The Adoption of a Virtual Learning Environment Among Digital Immigrant Engineering Lecturers: a Case Study

The use of Virtual Learning Environments in Higher Education has increased significantly in recent years. Despite this there are some teaching staff whose usage is minimal. This research seeks to establish the reasons for this lack of adoption, particularly with regard to staff born before the widespread use of digital technology. A single case study approach with multiple embedded units was utilised. The participants were drawn from the Engineering faculty of an Irish Institute of Technology. The significant findings were that the main factors hindering the adoption of VLEs were the belief that VLEs discourage attendance in class which is essential for some learners, and that academic staff would lose control of their materials to their detriment were they to utilise the VLE. The implications of this research are that the perceptions and beliefs of academic staff play an important role in their adoption of a VLE. Some of these beliefs give rise to concerns as to the appropriateness of VLE use. In order to further promote the use of VLEs there is a need for both academic staff and their Institutes to reflect on how these concerns can be properly addressed

Indirect measures of learning transfer between real and virtual environments

This paper reports on research undertaken to determine the effectiveness of a 3D simulation environment used to train mining personnel in emergency evacuation procedures, designated the Fires in Underground Mines Evacuation Simulator (FUMES). Owing to the operational constraints of the mining facility, methods for measuring learning transfer were employed which did not require real world performance evaluation. Transfer measures that examined simulator performance relative to real world experience, fidelity assessment, and appraisal of the training value of the platform were utilised. Triangulation of results across all three measures indicated the presence of learning transfer, suggesting the viability of indirect measures in instances where real world performance testing is not possible. Furthermore, these indirect measures of learning transfer also provided some insight as to the strengths and weaknesses of the simulation design, which could be used to inform the development of future versions of the product

Inferring angiosperm phylogeny from EST data with widespread gene duplication

BACKGROUND: Most studies inferring species phylogenies use sequences from single copy genes or sets of orthologs culled from gene families. For taxa such as plants, with very high levels of gene duplication in their nuclear genomes, this has limited the exploitation of nuclear sequences for phylogenetic studies, such as those available in large EST libraries. One rarely used method of inference, gene tree parsimony, can infer species trees from gene families undergoing duplication and loss, but its performance has not been evaluated at a phylogenomic scale for EST data in plants. RESULTS: A gene tree parsimony analysis based on EST data was undertaken for six angiosperm model species and Pinus, an outgroup. Although a large fraction of the tentative consensus sequences obtained from the TIGR database of ESTs was assembled into homologous clusters too small to be phylogenetically informative, some 557 clusters contained promising levels of information. Based on maximum likelihood estimates of the gene trees obtained from these clusters, gene tree parsimony correctly inferred the accepted species tree with strong statistical support. A slight variant of this species tree was obtained when maximum parsimony was used to infer the individual gene trees instead. CONCLUSION: Despite the complexity of the EST data and the relatively small fraction eventually used in inferring a species tree, the gene tree parsimony method performed well in the face of very high apparent rates of duplication

HDAC inhibitors increase NRF2-signaling in tumour cells and blunt the efficacy of co-adminstered cytotoxic agents

The NRF2 signalling cascade provides a primary response against electrophilic chemicals and oxidative stress. The activation of NRF2-signaling is anticipated to have adverse clinical consequences; NRF2 is activated in a number of cancers and, additionally, its pharmacological activation by one compound can reduce the toxicity or efficiency of a second agent administered concomitantly. In this work, we have analysed systematically the ability of 152 research, pre-clinical or clinically used drugs to induce an NRF2 response using the MCF7-AREc32 NRF2 reporter. Ten percent of the tested drugs induced an NRF2 response. The NRF2 activators were not restricted to classical cytotoxic alkylating agents but also included a number of emerging anticancer drugs, including an IGF1-R inhibitor (NVP-AEW541), a PIM-1 kinase inhibitor (Pim1 inhibitor 2), a PLK1 inhibitor (BI 2536) and most strikingly seven of nine tested HDAC inhibitors. These findings were further confirmed by demonstrating NRF2-dependent induction of endogenous AKR genes, biomarkers of NRF2 activity. The ability of HDAC inhibitors to stimulate NRF2-signalling did not diminish their own potency as antitumour agents. However, when used to pre-treat cells, they did reduce the efficacy of acrolein. Taken together, our data suggest that the ability of drugs to stimulate NRF2 activity is common and should be investigated as part of the drug-development process

Phylogenomics with incomplete taxon coverage: the limits to inference

<p>Abstract</p> <p>Background</p> <p>Phylogenomic studies based on multi-locus sequence data sets are usually characterized by partial taxon coverage, in which sequences for some loci are missing for some taxa. The impact of missing data has been widely studied in phylogenetics, but it has proven difficult to distinguish effects due to error in tree reconstruction from effects due to missing data per se. We approach this problem using a explicitly phylogenomic criterion of success, <it>decisiveness</it>, which refers to whether the pattern of taxon coverage allows for uniquely defining a single tree for all taxa.</p> <p>Results</p> <p>We establish theoretical bounds on the impact of missing data on decisiveness. Results are derived for two contexts: a fixed taxon coverage pattern, such as that observed from an already assembled data set, and a randomly generated pattern derived from a process of sampling new data, such as might be observed in an ongoing comparative genomics sequencing project. Lower bounds on how many loci are needed for decisiveness are derived for the former case, and both lower and upper bounds for the latter. When data are not decisive for all trees, we estimate the probability of decisiveness and the chances that a given edge in the tree will be distinguishable. Theoretical results are illustrated using several empirical examples constructed by mining sequence databases, genomic libraries such as ESTs and BACs, and complete genome sequences.</p> <p>Conclusion</p> <p>Partial taxon coverage among loci can limit phylogenomic inference by making it impossible to distinguish among multiple alternative trees. However, even though lack of decisiveness is typical of many sparse phylogenomic data sets, it is often still possible to distinguish a large fraction of edges in the tree.</p

Error-tolerant quantum convolutional neural networks for symmetry-protected topological phases

The analysis of noisy quantum states prepared on current quantum computers is getting beyond the capabilities of classical computing. Quantum neural networks based on parametrized quantum circuits, measurements and feed-forward can process large amounts of quantum data to reduce measurement and computational costs of detecting non-local quantum correlations. The tolerance of errors due to decoherence and gate infidelities is a key requirement for the application of quantum neural networks on near-term quantum computers. Here we construct quantum convolutional neural networks (QCNNs) that can, in the presence of incoherent errors, recognize different symmetry-protected topological phases of generalized cluster-Ising Hamiltonians from one another as well as from topologically trivial phases. Using matrix product state simulations, we show that the QCNN output is robust against symmetry-breaking errors below a threshold error probability and against all symmetry-preserving errors provided the error channel is invertible. This is in contrast to string order parameters and the output of previously designed QCNNs, which vanish in the presence of any symmetry-breaking errors. To facilitate the implementation of the QCNNs on near-term quantum computers, the QCNN circuits can be shortened from logarithmic to constant depth in system size by performing a large part of the computation in classical post-processing. These constant-depth QCNNs reduce sample complexity exponentially with system size in comparison to the direct sampling using local Pauli measurements.Comment: 24 pages, 12 figure

Flexible labour markets, real wages and economic recoveries

Economists discuss the impact of low wages on the economy, and differences across EU countries - by Wouter Den Haan, Ethan Ilzetzki, Martin Ellison and Michael McMaho

Brexit and the economy: are economists out of touch with voters and politicians?

Given the majority of economic studies produced on Brexit prior to the UK’s referendum suggested that leaving the EU would leave the country worse off, should we conclude that economists are simply out of step with the views of voters? Drawing on evidence from a new survey by the Centre for Macroeconomics, Wouter Den Haan, Ethan Ilzetzki, Martin Ellison and Michael McMahon discuss the implications of Brexit for the economics profession