A Hundred Comets: The Visual-Wavelength Observations of the Survey of Ensemble Physical Properties of Cometary Nuclei (SEPPCoN)

We present new results from SEPPCoN, a Survey of Ensemble Physical Properties of Cometary Nuclei. This project is currently surveying 100 Jupiter-family comets (JFCs) to measure the mid-infrared thermal emission and visible reflected sunlight of the nuclei. The scientific goal is to determine the distributions of radius, geometric albedo, thermal inertia, axial ratio, and color among the JFC nuclei. In the past we have presented results from the completed mid-IR observations of our sample [1]; here we present preliminary results from ongoing, broadband visible-wavelength observations of nuclei obtained from a variety of ground-based facilities (Mauna Kea, Cerro Pachon, La Silla, La Palma, Apache Point, Table Mtn., and Palomar Mtn.), including contributions from the Near Earth Asteroid Telescope project (NEAT) archive. The nuclei were observed at high heliocentric distance (usually over 4 AU) and so many comets show either no or little contamination from dust coma. While several nuclei have been observed as snapshots, we have multiepoch photometry for many of our targets. With our datasets we are building a large database of photometry, and such a database is essential to the derivation of albedo and shape of a large number of nuclei, and to the understanding of biases in the survey. Support for this work was provided by NSF and the NASA Planetary Astronomy program. Reference: [1] Fernandez, Y.R., et al. 2007, BAAS 39, 827.

Involvement of the endosomal autoantigen EEA1 in homotypic fusion of early endosomes

In mammalian cells, fusion between early endocytic vesicles has been shown to require the ubiquitous intracellular fusion factors N-ethylmaleimide-sensitive factor (NSF) and alpha-SNAP, as well as a factor specific for early endosomes, the small GTPase Rab5 [1-3]. We have previously demonstrated an additional requirement for phosphatidylinositol 3-kinase (PI 3-kinase) activity [4]. The membrane association of early endosomal antigen 1 (EEA1), a specific marker of early endosomes [5,6], has recently been shown to be similarly dependent on PI 3-kinase activity [7], and we therefore postulated that it might be involved in endosome fusion. Here, we present evidence that EEA1 has an important role in determining the efficiency of endosome fusion in vitro. Both the carboxy-terminal domain of EEA1 (residues 1098-1411) and specific antibodies against EEA1 inhibited endosome fusion when included in an in vitro assay. Furthermore, depletion of EEA1, both from the membrane fraction used in the assay by washing with salt and from the cytosol using an EEA1-specific antibody, resulted in inhibition of endosome fusion. The involvement of EEA1 in endosome fusion accounts for the sensitivity of the endosome fusion assay to inhibitors of PI 3-kinase.

Learning from Outdoor Webcams: Surveillance of Physical Activity Across Environments

Abstract
Publicly available, outdoor webcams continuously view the world and share images. These cameras include traffic cams, campus cams, ski-resort cams, etc. The Archive of Many Outdoor Scenes (AMOS) is a project aiming to geolocate, annotate, archive, and visualize these cameras and images to serve as a resource for a wide variety of scientific applications. The AMOS dataset has archived over 750 million images of outdoor environments from 27,000 webcams since 2006. Our goal is to utilize the AMOS image dataset and crowdsourcing to develop reliable and valid tools to improve physical activity assessment via online, outdoor webcam capture of global physical activity patterns and urban built environment characteristics.
This project’s grand scale-up of capturing physical activity patterns and built environments is a methodological step forward in advancing a real-time, non-labor intensive assessment using webcams, crowdsourcing, and eventually machine learning. The combined use of webcams capturing outdoor scenes every 30 min and crowdsources providing the labor of annotating the scenes allows for accelerated public health surveillance related to physical activity across numerous built environments. The ultimate goal of this public health and computer vision collaboration is to develop machine learning algorithms that will automatically identify and calculate physical activity patterns.

Electron-Impact Uncertainty Analysis and its Impact on Certain Temperature Diagnostics

In this study we calculate the electron-impact uncertainties in atomic data for direct ionization and recombination and investigate the role of these uncertainties on spectral diagnostics. We outline a systematic approach to assigning meaningful uncertainties that vary with electron temperature. Once these uncertainty parameters have been evaluated, we can then calculate the uncertainties on key diagnostics through a Monte Carlo routine, using the Astrophysical Emission Code (APEC) [Smith et al. 2001]. We incorporate these uncertainties into well known temperature diagnostics, such as the Lyman alpha versus resonance line ratio and the G ratio. We compare these calculations to a study performed by [Testa et al. 2004], where significant discrepancies in the two diagnostic ratios were observed. We conclude that while the atomic physics uncertainties play a noticeable role in the discrepancies observed by Testa, they do not explain all of them. This indicates that there is another physical process occurring in the system that is not being taken into account. This work is supported in part by the National Science Foundation REU and Department of Defense ASSURE programs under NSF Grant no. 1262851 and by the Smithsonian Institution.