Evaluating multivariate forecast densities: a comparison of two approaches

We consider methods of evaluating multivariate density forecasts. A recently proposed method is found to lack power when the correlation structure is mis-specified. Tests that have good power to detect mis-specifications of this sort are described. We also consider the properties of the tests in the presence of more general mis-specifications.

Evaluation of biospheric components in Earth system models using modern and palaeo-observations: the state-of-the-art

Earth system models (ESMs) are increasing in complexity by incorporating more processes than their predecessors, making them potentially important tools for studying the evolution of climate and associated biogeochemical cycles. However, their coupled behaviour has only recently been examined in any detail, and has yielded a very wide range of outcomes. For example, coupled climate–carbon cycle models that represent land-use change simulate total land carbon stores at 2100 that vary by as much as 600 Pg C, given the same emissions scenario. This large uncertainty is associated with differences in how key processes are simulated in different models, and illustrates the necessity of determining which models are most realistic using rigorous methods of model evaluation. Here we assess the state-of-the-art in evaluation of ESMs, with a particular emphasis on the simulation of the carbon cycle and associated biospheric processes. We examine some of the new advances and remaining uncertainties relating to (i) modern and palaeodata and (ii) metrics for evaluation. We note that the practice of averaging results from many models is unreliable and no substitute for proper evaluation of individual models. We discuss a range of strategies, such as the inclusion of pre-calibration, combined process- and system-level evaluation, and the use of emergent constraints, that can contribute to the development of more robust evaluation schemes. An increasingly data-rich environment offers more opportunities for model evaluation, but also presents a challenge. Improved knowledge of data uncertainties is still necessary to move the field of ESM evaluation away from a "beauty contest" towards the development of useful constraints on model outcomes.

Tracking (poly)phenol components from raspberries in ileal fluid

The (poly)phenols in ileal fluid after ingestion of raspberries were analysed by targeted and non-targeted LC-MSn approaches. Targeted approaches identified major anthocyanin and ellagitannin components at varying recoveries and with considerable inter-individual variation. Non-targeted LC-MSn analysis using an Orbitrap mass spectrometer gave exact mass MS data which was sifted using a software program to select peaks that changed significantly after supplementation. This method confirmed the recovery of the targeted components but also identified novel raspberry-specific metabolites. Some components (including ellagitannin and previously unidentified proanthocyanidin derivatives) may have arisen from raspberry seeds that survived intact in ileal samples. Other components include potential breakdown products of anthocyanins, unidentified components and phenolic metabolites formed in either the gut epithelia or after absorption into the circulatory system and efflux back into the gut lumen. The possible physiological roles of the ileal metabolites in the large bowel are discussed.

Forecasting with Bayesian multivariate vintage-based VARs

We consider the forecasting of macroeconomic variables that are subject to revisions, using Bayesian vintage-based vector autoregressions. The prior incorporates the belief that, after the first few data releases, subsequent ones are likely to consist of revisions that are largely unpredictable. The Bayesian approach allows the joint modelling of the data revisions of more than one variable, while keeping the concomitant increase in parameter estimation uncertainty manageable. Our model provides markedly more accurate forecasts of post-revision values of inflation than do other models in the literature.

Dominant components of the Thoroughbred metabolome characterised by 1H‐NMR spectroscopy: a metabolite atlas of common biofluids

Summary Reasons for performing study: Metabonomics is emerging as a powerful tool for disease screening and investigating mammalian metabolism. This study aims to create a metabolic framework by producing a preliminary reference guide for the normal equine metabolic milieu. Objectives: To metabolically profile plasma, urine and faecal water from healthy racehorses using high resolution 1H-NMR spectroscopy and to provide a list of dominant metabolites present in each biofluid for the benefit of future research in this area. Study design: This study was performed using seven Thoroughbreds in race training at a single time-point. Urine and faecal samples were collected non-invasively and plasma was obtained from samples taken for routine clinical chemistry purposes. Methods: Biofluids were analysed using 1H-NMR spectroscopy. Metabolite assignment was achieved via a range of 1D and 2D experiments. Results: A total of 102 metabolites were assigned across the three biological matrices. A core metabonome of 14 metabolites was ubiquitous across all biofluids. All biological matrices provided a unique window on different aspects of systematic metabolism. Urine was the most populated metabolite matrix with 65 identified metabolites, 39 of which were unique to this biological compartment. A number of these were related to gut microbial host co-metabolism. Faecal samples were the most metabolically variable between animals; acetate was responsible for the majority (28%) of this variation. Short chain fatty acids were the predominant features identified within this biofluid by 1H-NMR spectroscopy. Conclusions: Metabonomics provides a platform for investigating complex and dynamic interactions between the host and its consortium of gut microbes and has the potential to uncover markers for health and disease in a variety of biofluids. Inherent variation in faecal extracts along with the relative abundance of microbial-mammalian metabolites in urine and invasive nature of plasma sampling, infers that urine is the most appropriate biofluid for the purposes of metabonomic analysis.

Short-term variability of solar wind number density, speed and dynamic pressure as a function of the interplanetary magnetic field components: A survey over two solar cycles

The variability of hourly values of solar wind number density, number density variation, speed, speed variation and dynamic pressure with IMF Bz and magnitude |B| has been examined for the period 1965–1986. We wish to draw attention to a strong correlation in number density and number density fluctuation with IMF Bz characterised by a symmetric increasing trend in these quantities away from Bz = 0 nT. The fluctuation level in solar wind speed is found to be relatively independent of Bz. We infer that number density and number density variability dominate in controlling solar wind dynamic pressure and dynamic pressure variability. It is also found that dynamic pressure is correlated with each component of IMF and that there is evidence of morphological differences between the variation with each component. Finally, we examine the variation of number density, speed, dynamic pressure and fluctuation level in number density and speed with IMF magnitude |B|. Again we find that number density variation dominates over solar wind speed in controlling dynamic pressure.

A novel combined biomarker including plasma carotenoids, vitamin C, and ferric reducing antioxidant power is more strongly associated with fruit and vegetable intake than the individual components

BACKGROUND: Monitoring of fruit and vegetable (F&V) intake is fraught with difficulties. Available dietary assessment methods are associated with considerable error, and the use of biomarkers offers an attractive alternative. Few studies to date have examined the use of plasma biomarkers to monitor or predict the F&V intake of volunteers consuming a wide range of intakes from both habitual F&V and manipulated diets. OBJECTIVE: This study tested the hypothesis that an integrated biomarker calculated from a combination of plasma vitamin C, cholesterol-adjusted carotenoid concentration and Ferric Reducing Antioxidant Power (FRAP) had more power to predict F&V intake than each individual biomarker. METHODS: Data from a randomized controlled dietary intervention study [FLAVURS (Flavonoids University of Reading Study); n = 154] in which the test groups observed sequential increases of 2.3, 3.2, and 4.2 portions of F&Vs every 6 wk across an 18-wk period were used in this study. RESULTS: An integrated plasma biomarker was devised that included plasma vitamin C, total cholesterol-adjusted carotenoids, and FRAP values, which better correlated with F&V intake (r = 0.47, P < 0.001) than the individual biomarkers (r = 0.33, P < 0.01; r = 0.37, P < 0.001; and r = 0.14, respectively; P = 0.099). Inclusion of urinary potassium concentration did not significantly improve the correlation. The integrated plasma biomarker predicted F&V intake more accurately than did plasma total cholesterol-adjusted carotenoid concentration, with the difference being significant at visit 2 (P < 0.001) and with a tendency to be significant at visit 1 (P = 0.07). CONCLUSION: Either plasma total cholesterol-adjusted carotenoid concentration or the integrated biomarker could be used to distinguish between high- and moderate-F&V consumers. This trial was registered at www.controlled-trials.com as ISRCTN47748735.

Experimental evolution for generalists and specialists reveals multivariate genetic constraints on thermal reaction norms

Theory predicts the emergence of generalists in variable environments and antagonistic pleiotropy to favour specialists in constant environments, but empirical data seldom support such generalist–specialist trade-offs. We selected for generalists and specialists in the dung fly Sepsis punctum (Diptera: Sepsidae) under conditions that we predicted would reveal antagonistic pleiotropy and multivariate trade-offs underlying thermal reaction norms for juvenile development. We performed replicated laboratory evolution using four treatments: adaptation at a hot (31 °C) or a cold (15 °C) temperature, or under regimes fluctuating between these temperatures, either within or between generations. After 20 generations, we assessed parental effects and genetic responses of thermal reaction norms for three correlated life-history traits: size at maturity, juvenile growth rate and juvenile survival. We find evidence for antagonistic pleiotropy for performance at hot and cold temperatures, and a temperature-mediated trade-off between juvenile survival and size at maturity, suggesting that trade-offs associated with environmental tolerance can arise via intensified evolutionary compromises between genetically correlated traits. However, despite this antagonistic pleiotropy, we found no support for the evolution of increased thermal tolerance breadth at the expense of reduced maximal performance, suggesting low genetic variance in the generalist–specialist dimension.

Common components of risk and uncertainty attitudes across contexts and domains: Evidence from 30 countries

Attitudes towards risk and uncertainty have been indicated to be highly context-dependent, and to be sensitive to the measurement technique employed. We present data collected in controlled experiments with 2,939 subjects in 30 countries measuring risk and uncertainty attitudes through incentivized measures as well as survey questions. Our data show clearly that measures correlate not only within decision contexts or measurement methods, but also across contexts and methods. This points to the existence of one underlying “risk preference”, which influences attitudes independently of the measurement method or choice domain. We furthermore find that answers to a general and a financial survey question correlate with incentivized lottery choices in most countries. Incentivized and survey measures also correlate significantly between countries. This opens the possibility to conduct cultural comparisons on risk attitudes using survey instruments.

Variation in nutritionally relevant components in retail Jersey and Guernsey whole milk

This study investigates the quality of retail milk labelled as Jersey & Guernsey (JG) when compared with milk without breed specifications (NS) and repeatability of differences over seasons and years. 16 different brands of milk (4 Jersey & Guernsey, 12 non specified breed) were sampled over 2 years on 4 occasions. JG milk was associated with both favourable traits for human health, such as the higher total protein, total casein, α-casein, β-casein, κ-casein and α-tocopherol contents, and unfavourable traits, such as the higher concentrations of saturated fat, C12:0, C14:0 and lower concentrations of monounsaturated fatty acids. In summer, JG milk had a higher omega-3:omega-6 ratio than had NS milk. Also, the relative increase in omega-3 fatty acids and α-tocopherol, from winter to summer, was greater in JG milk. The latter characteristic could be of use in breeding schemes and farming systems producing niche dairy products. Seasonality had a more marked impact on the fatty acid composition of JG milk than had NS milk, while the opposite was found for protein composition. Potential implication for the findings in human health, producers, industry and consumers are considered.

Advanced multivariate analysis to assess remediation of hydrocarbons in soils

Accurate monitoring of degradation levels in soils is essential in order to understand and achieve complete degradation of petroleum hydrocarbons in contaminated soils. We aimed to develop the use of multivariate methods for the monitoring of biodegradation of diesel in soils and to determine if diesel contaminated soils could be remediated to a chemical composition similar to that of an uncontaminated soil. An incubation experiment was set up with three contrasting soil types. Each soil was exposed to diesel at varying stages of degradation and then analysed for key hydrocarbons throughout 161 days of incubation. Hydrocarbon distributions were analysed by Principal Coordinate Analysis and similar samples grouped by cluster analysis. Variation and differences between samples were determined using permutational multivariate analysis of variance. It was found that all soils followed trajectories approaching the chemical composition of the unpolluted soil. Some contaminated soils were no longer significantly different to that of uncontaminated soil after 161 days of incubation. The use of cluster analysis allows the assignment of a percentage chemical similarity of a diesel contaminated soil to an uncontaminated soil sample. This will aid in the monitoring of hydrocarbon contaminated sites and the establishment of potential endpoints for successful remediation.

Taste components of Sherry wines

Sherry wine has characteristic taste and aroma, different from other wine-based alcoholic beverages. This paper reports a study of the non-volatile, low-molecular weight compounds found in sherry and related alcoholic beverages that may contribute to taste. Compounds analysed included free amino acids, organic acids, sugars and small peptides (linear and cyclic). A series of seven diketopiperazines (cyclic dipeptides) namely, cyclo(Leu-Leu), cyclo(Pro-Leu), cyclo(Pro-Ile), cyclo(Pro-Met), cyclo( Pro-Val), cyclo(Pro-Pro) and cyclo(Val-Ala) were identified for the first time in sherry. Although traces were found in some other alcoholic beverages, levels were low compared with sherry. The base wine used in the sherry production had only traces of diketopiperazines, indicating that the casking stage of sherry production might be responsible for their formation.