87,092 research outputs found
Structure and functional motifs of GCR1, the only plant protein with a GPCR fold?
Whether GPCRs exist in plants is a fundamental biological question. Interest in deorphanizing new G
protein coupled receptors (GPCRs), arises because of their importance in signaling. Within plants, this
is controversial as genome analysis has identified 56 putative GPCRs, including GCR1 which is
reportedly a remote homologue to class A, B and E GPCRs. Of these, GCR2, is not a GPCR; more
recently it has been proposed that none are, not even GCR1. We have addressed this disparity
between genome analysis and biological evidence through a structural bioinformatics study, involving
fold recognition methods, from which only GCR1 emerges as a strong candidate. To further probe
GCR1, we have developed a novel helix alignment method, which has been benchmarked against the
the class A – class B - class F GPCR alignments. In addition, we have presented a mutually consistent
set of alignments of GCR1 homologues to class A, class B and class F GPCRs, and shown that GCR1
is closer to class A and /or class B GPCRs than class A, class B or class F GPCRs are to each other.
To further probe GCR1, we have aligned transmembrane helix 3 of GCR1 to each of the 6 GPCR
classes. Variability comparisons provide additional evidence that GCR1 homologues have the GPCR
fold. From the alignments and a GCR1 comparative model we have identified motifs that are common
to GCR1, class A, B and E GPCRs. We discuss the possibilities that emerge from this controversial
evidence that GCR1 has a GPCR fol
A comparison of magnetic resonance imaging and neuropsychological examination in the diagnostic distinction of Alzheimer’s disease and behavioral variant frontotemporal dementia
The clinical distinction between Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) remains challenging and largely dependent on the experience of the clinician. This study investigates whether objective machine learning algorithms using supportive neuroimaging and neuropsychological clinical features can aid the distinction between both diseases. Retrospective neuroimaging and neuropsychological data of 166 participants (54 AD; 55 bvFTD; 57 healthy controls) was analyzed via a Naïve Bayes classification model. A subgroup of patients (n = 22) had pathologically-confirmed diagnoses. Results show that a combination of gray matter atrophy and neuropsychological features allowed a correct classification of 61.47% of cases at clinical presentation. More importantly, there was a clear dissociation between imaging and neuropsychological features, with the latter having the greater diagnostic accuracy (respectively 51.38 vs. 62.39%). These findings indicate that, at presentation, machine learning classification of bvFTD and AD is mostly based on cognitive and not imaging features. This clearly highlights the urgent need to develop better biomarkers for both diseases, but also emphasizes the value of machine learning in determining the predictive diagnostic features in neurodegeneration
Automatic Quality Estimation for ASR System Combination
Recognizer Output Voting Error Reduction (ROVER) has been widely used for
system combination in automatic speech recognition (ASR). In order to select
the most appropriate words to insert at each position in the output
transcriptions, some ROVER extensions rely on critical information such as
confidence scores and other ASR decoder features. This information, which is
not always available, highly depends on the decoding process and sometimes
tends to over estimate the real quality of the recognized words. In this paper
we propose a novel variant of ROVER that takes advantage of ASR quality
estimation (QE) for ranking the transcriptions at "segment level" instead of:
i) relying on confidence scores, or ii) feeding ROVER with randomly ordered
hypotheses. We first introduce an effective set of features to compensate for
the absence of ASR decoder information. Then, we apply QE techniques to perform
accurate hypothesis ranking at segment-level before starting the fusion
process. The evaluation is carried out on two different tasks, in which we
respectively combine hypotheses coming from independent ASR systems and
multi-microphone recordings. In both tasks, it is assumed that the ASR decoder
information is not available. The proposed approach significantly outperforms
standard ROVER and it is competitive with two strong oracles that e xploit
prior knowledge about the real quality of the hypotheses to be combined.
Compared to standard ROVER, the abs olute WER improvements in the two
evaluation scenarios range from 0.5% to 7.3%
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