) / There have been many endeavors to design humanoid robots that have human characteristics such as dexterity, autonomy and intelligence. Humanoid robots are intended to cooperate with humans and perform useful work that humans can perform. The main advantage of humanoid robots over other machines is that they are flexible and multi-purpose. In this thesis, a human-like robotic arm is designed and used in a task which is typically performed by humans, namely, catching a ball. The robotic arm was designed to closely resemble a human arm, based on anthropometric studies. A rigid multibody dynamics software was used to create a virtual model of the robotic arm, perform experiments, and collect data. The inverse kinematics of the robotic arm was solved using a Newton-Raphson numerical method with a numerically calculated Jacobian. The system was validated by testing its ability to find a kinematic solution for the catch position and successfully catch the ball within the robot's workspace. The tests were conducted by throwing the ball such that its path intersects different target points within the robot's workspace. The method used for determining the catch location consists of finding the intersection of the ball's trajectory with a virtual catch plane. The hand orientation was set so that the normal vector to the palm of the hand is parallel to the trajectory of the ball at the intersection point and a vector perpendicular to this normal vector remains in a constant orientation during the catch. It was found that this catch orientation approach was reliable within a 0.35 x 0.4 meter window in the robot's workspace. For all tests within this window, the robotic arm successfully caught and dropped the ball in a bin. Also, for the tests within this window, the maximum position and orientation (Euler angle) tracking errors were 13.6 mm and 4.3 degrees, respectively. The average position and orientation tracking errors were 3.5 mm and 0.3 degrees, respectively. The work presented in this study can be applied to humanoid robots in industrial assembly lines and hazardous environment recovery tasks, amongst other applications.

Cette thèse expérimentale s’inscrit dans le domaine des sciences séparatives et se base sur la technique de SPLITT (SPLIT-flow Thin fractionation). Son objectif consiste en l’étude des mécanismes qui sont à l’origine de la séparation, en continu et sans membrane, d’objets de taille micrométrique dans des mini-séparateurs fluidiques (Step-SPLITT). Les expériences menées, en laboratoire et lors de vols paraboliques, ont révélé le couplage complexe comme l’influence des effets hydrodynamiques et du champ gravitationnel sur la migration transverse des espèces en écoulement. Des visualisations tridimensionnelles par holographie digitale ont corroboré nos résultats et dévoilé des comportements inattendus. Les capacités séparatives des Step-SPLITT ont rendu possible l’analyse et la séparation d’objets biologiques et biomimétiques. Enfin, cette étude complétée par une modélisation tridimensionnelle de l’écoulement nous a permis de mettre au point un nouveau prototype de séparateur. This experimental thesis belongs to the field of separative sciences and is based on the SPLITT technique (SPLIT-flow Thin fractionation). The objective is to study the mechanisms that are at the origin of continuous and membraneless separation of micron-size species in mini fluidic separators (Step-SPLITT). Experiments undertaken in laboratory and during parabolic flights revealed the complex coupling of the hydrodynamic effects and the gravitational field influencing the transverse migration of the flowing species. Three-dimensional visualizations performed by digital holography confirmed our results and disclosed unexpected behaviours. The separation capacities of Step-SPLITT made the analysis and the separation of biological and biomimetic species possible. In addition this study in conjunction with a three-dimensional flow modelling enabled us to develop a new prototype of separator.

Cette thèse expérimentale s’inscrit dans le domaine des sciences séparatives et se base sur la technique de SPLITT (SPLIT-flow Thin fractionation). Son objectif consiste en l’étude des mécanismes qui sont à l’origine de la séparation, en continu et sans membrane, d’objets de taille micrométrique dans des mini-séparateurs fluidiques (Step-SPLITT). Les expériences menées, en laboratoire et lors de vols paraboliques, ont révélé le couplage complexe comme l’influence des effets hydrodynamiques et du champ gravitationnel sur la migration transverse des espèces en écoulement. Des visualisations tridimensionnelles par holographie digitale ont corroboré nos résultats et dévoilé des comportements inattendus. Les capacités séparatives des Step-SPLITT ont rendu possible l’analyse et la séparation d’objets biologiques et biomimétiques. Enfin, cette étude complétée par une modélisation tridimensionnelle de l’écoulement nous a permis de mettre au point un nouveau prototype de séparateur.<p><p>This experimental thesis belongs to the field of separative sciences and is based on the SPLITT technique (SPLIT-flow Thin fractionation). The objective is to study the mechanisms that are at the origin of continuous and membraneless separation of micron-size species in mini fluidic separators (Step-SPLITT). Experiments undertaken in laboratory and during parabolic flights revealed the complex coupling of the hydrodynamic effects and the gravitational field influencing the transverse migration of the flowing species. Three-dimensional visualizations performed by digital holography confirmed our results and disclosed unexpected behaviours. The separation capacities of Step-SPLITT made the analysis and the separation of biological and biomimetic species possible. In addition this study in conjunction with a three-dimensional flow modelling enabled us to develop a new prototype of separator. / Doctorat en sciences appliquées / info:eu-repo/semantics/nonPublished

Indiana University-Purdue University Indianapolis (IUPUI) / Environmental planning is very cumbersome work for environmentalists, government agencies like USDA and NRCS, and farmers. There are a number of conflicts and issues involved in such a decision making process. This research is based on the work to provide a common platform for environmental planning called WRESTORE (Watershed Restoration using Spatio-Temporal Optimization of Resources). We have designed a system that can be used to provide the best management practices for environmental planning. A distributed system was designed to combine high performance computing power of clusters/supercomputers in running various environmental model simulations. The system is designed to be a multi-user system just like a multi-user operating system. A number of stakeholders can log-on and run environmental model simulations simultaneously, seamlessly collaborate, and make collective judgments by visualizing their landscapes. In the research, we identified challenges in running such a system and proposed various solutions. One challenge was the lack of fast optimization algorithm. In our research, several algorithms are utilized such as Genetic Algorithm (GA) and Learning Automaton (LA). However, the criticism is that LA has a slow rate of convergence and that both LA and GA have the problem of getting stuck in local optima. We tried to solve the multi-objective problems using LA in batch mode to make the learning faster and accurate. The problems where the evaluation of the fitness functions for optimization is a bottleneck, like running environmental model simulation, evaluation of a number of such models in parallel can give considerable speed-up. In the multi-objective LA, different weight pair solutions were evaluated independently. We created their parallel versions to make them practically faster in computation. Additionally, we extended the parallelism concept with the batch mode learning. Another challenge we faced was in User Modeling. There are a number of User Modeling techniques available. Selection of the best user modeling technique is a hard problem. In this research, we modeled user's preferences and search criteria using an ANN (Artificial Neural Network). Training an ANN with limited data is not always feasible. There are many situations where a simple modeling technique works better if the learning data set is small. We formulated ways to fine tune the ANN in case of limited data and also introduced the concept of Deep Learning in User Modeling for environmental planning system.