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Plans are currently being drafted for the next decade of action on biodiversity-both the post-2020 Global Biodiversity Framework of the Convention on Biological Diversity (CBD) and Biodiversity Strategy of the European Union (EU). Freshwater biodiversity is disproportionately threatened and underprioritized relative to the marine and terrestrial biota, despite supporting a richness of species and ecosystems with their own intrinsic value and providing multiple essential ecosystem services. Future policies and strategies must have a greater focus on the unique ecology of freshwater life and its multiple threats, and now is a critical time to reflect on how this may be achieved. We identify priority topics including environmental flows, water quality, invasive species, integrated water resources management, strategic conservation planning, and emerging technologies for freshwater ecosystem monitoring. We synthesize these topics with decades of first-hand experience and recent literature into 14 special recommendations for global freshwater biodiversity conservation based on the successes and setbacks of European policy, management, and research. Applying and following these recommendations will inform and enhance the ability of global and European post-2020 biodiversity agreements to halt and reverse the rapid global decline of freshwater biodiversity.
Purpose: The present work aimed to delineate (i) a revised protocol according to recent methodological developments in evidence generation, to (ii) describe its interpretation, the assessment of the overall certainty of evidence and to (iii) outline an Evidence to Decision framework for deriving an evidence-based guideline on quantitative and qualitative aspects of dietary protein intake. Methods A methodological protocol to systematically investigate the association between dietary protein intake and several health outcomes and for deriving dietary protein intake recommendations for the primary prevention of various non-communicable diseases in the general adult population was developed. Results The developed methodological protocol relies on umbrella reviews including systematic reviews with or without meta-analyses. Systematic literature searches in three databases will be performed for each health-related outcome. The methodological quality of all selected systematic reviews will be evaluated using a modified version of AMSTAR 2, and the outcome-specific certainty of evidence for systematic reviews with or without meta-analysis will be assessed with NutriGrade. The general outline of the Evidence to Decision framework foresees that recommendations in the derived guideline will be given based on the overall certainty of evidence as well as on additional criteria such as sustainability. Conclusion The methodological protocol permits a systematic evaluation of published systematic reviews on dietary protein intake and its association with selected health-related outcomes. An Evidence to Decision framework will be the basis for the overall conclusions and the resulting recommendations for dietary protein intake.
Polygenic risk scores (PRS) aggregating results from genome-wide association studies are the state of the art in the prediction of susceptibility to complex traits or diseases, yet their predictive performance is limited for various reasons, not least of which is their failure to incorporate the effects of gene-gene interactions. Novel machine learning algorithms that use large amounts of data promise to find gene-gene interactions in order to build models with better predictive performance than PRS. Here, we present a data preprocessing step by using data-mining of contextual information to reduce the number of features, enabling machine learning algorithms to identify gene-gene interactions. We applied our approach to the Parkinson's Progression Markers Initiative (PPMI) dataset, an observational clinical study of 471 genotyped subjects (368 cases and 152 controls). With an AUC of 0.85 (95% CI = [0.72; 0.96]), the interaction-based prediction model outperforms the PRS (AUC of 0.58 (95% CI = [0.42; 0.81])). Furthermore, feature importance analysis of the model provided insights into the mechanism of Parkinson's disease. For instance, the model revealed an interaction of previously described drug target candidate genes TMEM175 and GAPDHP25. These results demonstrate that interaction-based machine learning models can improve genetic prediction models and might provide an answer to the missing heritability problem.
Objective: Demographic changes are increasing the pressure to improve therapeutic strategies against cognitive decline in Alzheimer disease (AD) and mild cognitive impairment (MCI). Besides drug treatment, physical activity seems to be a promising intervention target as epidemiological and clinical studies suggest beneficial effects of exercise training on cognition. Using comparable inclusion and exclusion criteria, we analyzed the efficacy of drug therapy (cholinesterase inhibitors, memantine, and Ginkgo biloba) and exercise interventions for improving cognition in AD and MCI populations. Methods: We searched The Cochrane Library, EBSCO, OVID, Web of Science, and U.S Food and Drug Administration data from inception through October 30, 2013. Randomized controlled trials in which at least one treatment arm consisted of an exercise or a pharmacological intervention for AD or MCI patients, and which had either a non-exposed control condition or a control condition that received another intervention. Treatment discontinuation rates and Standardized Mean Change score using Raw score standardization (SMCR) of cognitive performance were calculated. Results: Discontinuation rates varied substantially and ranged between 0% and 49% with a median of 18%. Significantly increased discontinuation rates were found for galantamine and rivastigmine as compared to placebo in AD studies. Drug treatments resulted in a small pooled effect on cognition (SMCR: 0.23, 95% CI: 0.20 to 0.25) in AD studies (N = 45, 18,434 patients) and no effect in any of the MCI studies (N = 5, 3,693 patients; SMCR: 0.03, 95% CI: 0.00 to 0.005). Exercise interventions had a moderate to strong pooled effect size (SMCR: 0.83, 95% CI: 0.59 to 1.07) in AD studies (N = 4, 119 patients), and a small effect size (SMCR: 0.20, 95% CI: 0.11 to 0.28) in MCI (N = 6, 443 patients). Conclusions: Drug treatments have a small but significant impact on cognitive functioning in AD and exercise has the potential to improve cognition in AD and MCI. Head-to-head trials with sufficient statistical power are necessary to directly compare efficacy, safety, and acceptability. Combining these two approaches might further increase the efficacy of each individual intervention. Identifier: PROSPERO (2013:CRD42013003910).
Diese eher ungewöhnliche, aber sehr persönlich gehaltene Festschrift ist dem langjährigen Wirken von Dr. Christiane Büchner als „Geschäftsführerin“ am Kommunalwissenschaftlichen Institut (KWI) der Universität Potsdam gewidmet. Die von Prof. Jochen Franzke zusammengestellte und herausgegebene Publikation enthält im ersten Teil neben dem Grußwort des Geschäftsführenden Direktors des KWI Herrn Prof. Thorsten Ingo Schmidt eine Reihe persönlicher Würdigungen von Kolleginnen und Kollegen, Gastwissenschaftlern und Mitarbeitenden, die seit 1994 in verschiedenen Phasen der Entwicklung des KWIs mit Dr. Christiane Büchner eng zusammengearbeitet haben. Der abschließende Dokumentationsteil der Publikation enthält neben Auszügen aus dem Schriftenverzeichnis von Dr. Christiane Büchner auch zwei Nachdrucke aus deren Feder zum Thema der Kreisgebietsreform in Brandenburg (von 2001) sowie über den Landkreis Barnim (von 2019).