Feature Tracking Cardiac Magnetic Resonance via Deep Learning and Spline Optimization

Feature tracking Cardiac Magnetic Resonance (CMR) has recently emerged as an area of interest for quantification of regional cardiac function from balanced, steady state free precession (SSFP) cine sequences. However, currently available techniques lack full automation, limiting reproducibility. We propose a fully automated technique whereby a CMR image sequence is first segmented with a deep, fully convolutional neural network (CNN) architecture, and quadratic basis splines are fitted simultaneously across all cardiac frames using least squares optimization. Experiments are performed using data from 42 patients with hypertrophic cardiomyopathy (HCM) and 21 healthy control subjects. In terms of segmentation, we compared state-of-the-art CNN frameworks, U-Net and dilated convolution architectures, with and without temporal context, using cross validation with three folds. Performance relative to expert manual segmentation was similar across all networks: pixel accuracy was ~97%, intersection-over-union (IoU) across all classes was ~87%, and IoU across foreground classes only was ~85%. Endocardial left ventricular circumferential strain calculated from the proposed pipeline was significantly different in control and disease subjects (-25.3% vs -29.1%, p = 0.006), in agreement with the current clinical literature.Comment: Accepted to Functional Imaging and Modeling of the Heart (FIMH) 201

The Local Food System Vitality Index: A Pilot Analysis to Demonstrate a Process for Measuring System Performance and Development

Identifying successful development priorities for local food systems (LFSs) is a challenge for pro­ducers, LFS advocates, Extension agents, and policymakers. Consumer perceptions and prefer­ences regarding what constitutes an active, healthy, and vibrant LFS often differ within and between diverse communities. Producers, development entities, and others would benefit from rapid assessment processes that provide detailed information on consumer preferences and potential market opportunities within their LFS. In this paper, we introduce the analytic possi­bilities of our Local Food System Vitality Index (LFSVI). Using data collected from a pilot survey in Lexington, Kentucky, we rapidly assess the per­formance of 20 different components of our LFS. The LFSVI differs from most other food system and quality-of-life indices by focusing on the per­ceptions of resident food consumers. In our analysis, we identify that Lexington resi­dents generally associate farmers markets, farm-to-fork restaurants, local product diversity, and retail sourcing of local food with high overall vitality of the local food system. While residents score the first three components as high performing, they perceive the retail component to be less functional. We use results such as these to compare which aspects of the LFS are valued versus which are high performing. We do this comparison across different resident food consumer segments in and between geographic locations. Throughout our analysis, we discuss how this index method is gen­erally applicable and conducive to identifying LFS development priorities

Power of grammatical evolution neural networks to detect gene-gene interactions in the presence of error

<p>Abstract</p> <p>Background</p> <p>With the advent of increasingly efficient means to obtain genetic information, a great insurgence of data has resulted, leading to the need for methods for analyzing this data beyond that of traditional parametric statistical approaches. Recently we introduced Grammatical Evolution Neural Network (GENN), a machine-learning approach to detect gene-gene or gene-environment interactions, also known as epistasis, in high dimensional genetic epidemiological data. GENN has been shown to be highly successful in a range of simulated data, but the impact of error common to real data is unknown. In the current study, we examine the power of GENN to detect interesting interactions in the presence of noise due to genotyping error, missing data, phenocopy, and genetic heterogeneity. Additionally, we compare the performance of GENN to that of another computational method – Multifactor Dimensionality Reduction (MDR).</p> <p>Findings</p> <p>GENN is extremely robust to missing data and genotyping error. Phenocopy in a dataset reduces the power of both GENN and MDR. GENN is reasonably robust to genetic heterogeneity and find that in some cases GENN has substantially higher power than MDR to detect functional loci in the presence of genetic heterogeneity.</p> <p>Conclusion</p> <p>GENN is a promising method to detect gene-gene interaction, even in the presence of common types of error found in real data.</p

AEGIS: Chandra Observation of DEEP2 Galaxy Groups and Clusters

We present a 200 ksec Chandra observation of seven spectroscopically selected, high redshift (0.75 < z < 1.03) galaxy groups and clusters discovered by the DEEP2 Galaxy Redshift Survey in the Extended Groth Strip (EGS). X-ray emission at the locations of these systems is consistent with background. The 3-sigma upper limits on the bolometric X-ray luminosities (L_X) of these systems put a strong constraint on the relation between L_X and the velocity dispersion of member galaxies sigma_gal at z~1; the DEEP2 systems have lower luminosity than would be predicted by the local relation. Our result is consistent with recent findings that at high redshift, optically selected clusters tend to be X-ray underluminous. A comparison with mock catalogs indicates that it is unlikely that this effect is entirely caused by a measurement bias between sigma_gal and the dark matter velocity dispersion. Physically, the DEEP2 systems may still be in the process of forming and hence not fully virialized, or they may be deficient in hot gas compared to local systems. We find only one possibly extended source in this Chandra field, which happens to lie outside the DEEP2 coverage.Comment: 5 pages, 3 figures. Accepted for publication in AEGIS ApJ Letters special editio

Editorial: Open Access – Our Golden Route in Academic Publishing in an Increasingly Open World

Open Access (OA) publishing, that is the immediate, online, free availability of research outputs without many of the restrictions imposed by traditional copyright agreements, is changing the landscape of scholarly publications. The Journal of Open, Flexible and Distance Learning is well positioned in the changing world of publishing with its focus on making high quality research in the Asia-Pacific region readily available to all. The recent inclusion of the Journal in the Directory of Open Access Journals highlights this commitment to accessibility. With that in mind, the three articles in this issue of the Journal explore the experiences of learners within three separate and distinct educational contexts in Aotearoa, New Zealand. Two of the articles are situated in the schooling sector where research on students learning at a distance is urgently needed to inform the development of more equitable practice worldwide. The third paper explores student engagement at the tertiary level, continuing the theme from the 2014 DEANZ conference relating to the ‘e’ in engagement. Using the article by Jeurissen as a focus, the editorial takes the time to highlight the role open, flexible and distance learning can play in the revitalisation of the New Zealand indigenous language, te reo Māori. The philosophy and methods of Open Access publishing are also discussed

The DEEP2 Galaxy Redshift Survey: Clustering of Groups and Group Galaxies at z~1

We study the clustering properties of groups and of galaxies in groups in the DEEP2 Galaxy Redshift Survey dataset at z~1. Four clustering measures are presented: 1) the group correlation function for 460 groups with estimated velocity dispersions of sigma>200 km/s, 2) the galaxy correlation for the full galaxy sample, using a flux-limited sample of 9800 objects between 0.7<z<1.0, 3) the galaxy correlation for galaxies in groups, and 4) the group-galaxy cross-correlation function. Using the observed number density and clustering amplitude of the groups, the estimated minimum group dark matter halo mass is M_min~6 10^12 h^-1 M_Sun for a flat LCDM cosmology. Groups are more clustered than galaxies, with a relative bias of b=1.7 +/-0.04 on scales r_p=0.5-15 Mpc/h. Galaxies in groups are also more clustered than the full galaxy sample, with a scale-dependent relative bias which falls from b~2.5 +/-0.3 at r_p=0.1 Mpc/h to b~1 +/-0.5 at r_p=10 Mpc/h. The correlation functions for all galaxies and galaxies in groups can be fit by a power-law on scales r_p=0.05-20 Mpc/h. We empirically measure the contribution to the projected correlation function for galaxies in groups from a `one-halo' term and a `two-halo' term by counting pairs of galaxies in the same or in different groups. The projected cross-correlation between shows that red galaxies are more centrally concentrated in groups than blue galaxies at z~1. DEEP2 galaxies in groups appear to have a shallower radial distribution than that of mock galaxy catalogs made from N-body simulations, which assume a central galaxy surrounded by satellite galaxies with an NFW profile. We show that the clustering of galaxies in groups can be used to place tighter constraints on the halo model than can be gained from using the usual galaxy correlation function alone.Comment: 22 pages, 12 figures, in emulateapj format, accepted to ApJ, minor changes made to match published versio

The DEEP2 Galaxy Redshift Survey: Mean Ages and Metallicities of Red Field Galaxies at z ~ 0.9 from Stacked Keck/DEIMOS Spectra

As part of the DEEP2 galaxy redshift survey, we analyze absorption line strengths in stacked Keck/DEIMOS spectra of red field galaxies with weak to no emission lines, at redshifts 0.7 <= z <= 1. Comparison with models of stellar population synthesis shows that red galaxies at z ~ 0.9 have mean luminosity-weighted ages of the order of only 1 Gyr and at least solar metallicities. This result cannot be reconciled with a scenario where all stars evolved passively after forming at very high z. Rather, a significant fraction of stars can be no more than 1 Gyr old, which means that star formation continued to at least z ~ 1.2. Furthermore, a comparison of these distant galaxies with a local SDSS sample, using stellar populations synthesis models, shows that the drop in the equivalent width of Hdelta from z ~ 0.9 to 0.1 is less than predicted by passively evolving models. This admits of two interpretations: either each individual galaxy experiences continuing low-level star formation, or the red-sequence galaxy population from z ~ 0.9 to 0.1 is continually being added to by new galaxies with younger stars.Comment: A few typos were corrected and numbers in Table 1 were revise

The DEEP3 Galaxy Redshift Survey: The Impact of Environment on the Size Evolution of Massive Early-type Galaxies at Intermediate Redshift

Using data drawn from the DEEP2 and DEEP3 Galaxy Redshift Surveys, we investigate the relationship between the environment and the structure of galaxies residing on the red sequence at intermediate redshift. Within the massive (10 < log(M*/Msun) < 11) early-type population at 0.4 < z <1.2, we find a significant correlation between local galaxy overdensity (or environment) and galaxy size, such that early-type systems in higher-density regions tend to have larger effective radii (by ~0.5 kpc or 25% larger) than their counterparts of equal stellar mass and Sersic index in lower-density environments. This observed size-density relation is consistent with a model of galaxy formation in which the evolution of early-type systems at z < 2 is accelerated in high-density environments such as groups and clusters and in which dry, minor mergers (versus mechanisms such as quasar feedback) play a central role in the structural evolution of the massive, early-type galaxy population.Comment: 11 pages, 5 figures, 2 tables; resubmitted to MNRAS after addressing referee's comments (originally submitted to journal on August 16, 2011