Enhancing Industry Exposure, Discovery-Based and Cooperative Learning in Mechanics of Solids

BACKGROUND Mechanics of Solids is a second year undergraduate subject, undertaken by both Civil and Mechanical engineering students at the University of Technology, Sydney (UTS). Mechanics of Solids has been delivered for many years in a traditional format with lectures and problem solving tutorials. As part of a national Australian project “Enhancing Industry Exposure in Engineering Degrees”, UTS in partnership with other universities and industry partners in Australia has sought industry involvement to engage students with the real-world challenges of engineering practice. PURPOSE The main objective of this project is to design, develop and implement learning modules in Mechanis of Solids that integrate industry exposure to provide context for the concepts included in this subject. DESIGN The project consisted of six guest lectures by industry representatives on topics related to typical Mechanics of Solids subject matter and two seminars on using MDSolids software. Students completed a collaborative assignment aligned with one of the industry presentations. Their reports and presentations were assessed on assessment criteria which included contextual understanding, judgement, effective collaboration and creativity, and their perceptions were captured to evaluate the impact of industry engagement in this subject. RESULTS One of the major benefits of this project was students’ better understanding of engineering practice. There were also positive effects on students’ motivation for learning engineering. CONCLUSIONS This paper reports the major findings, outcomes and challenges for implementing enhancing industry exposure approach in Mechanics of Solids subject at UTS. The main finding of this research concluded that this project is very valuable to both students as it promotes exposure to real-world engineering challenges. The students’ exposure to real and substantive challenges improves their contextual understanding, plus their judgement, practice based planning, teamwork, and initiative learning skills

Neural stem cell transcriptional networks highlight genes essential for nervous system development

Neural stem cells must strike a balance between self-renewal and multipotency, and differentiation. Identification of the transcriptional networks regulating stem cell division is an essential step in understanding how this balance is achieved. We have shown that the homeodomain transcription factor, Prospero, acts to repress self-renewal and promote differentiation. Among its targets are three neural stem cell transcription factors, Asense, Deadpan and Snail, of which Asense and Deadpan are repressed by Prospero. Here, we identify the targets of these three factors throughout the genome. We find a large overlap in their target genes, and indeed with the targets of Prospero, with 245 genomic loci bound by all factors. Many of the genes have been implicated in vertebrate stem cell self-renewal, suggesting that this core set of genes is crucial in the switch between self-renewal and differentiation. We also show that multiply bound loci are enriched for genes previously linked to nervous system phenotypes, thereby providing a shortcut to identifying genes important for nervous system development

Robust Conditional Independence maps of single-voxel Magnetic Resonance Spectra to elucidate associations between brain tumours and metabolites.

The aim of the paper is two-fold. First, we show that structure finding with the PC algorithm can be inherently unstable and requires further operational constraints in order to consistently obtain models that are faithful to the data. We propose a methodology to stabilise the structure finding process, minimising both false positive and false negative error rates. This is demonstrated with synthetic data. Second, to apply the proposed structure finding methodology to a data set comprising single-voxel Magnetic Resonance Spectra of normal brain and three classes of brain tumours, to elucidate the associations between brain tumour types and a range of observed metabolites that are known to be relevant for their characterisation. The data set is bootstrapped in order to maximise the robustness of feature selection for nominated target variables. Specifically, Conditional Independence maps (CI-maps) built from the data and their derived Bayesian networks have been used. A Directed Acyclic Graph (DAG) is built from CI-maps, being a major challenge the minimization of errors in the graph structure. This work presents empirical evidence on how to reduce false positive errors via the False Discovery Rate, and how to identify appropriate parameter settings to improve the False Negative Reduction. In addition, several node ordering policies are investigated that transform the graph into a DAG. The obtained results show that ordering nodes by strength of mutual information can recover a representative DAG in a reasonable time, although a more accurate graph can be recovered using a random order of samples at the expense of increasing the computation time

Merged consensus clustering to assess and improve class discovery with microarray data

<p>Abstract</p> <p>Background</p> <p>One of the most commonly performed tasks when analysing high throughput gene expression data is to use clustering methods to classify the data into groups. There are a large number of methods available to perform clustering, but it is often unclear which method is best suited to the data and how to quantify the quality of the classifications produced.</p> <p>Results</p> <p>Here we describe an R package containing methods to analyse the consistency of clustering results from any number of different clustering methods using resampling statistics. These methods allow the identification of the the best supported clusters and additionally rank cluster members by their fidelity within the cluster. These metrics allow us to compare the performance of different clustering algorithms under different experimental conditions and to select those that produce the most reliable clustering structures. We show the application of this method to simulated data, canonical gene expression experiments and our own novel analysis of genes involved in the specification of the peripheral nervous system in the fruitfly, <it>Drosophila melanogaster</it>.</p> <p>Conclusions</p> <p>Our package enables users to apply the merged consensus clustering methodology conveniently within the R programming environment, providing both analysis and graphical display functions for exploring clustering approaches. It extends the basic principle of consensus clustering by allowing the merging of results between different methods to provide an averaged clustering robustness. We show that this extension is useful in correcting for the tendency of clustering algorithms to treat outliers differently within datasets. The R package, <it>clusterCons</it>, is freely available at CRAN and sourceforge under the GNU public licence.</p

The Drosophila homologue of Rootletin is required for mechanosensory function and ciliary rootlet formation in chordotonal sensory neurons

BACKGROUND: In vertebrates, rootletin is the major structural component of the ciliary rootlet and is also part of the tether linking the centrioles of the centrosome. Various functions have been ascribed to the rootlet, including maintenance of ciliary integrity through anchoring and facilitation of transport to the cilium or at the base of the cilium. In Drosophila, Rootletin function has not been explored. RESULTS: In the Drosophila embryo, Rootletin is expressed exclusively in cell lineages of type I sensory neurons, the only somatic cells bearing a cilium. Expression is strongest in mechanosensory chordotonal neurons. Knock-down of Rootletin results in loss of ciliary rootlet in these neurons and severe disruption of their sensory function. However, the sensory cilium appears largely normal in structure and in localisation of proteins suggesting no strong defect in ciliogenesis. No evidence was found for a defect in cell division. CONCLUSIONS: The role of Rootletin as a component of the ciliary rootlet is conserved in Drosophila. In contrast, lack of a general role in cell division is consistent with lack of centriole tethering during the centrosome cycle in Drosophila. Although our evidence is consistent with an anchoring role for the rootlet, severe loss of mechanosensory function of chordotonal (Ch) neurons upon Rootletin knock-down may suggest a direct role for the rootlet in the mechanotransduction mechanism itself

Integrating Computational Biology and Forward Genetics in Drosophila

Genetic screens are powerful methods for the discovery of gene–phenotype associations. However, a systems biology approach to genetics must leverage the massive amount of “omics” data to enhance the power and speed of functional gene discovery in vivo. Thus far, few computational methods for gene function prediction have been rigorously tested for their performance on a genome-wide scale in vivo. In this work, we demonstrate that integrating genome-wide computational gene prioritization with large-scale genetic screening is a powerful tool for functional gene discovery. To discover genes involved in neural development in Drosophila, we extend our strategy for the prioritization of human candidate disease genes to functional prioritization in Drosophila. We then integrate this prioritization strategy with a large-scale genetic screen for interactors of the proneural transcription factor Atonal using genomic deficiencies and mutant and RNAi collections. Using the prioritized genes validated in our genetic screen, we describe a novel genetic interaction network for Atonal. Lastly, we prioritize the whole Drosophila genome and identify candidate gene associations for ten receptor-signaling pathways. This novel database of prioritized pathway candidates, as well as a web application for functional prioritization in Drosophila, called Endeavour-HighFly, and the Atonal network, are publicly available resources. A systems genetics approach that combines the power of computational predictions with in vivo genetic screens strongly enhances the process of gene function and gene–gene association discovery

Indicators of breast cancer severity and appropriateness of surgery based on hospital administrative data in the Lazio Region, Italy

BACKGROUND: Administrative data can serve as an easily available source for epidemiological and evaluation studies. The aim of this study is to evaluate the use of hospital administrative data to determine breast cancer severity and the appropriateness of surgical treatment. METHODS: the study population consisted of 398 patients randomly selected from a cohort of women hospitalized for first-time breast cancer surgery in the Lazio Region, Italy. Tumor severity was defined in three different ways: 1) tumor size; 2) clinical stage (TNM); 3) severity indicator based on HIS data (SI). Sensitivity, specificity, and positive predictive value (PPV) of the severity indicator in evaluating appropriateness of surgery were calculated. The accuracy of HIS data was measured using Kappa statistic. RESULTS: Most of 387 cases were classified as T1 and T2 (tumor size), more than 70% were in stage I or II and the SI classified 60% of cases in medium-low category. Variation from guidelines indications identified under and over treatments. The accuracy of the SI to predict under-treatment was relatively good (58% of all procedures classified as under-treatment using pT where also classified as such using SI), and even greater predicting over-treatment (88.2% of all procedures classified as over treatment using pT where also classified as such using SI). Agreement between clinical chart and hospital discharge reports was K = 0.35. CONCLUSION: Our findings suggest that administrative data need to be used with caution when evaluating surgical appropriateness, mainly because of the limited ability of SI to predict tumor size and the questionable quality of HIS data as observed in other studies

Atoh8, a bHLH Transcription Factor, Is Required for the Development of Retina and Skeletal Muscle in Zebrafish

Math6/atoh8, a bHLH transcription factor, is thought to be indispensable for early embryonic development and likely has important roles in vertebrate tissue-specific differentiation. However, the function of Atoh8 during early development is not clear because homozygous knockout causes embryonic lethality in mice. We have examined the effects of the atoh8 gene on the differentiation of retina and skeletal muscle during early development in zebrafish.We isolated a Math6 homologue in zebrafish, designated as zebrafish atoh8. Whole -mount in situ hybridization analysis showed that zebrafish atoh8 is dynamically expressed mainly in developing retina and skeletal muscle. Atoh8-MO knock-down resulted in reduced eye size with disorganization of retinal lamination. The reduction of atoh8 function also affected the arrangement of paraxial cells and differentiated muscle fibers during somite morphogenesis.Our results show that Atoh8 is an important regulator for the development of both the retina and skeletal muscles necessary for neural retinal cell and myogenic differentiation during zebrafish embryogenesis