The Calibration of the HST Kuiper Belt Object Search: Setting the Record Straight

The limiting magnitude of the HST data set used by Cochran et al. (1995) to detect small objects in the Kuiper belt is reevaluated, and the methods used are described in detail. It is shown, by implanting artificial objects in the original HST images, and re-reducing the images using our original algorithm, that the limiting magnitude of our images (as defined by the 50% detectability limit) is $V=28.4$. This value is statistically the same as the value found in the original analysis. We find that $\sim50$ of the moving Kuiper belt objects with $V=27.9$ are detected when trailing losses are included. In the same data in which these faint objects are detected, we find that the number of false detections brighter than $V=28.8$ is less than one per WFPC2 image. We show that, primarily due to a zero-point calibration error, but partly due to inadequacies in modeling the HST'S data noise characteristics and Cochran et al.'s reduction techniques, Brown et al. 1997 underestimate the SNR of objects in the HST dataset by over a factor of 2, and their conclusions are therefore invalid.Comment: Accepted to ApJ Letters; 10 pages plus 3 figures, LaTe

Modeling Biological Membranes with Circuit Boards and Measuring Electrical Signals in Axons: Student Laboratory Exercises

This is a demonstration of how electrical models can be used to characterize biological membranes. This exercise also introduces biophysical terminology used in electrophysiology. The same equipment is used in the membrane model as on live preparations. Some properties of an isolated nerve cord are investigated: nerve action potentials, recruitment of neurons, and responsiveness of the nerve cord to environmental factors

Retrospective Application of Human Reliability Analysis for Oil and Gas Incidents: A Case Study Using the Petro-HRA Method

Human reliability analysis (HRA) may be performed prospectively for a newly designed system or retrospectively for an as-built system, typically in response to a safety incident. The SPAR-H HRA method was originally developed for retrospective analysis in the U.S. nuclear industry. As HRA has found homes in new safety critical areas, HRA methods developed predominantly for nuclear power applications are being used in novel ways. The Petro-HRA method represents a significant adaptation of the SPAR-H method for petroleum applications. Current guidance on Petro-HRA considers only prospective applications of the method, such as for review of new systems to be installed at offshore installations. In this paper, we review retrospective applications of Petro-HRA and analyze the Macando Oil Well-Deepwater Horizon accident as a case study

Observational and Genetic Evidence for Bidirectional Effects Between Red Blood Cell Traits and Diastolic Blood Pressure

Background: Previous studies have found associations of red blood cell traits (hemoglobin and red blood cell count, RBC) with blood pressure; whether these associations are causal is unknown.Methods: We performed cross-sectional analyses in the Lifelines Cohort Study (n=167,785). Additionally, we performed bidirectional two sample Mendelian randomization (MR) analyses to explore the causal effect of the two traits on systolic (SBP) and diastolic blood pressure (DBP), using genetic instrumental variables regarding hemoglobin and RBC identified in UK Biobank (n=350,475) and International Consortium of Blood Pressure studies for SBP and DBP (n= 757,601).Results: In cross-sectional analyses we observed positive associations with hypertension and blood pressure for both hemoglobin (OR=1.18, 95% CI: 1.16 to 1.20 for hypertension; B=0.11, 95% CI: 0.11 to 0.12 for SBP; B=0.11, 95% CI: 0.10 to 0.11 for DBP, all per SD) and RBC (OR=1.14, 95% CI: 1.12 to 1.16 for hypertension; B=0.11, 95% CI: 0.10 to 0.12 for SBP; B=0.08, 95% CI: 0.08 to 0.09 for DBP, all per SD). MR analyses suggested that higher hemoglobin and RBC cause higher DBP (inverse variance weighted [IVW] B=0.11, 95% CI: 0.07 to 0.16 for hemoglobin; B=0.07, 95% CI: 0.04 to 0.10 for RBC, all per SD). Reverse MR analyses (all per SD) suggested causal effects of DBP on both hemoglobin (B=0.06, 95% CI: 0.03 to 0.09) and RBC (B=0.08, 95% CI: 0.04 to 0.11). No significant effects on SBP were found.Conclusions: Our results suggest bidirectional causal relationships of hemoglobin and RBC with DBP, but not with SBP

Nondestructive Evaluation Approaches Developed for Material Characterization in Aeronautics and Space Applications

At the NASA Glenn Research Center, nondestructive evaluation (NDE) approaches were developed or tailored for characterizing advanced material systems. The emphasis was on high-temperature aerospace propulsion applications. The material systems included monolithic ceramics, superalloys, and high-temperature composites. In the aeronautics area, the major applications were cooled ceramic plate structures for turbine applications, gamma-TiAl blade materials for low-pressure turbines, thermoelastic stress analysis for residual stress measurements in titanium-based and nickel-based engine materials, and acousto-ultrasonics for creep damage assessment in nickel-based alloys. In the space area, applications consisted of cooled carbon-carbon composites for gas generator combustors and flywheel rotors composed of carbon-fiber-reinforced polymer matrix composites for energy storage on the International Space Station

A Modular Framework for Implicit 3D-0D Coupling in Cardiac Mechanics

In numerical simulations of cardiac mechanics, coupling the heart to a model of the circulatory system is essential for capturing physiological cardiac behavior. A popular and efficient technique is to use an electrical circuit analogy, known as a lumped parameter network or zero-dimensional (0D) fluid model, to represent blood flow throughout the cardiovascular system. Due to the strong physical interaction between the heart and the blood circulation, developing accurate and efficient numerical coupling methods remains an active area of research. In this work, we present a modular framework for implicitly coupling three-dimensional (3D) finite element simulations of cardiac mechanics to 0D models of blood circulation. The framework is modular in that the circulation model can be modified independently of the 3D finite element solver, and vice versa. The numerical scheme builds upon a previous work that combines 3D blood flow models with 0D circulation models (3D fluid - 0D fluid). Here, we extend it to couple 3D cardiac tissue mechanics models with 0D circulation models (3D structure - 0D fluid), showing that both mathematical problems can be solved within a unified coupling scheme. The effectiveness, temporal convergence, and computational cost of the algorithm are assessed through multiple examples relevant to the cardiovascular modeling community. Importantly, in an idealized left ventricle example, we show that the coupled model yields physiological pressure-volume loops and naturally recapitulates the isovolumic contraction and relaxation phases of the cardiac cycle without any additional numerical techniques. Furthermore, we provide a new derivation of the scheme inspired by the Approximate Newton Method of Chan (1985), explaining how the proposed numerical scheme combines the stability of monolithic approaches with the modularity and flexibility of partitioned approaches

Estimation of Individual Micro Data from Aggregated Open Data

In this paper, we propose a method of estimating individual micro data from aggregated open data based on semi-supervised learning and conditional probability. Firstly, the proposed method collects aggregated open data and support data, which are related to the individual micro data to be estimated. Then, we perform the locality sensitive hashing (LSH) algorithm to find a subset of the support data that is similar to the aggregated open data and then classify them by using the Ensemble classification model, which is learned by semi-supervised learning. Finally, we use conditional probability to estimate the individual micro data by finding the most suitable record for the probability distribution of the individual micro data among the classification results. To evaluate the performance of the proposed method, we estimated the individual building data where the fire occurred using the aggregated fire open data. According to the experimental results, the micro data estimation performance of the proposed method is 59.41% on average in terms of accuracy.Comment: 7 page

Pulling Back the Curtain on the Wizards of Oz

The Wizard of Oz method is an increasingly common practice in HCI and CSCW studies as part of iterative design processes for interactive systems. Instead of designing a fully-fledged system, the ‘technical work’ of key system components is completed by human operators yet presented to study participants as if computed by a machine. However, little is known about how Wizard of Oz studies are interactionally and collaboratively achieved in situ by researchers and participants. By adopting an ethnomethodological perspective, we analyse our use of the method in studies with a voice-controlled vacuum robot and two researchers present. We present data that reveals how such studies are organised and presented to participants and unpack the coordinated orchestration work that unfolds ‘behind the scenes’ to complete the study. We examine how the researchers attend to participant requests and technical breakdowns, and discuss the performative, collaborative, and methodological nature of their work. We conclude by offering insights from our application of the approach to others in the HCI and CSCW communities for using the method

RTOG 0518: Randomized Phase III Trial to Evaluate Zoledronic Acid for Prevention of Osteoporosis and Associated Fractures in Prostate Cancer Patients

Background: RTOG 0518 evaluated the potential benefit of zoledronic acid therapy in preventing bone fractures for patients with high grade and/or locally advanced, non-metastatic prostate adenocarcinoma receiving luteinizing hormone-releasing hormone (LHRH) agonist and radiotherapy (RT). Methods: Eligible patients with T-scores of the hip ( −2.5 vs. > −1.0) and negative bone scans were prospectively randomized to either zoledronic acid, 4 mg, concurrently with the start of RT and then every six months for a total of 6 infusions (Arm 1) or observation (Arm 2). Vitamin D and calcium supplements were given to all patients. Secondary objectives included quality of life (QOL) and bone mineral density (BMD) changes over a period of three years. Results: Of 109 patients accrued before early closure, 96 were eligible. Median follow-up was 36.3 months for Arm I and 34.8 months for Arm 2. Only two patients experienced a bone fracture (1 in each arm) resulting in no difference in freedom from any bone fracture (p=0.95), nor in QOL. BMD percent changes from baseline to 36 months were statistically improved with the use of zoledronic acid compared to observation for the lumbar spine (6% vs. −5%, p<0.0001), left total hip (1% vs. −8%, p=0.0002), and left femoral neck (3% vs. −8%, p=0.0007). Conclusions: For patients with advanced, non-metastatic prostate cancer receiving LHRH agonist and RT, the use of zoledronic acid was associated with statistically improved BMD percent changes. The small number of accrued patients resulted in decreased statistical power to detect any differences in the incidence of bone fractures or QOL

Electronic Health Record-Based Recruitment and Retention and Mobile Health App Usage: Multisite Cohort Study.

BACKGROUND: To address the obesity epidemic, there is a need for novel paradigms, including those that address the timing of eating and sleep in relation to circadian rhythms. Electronic health records (EHRs) are an efficient way to identify potentially eligible participants for health research studies. Mobile health (mHealth) apps offer available and convenient data collection of health behaviors, such as timing of eating and sleep. OBJECTIVE: The aim of this descriptive analysis was to report on recruitment, retention, and app use from a 6-month cohort study using a mobile app called Daily24. METHODS: Using an EHR query, adult patients from three health care systems in the PaTH clinical research network were identified as potentially eligible, invited electronically to participate, and instructed to download and use the Daily24 mobile app, which focuses on eating and sleep timing. Online surveys were completed at baseline and 4 months. We described app use and identified predictors of app use, defined as 1 or more days of use, versus nonuse and usage categories (ie, immediate, consistent, and sustained) using multivariate regression analyses. RESULTS: Of 70,661 patients who were sent research invitations, 1021 (1.44%) completed electronic consent forms and online baseline surveys; 4 withdrew, leaving a total of 1017 participants in the analytic sample. A total of 53.79% (n=547) of the participants were app users and, of those, 75.3% (n=412), 50.1% (n=274), and 25.4% (n=139) were immediate, consistent, and sustained users, respectively. Median app use was 28 (IQR 7-75) days over 6 months. Younger age, White race, higher educational level, higher income, having no children younger than 18 years, and having used 1 to 5 health apps significantly predicted app use (vs nonuse) in adjusted models. Older age and lower BMI predicted early, consistent, and sustained use. About half (532/1017, 52.31%) of the participants completed the 4-month online surveys. A total of 33.5% (183/547), 29.3% (157/536), and 27.1% (143/527) of app users were still using the app for at least 2 days per month during months 4, 5, and 6 of the study, respectively. CONCLUSIONS: EHR recruitment offers an efficient (ie, high reach, low touch, and minimal participant burden) approach to recruiting participants from health care settings into mHealth research. Efforts to recruit and retain less engaged subgroups are needed to collect more generalizable data. Additionally, future app iterations should include more evidence-based features to increase participant use