UASIS: Universal Automatic SNP Identification System

<p>Abstract</p> <p>Background</p> <p>SNP (Single Nucleotide Polymorphism), the most common genetic variations between human beings, is believed to be a promising way towards personalized medicine. As more and more research on SNPs are being conducted, non-standard nomenclatures may generate potential problems. The most serious issue is that researchers cannot perform cross referencing among different SNP databases. This will result in more resources and time required to track SNPs. It could be detrimental to the entire academic community.</p> <p>Results</p> <p>UASIS (Universal Automated SNP Identification System) is a web-based server for SNP nomenclature standardization and translation at DNA level. Three utilities are available. They are UASIS Aligner, Universal SNP Name Generator and SNP Name Mapper. UASIS maps SNPs from different databases, including dbSNP, GWAS, HapMap and JSNP etc., into an uniform view efficiently using a proposed universal nomenclature and state-of-art alignment algorithms. UASIS is freely available at <url>http://www.uasis.tk</url> with no requirement of log-in.</p> <p>Conclusions</p> <p>UASIS is a helpful platform for SNP cross referencing and tracking. By providing an informative, unique and unambiguous nomenclature, which utilizes unique position of a SNP, we aim to resolve the ambiguity of SNP nomenclatures currently practised. Our universal nomenclature is a good complement to mainstream SNP notations such as rs# and HGVS guidelines. UASIS acts as a bridge to connect heterogeneous representations of SNPs.</p

Learning with multiple representations: An example of a revision lesson in mechanics

We describe an example of learning with multiple representations in an A-level revision lesson on mechanics. The context of the problem involved the motion of a ball thrown vertically upwards in air and studying how the associated physical quantities changed during its flight. Different groups of students were assigned to look at the ball's motion using various representations: motion diagrams, vector diagrams, free-body diagrams, verbal description, equations and graphs, drawn against time as well as against displacement. Overall, feedback from students about the lesson was positive. We further discuss the benefits of using computer simulation to support and extend student learning.Comment: 10 pages, 5 figures, 2 tables http://iopscience.iop.org/0031-912

The 9th annual computational and systems neuroscience (cosyne) meeting

The 9th annual Computational and Systems Neuroscience meeting (Cosyne) was held 23-26 February in Salt Lake City, Utah. Cosyne meeting is the forum for exchange of experimental and theoretical/computational approaches to studying systems neuroscience

Exploring patterns of recurrent melanoma in Northeast Scotland to inform the introduction a digital self-examination intervention

Peer reviewedPublisher PD

Climate adaptation for airports by climate change risk indicators (CCRI)

The study is to develop a Climate Change Risk Indicator (CCRI) framework for airport adaptation to climate change. This paper firstly describes the results of a literature review of the studies by international and professional bodies on climate change adaptation for transportation systems, with a focus on the aviation sector. Secondly, a Fuzzy Evidential Reasoning (FER) method is employed to develop a new CCRI framework to evaluate the climate risks of airports. Finally, six airports in the United Kingdom (UK) are selected as the real cases to demonstrate the feasibility of using the proposed CCRI framework

Entrainment of slow oscillations of auditory thalamic neurons by repetitive sound stimuli

2008-2009 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Differencing techniques in semi-parametric panel data varying coefficient models with fixed effects: a Monte Carlo study.

Recently, some new techniques have been proposed for the estimation of semi-parametric fixed effects varying coefficient panel data models. These new techniques fall within the class of the so-called differencing estimators. In particular, we consider first-differences and within local linear regression estimators. Analyzing their asymptotic properties it turns out that, keeping the same order of magnitude for the bias term, these estimators exhibit different asymptotic bounds for the variance. In both cases, the consequences are suboptimal non-parametric rates of convergence. In order to solve this problem, by exploiting the additive structure of this model, a one-step backfitting algorithm is proposed. Under fairly general conditions, it turns out that the resulting estimators show optimal rates of convergence and exhibit the oracle efficiency property. Since both estimators are asymptotically equivalent, it is of interest to analyze their behavior in small sample sizes. In a fully parametric context, it is well-known that, under strict exogeneity assumptions the performance of both first-differences and within estimators is going to depend on the stochastic structure of the idiosyncratic random errors. However, in the non-parametric setting, apart from the previous issues other factors such as dimensionality or sample size are of great interest. In particular, we would be interested in learning about their relative average mean square error under different scenarios. The simulation results basically confirm the theoretical findings for both local linear regression and one-step backfitting estimators. However, we have found out that within estimators are rather sensitive to the size of number of time observations

Prioritizing Vehicle Cleanliness (PVC) using Key Green Performance Indicators (KGPI)

Climate catastrophes (e.g. hurricane, flooding and heat waves) are generating increasing impact on port operations and hence configuration of shipping networks. This paper formulates the routing problem to optimise the resilience of shipping networks, by taking into account the disruptions due to climate risks to port operations. It first describes a literature review with the emphasis on environmental sustainability, port disruptions due to climate extremes and routing optimisation in shipping operations. Second, a centrality assessment of port cities by a novel multi-centrality-based indicator is implemented. Third, a climate resilience model is developed by incorporating the port disruption days by climate risks into shipping route optimisation. Its main contribution is constructing a novel methodology to connect climate risk indices, centrality assessment, and shipping routing to observe the changes of global shipping network by climate change impacts

What is memory? The present state of the engram

The mechanism of memory remains one of the great unsolved problems of biology. Grappling with the question more than a hundred years ago, the German zoologist Richard Semon formulated the concept of the engram, lasting connections in the brain that result from simultaneous "excitations", whose precise physical nature and consequences were out of reach of the biology of his day. Neuroscientists now have the knowledge and tools to tackle this question, however, and this Forum brings together leading contemporary views on the mechanisms of memory and what the engram means today