Directional Distributions in Tracking of Space Debris

Directional distributions play an important role in describing uncertainty in spherical coordinates. A review is given of some standard distributions on the sphere which arise as special cases of the Fisher-Bingham distribution. A new distribution, called the “extreme FB5” istribution, is introduced to describe semi-concentrated behavior on the sphere, that is, patterns of data that are unimodal and concentrated near a great circle. This behavior is particularly relevant to tracking problems. Properties of the new distribution are discussed and methods are given for simulation and estimation. Two simple error propagation illustrations are given to demonstrate the usefulness of the new model

Filtering When Object Custody is Ambiguous

Filtering involves predicting the future state of a space object in orbit about the earth given observations (e.g. angles-only or radar measurements) about its current and past states. The task is simplest when the identity of the object is known. A recently developed “Adapted STructural (AST)” coordinate system enables the task to be carried out in a computationally efficient manner. Propagation for a single state (or a small number of sigma points) can be carried out using Keplerian dynamics or using a numerically more expensive propagator to accommodate perturbation effects. In either case, the uncertainty can be represented in AST coordinates as Gaussian to a high level of accuracy. An Unscented Kalman Filter (UKF) has been developed in this situation; in particular, there is no need to use particle filters. However, when object custody is uncertain, i.e. when the latest observation might correspond to two or more objects in a catalog, the filtering task is more complicated. In this case we propose a mixture of Gaussians in AST coordinates to represent the state. The paper will demonstrate the feasibility of this approach

The flow of anisotropic nanoparticles in solution and in blood

The alignment of anisotropic nanoparticles in flow has been used for a range of applications such as the preparation of strong fibres and the assembly of in-plane aligned 1D-nanoobjects that are used for electronic devices, sensors, energy and biological application. Important is also the flow behaviour of nanoparticles that were designed for nanomedical applications such as drug delivery. It is widely observed that non-spherical nanoparticles have longer circulation times and a more favourable biodistribution. To be able to understand this behaviour, researchers have turned to analyzing the flow of non-spherical nanoparticles in the blood stream. In this review, an overview of microfluidic techniques that are used to monitor the alignment of anisotropic nanoparticles in solution will be provided, which includes analysis by small angle X-ray scattering (SAXS) and polarized light microscopy. The flow of these nanoparticles in blood is then discussed as the presence of red blood cells causes margination of some nanoparticles. Using fluorescence microscopy, the extent of margination can be identified, which coincides with the ability of nanoparticles to adhere to the cells grown along the wall. While these studies are mainly carried out in vitro using blood, initial investigations in vivo were able to confirm the unusual flow of anisotropic nanoparticles

A New Unified Approach for the Simulation of a Wide Class of Directional Distributions

The need for effective simulation methods for directional distributions has grown as they have become components in more sophisticated statistical models. A new acceptance-rejection method is proposed and investigated for the Bingham distribution on the sphere using the angular central Gaussian distribution as an envelope. It is shown that the proposed method has high efficiency and is also straightforward to use. Next, the simulation method is extended to the Fisher and Fisher-Bingham distributions on spheres and related manifolds. Together, these results provide a widely applicable and efficient methodology to simulate many of the standard models in directional data analysis. An R package simdd, available in the online supplementary material, implements these simulation methods

Shrinkage estimation with a matrix loss function

Consider estimating an n×p matrix of means Θ, say, from an n×p matrix of observations X, where the elements of X are assumed to be independently normally distributed with E(xij)=θij and constant variance, and where the performance of an estimator is judged using a p×p matrix quadratic error loss function. A matrix version of the James-Stein estimator is proposed, depending on a tuning constant a. It is shown to dominate the usual maximum likelihood estimator for some choices of a when n≥3. This result also extends to other shrinkage estimators and settings

The effect of Neuragen PN® on Neuropathic pain: A randomized, double blind, placebo controlled clinical trial

<p>Abstract</p> <p>Background</p> <p>A double blind, randomized, placebo controlled study to evaluate the safety and efficacy of the naturally derived topical oil, "Neuragen PN^®" for the treatment of neuropathic pain.</p> <p>Methods</p> <p>Sixty participants with plantar cutaneous (foot sole) pain due to all cause peripheral neuropathy were recruited from the community. Each subject was randomly assigned to receive one of two treatments (Neuragen PN^®or placebo) per week in a crossover design. The primary outcome measure was acute spontaneous pain level as reported on a visual analog scale.</p> <p>Results</p> <p>There was an overall pain reduction for both treatments from pre to post application. As compared to the placebo, Neuragen PN^®led to significantly (p < .05) greater pain reduction. Fifty six of sixty subjects (93.3%) receiving Neuragen PN^®reported pain reduction within 30 minutes. This reduction within 30 minutes occurred in only twenty one of sixty (35.0%) subjects receiving the placebo. In a break out analysis of the diabetic only subgroup, 94% of subjects in the Neuragen PN^®group achieved pain reduction within 30 minutes vs 11.0% of the placebo group. No adverse events were observed.</p> <p>Conclusions</p> <p>This randomized, placebo controlled, clinical trial with crossover design revealed that the naturally derived oil, Neuragen PN^®, provided significant relief from neuropathic pain in an all cause neuropathy group. Participants with diabetes within this group experienced similar pain relief.</p> <p>Trial registration</p> <p><b>ISRCTN registered: </b>ISRCTN13226601</p

GIVE: portable genome browsers for personal websites.

Growing popularity and diversity of genomic data demand portable and versatile genome browsers. Here, we present an open source programming library called GIVE that facilitates the creation of personalized genome browsers without requiring a system administrator. By inserting HTML tags, one can add to a personal webpage interactive visualization of multiple types of genomics data, including genome annotation, "linear" quantitative data, and genome interaction data. GIVE includes a graphical interface called HUG (HTML Universal Generator) that automatically generates HTML code for displaying user chosen data, which can be copy-pasted into user's personal website or saved and shared with collaborators. GIVE is available at: https://www.givengine.org/

An elliptically symmetric angular Gaussian distribution

We define a distribution on the unit sphere Sd−1 called the elliptically symmetric angular Gaussian distribution. This distribution, which to our knowledge has not been studied before, is a subfamily of the angular Gaussian distribution closely analogous to the Kent subfamily of the general Fisher–Bingham distribution. Like the Kent distribution, it has elliptical contours, enabling modelling of rotational asymmetry about the mean direction, but it has the additional advantages of being simple and fast to simulate from, and having a density and hence likelihood that is easy and very quick to compute exactly. These advantages are especially beneficial for computationally intensive statistical methods, one example of which is a parametric bootstrap procedure for inference for the directional mean that we describe

Genetic signal maximization using environmental regression

Joint analyses of correlated phenotypes in genetic epidemiology studies are common. However, these analyses primarily focus on genetic correlation between traits and do not take into account environmental correlation. We describe a method that optimizes the genetic signal by accounting for stochastic environmental noise through joint analysis of a discrete trait and a correlated quantitative marker. We conducted bivariate analyses where heritability and the environmental correlation between the discrete and quantitative traits were calculated using Genetic Analysis Workshop 17 (GAW17) family data. The resulting inverse value of the environmental correlation between these traits was then used to determine a new β coefficient for each quantitative trait and was constrained in a univariate model. We conducted genetic association tests on 7,087 nonsynonymous SNPs in three GAW17 family replicates for Affected status with the β coefficient fixed for three quantitative phenotypes and compared these to an association model where the β coefficient was allowed to vary. Bivariate environmental correlations were 0.64 (± 0.09) for Q1, 0.798 (± 0.076) for Q2, and −0.169 (± 0.18) for Q4. Heritability of Affected status improved in each univariate model where a constrained β coefficient was used to account for stochastic environmental effects. No genome-wide significant associations were identified for either method but we demonstrated that constraining β for covariates slightly improved the genetic signal for Affected status. This environmental regression approach allows for increased heritability when the β coefficient for a highly correlated quantitative covariate is constrained and increases the genetic signal for the discrete trait

High throughput mutagenesis for identification of residues regulating human prostacyclin (hIP) receptor

The human prostacyclin receptor (hIP receptor) is a seven-transmembrane G protein-coupled receptor (GPCR) that plays a critical role in vascular smooth muscle relaxation and platelet aggregation. hIP receptor dysfunction has been implicated in numerous cardiovascular abnormalities, including myocardial infarction, hypertension, thrombosis and atherosclerosis. Genomic sequencing has discovered several genetic variations in the PTGIR gene coding for hIP receptor, however, its structure-function relationship has not been sufficiently explored. Here we set out to investigate the applicability of high throughput random mutagenesis to study the structure-function relationship of hIP receptor. While chemical mutagenesis was not suitable to generate a mutagenesis library with sufficient coverage, our data demonstrate error-prone PCR (epPCR) mediated mutagenesis as a valuable method for the unbiased screening of residues regulating hIP receptor function and expression. Here we describe the generation and functional characterization of an epPCR derived mutagenesis library compromising >4000 mutants of the hIP receptor. We introduce next generation sequencing as a useful tool to validate the quality of mutagenesis libraries by providing information about the coverage, mutation rate and mutational bias. We identified 18 mutants of the hIP receptor that were expressed at the cell surface, but demonstrated impaired receptor function. A total of 38 non-synonymous mutations were identified within the coding region of the hIP receptor, mapping to 36 distinct residues, including several mutations previously reported to affect the signaling of the hIP receptor. Thus, our data demonstrates epPCR mediated random mutagenesis as a valuable and practical method to study the structurefunction relationship of GPCRs. © 2014 Bill et al