Integrals of Periodic Functions

Computing integrals of powers of the sine function is a standard exercise in calculus. The authors show that the first integral is representative of the integral of any periodic function

Speckle Observations of Binary Stars with the WIYN Telescope. III. A Partial Survey of A, F, and G Dwarfs

Two hundred thirty nearby main-sequence stars with spectral types in the range of A to G have been observed by way of speckle interferometry using the WIYN 3.5 m telescope at Kitt Peak, Arizona. The stars had no previous mention of duplicity in the literature. Of those observed, 14 showed clear evidence of a companion, and 63 were classified as suspected nonsingle based on a power spectrum analysis. The remaining stars discussed show no evidence of duplicity to the limit of the detection system in high-quality observations

ICAM-reg: Interpretable Classification and Regression with Feature Attribution for Mapping Neurological Phenotypes in Individual Scans

An important goal of medical imaging is to be able to precisely detect patterns of disease specific to individual scans; however, this is challenged in brain imaging by the degree of heterogeneity of shape and appearance. Traditional methods, based on image registration to a global template, historically fail to detect variable features of disease, as they utilise population-based analyses, suited primarily to studying group-average effects. In this paper we therefore take advantage of recent developments in generative deep learning to develop a method for simultaneous classification, or regression, and feature attribution (FA). Specifically, we explore the use of a VAE-GAN translation network called ICAM, to explicitly disentangle class relevant features from background confounds for improved interpretability and regression of neurological phenotypes. We validate our method on the tasks of Mini-Mental State Examination (MMSE) cognitive test score prediction for the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, as well as brain age prediction, for both neurodevelopment and neurodegeneration, using the developing Human Connectome Project (dHCP) and UK Biobank datasets. We show that the generated FA maps can be used to explain outlier predictions and demonstrate that the inclusion of a regression module improves the disentanglement of the latent space. Our code is freely available on Github https://github.com/CherBass/ICAM

ICAM-reg: Interpretable Classification and Regression with Feature Attribution for Mapping Neurological Phenotypes in Individual Scans

Feature attribution (FA), or the assignment of class-relevance to different locations in an image, is important for many classification and regression problems but is particularly crucial within the neuroscience domain, where accurate mechanistic models of behaviours, or disease, require knowledge of all features discriminative of a trait. At the same time, predicting class relevance from brain images is challenging as phenotypes are typically heterogeneous, and changes occur against a background of significant natural variation. Here, we present an extension of the ICAM framework for creating prediction specific FA maps through image-to-image translation

Gleam: the GLAST Large Area Telescope Simulation Framework

This paper presents the simulation of the GLAST high energy gamma-ray telescope. The simulation package, written in C++, is based on the Geant4 toolkit, and it is integrated into a general framework used to process events. A detailed simulation of the electronic signals inside Silicon detectors has been provided and it is used for the particle tracking, which is handled by a dedicated software. A unique repository for the geometrical description of the detector has been realized using the XML language and a C++ library to access this information has been designed and implemented.Comment: 10 pages, Late

Importance of Driving and Potential Impact of Driving Cessation for Rural and Urban Older Adults

PurposeAnalyses compared older drivers from urban, suburban, and rural areas on perceived importance of continuing to drive and potential impact that driving cessation would have on what they want and need to do.MethodsThe AAA LongROAD Study is a prospective study of driving behaviors, patterns, and outcomes of older adults. A cohort of 2,990 women and men 65‐79 years of age was recruited during 2015‐2017 from health systems or primary care practices near 5 study sites in different parts of the United States. Participants were classified as living in urban, surburban, or rural areas and were asked to rate the importance of driving and potential impact of driving cessation. Logistic regression models adjusted for sociodemographic and driving‐related characteristics.FindingsThe percentages of older drivers rating driving as “completely important” were 76.9%, 79.0%, and 83.8% for urban, suburban, and rural drivers, respectively (P = .009). The rural drivers were also most likely to indicate driving cessation would have a high impact on what they want or need to do (P < .001). After adjustment for sociodemographic and driving‐related characteristics, there was a 2‐fold difference for rural versus urban older drivers in odds that driving cessation would have a high impact on what they need to do (OR = 2.03; 95% CI: 1.60‐2.58).ConclusionsOlder drivers from rural areas were more likely to rate driving as highly important and the prospect of driving cessation as very impactful. Strategies to enhance both the ability to drive safely and the accessibility of alternative sources of transportation may be especially important for older rural adults.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153160/1/jrh12369_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153160/2/jrh12369.pd

The Mock LISA Data Challenges: from Challenge 3 to Challenge 4

The Mock LISA Data Challenges are a program to demonstrate LISA data-analysis capabilities and to encourage their development. Each round of challenges consists of one or more datasets containing simulated instrument noise and gravitational waves from sources of undisclosed parameters. Participants analyze the datasets and report best-fit solutions for the source parameters. Here we present the results of the third challenge, issued in Apr 2008, which demonstrated the positive recovery of signals from chirping Galactic binaries, from spinning supermassive--black-hole binaries (with optimal SNRs between ~ 10 and 2000), from simultaneous extreme-mass-ratio inspirals (SNRs of 10-50), from cosmic-string-cusp bursts (SNRs of 10-100), and from a relatively loud isotropic background with Omega_gw(f) ~ 10^-11, slightly below the LISA instrument noise.Comment: 12 pages, 2 figures, proceedings of the 8th Edoardo Amaldi Conference on Gravitational Waves, New York, June 21-26, 200

An emergency care research course for healthcare career preparation

Background University students have limited opportunities to gain healthcare clinical exposure within an academic curriculum. Furthermore, traditional pre-medical clinical experiences like shadowing lack active learning components. This may make it difficult for students to make an informed decision about pursuing biomedical professions. An academic university level research course with bedside experience provides students direct clinical participation in the healthcare setting. Methods Described is a research immersion course for senior university students (3rd to 5th year) interested in healthcare and reported study enrollment with final course evaluations. The setting was an adult, academic, urban, level 1 trauma center emergency department (ED) within a tertiary-care, 1000-bed, medical center. Our course, “Immersion in Emergency Care Research”, was offered as a university senior level class delivered consecutively over 16-weeks for students interested in healthcare careers. Faculty and staff from the Department of Emergency Medicine provided a classroom lecture program and extensive bedside, hands-on clinical research experience. Students enrolled patients in a survey study requiring informed consent, interviews, data abstraction and data entry. Additionally, they were required to write and present a mock emergency care research proposal inspired by their clinical experience. The course evaluations from students’ ordinal rankings and blinded text responses report possible career impact. Results Thirty-two students, completed the 16-week, 6–9 h per week, course from August to December in 1 of 4 years (2016 to 2019). Collectively, students enrolled 759 ED patients in the 4 survey studies and reported increased confidence in the clinical research process as each week progressed. Ranked evaluations were extremely positive, with many students describing how the course significantly impacted their career pathways and addressed an unmet need in biomedical education. Six students continued the research experience from the course through independent study using the survey data to develop 3 manuscripts for submission to peer-reviewed journals. Conclusions A bedside emergency care research course for students with pre-healthcare career aspirations can successfully provide early exposure to patients and emergency care, allow direct experience with clinical bedside research, research data collection, and may impact biomedical science career choices

Testing gravitational-wave searches with numerical relativity waveforms: Results from the first Numerical INJection Analysis (NINJA) project

The Numerical INJection Analysis (NINJA) project is a collaborative effort between members of the numerical relativity and gravitational-wave data analysis communities. The purpose of NINJA is to study the sensitivity of existing gravitational-wave search algorithms using numerically generated waveforms and to foster closer collaboration between the numerical relativity and data analysis communities. We describe the results of the first NINJA analysis which focused on gravitational waveforms from binary black hole coalescence. Ten numerical relativity groups contributed numerical data which were used to generate a set of gravitational-wave signals. These signals were injected into a simulated data set, designed to mimic the response of the Initial LIGO and Virgo gravitational-wave detectors. Nine groups analysed this data using search and parameter-estimation pipelines. Matched filter algorithms, un-modelled-burst searches and Bayesian parameter-estimation and model-selection algorithms were applied to the data. We report the efficiency of these search methods in detecting the numerical waveforms and measuring their parameters. We describe preliminary comparisons between the different search methods and suggest improvements for future NINJA analyses.Comment: 56 pages, 25 figures; various clarifications; accepted to CQ