The International LAM Registry: a component of an innovative web-based clinician, researcher, and patient-driven rare disease research platform.

BACKGROUND: A relative inability to capture a sufficiently large patient population in any one geographic location has traditionally limited research into rare diseases. METHODS AND RESULTS: Clinicians interested in the rare disease lymphangioleiomyomatosis (LAM) have worked with the LAM Treatment Alliance, the MIT Media Lab, and Clozure Associates to cooperate in the design of a state-of-the-art data coordination platform that can be used for clinical trials and other research focused on the global LAM patient population. This platform is a component of a set of web-based resources, including a patient self-report data portal, aimed at accelerating research in rare diseases in a rigorous fashion. CONCLUSIONS: Collaboration between clinicians, researchers, advocacy groups, and patients can create essential community resource infrastructure to accelerate rare disease research. The International LAM Registry is an example of such an effort. 82.

Numerical analysis of wave-induced fluid flow effects on seismic data: Application to monitoring of CO2 storage at the Sleipner Field

In this work we analyze how patchy distributions of CO2 and brine within sand reservoirs may lead to significant attenuation and velocity dispersion effects, which in turn may have a profound impact on surface seismic data. The ultimate goal of this paper is to contribute to the understanding of these processes within the framework of the seismic monitoring of CO2 sequestration, a key strategy to mitigate global warming. We first carry out a Monte Carlo analysis to study the statistical behavior of attenuation and velocity dispersion of compressional waves traveling through rocks with properties similar to those at the Utsira Sand, Sleipner field, containing quasi-fractal patchy distributions of CO2 and brine. These results show that the mean patch size and CO2 saturation play key roles in the observed wave-induced fluid flow effects. The latter can be remarkably important when CO2 concentrations are low and mean patch sizes are relatively large. To analyze these effects on the corresponding surface seismic data, we perform numerical simulations of wave propagation considering reservoir models and CO2 accumulation patterns similar to the CO2 injection site in the Sleipner field. These numerical experiments suggest that wave-induced fluid flow effects may produce changes in the reservoir's seismic response, modifying significantly the main seismic attributes usually employed in the characterization of these environments. Consequently, the determination of the nature of the fluid distributions as well as the proper modeling of the seismic data constitute important aspects that should not be ignored in the seismic monitoring of CO2 sequestration problems.

Applicability of a distributed watershed pollution model in a data-poor environment in Santa Catarina State, Brazil

Intensification of agricultural production without a sound management and regulations can lead to severe environmental problems, as in Western Santa Catarina State, Brazil, where intensive swine production has caused large accumulations of manure and consequently water pollution. Natural resource scientists are asked by decision-makers for advice on management and regulatory decisions. Distributed environmental models are useful tools, since they can be used to explore consequences of various management practices. However, in many areas of the world, quantitative data for model calibration and validation are lacking. The data-intensive distributed environmental model AgNPS was applied in a data-poor environment, the upper catchment (2,520 ha) of the Ariranhazinho River, near the city of Seara, in Santa Catarina State. Steps included data preparation, cell size selection, sensitivity analysis, model calibration and application to different management scenarios. The model was calibrated based on a best guess for model parameters and on a pragmatic sensitivity analysis. The parameters were adjusted to match model outputs (runoff volume, peak runoff rate and sediment concentration) closely with the sparse observed data. A modelling grid cell resolution of 150 m adduced appropriate and computer-fit results. The rainfall runoff response of the AgNPS model was calibrated using three separate rainfall ranges (< 25, 25-60, > 60 mm). Predicted sediment concentrations were consistently six to ten times higher than observed, probably due to sediment trapping along vegetated channel banks. Predicted N and P concentrations in stream water ranged from just below to well above regulatory norms. Expert knowledge of the area, in addition to experience reported in the literature, was able to compensate in part for limited calibration data. Several scenarios (actual, recommended and excessive manure applications, and point source pollution from swine operations) could be compared by the model, using a relative ranking rather than quantitative predictions.

Diagnostic techniques applied in geostatistics for agricultural data analysis

The structural modeling of spatial dependence, using a geostatistical approach, is an indispensable tool to determine parameters that define this structure, applied on interpolation of values at unsampled points by kriging techniques. However, the estimation of parameters can be greatly affected by the presence of atypical observations in sampled data. The purpose of this study was to use diagnostic techniques in Gaussian spatial linear models in geostatistics to evaluate the sensitivity of maximum likelihood and restrict maximum likelihood estimators to small perturbations in these data. For this purpose, studies with simulated and experimental data were conducted. Results with simulated data showed that the diagnostic techniques were efficient to identify the perturbation in data. The results with real data indicated that atypical values among the sampled data may have a strong influence on thematic maps, thus changing the spatial dependence structure. The application of diagnostic techniques should be part of any geostatistical analysis, to ensure a better quality of the information from thematic maps.

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

Modeling premartensitic effects in Ni2MnGa: A mean-field and Monte Carlo simulation study

ic first-order transition line ending in a critical point. This critical point is responsible for the existence of large premartensitic fluctuations which manifest as broad peaks in the specific heat, not always associated with a true phase transition. The main conclusion is that premartensitic effects result from the interplay between the softness of the anomalous phonon driving the modulation and the magnetoelastic coupling. In particular, the premartensitic transition occurs when such coupling is strong enough to freeze the involved mode phonon. The implication of the results in relation to the available experimental data is discussed.

Modeling changes in organic carbon stocks for distinct soils in southeastern brazil after four eucalyptus rotations using the century model

Soil organic matter (SOM) plays an important role in carbon (C) cycle and soil quality. Considering the complexity of factors that control SOM cycling and the long time it usually takes to observe changes in SOM stocks, modeling constitutes a very important tool to understand SOM cycling in forest soils. The following hypotheses were tested: (i) soil organic carbon (SOC) stocks would be higher after several rotations of eucalyptus than in low-productivity pastures; (ii) SOC values simulated by the Century model would describe the data better than the mean of observations. So, the aims of the current study were: (i) to evaluate the SOM dynamics using the Century model to simulate the changes of C stocks for two eucalyptus chronosequences in the Rio Doce Valley, Minas Gerais State, Brazil; and (ii) to compare the C stocks simulated by Century with the C stocks measured in soils of different Orders and regions of the Rio Doce Valley growing eucalyptus. In Belo Oriente (BO), short-rotation eucalyptus plantations had been cultivated for 4.0; 13.0, 22.0, 32.0 and 34.0 years, at a lower elevation and in a warmer climate, while in Virginópolis (VG), these time periods were 8.0, 19.0 and 33.0 years, at a higher elevation and in a milder climate. Soil samples were collected from the 0-20 cm layer to estimate C stocks. Results indicate that the C stocks simulated by the Century model decreased after 37 years of poorly managed pastures in areas previously covered by native forest in the regions of BO and VG. The substitution of poorly managed pastures by eucalyptus in the early 1970´s led to an average increase of C of 0.28 and 0.42 t ha-1 year-1 in BO and VG, respectively. The measured C stocks under eucalyptus in distinct soil Orders and independent regions with variable edapho-climate conditions were not far from the values estimated by the Century model (root mean square error - RMSE = 20.9; model efficiency - EF = 0.29) despite the opposite result obtained with the statistical procedure to test the identity of analytical methods. Only for lower soil C stocks, the model over-estimated the C stock in the 0-20 cm layer. Thus, the Century model is highly promising to detect changes in C stocks in distinct soil orders under eucalyptus, as well as to indicate the impact of harvest residue management on SOM in future rotations.

Determination of hydraulic properties of a tropical soil of Hawaii using column experiments and inverse modeling

A method for determining soil hydraulic properties of a weathered tropical soil (Oxisol) using a medium-sized column with undisturbed soil is presented. The method was used to determine fitting parameters of the water retention curve and hydraulic conductivity functions of a soil column in support of a pesticide leaching study. The soil column was extracted from a continuously-used research plot in Central Oahu (Hawaii, USA) and its internal structure was examined by computed tomography. The experiment was based on tension infiltration into the soil column with free outflow at the lower end. Water flow through the soil core was mathematically modeled using a computer code that numerically solves the one-dimensional Richards equation. Measured soil hydraulic parameters were used for direct simulation, and the retention and soil hydraulic parameters were estimated by inverse modeling. The inverse modeling produced very good agreement between model outputs and measured flux and pressure head data for the relatively homogeneous column. The moisture content at a given pressure from the retention curve measured directly in small soil samples was lower than that obtained through parameter optimization based on experiments using a medium-sized undisturbed soil column.

Relaxation time of processes driven by multiplicative noise

We consider systems described by nonlinear stochastic differential equations with multiplicative noise. We study the relaxation time of the steady-state correlation function as a function of noise parameters. We consider the white- and nonwhite-noise case for a prototype model for which numerical data are available. We discuss the validity of analytical approximation schemes. For the white-noise case we discuss the results of a projector-operator technique. This discussion allows us to give a generalization of the method to the non-white-noise case. Within this generalization, we account for the growth of the relaxation time as a function of the correlation time of the noise. This behavior is traced back to the existence of a non-Markovian term in the equation for the correlation function.

A test of Jessor's problem behavior theory in a Eurasian and a Western European developmental context.

PURPOSE: The current study tested the applicability of Jessor's problem behavior theory (PBT) in national probability samples from Georgia and Switzerland. Comparisons focused on (1) the applicability of the problem behavior syndrome (PBS) in both developmental contexts, and (2) on the applicability of employing a set of theory-driven risk and protective factors in the prediction of problem behaviors. METHODS: School-based questionnaire data were collected from n = 18,239 adolescents in Georgia (n = 9499) and Switzerland (n = 8740) following the same protocol. Participants rated five measures of problem behaviors (alcohol and drug use, problems because of alcohol and drug use, and deviance), three risk factors (future uncertainty, depression, and stress), and three protective factors (family, peer, and school attachment). Final study samples included n = 9043 Georgian youth (mean age = 15.57; 58.8% females) and n = 8348 Swiss youth (mean age = 17.95; 48.5% females). Data analyses were completed using structural equation modeling, path analyses, and post hoc z-tests for comparisons of regression coefficients. RESULTS: Findings indicated that the PBS replicated in both samples, and that theory-driven risk and protective factors accounted for 13% and 10% in Georgian and Swiss samples, respectively in the PBS, net the effects by demographic variables. Follow-up z-tests provided evidence of some differences in the magnitude, but not direction, in five of six individual paths by country. CONCLUSION: PBT and the PBS find empirical support in these Eurasian and Western European samples; thus, Jessor's theory holds value and promise in understanding the etiology of adolescent problem behaviors outside of the United States.

Mean first-passage time of continuous non-Markovian processes driven by colored noise

An equation for mean first-passage times of non-Markovian processes driven by colored noise is derived through an appropriate backward integro-differential equation. The equation is solved in a Bourret-like approximation. In a weak-noise bistable situation, non-Markovian effects are taken into account by an effective diffusion coefficient. In this situation, our results compare satisfactorily with other approaches and experimental data.

Preisach modeling of hysteresis for a pseudoelastic Cu-Zn-Al single crystal

Stress-strain trajectories associated with pseudoelastic behavior of a Cu¿19.4 Zn¿13.1 Al (at.%) single crystal at room temperature have been determined experimentally. For a constant cross-head speed the trajectories and the associated hysteresis behavior are perfectly reproducible; the trajectories exhibit memory properties, dependent only on the values of return points, where transformation direction is reverted. An adapted version of the Preisach model for hysteresis has been implemented to predict the observed trajectories, using a set of experimental first¿order reversal curves as input data. Explicit formulas have been derived giving all trajectories in terms of this data set, with no adjustable parameters. Comparison between experimental and calculated trajectories shows a much better agreement for descending than for ascending paths, an indication of a dissymmetry between the dissipation mechanisms operative in forward and reverse directions of martensitic transformation.

Cost-effective experimental design to support modeling of concentration-response functions

Modeling concentration-response function became extremely popular in ecotoxicology during the last decade. Indeed, modeling allows determining the total response pattern of a given substance. However, reliable modeling is consuming in term of data, which is in contradiction with the current trend in ecotoxicology, which aims to reduce, for cost and ethical reasons, the number of data produced during an experiment. It is therefore crucial to determine experimental design in a cost-effective manner. In this paper, we propose to use the theory of locally D-optimal designs to determine the set of concentrations to be tested so that the parameters of the concentration-response function can be estimated with high precision. We illustrated this approach by determining the locally D-optimal designs to estimate the toxicity of the herbicide dinoseb on daphnids and algae. The results show that the number of concentrations to be tested is often equal to the number of parameters and often related to the their meaning, i.e. they are located close to the parameters. Furthermore, the results show that the locally D-optimal design often has the minimal number of support points and is not much sensitive to small changes in nominal values of the parameters. In order to reduce the experimental cost and the use of test organisms, especially in case of long-term studies, reliable nominal values may therefore be fixed based on prior knowledge and literature research instead of on preliminary experiments

Prediction of Infant Drug Exposure Through Breastfeeding: Population PK Modeling and Simulation of Fluoxetine Exposure.

The likelihood of significant exposure to drugs in infants through breast milk is poorly defined, given the difficulties of conducting pharmacokinetics (PK) studies. Using fluoxetine (FX) as an example, we conducted a proof-of-principle study applying population PK (popPK) modeling and simulation to estimate drug exposure in infants through breast milk. We simulated data for 1,000 mother-infant pairs, assuming conservatively that the FX clearance in an infant is 20% of the allometrically adjusted value in adults. The model-generated estimate of the milk-to-plasma ratio for FX (mean: 0.59) was consistent with those reported in other studies. The median infant-to-mother ratio of FX steady-state plasma concentrations predicted by the simulation was 8.5%. Although the disposition of the active metabolite, norfluoxetine, could not be modeled, popPK-informed simulation may be valid for other drugs, particularly those without active metabolites, thereby providing a practical alternative to conventional PK studies for exposure risk assessment in this population.

Soil thermal diffusivity estimated from data of soil temperature and single soil component properties

Under field conditions, thermal diffusivity can be estimated from soil temperature data but also from the properties of soil components together with their spatial organization. We aimed to determine soil thermal diffusivity from half-hourly temperature measurements in a Rhodic Kanhapludalf, using three calculation procedures (the amplitude ratio, phase lag and Seemann procedures), as well as from soil component properties, for a comparison of procedures and methods. To determine thermal conductivity for short wave periods (one day), the phase lag method was more reliable than the amplitude ratio or the Seemann method, especially in deeper layers, where temperature variations are small. The phase lag method resulted in coherent values of thermal diffusivity. The method using properties of single soil components with the values of thermal conductivity for sandstone and kaolinite resulted in thermal diffusivity values of the same order. In the observed water content range (0.26-0.34 m³ m-3), the average thermal diffusivity was 0.034 m² d-1 in the top layer (0.05-0.15 m) and 0.027 m² d-1 in the subsurface layer (0.15-0.30 m).