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

© 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

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Die politische Tendenz der vierten Satire des Aulus Persius Flaccus

von Anton StädtlerText handschriftl.Graz, Univ., Diss., 1918(VLID)231496

Mean and standard deviation of the acoustic parameters for each call type as well as results of the univariate ANOVA comparing the four call types.

<p>Mean and standard deviation of the acoustic parameters for each call type as well as results of the univariate ANOVA comparing the four call types.</p

Scatterplot of the discriminant function analysis.

<p><b>(</b>a) DFA function 1 separates the Whines from the noisy call types. (b) DFA functions 2 and 3 separate the three noisy call types Snort, Threat and Pant.</p

Description of measured acoustic parameters.

<p>Description of measured acoustic parameters.</p

Examples of sonograms for the different call types.

<p>Whines (A-D) showing temporal and spectral variations of the contour of the fundamental frequency; Snort without and with pulsed structure; Threat and Pant.</p

Comparison of infant white rhinoceros vocalisations (present study) and the literature on adult vocalisations of the Northern [17] and Southern white rhinoceros [38].

<p>Comparison of infant white rhinoceros vocalisations (present study) and the literature on adult vocalisations of the Northern [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192166#pone.0192166.ref017" target="_blank">17</a>] and Southern white rhinoceros [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192166#pone.0192166.ref038" target="_blank">38</a>].</p