High value trees: Africa RISING science, innovations and technologies with scaling potential from the Ethiopian Highlands

United States Agency for International Developmen

It’s all about information? The Following Behaviour of Professors and PhD Students on Twitter

In this paper we investigate the role of the academic status in the following behaviour of computer scientists on Twitter. Based on a uses and gratifications perspective, we focus on the activity of a Twitter account and the reciprocity of following relationships. We propose that the account activity addresses the users' information motive only, whereas the user's academic status relates to both the information motive and community development (as in peer networking or career planning). Variables were extracted from Twitter user data. We applied a biographical approach to correctly identify the academic status (professor versus PhD student). We calculated a $2\times 2$ MANOVA on the influence of the activity of the account and the academic status (on different groups of followers) to differentiate the influence of the information motive versus the motive for community development. Results suggest that for computer scientists Twitter is mainly an information network. However, we found significant effects in the sense of career planning, that is, the accounts of professors had even in the case of low activity a relatively high number of researcher followers -- both PhD followers as well as professor followers. Additionally, there was also some weak evidence for community development gratifications in the sense of peer-networking of professors. Overall, we conclude that the academic use of Twitter is not only about information, but also about career planning and networking

Chlamydia trachomatis Test-of-Cure Cannot Be Based on a Single Highly Sensitive Laboratory Test Taken at Least 3 Weeks after Treatment

Current test-of-cure practice in patients with Chlamydia trachomatis (Ct) infection is to confirm cure with a single test taken at least 3 weeks after treatment. Effectiveness of single-time-point testing however lacks a scientific evidence basis and the high sensitivity of laboratory assays nowadays in use for this purpose may compromise the clinical significance of their results. Prospectively following 59 treated Ct infections, administering care as usual, the presence of Ct plasmid DNA and rRNA was systematically assessed by multiple time-sequential measurements, i.e. on 18 samples taken per patient during 8 weeks following treatment with a single dose of 1000 mg Azythromycin. A high proportion (42%) of Ct infections tested positive on at least one of the samples taken after 3 weeks. Patients' test results showed substantial inter-individual and intra-individual variation over time and by type of NAAT used. We demonstrated frequent intermittent positive patterns in Ct test results over time, and strongly argue against current test-of-cure practice

Of gastro and the gold standard: evaluation and policy implications of norovirus test performance for outbreak detection

<p>Abstract</p> <p>Background</p> <p>The norovirus group (NVG) of caliciviruses are the etiological agents of most institutional outbreaks of gastroenteritis in North America and Europe. Identification of NVG is complicated by the non-culturable nature of this virus, and the absence of a diagnostic gold standard makes traditional evaluation of test characteristics problematic.</p> <p>Methods</p> <p>We evaluated 189 specimens derived from 440 acute gastroenteritis outbreaks investigated in Ontario in 2006–07. Parallel testing for NVG was performed with real-time reverse-transcriptase polymerase chain reaction (RT²-PCR), enzyme immunoassay (EIA) and electron microscopy (EM). Test characteristics (sensitivity and specificity) were estimated using latent class models and composite reference standard methods. The practical implications of test characteristics were evaluated using binomial probability models.</p> <p>Results</p> <p>Latent class modelling estimated sensitivities of RT²-PCR, EIA, and EM as 100%, 86%, and 17% respectively; specificities were 84%, 92%, and 100%; estimates obtained using a composite reference standard were similar. If all specimens contained norovirus, RT²-PCR or EIA would be associated with > 99.9% likelihood of at least one test being positive after three specimens tested. Testing of more than 5 true negative specimens with RT²-PCR would be associated with a greater than 50% likelihood of a false positive test.</p> <p>Conclusion</p> <p>Our findings support the characterization of EM as lacking sensitivity for NVG outbreaks. The high sensitivity of RT²-PCR and EIA permit identification of NVG outbreaks with testing of limited numbers of clinical specimens. Given risks of false positive test results, it is reasonable to limit the number of specimens tested when RT²-PCR or EIA are available.</p

Estimating the Accuracy of Anal Cytology in the Presence of an Imperfect Reference Standard

Background: The study aim is to estimate sensitivity and specificity of anal cytology for histologic HSIL in analyses adjusted for the imperfect biopsy reference standard. Methods and Principal Findings: Retrospective cohort study of an anal dysplasia screening program for HIV infected adults. We estimated the prevalence of histologic HSIL by concurrent cytology category and the associated cytology ROC area. Cytology operating characteristics for HSIL were estimated and adjusted for the imperfect reference standard by 3 methodologies. The study cohort included 261 patients with 3 available measures: (1) referral cytology; (2) HRA cytology; and (3) HRA directed biopsy. The prevalence of biopsy HSIL varied according to the concurrent HRA cytology result: 64.5

Bringing evidence to bear for negotiating tradeoffs in sustainable agricultural intensification using a structured stakeholder engagement process

Sustainable agricultural intensification (SAI) has the potential to increase food security without detrimental effects on ecosystem services. However, adoption of SAI practices across sub-Saharan Africa has not reached transformational numbers to date. It is often hampered by lack of context-specific practices, sub-optimal understanding of tradeoffs and synergies among stakeholders, and lack of approaches that bring diverse evidence sources together with stakeholders to collectively tackle complex problems. In this study, we asked three interconnected questions: (i) What is the accessibility and use of evidence for SAI decision making; (ii) What tools could enhance access and interaction with evidence for tradeoff analysis; and (iii) Which stakeholders must be included? This study employed a range of research and engagement methods including surveys, stakeholder analysis, participatory trade-off assessments and co-design of decision dashboards to better support evidence-based decision making in Zambia, Tanzania and Ethiopia. At the inception, SAI evidence was accessible and used by less than half of the decision makers across the three countries and online dashboards hold promise to enhance access. Many of the stakeholders working on SAI were not collaborating and tradeoff analysis was an under-utilized tool. Structured engagement across multiple stakeholder groups with evidence is critical

Mixture of latent trait analyzers for model-based clustering of categorical data

Model-based clustering methods for continuous data are well established and commonly used in a wide range of applications. However, model-based clustering methods for categorical data are less standard. Latent class analysis is a commonly used method for model-based clustering of binary data and/or categorical data, but due to an assumed local independence structure there may not be a correspondence between the estimated latent classes and groups in the population of interest. The mixture of latent trait analyzers model extends latent class analysis by assuming a model for the categorical response variables that depends on both a categorical latent class and a continuous latent trait variable; the discrete latent class accommodates group structure and the continuous latent trait accommodates dependence within these groups. Fitting the mixture of latent trait analyzers model is potentially difficult because the likelihood function involves an integral that cannot be evaluated analytically. We develop a variational approach for fitting the mixture of latent trait models and this provides an efficient model fitting strategy. The mixture of latent trait analyzers model is demonstrated on the analysis of data from the National Long Term Care Survey (NLTCS) and voting in the U.S. Congress. The model is shown to yield intuitive clustering results and it gives a much better fit than either latent class analysis or latent trait analysis alone

Assessing Performance of Orthology Detection Strategies Applied to Eukaryotic Genomes

Orthology detection is critically important for accurate functional annotation, and has been widely used to facilitate studies on comparative and evolutionary genomics. Although various methods are now available, there has been no comprehensive analysis of performance, due to the lack of a genomic-scale ‘gold standard’ orthology dataset. Even in the absence of such datasets, the comparison of results from alternative methodologies contains useful information, as agreement enhances confidence and disagreement indicates possible errors. Latent Class Analysis (LCA) is a statistical technique that can exploit this information to reasonably infer sensitivities and specificities, and is applied here to evaluate the performance of various orthology detection methods on a eukaryotic dataset. Overall, we observe a trade-off between sensitivity and specificity in orthology detection, with BLAST-based methods characterized by high sensitivity, and tree-based methods by high specificity. Two algorithms exhibit the best overall balance, with both sensitivity and specificity>80%: INPARANOID identifies orthologs across two species while OrthoMCL clusters orthologs from multiple species. Among methods that permit clustering of ortholog groups spanning multiple genomes, the (automated) OrthoMCL algorithm exhibits better within-group consistency with respect to protein function and domain architecture than the (manually curated) KOG database, and the homolog clustering algorithm TribeMCL as well. By way of using LCA, we are also able to comprehensively assess similarities and statistical dependence between various strategies, and evaluate the effects of parameter settings on performance. In summary, we present a comprehensive evaluation of orthology detection on a divergent set of eukaryotic genomes, thus providing insights and guides for method selection, tuning and development for different applications. Many biological questions have been addressed by multiple tests yielding binary (yes/no) outcomes but no clear definition of truth, making LCA an attractive approach for computational biology