The branch-site test of positive selection is surprisingly robust but lacks power under synonymous substitution saturation and variation in GC

Positive selection is widely estimated from protein coding sequence alignments by the nonsynonymous-to-synonymous ratio omega. Increasingly elaborate codon models are used in a likelihood framework for this estimation. Although there is widespread concern about the robustness of the estimation of the omega ratio, more efforts are needed to estimate this robustness, especially in the context of complex models. Here, we focused on the branch-site codon model. We investigated its robustness on a large set of simulated data. First, we investigated the impact of sequence divergence. We found evidence of underestimation of the synonymous substitution rate for values as small as 0.5, with a slight increase in false positives for the branch-site test. When dS increases further, underestimation of dS is worse, but false positives decrease. Interestingly, the detection of true positives follows a similar distribution, with a maximum for intermediary values of dS. Thus, high dS is more of a concern for a loss of power (false negatives) than for false positives of the test. Second, we investigated the impact of GC content. We showed that there is no significant difference of false positives between high GC (up to similar to 80%) and low GC (similar to 30%) genes. Moreover, neither shifts of GC content on a specific branch nor major shifts in GC along the gene sequence generate many false positives. Our results confirm that the branch-site is a very conservative test.

On the diurnal soil water content dynamics during evaporation using dielectric methods

The water content dynamics in the upper soil surface during evaporation is a key element in land-atmosphere exchanges. Previous experimental studies have suggested that the soil water content increases at the depth of 5 to 15 cm below the soil surface during evapo- ration, while the layer in the immediate vicinity of the soil surface is drying. In this study, the dynamics of water content profiles exposed to solar radiative forcing was monitored at a high temporal resolution using dielectric methods both in the presence and absence of evaporation. A 4-d comparison of reported moisture content in coarse sand in covered and uncovered buckets using a commercial dielectric-based probe (70 MHz ECH2O-5TE, Decagon Devices, Pullman, WA) and the standard 1-GHz time domain reflectometry method. Both sensors reported a positive correlation between temperature and water content in the 5- to 10-cm depth, most pronounced in the morning during heating and in the afternoon during cooling. Such positive correlation might have a physical origin induced by evaporation at the surface and redistribution due to liquid water fluxes resulting from the temperature- gradient dynamics within the sand profile at those depths. Our experimental data suggest that the combined effect of surface evaporation and temperature-gradient dynamics should be considered to analyze experimental soil water profiles. Additional effects related to the frequency of operation and to protocols for temperature compensation of the dielectric sensors may also affect the probes' response during large temperature changes.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Urinary saturation and nephrocalcinosis in preterm infants: effect of parenteral nutrition.

Urinary lithogenic and inhibitory factors were studied in 27 preterm infants; 16 had total parenteral nutrition (TPN) and 11 had breastmilk with an additional glucose-sodium chloride infusion. Urines were collected for 24 hours on day 2 (period A), day 3 (B), and once between days 4 and 10 (C). Urinary calcium oxalate saturation was calculated by the computer program EQUIL 2. Renal ultrasonography was performed every second week until discharge. The calcium/creatinine ratio increased in infants on TPN (A 0.91; C 1.68 mol/mol) and was significantly higher at period C than that in infants on breastmilk/infusion (A 0.52; C 0.36). The oxalate/creatinine ratio was persistently higher with TPN (203 mmol/mol) than with breastmilk/infusion (98; 137). The citrate/creatinine remained constant with TPN (0.44 mol/mol), whereas it increased significantly with breastmilk/infusion (0.26; 0.49). Calcium/citrate rose considerably with TPN, but decreased with breastmilk/infusion to a significantly lower level than with TPN. The urinary calcium oxalate saturation increased with TPN (2.4; 4.5) and decreased with breastmilk/infusion (2.1; 1.5) to a significantly lower value than with TPN. Nephrocalcinosis developed in two infants on TPN. Mean daily calcium intake was similar in both groups, whereas protein, sodium, and phosphorus intake were significantly higher on TPN. It is concluded that the increase in urinary calcium oxalate saturation observed with TPN is due to the combined effect of an increased urinary calcium excretion and higher urinary oxalate/creatinine and calcium/citrate ratios. The changes observed are likely to be caused by TPN itself, which differs in several respects from breastmilk feeding.

Soil diffuse degassing and thermal energy fluxes from the southern Lakki plain, Nisyros (Greece)

Two diffuse soil CO2 flux surveys from the southern Lakki plain show that CO2 is mainly released from the hydrothermal explosion craters. The correspondence between high CO2 fluxes and elevated soil temperatures suggests that a flux of hot hydrothermal fluids ascends towards the surface. Steam mostly condenses near the surface and the heat given off is conductively transferred to the atmosphere through the soil, accompanied by a large CO2 flux. Tt was calculated, that 68 t d(-1) of hydrothermal CO2 are released through the total surveyed area of similar to1.3 km(2) Admitting that a steam flux of 2200 t d(-1) accompanies this CO2 flux, the thermal energy released through steam condensation amounts to 58 MW.

Controls on 13C and 14C variability in soil CO2

14C dating of groundwater depends on the isotopic composition of both the solid carbonate and the soil CO2 and requires the use of 14C age correction models. To better assess the variability of the 14C activity of soil CO2 (A14Csoil-CO2) and the δ13C of soil CO2 (δ13Csoil-CO2), which are two parameters used in 14C age correction models, we studied the different processes involving carbon isotopes in the soil. The approach used experimental data from two sites in France (Fontainebleau sands and Astian sands) and a steady-state transport model. In most cases, the 14C activity (A14C) of atmospheric CO2 is directly used in the 14C age correction models as the A14Csoil-CO2. However, we demonstrate that since 1950, the evolution of the A14Csoil-CO2 reflects the competition between the fluxes of root-derived CO2 and organic matter-derived CO2. Therefore, the A14Csoil-CO2 must be used to date groundwater that is younger than 60 years old. Moreover, the δ13C of soil CO2 (δ13Csoil-CO2) showed large seasonal variations that must be taken into account in selecting the δ13Csoil-CO2 for 14C age correction models.

Testing the influence of topography and material properties on catchment-scale soil moisture patterns using remotely sensed vegetation patterns in a humid temperate catchment, northern Britain

In order to evaluate the relationship between the apparent complexity of hillslope soil moisture and the emergent patterns of catchment hydrological behaviour and water quality, we need fine-resolution catchment-wide data on soil moisture characteristics. This study proposes a methodology whereby vegetation patterns obtained from high-resolution orthorectified aerial photographs are used as an indicator of soil moisture characteristics. This enables us to examine a set of hypotheses regarding what drives the spatial patterns of soil moisture at the catchment scale (material properties or topography). We find that the pattern of Juncus effusus vegetation is controlled largely by topography and mediated by the catchment's material properties. Characterizing topography using the topographic index adds value to the soil moisture predictions relative to slope or upslope contributing area (UCA). However, these predictions depart from the observed soil moisture patterns at very steep slopes or low UCAs. Copyright (c) 2012 John Wiley & Sons, Ltd.