19 research outputs found

    Impairment of episodic memory in genetic frontotemporal dementia : a GENFI study

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    © 2021 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, LLC on behalf of Alzheimer's Association. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.Introduction: We aimed to assess episodic memory in genetic frontotemporal dementia (FTD) with the Free and Cued Selective Reminding Test (FCSRT). Methods: The FCSRT was administered in 417 presymptomatic and symptomatic mutation carriers (181 chromosome 9 open reading frame 72 [C9orf72], 163 progranulin [GRN], and 73 microtubule-associated protein tau [MAPT]) and 290 controls. Group differences and correlations with other neuropsychological tests were examined. We performed voxel-based morphometry to investigate the underlying neural substrates of the FCSRT. Results: All symptomatic mutation carrier groups and presymptomatic MAPT mutation carriers performed significantly worse on all FCSRT scores compared to controls. In the presymptomatic C9orf72 group, deficits were found on all scores except for the delayed total recall task, while no deficits were found in presymptomatic GRN mutation carriers. Performance on the FCSRT correlated with executive function, particularly in C9orf72 mutation carriers, but also with memory and naming tasks in the MAPT group. FCSRT performance also correlated with gray matter volumes of frontal, temporal, and subcortical regions in C9orf72 and GRN, but mainly temporal areas in MAPT mutation carriers. Discussion: The FCSRT detects presymptomatic deficits in C9orf72- and MAPT-associated FTD and provides important insight into the underlying cause of memory impairment in different forms of FTD.The Dementia Research Centre is supported by Alzheimer's Research UK, Alzheimer's Society, Brain Research UK, and The Wolfson Foundation. This work was supported by the NIHR UCL/H Biomedical Research Centre, the Leonard Wolfson Experimental Neurology Centre (LWENC) Clinical Research Facility, and the UK Dementia Research Institute, which receives its funding from UK DRI Ltd, funded by the UK Medical Research Council, Alzheimer's Society, and Alzheimer's Research UK. J. D. Rohrer is supported by an MRC Clinician Scientist Fellowship (MR/M008525/1) and has received funding from the NIHR Rare Disease Translational Research Collaboration (BRC149/NS/MH). This work was also supported by the MRC UK GENFI grant (MR/M023664/1); the Bluefield Project; the JPND GENFI-PROX grant (2019-02248); the Dioraphte Foundation (grant numbers 09-02-00); the Association for Frontotemporal Dementias Research Grant 2009; The Netherlands Organization for Scientific Research (NWO; grant HCMI 056-13-018); ZonMw Memorabel (Deltaplan Dementie, project numbers 733 050 103 and 733 050 813); JPND PreFrontAls consortium (project number 733051042). J. M. Poos is supported by a Fellowship award from Alzheimer Nederland (WE.15-2019.02). This work was conducted using the MRC Dementias Platform UK (MR/L023784/1 and MR/009076/1). Several authors of this publication are members of the European Reference Network for Rare Neurological Diseases - Project ID No 739510.info:eu-repo/semantics/publishedVersio

    Methoden zur Reduktion der Rechenzeit linearer Optimierungsmodelle in der Energiewirtschaft – Eine Performance-Analyse

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    Dieser Beitrag stellt mögliche AnsĂ€tze zur Reduktion der Rechenzeit von linearen Optimierungsproblemen mit energiewirtschaftlichem Anwendungshintergrund vor. Diese AnsĂ€tze bilden im Allgemeinen die Grundlage fĂŒr konzeptionelle Strategien zur Be-schleunigung von Energiesystemmodellen. Zu den einfachsten Beschleunigungsstrategien zĂ€hlt die Verkleinerung der Modelldimensionen, was beispielsweise durch Ändern der zeitlichen, rĂ€umlichen oder technologischen Auflösung eines Energiesystemmodells erreicht wer-den kann. Diese Strategien sind zwar hĂ€ufig ein Teil der Methodik in der Energiesystemanalyse, systematische Benchmarks zur Bewertung ihrer EffektivitĂ€t werden jedoch meist nicht durchgefĂŒhrt. Die vorliegende Arbeit adressiert genau diesen Sachverhalt. Hierzu werden Modellinstanzen des Modells REMix in verschiedenen GrĂ¶ĂŸenordnungen mittels einer Per-formance-Benchmark-Analyse untersucht. Die Ergebnisse legen zum einen den Schluss nahe, dass verkĂŒrzte BetrachtungszeitrĂ€ume das grĂ¶ĂŸte Potential unter den hier analysierten Strategien zur Reduktion von Rechenzeit bieten. Zum anderen empfiehlt sich die Verwendung des Barrier-Lösungsverfahrens mit multiplen Threads unter VernachlĂ€ssigung des Cross-Over

    Historisches Predigtenkorpus zum Nachfeld (HIPKON Version 1.0) - Dokumentation

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    Das Korpus HIPKON (Historisches Predigtenkorpus zum Nachfeld) wurde zur Untersuchung der Nachfeldbesetzung in der Entwicklung des Deutschen erstellt. Zwar existieren schon verschiedene datenbasierte Untersuchungen zum Nachfeld, jedoch sind deren Ergebnisse kaum miteinander vergleichbar, da sich die zugrunde gelegten Daten hinsichtlich der Textsorte oder des Dialektgebiets unterscheiden bzw. die verwendete Terminologie verschiedene PhĂ€nomene erfasst. Das neue Korpus soll dementsprechend zum ersten Mal die Vergleichbarkeit der Daten ĂŒber einen grĂ¶ĂŸeren Zeitraum hinweg ermöglichen.Not Reviewe

    The SCIP Optimization Suite 6.0

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    The SCIP Optimization Suite provides a collection of software packages for mathematical optimization centered around the constraint integer programming framework SCIP. This paper discusses enhancements and extensions contained in version 6.0 of the SCIP Optimization Suite. Besides performance improvements of the MIP and MINLP core achieved by new primal heuristics and a new selection criterion for cutting planes, one focus of this release are decomposition algorithms. Both SCIP and the automatic decomposition solver GCG now include advanced functionality for performing Benders’ decomposition in a generic framework. GCG’s detection loop for structured matrices and the coordination of pricing routines for Dantzig-Wolfe decomposition has been significantly revised for greater flexibility. Two SCIP extensions have been added to solve the recursive circle packing problem by a problem-specific column generation scheme and to demonstrate the use of the new Benders’ framework for stochastic capacitated facility location. Last, not least, the report presents updates and additions to the other components and extensions of the SCIP Optimization Suite: the LP solver SoPlex, the modeling language Zimpl, the parallelization framework UG, the Steiner tree solver SCIP-Jack, and the mixed-integer semidefinite programming solver SCIP-SDP
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