560 research outputs found
Getting the picture : iconicity does not affect representation-referent confusion
Three experiments examined 3- to 5-year-olds' (N = 428) understanding of the relationship between pictorial iconicity (photograph, colored drawing, schematic drawing) and the real world referent. Experiments 1 and 2 explored pictorial iconicity in picture-referent confusion after the picture-object relationship has been established. Pictorial iconicity had no effect on referential confusion when the referent changed after the picture had been taken/drawn (Experiment 1) and when the referent and the picture were different from the outset (Experiment 2). Experiment 3 investigated whether children are sensitive to iconicity to begin with. Children deemed photographs from a choice of varying iconicity representations as best representations for object reference. Together, findings suggest that iconicity plays a role in establishing a picture-object relation per se but is irrelevant once children have accepted that a picture represents an object. The latter finding may reflect domain general representational abilities
Tracking TCRß sequence clonotype expansions during antiviral therapy using high-throughput sequencing of the hypervariable region
To maintain a persistent infection viruses such as hepatitis C virus (HCV) employ a range of mechanisms that subvert protective T cell responses. The suppression of antigen-specific T cell responses by HCV hinders efforts to profile T cell responses during chronic infection and antiviral therapy. Conventional methods of detecting antigen-specific T cells utilize either antigen stimulation (e.g., ELISpot, proliferation assays, cytokine production) or antigen-loaded tetramer staining. This limits the ability to profile T cell responses during chronic infection due to suppressed effector function and the requirement for prior knowledge of antigenic viral peptide sequences. Recently, high-throughput sequencing (HTS) technologies have been developed for the analysis of T cell repertoires. In the present study, we have assessed the feasibility of HTS of the TCRβ complementarity determining region (CDR)3 to track T cell expansions in an antigen-independent manner. Using sequential blood samples from HCV-infected individuals undergoing antiviral therapy, we were able to measure the population frequencies of >35,000 TCRβ sequence clonotypes in each individual over the course of 12 weeks. TRBV/TRBJ gene segment usage varied markedly between individuals but remained relatively constant within individuals across the course of therapy. Despite this stable TRBV/TRBJ gene segment usage, a number of TCRβ sequence clonotypes showed dramatic changes in read frequency. These changes could not be linked to therapy outcomes in the present study; however, the TCRβ CDR3 sequences with the largest fold changes did include sequences with identical TRBV/TRBJ gene segment usage and high junction region homology to previously published CDR3 sequences from HCV-specific T cells targeting the HLA-B*0801-restricted 1395HSKKKCDEL1403 and HLA-A*0101-restricted 1435ATDALMTGY1443 epitopes. The pipeline developed in this proof of concept study provides a platform for the design of future experiments to accurately address the question of whether T cell responses contribute to SVR upon antiviral therapy. This pipeline represents a novel technique to analyze T cell dynamics in situations where conventional antigen-dependent methods are limited due to suppression of T cell functions and highly diverse antigenic sequences
Repetitive Stimulation of the Pituitary with Growth-Hormone-Releasing Hormone Alters the Proportion of 22 and 20 Kilodalton Human-Growth Hormone Released
Background/Aims. 20 Kilodalton-hGH (20 K-hGH) is the second most abundant pituitary GH variant after 22 K-hGH. In the steady state the proportion of 20 : 22 K-hGH appears constant; does this proportion change with repetitive somatotroph stimulation? Methods. Forty adult males were randomised to receive a GHRH(1–29)NH2 bolus (0.5 μg/kg (n = 20) or 1.0 μg/kg (n = 20)), preceded or followed by a saline bolus, 1 week apart. Four to six weeks later, 10 subjects received 0.5 μg/kg GHRH(1–29)NH2 at 0, 60, 120, and 180 minutes. Clearance rate of 22 and 20 K-hGH was measured in 10 subjects. Results. Total amount/proportion of 22 K-hGH/20 K-hGH secreted was similar for both GHRH(1–29)NH2 doses. Repetitive stimulation reduced the amount of 22 K-hGH released whereas the amount of 20 K-hGH did not change significantly leading to an increase in the proportion of 20 K-hGH (P = .05). Half-life of 20 and 22 K-hGH were not significantly different (P = .55). Conclusions. Repetitive stimulation of the somatotroph may alter the proportion of GH variant released
Unsupervised Multimodal Surface Registration with Geometric Deep Learning
This paper introduces GeoMorph, a novel geometric deep-learning framework
designed for image registration of cortical surfaces. The registration process
consists of two main steps. First, independent feature extraction is performed
on each input surface using graph convolutions, generating low-dimensional
feature representations that capture important cortical surface
characteristics. Subsequently, features are registered in a deep-discrete
manner to optimize the overlap of common structures across surfaces by learning
displacements of a set of control points. To ensure smooth and biologically
plausible deformations, we implement regularization through a deep conditional
random field implemented with a recurrent neural network. Experimental results
demonstrate that GeoMorph surpasses existing deep-learning methods by achieving
improved alignment with smoother deformations. Furthermore, GeoMorph exhibits
competitive performance compared to classical frameworks. Such versatility and
robustness suggest strong potential for various neuroscience applications
Appropriate dairy calf feeding from birth to weaning: “it’s an investment for the future”
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Dairy calves must be fed appropriately to meet their nutritional needs, supporting optimal growth and development to achieve the recommended target age at first calving (AFC) of 24 months. Traditional restricted milk feeding practices suppress growth, contribute to negative welfare states and may result in malnutrition and immunosuppression. Despite more recent recommendations to increase milk allowances for pre-weaned calves, restricted feeding remains a common practice. This study explored the rationales behind the calf feeding protocols used by dairy farmers in England. Forty qualitative interviews (26 farmers, 14 advisors) were conducted between May 2016 and June 2017, transcribed in full, then coded into themes. Results indicate that a variety of calf feeding regimes are used on farms, largely determined by farmers’ attitudes regarding ease of management and the wellbeing of calves. Advisors were concerned about widespread underfeeding of calves, which may be partially due to insufficiently clear recommendations for calf milk replacer (CMR) feeding rates. There was also evidence of uncertainty regarding best practices for weaning calves. Collaboration between academic research and industry is essential to establish a consensus on calf feeding standards which support physiological function, facilitate weaning, support growth targets and ensure calf health and welfare is protected
Trends in atmospheric evaporative demand in Great Britain using high-resolution meteorological data
Observations of climate are often available on very different spatial scales from observations of the natural environments and resources that are affected by climate change. In order to help bridge the gap between these scales using modelling, a new dataset of daily meteorological variables was created at 1 km resolution over Great Britain for the years 1961–2012, by interpolating coarser resolution climate data and including the effects of local topography. These variables were used to calculate atmospheric evaporative demand (AED) at the same spatial and temporal resolution. Two functions that represent AED were chosen: one is a standard form of potential evapotranspiration (PET) and the other is a derived PET measure used by hydrologists that includes the effect of water intercepted by the canopy (PETI). Temporal trends in these functions were calculated, with PET found to be increasing in all regions, and at an overall rate of 0.021 ± 0.021 mm day−1 decade−1 in Great Britain. PETI was found to be increasing at a rate of 0.019 ± 0.020 mm day−1 decade−1 in Great Britain, but this was not statistically significant. However, there was a trend in PETI in England of 0.023 ± 0.023 mm day−1 decade−1. The trends were found to vary by season, with spring PET increasing by 0.043 ± 0.019 mm day−1 decade−1 (0.038 ± 0.018 mm day−1 decade−1 when the interception correction is included) in Great Britain, while there is no statistically significant trend in other seasons. The trends were attributed analytically to trends in the climate variables; the overall positive trend was predominantly driven by rising air temperature, although rising specific humidity had a negative effect on the trend. Recasting the analysis in terms of relative humidity revealed that the overall effect is that falling relative humidity causes the PET to rise. Increasing downward short- and longwave radiation made an overall positive contribution to the PET trend, while decreasing wind speed made a negative contribution to the trend in PET. The trend in spring PET was particularly strong due to a strong decrease in relative humidity and increase in downward shortwave radiation in the spring
Conditional Temporal Attention Networks for Neonatal Cortical Surface Reconstruction
Cortical surface reconstruction plays a fundamental role in modeling the
rapid brain development during the perinatal period. In this work, we propose
Conditional Temporal Attention Network (CoTAN), a fast end-to-end framework for
diffeomorphic neonatal cortical surface reconstruction. CoTAN predicts
multi-resolution stationary velocity fields (SVF) from neonatal brain magnetic
resonance images (MRI). Instead of integrating multiple SVFs, CoTAN introduces
attention mechanisms to learn a conditional time-varying velocity field (CTVF)
by computing the weighted sum of all SVFs at each integration step. The
importance of each SVF, which is estimated by learned attention maps, is
conditioned on the age of the neonates and varies with the time step of
integration. The proposed CTVF defines a diffeomorphic surface deformation,
which reduces mesh self-intersection errors effectively. It only requires 0.21
seconds to deform an initial template mesh to cortical white matter and pial
surfaces for each brain hemisphere. CoTAN is validated on the Developing Human
Connectome Project (dHCP) dataset with 877 3D brain MR images acquired from
preterm and term born neonates. Compared to state-of-the-art baselines, CoTAN
achieves superior performance with only 0.12mm geometric error and 0.07%
self-intersecting faces. The visualization of our attention maps illustrates
that CoTAN indeed learns coarse-to-fine surface deformations automatically
without intermediate supervision.Comment: Accepted by the 26th International Conference on Medical Image
Computing and Computer Assisted Intervention, MICCAI 202
ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping
Feature attribution (FA), or the assignment of class-relevance to different
locations in an image, is important for many classification problems but is
particularly crucial within the neuroscience domain, where accurate mechanistic
models of behaviours, or disease, require knowledge of all features
discriminative of a trait. At the same time, predicting class relevance from
brain images is challenging as phenotypes are typically heterogeneous, and
changes occur against a background of significant natural variation. Here, we
present a novel framework for creating class specific FA maps through
image-to-image translation. We propose the use of a VAE-GAN to explicitly
disentangle class relevance from background features for improved
interpretability properties, which results in meaningful FA maps. We validate
our method on 2D and 3D brain image datasets of dementia (ADNI dataset), ageing
(UK Biobank), and (simulated) lesion detection. We show that FA maps generated
by our method outperform baseline FA methods when validated against ground
truth. More significantly, our approach is the first to use latent space
sampling to support exploration of phenotype variation. Our code will be
available online at https://github.com/CherBass/ICAM.Comment: Submitted to NeurIPS 2020: Neural Information Processing Systems.
Keywords: interpretable, classification, feature attribution, domain
translation, variational autoencoder, generative adversarial network,
neuroimagin
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