Comparing plasma and faecal measures of steroid hormones in Adelie penguins Pygoscelis adeliae

Physiological measurements of both stress and sex hormones are often used to estimate the consequences of natural or human-induced change in ecological studies of various animals. Different methods of hormone measurement exist, potentially explaining variation in results across studies; methods should be cross-validated to ensure that they correlate. We directly compared faecal and plasma hormone measurements for the first time in a wild free-living species, the Adelie penguin (Pygoscelis adeliae). Blood and faecal samples were simultaneously collected from individual penguins for comparison and assayed for testosterone and corticosterone (or their metabolites). Sex differences and variability within each measure, and correlation of values across measures were compared. For both hormones, plasma samples showed greater variation than faecal samples. Males had higher mean corticosterone concentrations than females, but the difference was only statistically significant in faecal samples. Plasma testosterone, but not faecal testosterone, was significantly higher in males than females. Correlation between sample types was poor overall, and weaker in females than in males, perhaps because measures from plasma represent hormones that are both free and bound to globulins, whereas measures from faeces represent only the free portion. Faecal samples also represent a cumulative measure of hormones over time, as opposed to a plasma ‘snapshot’ concentration. Our data indicate that faecal sampling appears more suitable for assessing baseline hormone concentrations, whilst plasma sampling may best define immediate responses to environmental events. Consequently, future studies should ensure that they select the most appropriate matrix and method of hormone measurement to answer their research questions

Adapting a computational multi agent model for humpback whale song research for use as a tool for algorithmic composition

Humpback whales (Megaptera Novaengliae) present one of the most complex displays of cultural transmission amongst non-humans. During breeding seasons, male humpback whales create long, hierarchical songs, which are shared amongst a population. Every male in the population conforms to the same song in a population. During the breeding season these songs slowly change and the song at the end of the breeding season is significantly different from the song heard at the start of the breeding season. The song of a population can also be replaced, if a new song from a different population is introduced.This is known as song revolution. Our research focuses on building computational multi agent models, which seek to recreate these phenomena observed in the wild.Our research relies on methods inspired by computational multi agent models for the evolution of music. This interdisciplinary approach has allowed us to adapt our model so that it may be used not only as a scientific tool, but also a creative tool for algorithmic composition. This paper discusses the model in detail, and then demonstrates how it may be adapted for use as an algorithmic composition tool

Testing general relativity with accretion onto compact objects

The X-ray emission of neutron stars and black holes presents a rich phenomenology that can lead us to a better understanding of their nature and to address more general physics questions: Does general relativity apply in the strong gravity regime? Is spacetime around black holes described by the Kerr metric? This white paper considers how we can investigate these questions by studying reverberation mapping and quasi-periodic oscillations in accreting systems with a combination of high-spectral and high-timing resolution. In the near future, we will be able to study compact objects in the X-rays in a new way: advancements in transition-edge sensors (TES) technology will allow for electron-volt-resolution spectroscopy combined with nanoseconds-precision timing.Comment: White paper submitted for Astro2020 Decadal Survey. 8 pages, 2 figure

Maximizing Equitable Reach and Accessibility of ETDs

This poster addresses accessibility issues of electronic theses and dissertations (ETDs) in digital libraries (DLs). ETDs are available primarily as PDF files, which present barriers to equitable access, especially for users with visual impairments, cognitive or learning disabilities, or for anyone needing more efficient and effective ways of finding relevant information within these long documents. We propose using AI techniques, including natural language processing (NLP), computer vision, and text analysis, to convert PDFs into machine-readable HTML documents with semantic tags and structure, extracting figures and tables, and generating summaries and keywords. Our goal is to increase the accessibility of ETDs and to make this important scholarship available to a wider audience

Opening Books and the National Corpus of Graduate Research

Virginia Tech University Libraries, in collaboration with Virginia Tech Department of Computer Science and Old Dominion University Department of Computer Science, request $505,214 in grant funding for a 3-year project, the goal of which is to bring computational access to book-length documents, demonstrating that with Electronic Theses and Dissertations (ETDs). The project is motivated by the following library and community needs. (1) Despite huge volumes of book-length documents in digital libraries, there is a lack of models offering effective and efficient computational access to these long documents. (2) Nationwide open access services for ETDs generally function at the metadata level. Much important knowledge and scientific data lie hidden in ETDs, and we need better tools to mine the content and facilitate the identification, discovery, and reuse of these important components. (3) A wide range of audiences can potentially benefit from this research, including but not limited to Librarians, Students, Authors, Educators, Researchers, and other interested readers. We will answer the following key research questions: (1) How can we effectively identify and extract key parts (chapters, sections, tables, figures, citations), in both born digital and page image formats? (2) How can we develop effective automatic classication as well as chapter summarization techniques? (3) How can our ETD digital library most effectively serve stakeholders? In response to these questions, we plan to first compile an ETD corpus consisting of at least 50,000 documents from multiple institutional repositories. We will make the corpus inclusive and diverse, covering a range of degrees (master’s and doctoral), years, graduate programs (STEM and non-STEM), and authors (from HBCUs and non-HBCUs). Testing first with this sample, we will investigate three major research areas (RAs), outlined below. RA 1: Document analysis and extraction, in which we experiment with machine/deep learning models for effective ETD segmentation and subsequent information extraction. Anticipated results of this research include new software tools that can be used and adapted by libraries for automatic extraction of structural metadata and document components (chapters, sections, figures, tables, citations, bibliographies) from ETDs - applied to both page image and born digital documents. RA 2: Adding value, in which we investigate techniques and build machine/deep learning models to automatically summarize and classify ETD chapters. Anticipated results of this research include software implementations of a chapter-level text summarizer that generates paragraph-length summaries of ETD chapters, and a multi-label classifier that assigns subject categories to ETD chapters. Our aim is to develop software that can be adapted or replicated by libraries to add value to their existing ETD services. RA 3: User services, in which we study users to identify and understand their information needs and information seeking behaviors, so that we may establish corresponding requirements for user interface and service components most useful for interacting with ETD content. Basing our design decisions on empirical evidence obtained from user analysis, we will construct a prototype system to demonstrate how these components can improve the user experience with ETD collections, and ultimately increase the capacity of libraries to provide access to ETDs and other long-form document content. Our project brings to bear cutting-edge computer science and machine/deep learning technologies to advance discovery, use, and potential for reuse of the knowledge hidden in the text of books and book-length documents. In addition, by focusing on libraries\u27 ETD collections (where legal restrictions from book publishers generally are not applicable), our research will open this rich corpus of graduate research and scholarship, leverage ETDs to advance further research and education, and allow libraries to achieve greater impact

Analyzing and Navigating ETDs Using Topic Models

Electronic theses and dissertations (ETDs) contain valuable knowledge that can be useful in a wide range of research areas. Accordingly, we are building electronic infrastructure leveraging advanced work on digital libraries, for discovering and accessing the knowledge buried in ETDs. In this paper we focus on our work to incorporate topic modeling into digital libraries for ETDs. We present ETD-Topics, a framework that extracts topics from a large text corpus in an unsupervised way. The representations learned from topic models can be useful for downstream tasks such as searching and/or browsing documents by topic, document recommendation, topic recommendation, and describing temporal topic trends (e.g., from the perspective of disciplines or universities)

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy

P4_4 The Lunar Recession

This article investigates the Moon's recession from the Earth. The semi-major axis at which the Moon will become tidally locked with the Earth is calculated, and is found to be 558,000 km. It was found that the period of the Earth-Moon system would be about 48 current days long at this time. Using the Moon's current rate of recession, it was estimated that this process would happen in about 4.6 Gyr