Empirische Shortcuts:Studentische Forschungsprojekte in der Methodenlehre

Die empirische Bildungsforschung hat in den letzten Jahren einen nachhaltigen quantitativen und qualitativen Aufschwung genommen. Erziehungswissenschaftliche Forschung erschöpft sich jedoch nicht im M&E des schulischen Lernstands im nationalen und internationalen Vergleich und seiner Ursachen sondern umfasst ein weites Feld an Fragestellungen aus unterschiedlichsten Bereichen. Dies muss den Studierenden, neben den methodischen Modellen und Instrumentarien exemplarisch vermittelt werden. Das ideale didaktische Modell hierfür ist zweifellos ein von den Studierenden selbst konzipiertes und durchgeführtes Forschungsprojekt, wie es seit Jahren im Programmbereich „Empirische Pädagogik“ des Instituts für Erziehungswissenschaft der WWU Münster praktiziert. Dadurch werden den Studierenden Dimensionen eröffnet, die im Zeitalter von Modularisierung, OECD-Statistiken und Ressourcenverknappung sich zunehmend verengen. Die damit verbundenen Impulse und Erfahrungsgewinne sind in eine Belebung und Bereicherung des Studiums

Dinucleotide controlled null models for comparative RNA gene prediction

<p>Abstract</p> <p>Background</p> <p>Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak <it>et al</it>. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available.</p> <p>Results</p> <p>We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content.</p> <p>Conclusion</p> <p>SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require randomization of multiple alignments can be considered.</p> <p>Availability</p> <p>SISSIz is available as open source C code that can be compiled for every major platform and downloaded here: <url>http://sourceforge.net/projects/sissiz</url>.</p

Simulating respiratory disease transmission within and between classrooms to assess pandemic management strategies at schools

The global spread of coronavirus disease 2019 (COVID-19) has emphasized the need for evidence-based strategies for the safe operation of schools during pandemics that balance infection risk with the society\u27s responsibility of allowing children to attend school. Due to limited empirical data, existing analyses assessing school-based interventions in pandemic situations often impose strong assumptions, for example, on the relationship between class size and transmission risk, which could bias the estimated effect of interventions, such as split classes and staggered attendance. To fill this gap in school outbreak studies, we parameterized an individual-based model that accounts for heterogeneous contact rates within and between classes and grades to a multischool outbreak data of influenza. We then simulated school outbreaks of respiratory infectious diseases of ongoing threat (i.e., COVID-19) and potential threat (i.e., pandemic influenza) under a variety of interventions (changing class structures, symptom screening, regular testing, cohorting, and responsive class closures). Our results suggest that interventions changing class structures (e.g., reduced class sizes) may not be effective in reducing the risk of major school outbreaks upon introduction of a case and that other precautionary measures (e.g., screening and isolation) need to be employed. Class-level closures in response to detection of a case were also suggested to be effective in reducing the size of an outbreak

Contact tracing is an imperfect tool for controlling COVID-19 transmission and relies on population adherence

Abstract: Emerging evidence suggests that contact tracing has had limited success in the UK in reducing the R number across the COVID-19 pandemic. We investigate potential pitfalls and areas for improvement by extending an existing branching process contact tracing model, adding diagnostic testing and refining parameter estimates. Our results demonstrate that reporting and adherence are the most important predictors of programme impact but tracing coverage and speed plus diagnostic sensitivity also play an important role. We conclude that well-implemented contact tracing could bring small but potentially important benefits to controlling and preventing outbreaks, providing up to a 15% reduction in R. We reaffirm that contact tracing is not currently appropriate as the sole control measure

Genetically Programmed Changes in Photosynthetic Cofactor Metabolism in Copper-deficient Chlamydomonas

Genetic and genomic studies indicate that copper deficiency triggers changes in the expression of genes encoding key enzymes in various chloroplast-localized lipid/pigment biosynthetic pathways. Among these are CGL78 involved in chlorophyll biosynthesis and HPPD1, encoding 4-hydroxyphenylpyruvate dioxygenase catalyzing the committed step of plastoquinone and tocopherol biosyntheses. Copper deficiency in wild-type cells does not change the chlorophyll content, but a survey of chlorophyll protein accumulation in this situation revealed increased accumulation of LHCSR3, which is blocked at the level of mRNA accumulation when either CGL78 expression is reduced or in the crd1 mutant, which has a copper-nutrition conditional defect at the same step in chlorophyll biosynthesis. Again, like copper-deficient crd1 strains, cgl78 knock-down lines also have reduced chlorophyll content concomitant with loss of PSI-LHCI super-complexes and reduced abundance of a chlorophyll binding subunit of PSI, PSAK, which connects LHCI to PSI. For HPPD1, increased mRNA results in increased abundance of the corresponding protein in copper-deficient cells concomitant with CRR1-dependent increased accumulation of γ-tocopherols, but not plastoquinone-9 nor total tocopherols. In crr1 mutants, where increased HPPD1 expression is blocked, plastochromanol-8, derived from plastoquinone-9 and purported to also have an antioxidant function, is found instead. Although not previously found in algae, this metabolite may occur only in stress conditions