Assessing Habitat Suitability of Ribbed Mussels (Geukensia Demissa) in Georgia Salt Marshes by Examining Predicted Mussel Densities and Mussel Population Parameters

Geukensia demissa (ribbed mussel) is an ecology important bivalve that has the potential to contribute to salt marsh restoration. Understanding the factors that contribute to the distribution of mussels can help inform managers on choosing locations to optimize the survivorship of mussels in restoration projects. This study sought to model mussel densities across the coast of Georgia and to compare predicted mussel densities with mussel population parameters as means to gauge habitat suitability. Mussel densities were collected through field surveys across a range of salt marshes along the coast of Georgia and were compared with spatial data such as distance to creek heads (the ends of intertidal creeks which flood the marsh platform), elevation, and slope. Highest predicted mussel densities occurred at an elevation of 0.7m relative to NAVD 88, close to creek heads and far from subtidal creeks. Using the predicted mussel densities from the model, low, medium and high density mussel sites were selected at two geographic locations, Cannon’s Point Preserve, St. Simons, Georgia and Dean Creak, Sapelo Island, Georgia, to conduct mussel growth, predation, and recruitment experiments. In areas with higher predicted mussel densities, mussel recruitment and growth rates were the highest. Despite being statistically significant, differences in growth rates may not be biologically meaningful. While not statistically significant, predation risk was lowest in areas of high predicted mussel densities and increased with decreasing density. This indicates that in areas of low predicted mussel densities, recruitment and predation risk are likely the limiting factor to mussel densities

Within‑marsh and Landscape Features Structure Ribbed Mussel Distribution in Georgia, USA, Marshes

Ribbed mussels, Geukensia demissa, are marsh fauna that are used in coastal management and restoration due to the ecosystem services they provide. Ribbed mussel restoration efforts may be improved with a greater understanding of the environmental drivers of ribbed mussel distribution at multiple spatial scales to predict areas where restoration could be successful. This study sought to estimate the effects of within-marsh (4 m) and landscape (500 m) factors on ribbed mussel distribution. Ribbed mussel densities were surveyed at 11 sites along the coast of Georgia, USA, and overlaid with spatial data for within-marsh factors (elevation, distance to marsh features, slope) as well as landscape factors (percent cover by subtidal creek, forest, and development within a 500-m radius). The distribution model was then validated using three previously unsurveyed marshes and explained 55% of the variance in ribbed mussel abundance. Ribbed mussel abundances and occupancy were most sensitive to changes in within-marsh factors (elevation and distance to subtidal creeks, bodies of water inundated during the full tidal cycle) but were also sensitive to landscape features (percent landcover of forests and development). The highest ribbed mussel densities were found in mid-elevation areas (~ 0.7 m NAVD88), far from subtidal creeks, and in marshes surrounded with forest and development. These results contrast with distributions in the northeastern USA, where ribbed mussels are distributed along subtidal creek banks. This work suggests that restoration may be most effective when focused on appropriate elevations and at locations away from the marsh-creek ecotone

Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning

Brain segmentation is a fundamental first step in neuroimage analysis. In the case of fetal MRI, it is particularly challenging and important due to the arbitrary orientation of the fetus, organs that surround the fetal head, and intermittent fetal motion. Several promising methods have been proposed but are limited in their performance in challenging cases and in real-time segmentation. We aimed to develop a fully automatic segmentation method that independently segments sections of the fetal brain in 2D fetal MRI slices in real-time. To this end, we developed and evaluated a deep fully convolutional neural network based on 2D U-net and autocontext, and compared it to two alternative fast methods based on 1) a voxelwise fully convolutional network and 2) a method based on SIFT features, random forest and conditional random field. We trained the networks with manual brain masks on 250 stacks of training images, and tested on 17 stacks of normal fetal brain images as well as 18 stacks of extremely challenging cases based on extreme motion, noise, and severely abnormal brain shape. Experimental results show that our U-net approach outperformed the other methods and achieved average Dice metrics of 96.52% and 78.83% in the normal and challenging test sets, respectively. With an unprecedented performance and a test run time of about 1 second, our network can be used to segment the fetal brain in real-time while fetal MRI slices are being acquired. This can enable real-time motion tracking, motion detection, and 3D reconstruction of fetal brain MRI.Comment: This work has been submitted to ISBI 201

Using Peer Discussion Facilitated by Clicker Questions in an Informal Education Setting : Enhancing Farmer Learning of Science

PLoS ONE, Vol. 7, No. 10Blueberry growers in Maine attend annual Cooperative Extension presentations given by university faculty members. These presentations cover topics, such as, how to prevent plant disease and monitor for insect pests. In 2012, in order to make the sessions more interactive and promote learning, clicker questions and peer discussion were incorporated into the presentations. Similar to what has been shown at the undergraduate level, after peer discussion, more blueberry growers gave correct answers to multiple-choice questions than when answering independently. Furthermore, because blueberry growers are characterized by diverse levels of education, experience in the field etc., we were able to determine whether demographic factors were associated with changes in performance after peer discussion. Taken together, our results suggest that clicker questions and peer discussion work equally well with adults from a variety of demographic backgrounds without disadvantaging a subset of the population and provide an important learning opportunity to the least formally educated members. Our results also indicate that clicker questions with peer discussion were viewed as a positive addition to university-related informal science education sessions

Early-type galaxies in the SDSS. II. Correlations between observables

A magnitude limited sample of nearly 9000 early-type galaxies, in the redshift range 0.01 < z < 0.3, was selected from the Sloan Digital Sky Survey using morphological and spectral criteria. The sample was used to study how early-type galaxy observables, including luminosity L, effective radius R_o, surface brightness I_o, color, and velocity dispersion sigma, are correlated with one another. Measurement biases are understood with mock catalogs which reproduce all of the observed scaling relations and their dependences on fitting technique. At any given redshift, the intrinsic distribution of luminosities, sizes and velocity dispersions in our sample are all approximately Gaussian. A maximum likelihood analysis shows that sigma ~ L^{0.25\pm 0.012}, R_o ~ L^{0.63\pm 0.025}, and R_o ~ I^{-0.75\pm 0.02} in the r* band. In addition, the mass-to-light ratio within the effective radius scales as M_o/L ~ L^{0.14\pm 0.02} or M_o/L ~ M_o^{0.22\pm 0.05}, and galaxies with larger effective masses have smaller effective densities: Delta_o ~ M_o^{-0.52\pm 0.03}. These relations are approximately the same in the g*, i* and z* bands. Relative to the population at the median redshift in the sample, galaxies at lower and higher redshifts have evolved only little, with more evolution in the bluer bands. The luminosity function is consistent with weak passive luminosity evolution and a formation time of about 9 Gyrs ago.Comment: 29 pages, 11 figures. Accepted by AJ (scheduled for April 2003). This paper is part II of a revised version of astro-ph/011034

Learning to segment fetal brain tissue from noisy annotations

Automatic fetal brain tissue segmentation can enhance the quantitative assessment of brain development at this critical stage. Deep learning methods represent the state of the art in medical image segmentation and have also achieved impressive results in brain segmentation. However, effective training of a deep learning model to perform this task requires a large number of training images to represent the rapid development of the transient fetal brain structures. On the other hand, manual multi-label segmentation of a large number of 3D images is prohibitive. To address this challenge, we segmented 272 training images, covering 19-39 gestational weeks, using an automatic multi-atlas segmentation strategy based on deformable registration and probabilistic atlas fusion, and manually corrected large errors in those segmentations. Since this process generated a large training dataset with noisy segmentations, we developed a novel label smoothing procedure and a loss function to train a deep learning model with smoothed noisy segmentations. Our proposed methods properly account for the uncertainty in tissue boundaries. We evaluated our method on 23 manually-segmented test images of a separate set of fetuses. Results show that our method achieves an average Dice similarity coefficient of 0.893 and 0.916 for the transient structures of younger and older fetuses, respectively. Our method generated results that were significantly more accurate than several state-of-the-art methods including nnU-Net that achieved the closest results to our method. Our trained model can serve as a valuable tool to enhance the accuracy and reproducibility of fetal brain analysis in MRI

A Systematic Search for High Surface Brightness Giant Arcs in a Sloan Digital Sky Survey Cluster Sample

We present the results of a search for gravitationally-lensed giant arcs conducted on a sample of 825 SDSS galaxy clusters. Both a visual inspection of the images and an automated search were performed and no arcs were found. This result is used to set an upper limit on the arc probability per cluster. We present selection functions for our survey, in the form of arc detection efficiency curves plotted as functions of arc parameters, both for the visual inspection and the automated search. The selection function is such that we are sensitive only to long, high surface brightness arcs with g-band surface brightness mu_g 10. Our upper limits on the arc probability are compatible with previous arc searches. Lastly, we report on a serendipitous discovery of a giant arc in the SDSS data, known inside the SDSS Collaboration as Hall's arc.Comment: 34 pages,8 Fig. Accepted ApJ:Jan-200

Improved constraints on H0 from a combined analysis of gravitational-wave and electromagnetic emission from GW170817

The luminosity distance measurement of GW170817 derived from GW analysis in Abbott et al. 2017 (here, A17:H0) is highly correlated with the measured inclination of the NS-NS system. To improve the precision of the distance measurement, we attempt to constrain the inclination by modeling the broad-band X-ray-to-radio emission from GW170817, which is dominated by the interaction of the jet with the environment. We update our previous analysis and we consider the radio and X-ray data obtained at $t<40$ days since merger. We find that the afterglow emission from GW170817 is consistent with an off-axis relativistic jet with energy $10^{48}\,\rm{erg}<E_{k}\le 3\times 10^{50} \,\rm{erg}$ propagating into an environment with density $n\sim10^{-2}-10^{-4} \,\rm{cm^{-3}}$, with preference for wider jets (opening angle $\theta_j=15$ deg). For these jets, our modeling indicates an off-axis angle $\theta_{\rm obs}\sim25-50$ deg. We combine our constraints on $\theta_{\rm obs}$ with the joint distance-inclination constraint from LIGO. Using the same $\sim 170$ km/sec peculiar velocity uncertainty assumed in A17:H0 but with an inclination constraint from the afterglow data, we get a value of $H_0=$$74.0 \pm \frac{11.5}{7.5}$ \mbox{km/s/Mpc}, which is higher than the value of $H_0=$$70.0 \pm \frac{12.0}{8.0}$ \mbox{km/s/Mpc} found in A17:H0. Further, using a more realistic peculiar velocity uncertainty of 250 km/sec derived from previous work, we find $H_0=$$75.5 \pm \frac{11.6}{9.6}$ km/s/Mpc for H0 from this system. We note that this is in modestly better agreement with the local distance ladder than the Planck CMB, though a significant such discrimination will require $\sim 50$ such events. Future measurements at $t>100$ days of the X-ray and radio emission will lead to tighter constraints.Comment: Submitted to ApJL. Comments Welcome. Revised uncertainties in v

The Electromagnetic Counterpart of the Binary Neutron Star Merger LIGO/VIRGO GW170817. V. Rising X-ray Emission from an Off-Axis Jet

We report the discovery of rising X-ray emission from the binary neutron star (BNS) merger event GW170817. This is the first detection of X-ray emission from a gravitational-wave source. Observations acquired with the Chandra X-ray Observatory (CXO) at t~2.3 days post merger reveal no significant emission, with L_x<=3.2x10^38 erg/s (isotropic-equivalent). Continued monitoring revealed the presence of an X-ray source that brightened with time, reaching L_x\sim 9x10^39 erg/s at ~15.1 days post merger. We interpret these findings in the context of isotropic and collimated relativistic outflows (both on- and off-axis). We find that the broad-band X-ray to radio observations are consistent with emission from a relativistic jet with kinetic energy E_k~10^49-10^50 erg, viewed off-axis with theta_obs~ 20-40 deg. Our models favor a circumbinary density n~ 0.0001-0.01 cm-3, depending on the value of the microphysical parameter epsilon_B=10^{-4}-10^{-2}. A central-engine origin of the X-ray emission is unlikely. Future X-ray observations at $t\gtrsim 100$ days, when the target will be observable again with the CXO, will provide additional constraints to solve the model degeneracies and test our predictions. Our inferences on theta_obs are testable with gravitational wave information on GW170817 from Advanced LIGO/Virgo on the binary inclination.Comment: 7 Pages, 4 Figures, ApJL, In Press. Keywords: GW170817, LV

The C4 Clustering Algorithm: Clusters of Galaxies in the Sloan Digital Sky Survey

We present the "C4 Cluster Catalog", a new sample of 748 clusters of galaxies identified in the spectroscopic sample of the Second Data Release (DR2) of the Sloan Digital Sky Survey (SDSS). The C4 cluster--finding algorithm identifies clusters as overdensities in a seven-dimensional position and color space, thus minimizing projection effects which plagued previous optical clusters selection. The present C4 catalog covers ~2600 square degrees of sky with groups containing 10 members to massive clusters having over 200 cluster members with redshifts. We provide cluster properties like sky location, mean redshift, galaxy membership, summed r--band optical luminosity (L_r), velocity dispersion, and measures of substructure. We use new mock galaxy catalogs to investigate the sensitivity to the various algorithm parameters, as well as to quantify purity and completeness. These mock catalogs indicate that the C4 catalog is ~90% complete and 95% pure above M_200 = 1x10^14 solar masses and within 0.03 <=z <= 0.12. The C4 algorithm finds 98% of X-ray identified clusters and 90% of Abell clusters within 0.03 <= z <= 0.12. We show that the L_r of a cluster is a more robust estimator of the halo mass (M_200) than the line-of-sight velocity dispersion or the richness of the cluster. L_r. The final SDSS data will provide ~2500 C4 clusters and will represent one of the largest and most homogeneous samples of local clusters.Comment: 32 pages of figures and text accepted in AJ. Electronic version with additional tables, links, and figures is available at http://www.ctio.noao.edu/~chrism/c