Model-based geostatistics: some issues in modelling and model diagnostics

Spatial modelling is examined in a model-based geostatistical context using the Gaussian linear mixed model in a likelihood framework. Complex spatial models developed provide practitioners with a practical and best-practice guide for spatial analysis. Adequate modelling theory and matrix algebra are provided to ground the methods demonstrated. A multivariate model over two time points and three-dimensional space is developed which is novel to the field of soil science. Soil organic carbon measurements at three soil depths and two time points from a cropping field with four soil classes are used. The spatial process is assessed for second-order stationarity and anisotropic correlation. Univariate spatial modelling is used to inform bivariate spatial modelling of pre- and post-harvest soil organic carbon at each soil depth. Bivariate modelling is extended to the multivariate level, where both time points and the three soil depths are incorporated in a single model to pool maximum information. A common correlation structure is tested and is supported for the response variable at each of the six time-depth combinations. Separable correlation structures are used for computational efficiency. The difficulty of estimating nugget effects suggests a sub-optimal sampling design. Preferred fitted models are all isotropic. Equations for predictions and the variance of prediction errors are extended from well-known results and maps of predicted values and variance of prediction errors are produced and show close correspondence with observed values. Finally, univariate models for spatially referenced seed counts from small sampling plots are examined within a Gaussian framework using Box-Cox transformations. The discrete nature of the data, small sample size and computational problems hamper model fitting. Anisotropy is examined using a variogram envelope diagnostic technique. ASReml-R software is shown to be a powerful analytical tool for spatial processes

Model-based geostatistics: some issues in modelling and model diagnostics

Spatial modelling is examined in a model-based geostatistical context using the Gaussian linear mixed model in a likelihood framework. Complex spatial models developed provide practitioners with a practical and best-practice guide for spatial analysis. Adequate modelling theory and matrix algebra are provided to ground the methods demonstrated. A multivariate model over two time points and three-dimensional space is developed which is novel to the field of soil science. Soil organic carbon measurements at three soil depths and two time points from a cropping field with four soil classes are used. The spatial process is assessed for second-order stationarity and anisotropic correlation. Univariate spatial modelling is used to inform bivariate spatial modelling of pre- and post-harvest soil organic carbon at each soil depth. Bivariate modelling is extended to the multivariate level, where both time points and the three soil depths are incorporated in a single model to pool maximum information. A common correlation structure is tested and is supported for the response variable at each of the six time-depth combinations. Separable correlation structures are used for computational efficiency. The difficulty of estimating nugget effects suggests a sub-optimal sampling design. Preferred fitted models are all isotropic. Equations for predictions and the variance of prediction errors are extended from well-known results and maps of predicted values and variance of prediction errors are produced and show close correspondence with observed values. Finally, univariate models for spatially referenced seed counts from small sampling plots are examined within a Gaussian framework using Box-Cox transformations. The discrete nature of the data, small sample size and computational problems hamper model fitting. Anisotropy is examined using a variogram envelope diagnostic technique. ASReml-R software is shown to be a powerful analytical tool for spatial processes

Using Search Queries to Understand Health Information Needs in Africa

The lack of comprehensive, high-quality health data in developing nations creates a roadblock for combating the impacts of disease. One key challenge is understanding the health information needs of people in these nations. Without understanding people's everyday needs, concerns, and misconceptions, health organizations and policymakers lack the ability to effectively target education and programming efforts. In this paper, we propose a bottom-up approach that uses search data from individuals to uncover and gain insight into health information needs in Africa. We analyze Bing searches related to HIV/AIDS, malaria, and tuberculosis from all 54 African nations. For each disease, we automatically derive a set of common search themes or topics, revealing a wide-spread interest in various types of information, including disease symptoms, drugs, concerns about breastfeeding, as well as stigma, beliefs in natural cures, and other topics that may be hard to uncover through traditional surveys. We expose the different patterns that emerge in health information needs by demographic groups (age and sex) and country. We also uncover discrepancies in the quality of content returned by search engines to users by topic. Combined, our results suggest that search data can help illuminate health information needs in Africa and inform discussions on health policy and targeted education efforts both on- and offline.Comment: Extended version of an ICWSM 2019 pape

DEVELOPING A STRATEGY OF PREDATOR CONTROL FOR THE PROTECTION OF THE CALIFORNIA LEAST TERN: A CASE HISTORY

In recent years, predation has been determined to be a seriously limiting factor in the reproduction of the endangered California least tern (Sterna antillarum browni) at many of its nesting colonies. Among them is a major colony at Camp Pendleton Marine Corps Base near Oceanside, CA. Early efforts to control predation were limited in effectiveness. In 1988, the U.S. Department of Agriculture, Animal Damage Control Program was contracted to provide control of mammalian and avian predators. The development of the successful strategy that has evolved over four years is discussed, with emphasis on the development and application of techniques, and the timing and areas of control

The Use of Bootstrapping when Using Propensity-Score Matching without Replacement: A Simulation Study

Propensity‐score matching is frequently used to estimate the effect of treatments, exposures, and interventions when using observational data. An important issue when using propensity‐score matching is how to estimate the standard error of the estimated treatment effect. Accurate variance estimation permits construction of confidence intervals that have the advertised coverage rates and tests of statistical significance that have the correct type I error rates. There is disagreement in the literature as to how standard errors should be estimated. The bootstrap is a commonly used resampling method that permits estimation of the sampling variability of estimated parameters. Bootstrap methods are rarely used in conjunction with propensity‐score matching. We propose two different bootstrap methods for use when using propensity‐score matching without replacement and examined their performance with a series of Monte Carlo simulations. The first method involved drawing bootstrap samples from the matched pairs in the propensity‐score‐matched sample. The second method involved drawing bootstrap samples from the original sample and estimating the propensity score separately in each bootstrap sample and creating a matched sample within each of these bootstrap samples. The former approach was found to result in estimates of the standard error that were closer to the empirical standard deviation of the sampling distribution of estimated effects

Capillary electrophoresis-fluorescence line narrowing system (CE-FLNS) for on-line structural characterization

Capillary electrophoresis (CE) is interfaced with low temperature fluorescenceline-narrowing (FLN) spectroscopy for on-line structural characterization of separated molecular analytes

Embryonic Pattern Scaling Achieved by Oppositely Directed Morphogen Gradients

Morphogens are proteins, often produced in a localised region, whose concentrations spatially demarcate regions of differing gene expression in developing embryos. The boundaries of expression must be set accurately and in proportion to the size of the one-dimensional developing field; this cannot be accomplished by a single gradient. Here, we show how a pair of morphogens produced at opposite ends of a developing field can solve the pattern-scaling problem. In the most promising scenario, the morphogens effectively interact according to the annihilation reaction $A+B\to\emptyset$ and the switch occurs according to the absolute concentration of $A$ or $B$. In this case embryonic markers across the entire developing field scale approximately with system size; this cannot be achieved with a pair of non-interacting gradients that combinatorially regulate downstream genes. This scaling occurs in a window of developing-field sizes centred at a few times the morphogen decay length.Comment: 24 pages; 11 figures; uses iopar

Patient experiences of adjusting to life in the first two years after bariatric surgery: a qualitative study

Background: There is a limited amount of research into the experiences of those who have undergone bariatric surgery, and how this impacts on their everyday lives and social interactions. Methods Semi-structured interviews were carried out with 18 participants (11 female, 7 male) who had undergone permanent bariatric surgical procedures 5-24 months prior to interview at a large NHS hospital in North East England. Constructivist grounded theory was used, with a constant comparative analytic framework. Results Participants conceptualised social encounters after bariatric surgery as being underpinned by risk. Their attitudes towards social situations guided their actions in the context of social interaction. Three profiles of attitudes towards risk were constructed: Risk Accepters, Risk Contenders and Risk Challengers. These profiles were based on participant-reported narratives of their experiences in the first two years post-surgically Conclusions The social complexities occurring as a consequence of bariatric surgery require adjustments to patients’ lives. Participants reported that the social aspects of bariatric surgery do not appear to be widely understood by those who have had bariatric surgery. The three risk attitude profiles that emerged from our data offer an understanding of ways in which patients adjust to life and can be used reflexively by healthcare professionals in the support of patients both pre- and post-operatively