Automated design analysis, assembly planning and motion study analysis using immersive virtual reality

Previous research work at Heriot-Watt University using immersive virtual reality (VR) for cable harness design showed that VR provided substantial productivity gains over traditional computer-aided design (CAD) systems. This follow-on work was aimed at understanding the degree to which aspects of this technology were contributed to these benefits and to determine if engineering design and planning processes could be analysed in detail by nonintrusively monitoring and logging engineering tasks. This involved using a CAD-equivalent VR system for cable harness routing design, harness assembly and installation planning that can be functionally evaluated using a set of creative design-tasks to measure the system and users' performance. A novel design task categorisation scheme was created and formalised which broke down the cable harness design process and associated activities. The system was also used to demonstrate the automatic generation of usable bulkhead connector, cable harness assembly and cable harness installation plans from non-intrusive user logging. Finally, the data generated from the user-logging allowed the automated activity categorisation of the user actions, automated generation of process flow diagrams and chronocyclegraphs

Expression stability of commonly used reference genes in canine articular connective tissues

Background: The quantification of gene expression in tissue samples requires the use of reference genes to normalise transcript numbers between different samples. Reference gene stability may vary between different tissues, and between the same tissue in different disease states. We evaluated the stability of 9 reference genes commonly used in human gene expression studies. Realtime reverse transcription PCR and a mathematical algorithm were used to establish which reference genes were most stably expressed in normal and diseased canine articular tissues and two canine cell lines stimulated with lipolysaccaride (LPS). Results: The optimal reference genes for comparing gene expression data between normal and diseased infrapatella fat pad were RPL13A and YWHAZ (M = 0.56). The ideal reference genes for comparing normal and osteoarthritic (OA) cartilage were RPL13A and SDHA (M = 0.57). The best reference genes for comparing normal and ruptured canine cranial cruciate ligament were B2M and TBP (M = 0.59). The best reference genes for normalising gene expression data from normal and LPS stimulated cell lines were SDHA and YWHAZ (K6) or SDHA and HMBS (DH82), which had expression stability (M) values of 0.05 (K6) and 0.07 (DH82) respectively. The number of reference genes required to reduce pairwise variation (V) to <0.20 was 4 for cell lines, 5 for cartilage, 7 for cranial cruciate ligament and 8 for fat tissue. Reference gene stability was not related to the level of gene expression. Conclusion: The reference genes demonstrating the most stable expression within each different canine articular tissue were identified, but no single reference gene was identified as having stable expression in all different tissue types. This study underlines the necessity to select reference genes on the basis of tissue and disease specific expression profile evaluation and highlights the requirement for the identification of new reference genes with greater expression stability for use in canine articular tissue gene expression studies

A new strategy for enhancing imputation quality of rare variants from next-generation sequencing data via combining SNP and exome chip data

Background: Rare variants have gathered increasing attention as a possible alternative source of missing heritability. Since next generation sequencing technology is not yet cost-effective for large-scale genomic studies, a widely used alternative approach is imputation. However, the imputation approach may be limited by the low accuracy of the imputed rare variants. To improve imputation accuracy of rare variants, various approaches have been suggested, including increasing the sample size of the reference panel, using sequencing data from study-specific samples (i.e., specific populations), and using local reference panels by genotyping or sequencing a subset of study samples. While these approaches mainly utilize reference panels, imputation accuracy of rare variants can also be increased by using exome chips containing rare variants. The exome chip contains 250 K rare variants selected from the discovered variants of about 12,000 sequenced samples. If exome chip data are available for previously genotyped samples, the combined approach using a genotype panel of merged data, including exome chips and SNP chips, should increase the imputation accuracy of rare variants. Results: In this study, we describe a combined imputation which uses both exome chip and SNP chip data simultaneously as a genotype panel. The effectiveness and performance of the combined approach was demonstrated using a reference panel of 848 samples constructed using exome sequencing data from the T2D-GENES consortium and 5,349 sample genotype panels consisting of an exome chip and SNP chip. As a result, the combined approach increased imputation quality up to 11 %, and genomic coverage for rare variants up to 117.7 % (MAF < 1 %), compared to imputation using the SNP chip alone. Also, we investigated the systematic effect of reference panels on imputation quality using five reference panels and three genotype panels. The best performing approach was the combination of the study specific reference panel and the genotype panel of combined data. Conclusions: Our study demonstrates that combined datasets, including SNP chips and exome chips, enhances both the imputation quality and genomic coverage of rare variants

Advances and perspectives in aptamer arrays

Aptamers are oligonucleotides (typically 10–60 bases in length) capable of binding target ligands with affinities similar to antibodies. The generation of high density multiplexed aptamer arrays for molecular diagnostics was first proposed nearly ten years ago for the quantification of the thousands of proteins within biological samples, including blood and urine. The tagless aptameric detection of small molecular compounds extends the application of such arrays to bioanalyses at the metabolite level. We present here a minireview on some existing technologies and highlight recent innovations that are being applied to this field, which may facilitate the vision of highly multi-parallelized arrays for the quantitative analysis of biological systems

Automated design process modelling and analysis using immersive virtual reality

The capture of engineering design processes and associated knowledge has traditionally been extremely difficult due to the high overhead associated with current intrusive and time-consuming manual methods used in industry, usually involving interruption of the designer during the design task and relying on them to remember how a design solution was developed after the event. This paper presents novel research which demonstrates how the detailed logging and analysis of an individual designer's actions in a cable harness virtual reality (VR) design and manufacturing system permits automated design task analysis with process mapping. Based on prior research, which utilised user-logging to automatically analyse design activities and generate assembly plans, this work involves the automatic capture of extracted design knowledge embedded within the log files and subsequently represented using IDEF0 diagrams, DRed graphs, PSL, XML, annotated movie clips and storyboard representations. Using this design knowledge, an online help system has been demonstrated which helps users to carry out design tasks similar to those performed previously by expert users. This is triggered by monitoring the designer's actions and functions in real time and pushes knowledge and advice to the user which was captured from experts and subsequently formalised during earlier design sessions

Analysis of aptamer sequence activity relationships

DNA sequences that can bind selectively and specifically to target molecules are known as aptamers. Normally such binding analyses are performed using soluble aptamers. However, there is much to be gained by using an on-chip or microarray format, where a large number of aptameric DNA sequences can be interrogated simultaneously. To calibrate the system, known thrombin binding aptamers (TBAs) have been mutated systematically, producing large populations that allow exploration of key structural aspects of the overall binding motif. The ability to discriminate between background noise and low affinity binding aptamers can be problematic on arrays, and we use the mutated sequences to establish appropriate experimental conditions and their limitations for two commonly used fluorescence-based detection methods. Having optimized experimental conditions, high-density oligonucleotide microarrays were used to explore the entire loop–sequence–functionality relationship creating a detailed model based on over 40 000 analyses, describing key features for quadruplex-forming sequences

Convergent evolution to an aptamer observed in small populations on DNA microarrays

The development of aptamers on custom synthesized DNA microarrays, which has been demonstrated in recent publications, can facilitate detailed analyses of sequence and fitness relationships. Here we use the technique to observe the paths taken through sequence-fitness space by three different evolutionary regimes: asexual reproduction, recombination and model-based evolution. The different evolutionary runs are made on the same array chip in triplicate, each one starting from a small population initialized independently at random. When evolving to a common target protein, glucose-6-phosphate dehydrogenase (G6PD), these nine distinct evolutionary runs are observed to develop aptamers with high affinity and to converge on the same motif not present in any of the starting populations. Regime specific differences in the evolutions, such as speed of convergence, could also be observed

Analysis of a complete DNA-protein affinity landscape

Properties of biological fitness landscapes are of interest to a wide sector of the life sciences, from ecology to genetics to synthetic biology. For biomolecular fitness landscapes, the information we currently possess comes primarily from two sources: sparse samples obtained from directed evolution experiments; and more fine-grained but less authentic information from ‘in silico’ models (such as NK-landscapes). Here we present the entire protein-binding profile of all variants of a nucleic acid oligomer 10 bases in length, which we have obtained experimentally by a series of highly parallel on-chip assays. The resulting complete landscape of sequence-binding pairs, comprising more than one million binding measurements in duplicate, has been analysed statistically using a number of metrics commonly applied to synthetic landscapes. These metrics show that the landscape is rugged, with many local optima, and that this arises from a combination of experimental variation and the natural structural properties of the oligonucleotides

Aptamer evolution for array-based diagnostics

Aptamer evolution for array-based diagnostic

Life histories shapes gene expression in salmonids

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