Can I Count on Getting Better? Association between Math Anxiety and Poorer Understanding of Medical Risk Reductions

BACKGROUND: Lower numerical ability is associated with poorer understanding of health statistics, such as risk reductions of medical treatment. For many people, despite good numeracy skills, math provokes anxiety that impedes an ability to evaluate numerical information. Math-anxious individuals also report less confidence in their ability to perform math tasks. We hypothesized that, independent of objective numeracy, math anxiety would be associated with poorer responding and lower confidence when calculating risk reductions of medical treatments.

METHODS: Objective numeracy was assessed using an 11-item objective numeracy scale. A 13-item self-report scale was used to assess math anxiety. In experiment 1, participants were asked to interpret the baseline risk of disease and risk reductions associated with treatment options. Participants in experiment 2 were additionally provided a graphical display designed to facilitate the processing of math information and alleviate effects of math anxiety. Confidence ratings were provided on a 7-point scale.

RESULTS: Individuals of higher objective numeracy were more likely to respond correctly to baseline risks and risk reductions associated with treatment options and were more confident in their interpretations. Individuals who scored high in math anxiety were instead less likely to correctly interpret the baseline risks and risk reductions and were less confident in their risk calculations as well as in their assessments of the effectiveness of treatment options. Math anxiety predicted confidence levels but not correct responding when controlling for objective numeracy. The graphical display was most effective in increasing confidence among math-anxious individuals.

CONCLUSIONS: The findings suggest that math anxiety is associated with poorer medical risk interpretation but is more strongly related to confidence in interpretations.

Effective collision strengths for fine-structure forbidden transitions among the 2s22p3 levels of Ne IV

Effective collision strengths for electron-impact excitation of the N-like ion NeIV are calculated in the close-coupling approximation using the multichannel R-matrix method. Specific attention is given to the 10 astrophysically important fine-structure forbidden transitions among the ⁴S^o, ²D^o and ²P^o levels in the 2s²2p³ ground-state configuration. The expansion of the total wavefunction incorporates the lowest 11 LS eigenstates of NeIV, consisting of eight n = 2 terms with configurations 2s²2p³, 2s2p⁴ and 2p⁵, together with three n = 3 states of configuration 2s²2p²3s. We present in graphical form the effective collision strengths obtained by thermally averaging the collision strengths over a Maxwellian distribution of velocities, for all 10 fine-structure transitions, over the range of electron temperatures log T(K) = 3.6 to log T(K) = 6.1 (the range appropriate for astrophysical applications). Comparisons are made with the earlier, less sophisticated close-coupling calculation of Giles, and excellent agreement is found in the limited temperature region where a comparison is possible [log T(K) = 3.7 to log 7(K) = 4.3]. At higher temperatures the present data are the only reliable results currently available.

Local Computation in Credal Networks

The goal of this contribution is to discuss local computation in credal networks — graphical models that can represent imprecise and indeterminate probability values. We analyze the inference problem in credal networks, discuss how inference algorithms can benefit from local computation, and suggest that local computation can be particularly important in approximate inference algorithms.

QUADrATiC: scalable gene expression connectivity mapping for repurposing FDA-approved therapeutics

Background: Gene expression connectivity mapping has proven to be a powerful and flexible tool for research. Its application has been shown in a broad range of research topics, most commonly as a means of identifying potential small molecule compounds, which may be further investigated as candidates for repurposing to treat diseases. The public release of voluminous data from the Library of Integrated Cellular Signatures (LINCS) programme further enhanced the utilities and potentials of gene expression connectivity mapping in biomedicine. Results: We describe QUADrATiC (http://go.qub.ac.uk/QUADrATiC), a user-friendly tool for the exploration of gene expression connectivity on the subset of the LINCS data set corresponding to FDA-approved small molecule compounds. It enables the identification of compounds for repurposing therapeutic potentials. The software is designed to cope with the increased volume of data over existing tools, by taking advantage of multicore computing architectures to provide a scalable solution, which may be installed and operated on a range of computers, from laptops to servers. This scalability is provided by the use of the modern concurrent programming paradigm provided by the Akka framework. The QUADrATiC Graphical User Interface (GUI) has been developed using advanced Javascript frameworks, providing novel visualization capabilities for further analysis of connections. There is also a web services interface, allowing integration with other programs or scripts.Conclusions: QUADrATiC has been shown to provide an improvement over existing connectivity map software, in terms of scope (based on the LINCS data set), applicability (using FDA-approved compounds), usability and speed. It offers potential to biological researchers to analyze transcriptional data and generate potential therapeutics for focussed study in the lab. QUADrATiC represents a step change in the process of investigating gene expression connectivity and provides more biologically-relevant results than previous alternative solutions.