Terugkeer van de otter in het rivierengebied

In het bestek van dit onderzoek is gekeken naar de kansen voor een duurzame populatie otters in het rivierengebied. Uit deze analyse komen de Gelderse Poort, de uiterwaarden van de IJssel en de Waal naar voren met elk een potentiële otterpopulatie van ca. 30 dieren. Ook in Noorden Midden-Limburg (Maasdal en haar zijbeken) vormt gezamenlijk een potentieel gebied voor ca. 50 otters. De belangrijkste knelpunten voor de ontwikkeling van een duurzame otterpopulatie liggen op het vlak van infrastructuurdichtheid. Otters lopen als oeverbewonend zoogdier een groot risico om in het verkeer te sneuvelen. De actuele waterkwaliteit van de grote rivieren vormt geen grote beperking meer voor de terugkeer van de otter

Feature engineering and a proposed decision-support system for systematic reviewers of medical evidence

Objectives: Evidence-based medicine depends on the timely synthesis of research findings. An important source of synthesized evidence resides in systematic reviews. However, a bottleneck in review production involves dual screening of citations with titles and abstracts to find eligible studies. For this research, we tested the effect of various kinds of textual information (features) on performance of a machine learning classifier. Based on our findings, we propose an automated system to reduce screeing burden, as well as offer quality assurance. Methods: We built a database of citations from 5 systematic reviews that varied with respect to domain, topic, and sponsor. Consensus judgments regarding eligibility were inferred from published reports. We extracted 5 feature sets from citations: alphabetic, alphanumeric +, indexing, features mapped to concepts in systematic reviews, and topic models. To simulate a two-person team, we divided the data into random halves. We optimized the parameters of a Bayesian classifier, then trained and tested models on alternate data halves. Overall, we conducted 50 independent tests. Results: All tests of summary performance (mean F3) surpassed the corresponding baseline, P<0.0001. The ranks for mean F3, precision, and classification error were statistically different across feature sets averaged over reviews; P-values for Friedman's test were .045, .002, and .002, respectively. Differences in ranks for mean recall were not statistically significant. Alphanumeric+ features were associated with best performance; mean reduction in screening burden for this feature type ranged from 88% to 98% for the second pass through citations and from 38% to 48% overall. Conclusions: A computer-assisted, decision support system based on our methods could substantially reduce the burden of screening citations for systematic review teams and solo reviewers. Additionally, such a system could deliver quality assurance both by confirming concordant decisions and by naming studies associated with discordant decisions for further consideration. © 2014 Bekhuis et al

Supporting systematic reviews using LDA-based document representations

BACKGROUND: Identifying relevant studies for inclusion in a systematic review (i.e. screening) is a complex, laborious and expensive task. Recently, a number of studies has shown that the use of machine learning and text mining methods to automatically identify relevant studies has the potential to drastically decrease the workload involved in the screening phase. The vast majority of these machine learning methods exploit the same underlying principle, i.e. a study is modelled as a bag-of-words (BOW). METHODS: We explore the use of topic modelling methods to derive a more informative representation of studies. We apply Latent Dirichlet allocation (LDA), an unsupervised topic modelling approach, to automatically identify topics in a collection of studies. We then represent each study as a distribution of LDA topics. Additionally, we enrich topics derived using LDA with multi-word terms identified by using an automatic term recognition (ATR) tool. For evaluation purposes, we carry out automatic identification of relevant studies using support vector machine (SVM)-based classifiers that employ both our novel topic-based representation and the BOW representation. RESULTS: Our results show that the SVM classifier is able to identify a greater number of relevant studies when using the LDA representation than the BOW representation. These observations hold for two systematic reviews of the clinical domain and three reviews of the social science domain. CONCLUSIONS: A topic-based feature representation of documents outperforms the BOW representation when applied to the task of automatic citation screening. The proposed term-enriched topics are more informative and less ambiguous to systematic reviewers. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13643-015-0117-0) contains supplementary material, which is available to authorized users

Crowdfunding in the Cultural Industries

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Pan-African Genetic Structure in the African Buffalo (Syncerus caffer): Investigating Intraspecific Divergence

The African buffalo (Syncerus caffer) exhibits extreme morphological variability, which has led to controversies about the validity and taxonomic status of the various recognized subspecies. The present study aims to clarify these by inferring the pan-African spatial distribution of genetic diversity, using a comprehensive set of mitochondrial D-loop sequences from across the entire range of the species. All analyses converged on the existence of two distinct lineages, corresponding to a group encompassing West and Central African populations and a group encompassing East and Southern African populations. The former is currently assigned to two to three subspecies (S. c. nanus, S. c. brachyceros, S. c. aequinoctialis) and the latter to a separate subspecies (S. c. caffer). Forty-two per cent of the total amount of genetic diversity is explained by the between-lineage component, with one to seventeen female migrants per generation inferred as consistent with the isolation-with-migration model. The two lineages diverged between 145 000 to 449 000 years ago, with strong indications for a population expansion in both lineages, as revealed by coalescent-based analyses, summary statistics and a star-like topology of the haplotype network for the S. c. caffer lineage. A Bayesian analysis identified the most probable historical migration routes, with the Cape buffalo undertaking successive colonization events from Eastern toward Southern Africa. Furthermore, our analyses indicate that, in the West-Central African lineage, the forest ecophenotype may be a derived form of the savanna ecophenotype and not vice versa, as has previously been proposed. The African buffalo most likely expanded and diverged in the late to middle Pleistocene from an ancestral population located around the current-day Central African Republic, adapting morphologically to colonize new habitats, hence developing the variety of ecophenotypes observed today

Kudos make you run! How runners influence each other on the online social network Strava

Strava is the largest online social network for athletes. We used Strava's big data to investigate how runners in the same virtual Strava club influenced each other's running behavior. We hypothesized that receiving kudos on recorded activities would spur exercise rates, and that the running behavior of clubmates to whom ego gave kudos would serve as a motivational example. We focused on five different Strava clubs that functioned as a virtual extension of real-life Dutch running clubs with a total of 329 members. Using data on kudos and recorded activities, we constructed a longitudinal dataset of complete networks and behavior over 11 periods with a one-month time window. We tested our hypotheses using SIENA. We found that receiving kudos induced runners to run more and more often. Moreover, athletes tended to adjust their running behavior to that of their 'kudos-friends' (i.e., those to whom they gave kudos). Contrary to our expectation, kudos-friends who ran more and more often than ego were not the most influential. If anything, the reverse was true; athletes were more likely to come to resemble the running behavior of their kudos-friends who ran less and less often