1,307 research outputs found
Estimating stellar oscillation-related parameters and their uncertainties with the moment method
The moment method is a well known mode identification technique in
asteroseismology (where `mode' is to be understood in an astronomical rather
than in a statistical sense), which uses a time series of the first 3 moments
of a spectral line to estimate the discrete oscillation mode parameters l and
m. The method, contrary to many other mode identification techniques, also
provides estimates of other important continuous parameters such as the
inclination angle alpha, and the rotational velocity v_e. We developed a
statistical formalism for the moment method based on so-called generalized
estimating equations (GEE). This formalism allows the estimation of the
uncertainty of the continuous parameters taking into account that the different
moments of a line profile are correlated and that the uncertainty of the
observed moments also depends on the model parameters. Furthermore, we set up a
procedure to take into account the mode uncertainty, i.e., the fact that often
several modes (l,m) can adequately describe the data. We also introduce a new
lack of fit function which works at least as well as a previous discriminant
function, and which in addition allows us to identify the sign of the azimuthal
order m. We applied our method to the star HD181558, using several numerical
methods, from which we learned that numerically solving the estimating
equations is an intensive task. We report on the numerical results, from which
we gain insight in the statistical uncertainties of the physical parameters
involved in the moment method.Comment: The electronic online version from the publisher can be found at
http://www.blackwell-synergy.com/doi/abs/10.1111/j.1467-9876.2005.00487.
Deep three-dimensional solid-state qubit arrays with long-lived spin coherence
Nitrogen-vacancy centers (NVCs) in diamond show promise for quantum computing, communication, and sensing. However, the best current method for entangling two NVCs requires that each one is in a separate cryostat, which is not scalable. We show that single NVCs can be laser written 6â15-”m deep inside of a diamond with spin coherence times that are an order of magnitude longer than previous laser-written NVCs and at least as long as naturally occurring NVCs. This depth is suitable for integration with solid immersion lenses or optical cavities and we present depth-dependent T2 measurements. 200â000 of these NVCs would fit into one diamond
Combinatorial quorum sensing allows bacteria to resolve their social and physical environment
Quorum sensing (QS) is a cellâcell communication system that controls gene expression in many bacterial species, mediated by diffusible signal molecules. Although the intracellular regulatory mechanisms of QS are often well-understood, the functional roles of QS remain controversial. In particular, the use of multiple signals by many bacterial species poses a serious challenge to current functional theories. Here, we address this challenge by showing that bacteria can use multiple QS signals to infer both their social (density) and physical (mass-transfer) environment. Analytical and evolutionary simulation models show that the detection of, and response to, complex social/physical contrasts requires multiple signals with distinct half-lives and combinatorial (nonadditive) responses to signal concentrations. We test these predictions using the opportunistic pathogen Pseudomonas aeruginosa and demonstrate significant differences in signal decay betweeallyn its two primary signal molecules, as well as diverse combinatorial responses to dual-signal inputs. QS is associated with the control of secreted factors, and we show that secretome genes are preferentially controlled by synergistic âAND-gateâ responses to multiple signal inputs, ensuring the effective expression of secreted factors in high-density and low mass-transfer environments. Our results support a new functional hypothesis for the use of multiple signals and, more generally, show that bacteria are capable of combinatorial communication
Vitamin D Status and its Association with Morbidity including Wasting and Opportunistic Illnesses in HIV-Infected Women in Tanzania.
Vitamin D has a potential role in preventing HIV-related complications, based on its extensive involvement in immune and metabolic function, including preventing osteoporosis and premature cardiovascular disease. However, this association has not been examined in large studies or in resource-limited settings. Vitamin D levels were assessed in 884 HIV-infected pregnant women at enrollment in a trial of multivitamin supplementation (excluding vitamin D) in Tanzania. Information on HIV related complications was recorded during follow-up (median, 70 months). Proportional hazards models and generalized estimating equations were used to assess the relationship of vitamin D status with these outcomes. Women with low vitamin D status (serum 25-hydroxyvitamin D<32 ng/mL) had 43% higher risk of reaching a body mass index (BMI) less than 18 kg/m(2) during the first 2 years of follow-up, compared to women with adequate vitamin D levels (hazard ratio [HR]: 1.43; 95% confidence intervals: [1.03-1.99]). The relationship between continuous vitamin D levels and risk of BMI less than 18 kg/m(2) during follow-up was inverse and linear (p=0.03). Women with low vitamin D levels had significantly higher incidence of acute upper respiratory infections (HR: 1.27 [1.04-1.54]) and thrush (HR: 2.74 [1.29-5.83]) diagnosed during the first 2 years of follow-up. Low vitamin D status was a significant risk factor for wasting and HIV-related complications such as thrush during follow-up in this prospective cohort in Tanzania. If these protective associations are confirmed in randomized trials, vitamin D supplementation could represent a simple and inexpensive method to improve health and quality of life of HIV-infected patients, particularly in resource-limited settings
Generalized estimating equations to estimate the ordered stereotype logit model for panel data
By modeling the effects of predictor variables as a multiplicative function of regression parameters being invariant over categories, and category-specific scalar effects, the ordered stereotype logit model is a flexible regression model for ordinal response variables. In this article, we propose a generalized estimating equations (GEE) approach to estimate the ordered stereotype logit model for panel data based on working covariance matrices, which are not required to be correctly specified. A simulation study compares the performance of GEE estimators based on various working correlation matrices and working covariance matrices using local odds ratios. Estimation of the model is illustrated using a real-world dataset. The results from the simulation study suggest that GEE estimation of this model is feasible in medium-sized and large samples and that estimators based on local odds ratios as realized in this study tend to be less efficient compared with estimators based on a working correlation matrix. For low true correlations, the efficiency gains seem to be rather small and if the working covariance structure is too flexible, the corresponding estimator may even be less efficient compared with the GEE estimator assuming independence. Like for GEE estimators more generally, if the true correlations over time are high, then a working covariance structure which is close to the true structure can lead to considerable efficiency gains compared with assuming independence.Peer ReviewedPostprint (published version
Using community-level prevalence of Loa loa infection to predict the proportion of highly-infected individuals:statistical modelling to support Lymphatic Filariasis and Onchocerciasis elimination programs
Lymphatic Filariasis and Onchocerciasis (river blindness) constitute pressing public health issues in tropical regions. Global elimination programs, involving mass drug administration (MDA), have been launched by the World Health Organisation. Although the drugs used are generally well tolerated, individuals who are highly co-infected with Loa loa are at risk of experiencing serious adverse events. Highly infected individuals are more likely to be found in communities with high prevalence. An understanding of the relationship between individual infection and population-level prevalence can therefore inform decisions on whether MDA can be safely administered in an endemic community. Based on Loa loa infection intensity data from individuals in Cameroon, the Republic of the Congo and the Democratic Republic of the Congo we develop a statistical model for the distribution of infection levels in communities. We then use this model to make predictive inferences regarding the proportion of individuals whose parasite count exceeds policy-relevant levels. In particular we show how to exploit the positive correlation between community-level prevalence and intensity of infection in order to predict the proportion of highly infected individuals in a community given only prevalence data from the community in question. The resulting prediction intervals are not substantially wider, and in some cases narrower, than the corresponding binomial confidence intervals obtained from data that include measurements of individual infection levels. Therefore the model developed here facilitates the estimation of the proportion of individuals highly infected with Loa loa using only estimated community level prevalence. It can be used to assess the risk of rolling out MDA in a specific community, or to guide policy decisions
SPHR Diabetes Prevention Model: Detailed Description of Model Background, Methods, Assumptions and Parameters
Type-2 diabetes is a complex disease with multiple risk factors and health consequences whose prevention is a major public health priority. We have developed a microsimulation model written in the R programming language that can evaluate the effectiveness and cost-effectiveness of a comprehensive range of different diabetes prevention interventions, either in the general population or in subgroups at high risk of diabetes. Within the model individual patients with different risk factors for diabetes follow metabolic trajectories (for body mass index, cholesterol, systolic blood pressure and glycaemia), develop diabetes, complications of diabetes and related disorders including cardiovascular disease and cancer, and eventually die. Lifetime costs and quality-adjusted life-years are collected for each patient. The model allows assessment of the wider social impact on employment and the equity impact of different interventions. Interventions may be population-based, community-based or individually targeted, and administered singly or layered together. The model is fully enabled for probabilistic sensitivity analysis (PSA) to provide an estimate of decision uncertainty. This discussion paper provides a detailed description of the model background, methods and assumptions, together with details of all parameters used in the model, their sources and distributions for PSA
Visualising spatio-temporal health data: the importance of capturing the 4th dimension
Confronted by a rapidly evolving health threat, such as an infectious disease
outbreak, it is essential that decision-makers are able to comprehend the
complex dynamics not just in space but also in the 4th dimension, time. In this
paper this is addressed by a novel visualisation tool, referred to as the
Dynamic Health Atlas web app, which is designed specifically for displaying the
spatial evolution of data over time while simultaneously acknowledging its
uncertainty. It is an interactive and open-source web app, coded predominantly
in JavaScript, in which the geospatial and temporal data are displayed
side-by-side. The first of two case studies of this visualisation tool relates
to an outbreak of canine gastroenteric disease in the United Kingdom, where
many veterinary practices experienced an unusually high case incidence. The
second study concerns the predicted COVID-19 reproduction number along with
incidence and prevalence forecasts in each local authority district in the
United Kingdom. These studies demonstrate the effectiveness of the Dynamic
Health Atlas web app at conveying geospatial and temporal dynamics along with
their corresponding uncertainties.Comment: 4 Figures, 27 page
Doubly charged silicon vacancy center, Si-N complexes, and photochromism in N and Si codoped diamond
Diamond samples containing silicon and nitrogen are shown to be heavily photochromic, with the dominant visible changes due to simultaneous change in total SiV0/â concentration. The photochromism treatment is not capable of creating or destroying SiV defects, and thus we infer the presence of the optically inactive
SiV2â . We measure spectroscopic signatures we attribute to substitutional silicon in diamond, and identify a silicon-vacancy complex decorated with a nearest-neighbor nitrogen SiVN, supported by theoretical calculations
Mark correlations: relating physical properties to spatial distributions
Mark correlations provide a systematic approach to look at objects both
distributed in space and bearing intrinsic information, for instance on
physical properties. The interplay of the objects' properties (marks) with the
spatial clustering is of vivid interest for many applications; are, e.g.,
galaxies with high luminosities more strongly clustered than dim ones? Do
neighbored pores in a sandstone have similar sizes? How does the shape of
impact craters on a planet depend on the geological surface properties? In this
article, we give an introduction into the appropriate mathematical framework to
deal with such questions, i.e. the theory of marked point processes. After
having clarified the notion of segregation effects, we define universal test
quantities applicable to realizations of a marked point processes. We show
their power using concrete data sets in analyzing the luminosity-dependence of
the galaxy clustering, the alignment of dark matter halos in gravitational
-body simulations, the morphology- and diameter-dependence of the Martian
crater distribution and the size correlations of pores in sandstone. In order
to understand our data in more detail, we discuss the Boolean depletion model,
the random field model and the Cox random field model. The first model
describes depletion effects in the distribution of Martian craters and pores in
sandstone, whereas the last one accounts at least qualitatively for the
observed luminosity-dependence of the galaxy clustering.Comment: 35 pages, 12 figures. to be published in Lecture Notes of Physics,
second Wuppertal conference "Spatial statistics and statistical physics
- âŠ