Los cítricos son el cultivo frutal económicamente más importante tanto en España como en el resto de los países productores. La clave para mantener la competitividad de este sector consiste en obtener material vegetal de alta calidad, para lo cual son indispensables los programas de mejora. La mejora de cítricos por métodos clásicos es muy complicada, por lo que hay que recurrir a las nuevas tecnologías para intentar acelerar y optimizar el procedimiento. La reciente secuenciación del genoma de dos especies de cítricos ha permitido identificar una larga lista de genes candidatos a participar en determinados procesos biológicos. Sin embargo, son necesarios nuevos análisis para asociar cada gen a un fenotipo específico o función biológica. El empleo de vectores virales para determinar la función de genes mediante silenciamiento génico inducido por virus (VIGS) ha demostrado ser una herramienta muy útil para los estudios de genética reversa realizados en plantas. Este sistema presenta ventajas respecto a los métodos tradicionales para estudiar la función de genes como son la mutagénesis o la transformación genética, ya que permite ensayar la función de numerosos genes en un corto periodo de tiempo. Esto es especialmente crítico en el caso de los cítricos, que poseen largos periodos juveniles de entre 6 y 8 años y donde la transformación de plantas adultas es muy difícil. Además, permite estudiar la función de genes que son esenciales para el crecimiento o el desarrollo de la planta y cuyo análisis es inviable con los métodos tradicionales. Al comienzo de la tesis se había desarrollado un vector viral para cítricos basado en el virus de la tristeza de los cítricos (CTV) con el que se pueden expresar proteínas pero que no se ha ensayado para estudiar la función de genes mediante VIGS. En el laboratorio disponíamos de un clon infeccioso de cDNA del genoma completo del virus del manchado foliar de los cítricos (CLBV), un virus que infecta a todas las especies y variedades de cítricos ensayadas y es asintomático en la mayoría de ellas. Este clon infeccioso se ha modificado para obtener vectores virales basados en el genoma de CLBV que pueden servir tanto para expresar proteínas como para silenciar mediante VIGS genes de cítricos para la mejora genética de este cultivo. Para ello, se ha introducido un punto de corte único PmlI en dos zonas del genoma de CLBV: en el extremo 3¿ no traducible (vector clbv3¿) o en la zona intergénica localizada entre los genes de las proteínas de movimiento y cápsida (CP) (vector clbvIN). Para la expresión de secuencias foráneas mediante la formación de un nuevo RNA subgenómico (sgRNA) se delimitó la secuencia mínima promotora del sgRNA CP mediante clonación de fragmentos de distinta longitud en torno al origen de transcripción de dicho sgRNA en el vector clbv3'. El fragmento de 92 bases localizado entre los nt -42 y +50 respecto al inicio de transcripción del sgRNA CP contenía todos los elementos necesarios para la promoción de un nuevo sgRNA in vivo. Esta secuencia mínima promotora se clonó en los 2 vectores virales previamente desarrollados para generar los vectores clbv3¿pr y clbvINpr, respectivamente. Ambos vectores fueron capaces de producir un nuevo sgRNA y de expresar proteínas recombinantes. Para determinar la estabilidad de los vectores obtenidos se clonaron en ellos fragmentos de secuencias lineales de distinto tamaño, o en tándem invertido para la formación de una estructura en horquilla, y se inocularon en plantas de N. benthamiana y cítricos. Todas las construcciones derivadas del vector clbv3' se mostraron estables a lo largo de las diferentes brotaciones analizadas durante al menos 3 años, comprobándose la replicación viral e integridad del inserto. Sin embargo, no se detectó multiplicación viral con ninguna de las construcciones derivadas del vector clbvIN. La estabilidad de las construcciones derivadas de los vectores con el promotor duplicado dependía del tamaño del inserto. Con todas ellas se detectó replicación viral pero se observaron eventos de recombinación cuando se clonaban fragmentos superiores a 720 nt en el vector clbvINpr o 408 nt en el vector clbv3'pr. Un factor importante para determinar la eficiencia y funcionalidad de los vectores desarrollados es conocer cómo se mueve y se distribuye el virus en los distintos tejidos de la planta. Para ello se inocularon plantas de N. benthamiana y cítricos con la construcción clbv3¿pr-GFP, que expresa GFP en los tejidos donde se localiza el virus. En N. benthamiana, la observación de GFP permitió detectar la presencia de CLBV en la mayoría de tejidos, acumulándose preferentemente en óvulos y regiones meristemáticas. En cítricos no se pudo visualizar GFP pero el virus se detectó en regiones meristemáticas mediante RT-PCR a tiempo real e hibridación molecular. La acumulación de CLBV en tejidos meristemáticos explicaría la dificultad de eliminar este virus mediante microinjerto. Para evaluar la capacidad de los vectores clbv3'pr y clbvINpr para expresar proteínas se clonó en ellos la secuencia completa del gen gfp y se cuantificó la cantidad de proteína GFP sintetizada en las plantas infectadas. En N. benthamiana la cantidad de GFP estimada para el vector clbv3'pr fue de 16 µg de proteína por gramo de peso fresco, cantidad que resultó entre 5 y 6 veces superior a la estimada para el vector clbvINpr. Sin embargo, en cítricos, debido a la inestabilidad del vector clbv3'pr, sólo se pudo cuantificar la proteína expresada por la construcción del vector clbvINpr, estimándose en 0.6 µg de GFP por gramo de peso fresco. La efectividad de los vectores clbv3', clbv3'pr y clbvINpr para silenciar genes mediante VIGS se ensayó clonando fragmentos de genes tanto endógenos de plantas (pds, actina, sulfur) como el gen gfp introducido experimentalmente en plantas transgénicas. En cítricos todas las construcciones de los tres vectores indujeron fenotipo de silenciamiento del gen ensayado, aunque el vector clbv3' fue el más efectivo para el estudio de VIGS en este huésped. Sin embargo, en N. benthamiana sólo se desencadenó el silenciamiento en las plantas inoculadas con la construcción clbv3¿pr-hp58PDS, que expresa una horquilla de doble cadena de un fragmento de 58 nt del gen pds. En todas las plantas silenciadas se detectó una disminución del correspondiente mRNA del gen ensayado y una acumulación de siRNAs derivados tanto del mRNA del gen insertado como del RNA genómico del virus. Por otro lado, el fenotipo de silenciamiento de los genes ensayados se observó en sucesivas brotaciones, lo que confirma la gran estabilidad de los vectores basados en el genoma de CLBV. Los vectores virales desarrollados en esta tesis constituyen una herramienta eficiente para el estudio de la función de genes mediante genética reversa utilizando la técnica VIGS. También pueden ser útiles para estudio de genética directa mediante expresión de proteínas o para la protección del cultivo frente a enfermedades producidas por virus, bacterias y hongos o frente a plagas de invertebrados. / Agüero González, J. (2013). Desarrollo de vectores virales basados en el virus del manchado foliar de los cítricos (CLBV) [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/34342

Wide arrays of applications exist for sensing systems capable of magnetic field detection. A broad range of sensors are already used in this capacity, but future sensors need to increase sensitivity while remaining economical. A promising sensor system to meet these requirements is that of magnetoelectric (ME) laminates. ME sensors produce an electric field when a magnetic field is applied. While this ME effect exists to a limited degree in single phase materials, it is more easily achieved by laminating a magnetostrictive material, which deforms when exposed to a magnetic field, to a piezoelectric material. The transfer of strain from the magnetostrictive material to the piezoelectric material results in an electric field proportional to the induced magnetic field. Other fabrication techniques may impart the directionality needed to classify the ME sensor as a vector magnetometer. ME laminate sensors are more affordable to fabricate than competing vector magnetometers and with recent increases in sensitivity, have potential for use in arrays and gradiometer configurations. However, little is known about their total field detection, the effects of multiple sensors in close proximity and the signal processing needed for target localization. The goal for this project is to closely examine the single axis ME sensor response in different orientations with a moving magnetic dipole to assess the field detection capabilities. Multiple sensors were tested together to determine if the response characteristics are altered by the DC magnetic bias of ME sensors in close proximity. And finally, the ME sensor characteristics were compared to alternate vector magnetometers. / Master of Science

In Earth-to-Space communications, well-known propagation effects such as path loss and atmospheric loss can lead to fluctuations in the strength of the communications link between a satellite and its ground station. Additionally, a less-often considered effect of shadowing due to the geometry of the satellite and its solar panels can also lead to link degradation. As a result of these anticipated channel impairments, NASA's communication links have been traditionally designed to handle the worst-case impact of these effects through high link margins and static, lower rate, modulation formats. This thesis first characterizes the propagation environment experienced by a software-defined radio on the NASA SCaN Testbed through a full link-budget analysis. Then, the following chapters propose, design, and model a link adaptation algorithm to provide an improved trade-off between data rate and link margin through varying the modulation format as the received signal-to-noise ratio fluctuates. / Master of Science

Gene regulatory networks (GRNs) provide a natural representation of relationships between regulators and target genes. Though inferring GRN is a challenging task, many methods, including unsupervised and supervised approaches, have been developed in the literature. However, most of these methods target non-context-specific GRNs. Because the regulatory relationships consistently reprogram under different tissues or biological processes, non-context-specific GRNs may not fit some specific conditions. In addition, a detailed investigation of the prediction results has remained elusive. In this study, I propose to use a machine learning approach to predict GRNs that occur in developmental stage-specific networks and to show how it improves our understanding of the GRN in seed development. I developed a Beacon GRN inference tool to predict a GRN in seed development in Arabidopsis based on a support vector machine (SVM) local model. Using the time series gene expression levels in seed development and prior known regulatory relationships, I evaluated and predicted the GRN at this specific biological process. The prediction results show that one gene may be controlled by multiple regulators. The targets that are strongly positively correlated with their regulators are mostly expressed at the beginning of seed development. The direct targets were detected when I found a match between the promoter regions of the targets and the regulator's binding sequence. Our prediction provides a novel testable hypotheses of a GRN in seed development in Arabidopsis, and the Beacon GRN inference tool provides a valuable model system for context-specific GRN inference. / Master of Science

Vector control technique is being widely used in ac motors drives for precise dynamic control of torque, speed and position. The application of vector control scheme to the induction motor drive and the complete modeling, analysis and simulation of the drive system are presented in this thesis. State space models of the motor and the speed controller and the real time models of the inverter switches and the vector controller are integrated to model the drive. Performance differences due to the use of PWM and hysteresis current controllers are examined. Simulation results of the torque and speed drive systems are given. The drive system is linearised around an operating point and the small signal response is evaluated. / Master of Science

Wavelet intensity based algorithm developed previously at VirginiaTech has been furthered and paired with an SVM based classifier. The wavelet intensity algorithm acts as a feature extraction algorithm. The wavelet transform is an effective tool as it allows one to narrow down upon the transient, high frequency events and is able to tell their exact location in time. According to prior work done in the field of signal processing, the local regularities of a signal can be estimated using a Lipchitz exponent at each time step of the signal. The local Lipchitz exponent can then be used to generate the wavelet intensity factor values. For each vertical acceleration value, corresponding to a specific location on the track, we now have a corresponding intensity factor. The intensity factor corresponds to break-no break information and can now be used as a feature to classify the vertical acceleration as a fault or no fault. Support Vector Machines (SVM) is used for this binary classification task. SVM is chosen as it is a well-studied topic with efficient implementations available. SVM instead of hard threshold of the data is expected to do a better job of classification without increasing the complexity of the system appreciably. / Master of Science

Emotion dysregulation plays a central role in psychopathology (B. Bradley et al., 2011) and has been linked to aberrant activation of neural circuitry involved in emotion regulation (Beauregard, Paquette, & Lévesque, 2006; Etkin & Schatzberg, 2011). In recent years, technological advances in neuroimaging methods coupled with developments in machine learning have allowed for the non-invasive measurement and prediction of brain states in real-time, which can be used to provide feedback to facilitate regulation of brain states (LaConte, 2011). Real-time functional magnetic resonance imaging (rt-fMRI)-guided neurofeedback, has promise as a novel therapeutic method in which individuals are provided with tailored feedback to improve regulation of emotional responses (Stoeckel et al., 2014). However, effective use of this technology for such purposes likely entails the development of (a) a normative model of emotion processing to provide feedback for individuals with emotion processing difficulties; and (b) best practices concerning how these types of group models are designed and translated for use in a rt-fMRI environment (Ruiz, Buyukturkoglu, Rana, Birbaumer, & Sitaram, 2014). To this end, the present study utilized fMRI data from a standard emotion elicitation paradigm to examine the impact of several design decisions made during the development of a whole-brain model of affective processing. Using support vector machine (SVM) learning, we developed a group model that reliably classified brain states associated with passive viewing of positive, negative, and neutral images. After validating the group whole-brain model, we adapted this model for use in an rt-fMRI experiment, and using a second imaging dataset along with our group model, we simulated rt-fMRI predictions and tested options for providing feedback. / Master of Science

Land cover classification using Synthetic Aperture Radar (SAR) data has been a topic of great interest in recent literature. Food commodities output prediction through crop identification, environmental monitoring, and forest regrowth tracking are some of the many problems that can be aided by land cover classification methods. The need for fast and automated classification methods is apparent in a variety of applications involving vast amounts of SAR data. One fundamental step in any supervised learning classification algorithm is the selection and/or extraction of features present in the dataset to be used for class discrimination. A popular method that has been proposed for feature extraction from polarimetric data is to decompose the data into the underlying scattering mechanisms. In this research, the Freeman and Durden scattering model is applied to ALOS PALSAR fully polarimetric data for feature extraction. Efficient methods for solving the complex system of equations present in the scattering model are developed and compared. Using the features from the Freeman and Durden work, the classification capability of the model is assessed on amazon rainforest land cover types using a supervised Support Vector Machine (SVM) classification algorithm. The quantity of land cover types that can be discriminated using the model is also determined. Additionally, the performance of the median as a robust estimator in noisy environments for multi-pixel windowing is also characterized. / Master of Science / Land type classification using Radar data has been a topic of great interest in recent literature. Food commodities output prediction through crop identification, environmental monitoring, and forest regrowth tracking are some of the many problems that can be aided by land cover classification methods. The need for fast and automated classification methods is apparent in a variety of applications involving vast amounts of Radar data. One fundamental step in any classification algorithm is the selection and/or extraction of discriminating features present in the dataset to be used for class discrimination. A popular method that has been proposed for feature extraction from polarized Radar data is to decompose the data into the underlying scatter components. In this research, a scattering model is applied to real world data for feature extraction. Efficient methods for solving the complex system of equations present in the scattering model are developed and compared. Using the features from the scattering model, the classification capability of the model is assessed on amazon rainforest land types using a Support Vector Machine (SVM) classification algorithm. The quantity of land cover types that can be discriminated using the model is also determined and compared using different estimators.

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The feasibility of using a vectorized banded direct solver for the compressible Euler and Navier-Stokes equations is examined for both single-grid and multi-grid strategies. A procedure is developed for comparing the computational effort required for the direct method with that of the vertical line Gauss-Seidel iteration scheme in order to provide a criteria for choosing between the two techniques. The direct method is shown to have a relatively wide range of application on a vector processor with large memory. Indeed, the primary limitation of the direct method at this time is machine memory. Results for both inviscid and viscous test problems over a range of Mach numbers and Reynolds numbers are examined for two dimensions. / Ph. D.