Optimizing Machine Learning Methods to Improve Predictive Models of Alzheimer's Disease.

BackgroundPredicting clinical course of cognitive decline can boost clinical trials' power and improve our clinical decision-making. Machine learning (ML) algorithms are specifically designed for the purpose of prediction; however. identifying optimal features or algorithms is still a challenge.ObjectiveTo investigate the accuracy of different ML methods and different features to classify cognitively normal (CN) individuals from Alzheimer's disease (AD) and to predict longitudinal outcome in participants with mild cognitive impairment (MCI).MethodsA total of 1,329 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were included: 424 CN, 656 MCI, and 249 AD individuals. Four feature-sets at baseline (hippocampal volume and volume of 47 cortical and subcortical regions with and without demographics and APOE4) and six machine learning methods (decision trees, support vector machines, K-nearest neighbor, ensemble linear discriminant, boosted trees, and random forests) were used to classify participants with normal cognition from participants with AD. Subsequently the model with best classification performance was used for predicting clinical outcome of MCI participants.ResultsEnsemble linear discriminant models using demographics and all volumetric magnetic resonance imaging measures as feature-set showed the best performance in classification of CN versus AD participants (accuracy = 92.8%, sensitivity = 95.8%, and specificity = 88.3%). Prediction accuracy of future conversion from MCI to AD for this ensemble linear discriminant at 6, 12, 24, 36, and 48 months was 63.8% (sensitivity = 74.4, specificity = 63.1), 68.9% (sensitivity = 75.9, specificity = 67.8), 74.9% (sensitivity = 71.5, specificity = 76.3), 75.3%, (sensitivity = 65.2, specificity = 79.7), and 77.0% (sensitivity = 59.6, specificity = 86.1), respectively.ConclusionsMachine learning models trained for classification of CN versus AD can improve our prediction ability of MCI conversion to AD

S-CLEVER. Schulentwicklung vor neuen Herausforderungen. Erste Ergebnisse der Schulleiter*innen-Befragung September und Oktober 2020 für Österreich

Die trinationale Studie „S-CLEVER. Schulentwicklung vor neuen Herausforderungen“ wird von einem wissenschaftlichen Konsortium durchgeführt. Untersucht werden längerfristige und nachhaltige Entwicklungen sowie die Herausforderungen, denen sich die Schulen im Zusammenhang mit der COVID-19-Pandemie stellen mussten. Der Fokus liegt auf den unterschiedlichen Erfahrungen der Schulleiter*innen, auf deren Wahrnehmung der Herausforderungen und ihren Handlungsstrategien. Folgende Fragestellungen leiten die Studie: Welche Herausforderungen haben sich den einzelnen Schulen im Zusammenhang mit der COVID-19-Pandemie gestellt und welche stellen sich im laufenden Schuljahr 2020/2021? Welche Strategien haben Schulen angewendet, um mit diesen Herausforderungen umzugehen? Welche langfristigen Lösungsansätze auf Schul- und Unterrichtsebene haben sie entwickelt und welche sind besonders geeignet und ertragreich? Welche nachhaltigen Effekte haben diese Lösungsansätze auf die schulische Praxis und das Lernen der Schüler*innen? Welche Faktoren beeinflussen die Entwicklungsprozesse in der Schule und deren Ergebnisse? Der vorliegende Bericht fasst die Ergebnisse der ersten Befragung im Herbst 2020 der S-CLEVER-Studie für Österreich zusammen. Er bezieht sich auf die erste von drei Online-Befragungen von Schulleiter*innen. Am ersten Befragungszeitpunkt beteiligten sich 1.188 Schulen aus Deutschland, Österreich und der Schweiz. Die weiteren Erhebungen finden im Frühjahr und Sommer 2021 statt. Alle Ergebnisse basieren auf den Angaben und Einschätzungen der Schulleiter*innen und spiegeln deren Perspektive auf das Schul- und Unterrichtsgeschehen wider. (DIPF/Orig.

Blind testing of routine, fully automated determination of protein structures from NMR data.

Item does not contain fulltextThe protocols currently used for protein structure determination by nuclear magnetic resonance (NMR) depend on the determination of a large number of upper distance limits for proton-proton pairs. Typically, this task is performed manually by an experienced researcher rather than automatically by using a specific computer program. To assess whether it is indeed possible to generate in a fully automated manner NMR structures adequate for deposition in the Protein Data Bank, we gathered 10 experimental data sets with unassigned nuclear Overhauser effect spectroscopy (NOESY) peak lists for various proteins of unknown structure, computed structures for each of them using different, fully automatic programs, and compared the results to each other and to the manually solved reference structures that were not available at the time the data were provided. This constitutes a stringent "blind" assessment similar to the CASP and CAPRI initiatives. This study demonstrates the feasibility of routine, fully automated protein structure determination by NMR

The sleep-deprived human brain

How does a lack of sleep affect our brains? In contrast to the benefits of sleep, frameworks exploring the impact of sleep loss are relatively lacking. Importantly, the effects of sleep deprivation (SD) do not simply reflect the absence of sleep and the benefits attributed to it; rather, they reflect the consequences of several additional factors, including extended wakefulness. With a focus on neuroimaging studies, we review the consequences of SD on attention and working memory, positive and negative emotion, and hippocampal learning. We explore how this evidence informs our mechanistic understanding of the known changes in cognition and emotion associated with SD, and the insights it provides regarding clinical conditions associated with sleep disruption