Wolbachia uses host microRNAs to manipulate host gene expression and facilitate colonization of the dengue vector Aedes aegypti

The obligate endosymbiont Wolbachia pipientis is found in a wide range of invertebrates where they are best known for manipulating host reproduction. Recent studies have shown that Wolbachia also can modulate the lifespan of host insects and interfere with the development of human pathogens in mosquito vectors. Despite considerable study, very little is known about the molecular interactions between Wolbachia and its hosts that might mediate these effects. Using microarrays, we show that the microRNA (miRNA) profile of the mosquito, Aedes aegypti, is significantly altered by the wMelPop-CLA strain of W. pipientis. We found that a host miRNA (aae-miR-2940) is induced after Wolbachia infection in both mosquitoes and cell lines. One target of aae-miR-2940 is the Ae. aegypti metalloprotease gene. Interestingly, expression of the target gene was induced after Wolbachia infection, ectopic expression of the miRNA independent of Wolbachia, or transfection of an artificial mimic of the miRNA into mosquito cells. We also confirmed the interaction of aae-miR-2940 with the target sequences using GFP as a reporter gene. Silencing of the metalloprotease gene in both Wolbachia-infected cells and adult mosquitoes led to a significant reduction in Wolbachia density, as did inhibition of the miRNA in cells. These results indicate that manipulation of the mosquito metalloprotease gene via aae-miR-2940 is crucial for efficient maintenance of the endosymbiont. This report shows how Wolbachia alters the host miRNA profile and provides insight into the mechanisms of host manipulation used by this widespread endosymbiont.

Real, complex, and binary semantic vectors

This paper presents a combined structure for using real, complex, and binary valued vectors for semantic representation. The theory, implementation, and application of this structure are all significant. For the theory underlying quantum interaction, it is important to develop a core set of mathematical operators that describe systems of information, just as core mathematical operators in quantum mechanics are used to describe the behavior of physical systems. The system described in this paper enables us to compare more traditional quantum mechanical models (which use complex state vectors), alongside more generalized quantum models that use real and binary vectors. The implementation of such a system presents fundamental computational challenges. For large and sometimes sparse datasets, the demands on time and space are different for real, complex, and binary vectors. To accommodate these demands, the Semantic Vectors package has been carefully adapted and can now switch between different number types comparatively seamlessly. This paper describes the key abstract operations in our semantic vector models, and describes the implementations for real, complex, and binary vectors. We also discuss some of the key questions that arise in the field of quantum interaction and informatics, explaining how the wide availability of modelling options for different number fields will help to investigate some of these questions.

High level protein expression in plants through the use of a novel autonomously replicating geminivirus shuttle vector

We constructed a novel autonomously replicating gene expression shuttle vector, with the aim of developing a system for transiently expressing proteins at levels useful for commercial production of vaccines and other proteins in plants. The vector, pRIC, is based on the mild strain of the geminivirus Bean yellow dwarf virus (BeYDV-m) and is replicationally released into plant cells from a recombinant Agrobacterium tumefaciens Ti plasmid. pRIC differs from most other geminivirus-based vectors in that the BeYDV replication-associated elements were included in cis rather than from a co-transfected plasmid, while the BeYDV capsid protein (CP) and movement protein (MP) genes were replaced by an antigen encoding transgene expression cassette derived from the non-replicating A. tumefaciens vector, pTRAc. We tested vector efficacy in Nicotiana benthamiana by comparing transient cytoplasmic expression between pRIC and pTRAc constructs encoding either enhanced green fluorescent protein (EGFP) or the subunit vaccine antigens, human papillomavirus subtype 16 (HPV-16) major CP L1 and human immunodeficiency virus subtype C p24 antigen. The pRIC constructs were amplified in planta by up to two orders of magnitude by replication, while 50% more HPV-16 L1 and three- to seven-fold more EGFP and HIV-1 p24 were expressed from pRIC than from pTRAc. Vector replication was shown to be correlated with increased protein expression. We anticipate that this new high-yielding plant expression vector will contribute towards the development of a viable plant production platform for vaccine candidates and other pharmaceuticals. © 2009 Blackwell Publishing Ltd.

Improving short utterance based I-vector speaker recognition using source and utterance-duration normalization techniques

A significant amount of speech is typically required for speaker verification system development and evaluation, especially in the presence of large intersession variability. This paper introduces a source and utterance duration normalized linear discriminant analysis (SUN-LDA) approaches to compensate session variability in short-utterance i-vector speaker verification systems. Two variations of SUN-LDA are proposed where normalization techniques are used to capture source variation from both short and full-length development i-vectors, one based upon pooling (SUN-LDA-pooled) and the other on concatenation (SUN-LDA-concat) across the duration and source-dependent session variation. Both the SUN-LDA-pooled and SUN-LDA-concat techniques are shown to provide improvement over traditional LDA on NIST 08 truncated 10sec-10sec evaluation conditions, with the highest improvement obtained with the SUN-LDA-concat technique achieving a relative improvement of 8% in EER for mis-matched conditions and over 3% for matched conditions over traditional LDA approaches.

Improved vectors for Agrobacterium tumefaciens-mediated transformation of monocot plants

A series of improved vectors have been constructed that are suitable for use in Agrobacterium tumefaciens-mediated monocot transformation. These binary vectors have several useful features, including the selectable marker genes bar (phosphinothricin resistance) or hph (hygromycin resistance) driven by either the cauliflower mosaic virus (CaMV) 35S promoter or the maize ubiquitin promoter, a high-copy-number replication origin that allows reliable mini-prep DNA isolation from Escherichia coli, and a polylinker sequence into which target genes can be easily inserted. A significant improvement has been made to the hph gene by the introduction of an intron into its coding region. The presence of the intron abolishes hph expression in A. tumefaciens, rendering the bacterium susceptible to the selective agent hygromycin B. The use of such an intron-hph vector thus enables the antibiotic in plant culture media to function as both a selective agent for transformed tissue and as a contraselective agent for A. tumefaciens growth, thus minimising the overgrowth of A. tumefaciens on plant tissues during transformation. Furthermore, the intron appears to be correctly spliced in plant cells and significantly enhances hph expression in transformed rice tissue. In our experiments, the use of the intron-hph vector increased the frequency of rice transformation and has enabled the production of transgenic barley.

Vectors and methods for hairpin RNA and artificial microRNA-mediated gene silencing in plants

In plant cells, DICER-LIKE4 processes perfectly double-stranded RNA (dsRNA) into short interfering (si) RNAs, and DICER-LIKE1 generates micro (mi) RNAs from primary miRNA transcripts (pri-miRNA) that form fold-back structures of imperfectly dsRNA. Both si and miRNAs direct the endogenous endonuclease, ARGONAUTE1 to cleave complementary target single-stranded RNAs and either small RNA (sRNA)-directed pathway can be harnessed to silence genes in plants. A routine way of inducing and directing RNA silencing by siRNAs is to express self-complementary single-stranded hairpin RNA (hpRNA), in which the duplexed region has the same sequence as part of the target gene's mRNA. Artificial miRNA (amiRNA)-mediated silencing uses an endogenous pri-miRNA, in which the original miRNA/miRNA* sequence has been replaced with a sequence complementary to the new target gene. In this chapter, we describe the plasmid vector systems routinely used by our research group for the generation of either hpRNA-derived siRNAs or amiRNAs.

High-throughput vectors for efficient gene silencing in plants

A major challenge in the post-genome era of plant biology is to determine the functions of all genes in the plant genome. A straightforward approach to this problem is to reduce or knockout expression of a gene with the hope of seeing a phenotype that is suggestive of its function. Insertional mutagenesis is a useful tool for this type of study but is limited by gene redundancy, lethal knockouts, non-tagged mutants, and the inability to target the inserted element to a specific gene. The efficacy of gene silencing in plants using inverted-repeat transgene constructs that encode a hairpin RNA (hpRNA) has been demonstrated by a number of groups, and has several advantages over insertional mutagenesis. In this paper we describe two improved pHellsgate vectors that facilitate rapid generation of hpRNA-encoding constructs, pHellsgate 4 allows the production of an hpRNA construct in a single step from a single polymerase chain reaction product, while pHellsgate 8 requires a two-step process via an intermediate vector. We show that these vectors are effective at silencing three endogenous genes in Arabidopsis, FLOWERING LOCUS C, PHYTOENE DESATURASE and ETHYLENE INSENSITIVE 2. We also show that a construct of sequences from two genes silences both genes.

Improving short utterance i-vector speaker verification using utterance variance modelling and compensation techniques

This paper proposes techniques to improve the performance of i-vector based speaker verification systems when only short utterances are available. Short-length utterance i-vectors vary with speaker, session variations, and the phonetic content of the utterance. Well established methods such as linear discriminant analysis (LDA), source-normalized LDA (SN-LDA) and within-class covariance normalisation (WCCN) exist for compensating the session variation but we have identified the variability introduced by phonetic content due to utterance variation as an additional source of degradation when short-duration utterances are used. To compensate for utterance variations in short i-vector speaker verification systems using cosine similarity scoring (CSS), we have introduced a short utterance variance normalization (SUVN) technique and a short utterance variance (SUV) modelling approach at the i-vector feature level. A combination of SUVN with LDA and SN-LDA is proposed to compensate the session and utterance variations and is shown to provide improvement in performance over the traditional approach of using LDA and/or SN-LDA followed by WCCN. An alternative approach is also introduced using probabilistic linear discriminant analysis (PLDA) approach to directly model the SUV. The combination of SUVN, LDA and SN-LDA followed by SUV PLDA modelling provides an improvement over the baseline PLDA approach. We also show that for this combination of techniques, the utterance variation information needs to be artificially added to full-length i-vectors for PLDA modelling.

Combining word semantics within complex Hilbert space for information retrieval

Complex numbers are a fundamental aspect of the mathematical formalism of quantum physics. Quantum-like models developed outside physics often overlooked the role of complex numbers. Specifically, previous models in Information Retrieval (IR) ignored complex numbers. We argue that to advance the use of quantum models of IR, one has to lift the constraint of real-valued representations of the information space, and package more information within the representation by means of complex numbers. As a first attempt, we propose a complex-valued representation for IR, which explicitly uses complex valued Hilbert spaces, and thus where terms, documents and queries are represented as complex-valued vectors. The proposal consists of integrating distributional semantics evidence within the real component of a term vector; whereas, ontological information is encoded in the imaginary component. Our proposal has the merit of lifting the role of complex numbers from a computational byproduct of the model to the very mathematical texture that unifies different levels of semantic information. An empirical instantiation of our proposal is tested in the TREC Medical Record task of retrieving cohorts for clinical studies.

Secure computation of the vector dominance problem

Suppose two parties, holding vectors A = (a 1,a 2,...,a n ) and B = (b 1,b 2,...,b n ) respectively, wish to know whether a i  > b i for all i, without disclosing any private input. This problem is called the vector dominance problem, and is closely related to the well-studied problem for securely comparing two numbers (Yao’s millionaires problem). In this paper, we propose several protocols for this problem, which improve upon existing protocols on round complexity or communication/computation complexity.

pGreen : a versatile and flexible binary Ti vector for Agrobacterium-mediated plant transformation

Binary Ti vectors are the plasmid vectors of choice in Agrobacterium-mediated plant transformation protocols. The pGreen series of binary Ti vectors are configured for ease-of-use and to meet the demands of a wide range of transformation procedures for many plant species. This plasmid system allows any arrangement of selectable marker and reporter gene at the right and left T-DNA borders without compromising the choice of restriction sites for cloning, since the pGreen cloning sites are based on the well-known pBluescript general vector plasmids. Its size and copy number in Escherichia coli offers increased efficiencies in routine in vitro recombination procedures. pGreen can replicate in Agrobacterium only if another plasmid, pSoup, is co-resident in the same strain. pSoup provides replication functions in trans for pGreen. The removal of RepA and Mob functions has enabled the size of pGreen to be kept to a minimum. Versions of pGreen have been used to transform several plant species with the same efficiencies as other binary Ti vectors. Information on the pGreen plasmid system is supplemented by an Internet site (http://www.pgreen.ac.uk) through which comprehensive information, protocols, order forms and lists of different pGreen marker gene permutations can be found.

Transient expression vectors for functional genomics, quantification of promoter activity and RNA silencing in plants

Background We describe novel plasmid vectors for transient gene expression using Agrobacterium, infiltrated into Nicotiana benthamiana leaves. We have generated a series of pGreenII cloning vectors that are ideally suited to transient gene expression, by removing elements of conventional binary vectors necessary for stable transformation such as transformation selection genes. Results We give an example of expression of heme-thiolate P450 to demonstrate effectiveness of this system. We have also designed vectors that take advantage of a dual luciferase assay system to analyse promoter sequences or post-transcriptional regulation of gene expression. We have demonstrated their utility by co-expression of putative transcription factors and the promoter sequence of potential target genes and show how orthologous promoter sequences respond to these genes. Finally, we have constructed a vector that has allowed us to investigate design features of hairpin constructs related to their ability to initiate RNA silencing, and have used these tools to study cis-regulatory effect of intron-containing gene constructs. Conclusion In developing a series of vectors ideally suited to transient expression analysis we have provided a resource that further advances the application of this technology. These minimal vectors are ideally suited to conventional cloning methods and we have used them to demonstrate their flexibility to investigate enzyme activity, transcription regulation and post-transcriptional regulatory processes in transient assays.

Multi-shot person re-identification via relational stein divergence

Person re-identification is particularly challenging due to significant appearance changes across separate camera views. In order to re-identify people, a representative human signature should effectively handle differences in illumination, pose and camera parameters. While general appearance-based methods are modelled in Euclidean spaces, it has been argued that some applications in image and video analysis are better modelled via non-Euclidean manifold geometry. To this end, recent approaches represent images as covariance matrices, and interpret such matrices as points on Riemannian manifolds. As direct classification on such manifolds can be difficult, in this paper we propose to represent each manifold point as a vector of similarities to class representers, via a recently introduced form of Bregman matrix divergence known as the Stein divergence. This is followed by using a discriminative mapping of similarity vectors for final classification. The use of similarity vectors is in contrast to the traditional approach of embedding manifolds into tangent spaces, which can suffer from representing the manifold structure inaccurately. Comparative evaluations on benchmark ETHZ and iLIDS datasets for the person re-identification task show that the proposed approach obtains better performance than recent techniques such as Histogram Plus Epitome, Partial Least Squares, and Symmetry-Driven Accumulation of Local Features.

Semantic spaces : measuring the distance between different subspaces

Semantic Space models, which provide a numerical representation of words’ meaning extracted from corpus of documents, have been formalized in terms of Hermitian operators over real valued Hilbert spaces by Bruza et al. [1]. The collapse of a word into a particular meaning has been investigated applying the notion of quantum collapse of superpositional states [2]. While the semantic association between words in a Semantic Space can be computed by means of the Minkowski distance [3] or the cosine of the angle between the vector representation of each pair of words, a new procedure is needed in order to establish relations between two or more Semantic Spaces. We address the question: how can the distance between different Semantic Spaces be computed? By representing each Semantic Space as a subspace of a more general Hilbert space, the relationship between Semantic Spaces can be computed by means of the subspace distance. Such distance needs to take into account the difference in the dimensions between subspaces. The availability of a distance for comparing different Semantic Subspaces would enable to achieve a deeper understanding about the geometry of Semantic Spaces which would possibly translate into better effectiveness in Information Retrieval tasks.

Topological localization using optical flow descriptors

We propose a topological localization method based on optical flow information. We analyse the statistical characteristics of the optical flow signal and demonstrate that the flow vectors can be used to identify and describe key locations in the environment. The key locations (nodes) correspond to significant scene changes and depth discontinuities. Since optical flow vectors contain position, magnitude and angle information, for each node, we extract low and high order statistical moments of the vectors and use them as descriptors for that node. Once a database of nodes and their corresponding optical flow features is created, the robot can perform topological localization by using the Mahalanobis distance between the current frame and the database. This is supported by field trials, which illustrate the repeatability of the proposed method for detecting and describing key locations in indoor and outdoor environments in challenging and diverse lighting conditions.