Predicting mesh density for adaptive modelling of the global atmosphere

The shallow water equations are solved using a mesh of polygons on the sphere, which adapts infrequently to the predicted future solution. Infrequent mesh adaptation reduces the cost of adaptation and load-balancing and will thus allow for more accurate mapping on adaptation. We simulate the growth of a barotropically unstable jet adapting the mesh every 12 h. Using an adaptation criterion based largely on the gradient of the vorticity leads to a mesh with around 20 per cent of the cells of a uniform mesh that gives equivalent results. This is a similar proportion to previous studies of the same test case with mesh adaptation every 1–20 min. The prediction of the mesh density involves solving the shallow water equations on a coarse mesh in advance of the locally refined mesh in order to estimate where features requiring higher resolution will grow, decay or move to. The adaptation criterion consists of two parts: that resolved on the coarse mesh, and that which is not resolved and so is passively advected on the coarse mesh. This combination leads to a balance between resolving features controlled by the large-scale dynamics and maintaining fine-scale features.

A simple model for predicting soil temperature in snow-covered and seasonally frozen soil: model description and testing

Microbial processes in soil are moisture, nutrient and temperature dependent and, consequently, accurate calculation of soil temperature is important for modelling nitrogen processes. Microbial activity in soil occurs even at sub-zero temperatures so that, in northern latitudes, a method to calculate soil temperature under snow cover and in frozen soils is required. This paper describes a new and simple model to calculate daily values for soil temperature at various depths in both frozen and unfrozen soils. The model requires four parameters average soil thermal conductivity, specific beat capacity of soil, specific heat capacity due to freezing and thawing and an empirical snow parameter. Precipitation, air temperature and snow depth (measured or calculated) are needed as input variables. The proposed model was applied to five sites in different parts of Finland representing different climates and soil types. Observed soil temperatures at depths of 20 and 50 cm (September 1981-August 1990) were used for model calibration. The calibrated model was then tested using observed soil temperatures from September 1990 to August 2001. R-2-values of the calibration period varied between 0.87 and 0.96 at a depth of 20 cm and between 0.78 and 0.97 at 50 cm. R-2 -values of the testing period were between 0.87 and 0.94 at a depth of 20cm. and between 0.80 and 0.98 at 50cm. Thus, despite the simplifications made, the model was able to simulate soil temperature at these study sites. This simple model simulates soil temperature well in the uppermost soil layers where most of the nitrogen processes occur. The small number of parameters required means, that the model is suitable for addition to catchment scale models.

Modelling monsoons: understanding and predicting current and future behaviour

The global monsoon system is so varied and complex that understanding and predicting its diverse behaviour remains a challenge that will occupy modellers for many years to come. Despite the difficult task ahead, an improved monsoon modelling capability has been realized through the inclusion of more detailed physics of the climate system and higher resolution in our numerical models. Perhaps the most crucial improvement to date has been the development of coupled ocean-atmosphere models. From subseasonal to interdecadal time scales, only through the inclusion of air-sea interaction can the proper phasing and teleconnections of convection be attained with respect to sea surface temperature variations. Even then, the response to slow variations in remote forcings (e.g., El Niño—Southern Oscillation) does not result in a robust solution, as there are a host of competing modes of variability that must be represented, including those that appear to be chaotic. Understanding the links between monsoons and land surface processes is not as mature as that explored regarding air-sea interactions. A land surface forcing signal appears to dominate the onset of wet season rainfall over the North American monsoon region, though the relative role of ocean versus land forcing remains a topic of investigation in all the monsoon systems. Also, improved forecasts have been made during periods in which additional sounding observations are available for data assimilation. Thus, there is untapped predictability that can only be attained through the development of a more comprehensive observing system for all monsoon regions. Additionally, improved parameterizations - for example, of convection, cloud, radiation, and boundary layer schemes as well as land surface processes - are essential to realize the full potential of monsoon predictability. A more comprehensive assessment is needed of the impact of black carbon aerosols, which may modulate that of other anthropogenic greenhouse gases. Dynamical considerations require ever increased horizontal resolution (probably to 0.5 degree or higher) in order to resolve many monsoon features including, but not limited to, the Mei-Yu/Baiu sudden onset and withdrawal, low-level jet orientation and variability, and orographic forced rainfall. Under anthropogenic climate change many competing factors complicate making robust projections of monsoon changes. Absent aerosol effects, increased land-sea temperature contrast suggests strengthened monsoon circulation due to climate change. However, increased aerosol emissions will reflect more solar radiation back to space, which may temper or even reduce the strength of monsoon circulations compared to the present day. Precipitation may behave independently from the circulation under warming conditions in which an increased atmospheric moisture loading, based purely on thermodynamic considerations, could result in increased monsoon rainfall under climate change. The challenge to improve model parameterizations and include more complex processes and feedbacks pushes computing resources to their limit, thus requiring continuous upgrades of computational infrastructure to ensure progress in understanding and predicting current and future behaviour of monsoons.

Predicting magnetospheric dynamics with a coupled sun-to-Earth model: challenges and first results

Results from the first Sun-to-Earth coupled numerical model developed at the Center for Integrated Space Weather Modeling are presented. The model simulates physical processes occurring in space spanning from the corona of the Sun to the Earth's ionosphere, and it represents the first step toward creating a physics-based numerical tool for predicting space weather conditions in the near-Earth environment. Two 6- to 7-d intervals, representing different heliospheric conditions in terms of the three-dimensional configuration of the heliospheric current sheet, are chosen for simulations. These conditions lead to drastically different responses of the simulated magnetosphere-ionosphere system, emphasizing, on the one hand, challenges one encounters in building such forecasting tools, and on the other hand, emphasizing successes that can already be achieved even at this initial stage of Sun-to-Earth modeling.

Recategorization and subgroup identification: predicting and preventing threats from common ingroups

Much work has supported the idea that recategorization of ingroups and outgroups into a superordinate category can have beneficial effects for intergroup relations. Recently, however, increases in bias following recategorization have been observed in some contexts. It is argued that such unwanted consequences of recategorization will only be apparent for perceivers who are highly committed to their ingroup subgroups. In Experiments 1 to 3, the authors observed, on both explicit and implicit measures, that an increase in bias following recategorization occurred only for high subgroup identifiers. In Experiment 4, it was found that maintaining the salience of subgroups within a recategorized superordinate group averted this increase in bias for high identifiers and led overall to the lowest levels of bias. These findings are discussed in the context of recent work on the Common Ingroup Identity Model.

Predicting the profile of nutrients available for absorption: from nutrient requirement to animal response and environmental impact

Current feed evaluation systems for dairy cattle aim to match nutrient requirements with nutrient intake at pre-defined production levels. These systems were not developed to address, and are not suitable to predict, the responses to dietary changes in terms of production level and product composition, excretion of nutrients to the environment, and nutrition related disorders. The change from a requirement to a response system to meet the needs of various stakeholders requires prediction of the profile of absorbed nutrients and its subsequent utilisation for various purposes. This contribution examines the challenges to predicting the profile of nutrients available for absorption in dairy cattle and provides guidelines for further improved prediction with regard to animal production responses and environmental pollution. The profile of nutrients available for absorption comprises volatile fatty acids, long-chain fatty acids, amino acids and glucose. Thus the importance of processes in the reticulo-rumen is obvious. Much research into rumen fermentation is aimed at determination of substrate degradation rates. Quantitative knowledge on rates of passage of nutrients out of the rumen is rather limited compared with that on degradation rates, and thus should be an important theme in future research. Current systems largely ignore microbial metabolic variation, and extant mechanistic models of rumen fermentation give only limited attention to explicit representation of microbial metabolic activity. Recent molecular techniques indicate that knowledge on the presence and activity of various microbial species is far from complete. Such techniques may give a wealth of information, but to include such findings in systems predicting the nutrient profile requires close collaboration between molecular scientists and mathematical modellers on interpreting and evaluating quantitative data. Protozoal metabolism is of particular interest here given the paucity of quantitative data. Empirical models lack the biological basis necessary to evaluate mitigation strategies to reduce excretion of waste, including nitrogen, phosphorus and methane. Such models may have little predictive value when comparing various feeding strategies. Examples include the Intergovernmental Panel on Climate Change (IPCC) Tier II models to quantify methane emissions and current protein evaluation systems to evaluate low protein diets to reduce nitrogen losses to the environment. Nutrient based mechanistic models can address such issues. Since environmental issues generally attract more funding from governmental offices, further development of nutrient based models may well take place within an environmental framework.

Research note: amendments to the model for predicting age at sexual maturity for growing pullets of layer strains following changes in photoperiod

A model was published by Lewis et al. (2002) to predict the mean age at first egg (AFE) for pullets of laying strains reared under non-limiting environmental conditions and exposed to a single change in photoperiod during the rearing stage. Subsequently, Lewis et al. (2003) reported the effects of two opposing changes in photoperiod, which showed that the first change appears to alter the pullet's physiological age so that it responds to the second change as though it had been given at an earlier age (if photoperiod was decreased), or later age (if photoperiod was increased) than the true chronological age. During the construction of a computer model based on these two publications, it became apparent that some of the components of the models needed adjustment. The amendments relate to (1) the standard deviation (S.D.) used for calculating the proportion of a young flock that has attained photosensitivity, (2) the equation for calculating the slope of the line relating AFE to age at transfer from one photoperiod to another, (3) the equation used for estimating the distribution of AFE as a function of the mean value, (4) the point of no return when pullets which have started spontaneous maturation in response to the current photoperiod can no longer respond to a late change in photoperiod and (5) the equations used for calculating the distribution of AFE when the trait is bimodal.

Alternative approaches to predicting methane emissions from dairy cows

Previous attempts to apply statistical models, which correlate nutrient intake with methane production, have been of limited. value where predictions are obtained for nutrient intakes and diet types outside those. used in model construction. Dynamic mechanistic models have proved more suitable for extrapolation, but they remain computationally expensive and are not applied easily in practical situations. The first objective of this research focused on employing conventional techniques to generate statistical models of methane production appropriate to United Kingdom dairy systems. The second objective was to evaluate these models and a model published previously using both United Kingdom and North American data sets. Thirdly, nonlinear models were considered as alternatives to the conventional linear regressions. The United Kingdom calorimetry data used to construct the linear models also were used to develop the three. nonlinear alternatives that were ball of modified Mitscherlich (monomolecular) form. Of the linear models tested,, an equation from the literature proved most reliable across the full range of evaluation data (root mean square prediction error = 21.3%). However, the Mitscherlich models demonstrated the greatest degree of adaptability across diet types and intake level. The most successful model for simulating the independent data was a modified Mitscherlich equation with the steepness parameter set to represent dietary starch-to-ADF ratio (root mean square prediction error = 20.6%). However, when such data were unavailable, simpler Mitscherlich forms relating dry matter or metabolizable energy intake to methane production remained better alternatives relative to their linear counterparts.

Predicting feed quality - chemical analysis and in vitro evaluation

As the ideal method of assessing the nutritive value of a feedstuff, namely offering it to the appropriate class of animal and recording the production response obtained, is neither practical nor cost effective a range of feed evaluation techniques have been developed. Each of these balances some degree of compromise with the practical situation against data generation. However, due to the impact of animal-feed interactions over and above that of feed composition, the target animal remains the ultimate arbitrator of nutritional value. In this review current in vitro feed evaluation techniques are examined according to the degree of animal-feed interaction. Chemical analysis provides absolute values and therefore differs from the majority of in vitro methods that simply rank feeds. However, with no host animal involvement, estimates of nutritional value are inferred by statistical association. In addition given the costs involved, the practical value of many analyses conducted should be reviewed. The in sacco technique has made a substantial contribution to both understanding rumen microbial degradative processes and the rapid evaluation of feeds, especially in developing countries. However, the numerous shortfalls of the technique, common to many in vitro methods, the desire to eliminate the use of surgically modified animals for routine feed evaluation, paralleled with improvements in in vitro techniques, will see this technique increasingly replaced. The majority of in vitro systems use substrate disappearance to assess degradation, however, this provides no information regarding the quantity of derived end-products available to the host animal. As measurement of volatile fatty acids or microbial biomass production greatly increases analytical costs, fermentation gas release, a simple and non-destructive measurement, has been used as an alternative. However, as gas release alone is of little use, gas-based systems, where both degradation and fermentation gas release are measured simultaneously, are attracting considerable interest. Alternative microbial inocula are being considered, as is the potential of using multi-enzyme systems to examine degradation dynamics. It is concluded that while chemical analysis will continue to form an indispensable part of feed evaluation, enhanced use will be made of increasingly complex in vitro systems. It is vital, however, the function and limitations of each methodology are fully understood and that the temptation to over-interpret the data is avoided so as to draw the appropriate conclusions. With careful selection and correct application in vitro systems offer powerful research tools with which to evaluate feedstuffs. (C) 2003 Elsevier B.V. All rights reserved.

Octanol-water partition coefficients for predicting the effects of tannins in ruminant nutrition

Tannins can cause beneficial or harmful nutritional effects, but their great diversity has until now prevented a rational distinction between tannin structures and their nutritional responses. An attempt has been made to study this problem by examining the octanol-water solubilities of tannins. A relatively simple HPLC method has been developed for screening mixtures of plant tannins for their octanol-water partition coefficients (K-ow coefficients). Tannins were isolated from the fruits and leaves of different Acacia, Calliandra, Dichrostachys, and Piliostigma species, which are known to produce beneficial or harmful effects. The K-ow coefficients of these tannins ranged from 0.061 to 13.9, average coefficients of variation were 9.2% and recoveries were 107%. Acacia nilotica fruits and leaves had the highest K-ow coefficients, that is, 2.0 and 13.9, respectively. These A. nilotica products also have high concentrations of tannins. The combined effects of high octanol solubilities and high tannin concentrations may explain their negative effects on animal nutrition and health. It is known that compounds with high octanol solubilities are more easily absorbed into tissues, and it is, therefore, proposed that such compounds are more likely to cause toxicity problems especially if consumed in large quantities. According to the literature, tannins in human foods tend to have low K-ow coefficients, and this was confirmed for the tannins in Piliostigma thonningii fruits. Therefore, unconventional feeds or browse products should be screened not only for their tannin concentrations but also for low octanol-water partition coefficients in order to identify nutritionally safe feeds and to avoid potentially toxic feeds.

Predicting the effects of nitrogen and planting density on maize water use in semi-arid Kenya

Models are important tools to assess the scope of management effects on crop productivity under different climatic and soil regimes. Accordingly, this study developed and used a simple model to assess the effects of nitrogen fertiliser and planting density on the water use efficiency (q) of maize in semi-arid Kenya. Field experiments were undertaken at Sonning, Berkshire, UK, in 1996 (one sowing) and 1997 (two sowings). The results from the field experiments plus soil and weather data for Machakos, Kenya (1 degree 33'S, 37 degree 14'E and 1560 m above sea level), were then used to predict the effects that N application and planting density may have on water use by a maize crop grown in semi-arid Kenya. The increase in q due to N application was greater under irrigated (15%-19%) than rainfed (7%-8%) conditions. Also, high planting density increased q (by 13%) under irrigation but decreased q (by 17%) under rainfed conditions. The current study has shown the significance of crop modelling techniques in assessing the influence of N and planting density on maize production in one region of semi-arid Kenya where there is high variability of rainfall.

A hierarchical Bayesian model for predicting the functional consequences of amino-acid polymorphisms

Genetic polymorphisms in deoxyribonucleic acid coding regions may have a phenotypic effect on the carrier, e.g. by influencing susceptibility to disease. Detection of deleterious mutations via association studies is hampered by the large number of candidate sites; therefore methods are needed to narrow down the search to the most promising sites. For this, a possible approach is to use structural and sequence-based information of the encoded protein to predict whether a mutation at a particular site is likely to disrupt the functionality of the protein itself. We propose a hierarchical Bayesian multivariate adaptive regression spline (BMARS) model for supervised learning in this context and assess its predictive performance by using data from mutagenesis experiments on lac repressor and lysozyme proteins. In these experiments, about 12 amino-acid substitutions were performed at each native amino-acid position and the effect on protein functionality was assessed. The training data thus consist of repeated observations at each position, which the hierarchical framework is needed to account for. The model is trained on the lac repressor data and tested on the lysozyme mutations and vice versa. In particular, we show that the hierarchical BMARS model, by allowing for the clustered nature of the data, yields lower out-of-sample misclassification rates compared with both a BMARS and a frequen-tist MARS model, a support vector machine classifier and an optimally pruned classification tree.

Changes in oestrogen receptor-alpha and -beta during progression to acquired resistance to tamoxifen and fulvestrant (Faslodex, ICI 182,780) in MCF7 human breast cancer cells

Cell culture models of antioestrogen resistance often involve applying selective pressures of oestrogen deprivation simultaneously with addition of tamoxifen or fulvestrant (Faslodex, ICI 182,780) which makes it difficult to distinguish events in development of antioestrogen resistance from those in loss of response to oestrogen or other components. We describe here time courses of loss of antioestrogen response using either oestrogen-maintained or oestrogen-deprived MCF7 cells in which the only alteration to the culture medium was addition of 10(-6) M tamoxifen or 10(-7) M fulvestrant. In both oestrogen-maintained and oestrogen-deprived models, loss of growth response to tamoxifen was not associated with loss of response to fulvestrant. However, loss of growth response to fulvestrant was associated in both models with concomitant loss of growth response to tamoxifen. Measurement of oestrogen receptor alpha (ER alpha) and oestrogen receptor beta (ER beta) mRNA by real-time RT-PCR together with ER alpha and ER beta protein by Western immunoblotting revealed substantial changes to ER alpha levels but very little alteration to ER beta levels following development of antioestrogen resistance. In oestrogen-maintained cells, tamoxifen resistance was associated with raised levels of ERa mRNA/protein. However by contrast, in oestrogen-deprived MCF7 cells, where oestrogen deprivation alone had already resulted in increased levels of ERa mRNA/protein, long-term tamoxifen exposure now reduced ER alpha levels. Whilst long-term exposure to fulvestrant reduced ERa. mRNA/protein levels in the oestrogen-maintained cells to a level barely detectable by Western immunoblotting and non-functional in inducing gene expression (ERE-LUC reporter or pS2), in oestrogen-deprived cells the reduction was much less substantial and these cells retained an oestrogen-induction of both the ERE-LUC reporter gene and the endogenous pS2 gene which could still be inhibited by antioestrogen. This demonstrates that whilst ER alpha can be abrogated by fulvestrant and increased by tamoxifen in some circumstances, this does not always hold true and mechanisms other than alteration to ER must be involved in the development of antioestrogen resistant growth. (c) 2006 Elsevier Ltd. All rights reserved.

Predicting the functional response of a farmland bird

1. Mechanistic models may be able to predict how changes in agricultural practice influence farmland bird populations. A key component of these models is the link between food and competitor densities and the rate at which birds consume food, i.e. the functional response. 2. This paper tests whether the functional response of a farmland bird, the rook Corvus frugilegus, can be predicted from three parameters: searching speed, food detection distance and handling time. It is often difficult to measure the functional response of farmland birds directly, but it may be possible to measure behavioural parameters more quickly. 3. We performed experiments in which rooks fed on a range of artificial food densities in two grass sward heights. Food detection distance was greater in the shorter sward, but sward height did not influence searching speed or handling time. The functional response could be accurately predicted in both sward heights. 4. We show that the functional response of a farmland bird can be predicted from parameters that can be measured more quickly than the alternative of measuring the functional response directly. This implies that the functional responses of other farmland birds may be predicted using a minimum of information.

Predicting functional gene links from phylogenetic-statistical analyses of whole genomes

An important element of the developing field of proteomics is to understand protein-protein interactions and other functional links amongst genes. Across-species correlation methods for detecting functional links work on the premise that functionally linked proteins will tend to show a common pattern of presence and absence across a range of genomes. We describe a maximum likelihood statistical model for predicting functional gene linkages. The method detects independent instances of the correlated gain or loss of pairs of proteins on phylogenetic trees, reducing the high rates of false positives observed in conventional across-species methods that do not explicitly incorporate a phylogeny. We show, in a dataset of 10,551 protein pairs, that the phylogenetic method improves by up to 35% on across-species analyses at identifying known functionally linked proteins. The method shows that protein pairs with at least two to three correlated events of gain or loss are almost certainly functionally linked. Contingent evolution, in which one gene's presence or absence depends upon the presence of another, can also be detected phylogenetically, and may identify genes whose functional significance depends upon its interaction with other genes. Incorporating phylogenetic information improves the prediction of functional linkages. The improvement derives from having a lower rate of false positives and from detecting trends that across-species analyses miss. Phylogenetic methods can easily be incorporated into the screening of large-scale bioinformatics datasets to identify sets of protein links and to characterise gene networks.