Quality based frame selection for video face recognition

Quality based frame selection is a crucial task in video face recognition, to both improve the recognition rate and to reduce the computational cost. In this paper we present a framework that uses a variety of cues (face symmetry, sharpness, contrast, closeness of mouth, brightness and openness of the eye) to select the highest quality facial images available in a video sequence for recognition. Normalized feature scores are fused using a neural network and frames with high quality scores are used in a Local Gabor Binary Pattern Histogram Sequence based face recognition system. Experiments on the Honda/UCSD database shows that the proposed method selects the best quality face images in the video sequence, resulting in improved recognition performance.

Analysis of genome survey sequences and SSR marker development for Siamese Mud Carp, Henicorhynchus siamensis, using 454 Pyrosequencing

Siamese mud carp (Henichorynchus siamensis) is a freshwater teleost of high economic importance in the Mekong River Basin. However, genetic data relevant for delineating wild stocks for management purposes currently are limited for this species. Here, we used 454 pyrosequencing to generate a partial genome survey sequence (GSS) dataset to develop simple sequence repeat (SSR) markers from H. siamensis genomic DNA. Data generated included a total of 65,954 sequence reads with average length of 264 nucleotides, of which 2.79% contain SSR motifs. Based on GSS-BLASTx results, 10.5% of contigs and 8.1% singletons possessed significant similarity (E value < 10–5) with the majority matching well to reported fish sequences. KEGG analysis identified several metabolic pathways that provide insights into specific potential roles and functions of sequences involved in molecular processes in H. siamensis. Top protein domains detected included reverse transcriptase and the top putative functional transcript identified was an ORF2-encoded protein. One thousand eight hundred and thirty seven sequences containing SSR motifs were identified, of which 422 qualified for primer design and eight polymorphic loci have been tested with average observed and expected heterozygosity estimated at 0.75 and 0.83, respectively. Regardless of their relative levels of polymorphism and heterozygosity, microsatellite loci developed here are suitable for further population genetic studies in H. siamensis and may also be applicable to other related taxa.

Supporting the learning of rapid application development in a database environment

Intelligent Tutoring Systems (ITSs) are computer systems designed to provide individualised help to students, learning in a problem solving context. The difference between an ITS and a Computer Assisted Instruction (CAI) system is that an ITS has a Student Model which allows it to provide a better educational environment. The Student Model contains information on what the student knows, and does not know, about the domain being learnt, as well as other personal characteristics such as preferred learning style. This research has resulted in the design and development of a new ITS: Personal Access Tutor (PAT). PAT is an ITS that helps students to learn Rapid Application Development in a database environment. More specifically, PAT focuses on helping students to learn how to create forms and reports in Microsoft Access. To provide an augmented learning environment, PAT’s architecture is different to most other ITSs. Instead of having a simulation, PAT uses a widelyused database development environment (Microsoft Access). This enables the students to ask for help, while developing real applications using real database software. As part of this research, I designed and created the knowledge base required for PAT. This contains four models: the domain, student, tutoring and exercises models. The Instructional Expert I created for PAT provides individualised help to the students to help them correctly finish each exercise, and also proposes the next exercise that a student should work on. PAT was evaluated by students enrolled in the Databases subject at QUT, and by staff members involved in teaching the subject. The results of the evaluation were positive and are discussed in the thesis.

Prediction of transcriptional regulatory interactions in bacteria : a comparative genomics approach

Exponential growth of genomic data in the last two decades has made manual analyses impractical for all but trial studies. As genomic analyses have become more sophisticated, and move toward comparisons across large datasets, computational approaches have become essential. One of the most important biological questions is to understand the mechanisms underlying gene regulation. Genetic regulation is commonly investigated and modelled through the use of transcriptional regulatory network (TRN) structures. These model the regulatory interactions between two key components: transcription factors (TFs) and the target genes (TGs) they regulate. Transcriptional regulatory networks have proven to be invaluable scientific tools in Bioinformatics. When used in conjunction with comparative genomics, they have provided substantial insights into the evolution of regulatory interactions. Current approaches to regulatory network inference, however, omit two additional key entities: promoters and transcription factor binding sites (TFBSs). In this study, we attempted to explore the relationships among these regulatory components in bacteria. Our primary goal was to identify relationships that can assist in reducing the high false positive rates associated with transcription factor binding site predictions and thereupon enhance the reliability of the inferred transcription regulatory networks. In our preliminary exploration of relationships between the key regulatory components in Escherichia coli transcription, we discovered a number of potentially useful features. The combination of location score and sequence dissimilarity scores increased de novo binding site prediction accuracy by 13.6%. Another important observation made was with regards to the relationship between transcription factors grouped by their regulatory role and corresponding promoter strength. Our study of E.coli ��70 promoters, found support at the 0.1 significance level for our hypothesis | that weak promoters are preferentially associated with activator binding sites to enhance gene expression, whilst strong promoters have more repressor binding sites to repress or inhibit gene transcription. Although the observations were specific to �70, they nevertheless strongly encourage additional investigations when more experimentally confirmed data are available. In our preliminary exploration of relationships between the key regulatory components in E.coli transcription, we discovered a number of potentially useful features { some of which proved successful in reducing the number of false positives when applied to re-evaluate binding site predictions. Of chief interest was the relationship observed between promoter strength and TFs with respect to their regulatory role. Based on the common assumption, where promoter homology positively correlates with transcription rate, we hypothesised that weak promoters would have more transcription factors that enhance gene expression, whilst strong promoters would have more repressor binding sites. The t-tests assessed for E.coli �70 promoters returned a p-value of 0.072, which at 0.1 significance level suggested support for our (alternative) hypothesis; albeit this trend may only be present for promoters where corresponding TFBSs are either all repressors or all activators. Nevertheless, such suggestive results strongly encourage additional investigations when more experimentally confirmed data will become available. Much of the remainder of the thesis concerns a machine learning study of binding site prediction, using the SVM and kernel methods, principally the spectrum kernel. Spectrum kernels have been successfully applied in previous studies of protein classification [91, 92], as well as the related problem of promoter predictions [59], and we have here successfully applied the technique to refining TFBS predictions. The advantages provided by the SVM classifier were best seen in `moderately'-conserved transcription factor binding sites as represented by our E.coli CRP case study. Inclusion of additional position feature attributes further increased accuracy by 9.1% but more notable was the considerable decrease in false positive rate from 0.8 to 0.5 while retaining 0.9 sensitivity. Improved prediction of transcription factor binding sites is in turn extremely valuable in improving inference of regulatory relationships, a problem notoriously prone to false positive predictions. Here, the number of false regulatory interactions inferred using the conventional two-component model was substantially reduced when we integrated de novo transcription factor binding site predictions as an additional criterion for acceptance in a case study of inference in the Fur regulon. This initial work was extended to a comparative study of the iron regulatory system across 20 Yersinia strains. This work revealed interesting, strain-specific difierences, especially between pathogenic and non-pathogenic strains. Such difierences were made clear through interactive visualisations using the TRNDifi software developed as part of this work, and would have remained undetected using conventional methods. This approach led to the nomination of the Yfe iron-uptake system as a candidate for further wet-lab experimentation due to its potential active functionality in non-pathogens and its known participation in full virulence of the bubonic plague strain. Building on this work, we introduced novel structures we have labelled as `regulatory trees', inspired by the phylogenetic tree concept. Instead of using gene or protein sequence similarity, the regulatory trees were constructed based on the number of similar regulatory interactions. While the common phylogentic trees convey information regarding changes in gene repertoire, which we might regard being analogous to `hardware', the regulatory tree informs us of the changes in regulatory circuitry, in some respects analogous to `software'. In this context, we explored the `pan-regulatory network' for the Fur system, the entire set of regulatory interactions found for the Fur transcription factor across a group of genomes. In the pan-regulatory network, emphasis is placed on how the regulatory network for each target genome is inferred from multiple sources instead of a single source, as is the common approach. The benefit of using multiple reference networks, is a more comprehensive survey of the relationships, and increased confidence in the regulatory interactions predicted. In the present study, we distinguish between relationships found across the full set of genomes as the `core-regulatory-set', and interactions found only in a subset of genomes explored as the `sub-regulatory-set'. We found nine Fur target gene clusters present across the four genomes studied, this core set potentially identifying basic regulatory processes essential for survival. Species level difierences are seen at the sub-regulatory-set level; for example the known virulence factors, YbtA and PchR were found in Y.pestis and P.aerguinosa respectively, but were not present in both E.coli and B.subtilis. Such factors and the iron-uptake systems they regulate, are ideal candidates for wet-lab investigation to determine whether or not they are pathogenic specific. In this study, we employed a broad range of approaches to address our goals and assessed these methods using the Fur regulon as our initial case study. We identified a set of promising feature attributes; demonstrated their success in increasing transcription factor binding site prediction specificity while retaining sensitivity, and showed the importance of binding site predictions in enhancing the reliability of regulatory interaction inferences. Most importantly, these outcomes led to the introduction of a range of visualisations and techniques, which are applicable across the entire bacterial spectrum and can be utilised in studies beyond the understanding of transcriptional regulatory networks.

A new classification and database for stratospheric dust particles

With the increasing number of stratospheric particles available for study (via the U2 and/or WB57F collections), it is essential that a simple, yet rational, classification scheme be developed for general use. Such a scheme should be applicable to all particles collected from the stratosphere, rather than limited to only extraterrestial or chemical sub-groups. Criteria for the efficacy of such a scheme would include: (a) objectivity , (b) ease of use, (c) acceptance within the broader scientific community and (d) how well the classification provides intrinsic categories which are consistent with our knowledge of particle types present in the stratosphere.

Does teaching sequence matter when teaching high school chemistry with scientific visualisations?

Five Canadian high school Chemistry classes in one school, taught by three different teachers, studied the concepts of dynamic chemical equilibria and Le Chatelier’s Principle. Some students received traditional teacher-led explanations of the concept first and used an interactive scientific visualisation second, while others worked with the visualisation first and received the teacher-led explanation second. Students completed a test of their conceptual understanding of the relevant concepts prior to instruction, after the first instructional session and at the end of instruction. Data on students’ academic achievement (highest, middle or lowest third of the class on the mid-term exam) and gender were also collected to explore the relationship between these factors, conceptual development and instructional sequencing. Results show, within this context at least, that teaching sequence is not important in terms of students’ conceptual learning gains.

A new database compilation of whole-rock chemical and geochronological data of igneous rocks in Queensland : a new resource for HDR geothermal resource exploration

The Geothermal industry in Australia and Queensland is in its infancy and for hot dry rock (HDR) geothermal energy, it is very much in the target identification and resource definition stages. As a key effort to assist the geothermal industry and exploration for HDR in Queensland, we are developing a comprehensive and new integrated geochemical and geochronological database on igneous rocks. To date, around 18,000 igneous rocks have been analysed across Queensland for chemical and/or age information. However, these data currently reside in a number of disparate datasets (e.g., Ozchron, Champion et al., 2007, Geological Survey of Queensland, journal publications, and unpublished university theses). The goal of this project is to collate and integrate these data on Queensland igneous rocks to improve our understanding of high heat producing granites in Queensland, in terms of their distribution (particularly in the subsurface), dimensions, ages, and controlling factors in their genesis.

Refinements and developments on the stratospheric dust database and classification scheme

Greater than 750 individual particles have now been selected from collection flags housed in the JSC Cosmic Dust Curatorial Facility and most have been documented in the Cosmic Dust Catalogs [1]. As increasing numbers of particles are placed in Cosmic Dust Collections, and a greater diversity of particles are introduced to the stratosphere through natural and man-made processes (e.g. decaying orbits of space debris [2]), there is an even greater need for a classification scheme to encompass all stratospheric particles rather than only extraterrestrial particles. The fundamental requirements for a suitable classification scheme have been outlined in earlier communications [3,4]. A quantitative survey of particles on collection flag W7017 indicates that there is some bias in the number of samples selected within a given category for the Cosmic Dust Catalog [5]. However, the sample diversity within this selection is still appropriate for the development of a reliable classification scheme. In this paper, we extend the earlier works on stratospheric particle classification to include particles collected during the period May 1981 to November 1983.

Towards a capabilities database to inform inclusive design : Experimental investigation of effective survey-based predictors of human-product interaction

A key issue in the field of inclusive design is the ability to provide designers with an understanding of people's range of capabilities. Since it is not feasible to assess product interactions with a large sample, this paper assesses a range of proxy measures of design-relevant capabilities. It describes a study that was conducted to identify which measures provide the best prediction of people's abilities to use a range of products. A detailed investigation with 100 respondents aged 50-80 years was undertaken to examine how they manage typical household products. Predictor variables included self-report and performance measures across a variety of capabilities (vision, hearing, dexterity and cognitive function), component activities used in product interactions (e.g. using a remote control, touch screen) and psychological characteristics (e.g. self-efficacy, confidence with using electronic devices). Results showed, as expected, a higher prevalence of visual, hearing, dexterity, cognitive and product interaction difficulties in the 65-80 age group. Regression analyses showed that, in addition to age, performance measures of vision (acuity, contrast sensitivity) and hearing (hearing threshold) and self-report and performance measures of component activities are strong predictors of successful product interactions. These findings will guide the choice of measures to be used in a subsequent national survey of design-relevant capabilities, which will lead to the creation of a capability database. This will be converted into a tool for designers to understand the implications of their design decisions, so that they can design products in a more inclusive way.

Human single-stranded DNA binding proteins are essential for maintaining genomic stability

The double-stranded conformation of cellular DNA is a central aspect of DNA stabilisation and protection. The helix preserves the genetic code against chemical and enzymatic degradation, metabolic activation, and formation of secondary structures. However, there are various instances where single-stranded DNA is exposed, such as during replication or transcription, in the synthesis of chromosome ends, and following DNA damage. In these instances, single-stranded DNA binding proteins are essential for the sequestration and processing of single-stranded DNA. In order to bind single-stranded DNA, these proteins utilise a characteristic and evolutionary conserved single-stranded DNA-binding domain, the oligonucleotide/oligosaccharide-binding (OB)-fold. In the current review we discuss a subset of these proteins involved in the direct maintenance of genomic stability, an important cellular process in the conservation of cellular viability and prevention of malignant transformation. We discuss the central roles of single-stranded DNA binding proteins from the OB-fold domain family in DNA replication, the restart of stalled replication forks, DNA damage repair, cell cycle-checkpoint activation, and telomere maintenance.

Integrated genomic characterization of endometrial carcinoma

We performed an integrated genomic, transcriptomic and proteomic characterization of 373 endometrial carcinomas using array- and sequencing-based technologies. Uterine serous tumours and ∼25% of high-grade endometrioid tumours had extensive copy number alterations, few DNA methylation changes, low oestrogen receptor/progesterone receptor levels, and frequent TP53 mutations. Most endometrioid tumours had few copy number alterations or TP53 mutations, but frequent mutations in PTEN, CTNNB1, PIK3CA, ARID1A and KRAS and novel mutations in the SWI/SNF chromatin remodelling complex gene ARID5B. A subset of endometrioid tumours that we identified had a markedly increased transversion mutation frequency and newly identified hotspot mutations in POLE. Our results classified endometrial cancers into four categories: POLE ultramutated, microsatellite instability hypermutated, copy-number low, and copy-number high. Uterine serous carcinomas share genomic features with ovarian serous and basal-like breast carcinomas. We demonstrated that the genomic features of endometrial carcinomas permit a reclassification that may affect post-surgical adjuvant treatment for women with aggressive tumours.

A performance test of query operation on encrypted database

Database security techniques are available widely. Among those techniques, the encryption method is a well-certified and established technology for protecting sensitive data. However, once encrypted, the data can no longer be easily queried. The performance of the database depends on how to encrypt the sensitive data, and an approach for searching and retrieval efficiencies that are implemented. In this paper we analyze the database queries and the data properties and propose a suitable mechanism to query the encrypted database. We proposed and analyzed the new database encryption algorithm using the Bloom Filter with the bucket index method. Finally, we demonstrated the superiority of the proposed algorithm through several experiments that should be useful for database encryption related research and application activities.

Which dissimilarity is to be used when extracting typologies in sequence analysis? A comparative study

Originally developed in bioinformatics, sequence analysis is being increasingly used in social sciences for the study of life-course processes. The methodology generally employed consists in computing dissimilarities between the trajectories and, if typologies are sought, in clustering the trajectories according to their similarities or dissemblances. The choice of an appropriate dissimilarity measure is a major issue when dealing with sequence analysis for life sequences. Several dissimilarities are available in the literature, but neither of them succeeds to become indisputable. In this paper, instead of deciding upon one dissimilarity measure, we propose to use an optimal convex combination of different dissimilarities. The optimality is automatically determined by the clustering procedure and is defined with respect to the within-class variance.

Post-genomic science & society : Getting our fangs into omics science and its implications

Genomics and genetic findings have been hailed with promises of unlocked codes and new frontiers of personalized medicine. Despite cautions about gene hype, the strong cultural pull of genes and genomics has allowed consideration of genomic personhood. Populated by the complicated records of mass spectrometer, proteomics, which studies the human protein, has not achieved either the funding or the popular cultural appeal proteomics scientists had hoped it would. While proteomics, being focused on the proteins that actually indicate and create disease states, has a more direct potential for clinical applications than genomic risk predictions, culturally, it has not provided the material for identity creation. In our ethnographic research, we explore how proteomic scientists attempting to shape an appeal to personhood through which legitimacy may be defined.