Statistical analysis for plant genetic resources: Clustering and indices in R made simple

This handbook aims to give researchers tools and methods to analyse mathematically complex morphological characterization data. R is a powerful software language and environment for statistical computing and graphics, developed by volunteers from around the world, working together over the Internet. The core development team consists of fewer than twenty programmers who maintain and develop the general R environment, with contributions from many others. A much larger number of contributors write individual statistical packages for R that address specific needs. Statisticians and researchers write many of these statistical packages for their own research purposes, ensuring that R is in the forefront of developments in statistics. Another of R's strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. On the down side, if you are used to pointand-click interfaces, you may find that R has a relatively steep learning curve. As one of the developers of R once put it, R is not designed to make easy things simple, but to make difficult things possible. Nonetheless, several projects are working on making R easier to use, and interfaces should improve in the near future. We would not propose R had we not found that it has many facilities useful to the analysis of plant genetic resources data, facilities not commonly found in other statistical packages. We also believe that with a step-by-step guide, providing examples of typical analyses required in plant genetic resources research, most researchers will be able to implement such analyses quite simply on their own data. This tutorial provides such a guide, beginning with simple examples that soon demonstrate the power of R, and allows users to gradually become acquainted with the more complex aspects. Just enough explanation of the statistics is given for readers with a basic understanding of the subject to understand the output of the analyses

A global information system for the conservation and sustainable use of Plant Genetic Resources for Food and Agriculture (PGRFA)

Poster presented at 2008 Annual Meeting of TD WG-Biodiversity Information Standards. Fremantle ( Australia), 19-24 Oct 200

Photo-elicitation and time-lining to enhance the research interview: Exploring the quarterlife crisis of young adults in India and the UK

The aim of this article is to convey our experience of using photo-elicitation along with time-lining to enhance the research interview. We reflect on a study on the ‘quarterlife crisis’ in India and the UK. Participants were aged 22-30 years and self-defined as having experienced difficulties ‘finding their place in the world.’ There were 16 British (8 women, 8 men) and 8 Indian participants (4 women; 4 men). First, we consider how photo-elicitation proved highly compatible with our method of analysis – interpretative phenomenological analysis – through affording a deep connection with participant experience. Second, we explore how participants engaged with photo-elicitation and time-lining, providing examples of image content (events and feelings), image form (literal and symbolic), and creative use of timelines. Third, we reflect on how photo-elicitation and time-lining appeared to enhance participant agency, and to have a therapeutic value for participants, as well as providing particularly rich material for analysis

Development and validation of a diagnostic aid for convulsive epilepsy in sub-Saharan Africa: a retrospective case-control study

Background: Identification of convulsive epilepsy in sub-Saharan Africa relies on access to resources that are often unavailable. Infrastructure and resource requirements can further complicate case verification. Using machine-learning techniques, we have developed and tested a region-specific questionnaire panel and predictive model to identify people who have had a convulsive seizure. These findings have been implemented into a free app for health-care workers in Kenya, Uganda, Ghana, Tanzania, and South Africa. Methods: In this retrospective case-control study, we used data from the Studies of the Epidemiology of Epilepsy in Demographic Sites in Kenya, Uganda, Ghana, Tanzania, and South Africa. We randomly split these individuals using a 7:3 ratio into a training dataset and a validation dataset. We used information gain and correlation-based feature selection to identify eight binary features to predict convulsive seizures. We then assessed several machine-learning algorithms to create a multivariate prediction model. We validated the best-performing model with the internal dataset and a prospectively collected external-validation dataset. We additionally evaluated a leave-one-site-out model (LOSO), in which the model was trained on data from all sites except one that, in turn, formed the validation dataset. We used these features to develop a questionnaire-based predictive panel that we implemented into a multilingual app (the Epilepsy Diagnostic Companion) for health-care workers in each geographical region. Findings: We analysed epilepsy-specific data from 4097 people, of whom 1985 (48·5%) had convulsive epilepsy, and 2112 were controls. From 170 clinical variables, we initially identified 20 candidate predictor features. Eight features were removed, six because of negligible information gain and two following review by a panel of qualified neurologists. Correlation-based feature selection identified eight variables that demonstrated predictive value; all were associated with an increased risk of an epileptic convulsion except one. The logistic regression, support vector, and naive Bayes models performed similarly, outperforming the decision-tree model. We chose the logistic regression model for its interpretability and implementability. The area under the receiver operator curve (AUC) was 0·92 (95% CI 0·91–0·94, sensitivity 85·0%, specificity 93·7%) in the internal-validation dataset and 0·95 (0·92–0·98, sensitivity 97·5%, specificity 82·4%) in the external-validation dataset. Similar results were observed for the LOSO model (AUC 0·94, 0·93–0·96, sensitivity 88·2%, specificity 95·3%). Interpretation: On the basis of these findings, we developed the Epilepsy Diagnostic Companion as a predictive model and app offering a validated culture-specific and region-specific solution to confirm the diagnosis of a convulsive epileptic seizure in people with suspected epilepsy. The questionnaire panel is simple and accessible for health-care workers without specialist knowledge to administer. This tool can be iteratively updated and could lead to earlier, more accurate diagnosis of seizures and improve care for people with epilepsy