An Automated System for Chromosome Analysis

The design, construction, and testing of a complete system to produce karyotypes and chromosome measurement data from human blood samples, and to provide a basis for statistical analysis of quantitative chromosome measurement data are described

An automated system for chromosome analysis. Volume 1: Goals, system design, and performance

The design, construction, and testing of a complete system to produce karyotypes and chromosome measurement data from human blood samples, and a basis for statistical analysis of quantitative chromosome measurement data is described. The prototype was assembled, tested, and evaluated on clinical material and thoroughly documented

Gaussian Processes for Machine Learning in Robotics

Mención Internacional en el título de doctorNowadays, machine learning is widely used in robotics for a variety of tasks such as perception, control, planning, and decision making. Machine learning involves learning, reasoning, and acting based on the data. This is achieved by constructing computer programs that process the data, extract useful information or features, make predictions to infer unknown properties, and suggest actions to take or decisions to make. This computer program corresponds to a mathematical model of the data that describes the relationship between the variables that represent the observed data and properties of interest. The aforementioned model is learned based on the available training data, which is accomplished using a learning algorithm capable of automatically adjusting the parameters of the model to agree with the data. Therefore, the architecture of the model needs to be selected accordingly, which is not a trivial task and usually depends on the machine-learning engineer’s insights and past experience. The number of parameters to be tuned varies significantly with the selected machine learning model, ranging from two or three parameters for Gaussian processes (GP) to hundreds of thousands for artificial neural networks. However, as more complex and novel robotic applications emerge, data complexity increases and prior experience may be insufficient to define adequate mathematical models. In addition, traditional machine learning methods are prone to problems such as overfitting, which can lead to inaccurate predictions and catastrophic failures in critical applications. These methods provide probabilistic distributions as model outputs, allowing for estimating the uncertainty associated with predictions and making more informed decisions. That is, they provide a mean and variance for the model responses. This thesis focuses on the application of machine learning solutions based on Gaussian processes to various problems in robotics, with the aim of improving current methods and providing a new perspective. Key areas such as trajectory planning for unmanned aerial vehicles (UAVs), motion planning for robotic manipulators and model identification of nonlinear systems are addressed. In the field of path planning for UAVs, algorithms based on Gaussian processes that allow for more efficient planning and energy savings in exploration missions have been developed. These algorithms are compared with traditional analytical approaches, demonstrating their superiority in terms of efficiency when using machine learning. Area coverage and linear coverage algorithms with UAV formations are presented, as well as a sea surface search algorithm. Finally, these algorithms are compared with a new method that uses Gaussian processes to perform probabilistic predictions and optimise trajectory planning, resulting in improved performance and reduced energy consumption. Regarding motion planning for robotic manipulators, an approach based on Gaussian process models that provides a significant reduction in computational times is proposed. A Gaussian process model is used to approximate the configuration space of a robot, which provides valuable information to avoid collisions and improve safety in dynamic environments. This approach is compared to conventional collision checking methods and its effectiveness in terms of computational time and accuracy is demonstrated. In this application, the variance provides information about dangerous zones for the manipulator. In terms of creating models of non-linear systems, Gaussian processes also offer significant advantages. This approach is applied to a soft robotic arm system and UAV energy consumption models, where experimental data is used to train Gaussian process models that capture the relationships between system inputs and outputs. The results show accurate identification of system parameters and the ability to make reliable future predictions. In summary, this thesis presents a variety of applications of Gaussian processes in robotics, from trajectory and motion planning to model identification. These machine learning-based solutions provide probabilistic predictions and improve the ability of robots to perform tasks safely and efficiently. Gaussian processes are positioned as a powerful tool to address current challenges in robotics and open up new possibilities in the field.El aprendizaje automático ha revolucionado el campo de la robótica al ofrecer una amplia gama de aplicaciones en áreas como la percepción, el control, la planificación y la toma de decisiones. Este enfoque implica desarrollar programas informáticos que pueden procesar datos, extraer información valiosa, realizar predicciones y ofrecer recomendaciones o sugerencias de acciones. Estos programas se basan en modelos matemáticos que capturan las relaciones entre las variables que representan los datos observados y las propiedades que se desean analizar. Los modelos se entrenan utilizando algoritmos de optimización que ajustan automáticamente los parámetros para lograr un rendimiento óptimo. Sin embargo, a medida que surgen aplicaciones robóticas más complejas y novedosas, la complejidad de los datos aumenta y la experiencia previa puede resultar insuficiente para definir modelos matemáticos adecuados. Además, los métodos de aprendizaje automático tradicionales son propensos a problemas como el sobreajuste, lo que puede llevar a predicciones inexactas y fallos catastróficos en aplicaciones críticas. Para superar estos desafíos, los métodos probabilísticos de aprendizaje automático, como los procesos gaussianos, han ganado popularidad. Estos métodos ofrecen distribuciones probabilísticas como salidas del modelo, lo que permite estimar la incertidumbre asociada a las predicciones y tomar decisiones más informadas. Esto es, proporcionan una media y una varianza para las respuestas del modelo. Esta tesis se centra en la aplicación de soluciones de aprendizaje automático basadas en procesos gaussianos a diversos problemas en robótica, con el objetivo de mejorar los métodos actuales y proporcionar una nueva perspectiva. Se abordan áreas clave como la planificación de trayectorias para vehículos aéreos no tripulados (UAVs), la planificación de movimientos para manipuladores robóticos y la identificación de modelos de sistemas no lineales. En el campo de la planificación de trayectorias para UAVs, se han desarrollado algoritmos basados en procesos gaussianos que permiten una planificación más eficiente y un ahorro de energía en misiones de exploración. Estos algoritmos se comparan con los enfoques analíticos tradicionales, demostrando su superioridad en términos de eficiencia al utilizar el aprendizaje automático. Se presentan algoritmos de recubrimiento de áreas y recubrimiento lineal con formaciones de UAVs, así como un algoritmo de búsqueda en superficies marinas. Finalmente, estos algoritmos se comparan con un nuevo método que utiliza procesos gaussianos para realizar predicciones probabilísticas y optimizar la planificación de trayectorias, lo que resulta en un rendimiento mejorado y una reducción del consumo de energía. En cuanto a la planificación de movimientos para manipuladores robóticos, se propone un enfoque basado en modelos gaussianos que permite una reducción significativa en los tiempos de cálculo. Se utiliza un modelo de procesos gaussianos para aproximar el espacio de configuraciones de un robot, lo que proporciona información valiosa para evitar colisiones y mejorar la seguridad en entornos dinámicos. Este enfoque se compara con los métodos convencionales de planificación de movimientos y se demuestra su eficacia en términos de tiempo de cálculo y precisión de los movimientos. En esta aplicación, la varianza proporciona información sobre zonas peligrosas para el manipulador. En cuanto a la identificación de modelos de sistemas no lineales, los procesos gaussianos también ofrecen ventajas significativas. Este enfoque se aplica a un sistema de brazo robótico blando y a modelos de consumo energético de UAVs, donde se utilizan datos experimentales para entrenar un modelo de proceso gaussiano que captura las relaciones entre las entradas y las salidas del sistema. Los resultados muestran una identificación precisa de los parámetros del sistema y la capacidad de realizar predicciones futuras confiables. En resumen, esta tesis presenta una variedad de aplicaciones de procesos gaussianos en robótica, desde la planificación de trayectorias y movimientos hasta la identificación de modelos. Estas soluciones basadas en aprendizaje automático ofrecen predicciones probabilísticas y mejoran la capacidad de los robots para realizar tareas de manera segura y eficiente. Los procesos gaussianos se posicionan como una herramienta poderosa para abordar los desafíos actuales en robótica y abrir nuevas posibilidades en el campo.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Juan Jesús Romero Cardalda.- Secretaria: María Dolores Blanco Rojas.- Vocal: Giuseppe Carbon

Optophone design: optical-to-auditory vision substitution for the blind

An optophone is a device that turns light into sound for the benefit of blind people. The present project is intended to produce a general-purpose optophone to be worn on the head about the house and in the street, to give the wearer a detailed description in sound of the'scene he is facing. The device will therefore consist'of an'electronic camera, some signal-processing electronics, earphones`, and a battery. The two major problems are the derivation of (a) the most suitable mapping from images to sounds, and (b) an algorithm to perform the mapping in real'time on existing electronic components. This thesis concerns problem (a). Chapter 2 goes into the general scene-to-sound mapping problem in some detail'and presents the work of earlier investigators. Chapter 3 1- discusses the design of tests to evaluate the performance of candidate mappings. A theoretical performance test (TPT) is derived. Chapter 4 applies the TPT to the most obvious mapping, the cartesian piano transform. Chapter 5 applies the TPT to a mapping based on the cosine transform. Chapter 6 attempts to derive a mapping by principal component analysis, using the inaccuracies of human sight and hearing and the statistical properties of real scenes and sounds. Chapter 7 presents a complete scheme, implemented in software, for representing digitised colour scenes by audible digitised stereo sound. Chapter 8 tries to decide how'many numbers are required to specify a steady spectrum with no noticeable degradation. Chapter 9 looks'at a scheme designed to produce more natural-sounding sounds related to more meaningful portions of the scene. This scheme maps windows in the scene to steady spectral patterns of short duration, the location of the window being conveyed by simulated free-field listening. Chapter 10 gives detailed recommendations as to further work

The Mixtec Pictorial Manuscripts

This handbook surveys and describes the illustrated Mixtec manuscripts that survive in Europe, the United States and Mexico

26. Theorietag Automaten und Formale Sprachen 23. Jahrestagung Logik in der Informatik: Tagungsband

Der Theorietag ist die Jahrestagung der Fachgruppe Automaten und Formale Sprachen der Gesellschaft für Informatik und fand erstmals 1991 in Magdeburg statt. Seit dem Jahr 1996 wird der Theorietag von einem eintägigen Workshop mit eingeladenen Vorträgen begleitet. Die Jahrestagung der Fachgruppe Logik in der Informatik der Gesellschaft für Informatik fand erstmals 1993 in Leipzig statt. Im Laufe beider Jahrestagungen finden auch die jährliche Fachgruppensitzungen statt. In diesem Jahr wird der Theorietag der Fachgruppe Automaten und Formale Sprachen erstmalig zusammen mit der Jahrestagung der Fachgruppe Logik in der Informatik abgehalten. Organisiert wurde die gemeinsame Veranstaltung von der Arbeitsgruppe Zuverlässige Systeme des Instituts für Informatik an der Christian-Albrechts-Universität Kiel vom 4. bis 7. Oktober im Tagungshotel Tannenfelde bei Neumünster. Während des Tre↵ens wird ein Workshop für alle Interessierten statt finden. In Tannenfelde werden • Christoph Löding (Aachen) • Tomás Masopust (Dresden) • Henning Schnoor (Kiel) • Nicole Schweikardt (Berlin) • Georg Zetzsche (Paris) eingeladene Vorträge zu ihrer aktuellen Arbeit halten. Darüber hinaus werden 26 Vorträge von Teilnehmern und Teilnehmerinnen gehalten, 17 auf dem Theorietag Automaten und formale Sprachen und neun auf der Jahrestagung Logik in der Informatik. Der vorliegende Band enthält Kurzfassungen aller Beiträge. Wir danken der Gesellschaft für Informatik, der Christian-Albrechts-Universität zu Kiel und dem Tagungshotel Tannenfelde für die Unterstützung dieses Theorietags. Ein besonderer Dank geht an das Organisationsteam: Maike Bradler, Philipp Sieweck, Joel Day. Kiel, Oktober 2016 Florin Manea, Dirk Nowotka und Thomas Wilk

Studying the structure of vertebrate kinetochore using a high-resolution microscopy approach

The kinetochore is a highly complex proteinaceous structure located at the primary constriction of mitotic chromosomes. Here, it performs an essential role in accurate chromosome segregation. Recently, much interest has been directed towards the Constitutive Centromere Associated Network (CCAN) components, as they participate in the formation of a scaffold involved in kinetochore assembly. It is therefore important to fully understand their role, and their distribution, at the kinetochore. Although many kinetochore proteins have already been identified, it is still unclear how centromeric chromatin folds to form the structure of the inner kinetochore. This is an interesting yet still open field of study, where the literature reports are still quite divided. In this study we take advantage of the high homologous recombination efficiency in DT40 B-lymphoma chicken cell lines, allowing the generation of conditional knockouts and deletion cell lines of several centromere proteins, subsequently engineered to stably express GFP:CENP-A. In the parental cell line the unfolding properties of the centromeric region were investigated by using TEEN buffer. Using fluorescence microscopy we were able to measure the length of many unfolded centromeric chromatin fibres, from both interphase and mitotic samples, based on the signal of GFP:CENP-A. A multi-peak analysis revealed the presence of discrete populations of fibres, recognised as peaks, in both interphase and mitotic samples. Compared with interphase, mitotic centromeres showed a greater level of compaction. Next, mutants for CCAN components, blocked in mitosis, were subjected to centromere chromatin unfolding. Results revealed that mitotic kinetochores depleted of CENP-C and CENP-S behaved similarly to the parental interphase samples, suggesting a role of those proteins in maintaining kinetochore structure. In contrast, CENP-O, CENP-H and CENP-I depletion did not seem to weaken the structure of the kinetochore. Additionally, we tested a hypothesis revealed by the multi-peak analyses, that chromatin layers exist in the inner kinetochore. Our data, when combined with published electron microscopy and crystallography measurements of centromere/kinetochore components, allowed us to assemble a robust and mathematically viable model that supports a boustrophedon organisation of the kinetochore chromatin. Finally, characterization studies of the novel kinetochore protein CENP-Z were performed. An involvement of CENP-Z in controlling the levels of di-methylation on lysine 4 of histone H3 was shown. This work represents an advance in our understanding of kinetochore structure in vertebrates

Automated clinical system for chromosome analysis

An automatic chromosome analysis system is provided wherein a suitably prepared slide with chromosome spreads thereon is placed on the stage of an automated microscope. The automated microscope stage is computer operated to move the slide to enable detection of chromosome spreads on the slide. The X and Y location of each chromosome spread that is detected is stored. The computer measures the chromosomes in a spread, classifies them by group or by type and also prepares a digital karyotype image. The computer system can also prepare a patient report summarizing the result of the analysis and listing suspected abnormalities