In-flight estimation of gyro noise on the Upper Atmosphere Research Satellite (UARS) and Extreme Ultraviolet Explorer (EUVE) missions

This paper characterizes the low-frequency noise response of the Teledyne dry rotor inertial reference unit (DRIRU) gyroscopes on the Upper Atmosphere Research Satellite (UARS) and the Extreme Ultraviolet Explorer (EUVE). The accuracy of spacecraft attitude estimation algorithms that use gyro data for propagating the spacecraft attitude is sensitive to gyro noise. EUVE gyro data were processed to validate a single-axis gyro noise model, which is used onboard various spacecraft. The paper addresses the potential impact of temperature effects on the gyro noise model and the overall impact on attitude determination accuracy. The power spectral density (PSD) of the gyro noise is estimated from UARS in-flight data by Fast Fourier Transform (FFT). The role of actuator dynamics on the PSD function is also discussed

Aquatic treadmill running reduces muscle soreness following intense sprint exercise in trained men

Delayed onset muscle soreness is associated with muscle damage, disturbances in proprioception, and decreases in muscular power. The purpose was to determine if short duration aquatic treadmill (ATM) running reduces muscle soreness following intense sprint exercise in trained men. Twenty trained men (180.3±4.4cm, 86.3±5.8kg, 20±1yr) were recruited and randomly divided into two groups: ATM recovery (ATMRec) and passive recovery (PRec). During testing, subjects performed a warm-up followed by sixteen 110yrd cutback runs with a sprint of 60yrds, sharp change of direction, and a return sprint of 50yrds. Work to rest ratio was set at 1:3. Additionally, following exercise, the ATMRec group performed ATM running using a HydroWorx® treadmill at 5mph, 50% maximal jet resistance, and water(33°C) level at chest depth for 10min. Both groups then evaluated their level of soreness/pain using a numerical rating scale (NRS: 0-10, 0=no pain, 10=worst pain) immediately following all exercise (IPE), 24h, and 48h post exercise in the following regions: ARMS, LEGS, BACK, CHEST, SHOULDERS, HIPS, ABDOMEN, NECK, OVERALL. Data were analyzed for group x time interactions using a 2x3 Generalized Linear Mixed Model for non-parametric data (α≤0.05). For significant interactions, the same procedure was used to analyze between group differences at the same measurement timepoint(α≤0.05)

Bis(N′-{(E)-[(2E)-1,3-diphenylprop-2-en-1-ylidene]amino}-N-ethylcarbamimidothioato-κ2 N′,S)zinc(II): crystal structure and Hirshfeld surface analysis

The title ZnII complex, [Zn(C18H18N3S)2], (I), features two independent but chemically equivalent molecules in the asymmetric unit. In each, the thiosemicarbazonate monoanion coordinates the ZnII atom via the thiolate-S and imine-N atoms, with the resulting N2S2 donor set defining a distorted tetrahedral geometry. The five-membered ZnSCN2 chelate rings adopt distinct conformations in each independent molecule, i.e. one ring is almost planar while the other is twisted about the Zn—S bond. In the crystal, the two molecules comprising the asymmetric unit are linked by amine-N—H...N(imine) and amine-N—H...S(thiolate) hydrogen bonds via an eight-membered heterosynthon, {...HNCN...HNCS}. The dimeric aggregates are further consolidated by benzene-C—H...S(thiolate) interactions and are linked into a zigzag supramolecular chain along the c axis via amine-N—H...S(thiolate) hydrogen bonds. The chains are connected into a three-dimensional architecture via phenyl-C—H...π(phenyl) and π–π interactions, the latter occurring between chelate and phenyl rings [inter-centroid separation = 3.6873 (11) Å]. The analysis of the Hirshfeld surfaces calculated for (I) emphasizes the different interactions formed by the independent molecules in the crystal and the impact of the π–π interactions between chelate and phenyl rings

Bis(4-methoxychalcone 4-ethylthiosemicarbazonato-κ2 N 1,S)zinc(II): Crystal structure and Hirshfeld surface analysis

The title ZnII complex, [Zn(C19H20N3OS)2] {systematic name: bis[(N-ethyl-N0-{(Z)-[(2E)-3-(4-methoxyphenyl)-1-phenylprop-2-en-1-ylidene]amino}carbamimidoyl)sulfanido]zinc(II)}, features a tetrahedrally coordinated ZnII ion within an N2S2 donor set provided by two N,S-chelating thiosemicarbazone anions. The resulting five-membered Zn,C,N2,S chelate rings adopt different conformations, i.e. almost planar and an envelope with the Zn atom being the flap atom. The configuration about the imine bond within the chelate ring is Z but those about the exocyclic imine and ethylene bonds are E. In the crystal, supramolecular [100] chains mediated by thioamide-N—H...S(thione) hydrogen bonds and eight-membered thioamide {.....HNCS}2 synthons are observed. A range of interactions, including C—H...O, C—H...., C—H....(chelate ring) and .(methoxybenzene)—.(chelate ring) consolidate the packing. The Hirshfeld surface analysis performed on the title complex also indicates the influence of the interactions involving the chelate rings upon the packing along with the more conventional contacts

Localizing the Latent Structure Canonical Uncertainty: Entropy Profiles for Hidden Markov Models

This report addresses state inference for hidden Markov models. These models rely on unobserved states, which often have a meaningful interpretation. This makes it necessary to develop diagnostic tools for quantification of state uncertainty. The entropy of the state sequence that explains an observed sequence for a given hidden Markov chain model can be considered as the canonical measure of state sequence uncertainty. This canonical measure of state sequence uncertainty is not reflected by the classic multivariate state profiles computed by the smoothing algorithm, which summarizes the possible state sequences. Here, we introduce a new type of profiles which have the following properties: (i) these profiles of conditional entropies are a decomposition of the canonical measure of state sequence uncertainty along the sequence and makes it possible to localize this uncertainty, (ii) these profiles are univariate and thus remain easily interpretable on tree structures. We show how to extend the smoothing algorithms for hidden Markov chain and tree models to compute these entropy profiles efficiently.Comment: Submitted to Journal of Machine Learning Research; No RR-7896 (2012

Expression of Epstein–Barr Virus–Encoded Small RNA (by the EBER-1 Gene) in Liver Specimens from Transplant Recipients with Post-Transplantation Lymphoproliferative Disease

Epstein-Barr virus (EBV)—associated post-transplantation lymphoproliferative disease (PTLD) develops in 1 to 10 percent of transplant recipients, in whom it can be treated by a reduction in the level of immunosuppression. We postulated that the tissue expression of the small RNA transcribed by the EBER-1 gene during latent EBV infection would identify patients at risk for PTLD. We studied EBER-1 gene expression in liver specimens obtained from 24 patients 2 days to 22 months before the development of PTLD, using in situ hybridization with an oligonucleotide probe. Control specimens were obtained from 20 recipients of allografts with signs of injury due to organ retrieval, acute graft rejection, or viral hepatitis in whom PTLD had not developed 9 to 71 months after the biopsy. Of the 24 patients with PTLD, 17 (71 percent) had specimens in which 1 to 40 percent of mononuclear cells were positive for the EBER-1 gene. In addition, 10 of these 17 patients (59 percent) had specimens with histopathological changes suggestive of EBV hepatitis. In every case, EBER-1—positive cells were found within the lymphoproliferative lesions identified at autopsy. Only 2 of the 20 controls (10 percent) had specimens with EBER-1—positive cells (P<0.001), and such cells were rare. EBER-1 gene expression in liver tissue precedes the occurrence of clinical and histologic PTLD. The possibility of identifying patients at risk by the method we describe here and preventing the occurrence of PTLD by a timely reduction of immunosuppression needs to be addressed by future prospective studies. (N Engl J Med 1992;327:1710–4.), POST-TRANSPLANTATION lymphoproliferative disease (PTLD), either polyclonal or monoclonal, complicates the clinical course of 1 to 10 percent of organ-transplant recipients.123 Immunohistochemical studies have demonstrated that the lymphoid cells within the lesions of PTLD almost invariably contain Epstein–Barr virus (EBV), primarily in a state of latent infection.4,5 The EBER-1 gene is expressed early during latent EBV infection and codes for a small messenger RNA (mRNA) expressed at up to 107 copies per cell.6 We and others have previously demonstrated the value of the detection of EBER-1 RNA for identifying EBV-infected cells in formalin-fixed paraffin-embedded tissues.7,8 In the current investigation, we used… © 1992, Massachusetts Medical Society. All rights reserved

What Type of Person Would Be Willing to Fly with Children? A Multi-Model Analysis

The purpose of this study was to assess the type of person who would be willing to fly with children in various scenarios. A quantitative methodology and a non-experimental research approach were used in this study. A two-stage approach created a regression equation then assessed model fit. Six hundred and twenty participants were recruited for the study. The dataset was split randomly into two groups to facilitate the two-stage approach, resulting in 310 participants per stage. The study used 14 possible predictors to determine willingness to fly in five different scenarios. Five models were created and found between two and four predictors of passengers who were willing to fly with children in various scenarios. We were able to explain between 14.3% and 18.6% of the variance. All five equations were assessed for model fit and found to support a good model fit. Many aviation studies have examined willingness to fly in various scenarios; however, no research specific to the type of person who would be willing to fly with children has been explored. This study aims to fill that gap by exploring the type of person who would fly with children in five different scenarios

Structured Sparsity: Discrete and Convex approaches

Compressive sensing (CS) exploits sparsity to recover sparse or compressible signals from dimensionality reducing, non-adaptive sensing mechanisms. Sparsity is also used to enhance interpretability in machine learning and statistics applications: While the ambient dimension is vast in modern data analysis problems, the relevant information therein typically resides in a much lower dimensional space. However, many solutions proposed nowadays do not leverage the true underlying structure. Recent results in CS extend the simple sparsity idea to more sophisticated {\em structured} sparsity models, which describe the interdependency between the nonzero components of a signal, allowing to increase the interpretability of the results and lead to better recovery performance. In order to better understand the impact of structured sparsity, in this chapter we analyze the connections between the discrete models and their convex relaxations, highlighting their relative advantages. We start with the general group sparse model and then elaborate on two important special cases: the dispersive and the hierarchical models. For each, we present the models in their discrete nature, discuss how to solve the ensuing discrete problems and then describe convex relaxations. We also consider more general structures as defined by set functions and present their convex proxies. Further, we discuss efficient optimization solutions for structured sparsity problems and illustrate structured sparsity in action via three applications.Comment: 30 pages, 18 figure