Developmental Dysplasia of the Hip (DDH) is an abnormal condition where hip joint dislocation, misalignment, or instability is present in infants. Rates of incidence of DDH in newborn infants have been reported to vary between 1 and 20 per 1000 births, making it the most common congenital malformation of the musculoskeletal system. DDH early detection and treatment is critical to avoid the use of surgical treatment in infants and to prevent future complications such as osteoarthritis in adult life. To this day several non-surgical treatments involving the use of harnesses and braces have been proposed to treat DDH in infants, with the Pavlik harness being the current non-surgical standard used to treat DDH at early stages. Although the Pavlik harness has been proven to be successful treating subtle dislocations, severe dislocations do not always reduce. Until now the use of the harness remains an empirical method, and its effectiveness often depends on physician expertise or trial-error procedures; thus a clear guideline has not been established to determine the best optimal harness configuration to treat both subtle and severe dislocations. The goal of this dissertation is to understand the connection between reductions for subtle and severe dislocations and passive muscle forces and moments generated while the harness is used during treatment. While the understanding of DDH biomechanics will provide a valuable clinically applicable approach to optimize and increase harness success rate, it is not without its difficulties. This research has created and developed a three-dimensional based on patient-specific geometry of an infant lower limb. The kinematics and dynamics of the lower limb were defined by modeling the hip, femur, tibia, fibula, ankle, foot, and toe bones. The lines of action of five (5) adductor muscles, namely, the Adductor Brevis, Adductor Longus, Adductor Magnus, Pectineus, and Gracilis were identified as mediators of reduction and its mechanical behavior was characterized using a passive response. Four grades (1-4) of dislocation as specified by the International Hip Dysplasia Institute (IHDI) were considered, and the computer model was computationally manipulated to represent physiological dislocations. To account for proper harness modeling, the femur was restrained to move in an envelope consistent with its constraints. The model of the infant lower limb has been used to analyze subtle and severe dislocations. Results are consistent with previous studies based on a simplified anatomically-consistent synthetic model and clinical reports of very low success of the Pavlik harness for severe dislocations. Furthermore the findings on this work suggest that for severe dislocations, the use of the harness could be optimized to achieve hyperflexion of the lower limb leading to successful reduction for cases where the harness fails. This approach provides three main advantages and innovations: 1) the used of patient-specific geometry to elucidate the biomechanics of DDH; 2) the ability to computationally dislocate the model to represent dislocation severity; and 3) the quantification of external forces needed to accomplish reduction for severe dislocations. This study aims to offer a practical solution to effective treatment that draws from engineering expertise and modeling capabilities and also draws upon medical input. The findings of this work will lay the foundation for future optimization of non-surgical methods critical for the treatment of DDH.

No description available.

Rigid body modeling has historically been used to study various features of the elbow joint including both physical and computational models. Computational modeling provides an inexpensive, easily customizable, and effective method by which to predict and investigate the response of a physiological system to in vivo stresses and applied perturbations. Utilizing computer topography scans of a cadaveric elbow, a virtual representation of the joint was created using the commercially available MIMICS(TM) and SolidWorks(TM) software packages. Accurate 3D articular surfaces, ligamentous constraints, and joint contact parameters dictated motion. The model was validated against two cadaveric studies performed by Chanlalit et al. (2011, 2012) considering monopolar and bipolar circular radial head replacements in their effects on radiocapitellar stability and respective reliance upon lateral soft tissues, as well as a comparison of these with a novel anatomic radial head replacement system in an elbow afflicted with the “terrible triad” injury. Rigid body simulations indicated that the computational model was able to accurately recreate the translation of forces in the joint and demonstrate results similar to those presented in the cadaveric data in both the intact elbow and in unstable injury states. Trends in the resulting data were reflective of the average behavior of the cadaveric specimens while percent changes between states correlated closely with the experimental data. Information on the transposition of forces within the joint and ligament tensions gleaned from the computational model provided further insight into the stability of the elbow with a compromised radial head.

The use of computational modeling is an effective and inexpensive way to predict the response of complex systems to various perturbations. However, not until the early 1990s had this technology been used to predict the behavior of physiological systems, specifically the human skeletal system. To that end, a computational model of the human elbow joint was developed using computed topography (CT) scans of cadaveric donor tissue, as well as the commercially available software package SolidWorks™. The kinematic function of the joint model was then defined through 3D reconstructions of the osteoarticular surfaces and various soft-tissue constraints. The model was validated against cadaveric experiments performed by Hull et al and Fern et al that measured the significance of coronoid process fractures, lateral ulnar collateral ligament ruptures, and radial head resection in elbow joint resistance to varus displacement of the forearm. Kinematic simulations showed that the computational model was able to mimic the physiological movements of the joint throughout various ranges of motion including flexion/extension and pronation/supination. Quantitatively, the model was able to accurately reproduce the trends, as well as the magnitudes, of varus resistance observed in the cadaveric specimens. Additionally, magnitudes of ligament tension and joint contact force predicted by the model were able to further elucidate the complex soft-tissue and osseous contributions to varus elbow stability.

One of the common research goals in disciplines such as computer graphics and robotics is to understand the subtleties of human motion and develop tools for recreating natural and meaningful motion. Physical simulation of virtual human characters is a promising approach since it provides a testbed for developing and testing control strategies required to execute various human behaviors. Designing generic control algorithms for simulating a wide range of human activities, which can robustly adapt to varying physical environments, has remained a primary challenge. This dissertation introduces methods for generic and robust control of virtual characters in an interactive physical environment. Our approach is to use the information of the physical contacts between the character and her environment in the control design. We leverage high-level knowledge of the kinematics goals and the interaction with the surroundings to develop active control strategies that robustly adapt to variations in the physical scene. For synthesizing intentional motion requiring long-term planning, we exploit properties of the physical model for creating efficient and robust controllers in an interactive framework. The control design leverages the reference motion capture data and the contact information with the environment for interactive long-term planning. Finally, we propose a compact soft contact model for handling contacts for rigid body virtual characters. This model aims at improving the robustness of existing control methods without adding any complexity to the control design and opens up possibilities for new control algorithms to synthesize agile human motion.

Le docking moléculaire est une méthode permettant de prédire l'orientation d'une molécule donnée relativement à une autre lorsque celles-ci forment un complexe. Le premier algorithme de docking moléculaire a vu jour en 1990 afin de trouver de nouveaux candidats face à la protéase du VIH-1. Depuis, l'utilisation de protocoles de docking est devenue une pratique standard dans le domaine de la conception de nouveaux médicaments. Typiquement, un protocole de docking comporte plusieurs phases. Il requiert l'échantillonnage exhaustif du site d'interaction où les éléments impliqués sont considérées rigides. Des algorithmes de clustering sont utilisés afin de regrouper les candidats à l'appariement similaires. Des méthodes d'affinage sont appliquées pour prendre en compte la flexibilité au sein complexe moléculaire et afin d'éliminer de possibles artefacts de docking. Enfin, des algorithmes d'évaluation sont utilisés pour sélectionner les meilleurs candidats pour le docking. Cette thèse présente de nouveaux algorithmes de protocoles de docking qui facilitent la prédiction des structures de complexes protéinaires, une des cibles les plus importantes parmi les cibles visées par les méthodes de conception de médicaments. Une première contribution concerne l‘algorithme Docktrina qui permet de prédire les conformations de trimères protéinaires triangulaires. Celui-ci prend en entrée des prédictions de contacts paire-à-paire à partir d'hypothèse de corps rigides. Ensuite toutes les combinaisons possibles de paires de monomères sont évalués à l'aide d'un test de distance RMSD efficace. Cette méthode à la fois rapide et efficace améliore l'état de l'art sur les protéines trimères. Deuxièmement, nous présentons RigidRMSD une librairie C++ qui évalue en temps constant les distances RMSD entre conformations moléculaires correspondant à des transformations rigides. Cette librairie est en pratique utile lors du clustering de positions de docking, conduisant à des temps de calcul améliorés d'un facteur dix, comparé aux temps de calcul des algorithmes standards. Une troisième contribution concerne KSENIA, une fonction d'évaluation à base de connaissance pour l'étude des interactions protéine-protéine. Le problème de la reconstruction de fonction d'évaluation est alors formulé et résolu comme un problème d'optimisation convexe. Quatrièmement, CARBON, un nouvel algorithme pour l'affinage des candidats au docking basés sur des modèles corps-rigides est proposé. Le problème d'optimisation de corps-rigides est vu comme le calcul de trajectoires quasi-statiques de corps rigides influencés par la fonction énergie. CARBON fonctionne aussi bien avec un champ de force classique qu'avec une fonction d'évaluation à base de connaissance. CARBON est aussi utile pour l'affinage de complexes moléculaires qui comportent des clashes stériques modérés à importants. Finalement, une nouvelle méthode permet d'estimer les capacités de prédiction des fonctions d'évaluation. Celle-ci permet d‘évaluer de façon rigoureuse la performance de la fonction d'évaluation concernée sur des benchmarks de complexes moléculaires. La méthode manipule la distribution des scores attribués et non pas directement les scores de conformations particulières, ce qui la rend avantageuse au regard des critères standard basés sur le score le plus élevé. Les méthodes décrites au sein de la thèse sont testées et validées sur différents benchmarks protéines-protéines. Les algorithmes implémentés ont été utilisés avec succès pour la compétition CAPRI concernant la prédiction de complexes protéine-protéine. La méthodologie développée peut facilement être adaptée pour de la reconnaissance d'autres types d'interactions moléculaires impliquant par exemple des ligands, de l'ARN… Les implémentations en C++ des différents algorithmes présentés seront mises à disposition comme SAMSON Elements de la plateforme logicielle SAMSON sur http://www.samson-connect.net ou sur http://nano-d.inrialpes.fr/software. / Molecular docking is a method that predicts orientation of one molecule with respect to another one when forming a complex. The first computational method of molecular docking was applied to find new candidates against HIV-1 protease in 1990. Since then, using of docking pipelines has become a standard practice in drug discovery. Typically, a docking protocol comprises different phases. The exhaustive sampling of the binding site upon rigid-body approximation of the docking subunits is required. Clustering algorithms are used to group similar binding candidates. Refinement methods are applied to take into account flexibility of the molecular complex and to eliminate possible docking artefacts. Finally, scoring algorithms are employed to select the best binding candidates. The current thesis presents novel algorithms of docking protocols that facilitate structure prediction of protein complexes, which belong to one of the most important target classes in the structure-based drug design. First, DockTrina - a new algorithm to predict conformations of triangular protein trimers (i.e. trimers with pair-wise contacts between all three pairs of proteins) is presented. The method takes as input pair-wise contact predictions from a rigid-body docking program. It then scans and scores all possible combinations of pairs of monomers using a very fast root mean square deviation (RMSD) test. Being fast and efficient, DockTrina outperforms state-of-the-art computational methods dedicated to predict structure of protein oligomers on the collected benchmark of protein trimers. Second, RigidRMSD - a C++ library that in constant time computes RMSDs between molecular poses corresponding to rigid-body transformations is presented. The library is practically useful for clustering docking poses, resulting in ten times speed up compared to standard RMSD-based clustering algorithms. Third, KSENIA - a novel knowledge-based scoring function for protein-protein interactions is developed. The problem of scoring function reconstruction is formulated and solved as a convex optimization problem. As a result, KSENIA is a smooth function and, thus, is suitable for the gradient-base refinement of molecular structures. Remarkably, it is shown that native interfaces of protein complexes provide sufficient information to reconstruct a well-discriminative scoring function. Fourth, CARBON - a new algorithm for the rigid-body refinement of docking candidates is proposed. The rigid-body optimization problem is viewed as the calculation of quasi-static trajectories of rigid bodies influenced by the energy function. To circumvent the typical problem of incorrect stepsizes for rotation and translation movements of molecular complexes, the concept of controlled advancement is introduced. CARBON works well both in combination with a classical force-field and a knowledge-based scoring function. CARBON is also suitable for refinement of molecular complexes with moderate and large steric clashes between its subunits. Finally, a novel method to evaluate prediction capability of scoring functions is introduced. It allows to rigorously assess the performance of the scoring function of interest on benchmarks of molecular complexes. The method manipulates with the score distributions rather than with scores of particular conformations, which makes it advantageous compared to the standard hit-rate criteria. The methods described in the thesis are tested and validated on various protein-protein benchmarks. The implemented algorithms are successfully used in the CAPRI contest for structure prediction of protein-protein complexes. The developed methodology can be easily adapted to the recognition of other types of molecular interactions, involving ligands, polysaccharides, RNAs, etc. The C++ versions of the presented algorithms will be made available as SAMSON Elements for the SAMSON software platform at http://www.samson-connect.net or at http://nano-d.inrialpes.fr/software.

Le docking moléculaire est une méthode permettant de prédire l'orientation d'une molécule donnée relativement à une autre lorsque celles-ci forment un complexe. Le premier algorithme de docking moléculaire a vu jour en 1990 afin de trouver de nouveaux candidats face à la protéase du VIH-1. Depuis, l'utilisation de protocoles de docking est devenue une pratique standard dans le domaine de la conception de nouveaux médicaments. Typiquement, un protocole de docking comporte plusieurs phases. Il requiert l'échantillonnage exhaustif du site d'interaction où les éléments impliqués sont considérées rigides. Des algorithmes de clustering sont utilisés afin de regrouper les candidats à l'appariement similaires. Des méthodes d'affinage sont appliquées pour prendre en compte la flexibilité au sein complexe moléculaire et afin d'éliminer de possibles artefacts de docking. Enfin, des algorithmes d'évaluation sont utilisés pour sélectionner les meilleurs candidats pour le docking. Cette thèse présente de nouveaux algorithmes de protocoles de docking qui facilitent la prédiction des structures de complexes protéinaires, une des cibles les plus importantes parmi les cibles visées par les méthodes de conception de médicaments. Une première contribution concerne l‘algorithme Docktrina qui permet de prédire les conformations de trimères protéinaires triangulaires. Celui-ci prend en entrée des prédictions de contacts paire-à-paire à partir d'hypothèse de corps rigides. Ensuite toutes les combinaisons possibles de paires de monomères sont évalués à l'aide d'un test de distance RMSD efficace. Cette méthode à la fois rapide et efficace améliore l'état de l'art sur les protéines trimères. Deuxièmement, nous présentons RigidRMSD une librairie C++ qui évalue en temps constant les distances RMSD entre conformations moléculaires correspondant à des transformations rigides. Cette librairie est en pratique utile lors du clustering de positions de docking, conduisant à des temps de calcul améliorés d'un facteur dix, comparé aux temps de calcul des algorithmes standards. Une troisième contribution concerne KSENIA, une fonction d'évaluation à base de connaissance pour l'étude des interactions protéine-protéine. Le problème de la reconstruction de fonction d'évaluation est alors formulé et résolu comme un problème d'optimisation convexe. Quatrièmement, CARBON, un nouvel algorithme pour l'affinage des candidats au docking basés sur des modèles corps-rigides est proposé. Le problème d'optimisation de corps-rigides est vu comme le calcul de trajectoires quasi-statiques de corps rigides influencés par la fonction énergie. CARBON fonctionne aussi bien avec un champ de force classique qu'avec une fonction d'évaluation à base de connaissance. CARBON est aussi utile pour l'affinage de complexes moléculaires qui comportent des clashes stériques modérés à importants. Finalement, une nouvelle méthode permet d'estimer les capacités de prédiction des fonctions d'évaluation. Celle-ci permet d‘évaluer de façon rigoureuse la performance de la fonction d'évaluation concernée sur des benchmarks de complexes moléculaires. La méthode manipule la distribution des scores attribués et non pas directement les scores de conformations particulières, ce qui la rend avantageuse au regard des critères standard basés sur le score le plus élevé. Les méthodes décrites au sein de la thèse sont testées et validées sur différents benchmarks protéines-protéines. Les algorithmes implémentés ont été utilisés avec succès pour la compétition CAPRI concernant la prédiction de complexes protéine-protéine. La méthodologie développée peut facilement être adaptée pour de la reconnaissance d'autres types d'interactions moléculaires impliquant par exemple des ligands, de l'ARN… Les implémentations en C++ des différents algorithmes présentés seront mises à disposition comme SAMSON Elements de la plateforme logicielle SAMSON sur http://www.samson-connect.net ou sur http://nano-d.inrialpes.fr/software. / Molecular docking is a method that predicts orientation of one molecule with respect to another one when forming a complex. The first computational method of molecular docking was applied to find new candidates against HIV-1 protease in 1990. Since then, using of docking pipelines has become a standard practice in drug discovery. Typically, a docking protocol comprises different phases. The exhaustive sampling of the binding site upon rigid-body approximation of the docking subunits is required. Clustering algorithms are used to group similar binding candidates. Refinement methods are applied to take into account flexibility of the molecular complex and to eliminate possible docking artefacts. Finally, scoring algorithms are employed to select the best binding candidates. The current thesis presents novel algorithms of docking protocols that facilitate structure prediction of protein complexes, which belong to one of the most important target classes in the structure-based drug design. First, DockTrina - a new algorithm to predict conformations of triangular protein trimers (i.e. trimers with pair-wise contacts between all three pairs of proteins) is presented. The method takes as input pair-wise contact predictions from a rigid-body docking program. It then scans and scores all possible combinations of pairs of monomers using a very fast root mean square deviation (RMSD) test. Being fast and efficient, DockTrina outperforms state-of-the-art computational methods dedicated to predict structure of protein oligomers on the collected benchmark of protein trimers. Second, RigidRMSD - a C++ library that in constant time computes RMSDs between molecular poses corresponding to rigid-body transformations is presented. The library is practically useful for clustering docking poses, resulting in ten times speed up compared to standard RMSD-based clustering algorithms. Third, KSENIA - a novel knowledge-based scoring function for protein-protein interactions is developed. The problem of scoring function reconstruction is formulated and solved as a convex optimization problem. As a result, KSENIA is a smooth function and, thus, is suitable for the gradient-base refinement of molecular structures. Remarkably, it is shown that native interfaces of protein complexes provide sufficient information to reconstruct a well-discriminative scoring function. Fourth, CARBON - a new algorithm for the rigid-body refinement of docking candidates is proposed. The rigid-body optimization problem is viewed as the calculation of quasi-static trajectories of rigid bodies influenced by the energy function. To circumvent the typical problem of incorrect stepsizes for rotation and translation movements of molecular complexes, the concept of controlled advancement is introduced. CARBON works well both in combination with a classical force-field and a knowledge-based scoring function. CARBON is also suitable for refinement of molecular complexes with moderate and large steric clashes between its subunits. Finally, a novel method to evaluate prediction capability of scoring functions is introduced. It allows to rigorously assess the performance of the scoring function of interest on benchmarks of molecular complexes. The method manipulates with the score distributions rather than with scores of particular conformations, which makes it advantageous compared to the standard hit-rate criteria. The methods described in the thesis are tested and validated on various protein-protein benchmarks. The implemented algorithms are successfully used in the CAPRI contest for structure prediction of protein-protein complexes. The developed methodology can be easily adapted to the recognition of other types of molecular interactions, involving ligands, polysaccharides, RNAs, etc. The C++ versions of the presented algorithms will be made available as SAMSON Elements for the SAMSON software platform at http://www.samson-connect.net or at http://nano-d.inrialpes.fr/software.

L’objectif de cette thèse est d’appliquer des méthodes de contrôle optimal en réso- nance magnétique nucléaire et en information quantique. Dans un premier temps, on introduit les domaines étudiés et la dynamique des modèles traités. On donne les outils nécessaires pour appliquer le principe du maximum de Pontryagin ainsi qu’un algorithme d’optimisation appelé GRAPE.Le premier travail consiste à appliquer le PMP pour contrôler une chaîne de trois spins inégalement couplés. On étudie ensuite un problème de physique classique appelé "l’effet de la raquette de tennis", qui est un phénomène non-linéaire du modèle de la toupie d’Euler. On se sert de cette étude pour déterminer des lois de commande d’un système quantique à deux niveaux dans le chapitre suivant. Le dernier chapitre présente une méthode numérique qui permet d’améliorer la robustesse d’une porte NOT et de tester la pertinence de différentes approches analytiques déjà développées dans la littérature. / The goal of this thesis is to apply the optimal control theory to Nuclear Magnetic Resonance and Quantum Information. In a first step, we introduce the different topics and the dynamics of the analyzed systems. We give the necessary tools to use the Pontryagin Maximum Principle, and also an optimization algorithm, namely GRAPE.The first work is an application of the PMP to the control of a three-spin chain with unequal couplings. We continue with the study of a classical problem called "the tennis racket effect", which is a non-linear phenomenon occuring during the free rotation of a three-dimensional rigid body. We use the results in the following chapter to determine some control laws for a two- level quantum system. The last chapter presents a numerical method which aims at improving the robustness of a quantum NOT gate and at investigating the efficiency of different analytical approaches proposed in the literature.

Master's Thesis is about the techniques of creating a small executable program with size limited to 64kB. Describes one of the possible ways to use OpenGL for such purposes. With more detail describe the rigid body simulation, creating shaders, dynamic generating of texture and make music in intro scene applications. Presents using of WinApi to create windows, V2 synthetizer for sound and GLSL language for creating shaders. Everything is demonstratively created under Windows.

Internal combustion engines are used in many applications. The same engine type may have different components mounted to it depending upon its use. These engine mounted components need to be designed against fatigue in order to withstand the engine vibrations. Measured engine vibrations are commonly used as input data for fatigue estimation. The focus in this thesis is set on heavy-duty diesel engines, typically used in trucks, buses and industrial applications. All of the appended papers use engine vibration measurements to evaluate the proposed methods. In Paper A, the engine block motion is described with a seven degree of freedom kinematic model. These degrees of freedom consist of six rigid body modes and one assumed twisting degree of freedom. With this description, measured engine block vibrations can be used to accurately predict the vibration in positions that have not been measured. Relating the measured vibrations of an engine mounted component with the projected motion of the engine block at that same position, makes it possible to identify local dynamic phenomena. In Paper B, the kinematic model of Paper A is extended with three assumed bending deformation mode shapes. For the current engine type, all of the assumed deformation modes are ranked within the 10-300 Hz frequency range. The deformation mode of highest importance is the engine block twist. Including bending deformation increases the accuracy of the engine block vibration description but it also increases the demands on instrumentation. In Paper C, the possibility to modify measured engine vibration signals, for addition or removal of engine mounted components, is investigated. For this purpose, engine vibration measurements were performed with and without a 29 kg brake air compressor mounted to the engine. For the task of removing the effect that this engine mounted component has on the engine block, the two cases of knowing, and not knowing the vibration of the component are both considered. The proposed methodology successfully predicts the changes in engine vibration due to system modification. The proposed method can also be used to estimate the time response of a component's centre of gravity. In this study the component's dynamic properties are derived from measurements but they could also be produced using finite element analysis. This can be useful early in the design process to find critically stressed areas due to base excitation. / <p>QC 20171222</p>