Detecting multivariate interactions in spatial point patterns with Gibbs models and variable selection

We propose a method for detecting significant interactions in very large multivariate spatial point patterns. This methodology develops high dimensional data understanding in the point process setting. The method is based on modelling the patterns using a flexible Gibbs point process model to directly characterise point-to-point interactions at different spatial scales. By using the Gibbs framework significant interactions can also be captured at small scales. Subsequently, the Gibbs point process is fitted using a pseudo-likelihood approximation, and we select significant interactions automatically using the group lasso penalty with this likelihood approximation. Thus we estimate the multivariate interactions stably even in this setting. We demonstrate the feasibility of the method with a simulation study and show its power by applying it to a large and complex rainforest plant population data set of 83 species

Death or survival from invasive pneumococcal disease in Scotland: associations with serogroups and multilocus sequence types

We describe associations between death from invasive pneumococcal disease (IPD) and particular serogroups and sequence types (STs) determined by multilocus sequence typing (MLST) using data from Scotland. All IPD episodes where blood or cerebrospinal fluid (CSF) culture isolates were referred to the Scottish Haemophilus, Legionella, Meningococcal and Pneumococcal Reference Laboratory (SHLMPRL) from January 1992 to February 2007 were matched to death certification records by the General Register Office for Scotland. This represented 5959 patients. The median number of IPD cases in Scotland each year was 292. Deaths, from any cause, within 30 days of pneumococcal culture from blood or CSF were considered to have IPD as a contributing factor. Eight hundred and thirty-three patients died within 30 days of culture of Streptococcus pneumoniae from blood or CSF [13.95%; 95% confidence interval (13.10, 14.80)]. The highest death rates were in patients over the age of 75. Serotyping data exist for all years but MLST data were only available from 2001 onward. The risk ratio of dying from infection due to particular serogroups or STs compared to dying from IPD due to all other serogroups or STs was calculated. Fisher's exact test with Bonferroni adjustment for multiple testing was used. Age adjustment was accomplished using the Cochran-Mantel-Haenszel test and 95% confidence intervals were reported. Serogroups 3, 11 and 16 have increased probability of causing fatal IPD in Scotland while serogroup 1 IPD has a reduced probability of causing death. None of the 20 most common STs were significantly associated with death within 30 days of pneumococcal culture, after age adjustment. We conclude that there is a stronger association between a fatal outcome and pneumococcal capsular serogroup than there is between a fatal outcome and ST

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.

Adaptive geostatistical design and analysis for prevalence surveys

Non-adaptive geostatistical designs (NAGDs) offer standard ways of collecting and analysing geostatistical data in which sampling locations are fixed in advance of any data collection. In contrast, adaptive geostatistical designs (AGDs) allow collection of geostatistical data over time to depend on information obtained from previous information to optimise data collection towards the analysis objective. AGDs are becoming more important in spatial mapping, particularly in poor resource settings where uniformly precise mapping may be unrealistically costly and the priority is often to identify critical areas where interventions can have the most health impact. Two constructions are: singleton and batch adaptive sampling. In singleton sampling, locations xi are chosen sequentially and at each stage, xk+1 depends on data obtained at locations x1,…,xk. In batch sampling, locations are chosen in batches of size b>1, allowing each new batch, {x(k+1),…,x(k+b)}, to depend on data obtained at locations x1,…,xkb. In most settings, batch sampling is more realistic than singleton sampling. We propose specific batch AGDs and assess their efficiency relative to their singleton adaptive and non-adaptive counterparts using simulations. We then show how we are applying these findings to inform an AGD of a rolling Malaria Indicator Survey, part of a large-scale, five-year malaria transmission reduction project in Malawi

Inhibitory geostatistical designs for spatial prediction taking account of uncertain covariance structure

The problem of choosing spatial sampling designs for investigating an unobserved spatial phenomenon S arises in many contexts, for example in identifying households to select for a prevalence survey to study disease burden and heterogeneity in a study region D. We studied randomised inhibitory spatial sampling designs to address the problem of spatial prediction whilst taking account of the need to estimate covariance structure. Two specific classes of design are inhibitory designs and inhibitory designs plus close pairs. In an inhibitory design, any pair of sample locations must be separated by at least an inhibition distance δ. In an inhibitory plus close pairs design, n − k sample locations in an inhibitory design with inhibition distance δ are augmented by k locations each positioned close to one of the randomly selected n − k locations in the inhibitory design, uniformly distributed within a disc of radius ζ. We present simulation results for the Mat´ern class of covariance structures. When the nugget variance is non-negligible, inhibitory plus close pairs designs demonstrate improved predictive efficiency over designs without close pairs. We illustrate how these findings can be applied to the design of a rolling Malaria Indicator Survey that forms part of an ongoing large-scale, five-year malaria transmission reduction project in Malawi

Combinatorial quorum sensing allows bacteria to resolve their social and physical environment

Quorum sensing (QS) is a cell–cell 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 “AND-gate” 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

Bayesian Inference and Data Augmentation Schemes for Spatial, Spatiotemporal and Multivariate Log-Gaussian Cox Processes in R

Log-Gaussian Cox processes are an important class of models for spatial and spatiotemporal point-pattern data. Delivering robust Bayesian inference for this class of models presents a substantial challenge, since Markov chain Monte Carlo (MCMC) algorithms require careful tuning in order to work well. To address this issue, we describe recent advances in MCMC methods for these models and their implementation in the R package lgcp. Our suite of R functions provides an extensible framework for inferring covariate effects as well as the parameters of the latent field. We also present methods for Bayesian inference in two further classes of model based on the log-Gaussian Cox process. The first of these concerns the case where we wish to fit a point process model to data consisting of event-counts aggregated to a set of spatial regions: we demonstrate how this can be achieved using data-augmentation. The second concerns Bayesian inference for a class of marked-point processes specified via a multivariate log-Gaussian Cox process model. For both of these extensions, we give details of their implementation in R

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 6–15-µm 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. 200 000 of these NVCs would fit into one diamond

Statistical Analysis of Surface Reconstruction Domains on InAs Wetting Layer Preceding Quantum Dot Formation

Surface of an InAs wetting layer on GaAs(001) preceding InAs quantum dot (QD) formation was observed at 300°C with in situ scanning tunneling microscopy (STM). Domains of (1 × 3)/(2 × 3) and (2 × 4) surface reconstructions were located in the STM image. The density of each surface reconstruction domain was comparable to that of subsequently nucleated QD precursors. The distribution of the domains was statistically investigated in terms of spatial point patterns. It was found that the domains were distributed in an ordered pattern rather than a random pattern. It implied the possibility that QD nucleation sites are related to the surface reconstruction domains