625 research outputs found
Finding strong lenses in CFHTLS using convolutional neural networks
We train and apply convolutional neural networks, a machine learning
technique developed to learn from and classify image data, to
Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) imaging for the
identification of potential strong lensing systems. An ensemble of four
convolutional neural networks was trained on images of simulated galaxy-galaxy
lenses. The training sets consisted of a total of 62,406 simulated lenses and
64,673 non-lens negative examples generated with two different methodologies.
The networks were able to learn the features of simulated lenses with accuracy
of up to 99.8% and a purity and completeness of 94-100% on a test set of 2000
simulations. An ensemble of trained networks was applied to all of the 171
square degrees of the CFHTLS wide field image data, identifying 18,861
candidates including 63 known and 139 other potential lens candidates. A second
search of 1.4 million early type galaxies selected from the survey catalog as
potential deflectors, identified 2,465 candidates including 117 previously
known lens candidates, 29 confirmed lenses/high-quality lens candidates, 266
novel probable or potential lenses and 2097 candidates we classify as false
positives. For the catalog-based search we estimate a completeness of 21-28%
with respect to detectable lenses and a purity of 15%, with a false-positive
rate of 1 in 671 images tested. We predict a human astronomer reviewing
candidates produced by the system would identify ~20 probable lenses and 100
possible lenses per hour in a sample selected by the robot. Convolutional
neural networks are therefore a promising tool for use in the search for lenses
in current and forthcoming surveys such as the Dark Energy Survey and the Large
Synoptic Survey Telescope.Comment: 16 pages, 8 figures. Accepted by MNRA
The genetic architecture underlying the evolution of a rare piscivorous life history form in brown trout after secondary contact and strong introgression
Identifying the genetic basis underlying phenotypic divergence and reproductive isolation is a longstanding problem in evolutionary biology. Genetic signals of adaptation and reproductive isolation are often confounded by a wide range of factors, such as variation in demographic history or genomic features. Brown trout ( ) in the Loch Maree catchment, Scotland, exhibit reproductively isolated divergent life history morphs, including a rare piscivorous (ferox) life history form displaying larger body size, greater longevity and delayed maturation compared to sympatric benthivorous brown trout. Using a dataset of 16,066 SNPs, we analyzed the evolutionary history and genetic architecture underlying this divergence. We found that ferox trout and benthivorous brown trout most likely evolved after recent secondary contact of two distinct glacial lineages, and identified 33 genomic outlier windows across the genome, of which several have most likely formed through selection. We further identified twelve candidate genes and biological pathways related to growth, development and immune response potentially underpinning the observed phenotypic differences. The identification of clear genomic signals divergent between life history phenotypes and potentially linked to reproductive isolation, through size assortative mating, as well as the identification of the underlying demographic history, highlights the power of genomic studies of young species pairs for understanding the factors shaping genetic differentiation
Innovations in thoracic imaging:CT, radiomics, AI and x-ray velocimetry
In recent years, pulmonary imaging has seen enormous progress, with the introduction, validation and implementation of new hardware and software. There is a general trend from mere visual evaluation of radiological images to quantification of abnormalities and biomarkers, and assessment of 'non visual' markers that contribute to establishing diagnosis or prognosis. Important catalysts to these developments in thoracic imaging include new indications (like computed tomography [CT] lung cancer screening) and the COVID-19 pandemic. This review focuses on developments in CT, radiomics, artificial intelligence (AI) and x-ray velocimetry for imaging of the lungs. Recent developments in CT include the potential for ultra-low-dose CT imaging for lung nodules, and the advent of a new generation of CT systems based on photon-counting detector technology. Radiomics has demonstrated potential towards predictive and prognostic tasks particularly in lung cancer, previously not achievable by visual inspection by radiologists, exploiting high dimensional patterns (mostly texture related) on medical imaging data. Deep learning technology has revolutionized the field of AI and as a result, performance of AI algorithms is approaching human performance for an increasing number of specific tasks. X-ray velocimetry integrates x-ray (fluoroscopic) imaging with unique image processing to produce quantitative four dimensional measurement of lung tissue motion, and accurate calculations of lung ventilation
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Potential implications of practice effects in Alzheimer's disease prevention trials.
IntroductionPractice effects (PEs) present a potential confound in clinical trials with cognitive outcomes. A single-blind placebo run-in design, with repeated cognitive outcome assessments before randomization to treatment, can minimize effects of practice on trial outcome.MethodsWe investigated the potential implications of PEs in Alzheimer's disease prevention trials using placebo arm data from the Alzheimer's Disease Cooperative Study donepezil/vitamin E trial in mild cognitive impairment. Frequent ADAS-Cog measurements early in the trial allowed us to compare two competing trial designs: a 19-month trial with randomization after initial assessment, versus a 15-month trial with a 4-month single-blind placebo run-in and randomization after the second administration of the ADAS-Cog. Standard power calculations assuming a mixed-model repeated-measure analysis plan were used to calculate sample size requirements for a hypothetical future trial designed to detect a 50% slowing of cognitive decline.ResultsOn average, ADAS-Cog 13 scores improved at first follow-up, consistent with a PE and progressively worsened thereafter. The observed change for a 19-month trial (1.18 points) was substantively smaller than that for a 15-month trial with 4-month run-in (1.79 points). To detect a 50% slowing in progression under the standard design (i.e., a 0.59 point slowing), a future trial would require 3.4 times more subjects than would be required to detect the comparable percent slowing (i.e., 0.90 points) with the run-in design.DiscussionAssuming the improvement at first follow-up observed in this trial represents PEs, the rate of change from the second assessment forward is a more accurate representation of symptom progression in this population and is the appropriate reference point for describing treatment effects characterized as percent slowing of symptom progression; failure to accommodate this leads to an oversized clinical trial. We conclude that PEs are an important potential consideration when planning future trials
Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans
Pulmonary lobe segmentation in computed tomography scans is essential for
regional assessment of pulmonary diseases. Recent works based on convolution
neural networks have achieved good performance for this task. However, they are
still limited in capturing structured relationships due to the nature of
convolution. The shape of the pulmonary lobes affect each other and their
borders relate to the appearance of other structures, such as vessels, airways,
and the pleural wall. We argue that such structural relationships play a
critical role in the accurate delineation of pulmonary lobes when the lungs are
affected by diseases such as COVID-19 or COPD.
In this paper, we propose a relational approach (RTSU-Net) that leverages
structured relationships by introducing a novel non-local neural network
module. The proposed module learns both visual and geometric relationships
among all convolution features to produce self-attention weights.
With a limited amount of training data available from COVID-19 subjects, we
initially train and validate RTSU-Net on a cohort of 5000 subjects from the
COPDGene study (4000 for training and 1000 for evaluation). Using models
pre-trained on COPDGene, we apply transfer learning to retrain and evaluate
RTSU-Net on 470 COVID-19 suspects (370 for retraining and 100 for evaluation).
Experimental results show that RTSU-Net outperforms three baselines and
performs robustly on cases with severe lung infection due to COVID-19
Emphysema Subtyping on Thoracic Computed Tomography Scans using Deep Neural Networks
Accurate identification of emphysema subtypes and severity is crucial for
effective management of COPD and the study of disease heterogeneity. Manual
analysis of emphysema subtypes and severity is laborious and subjective. To
address this challenge, we present a deep learning-based approach for
automating the Fleischner Society's visual score system for emphysema subtyping
and severity analysis. We trained and evaluated our algorithm using 9650
subjects from the COPDGene study. Our algorithm achieved the predictive
accuracy at 52\%, outperforming a previously published method's accuracy of
45\%. In addition, the agreement between the predicted scores of our method and
the visual scores was good, where the previous method obtained only moderate
agreement. Our approach employs a regression training strategy to generate
categorical labels while simultaneously producing high-resolution localized
activation maps for visualizing the network predictions. By leveraging these
dense activation maps, our method possesses the capability to compute the
percentage of emphysema involvement per lung in addition to categorical
severity scores. Furthermore, the proposed method extends its predictive
capabilities beyond centrilobular emphysema to include paraseptal emphysema
subtypes
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