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Author(s):: Aniano, G.; Draine, B.T.; Hunt, L.K.; Sandstrom, K.; Calzetti, D.; Kennicutt, R.C.; Dale, D.A.; Galametz, M.; Gordon, K.D.; Leroy, A.K.; Smith, J.-D.T.; Roussel, H.; Sauvage, M.; Walter, F.; Armus, L.; Bolatto, A.D.; Boquien, M.; Crocker, A.; De Looze, I.; Donovan Meyer, J.; Helou, G.; Hinz, J.; Johnson, B.D.; Koda, J.; Miller, A.; Montiel, E.; Murphy, E.J.; Relano, M.; Rix, H.-W.; Schinnerer, E.; Skibba, R.; Wolfire, M.G.; Engelbracht, C.W.
Abstract:: Dust and starlight have been modeled for the KINGFISH project galaxies. For each pixel in each galaxy, we estimate: (1) dust surface density; (2) q_PAH, the dust mass fraction in PAHs; (3) distribution of starlight intensities heating the dust; (4) luminosity emitted by the dust; and (5) dust luminosity from regions with high starlight intensity. The modeling is as described in the paper "Modeling Dust and Starlight in Galaxies Observed by Spitzer and Herschel: The KINGFISH Sample", by G. Aniano, B.T. Draine, L.K. Hunt, K. Sandstrom, D. Calzetti, R.C. Kennicutt, D.A, Dale, and 26 other authors, accepted for publication in The Astrophysical Journal.
Type:: Dataset and Image

Author(s):: Manzuk, Ryan; Maloof, Adam
Abstract:: In our study, we compare the three dimensional (3D) morphologic characteristics of Earth's first reef-building animals (archaeocyath sponges) with those of modern, photosynthetic corals. Within this repository are the 3D image data products for both groups of animals. The archaeocyath images were produced through serial grinding and imaging with the Grinding, Imaging, and Reconstruction Instrument at Princeton University. The images in this repository are the downsampled data products used in our study, and the full resolution (>2TB) image stacks are available upon request from the author. For the coral image data, the computed tomography (CT) images of all samples are included at full resolution. Also included in this repository are the manual and automated outline coordinates of the archaeocyath and coral branches, which can be directly used for morphological study.
Type:: Dataset, Image, MovingImage, and StillImage
Issue Date:: August 2022

Author(s):: Taylor, Jenny A.; Bratton, Benjamin P.; Sichel, Sophie R.; Blair, Kris M.; Jacobs, Holly M.; DeMeester, Kristen E.; Kuru, Erkin; Gray, Joe; Biboy, Jacob; VanNieuwenhze, Michael S.; Vollmer, Waldemar; Grimes, Catherine L.; Shaevitz, Joshua W.; Salama, Nina R.
Abstract:: Helical cell shape is necessary for efficient stomach colonization by Helicobacter pylori, but the molecular mechanisms for generating helical shape remain unclear. We show that the helical centerline pitch and radius of wild-type H. pylori cells dictate surface curvatures of considerably higher positive and negative Gaussian curvatures than those present in straight- or curved-rod bacteria. Quantitative 3D microscopy analysis of short pulses with either N-acetylmuramic acid or D-alanine metabolic probes showed that cell wall growth is enhanced at both sidewall curvature extremes. Immunofluorescence revealed MreB is most abundant at negative Gaussian curvature, while the bactofilin CcmA is most abundant at positive Gaussian curvature. Strains expressing CcmA variants with altered polymerization properties lose helical shape and associated positive Gaussian curvatures. We thus propose a model where CcmA and MreB promote PG synthesis at positive and negative Gaussian curvatures, respectively, and that this patterning is one mechanism necessary for maintaining helical shape.
Type:: Dataset and Image
Issue Date:: April 2019

Author(s):: Webb, Taylor; Dulberg, Zachary; Frankland, Steven; Petrov, Alexander; O'Reilly, Randall; Cohen, Jonathan
Abstract:: Extrapolation -- the ability to make inferences that go beyond the scope of one's experiences -- is a hallmark of human intelligence. By contrast, the generalization exhibited by contemporary neural network algorithms is largely limited to interpolation between data points in their training corpora. In this paper, we consider the challenge of learning representations that support extrapolation. We introduce a novel visual analogy benchmark that allows the graded evaluation of extrapolation as a function of distance from the convex domain defined by the training data. We also introduce a simple technique, context normalization, that encourages representations that emphasize the relations between objects. We find that this technique enables a significant improvement in the ability to extrapolate, considerably outperforming a number of competitive techniques.
Type:: Dataset and Image
Issue Date:: 2020

Abstract:: This dataset contains all the data, model and MATLAB codes used to generate the figures and data reported in the article (DOI: 10.1002/2014JD022278). The data was generated during September 2013 and February 2014 using the Ocean-Land-Atmosphere Model also provided with this package. The data was generated using the computational resources supported by the PICSciE OIT High Performance Computing Center and Visualization Laboratory at Princeton University. The dataset contains a pdf Readme file which explains in detail how the data can be used. Users are recommended to go through this file before using the data.
Type:: Dataset, Image, Software, and text
Issue Date:: 19 November 2014