Calculation of AGARD Wing 445.6 Flutter Using Navier-Stokes Aerodynamics

An unsteady, 3D, implicit upwind Euler/Navier-Stokes algorithm is here used to compute the flutter characteristics of Wing 445.6, the AGARD standard aeroelastic configuration for dynamic response, with a view to the discrepancy between Euler characteristics and experimental data. Attention is given to effects of fluid viscosity, structural damping, and number of structural model nodes. The flutter characteristics of the wing are determined using these unsteady generalized aerodynamic forces in a traditional V-g analysis. The V-g analysis indicates that fluid viscosity has a significant effect on the supersonic flutter boundary for this wing

Proper Orthogonal Decomposition Methods for the Analysis of Real-Time Data: Exploring Peak Clustering in a Secondhand Smoke Exposure Intervention

This work explores a method for classifying peaks appearing within a data-intensive time-series. We summarize a case study from a clinical trial aimed at reducing secondhand smoke exposure via the installation of air particle monitors in households. Proper orthogonal decomposition (POD) in conjunction with a k-means clustering algorithm assigns each data peak to one of two clusters. Aversive feedback from the monitors increased the proportion of short-duration, attenuated peaks from 38.8% to 96.6%. For each cluster, a distribution of parameters from a physics-based model of airborne particles is estimated. Peaks generated from these distributions are correctly identified by POD/clustering with \u3e60% accuracy

Developing and Selecting Auditory Warnings for a Real-Time Behavioral Intervention

Real-time sensing and computing technologies are increasingly used in the delivery of real-time health behavior interventions. Auditory signals play a critical role in many of these interventions, impacting not only behavioral response but also treatment adherence and participant retention. Yet, few behavioral interventions that employ auditory feedback report the characteristics of sounds used and even fewer design signals specifically for their intervention. This paper describes a four-step process used in developing and selecting auditory warnings for a behavioral trial designed to reduce indoor secondhand smoke exposure. In step one, relevant information was gathered from ergonomic and behavioral science literature to assist a panel of research assistants in developing criteria for intervention-specific auditory feedback. In step two, multiple sounds were identified through internet searches and modified in accordance with the developed criteria, and two sounds were selected that best met those criteria. In step three, a survey was conducted among 64 persons from the primary sampling frame of the larger behavioral trial to compare the relative aversiveness of sounds, determine respondents\u27 reported behavioral reactions to those signals, and assess participant’s preference between sounds. In the final step, survey results were used to select the appropriate sound for auditory warnings. Ultimately, a single-tone pulse, 500 milliseconds (ms) in length that repeats every 270 ms for three cycles was chosen for the behavioral trial. The methods described herein represent one example of steps that can be followed to develop and select auditory feedback tailored for a given behavioral intervention

Randomized Trial to Reduce Air Particle Levels in Homes of Smokers and Children

Introduction Exposure to fine particulate matter in the home from sources such as smoking, cooking, and cleaning may put residents, especially children, at risk for detrimental health effects. A randomized clinical trial was conducted from 2011 to 2016 to determine whether real-time feedback in the home plus brief coaching of parents or guardians could reduce fine particle levels in homes with smokers and children. Design A randomized trial with two groups—intervention and control. Setting/participants A total of 298 participants from predominantly low-income households with an adult smoker and a child aged \u3c14 years. Participants were recruited during 2012–2015 from multiple sources in San Diego, mainly Women, Infants and Children Program sites. Intervention The multicomponent intervention consisted of continuous lights and brief sound alerts based on fine particle levels in real time and four brief coaching sessions using particle level graphs and motivational interviewing techniques. Motivational interviewing coaching focused on particle reduction to protect children and other occupants from elevated particle levels, especially from tobacco-related sources. Main outcome measures In-home air particle levels were measured by laser particle counters continuously in both study groups. The two outcomes were daily mean particle counts and percentage time with high particle concentrations (\u3e15,000 particles/0.01 ft3). Linear mixed models were used to analyze the differential change in the outcomes over time by group, during 2016–2017. Results Intervention homes had significantly larger reductions than controls in daily geometric mean particle concentrations (18.8% reduction vs 6.5% reduction, p\u3c0.001). Intervention homes’ average percentage time with high particle concentrations decreased 45.1% compared with a 4.2% increase among controls (difference between groups p\u3c0.001). Conclusions Real-time feedback for air particle levels and brief coaching can reduce fine particle levels in homes with smokers and young children. Results set the stage for refining feedback and possible reinforcing consequences for not generating smoke-related particles. Trial registration This study is registered at www.clinicaltrials.gov NCT01634334

Discovering study-specific gene regulatory networks

This article has been made available through the Brunel Open Access Publishing Fund.Microarrays are commonly used in biology because of their ability to simultaneously measure thousands of genes under different conditions. Due to their structure, typically containing a high amount of variables but far fewer samples, scalable network analysis techniques are often employed. In particular, consensus approaches have been recently used that combine multiple microarray studies in order to find networks that are more robust. The purpose of this paper, however, is to combine multiple microarray studies to automatically identify subnetworks that are distinctive to specific experimental conditions rather than common to them all. To better understand key regulatory mechanisms and how they change under different conditions, we derive unique networks from multiple independent networks built using glasso which goes beyond standard correlations. This involves calculating cluster prediction accuracies to detect the most predictive genes for a specific set of conditions. We differentiate between accuracies calculated using cross-validation within a selected cluster of studies (the intra prediction accuracy) and those calculated on a set of independent studies belonging to different study clusters (inter prediction accuracy). Finally, we compare our method's results to related state-of-the art techniques. We explore how the proposed pipeline performs on both synthetic data and real data (wheat and Fusarium). Our results show that subnetworks can be identified reliably that are specific to subsets of studies and that these networks reflect key mechanisms that are fundamental to the experimental conditions in each of those subsets

Thin Ice Target for 16^{16}O(p,p') experiment

A windowless and self-supporting ice target is described. An ice sheet with a thickness of 29.7 mg/cm$^2$ cooled by liquid nitrogen was placed at the target position of a magnetic spectrometer and worked stably in the $^{16}$O$(p,p')$ experiment at $E_{p}=392$ MeV. Background-free spectra were obtained.Comment: 14 pages, 4 figures, Nucl. Instr. & Meth. A (in press

Molecular landscape of the ribosome pre-initiation complex during mRNA scanning: structural role for eIF3c and its control by eIF5

During eukaryotic translation initiation, eIF3 binds the solvent-accessible side of the 40S ribosome and recruits the gate-keeper protein eIF1 and eIF5 to the decoding center. This is largely mediated by the N-terminal domain (NTD) of eIF3c, which can be divided into three parts: 3c0, 3c1 and 3c2. The N-terminal part, 3c0, binds eIF5 strongly, but only weakly to the ribosome-binding surface of eIF1, whereas 3c1 and 3c2 form a stoichiometric complex with eIF1. 3c1 contacts eIF1 through Arg-53 and Leu-96, while 3c2 faces 40S protein uS15/S13, to anchor eIF1 to the scanning pre-initiation complex (PIC). We propose that the 3c0:eIF1 interaction diminishes eIF1 binding to the 40S, whereas 3c0:eIF5 interaction stabilizes the scanning PIC by precluding this inhibitory interaction. Upon start codon recognition, interactions involving eIF5, and ultimately 3c0:eIF1 association facilitate eIF1 release. Our results reveal intricate molecular interactions within the PIC, programmed for rapid scanning-arrest at the start codon