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1. A Quick Look at the 3 GHz Radio Sky. II. Hunting for DRAGNs in the VLA Sky Survey

   https://doi.org/10.3847/1538-4365/acda30  

   Gordon, Yjan A. ; Rudnick, Lawrence ; Andernach, Heinz ; Morabito, Leah K. ; O’Dea, Christopher P. ; Achong, Kaylan-Marie ; Baum, Stefi A. ; Bayona-Figueroa, Caryelis ; Hooper, Eric J. ; Mingo, Beatriz ; et al ( August 2023 , The Astrophysical Journal Supplement Series)

   Abstract Active galactic nuclei (AGNs) can often be identified in radio images as two lobes, sometimes connected to a core by a radio jet. This multicomponent morphology unfortunately creates difficulties for source finders, leading to components that are (a) separate parts of a wider whole, and (b) offset from the multiwavelength cross identification of the host galaxy. In this work we define an algorithm, DRAGN hunter , for identifying double radio sources associated with AGNs (DRAGNs) from component catalog data in the first epoch Quick Look images of the high-resolution (≈3″ beam size) Very Large Array Sky Survey (VLASS). We use DRAGN hunter to construct a catalog of >17,000 DRAGNs in VLASS for which contamination from spurious sources is estimated at ≈11%. A “high-fidelity” sample consisting of 90% of our catalog is identified for which contamination is <3%. Host galaxies are found for ≈13,000 DRAGNs as well as for an additional 234,000 single-component radio sources. Using these data, we explore the properties of our DRAGNs, finding them to be typically consistent with Fanaroff–Riley class II sources and to allow us to report the discovery of 31 new giant radio galaxies identified using VLASS. 

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2. Quick and Accurate Estimates of Mutation Effects on Transition-State Stabilization of Enzymes from Molecular Simulations with Restrained Transition States

   https://doi.org/10.1021/acs.jpcb.2c04802  

   Burgin, Tucker ; Pfaendtner, Jim ; Beck, David A. ( December 2022 , The Journal of Physical Chemistry B)

   Data science and machine learning are revolutionizing enzyme engineering; however, high-throughput simulations for screening large libraries of enzyme variants remain a challenge. Here, we present a novel but highly simple approach to comparing enzyme variants with fully atomistic classical molecular dynamics (MD) simulations on a tractable timescale. Our method greatly simplifies the problem by restricting sampling only to the reaction transition state, and we show that the resulting measurements of transition-state stability are well correlated with experimental activity measurements across two highly distinct enzymes, even for mutations with effects too small to resolve with free energy methods. This method will enable atomistic simulations to achieve sampling coverage for enzyme variant prescreening and machine learning model training on a scale that was previously not possible. 

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3. Quick and facile preparation of histone proteins from the green microalga Chlamydomonas reinhardtii and other photosynthetic organisms

   https://doi.org/10.1016/j.ymeth.2020.01.019  

   Wong, Amanda L. ; Totah, Nicholas N. ; Iavarone, Anthony T. ; Pesavento, James J. ( January 2020 , Methods)

   null (Ed.)
4. Quick line outage identification in urban distribution grids via smart meters

   https://doi.org/10.17775/CSEEJPES.2020.04640  

   Yizheng Liao ; Yang Weng ; Chin-Woo Tan ; Ram Rajagopal ( July 2022 , CSEE Journal of Power and Energy Systems)

   The growing integration of distributed energy resources (DERs) in distribution grids raises various reliability issues due to DER's uncertain and complex behaviors. With large-scale DER penetration in distribution grids, traditional outage detection methods, which rely on customers report and smart meters' “last gasp” signals, will have poor performance, because renewable generators and storage and the mesh structure in urban distribution grids can continue supplying power after line outages. To address these challenges, we propose a data-driven outage monitoring approach based on the stochastic time series analysis with a theoretical guarantee. Specifically, we prove via power flow analysis that dependency of time-series voltage measurements exhibits significant statistical changes after line outages. This makes the theory on optimal change-point detection suitable to identify line outages. However, existing change point detection methods require post-outage voltage distribution, which are unknown in distribution systems. Therefore, we design a maximum likelihood estimator to directly learn distribution pa-rameters from voltage data. We prove the estimated parameters-based detection also achieves optimal performance, making it extremely useful for fast distribution grid outage identifications. Furthermore, since smart meters have been widely installed in distribution grids and advanced infrastructure (e.g., PMU) has not widely been available, our approach only requires voltage magnitude for quick outage identification. Simulation results show highly accurate outage identification in eight distribution grids with 17 configurations with and without DERs using smart meter data. 

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5. Classification accuracy of the Quick Interactive Language Screener for preschool children with and without developmental language disorder

   https://doi.org/10.1016/j.jcomdis.2022.106276  

   Pace, Amy ; Curran, Maura ; Van Horne, Amanda Owen ; de Villiers, Jill ; Iglesias, Aquiles ; Golinkoff, Roberta Michnick ; Wilson, Mary S. ; Hirsh-Pasek, Kathy ( November 2022 , Journal of Communication Disorders)

   Abstract Introduction This research examined the classification accuracy of the Quick Interactive Language Screener (QUILS) for identifying preschool-aged children (3;0 to 6;9) with developmental language disorder (DLD). We present data from two independent samples that varied in prevalence and diagnostic reference standard. Methods Study 1 included a clinical sample of children (54 with DLD; 13 without) who completed the QUILS and a standardized assessment of expressive grammar (Syntax subtest from the Diagnostic Evaluation of Language Variation–Norm Referenced; Structured Photographic Expressive Language Test–Preschool 2nd Edition; or Structured Photographic Expressive Language Test–3 rd Edition). Study 2 included a community sample of children (25 with DLD; 101 without) who completed the QUILS and the Auditory Comprehension subtest of the Preschool Language Scales–5th Edition (PLS-5; Zimmerman et al., 2011). Discriminant analyses were conducted to compare classification accuracy (i.e., sensitivity and specificity) using the normreferenced cut score (< 25th percentile) with empirically derived cut scores. Results In Study 1, the QUILS led to low fail rates (i.e., high specificity) in children without impairment and statistically significant group differences as a function of children's clinical status; however, only 65% of children with DLD were accurately identified using the norm-referenced cutoff. In Study 2, 76% of children with DLD were accurately identified at the 25th percentile cutoff and accuracy improved to 84% when an empirically derived cutoff (<32nd percentile) was applied. Conclusions Findings support the clinical application of the QUILS as a component of the screening process for identifying the presence or absence of DLD in community samples of preschool-aged children. 

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