Tests of the Accelerating Universe with Near-Infrared Observations of a High-Redshift Type Ia Supernova

We have measured the rest-frame B,V, and I-band light curves of a high-redshift type Ia supernova (SN Ia), SN 1999Q (z=0.46), using HST and ground-based near-infrared detectors. A goal of this study is the measurement of the color excess, E_{B-I}, which is a sensitive indicator of interstellar or intergalactic dust which could affect recent cosmological measurements from high-redshift SNe Ia. Our observations disfavor a 30% opacity of SN Ia visual light by dust as an alternative to an accelerating Universe. This statement applies to both Galactic-type dust (rejected at the 3.4 sigma confidence level) and greyer dust (grain size > 0.1 microns; rejected at the 2.3 to 2.6 sigma confidence level) as proposed by Aguirre (1999). The rest-frame $I$-band light cur ve shows the secondary maximum a month after B maximum typical of nearby SNe Ia of normal luminosi ty, providing no indication of evolution as a function of redshift out to z~0.5. A n expanded set of similar observations could improve the constraints on any contribution of extragalactic dust to the dimming of high-redshift SNe Ia.Comment: Accepted to the Astrophysical Journal, 12 pages, 2 figure

Weighted gene coexpression network analysis strategies applied to mouse weight

Systems-oriented genetic approaches that incorporate gene expression and genotype data are valuable in the quest for genetic regulatory loci underlying complex traits. Gene coexpression network analysis lends itself to identification of entire groups of differentially regulated genes—a highly relevant endeavor in finding the underpinnings of complex traits that are, by definition, polygenic in nature. Here we describe one such approach based on liver gene expression and genotype data from an F2 mouse intercross utilizing weighted gene coexpression network analysis (WGCNA) of gene expression data to identify physiologically relevant modules. We describe two strategies: single-network analysis and differential network analysis. Single-network analysis reveals the presence of a physiologically interesting module that can be found in two distinct mouse crosses. Module quantitative trait loci (mQTLs) that perturb this module were discovered. In addition, we report a list of genetic drivers for this module. Differential network analysis reveals differences in connectivity and module structure between two networks based on the liver expression data of lean and obese mice. Functional annotation of these genes suggests a biological pathway involving epidermal growth factor (EGF). Our results demonstrate the utility of WGCNA in identifying genetic drivers and in finding genetic pathways represented by gene modules. These examples provide evidence that integration of network properties may well help chart the path across the gene–trait chasm

The Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA)

To explore the various couplings across space and time and between ecosystems in a consistent manner, atmospheric modeling is moving away from the fractured limited-scale modeling strategy of the past toward a unification of the range of scales inherent in the Earth system. This paper describes the forward-looking Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA), which is intended to become the next-generation community infrastructure for research involving atmospheric chemistry and aerosols. MUSICA will be developed collaboratively by the National Center for Atmospheric Research (NCAR) and university and government researchers, with the goal of serving the international research and applications communities. The capability of unifying various spatiotemporal scales, coupling to other Earth system components, and process-level modularization will allow advances in both fundamental and applied research in atmospheric composition, air quality, and climate and is also envisioned to become a platform that addresses the needs of policy makers and stakeholders

Transforming upperdivision quantum mechanics: Learning goals and their assessment

Abstract. In order to help students overcome documented difficulties learning quantum mechanics (QM) concepts, we have transformed our upper-division QM I course using principles of learning theory and active engagement. Key components of this process include establishing learning goals and developing a valid, reliable conceptual assessment tool to measure the extent to which students achieve these learning goals. The course learning goals were developed with broad faculty input, and serve as the basis for the design of the course assessment tool. The development of the assessment tool has included significant faculty input and feedback, twenty-one student interviews, a review of PER literature, and administration of the survey to two semesters of QM I students as well as to a cohort of graduate students. Here, we discuss this ongoing development process and present initial findings from our QM class for the past two semesters