A Method to Distinguish Quiescent and Dusty Star-forming Galaxies with Machine Learning

Large photometric surveys provide a rich source of observations of quiescent galaxies, including a surprisingly large population at z > 1. However, identifying large, but clean, samples of quiescent galaxies has proven difficult because of their near-degeneracy with interlopers such as dusty, star-forming galaxies. We describe a new technique for selecting quiescent galaxies based upon t-distributed stochastic neighbor embedding (t-SNE), an unsupervised machine-learning algorithm for dimensionality reduction. This t-SNE selection provides an improvement both over UVJ, removing interlopers that otherwise would pass color selection, and over photometric template fitting, more strongly toward high redshift. Due to the similarity between the colors of high- and low-redshift quiescent galaxies, under our assumptions, t-SNE outperforms template fitting in 63% of trials at redshifts where a large training sample already exists. It also may be able to select quiescent galaxies more efficiently at higher redshifts than the training sample

The General Morphology of the Elasmobranch Hypophysis

The terms hypophysis and pituitary are used loosely and interchangeably by many if not most writers on endocrine organs. By pituitary the writers of this paper understand an assemblage of two diverse elements: an epithelial hypophysis derived from the buccal ectoderm of the embryo and a pars nervosa originating from the infundibulum of the brain. The junction and fusion of these two parts form the pituitary proper. It is with such an interpretation that we shall use the terms hypophysis and pituitary

A Method to Distinguish Quiescent and Dusty Star-forming Galaxies with Machine Learning

Large photometric surveys provide a rich source of observations of quiescent galaxies, including a surprisingly large population at z > 1. However, identifying large, but clean, samples of quiescent galaxies has proven difficult because of their near-degeneracy with interlopers such as dusty, star-forming galaxies. We describe a new technique for selecting quiescent galaxies based upon t-distributed stochastic neighbor embedding (t-SNE), an unsupervised machine-learning algorithm for dimensionality reduction. This t-SNE selection provides an improvement both over UVJ, removing interlopers that otherwise would pass color selection, and over photometric template fitting, more strongly toward high redshift. Due to the similarity between the colors of high- and low-redshift quiescent galaxies, under our assumptions, t-SNE outperforms template fitting in 63% of trials at redshifts where a large training sample already exists. It also may be able to select quiescent galaxies more efficiently at higher redshifts than the training sample

Studio Recital

Studio recital presenting the students of Charlotte Petersen. Featuring cellists and string ensembles.https://digitalcommons.usu.edu/music_programs/1124/thumbnail.jp

The SNX-PX-BAR Family in Macropinocytosis: The Regulation of Macropinosome Formation by SNX-PX-BAR Proteins

Background: Macropinocytosis is an actin-driven endocytic process, whereby membrane ruffles fold back onto the plasma membrane to form large (> 0.2 mu m in diameter) endocytic organelles called macropinosomes. Relative to other endocytic pathways, little is known about the molecular mechanisms involved in macropinocytosis. Recently, members of the Sorting Nexin (SNX) family have been localized to the cell surface and early macropinosomes, and implicated in macropinosome formation. SNX-PX-BAR proteins form a subset of the SNX family and their lipid-binding (PX) and membrane-curvature sensing (BAR) domain architecture further implicates their functional involvement in macropinosome formation

Literacy coaching: Research & practice

Copyright © 2010 Center for Educational Development, Evaluation, and Research; Texas A&M University-Corpus Christi College of EducationThe chapters in this volume are based on presentations from the First National Literacy Coaching Summit held at Texas A&M University-Corpus Christi on April 3 and 4, 2009. The conference drew more than 400 participants from 21 states, the Virgin Islands, Canada, and Washington, D.C. Keynote speakers included Dr. Nancy Shanklin of the University of Colorado, then-head of the Literacy Coaching Clearinghouse; Dr. Rita Bean of the University of Pittsburgh, a longtime researcher on the role of the reading specialist/literacy professional; Dr. MaryEllen Vogt of California State University Long Beach, an expert on teaching English language learners; and Gary Soto, a noted children’s author. Literacy coaching has been a hot topic in the field for most of the past decade (Cassidy & Cassidy, 2009-10). A literacy coach should be a well-qualified and highly-regarded classroom teacher with advanced training in literacy. Ideally, the literacy coach is assigned to one school and primarily works as a staff developer. However, the International Reading Association recognized the “changing roles … and variety of new titles, such as reading coach and literacy coach, and …the variability in the job descriptions for these coaches” (2004, p. 2). Chapters included in this book represent both research and practice in the field of literacy coaching. The 26 authors hail from 10 different states. These chapter authors are both school-based and university-based professionals. Each of the articles was blindly peer reviewed by at least two literacy professionals. Like the authors, Jack Cassidy & Sherrye Dee Garrett, these peer reviewers were both school-based and university based and came from ten different states. The first chapter introduces the major coaching themes and research presented in this book. The second chapter provides an overview provides an overview of the history and precursors of current literacy coaching. The next five chapters represent some of the research conducted on literacy coaching. Nancy Shanklin’s article begins the research section, and it highlights some of the most significant research on literacy coaching. The second section of the book focuses on specific practices associated with literacy coaching. This chapter opens with a piece by Rita Bean, delineating five lessons from her years working with and observing literacy coaching in schools. The remaining five chapters address specific programs and strategies that have proven effective