348 research outputs found
Improved test-retest reliability of and susceptibility quantification using multi-shot multi echo 3D EPI
This study aimed to evaluate the potential of 3D echo-planar imaging (EPI)
for improving the reliability of -weighted () data and
quantification of decay rate and susceptibility ()
compared to conventional gradient echo (GRE)-based acquisition. Eight healthy
subjects in a wide age range were recruited. Each subject received repeated
scans for both GRE and EPI acquisitions with an isotropic 1 mm resolution at 3
T. Maps of and were quantified and compared using their
inter-scan difference to evaluate the test-retest reliability. Inter-protocol
differences of and between GRE and EPI were also
measured voxel by voxel and in selected ROIs to test the consistency between
the two acquisition methods. The quantifications of and
using EPI protocols showed increased test-retest reliability with higher EPI
factors up to 5 as performed in the experiment and were consistent with those
based on GRE. This result suggested multi-shot multi-echo 3D EPI can be a
useful alternative acquisition method for MRI and quantification of
and with reduced scan time, improved test-retest
reliability and similar accuracy compared to commonly used 3D GRE.Comment: 18 pages, 8 figures and 1 tabl
Multiplication between elements in Martingale Hardy spaces and their duals
In this paper, we establish continuous bilinear decompositions that arise in
the study of products between elements in martingale Hardy spaces $ H^p\
(0<p\leqslant 1) $ and functions in their dual spaces. Our decompositions are
based on martingale paraproducts. As a consequence of our work, we also obtain
analogous results for dyadic martingales on spaces of homogeneous type equipped
with a doubling measure
ThumbNet: One Thumbnail Image Contains All You Need for Recognition
Although deep convolutional neural networks (CNNs) have achieved great
success in computer vision tasks, its real-world application is still impeded
by its voracious demand of computational resources. Current works mostly seek
to compress the network by reducing its parameters or parameter-incurred
computation, neglecting the influence of the input image on the system
complexity. Based on the fact that input images of a CNN contain substantial
redundancy, in this paper, we propose a unified framework, dubbed as ThumbNet,
to simultaneously accelerate and compress CNN models by enabling them to infer
on one thumbnail image. We provide three effective strategies to train
ThumbNet. In doing so, ThumbNet learns an inference network that performs
equally well on small images as the original-input network on large images.
With ThumbNet, not only do we obtain the thumbnail-input inference network that
can drastically reduce computation and memory requirements, but also we obtain
an image downscaler that can generate thumbnail images for generic
classification tasks. Extensive experiments show the effectiveness of ThumbNet,
and demonstrate that the thumbnail-input inference network learned by ThumbNet
can adequately retain the accuracy of the original-input network even when the
input images are downscaled 16 times
MEWL: Few-shot multimodal word learning with referential uncertainty
Without explicit feedback, humans can rapidly learn the meaning of words.
Children can acquire a new word after just a few passive exposures, a process
known as fast mapping. This word learning capability is believed to be the most
fundamental building block of multimodal understanding and reasoning. Despite
recent advancements in multimodal learning, a systematic and rigorous
evaluation is still missing for human-like word learning in machines. To fill
in this gap, we introduce the MachinE Word Learning (MEWL) benchmark to assess
how machines learn word meaning in grounded visual scenes. MEWL covers human's
core cognitive toolkits in word learning: cross-situational reasoning,
bootstrapping, and pragmatic learning. Specifically, MEWL is a few-shot
benchmark suite consisting of nine tasks for probing various word learning
capabilities. These tasks are carefully designed to be aligned with the
children's core abilities in word learning and echo the theories in the
developmental literature. By evaluating multimodal and unimodal agents'
performance with a comparative analysis of human performance, we notice a sharp
divergence in human and machine word learning. We further discuss these
differences between humans and machines and call for human-like few-shot word
learning in machines.Comment: Accepted at ICML 202
Representation of EHR data for predictive modeling: a comparison between UMLS and other terminologies.
OBJECTIVE: Predictive disease modeling using electronic health record data is a growing field. Although clinical data in their raw form can be used directly for predictive modeling, it is a common practice to map data to standard terminologies to facilitate data aggregation and reuse. There is, however, a lack of systematic investigation of how different representations could affect the performance of predictive models, especially in the context of machine learning and deep learning.
MATERIALS AND METHODS: We projected the input diagnoses data in the Cerner HealthFacts database to Unified Medical Language System (UMLS) and 5 other terminologies, including CCS, CCSR, ICD-9, ICD-10, and PheWAS, and evaluated the prediction performances of these terminologies on 2 different tasks: the risk prediction of heart failure in diabetes patients and the risk prediction of pancreatic cancer. Two popular models were evaluated: logistic regression and a recurrent neural network.
RESULTS: For logistic regression, using UMLS delivered the optimal area under the receiver operating characteristics (AUROC) results in both dengue hemorrhagic fever (81.15%) and pancreatic cancer (80.53%) tasks. For recurrent neural network, UMLS worked best for pancreatic cancer prediction (AUROC 82.24%), second only (AUROC 85.55%) to PheWAS (AUROC 85.87%) for dengue hemorrhagic fever prediction.
DISCUSSION/CONCLUSION: In our experiments, terminologies with larger vocabularies and finer-grained representations were associated with better prediction performances. In particular, UMLS is consistently 1 of the best-performing ones. We believe that our work may help to inform better designs of predictive models, although further investigation is warranted
Systematically characterizing dysfunctional long intergenic noncoding RNAs in multiple brain regions of major psychosis
Schizophrenia (SZ) and bipolar disorder (BD) are severe neuropsychiatric disorders with serious impact on patients, together termed “major psychosis”. Recently, long intergenic non-coding RNAs (lincRNAs) were reported to play important roles in mental diseases. However, little was known about their molecular mechanism in pathogenesis of SZ and BD. Here, we performed RNA sequencing on 82 postmortem brain tissues from three brain regions (orbitofrontal cortex (BA11), anterior cingulate cortex (BA24) and dorsolateral prefrontal cortex (BA9)) of patients with SZ and BD and control subjects, generating over one billion reads. We characterized lincRNA transcriptome in the three brain regions and identified 20 differentially expressed lincRNAs (DELincRNAs) in BA11 for BD, 34 and 1 in BA24 and BA9 for SZ, respectively. Our results showed that these DELincRNAs exhibited brain region-specific patterns. Applying weighted gene co-expression network analysis, we revealed that DELincRNAs together with other genes can function as modules to perform different functions in different brain regions, such as immune system development in BA24 and oligodendrocyte differentiation in BA9. Additionally, we found that DNA methylation alteration could partly explain the dysregulation of lincRNAs, some of which could function as enhancers in the pathogenesis of major psychosis. Together, we performed systematical characterization of dysfunctional lincRNAs in multiple brain regions of major psychosis, which provided a valuable resource to understand their roles in SZ and BD pathology and helped to discover novel biomarkers
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