Kenya has experienced severe arboviral outbreaks of public health concern in the recent past, including yellow fever (YF), Crimean Congo hemorrhagic fever (CCHF), chikungunya, and Rift Valley fever (RVF) among others. Most of these infections are under diagnosed and hence neglected due to non-specific nature of their symptoms. Often they are mistaken for endemic tropical diseases such as malaria and typhoid infections and are only recognized during major outbreaks which result in adverse public health and economic consequences to the affected communities. Ongoing inter-epidemic surveillance in RVF virus hotspots in Kenya has indicated continued intense transmission of Bunyamwera virus (BUNV) in the absence or under low level activity of RVF virus. BUNV belongs to the genus Orthobunyavirus of the family Bunyaviridae. These are segmented RNA viruses whose members have the potential for genetic reassotment and/or drift. Recently, Ngari virus (NRIV), a natural reassortant virus associated with hemorrhagic fever was documented to have emerged from BUNV, which previously was not associated with such symptoms. However, the vectors that are involved in the maintenance and transmission of BUNV and NRIV are diverse and their role in virus maintenance/dynamics is poorly known. It is thus important to investigate the dynamics of BUNV and NRIV in selected transmission foci in an effort to understand their circulation better in order to be able to control and predict outbreaks. In this study, we determined the evolutionary and phenotypic diversity of BUNV and NRIV isolates previously obtained from vectors in parts of Kenya. We have provided genetic sequences of two BUNV and three NRIV isolates which contribute to addressing paucity of genetic sequences associated with this group of viruses. Phylogenetic analysis of these sequences in addition to other sequences in GenBank revealed evidence of geographic/temporal clustering that requires further investigation. Next, we demonstrated that plaque purified phenotypes of selected BUNV and NRIV isolates differ in in vivo growth kinetics and pathogenicity in mice, possibly related to specific mutations within the genome. The phenotypic changes and identification of mutations possibly associated with these changes support further investigation of specific mutations using site directed mutagenesis. In addition, we determined the competence of some of the mosquito species implicated in their transmission, Anopheles gambiae, Aedes aegypti and Culex quinquefaciatus and evaluated the dynamics of their transmission in these vectors. We conclude that Anopheles gambiae is likely a more competent vector for NRIV than Aedes aegypti and is a moderately competent vector for BUNV, which has implications for animal movement in malaria endemic areas where the vector is present. We also report evidence of BUNV transovarial transmission in both Aedes aegypti and Anopheles gambiae with the prevalence of transmission related to the ovarian cycle. Finally, we determined the level of human exposure to these viruses in the transmission foci. Orthobunyavirus-specific antibodies were detected by plaque reduction neutralization test in 89 (25.8%) of 345 persons tested. Multivariable analysis revealed age and residence in North Eastern Kenya as risk factors. In conclusion, we propose that acute febrile disease surveillance needs to be implemented in North Eastern Kenya. This study helps identify the virus strains/populations and the vector species that play a critical role in sustenance and transmission of BUNV and NRIV in different ecosystems in the country. All these are important in understanding virus circulation, potential for emergence and risk to populations in areas of circulation, and will help in making decisions for intervention and management. Generated sequence data in this study contributes to global phylogenetic characterization of Orthobunyaviruses worldwide and their molecular epidemiology. The study also shed light/improve our knowledge on the genetic stability or diversity and evolutionary trends of Orthobunyavirus strains in Kenya. / Thesis (PhD)--University of Pretoria, 2015. / Medical Virology / PhD / Unrestricted

In this thesis we introduce the spherical, central, angular, halfspace and regression symmetry of random vectors and their measures. Firstly we deal with their mutual relations and equivalent expressions. We also study the uniqueness of the center of individual symmetries and other interesting properties. Then we define the halfspace, projection, spatial and regression multidimensional median and show their properties. Finally we look at the relationships between these medians and symmetric distributions. 1

京都大学 / 0048 / 新制・論文博士 / 博士(農学) / 乙第13073号 / 論農博第2843号 / 新制||農||1046(附属図書館) / 学位論文||H29||N5029(農学部図書室) / 33224 / 京都大学大学院農学研究科農学専攻 / (主査)教授 北島 宣, 教授 土井 元章, 教授 田尾 龍太郎 / 学位規則第4条第2項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

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Machine learning is emerging as a powerful tool to data science and is being applied in almost all subjects. In many applications, the number of features is com- parable to the number of samples, and both grow large. This setting is usually named the high-dimensional regime. In this regime, new challenges arise when it comes to the application of machine learning. In this work, we conduct a high-dimensional performance analysis of some popular classification and regression techniques. In a first part, discriminant analysis classifiers are considered. A major challenge towards the use of these classifiers in practice is that they depend on the inverse of covariance matrices that need to be estimated from training data. Several estimators for the inverse of the covariance matrices can be used. The most common ones are estimators based on the regularization approach. In this thesis, we propose new estimators that are shown to yield better performance. The main principle of our proposed approach is the design of an optimized inverse covariance matrix estimator based on the assumption that the covariance matrix is a low-rank perturbation of a scaled identity matrix. We show that not only the proposed classifiers are easier to implement but also, outperform the classical regularization-based discriminant analysis classifiers. In a second part, we carry out a high-dimensional statistical analysis of linear support vector regression. Under some plausible assumptions on the statistical dis- tribution of the data, we characterize the feasibility condition for the hard support vector regression and, when feasible, derive an asymptotic approximation for its risk. Similarly, we study the test risk for the soft support vector regression as a function of its parameters. The analysis is then extended to the case of kernel support vector regression under generalized linear models assumption. Based on our analysis, we illustrate that adding more samples may be harmful to the test performance of these regression algorithms, while it is always beneficial when the parameters are optimally selected. Our results pave the way to understand the effect of the underlying hyper- parameters and provide insights on how to optimally choose the kernel function.

Virtually all organisms exhibit circadian rhythms, this includes mosquitoes. Many aspects of their biology are under the control of their circadian clocks like their vision, olfaction, host-seeking, mating, oviposition, metabolism, locomotion, and more. However, how the circadian clock regulates mosquito-host interactions and adapts to specific environmental conditions remains largely unknown, despite its importance to vector disease control. Here, we relied on a multidisciplinary, integrative, and comparative approach to shed light on the mechanisms underlying mosquitoes adaptations to various temporal niches. We use CRISPR/Cas9 to knockout timeless in Aedes aegypti and show this mutation causes an increase in their free running periods under continuous darkness conditions. External factors can also influence a vector's activity pattern like climate, light pollution, as well as host preference and availability. To investigate the influence these factors have on activity patterns, we compare the activity patterns of multiple lab rear strains of Aedes albopictus as well as two field collected populations of Aedes japonicus. Our results suggest host availability and light pollution could cause the differences in activity profiles that we observed. With vision playing an important role in both circadian rhythms, host seeking, and threat detection, we compared a nocturnal mosquito (Aedes japonicus) and a diurnal mosquito (Aedes aegypti). We introduced a looming visual stimulus in an LED arena and found Aedes aegypti to be more responsive to the looming stimulus than Aedes japonicus. Finally, the underlying mechanisms that mediate this threat detection and escape behavior were examined, revealing a possible candidate for a giant fiber neuron in Aedes aegypti. Overall these results provide novel insights into mosquito biology that have the potential to be applied towards vector control methods. / Doctor of Philosophy / Most living things, including mosquitoes, follow a daily pattern called a circadian rhythm. This rhythm affects many parts of their lives including their vision, smell, and activity. Understanding how their circadian clock functions can help us control populations of mosquitoes and prevent the transmission of diseases they may carry. By studying different species and populations of mosquitoes in the lab and from the wild, we have found that factors like light pollution and host availability could affect when mosquitoes are active. In addition to these results we have also demonstrated that mosquitoes that are active during the day and mosquitoes that are active during the night will respond differently to a looming visual stimulus. We examined the brain of the mosquitoes that were most responsive to the looming stimulus and found a system of large neurons that could potentially be responsible for eliciting their escape behaviors. Overall these results provide new and important information about mosquito biology and can be applied to mosquito control.

This licentiate thesis consists of two papers. In paper I the tautological ring of a Hilbert modular variety at an unramified prime is computed. The method of van der Geer in the case of A_{g} is extended to deal with the case of the Hilbert modular variety, which is more complicated. An example involving the unitary group is given which shows that this method cannot be used to compute the tautological rings of all Shimura varieties of Hodge type. In paper II we compute the pushforward map from a sub flag variety defined by a Levi subgroup to the Siegel flag variety. Specifically, this is the Levi factor of the parabolic associated with the maximal rational boundary component of the Siegel Shimura datum. The method involves an explicit understanding of the pullback map and an application of the self intersection formula.

Rotylenchulus reniformis is a nematode species affecting the cotton crop and quickly spreading throughout the southeastern United States. Effective use of nematicides at a variable rate is the only economic counter measure. It requires the intraield variable nematode population, which in turn depends on the collection of soil samples from the field and analyzing them in the laboratory. This process is economically prohibitive. Hence estimating the nematode infestation on the cotton crop using remote sensing and machine learning techniques which are cost and time effective is the motivation for this study. In the current research, the concept of multi-temporal remote sensing has been implemented in order to design a robust and generalized Nematode detection regression model. Finally, a user friendly web-service is created which is gives trustworthy results for the given input data and thereby reducing the nematode infestation in the crop and their expenses on nematicides.

While uncertainty in scientific data attracts an increasing research interest in the visualization community, two critical issues remain insufficiently studied: (1) visualizing the impact of the uncertainty of a data set on its features and (2) interactively exploring 3D or large 2D data sets with uncertainties. In this study, a suite of feature-based techniques is developed to address these issues. First, a framework of feature-level uncertainty visualization is presented to study the uncertainty of the features in scalar and vector data. The uncertainty in the number and locations of features such as sinks or sources of vector fields are referred to as feature-level uncertainty while the uncertainty in the numerical values of the data is referred to as data-level uncertainty. The features of different ensemble members are indentified and correlated. The feature-level uncertainties are expressed as the transitions between corresponding features through new elliptical glyphs. Second, an interactive visualization tool for exploring scalar data with data-level and two types of feature-level uncertainties — contour-level and topology-level uncertainties — is developed. To avoid visual cluttering and occlusion, the uncertainty information is attached to a contour tree instead of being integrated with the visualization of the data. An efficient contour tree-based interface is designed to reduce users’ workload in viewing and analyzing complicated data with uncertainties and to facilitate a quick and accurate selection of prominent contours. This thesis advances the current uncertainty studies with an in-depth investigation of the feature-level uncertainties and an exploration of topology tools for effective and interactive uncertainty visualizations. With quantified representation and interactive capability, feature-based visualization helps people gain new insights into the uncertainties of their data, especially the uncertainties of extracted features which otherwise would remain unknown with the visualization of only data-level uncertainties.

Three-phase voltage source inverters are increasingly employed in power systems and industrial applications. Various pulse width modulation strategies have been applied to control the voltage source inverters. This dissertation presents a hybrid 2D-3D space vector modulation algorithm for three-phase voltage source inverters with both three-wire and four-wire topologies. The voltage magnitude and phase angle of the inverters fundamental output phase voltage are precisely controlled under either balanced or unbalanced load conditions, and hence, the space vector algorithm offers synchronization controllability over generation control in distributed generation systems. The numerical efficiency and simplicity of the proposed algorithm are validated through conducting MATLAB/Simulink simulations and hardware experiments. Mathematical description and harmonic analyses of output phase voltages of three-phase voltage source inverter which employs a hybrid 2D-3D SVM are presented in this dissertation. Explicit time domain representation of the harmonic components in addition to the total harmonic distortion of the output phase voltages are given in terms of system and switching parameters. The dissertation also investigates the harmonic characteristics and low total harmonic distortion performance against the linearity of modulation region which helps in the harmonic performance and design studies of such inverters employing the hybrid 2D-3D SVM. Experimental results are used to validate these analyses. In addition, the performance and the harmonic contents of the inverter output phase voltage when applying the proposed hybrid 2D-3D SVM are compared to that obtained from conventional 2D SVM and 3D SVM. As a result, the proposed new algorithm shows advantages in terms of low total harmonic distortion and reduced harmonic contents in both three-wire and four-wire systems.