1,285,402 research outputs found
Early neurotransmitters changes in prodromal frontotemporal dementia: A GENFI study
Background: Neurotransmitters deficits in Frontotemporal Dementia (FTD) are still poorly understood. Better knowledge of neurotransmitters impairment, especially in prodromal disease stages, might tailor symptomatic treatment approaches.Methods: In the present study, we applied JuSpace toolbox, which allowed for cross-modal correlation of Mag-netic Resonance Imaging (MRI)-based measures with nuclear imaging derived estimates covering various neurotransmitter systems including dopaminergic, serotonergic, noradrenergic, GABAergic and glutamatergic neurotransmission.We included 392 mutation carriers (157 GRN, 164 C9orf72, 71 MAPT), together with 276 non-carrier cognitively healthy controls (HC). We tested if the spatial patterns of grey matter volume (GMV) alterations in mutation carriers (relative to HC) are correlated with specific neurotransmitter systems in prodromal (CDR (R) plus NACC FTLD = 0.5) and in symptomatic (CDR (R) plus NACC FTLD >= 1) FTD. Results: In prodromal stages of C9orf72 disease, voxel-based brain changes were significantly associated with spatial distribution of dopamine and acetylcholine pathways;in prodromal MAPT disease with dopamine and serotonin pathways, while in prodromal GRN disease no significant findings were reported (p < 0.05, Family Wise Error corrected). In symptomatic FTD, a widespread involvement of dopamine, serotonin, glutamate and acetylcholine pathways across all genetic subtypes was found. Social cognition scores, loss of empathy and poor response to emotional cues were found to correlate with the strength of GMV colocalization of dopamine and serotonin pathways (all p < 0.01).Conclusions: This study, indirectly assessing neurotransmitter deficits in monogenic FTD, provides novel insight into disease mechanisms and might suggest potential therapeutic targets to counteract disease-related symptoms
SciTech News Volume 71, No. 3 (2017)
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SciTech News Volume 71, No. 2 (2017)
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SciTech News Volume 71, No. 1 (2017)
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Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity.
A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework--a dynamic knowledge repository--wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline
Knowledge Base Population using Semantic Label Propagation
A crucial aspect of a knowledge base population system that extracts new
facts from text corpora, is the generation of training data for its relation
extractors. In this paper, we present a method that maximizes the effectiveness
of newly trained relation extractors at a minimal annotation cost. Manual
labeling can be significantly reduced by Distant Supervision, which is a method
to construct training data automatically by aligning a large text corpus with
an existing knowledge base of known facts. For example, all sentences
mentioning both 'Barack Obama' and 'US' may serve as positive training
instances for the relation born_in(subject,object). However, distant
supervision typically results in a highly noisy training set: many training
sentences do not really express the intended relation. We propose to combine
distant supervision with minimal manual supervision in a technique called
feature labeling, to eliminate noise from the large and noisy initial training
set, resulting in a significant increase of precision. We further improve on
this approach by introducing the Semantic Label Propagation method, which uses
the similarity between low-dimensional representations of candidate training
instances, to extend the training set in order to increase recall while
maintaining high precision. Our proposed strategy for generating training data
is studied and evaluated on an established test collection designed for
knowledge base population tasks. The experimental results show that the
Semantic Label Propagation strategy leads to substantial performance gains when
compared to existing approaches, while requiring an almost negligible manual
annotation effort.Comment: Submitted to Knowledge Based Systems, special issue on Knowledge
Bases for Natural Language Processin
Applying a User-centred Approach to Interactive Visualization Design
Analysing users in their context of work and finding out how and why they use different information resources is essential to provide interactive visualisation systems that match their goals and needs. Designers should actively involve the intended users throughout the whole process. This chapter presents a user-centered approach for the design of interactive visualisation systems. We describe three phases of the iterative visualisation design process: the early envisioning phase, the global specification hase, and the detailed specification phase. The whole design cycle is repeated until some criterion of success is reached. We discuss different techniques for the analysis of users, their tasks and domain. Subsequently, the design of prototypes and evaluation methods in visualisation practice are presented. Finally, we discuss the practical challenges in design and evaluation of collaborative visualisation environments. Our own case studies and those of others are used throughout the whole chapter to illustrate various approaches
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