Cloning and characterization of a novel human gene related to vascular endothelial growth factor

This paper describes the cloning and characterization of a new member of the vascular endothelial growth factor (VEGF) gene family, which we have designated VRF for VEGF-related-factor. Sequencing of cDNAs from a human fetal brain library and RT-PCR products from normal and tumor tissue cDNA pools indicate two alternatively spliced messages with open reading frames of 621 and 564 bp, respectively. The predicted proteins differ at their carboxyl ends resulting from a shift in the open reading frame. Both isoforms show strong homology to VEGF at their amino termini, but only the shorter isoform maintains homology to VEGF at its carboxyl terminus and conserves all 16 cysteine residues of VEGF165. Similarity comparisons of this isoform revealed overall protein identity of 48% and conservative substitution of 69% with VEGF189. VRF is predicted to contain a signal peptide, suggesting that it may be a secreted factor. The VRF gene maps to the D11S750 locus at chromosome band 11q13, and the protein coding region, spanning approximately 5 kb, is comprised of 8 exons that range in size from 36 to 431 bp. Exons 6 and 7 are contiguous and the two isoforms of VRF arise through alternate splicing of exon 6. VRF appears to be ubiquitously expressed as two transcripts of 2.0 and 5.5 kb; the level of expression is similar among normal and malignant tissues.

Towards condition-invariant sequence-based route recognition

In this paper we present a novel algorithm for localization during navigation that performs matching over local image sequences. Instead of calculating the single location most likely to correspond to a current visual scene, the approach finds candidate matching locations within every section (subroute) of all learned routes. Through this approach, we reduce the demands upon the image processing front-end, requiring it to only be able to correctly pick the best matching image from within a short local image sequence, rather than globally. We applied this algorithm to a challenging downhill mountainbiking visual dataset where there was significant perceptual or environment change between repeated traverses of the environment, and compared performance to applying the feature-based algorithm FAB-MAP. The results demonstrate the potential for localization using visual sequences, even when there are no visual features that can be reliably detected.

User behaviour modelling in a multi-dimensional environment for personalization and recommendation

Handling information overload online, from the user's point of view is a big challenge, especially when the number of websites is growing rapidly due to growth in e-commerce and other related activities. Personalization based on user needs is the key to solving the problem of information overload. Personalization methods help in identifying relevant information, which may be liked by a user. User profile and object profile are the important elements of a personalization system. When creating user and object profiles, most of the existing methods adopt two-dimensional similarity methods based on vector or matrix models in order to find inter-user and inter-object similarity. Moreover, for recommending similar objects to users, personalization systems use the users-users, items-items and users-items similarity measures. In most cases similarity measures such as Euclidian, Manhattan, cosine and many others based on vector or matrix methods are used to find the similarities. Web logs are high-dimensional datasets, consisting of multiple users, multiple searches with many attributes to each. Two-dimensional data analysis methods may often overlook latent relationships that may exist between users and items. In contrast to other studies, this thesis utilises tensors, the high-dimensional data models, to build user and object profiles and to find the inter-relationships between users-users and users-items. To create an improved personalized Web system, this thesis proposes to build three types of profiles: individual user, group users and object profiles utilising decomposition factors of tensor data models. A hybrid recommendation approach utilising group profiles (forming the basis of a collaborative filtering method) and object profiles (forming the basis of a content-based method) in conjunction with individual user profiles (forming the basis of a model based approach) is proposed for making effective recommendations. A tensor-based clustering method is proposed that utilises the outcomes of popular tensor decomposition techniques such as PARAFAC, Tucker and HOSVD to group similar instances. An individual user profile, showing the user's highest interest, is represented by the top dimension values, extracted from the component matrix obtained after tensor decomposition. A group profile, showing similar users and their highest interest, is built by clustering similar users based on tensor decomposed values. A group profile is represented by the top association rules (containing various unique object combinations) that are derived from the searches made by the users of the cluster. An object profile is created to represent similar objects clustered on the basis of their similarity of features. Depending on the category of a user (known, anonymous or frequent visitor to the website), any of the profiles or their combinations is used for making personalized recommendations. A ranking algorithm is also proposed that utilizes the personalized information to order and rank the recommendations. The proposed methodology is evaluated on data collected from a real life car website. Empirical analysis confirms the effectiveness of recommendations made by the proposed approach over other collaborative filtering and content-based recommendation approaches based on two-dimensional data analysis methods.

Multifractal characterisation and analysis of complex networks

Complex networks have been studied extensively due to their relevance to many real-world systems such as the world-wide web, the internet, biological and social systems. During the past two decades, studies of such networks in different fields have produced many significant results concerning their structures, topological properties, and dynamics. Three well-known properties of complex networks are scale-free degree distribution, small-world effect and self-similarity. The search for additional meaningful properties and the relationships among these properties is an active area of current research. This thesis investigates a newer aspect of complex networks, namely their multifractality, which is an extension of the concept of selfsimilarity. The first part of the thesis aims to confirm that the study of properties of complex networks can be expanded to a wider field including more complex weighted networks. Those real networks that have been shown to possess the self-similarity property in the existing literature are all unweighted networks. We use the proteinprotein interaction (PPI) networks as a key example to show that their weighted networks inherit the self-similarity from the original unweighted networks. Firstly, we confirm that the random sequential box-covering algorithm is an effective tool to compute the fractal dimension of complex networks. This is demonstrated on the Homo sapiens and E. coli PPI networks as well as their skeletons. Our results verify that the fractal dimension of the skeleton is smaller than that of the original network due to the shortest distance between nodes is larger in the skeleton, hence for a fixed box-size more boxes will be needed to cover the skeleton. Then we adopt the iterative scoring method to generate weighted PPI networks of five species, namely Homo sapiens, E. coli, yeast, C. elegans and Arabidopsis Thaliana. By using the random sequential box-covering algorithm, we calculate the fractal dimensions for both the original unweighted PPI networks and the generated weighted networks. The results show that self-similarity is still present in generated weighted PPI networks. This implication will be useful for our treatment of the networks in the third part of the thesis. The second part of the thesis aims to explore the multifractal behavior of different complex networks. Fractals such as the Cantor set, the Koch curve and the Sierspinski gasket are homogeneous since these fractals consist of a geometrical figure which repeats on an ever-reduced scale. Fractal analysis is a useful method for their study. However, real-world fractals are not homogeneous; there is rarely an identical motif repeated on all scales. Their singularity may vary on different subsets; implying that these objects are multifractal. Multifractal analysis is a useful way to systematically characterize the spatial heterogeneity of both theoretical and experimental fractal patterns. However, the tools for multifractal analysis of objects in Euclidean space are not suitable for complex networks. In this thesis, we propose a new box covering algorithm for multifractal analysis of complex networks. This algorithm is demonstrated in the computation of the generalized fractal dimensions of some theoretical networks, namely scale-free networks, small-world networks, random networks, and a kind of real networks, namely PPI networks of different species. Our main finding is the existence of multifractality in scale-free networks and PPI networks, while the multifractal behaviour is not confirmed for small-world networks and random networks. As another application, we generate gene interactions networks for patients and healthy people using the correlation coefficients between microarrays of different genes. Our results confirm the existence of multifractality in gene interactions networks. This multifractal analysis then provides a potentially useful tool for gene clustering and identification. The third part of the thesis aims to investigate the topological properties of networks constructed from time series. Characterizing complicated dynamics from time series is a fundamental problem of continuing interest in a wide variety of fields. Recent works indicate that complex network theory can be a powerful tool to analyse time series. Many existing methods for transforming time series into complex networks share a common feature: they define the connectivity of a complex network by the mutual proximity of different parts (e.g., individual states, state vectors, or cycles) of a single trajectory. In this thesis, we propose a new method to construct networks of time series: we define nodes by vectors of a certain length in the time series, and weight of edges between any two nodes by the Euclidean distance between the corresponding two vectors. We apply this method to build networks for fractional Brownian motions, whose long-range dependence is characterised by their Hurst exponent. We verify the validity of this method by showing that time series with stronger correlation, hence larger Hurst exponent, tend to have smaller fractal dimension, hence smoother sample paths. We then construct networks via the technique of horizontal visibility graph (HVG), which has been widely used recently. We confirm a known linear relationship between the Hurst exponent of fractional Brownian motion and the fractal dimension of the corresponding HVG network. In the first application, we apply our newly developed box-covering algorithm to calculate the generalized fractal dimensions of the HVG networks of fractional Brownian motions as well as those for binomial cascades and five bacterial genomes. The results confirm the monoscaling of fractional Brownian motion and the multifractality of the rest. As an additional application, we discuss the resilience of networks constructed from time series via two different approaches: visibility graph and horizontal visibility graph. Our finding is that the degree distribution of VG networks of fractional Brownian motions is scale-free (i.e., having a power law) meaning that one needs to destroy a large percentage of nodes before the network collapses into isolated parts; while for HVG networks of fractional Brownian motions, the degree distribution has exponential tails, implying that HVG networks would not survive the same kind of attack.

CgDN24 : a gene involved in hyphal development in the fungal phytopathogen Colletotrichum gloeosporioides

A cDNA corresponding to a transcript induced in culture by N starvation, was identified in Colletotrichum gloeosporioides by a differential hybridisation strategy. The cDNA comprised 905 bp and predicted a 215 aa protein; the gene encoding the cDNA was termed CgDN24. No function for CgDN24 could be predicted by database homology searches using the cDNA sequence and no homologues were found in the sequenced fungal genomes. Transcripts of CgDN24 were detected in infected leaves of Stylosanthes guianensis at stages of infection that corresponded with symptom development. The CgDN24 gene was disrupted by homologous recombination and this led to reduced radial growth rates and the production of hyphae with a hyperbranching phenotype. Normal sporulation was observed, and following conidial inoculation of S. guianensis, normal disease development was obtained. These results demonstrate that CgDN24 is necessary for normal hyphal development in axenic culture but dispensable for phytopathogenicity. © 2005 Elsevier GmbH. All rights reserved.

A sequence analytic model of entrepreneurial discovery and exploitation process

This study examined the effect that temporal order within the entrepreneurial discovery exploitation process has on the outcomes of venture creation. Consistent with sequential theories of discovery-exploitation, the general flow of venture creation was found to be directed from discovery toward exploitation in a random sample of nascent ventures. However, venture creation attempts which specifically follow this sequence derive poor outcomes. Moreover, simultaneous discovery-exploitation was the most prevalent temporal order observed, and venture attempts that proceed in this manner more likely become operational. These findings suggest that venture creation is a multi-scale phenomenon that is at once directional in time, and simultaneously driven by symbiotically coupled discovery and exploitation.

How Does Information Processing Speed Relate to the Attentional Blink?

Background When observers are asked to identify two targets in rapid sequence, they often suffer profound performance deficits for the second target, even when the spatial location of the targets is known. This attentional blink (AB) is usually attributed to the time required to process a previous target, implying that a link should exist between individual differences in information processing speed and the AB. Methodology/Principal Findings The present work investigated this question by examining the relationship between a rapid automatized naming task typically used to assess information-processing speed and the magnitude of the AB. The results indicated that faster processing actually resulted in a greater AB, but only when targets were presented amongst high similarity distractors. When target-distractor similarity was minimal, processing speed was unrelated to the AB. Conclusions/Significance Our findings indicate that information-processing speed is unrelated to target processing efficiency per se, but rather to individual differences in observers' ability to suppress distractors. This is consistent with evidence that individuals who are able to avoid distraction are more efficient at deploying temporal attention, but argues against a direct link between general processing speed and efficient information selection.

Tinamous and moa flock together : mitochondrial genome sequence analysis reveals independent losses of flight among ratites

Ratites are large, flightless birds and include the ostrich, rheas, kiwi, emu, and cassowaries, along with extinct members, such as moa and elephant birds. Previous phylogenetic analyses of complete mitochondrial genome sequences have reinforced the traditional belief that ratites are monophyletic and tinamous are their sister group. However, in these studies ratite monophyly was enforced in the analyses that modeled rate heterogeneity among variable sites. Relaxing this topological constraint results in strong support for the tinamous (which fly) nesting within ratites. Furthermore, upon reducing base compositional bias and partitioning models of sequence evolution among protein codon positions and RNA structures, the tinamou–moa clade grouped with kiwi, emu, and cassowaries to the exclusion of the successively more divergent rheas and ostrich. These relationships are consistent with recent results from a large nuclear data set, whereas our strongly supported finding of a tinamou–moa grouping further resolves palaeognath phylogeny. We infer flight to have been lost among ratites multiple times in temporally close association with the Cretaceous–Tertiary extinction event. This circumvents requirements for transient microcontinents and island chains to explain discordance between ratite phylogeny and patterns of continental breakup. Ostriches may have dispersed to Africa from Eurasia, putting in question the status of ratites as an iconic Gondwanan relict taxon. [Base composition; flightless; Gondwana; mitochondrial genome; Palaeognathae; phylogeny; ratites.]

Catalytic components of proteasomes and the regulation of proteinase activity

The proteasome (multicatalytic proteinase complex) is a large multimeric complex which is found in the nucleus and cytoplasm of eukaryotic cells. It plays a major role in both ubiquitin-dependent and ubiquitin-independent nonlysosomal pathways of protein degradation. Proteasome subunits are encoded by members of the same gene family and can be divided into two groups based on their similarity to the c~ and /3 subunits of the simpler proteasome isolated from Thermoplasma acidophilum. Proteasomes have a cylindrical structure composed of four rings of seven subunits. The 26S form of the proteasome, which is responsible for ubiquitin-dependent proteolysis, contains additional regulatory complexes. Eukaryotic proteasomes have multiple catalytic activities which are catalysed at distinct sites. Since proteasomes are unrelated to other known proteases, there are no clues as to which are the catalytic components from sequence alignments. It has been assumed from studies with yeast mutants that /3-type subunits play a catalytic role. Using a radiolabelled peptidyl chloromethane inhibitor of rat liver proteasomes we have directly identified RC7 as a catalytic component. Interestingly, mutants in Prel, the yeast homologue of RC7, have already been reported to have defective chymotrypsin-like activity. These results taken together confirm a direct catalytic role for these/3-type subunits. Proteasome activities are sensitive to conformational changes and there are several ways in which proteasome function may be modulated in vivo. Our recent studies have shown that in animal cells at least two proteasome subunits can undergo phosphorylation, the level of which is likely to be important for determining proteasome localization, activity or ability to form larger complexes. In addition, we have isolated two isoforms of the 26S proteinase.

Viruses of banana in East Africa

Bananas are one of the world's most important food crops, providing sustenance and income for millions of people in developing countries and supporting large export industries. Viruses are considered major constraints to banana production, germplasm multiplication and exchange, and to genetic improvement of banana through traditional breeding. In Africa, the two most important virus diseases are bunchy top, caused by Banana bunchy top virus (BBTV), and banana streak disease, caused by Banana streak virus (BSV). BBTV is a serious production constraint in a number of countries within/bordering East Africa, such as Burundi, Democratic Republic of Congo, Malawi, Mozambique, Rwanda and Zambia, but is not present in Kenya, Tanzania and Uganda. Additionally, epidemics of banana streak disease are occurring in Kenya and Uganda. The rapidly growing tissue culture (TC) industry within East Africa, aiming to provide planting material to banana farmers, has stimulated discussion about the need for virus indexing to certify planting material as virus-free. Diagnostic methods for BBTV and BSV have been reported and, for BBTV, PCR-based assays are reliable and relatively straightforward. However for BSV, high levels of serological and genetic variability and the presence of endogenous virus sequences within the banana genome complicate diagnosis. Uganda has been shown to contain the greatest diversity in BSV isolates found anywhere in the world. A broad-spectrum diagnostic test for BSV detection, which can discriminate between endogenous and episomal BSV sequences, is a priority. This PhD project aimed to establish diagnostic methods for banana viruses, with a particular focus on the development of novel methods for BSV detection, and to use these diagnostic methods for the detection and characterisation of banana viruses in East Africa. A novel rolling-circle amplification (RCA) method was developed for the detection of BSV. Using samples of Banana streak MY virus (BSMYV) and Banana streak OL virus (BSOLV) from Australia, this method was shown to distinguish between endogenous and episomal BSV sequences in banana plants. The RCA assay was used to screen a collection of 56 banana samples from south-west Uganda for BSV. RCA detected at least five distinct BSV isolates in these samples, including BSOLV and Banana streak GF virus (BSGFV) as well as three BSV isolates (Banana streak Uganda-I, -L and -M virus) for which only partial sequences had been previously reported. These latter three BSV had only been detected using immuno-capture (IC)-PCR and thus were possible endogenous sequences. In addition to its ability to detect BSV, the RCA protocol was also demonstrated to detect other viruses within the family Caulimoviridae, including Sugar cane bacilliform virus, and Cauliflower mosaic virus. Using the novel RCA method, three distinct BSV isolates from both Kenya and Uganda were identified and characterised. The complete genome of these isolates was sequenced and annotated. All six isolates were shown to have a characteristic badnavirus genome organisation with three open reading frames (ORFs) and the large polyprotein encoded by ORF 3 was shown to contain conserved amino acid motifs for movement, aspartic protease, reverse transcriptase and ribonuclease H activities. As well, several sequences important for expression and replication of the virus genome were identified including the conserved tRNAmet primer binding site present in the intergenic region of all badnaviruses. Based on the International Committee on Taxonomy of Viruses (ICTV) guidelines for species demarcation in the genus Badnavirus, these six isolates were proposed as distinct species, and named Banana streak UA virus (BSUAV), Banana streak UI virus (BSUIV), Banana streak UL virus (BSULV), Banana streak UM virus (BSUMV), Banana streak CA virus (BSCAV) and Banana streak IM virus (BSIMV). Using PCR with species-specific primers designed to each isolate, a genotypically diverse collection of 12 virus-free banana cultivars were tested for the presence of endogenous sequences. For five of the BSV no amplification was observed in any cultivar tested, while for BSIMV, four positive samples were identified in cultivars with a B-genome component. During field visits to Kenya, Tanzania and Uganda, 143 samples were collected and assayed for BSV. PCR using nine sets of species-specific primers, and RCA, were compared for BSV detection. For five BSV species with no known endogenous counterpart (namely BSCAV, BSUAV, BSUIV, BSULV and BSUMV), PCR was used to detect 30 infections from the 143 samples. Using RCA, 96.4% of these samples were considered positive, with one additional sample detected using RCA which was not positive using PCR. For these five BSV, PCR and RCA were both useful for identifying infected samples, irrespective of the host cultivar genotype (Musa A- or B-genome components). For four additional BSV with known endogenous counterparts in the M. balbisiana genome (BSOLV, BSGFV, BSMYV and BSIMV), PCR was shown to detect 75 infections from the 143 samples. In 30 samples from cultivars with an A-only genome component there was 96.3% agreement between PCR positive samples and detection using RCA, again demonstrating either PCR or RCA are suitable methods for detection. However, in 45 samples from cultivars with some B-genome component, the level of agreement between PCR positive samples and RCA positive samples was 70.5%. This suggests that, in cultivars with some B-genome component, many infections were detected using PCR which were the result of amplification of endogenous sequences. In these latter cases, RCA or another method which discriminates between endogenous and episomal sequences, such as immuno-capture PCR, is needed to diagnose episomal BSV infection. Field visits were made to Malawi and Rwanda to collect local isolates of BBTV for validation of a PCR-based diagnostic assay. The presence of BBTV in samples of bananas with bunchy top disease was confirmed in 28 out of 39 samples from Malawi and all nine samples collected in Rwanda, using PCR and RCA. For three isolates, one from Malawi and two from Rwanda, the complete nucleotide sequences were determined and shown to have a similar genome organisation to previously published BBTV isolates. The two isolates from Rwanda had at least 98.1% nucleotide sequence identity between each of the six DNA components, while the similarity between isolates from Rwanda and Malawi was between 96.2% and 99.4% depending on the DNA component. At the amino acid level, similarities in the putative proteins encoded by DNA-R, -S, -M, - C and -N were found to range between 98.8% to 100%. In a phylogenetic analysis, the three East African isolates clustered together within the South Pacific subgroup of BBTV isolates. Nucleotide sequence comparison to isolates of BBTV from outside Africa identified India as the possible origin of East African isolates of BBTV.