Combining satellite data and models to estimate cloud radiative effect at the surface and in the atmosphere

Satellite measurements and numerical forecast model reanalysis data are used to compute an updated estimate of the cloud radiative effect on the global multi-annual mean radiative energy budget of the atmosphere and surface. The cloud radiative cooling effect through reflection of shortwave radiation dominates over the longwave heating effect, resulting in a net cooling of the climate system of –21 Wm-2. The shortwave radiative effect of cloud is primarily manifest as a reduction in the solar radiation absorbed at the surface of -53 Wm-2. Clouds impact longwave radiation by heating the moist tropical atmosphere (up to around 40 Wm-2 for global annual means) while enhancing the radiative cooling of the atmosphere over other regions, in particular higher latitudes and sub-tropical marine stratocumulus regimes. While clouds act to cool the climate system during the daytime, the cloud greenhouse effect heats the climate system at night. The influence of cloud radiative effect on determining cloud feedbacks and changes in the water cycle are discussed.

Energy and protein interactions and their effect on nitrogen excretion in dairy cows

The principal driver of nitrogen (N) losses from the body including excretion and secretion in milk is N intake. However, other covariates may also play a role in modifying the partitioning of N. This study tests the hypothesis that N partitioning in dairy cows is affected by energy and protein interactions. A database containing 470 dairy cow observations was collated from calorimetry experiments. The data include N and energy parameters of the diet and N utilization by the animal. Univariate and multivariate meta-analyses that considered both within and between study effects were conducted to generate prediction equations based on N intake alone or with an energy component. The univariate models showed that there was a strong positive linear relationships between N intake and N excretion in faeces, urine and milk. The slopes were 0.28 faeces N, 0.38 urine N and 0.20 milk N. Multivariate model analysis did not improve the fit. Metabolizable energy intake had a significant positive effect on the amount of milk N in proportion to faeces and urine N, which is also supported by other studies. Another measure of energy considered as a covariate to N intake was diet quality or metabolizability (the concentration of metabolizable energy relative to gross energy of the diet). Diet quality also had a positive linear relationship with the proportion of milk N relative to N excreted in faeces and urine. Metabolizability had the largest effect on faeces N due to lower protein digestibility of low quality diets. Urine N was also affected by diet quality and the magnitude of the effect was higher than for milk N. This research shows that including a measure of diet quality as a covariate with N intake in a model of N execration can enhance our understanding of the effects of diet composition on N losses from dairy cows. The new prediction equations developed in this study could be used to monitor N losses from dairy systems.

Hyper-parallel tempering Monte Carlo simulations of Ar adsorption in new models of microporous non-graphitizing activated carbon: effect of microporosity

The adsorption of gases on microporous carbons is still poorly understood, partly because the structure of these carbons is not well known. Here, a model of microporous carbons based on fullerene- like fragments is used as the basis for a theoretical study of Ar adsorption on carbon. First, a simulation box was constructed, containing a plausible arrangement of carbon fragments. Next, using a new Monte Carlo simulation algorithm, two types of carbon fragments were gradually placed into the initial structure to increase its microporosity. Thirty six different microporous carbon structures were generated in this way. Using the method proposed recently by Bhattacharya and Gubbins ( BG), the micropore size distributions of the obtained carbon models and the average micropore diameters were calculated. For ten chosen structures, Ar adsorption isotherms ( 87 K) were simulated via the hyper- parallel tempering Monte Carlo simulation method. The isotherms obtained in this way were described by widely applied methods of microporous carbon characterisation, i. e. Nguyen and Do, Horvath - Kawazoe, high- resolution alpha(a)s plots, adsorption potential distributions and the Dubinin - Astakhov ( DA) equation. From simulated isotherms described by the DA equation, the average micropore diameters were calculated using empirical relationships proposed by different authors and they were compared with those from the BG method.

The effect of Arabian Sea optical properties on SST biases and the South Asian summer monsoon in a coupled GCM

This study examines the effect of seasonally varying chlorophyll on the climate of the Arabian Sea and South Asian monsoon. The effect of such seasonality on the radiative properties of the upper ocean is often a missing process in coupled general circulation models and its large amplitude in the region makes it a pertinent choice for study to determine any impact on systematic biases in the mean and seasonality of the Arabian Sea. In this study we examine the effects of incorporating a seasonal cycle in chlorophyll due to phytoplankton blooms in the UK Met Office coupled atmosphere-ocean GCM HadCM3. This is achieved by performing experiments in which the optical properties of water in the Arabian Sea - a key signal of the semi-annual cycle of phytoplankton blooms in the region - are calculated from a chlorophyll climatology derived from Sea-viewing Wide Field-of-View Sensor (SeaWiFS) data. The SeaWiFS chlorophyll is prescribed in annual mean and seasonally-varying experiments. In response to the chlorophyll bloom in late spring, biases in mixed layer depth are reduced by up to 50% and the surface is warmed, leading to increases in monsoon rainfall during the onset period. However when the monsoons are fully established in boreal winter and summer and there are strong surface winds and a deep mixed layer, biases in the mixed layer depth are reduced but the surface undergoes cooling. The seasonality of the response of SST to chlorophyll is found to depend on the relative depth of the mixed layer to that of the anomalous penetration depth of solar fluxes. Thus the inclusion of the effects of chlorophyll on radiative properties of the upper ocean acts to reduce biases in mixed layer depth and increase seasonality in SST.

Effect of food models and low-temperature storage on the adhesion of Lactobacillus rhamnosus GG to Caco-2 cells

This study evaluated the effects of fat and sugar levels on the surface properties of Lactobacillus rhamnosus GG during storage in food model systems, simulating yogurt and ice cream, and related them with the ability of the bacterial cells to adhere to Caco-2 cells. Freeze-dried L. rhamnosus GG cells were added to the model food systems and stored for 7 days. The bacterial cells were analyzed for cell viability, hydrophobicity, ζ potential, and their ability to adhere to Caco-2 cells. The results indicated that the food type and its composition affected the surface and adhesion properties of the bacterial cells during storage, with yogurt being a better delivery vehicle than ice cream in terms of bacterial adhesion to Caco-2 cells. The most important factor influencing bacterial adhesion was the storage time rather than the levels of fats and sugars, indicating that conformational changes were taking place on the surface of the bacterial cells during storage.

Superensembles of linear viscoelastic models of polymer melts

The idea of incorporating multiple models of linear rheology into a superensemble, to forge a consensus forecast from the individual model predictions, is investigated. The relative importance of the individual models in the so-called multimodel superensemble (MMSE) was inferred by evaluating their performance on a set of experimental training data, via nonlinear regression. The predictive ability of the MMSE model was tested by comparing its predictions on test data that were similar (in-sample) and dissimilar (out-of-sample) to the training data used in the calibration. For the in-sample forecasts, we found that the MMSE model easily outperformed the best constituent model. The presence of good individual models greatly enhanced the MMSE forecast, while the presence of some bad models in the superensemble also improved the MMSE forecast modestly. While the performance of the MMSE model on the out-of-sample training data was not as spectacular, it demonstrated the robustness of this approach.

Optimal linearization trajectories for tangent linear models

We examine differential equations where nonlinearity is a result of the advection part of the total derivative or the use of quadratic algebraic constraints between state variables (such as the ideal gas law). We show that these types of nonlinearity can be accounted for in the tangent linear model by a suitable choice of the linearization trajectory. Using this optimal linearization trajectory, we show that the tangent linear model can be used to reproduce the exact nonlinear error growth of perturbations for more than 200 days in a quasi-geostrophic model and more than (the equivalent of) 150 days in the Lorenz 96 model. We introduce an iterative method, purely based on tangent linear integrations, that converges to this optimal linearization trajectory. The main conclusion from this article is that this iterative method can be used to account for nonlinearity in estimation problems without using the nonlinear model. We demonstrate this by performing forecast sensitivity experiments in the Lorenz 96 model and show that we are able to estimate analysis increments that improve the two-day forecast using only four backward integrations with the tangent linear model. Copyright © 2011 Royal Meteorological Society

Potential anti-cancer effects of virgin olive oil phenols on colorectal carcinogenesis models in vitro

The traditional Mediterranean diet is thought to represent a healthy lifestyle; especially given the incidence of several cancers including colorectal cancer is lower in Mediterranean countries compared to Northern Europe. Olive oil, a central component of the Mediterranean diet, is believed to beneficially affect numerous biological processes. We used phenols extracted from virgin olive oil on a series of in vitro systems that model important stages of colon carcinogenesis. The effect the extract on DNA damage induced by hydrogen peroxide was measured in HT29 cells using single cell microgel-electrophoresis. A significant anti-genotoxic linear trend (p=0.011) was observed when HT29 cells were pre-incubated with olive oil phenols (0, 5, 10, 25, 50, 75, 100 microg/ml) for 24 hr, then challenged with hydrogen peroxide. The olive oil phenols (50, 100 microg/ml) significantly (p=0.004, p=0.002) improved barrier function of CACO2 cells after 48 hr as measured by trans-epithelial resistance. Significant inhibition of HT115 invasion (p<0.01) was observed at olive oil phenols concentrations of 25, 50, 75, 100 microg/ml using the matrigel invasion assay. No effect was observed on HT115 viability over the concentration range 0, 25, 50 75, 100 microg/ml after 24 hr, although 75 and 100 microg/ml olive oil phenols significantly inhibited HT115 cell attachment (p=0.011, p=0.006). Olive oil phenols had no significant effect on metastasis-related gene expression in HT115 cells. We have demonstrated that phenols extracted from virgin olive oil are capable of inhibiting several stages in colon carcinogenesis in vitro.

Conditional mean functions of non-linear models of US output

We compare a number of models of post War US output growth in terms of the degree and pattern of non-linearity they impart to the conditional mean, where we condition on either the previous period's growth rate, or the previous two periods' growth rates. The conditional means are estimated non-parametrically using a nearest-neighbour technique on data simulated from the models. In this way, we condense the complex, dynamic, responses that may be present in to graphical displays of the implied conditional mean.

Forecasting economic and financial time series with non-linear models

In this paper we discuss the current state-of-the-art in estimating, evaluating, and selecting among non-linear forecasting models for economic and financial time series. We review theoretical and empirical issues, including predictive density, interval and point evaluation and model selection, loss functions, data-mining, and aggregation. In addition, we argue that although the evidence in favor of constructing forecasts using non-linear models is rather sparse, there is reason to be optimistic. However, much remains to be done. Finally, we outline a variety of topics for future research, and discuss a number of areas which have received considerable attention in the recent literature, but where many questions remain.

Use of batch culture and a two-stage continuous culture system to study the effect of supplemental a-lactalbumin and glycomacropeptide on mixed populations of human gut bacteria

Certain milk factors can promote the growth of a host-friendly gastrointestinal microflora. This may explain why breast-fed infants experience fewer intestinal infections than their formula-fed counterparts. The effect of formula supplementation with two such factors was investigated in this study. Infant faecal specimens were used to ferment formulas supplemented with glycomacropeptide and α-lactalbumin in a two-stage compound continuous culture model. Bacteriology was determined by fluorescence in situ hybridisation. Vessels that contained breast milk as well as α-lactalbumin and glycomacropeptide had stable counts of bifidobacteria while lactobacilli increased significantly only in vessels with breast milk. Bacteroides, clostridia and Escherichia coli decreased significantly in all runs. Acetate was the principal acid found along with high amounts of propionate and lactate. Supplementation of infant formulas with appropriate milk proteins may be useful in simulating the beneficial bacteriological effects of breast milk.

Clinical predictors of response to cognitive-behavioural therapy in pediatric anxiety disorders: the Genes for Treatment (GXT) Study

Objective The Genes for Treatment study is an international, multisite collaboration exploring the role of genetic, demographic, and clinical predictors in response to cognitive-behavioral therapy (CBT) in pediatric anxiety disorders. The current article, the first from the study, examined demographic and clinical predictors of response to CBT. We hypothesized that the child’s gender, type of anxiety disorder, initial severity and comorbidity, and parents’ psychopathology would significantly predict outcome. Method A sample of 1,519 children 5 to 18 years of age with a primary anxiety diagnosis received CBT across 11 sites. Outcome was defined as response (change in diagnostic severity) and remission (absence of the primary diagnosis) at each time point (posttreatment, 3-, 6-, and/or 12-month follow-up) and analyzed using linear and logistic mixed models. Separate analyses were conducted using data from posttreatment and follow-up assessments to explore the relative importance of predictors at these time points. Results Individuals with social anxiety disorder (SoAD) had significantly poorer outcomes (poorer response and lower rates of remission) than those with generalized anxiety disorder (GAD). Although individuals with specific phobia (SP) also had poorer outcomes than those with GAD at posttreatment, these differences were not maintained at follow-up. Both comorbid mood and externalizing disorders significantly predicted poorer outcomes at posttreatment and follow-up, whereas self-reported parental psychopathology had little effect on posttreatment outcomes but significantly predicted response (although not remission) at follow-up. Conclusion SoAD, nonanxiety comorbidity, and parental psychopathology were associated with poorer outcomes after CBT. The results highlight the need for enhanced treatments for children at risk for poorer outcomes.