Decrease in water clarity of the southern and central North Sea during the 20th century

Light in the marine environment is a key environmental variable coupling physics to marine biogeochemistry and ecology. Weak light penetration reduces light available for photosynthesis, changing energy fluxes through the marine food web. Based on published and unpublished data, this study shows that the central and southern North Sea has become significantly less clear over the second half of the 20th century. In particular, in the different regions and seasons investigated, the average Secchi depth pre-1950 decreased between 25% and 75% compared to the average Secchi depth post-1950. Consequently, in summer pre-1950, most (74%) of the sea floor in the permanently mixed area off East Anglia was within the photic zone. For the last 25+ years, changes in water clarity were more likely driven by an increase in the concentration of suspended sediments, rather than phytoplankton. We suggest that a combination of causes have contributed to this increase in suspended sediments such as changes in sea-bed communities and in weather patterns, decreased sink of sediments in estuaries, and increased coastal erosion. A predicted future increase in storminess (Beniston et al., 2007; Kovats et al., 2014) could enhance the concentration of suspended sediments in the water column and consequently lead to a further decrease in clarity, with potential impacts on phytoplankton production, CO2 fluxes, and fishery production

A Spatially Discrete Approximation to Log-Gaussian Cox Processes for Modelling Aggregated Disease Count Data

In this paper, we develop a computationally efficient discrete approximation to log‐Gaussian Cox process (LGCP) models for the analysis of spatially aggregated disease count data. Our approach overcomes an inherent limitation of spatial models based on Markov structures, namely, that each such model is tied to a specific partition of the study area, and allows for spatially continuous prediction. We compare the predictive performance of our modelling approach with LGCP through a simulation study and an application to primary biliary cirrhosis incidence data in Newcastle upon Tyne, UK. Our results suggest that, when disease risk is assumed to be a spatially continuous process, the proposed approximation to LGCP provides reliable estimates of disease risk both on spatially continuous and aggregated scales. The proposed methodology is implemented in the open‐source R package SDALGCP

A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation

A reliable Ultrasound (US)-to-US registration method to compensate for brain shift would substantially improve Image-Guided Neurological Surgery. Developing such a registration method is very challenging, due to factors such as missing correspondence in images, the complexity of brain pathology and the demand for fast computation. We propose a novel feature-driven active framework. Here, landmarks and their displacement are first estimated from a pair of US images using corresponding local image features. Subsequently, a Gaussian Process (GP) model is used to interpolate a dense deformation field from the sparse landmarks. Kernels of the GP are estimated by using variograms and a discrete grid search method. If necessary, the user can actively add new landmarks based on the image context and visualization of the uncertainty measure provided by the GP to further improve the result. We retrospectively demonstrate our registration framework as a robust and accurate brain shift compensation solution on clinical data acquired during neurosurgery

Horizontal patterns of water temperature and salinity in an estuarine tidal channel: Ria de Aveiro

This work presents results from two complementary and interconnected approaches to study water temperature and salinity patterns in an estuarine tidal channel. This channel is one of the four main branches of the Ria de Aveiro, a shallow lagoon located in the Northwest coast of the Iberian Peninsula. Longitudinal and cross-sectional fields of water temperature and salinity were determined by spatial interpolation of field measurements. A numerical model (Mohid) was used in a 2D depth-integrated mode in order to compute water temperature and salinity patterns. The main purpose of this work was to determine the horizontal patterns of water temperature and salinity in the study area, evaluating the effects of the main forcing factors. The field results were depth-integrated and compared to numerical model results. These results obtained using extreme tidal and river runoff forcing, are also presented. The field results reveal that, when the river flow is weak, the tidal intrusion is the main forcing mechanism, generating saline and thermal fronts which migrate with the neap/spring tidal cycle. When the river flow increases, the influence of the freshwater extends almost as far as the mouth of the lagoon and vertical stratification is established. Results of numerical modelling reveal that the implemented model reproduces quite well the observed horizontal patterns. The model was also used to study the hydrology of the study area under extreme forcing conditions. When the model is forced with a low river flow (1 m3 s−1) the results confirm that the hydrology is tidally dominated. When the model is forced with a high river flow (1,000 m3 s−1) the hydrology is dominated by freshwater, as would be expected in such an area

Bayesian Multimodel Inference for Geostatistical Regression Models

The problem of simultaneous covariate selection and parameter inference for spatial regression models is considered. Previous research has shown that failure to take spatial correlation into account can influence the outcome of standard model selection methods. A Markov chain Monte Carlo (MCMC) method is investigated for the calculation of parameter estimates and posterior model probabilities for spatial regression models. The method can accommodate normal and non-normal response data and a large number of covariates. Thus the method is very flexible and can be used to fit spatial linear models, spatial linear mixed models, and spatial generalized linear mixed models (GLMMs). The Bayesian MCMC method also allows a priori unequal weighting of covariates, which is not possible with many model selection methods such as Akaike's information criterion (AIC). The proposed method is demonstrated on two data sets. The first is the whiptail lizard data set which has been previously analyzed by other researchers investigating model selection methods. Our results confirmed the previous analysis suggesting that sandy soil and ant abundance were strongly associated with lizard abundance. The second data set concerned pollution tolerant fish abundance in relation to several environmental factors. Results indicate that abundance is positively related to Strahler stream order and a habitat quality index. Abundance is negatively related to percent watershed disturbance

Computer Controlled Automated Assay for Comprehensive Studies of Enzyme Kinetic Parameters

Stability and biological activity of proteins is highly dependent on their physicochemical environment. The development of realistic models of biological systems necessitates quantitative information on the response to changes of external conditions like pH, salinity and concentrations of substrates and allosteric modulators. Changes in just a few variable parameters rapidly lead to large numbers of experimental conditions, which go beyond the experimental capacity of most research groups. We implemented a computer-aided experimenting framework (“robot lab assistant”) that allows us to parameterize abstract, human-readable descriptions of micro-plate based experiments with variable parameters and execute them on a conventional 8 channel liquid handling robot fitted with a sensitive plate reader. A set of newly developed R-packages translates the instructions into machine commands, executes them, collects the data and processes it without user-interaction. By combining script-driven experimental planning, execution and data-analysis, our system can react to experimental outcomes autonomously, allowing outcome-based iterative experimental strategies. The framework was applied in a response-surface model based iterative optimization of buffer conditions and investigation of substrate, allosteric effector, pH and salt dependent activity profiles of pyruvate kinase (PYK). A diprotic model of enzyme kinetics was used to model the combined effects of changing pH and substrate concentrations. The 8 parameters of the model could be estimated from a single two-hour experiment using nonlinear least-squares regression. The model with the estimated parameters successfully predicted pH and PEP dependence of initial reaction rates, while the PEP concentration dependent shift of optimal pH could only be reproduced with a set of manually tweaked parameters. Differences between model-predictions and experimental observations at low pH suggest additional protonation-sites at the enzyme or substrates critical for enzymatic activity. The developed framework is a powerful tool to investigate enzyme reaction specifics and explore biological system behaviour in a wide range of experimental conditions

Spatial effects of mosquito bednets on child mortality

<p>Abstract</p> <p>Background</p> <p>Insecticide treated nets (ITN) have been proven to be an effective tool in reducing the burden of malaria. Few randomized clinical trials examined the spatial effect of ITNs on child mortality at a high coverage level, hence it is essential to better understand these effects in real-life situation with varying levels of coverage. We analyzed for the first time data from a large follow-up study in an area of high perennial malaria transmission in southern Tanzania to describe the spatial effects of bednets on all-cause child mortality.</p> <p>Methods</p> <p>The study was carried out between October 2001 and September 2003 in 25 villages in Kilombero Valley, southern Tanzania. Bayesian geostatistical models were fitted to assess the effect of different bednet density measures on child mortality adjusting for possible confounders.</p> <p>Results</p> <p>In the multivariate model addressing potential confounding, the only measure significantly associated with child mortality was the bed net density at household level; we failed to observe additional community effect benefit from bed net coverage in the community.</p> <p>Conclusion</p> <p>In this multiyear, 25 village assessment, despite substantial known inadequate insecticide-treatment for bed nets, the density of household bed net ownership was significantly associated with all cause child mortality reduction. The absence of community effect of bednets in our study area might be explained by (1) the small proportion of nets which are treated with insecticide, and (2) the relative homogeneity of coverage with nets in the area. To reduce malaria transmission for both users and non-users it is important to increase the ITNs and long-lasting nets coverage to at least the present untreated nets coverage.</p

A Platform for Processing Expression of Short Time Series (PESTS)

<p>Abstract</p> <p>Background</p> <p>Time course microarray profiles examine the expression of genes over a time domain. They are necessary in order to determine the complete set of genes that are dynamically expressed under given conditions, and to determine the interaction between these genes. Because of cost and resource issues, most time series datasets contain less than 9 points and there are few tools available geared towards the analysis of this type of data.</p> <p>Results</p> <p>To this end, we introduce a platform for Processing Expression of Short Time Series (PESTS). It was designed with a focus on usability and interpretability of analyses for the researcher. As such, it implements several standard techniques for comparability as well as visualization functions. However, it is designed specifically for the unique methods we have developed for significance analysis, multiple test correction and clustering of short time series data. The central tenet of these methods is the use of biologically relevant features for analysis. Features summarize short gene expression profiles, inherently incorporate dependence across time, and allow for both full description of the examined curve and missing data points.</p> <p>Conclusions</p> <p>PESTS is fully generalizable to other types of time series analyses. PESTS implements novel methods as well as several standard techniques for comparability and visualization functions. These features and functionality make PESTS a valuable resource for a researcher's toolkit. PESTS is available to download for free to academic and non-profit users at <url>http://www.mailman.columbia.edu/academic-departments/biostatistics/research-service/software-development</url>.</p

Estimation and inference under economic restrictions

Estimation of economic relationships often requires imposition of constraints such as positivity or monotonicity on each observation. Methods to impose such constraints, however, vary depending upon the estimation technique employed. We describe a general methodology to impose (observation-specific) constraints for the class of linear regression estimators using a method known as constraint weighted bootstrapping. While this method has received attention in the nonparametric regression literature, we show how it can be applied for both parametric and nonparametric estimators. A benefit of this method is that imposing numerous constraints simultaneously can be performed seamlessly. We apply this method to Norwegian dairy farm data to estimate both unconstrained and constrained parametric and nonparametric models