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Large-scale genome sequencing has identified millions of protein-coding genes whose function is unknown. Many of these proteins are similar to characterized proteins from other organisms, but much of this information is missing from annotation databases and is hidden in the scientific literature. To make this information accessible, PaperBLAST uses EuropePMC to search the full text of scientific articles for references to genes. PaperBLAST also takes advantage of curated resources (Swiss-Prot, GeneRIF, and EcoCyc) that link protein sequences to scientific articles. PaperBLAST's database includes over 700,000 scientific articles that mention over 400,000 different proteins. Given a protein of interest, PaperBLAST quickly finds similar proteins that are discussed in the literature and presents snippets of text from relevant articles or from the curators. PaperBLAST is available at http://papers.genomics.lbl.gov/. IMPORTANCE With the recent explosion of genome sequencing data, there are now millions of uncharacterized proteins. If a scientist becomes interested in one of these proteins, it can be very difficult to find information as to its likely function. Often a protein whose sequence is similar, and which is likely to have a similar function, has been studied already, but this information is not available in any database. To help find articles about similar proteins, PaperBLAST searches the full text of scientific articles for protein identifiers or gene identifiers, and it links these articles to protein sequences. Then, given a protein of interest, it can quickly find similar proteins in its database by using standard software (BLAST), and it can show snippets of text from relevant papers. We hope that PaperBLAST will make it easier for biologists to predict proteins' functions.

Toward the goal of maximizing the impact of computer modeling on the design of future particle accelerators and the development of new accelerator techniques & technologies, this white paper presents the rationale for: (a) strengthening and expanding programmatic activities in accelerator modeling science within the Department of Energy (DOE) Office of High Energy Physics (HEP) and (b) increasing the community-wide coordination and integration of code development.

Several years ago, one of us, having noticed that inexperienced scientists tend to make largely the same mistakes while writing their first papers, was compelled to write a one-page note summarizing some dos and don'ts intended to help take care of common problems before they occur. Since these days the majority of research papers are written collaboratively by groups of co-authors, we are compelled to extend these recommendations to collaborative writing as we observe groups of co-authors falling into the same traps again and again.

Inspired by the success of convolutional neural networks in image classification, and other higher level vision tasks, we explore two applications of such deep convolutional neural networks to model tasks typically involving human assessment, viz. i) prediction of personality from social media images, and ii) prediction of citations from the visual elements of an academic paper. The aim in this context is to discover if there is any predictable and learnable signal in the input data. As an extension, we attempt to discover what aspects of the signal are indeed learnt that lead to the results presented. For instance, if personality can be predicted, what aspects of the image are causing that? Similarly if an academic paper is highly cited, what are the characteristic visual elements that cause this? We employ convolutional neural networks in order to understand what imputable attributes we may derive that are simpler to reason.

Introduction: Scholarship is an essential part of academic success. Junior faculty members are often unfamiliar with the grounding literature that defines educational scholarship. In this article, the authors aim to summarize five key papers outlining scholarship in the setting of academic contributions for emerging clinician educators. Methods: The authors conducted a consensus-building process to generate a list of key papers that describe the importance and significance of academic scholarship, informed by social media sources. We then used a three-round voting methodology, akin to a Delphi study, to determine the papers with the most impact. Results: A summary of the five most important papers on the topic domain of academic scholarship, as determined by this mixed group of junior faculty members and faculty developers, is presented in this paper. We subsequently wrote a summary of these five papers and their relevance to junior faculty members and faculty developers, as well. Conclusion: Five papers on educational scholarship, deemed essential by the authors’ consensus process, are presented in this paper. These papers may help provide the foundational background to help junior faculty members gain a grasp of the academic scholarly environment. This list may also inform senior faculty and faculty developers on the needs of junior faculty members in the nascent stages of their careers. [West JEmerg Med. 2016;17(5)519-526.]

Transwomen are among the most vulnerable populations in the United States and are disproportionately at risk for HIV and other negative health outcomes. Research with transwomen has been largely descriptive, documenting the high prevalence of various co-morbidities, ranging from substance use to negative mental health outcomes. In these studies, singular axes of identity have generally been analyzed. For example, studies have found that race is especially salient in the health of transwomen, finding that transwomen of color experience numerous structural inequalities that drive disparities in health. More intersectional research to examine social inequities in health and illness among transwomen is needed to better understand factors situated in the lived experiences of transwomen. This dissertation draws upon quantitative methods to examine disparities in HIV risk behavior and substance use among transwomen at the intersection of race and gender (Paper 1) and sexuality and gender (Paper 2). Qualitatively, this dissertation examines transmisogyny as a form of intersectional stress among transwomen (Paper 3). This first study is a cross-sectional analysis of data from a sample of 149 HIV-negative adult transwomen in San Francisco collected in 2013 from the TEACH 2 study. The second study is a cross-sectional analysis of data from a sample of 259 young transwomen in the San Francisco Bay Area collected in 2012-2014 from the SHINE study. The third study is a secondary analysis of qualitative, in-depth interview data from the Transgender Research Youth Project (TRYP), collected from 34 young transwomen in Chicago and Los Angeles in 2006. Altogether, these studies argue that future transgender research should increasingly take up intersectionality in order to understand the unique social location of transwomen.

This dissertation is comprised of three distinct papers covering topics in appliedmicroeconomics and applied econometrics. The first paper addresses a common problem faced by empirical researchers wishing to estimate Markov regime-switching models. For these models, testing for the possible presence of more than one regime requires the use of a non-standard test statistic. The analytic steps needed to implement the test of Markov regime-switching proposed by Cho & White (2007) are derived in detail in Carter & Steigerwald (2013). We summarize those implementation steps and address the computational issues that arise. A new Stata command to compute the regime-switching critical values, rscv, is introduced and presented in the context of empirical economic research. This paper is joint work with Douglas Steigerwald, and has previously appeared in the Stata Journal (Bostwick and Steigerwald, 2014).In the second paper, I address a question in the field of economics of education: that is,whether college students use their choice of major as a signal of unobserved productivityin the labor market. I propose a model of postsecondary education in which major fieldof study can be used by individuals to signal productivity to employers. Under thissignaling model, I show that geographic areas with high access to elite universities resultin fewer science, technology, engineering, and mathematics (STEM) majors among lower ability students at non-elite colleges. Using data from the National Center for Education Statistics' Baccalaureate and Beyond survey, I find evidence that is consistent with the signaling model prediction, specifically a 2.3-3.7 percentage point (or 16-25%) decrease in the probability of choosing a STEM major among lower ability students in areas with greater access to elite colleges. This paper has previously appeared in Economic Inquiry (Bostwick, 2016).In the third paper, I analyze an unexpected consequence of a highly debated educationpolicy. Many s

Machine learning is an important applied research area in particle physics, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas in machine learning in particle physics with a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.

The Wide-Field Infrared Survey Telescope (WFIRST) is expected to launch in the mid-2020s. With its wide-field near-infrared (NIR) camera, it will survey the sky to unprecedented detail. As part of normal operations and as the result of multiple expected dedicated surveys, WFIRST will produce several relatively wide-field (tens of square degrees) deep (limiting magnitude of 28 or fainter) fields. In particular, a planned supernova survey is expected to image 3 deep fields in the LSST footprint roughly every 5 days over 2 years. Stacking all data, this survey will produce, over all WFIRST supernova fields in the LSST footprint, ~12-25 deg^2 and ~5-15 deg^2 regions to depths of ~28 mag and ~29 mag, respectively. We suggest LSST undertake mini-surveys that will match the WFIRST cadence and simultaneously observe the supernova survey fields during the 2-year WFIRST supernova survey, achieving a stacked depth similar to that of the WFIRST data. We also suggest additional observations of these same regions throughout the LSST survey to get deep images earlier, have long-term monitoring in the fields, and produce deeper images overall. These fields will provide a legacy for cosmology, extragalactic, and transient/variable science.

Aqueous two-phase systems (ATPSs) have been widely utilized for liquid-liquid extraction and purification of biomolecules, with some studies also demonstrating their capacity as a biomarker concentration technique for use in diagnostic settings. As the limited polarity range of conventional polymer-based ATPSs can restrict their use, ionic liquid (IL)-based ATPSs have been recently proposed as a promising alternative to polymer-based ATPSs, since ILs are regarded as tunable solvents with excellent solvation capabilities for a variety of natural compounds and proteins. This study demonstrates the first application of IL ATPSs to point-of-care diagnostics. ATPSs consisting of 1-butyl-3-methylimidazolium tetrafluoroborate ([Bmim][BF4]) and sodium phosphate salt were utilized to quickly concentrate biomarkers prior to detection using the lateral-flow immunoassay (LFA). We found the phase separation speed of the IL ATPS to be very rapid and a significant improvement upon the separation speed of both polymer-salt and micellar ATPSs. This system was successfully applied to both sandwich and competitive LFA formats and enhanced the detection of both Escherichia coli bacteria and the transferrin protein up to 8- and 20-fold, respectively. This system's compatibility with a broad range of biomolecules, rapid phase separation speed, and tunability suggest wide applicability for a large range of different antigens and biomarkers.

The Liver Imaging and Reporting Data System (LI-RADS) is a comprehensive system for standardizing the terminology, technique, interpretation, reporting, and data collection of liver imaging with the overarching goal of improving communication, clinical care, education, and research relating to patients at risk for or diagnosed with hepatocellular carcinoma (HCC). In 2018, the American Association for the Study of Liver Diseases (AASLD) integrated LI-RADS into its clinical practice guidance for the imaging-based diagnosis of HCC. The harmonization between the AASLD and LI-RADS diagnostic imaging criteria required minor modifications to the recently released LI-RADS v2017 guidelines, necessitating a LI-RADS v2018 update. This article provides an overview of the key changes included in LI-RADS v2018 as well as a look at the LI-RADS v2018 diagnostic algorithm and criteria, technical recommendations, and management suggestions. Substantive changes in LI-RADS v2018 are the removal of the requirement for visibility on antecedent surveillance ultrasound for LI-RADS 5 (LR-5) categorization of 10-19 mm observations with nonrim arterial phase hyper-enhancement and nonperipheral "washout", and adoption of the Organ Procurement and Transplantation Network definition of threshold growth (≥ 50% size increase of a mass in ≤ 6 months). Nomenclatural changes in LI-RADS v2018 are the removal of -us and -g as LR-5 qualifiers.

Transportation is arguably experiencing its most transformative revolution since the introduction of the automobile. Concerns over climate change and equity are converging with dramatic technological advances. Although these changes – including shared mobility and automation – are rapidly altering the mobility landscape, predictions about the future of transportation are complex, nuanced, and widely debated. California is required by law to renew the California Transportation Plan (CTP), updating its models and policy considerations to reflect industry changes every five years. This document is envisioned as a reference for modelers and decision makers. We aggregate current information and research on the state of key trends and emerging technologies/services, documented impacts on California’s transportation ecosystem, and future growth projections (as appropriate). During 2017, we reviewed an expanded list of 20 topics by referencing state agency publications, peer-reviewed journal articles, and forecast reports from consulting firms and think tanks. We followed transportation newsletters and media sources to track industry developments, and interviewed six experts to explore their opinions on the future of transportation. We consulted an advisory committee of over 50 representatives from local and state transportation agencies, who provided input throughout the project’s evolution. We also obtained feedback on our draft report from a panel of U.S. experts.

The advancement of science, as well as scientific careers, depends upon good and clear scientific writing. Science is the most democratic of human endeavours because, in principle, anyone can replicate a scientific discovery. In order for this to continue, writing must be clear enough to be understood well enough to allow replication, either in principle or in fact. In this paper I will present data on the publication process in Evolutionary Ecology, use it to illustrate some of the problems in scientific papers, make some general remarks about writing scientific papers, summarise two new paper categories in the journal which will fill gaps that appear to be expanding in the literature, and summarise new journal policies to help mitigate existing problems. Most of the suggestions about writing would apply to any scientific journal.

This poster describes a project to arrange, describe, and preserve two archival collections at UC Santa Cruz Special Collections & Archives: the Lick Observatory Records and the papers of Kenneth S. Norris. Both collections include essential records that highlight an innovative way of exploring nature at the time: the Lick Observatory embarked on expeditions all over the world in the early 20th century to study total solar eclipses, and Ken Norris helped establish the Field Quarter class at UC Santa Cruz, which remains to this day a unique opportunity for students to explore and study natural reserves across California for an entire academic quarter.

Introduction background to metabolomicsMetabolomics is the comprehensive study of the metabolome, the repertoire of biochemicals (or small molecules) present in cells, tissues, and body fluids. The study of metabolism at the global or "-omics" level is a rapidly growing field that has the potential to have a profound impact upon medical practice. At the center of metabolomics, is the concept that a person's metabolic state provides a close representation of that individual's overall health status. This metabolic state reflects what has been encoded by the genome, and modified by diet, environmental factors, and the gut microbiome. The metabolic profile provides a quantifiable readout of biochemical state from normal physiology to diverse pathophysiologies in a manner that is often not obvious from gene expression analyses. Today, clinicians capture only a very small part of the information contained in the metabolome, as they routinely measure only a narrow set of blood chemistry analytes to assess health and disease states. Examples include measuring glucose to monitor diabetes, measuring cholesterol and high density lipoprotein/low density lipoprotein ratio to assess cardiovascular health, BUN and creatinine for renal disorders, and measuring a panel of metabolites to diagnose potential inborn errors of metabolism in neonates.Objectives of white paper—expected treatment outcomes and metabolomics enabling tool for precision medicineWe anticipate that the narrow range of chemical analyses in current use by the medical community today will be replaced in the future by analyses that reveal a far more comprehensive metabolic signature. This signature is expected to describe global biochemical aberrations that reflect patterns of variance in states of wellness, more accurately describe specific diseases and their progression, and greatly aid in differential diagnosis. Such future metabolic signatures will: (1) provide predictive, prognostic, diagnostic, and surrogate ma