Panel of real-time PCRs for the multiplexed detection of two tomato-infecting begomoviruses and their cognate whitefly vector species

A new approach for the simultaneous identification of the viruses and vectors responsible for tomato yellow leaf curl disease (TYLCD) epidemics is presented. A panel of quantitative multiplexed real-time PCR assays was developed for the sensitive and reliable detection of Tomato yellow leaf curl virus-Israel (TYLCV-IL), Tomato leaf curl virus (ToLCV), Bemisia tabaci Middle East Asia Minor 1 species (MEAM1, B biotype) and B.tabaci Mediterranean species (MED, Q biotype) from either plant or whitefly samples. For quality-assurance purposes, two internal control assays were included in the assay panel for the co-amplification of solanaceous plant DNA or B.tabaci DNA. All assays were shown to be specific and reproducible. The multiplexed assays were able to reliably detect as few as 10 plasmid copies of TYLCV-IL, 100 plasmid copies of ToLCV, 500fg B.tabaci MEAM1 and 300fg B.tabaci MED DNA. Evaluated methods for routine testing of field-collected whiteflies are presented, including protocols for processing B.tabaci captured on yellow sticky traps and for bulking of multiple B.tabaci individuals prior to DNA extraction. This work assembles all of the essential features of a validated and quality-assured diagnostic method for the identification and discrimination of tomato-infecting begomovirus and B.tabaci vector species in Australia. This flexible panel of assays will facilitate improved quarantine, biosecurity and disease-management programmes both in Australia and worldwide.

Cytorhabdovirus phosphoprotein shows RNA silencing suppressor activity in plants, but not in insect cells

RNA silencing in plants and insects provides an antiviral defense and as a countermeasure most viruses encode RNA silencing suppressors (RSS). For the family Rhabdoviridae, no detailed functional RSS studies have been reported in plant hosts and insect vectors. In agroinfiltrated Nicotiana benthamiana leaves we show for the first time for a cytorhabdovirus, lettuce necrotic yellows virus (LNYV), that one of the nucleocapsid core proteins, phosphoprotein (P) has relatively weak local RSS activity and delays systemic silencing of a GFP reporter. Analysis of GFP small RNAs indicated that the P protein did not prevent siRNA accumulation. To explore RSS activity in insects, we used a Flock House virus replicon system in Drosophila S2 cells. In contrast to the plant host, LNYV P protein did not exhibit RSS activity in the insect cells. Taken together our results suggest that P protein may target plant-specific components of RNA silencing post siRNA biogenesis.

The insecticidal spider toxin SFI1 is a knottin peptide that blocks the pore of insect voltage-gated sodium channels via a large β-hairpin loop

Spider venoms contain a plethora of insecticidal peptides that act on neuronal ion channels and receptors. Because of their high specificity, potency and stability, these peptides have attracted much attention as potential environmentally friendly insecticides. Although many insecticidal spider venom peptides have been isolated, the molecular target, mode of action and structure of only a small minority have been explored. Sf1a, a 46-residue peptide isolated from the venom of the tube-web spider Segesteria florentina, is insecticidal to a wide range of insects, but nontoxic to vertebrates. In order to investigate its structure and mode of action, we developed an efficient bacterial expression system for the production of Sf1a. We determined a high-resolution solution structure of Sf1a using multidimensional 3D/4D NMR spectroscopy. This revealed that Sf1a is a knottin peptide with an unusually large β-hairpin loop that accounts for a third of the peptide length. This loop is delimited by a fourth disulfide bond that is not commonly found in knottin peptides. We showed, through mutagenesis, that this large loop is functionally critical for insecticidal activity. Sf1a was further shown to be a selective inhibitor of insect voltage-gated sodium channels, consistent with its 'depressant' paralytic phenotype in insects. However, in contrast to the majority of spider-derived sodium channel toxins that function as gating modifiers via interaction with one or more of the voltage-sensor domains, Sf1a appears to act as a pore blocker.

Composition operators on vector-valued BMOA and related function spaces

A composition operator is a linear operator between spaces of analytic or harmonic functions on the unit disk, which precomposes a function with a fixed self-map of the disk. A fundamental problem is to relate properties of a composition operator to the function-theoretic properties of the self-map. During the recent decades these operators have been very actively studied in connection with various function spaces. The study of composition operators lies in the intersection of two central fields of mathematical analysis; function theory and operator theory. This thesis consists of four research articles and an overview. In the first three articles the weak compactness of composition operators is studied on certain vector-valued function spaces. A vector-valued function takes its values in some complex Banach space. In the first and third article sufficient conditions are given for a composition operator to be weakly compact on different versions of vector-valued BMOA spaces. In the second article characterizations are given for the weak compactness of a composition operator on harmonic Hardy spaces and spaces of Cauchy transforms, provided the functions take values in a reflexive Banach space. Composition operators are also considered on certain weak versions of the above function spaces. In addition, the relationship of different vector-valued function spaces is analyzed. In the fourth article weighted composition operators are studied on the scalar-valued BMOA space and its subspace VMOA. A weighted composition operator is obtained by first applying a composition operator and then a pointwise multiplier. A complete characterization is given for the boundedness and compactness of a weighted composition operator on BMOA and VMOA. Moreover, the essential norm of a weighted composition operator on VMOA is estimated. These results generalize many previously known results about composition operators and pointwise multipliers on these spaces.

Passive vector turbulence

This thesis consists of three articles on passive vector fields in turbulence. The vector fields interact with a turbulent velocity field, which is described by the Kraichnan model. The effect of the Kraichnan model on the passive vectors is studied via an equation for the pair correlation function and its solutions. The first paper is concerned with the passive magnetohydrodynamic equations. Emphasis is placed on the so called "dynamo effect", which in the present context is understood as an unbounded growth of the pair correlation function. The exact analytical conditions for such growth are found in the cases of zero and infinite Prandtl numbers. The second paper contains an extensive study of a number of passive vector models. Emphasis is now on the properties of the (assumed) steady state, namely anomalous scaling, anisotropy and small and large scale behavior with different types of forcing or stirring. The third paper is in many ways a completion to the previous one in its study of the steady state existence problem. Conditions for the existence of the steady state are found in terms of the spatial roughness parameter of the turbulent velocity field.

Two Stage Transform Vector Quantization of LSFs for Wideband Speech Coding

We investigate the use of a two stage transform vector quantizer (TSTVQ) for coding of line spectral frequency (LSF) parameters in wideband speech coding. The first stage quantizer of TSTVQ, provides better matching of source distribution and the second stage quantizer provides additional coding gain through using an individual cluster specific decorrelating transform and variance normalization. Further coding gain is shown to be achieved by exploiting the slow time-varying nature of speech spectra and thus using inter-frame cluster continuity (ICC) property in the first stage of TSTVQ method. The proposed method saves 3-4 bits and reduces the computational complexity by 58-66%, compared to the traditional split vector quantizer (SVQ), but at the expense of 1.5-2.5 times of memory.

Spatial ecology of a specialist insect herbivore : the leaf-mining moth Tischeria ekebladella on the pedunculate oak Quercus robur

Herbivorous insects comprise a major part of terrestrial biodiversity, and their interactions with their host plants and natural enemies are of vast ecological importance. A large body of research demonstrates that the ecology and evolution of these insects may be affected by trophic interactions, by abiotic influences, and by intraspecific processes, but so far research on these individual aspects has rarely been combined. This thesis uses the leaf-mining moth Tischeria ekebladella and the pedunculate oak (Quercus robur) as a case study to assess how spatial variation in trophic interactions and the physical distribution of host trees jointly affect the distribution, dynamics and evolution of a host-specific herbivore. With respect to habitat quality, Tischeria ekebladella experiences abundant variation at several spatial scales. Most of this variation occurs at small scales notably among leaves and shoots within individual trees. While hypothetically this could cause moths to evolve an ability to select leaves and shoots of high quality, I did not find any coupling between female preference and offspring performance. Based on my studies on temporal variation in resource quality I therefore propose that unpredictable temporal changes in the relative rankings of individual resource units may render it difficult for females to predict the fate of their developing offspring. With respect to intraspecific processes, my results suggest that limited moth dispersal in relation to the spatial distribution of oak trees plays a key role in determining the regional distribution of Tischeria ekebladella. The distribution of the moth is aggregated at the landscape level, where local leaf miner populations are less likely to be present where oaks are scarce. A modelling exercise based on empirical dispersal estimates revealed that the moth population on Wattkast an island in south-western Finland is spatially structured overall, but that the relative importance of local and regional processes on tree-specific moth dynamics varies drastically across the landscape. To conclude, my work in the oak-Tischeria ekebladella system demonstrates that the local abundance and regional distribution of a herbivore may be more strongly influenced by the spatial location of host trees than by their relative quality. Hence, it reveals the importance of considering spatial context in the study of herbivorous insects, and forms a bridge between the classical fields of plant-insect interactions and spatial ecology.

Subspace intersection method of high-resolution bearing estimation in shallow ocean using acoustic vector sensors

The subspace intersection method (SIM) provides unbiased bearing estimates of multiple acoustic sources in a range-independent shallow ocean using a one-dimensional search without prior knowledge of source ranges and depths. The original formulation of this method is based on deployment of a horizontal linear array of hydrophones which measure acoustic pressure. In this paper, we extend SIM to an array of acoustic vector sensors which measure pressure as well as all components of particle velocity. Use of vector sensors reduces the minimum number of sensors required by a factor of 4, and also eliminates the constraint that the intersensor spacing should not exceed half wavelength. The additional information provided by the vector sensors leads to performance enhancement in the form of lower estimation error and higher resolution.

Prediction of Candidate Primary Immunodeficiency Disease Genes Using a Support Vector Machine Learning Approach

Screening and early identification of primary immunodeficiency disease (PID) genes is a major challenge for physicians. Many resources have catalogued molecular alterations in known PID genes along with their associated clinical and immunological phenotypes. However, these resources do not assist in identifying candidate PID genes. We have recently developed a platform designated Resource of Asian PDIs, which hosts information pertaining to molecular alterations, protein-protein interaction networks, mouse studies and microarray gene expression profiling of all known PID genes. Using this resource as a discovery tool, we describe the development of an algorithm for prediction of candidate PID genes. Using a support vector machine learning approach, we have predicted 1442 candidate PID genes using 69 binary features of 148 known PID genes and 3162 non-PID genes as a training data set. The power of this approach is illustrated by the fact that six of the predicted genes have recently been experimentally confirmed to be PID genes. The remaining genes in this predicted data set represent attractive candidates for testing in patients where the etiology cannot be ascribed to any of the known PID genes.

Gaussian mixture model based switched split vector quantization of LSF parameters

We address the issue of rate-distortion (R/D) performance optimality of the recently proposed switched split vector quantization (SSVQ) method. The distribution of the source is modeled using Gaussian mixture density and thus, the non-parametric SSVQ is analyzed in a parametric model based framework for achieving optimum R/D performance. Using high rate quantization theory, we derive the optimum bit allocation formulae for the intra-cluster split vector quantizer (SVQ) and the inter-cluster switching. For the wide-band speech line spectrum frequency (LSF) parameter quantization, it is shown that the Gaussian mixture model (GMM) based parametric SSVQ method provides 1 bit/vector advantage over the non-parametric SSVQ method.