REMA: A computer-based mapping tool for analysis of restriction sites in multiple DNA sequences

REMA is an interactive web-based program which predicts endonuclease cut sites in DNA sequences. It analyses Multiple sequences simultaneously and predicts the number and size of fragments as well as provides restriction maps. The users can select single or paired combinations of all commercially available enzymes. Additionally, REMA permits prediction of multiple sequence terminal fragment sizes and suggests suitable restriction enzymes for maximally discriminatory results. REMA is an easy to use, web based program which will have a wide application in molecular biology research. Availability: REMA is written in Perl and is freely available for non-commercial use. Detailed information on installation can be obtained from Jan Szubert (jan.szubert@gmail.com) and the web based application is accessible on the internet at the URL http://www.macaulay.ac.uk/rema. Contact: b.singh@macaulay.ac.uk. (C) 2007 Elsevier B.V. All rights reserved.

Assessing the evidence: Student response system versus computer based testing for undertaking multiple choice question assessment in undergraduate nursing education

There is a dearth of evidence focusing on student preferences for computer-based testing versus
testing via student response systems for summative assessment in undergraduate education.
This quantitative study compared the preference and acceptability of computer-based testing
and a student response system for completing multiple choice questions in undergraduate
nursing education. After using both computer-based testing and a student response system to
complete multiple choice questions, 192 first year undergraduate nursing students rated their
preferences and attitudes towards using computer-based testing and a student response system.
Results indicated that seventy four percent felt the student response system was easy to use.
Fifty six percent felt the student response system took more time than the computer-based testing
to become familiar with. Sixty Percent felt computer-based testing was more users friendly.
Seventy Percent of students would prefer to take a multiple choice question summative exam
via computer-based testing, although Fifty percent would be happy to take using student response
system. Results are useful for undergraduate educators in relation to student’s preference
for using computer-based testing or student response system to undertake a summative
multiple choice question exam

Development and Evaluation of a Computer-Based, Self-Management Tool for People Recently Diagnosed with Type 2 Diabetes

Aim. The purpose of this study was to develop and evaluate a computer-based, dietary, and physical activity self-management program for people recently diagnosed with type 2 diabetes. 
Methods. The computer-based program was developed in conjunction with the target group and evaluated in a 12-week randomised controlled trial (RCT). Participants were randomised to the intervention (computer-program) or control group (usual care). Primary outcomes were diabetes knowledge and goal setting (ADKnowl questionnaire, Diabetes Obstacles Questionnaire (DOQ)) measured at baseline and week 12. User feedback on the program was obtained via a questionnaire and focus groups. Results. Seventy participants completed the 12-week RCT (32 intervention, 38 control, mean age 59 (SD) years). After completion there was a significant between-group difference in the “knowledge and beliefs scale” of the DOQ. Two-thirds of the intervention group rated the program as either good or very good, 92% would recommend the program to others, and 96% agreed that the information within the program was clear and easy to understand. 
Conclusions. The computer-program resulted in a small but statistically significant improvement in diet-related knowledge and user satisfaction was high. With some further development, this computer-based educational tool may be a useful adjunct to diabetes self-management.

A randomized controlled trial of an early-intervention, computer-based literacy program to boost phonological skills in 4- to 6-year-old children

Background: Many school-based interventions are being delivered in the absence of evidence of effectiveness (Snowling & Hulme, 2011, Br. J. Educ. Psychol., 81, 1).Aim: This study sought to address this oversight by evaluating the effectiveness of the commonly used the Lexia Reading Core5 intervention, with 4- to 6-year-old pupils in Northern Ireland.Sample: A total of 126 primary school pupils in year 1 and year 2 were screened on the Phonological Assessment Battery 2nd Edition (PhAB-2). Children were recruited from the equivalent year groups to Reception and Year 1 in England and Wales, and Pre-kindergarten and Kindergarten in North America.
Methods: A total of 98 below-average pupils were randomized (T0) to either an 8-week block (inline image = 647.51 min, SD = 158.21) of daily access to Lexia Reading Core5 (n = 49) or a waiting-list control group (n = 49). Assessment of phonological skills was completed at post-intervention (T1) and at 2-month follow-up (T2) for the intervention group only.
Results: Analysis of covariance which controlled for baseline scores found that the Lexia Reading Core5 intervention group made significantly greater gains in blending, F(1, 95) = 6.50, p = .012, partial η2 = .064 (small effect size) and non-word reading, F(1, 95) = 7.20, p = .009, partial η2 = .070 (small effect size). Analysis of the 2-month follow-up of the intervention group found that all group treatment gains were maintained. However, improvements were not uniform among the intervention group with 35% failing to make progress despite access to support. Post-hoc analysis revealed that higher T0 phonological working memory scores predicted improvements made in phonological skills.
Conclusions: An early-intervention, computer-based literacy program can be effective in boosting the phonological skills of 4- to 6-year-olds, particularly if these literacy difficulties are not linked to phonological working memory deficits.

Proposing the deep dynamic Bayesian network as a future computer based medical system

The development of new learning models has been of great importance throughout recent years, with a focus on creating advances in the area of deep learning. Deep learning was first noted in 2006, and has since become a major area of research in a number of disciplines. This paper will delve into the area of deep learning to present its current limitations and provide a new idea for a fully integrated deep and dynamic probabilistic system. The new model will be applicable to a vast number of areas initially focusing on applications into medical image analysis with an overall goal of utilising this approach for prediction purposes in computer based medical systems.