Top single nucleotide polymorphisms affecting carbohydrate metabolism in Metabolic Syndrome: from the LIPGENE study

Rationale:Metabolic Syndrome (MetS) is a high prevalence condition characterized by altered energy metabolism, insulin resistance and elevated cardiovascular risk.Objectives:Although many individual single nucleotide polymorphisms (SNPs) have been linked to certain MetS features, there are few studies analyzing the influence of SNPs on carbohydrate metabolism in MetS.Methods:904 SNPs (tag SNPs and functional SNPs) were tested for influence in eight fasting and dynamic markers of carbohydrate metabolism, performing an intravenous glucose tolerance test in 450 participants of the LIPGENE study.Findings:From 382 initial gene-phenotype associations between SNPs and any phenotypic variables, 61 (a 16 % of the pre-selected) remained significant after Bootstrapping. Top SNPs affecting glucose metabolism variables were as follows: fasting glucose: rs26125 (PPARGC1B); fasting insulin: rs4759277 (LRP1); C peptide: rs4759277 (LRP1); HOMA-IR: rs4759277 (LRP1); QUICKI: rs184003 (AGER); SI: rs7301876 (ABCC9), AIRg: rs290481 (TCF7L2) and DI: rs12691 (CEBPA).Conclusions:We describe here the top SNPs linked to phenotypic features in carbohydrate metabolism among aproximately 1000 candidate gene variations in fasting and postprandial samples of 450 patients with MetS from the LIPGENE study.

The ESA climate change initiative: satellite data records for essential climate variables

Observations of Earth from space have been made for over 40 years and have contributed to advances in many aspects of climate science. However, attempts to exploit this wealth of data are often hampered by a lack of homogeneity and continuity and by insufficient understanding of the products and their uncertainties. There is, therefore, a need to reassess and reprocess satellite datasets to maximize their usefulness for climate science. The European Space Agency has responded to this need by establishing the Climate Change Initiative (CCI). The CCI will create new climate data records for (currently) 13 essential climate variables (ECVs) and make these open and easily accessible to all. Each ECV project works closely with users to produce time series from the available satellite observations relevant to users' needs. A climate modeling users' group provides a climate system perspective and a forum to bring the data and modeling communities together. This paper presents the CCI program. It outlines its benefit and presents approaches and challenges for each ECV project, covering clouds, aerosols, ozone, greenhouse gases, sea surface temperature, ocean color, sea level, sea ice, land cover, fire, glaciers, soil moisture, and ice sheets. It also discusses how the CCI approach may contribute to defining and shaping future developments in Earth observation for climate science.

The depression of tropical snow lines at the Last Glacial Maximum: what can we learn from climate and model experiments?

Analyses of simulations of the last glacial maximum (LGM) made with 17 atmospheric general circulation models (AGCMs) participating in the Paleoclimate Modelling Intercomparison Project, and a high-resolution (T106) version of one of the models (CCSR1), show that changes in the elevation of tropical snowlines (as estimated by the depression of the maximum altitude of the 0 °C isotherm) are primarily controlled by changes in sea-surface temperatures (SSTs). The correlation between the two variables, averaged for the tropics as a whole, is 95%, and remains >80% even at a regional scale. The reduction of tropical SSTs at the LGM results in a drier atmosphere and hence steeper lapse rates. Changes in atmospheric circulation patterns, particularly the weakening of the Asian monsoon system and related atmospheric humidity changes, amplify the reduction in snowline elevation in the northern tropics. Colder conditions over the tropical oceans combined with a weakened Asian monsoon could produce snowline lowering of up to 1000 m in certain regions, comparable to the changes shown by observations. Nevertheless, such large changes are not typical of all regions of the tropics. Analysis of the higher resolution CCSR1 simulation shows that differences between the free atmospheric and along-slope lapse rate can be large, and may provide an additional factor to explain regional variations in observed snowline changes.

Parameter spaces of environmental fields responsible for cyclone development from tropics to extratropics

Objective cyclone tracking applied to a 30-yr reanalysis dataset shows that cyclone development in the summer and autumn seasons is active in the tropics and extratropics and inactive in the subtropics. To understand this geographically bimodal distribution of cyclone development associated with tropical and extratropical cyclones quantitatively, the direct relationship between cyclone types and their environments are assessed by using a parameter space of environmental variables [environmental parameter space (EPS)]. The number of cyclones is analyzed in terms of two different factors: the environmental conditions favorable for cyclone development and the area size that satisfies the favorable condition. The EPS analysis is mainly conducted for two representative environmental parameters that are commonly used for cyclone analysis: potential intensity for tropical cyclones and baroclinicity for extratropical cyclones. The geographically bimodal distribution is attributed to the high sensitivity of the cyclone development to the change in the environmental fields from tropics to extratropics. In addition, the bimodal distribution is partly attributed to the rapid change in the environmental fields from tropics to extratropics. The EPS analysis also shows that other environmental parameters, including relative humidity and vertical velocity, may enhance the contrast between the tropics (extratropics) and subtropics, whereas they are not essential for determining cyclone types. The relationship between cyclones and their environments is found to be similar between the hemispheres in the EPS, although the geographical distribution, particularly the longitudinal uniformity, is markedly different between the hemispheres.

Evaluation of milk compositional variables on coagulation properties using partial least squares

The aim of this study was to investigate the effects of numerous milk compositional factors on milk coagulation properties using Partial Least Squares (PLS). Milk from herds of Jersey and Holstein-Friesian cattle was collected across the year and blended (n=55), to maximize variation in composition and coagulation. The milk was analysed for casein, protein, fat, titratable acidity, lactose, Ca2+, urea content, micelles size, fat globule size, somatic cell count and pH. Milk coagulation properties were defined as coagulation time, curd firmness and curd firmness rate measured by a controlled strain rheometer. The models derived from PLS had higher predictive power than previous models demonstrating the value of measuring more milk components. In addition to the well-established relationships with casein and protein levels, CMS and fat globule size were found to have as strong impact on all of the three models. The study also found a positive impact of fat on milk coagulation properties and a strong relationship between lactose and curd firmness, and urea and curd firmness rate, all of which warrant further investigation due to current lack of knowledge of the underlying mechanism. These findings demonstrate the importance of using a wider range of milk compositional variable for the prediction of the milk coagulation properties, and hence as indicators of milk suitability for cheese making.

The interdependence of continental warm cloud properties derived from unexploited solar background signals in ground-based lidar measurements

We have extensively analysed the interdependence between cloud optical depth, droplet effective radius, liquid water path (LWP) and geometric thickness for stratiform warm clouds using ground-based observations. In particular, this analysis uses cloud optical depths retrieved from untapped solar background signals that are previously unwanted and need to be removed in most lidar applications. Combining these new optical depth retrievals with radar and microwave observations at the Atmospheric Radiation Measurement (ARM) Climate Research Facility in Oklahoma during 2005–2007, we have found that LWP and geometric thickness increase and follow a power-law relationship with cloud optical depth regardless of the presence of drizzle; LWP and geometric thickness in drizzling clouds can be generally 20–40 % and at least 10 % higher than those in non-drizzling clouds, respectively. In contrast, droplet effective radius shows a negative correlation with optical depth in drizzling clouds and a positive correlation in non-drizzling clouds, where, for large optical depths, it asymptotes to 10 μm. This asymptotic behaviour in non-drizzling clouds is found in both the droplet effective radius and optical depth, making it possible to use simple thresholds of optical depth, droplet size, or a combination of these two variables for drizzle delineation. This paper demonstrates a new way to enhance ground-based cloud observations and drizzle delineations using existing lidar networks.

Meteorological forcing data for urban outdoor thermal comfort models from a coupled convective boundary layer and surface energy balance scheme

Site-specific meteorological forcing appropriate for applications such as urban outdoor thermal comfort simulations can be obtained using a newly coupled scheme that combines a simple slab convective boundary layer (CBL) model and urban land surface model (ULSM) (here two ULSMs are considered). The former simulates daytime CBL height, air temperature and humidity, and the latter estimates urban surface energy and water balance fluxes accounting for changes in land surface cover. The coupled models are tested at a suburban site and two rural sites, one irrigated and one unirrigated grass, in Sacramento, U.S.A. All the variables modelled compare well to measurements (e.g. coefficient of determination = 0.97 and root mean square error = 1.5 °C for air temperature). The current version is applicable to daytime conditions and needs initial state conditions for the CBL model in the appropriate range to obtain the required performance. The coupled model allows routine observations from distant sites (e.g. rural, airport) to be used to predict air temperature and relative humidity in an urban area of interest. This simple model, which can be rapidly applied, could provide urban data for applications such as air quality forecasting and building energy modelling, in addition to outdoor thermal comfort.

Climate variability and human impact in South America during the last 2000 years: synthesis and perspectives from pollen records

An improved understanding of present-day climate variability and change relies on high-quality data sets from the past 2 millennia. Global efforts to model regional climate modes are in the process of being validated against, and integrated with, records of past vegetation change. For South America, however, the full potential of vegetation records for evaluating and improving climate models has hitherto not been sufficiently acknowledged due to an absence of information on the spatial and temporal coverage of study sites. This paper therefore serves as a guide to high-quality pollen records that capture environmental variability during the last 2 millennia. We identify 60 vegetation (pollen) records from across South America which satisfy geochronological requirements set out for climate modelling, and we discuss their sensitivity to the spatial signature of climate modes throughout the continent. Diverse patterns of vegetation response to climate change are observed, with more similar patterns of change in the lowlands and varying intensity and direction of responses in the highlands. Pollen records display local-scale responses to climate modes; thus, it is necessary to understand how vegetation–climate interactions might diverge under variable settings. We provide a qualitative translation from pollen metrics to climate variables. Additionally, pollen is an excellent indicator of human impact through time. We discuss evidence for human land use in pollen records and provide an overview considered useful for archaeological hypothesis testing and important in distinguishing natural from anthropogenically driven vegetation change. We stress the need for the palynological community to be more familiar with climate variability patterns to correctly attribute the potential causes of observed vegetation dynamics. This manuscript forms part of the wider LOng-Term multi-proxy climate REconstructions and Dynamics in South America – 2k initiative that provides the ideal framework for the integration of the various palaeoclimatic subdisciplines and palaeo-science, thereby jump-starting and fostering multidisciplinary research into environmental change on centennial and millennial timescales.

Effects of forage source and extruded linseed supplementation on methane emissions from growing dairy cattle of differing body weights

Changes in diet carbohydrate amount and type (i.e., starch vs. fiber) and dietary oil supplements can affect ruminant methane emissions. Our objectives were to measure methane emissions, whole-tract digestibility, and energy and nitrogen utilization from growing dairy cattle at 2 body weight (BW) ranges, fed diets containing either high maize silage (MS) or high grass silage (GS), without or with supplemental oil from extruded linseed (ELS). Four Holstein-Friesian heifers aged 13 mo (BW range from start to finish of 382 to 526 kg) were used in experiment 1, whereas 4 lighter heifers aged 12 mo (BW range from start to finish of 292 to 419 kg) were used in experiment 2. Diets were fed as total mixed rations with forage dry matter (DM) containing high MS or high GS and concentrates in proportions (forage:concentrate, DM basis) of either 75:25 (experiment 1) or 60:40 (experiment 2), respectively. Diets were supplemented without or with ELS (Lintec[AU1: Add manufacturer name and location.]; 260 g of oil/ kg of DM) at 6% of ration DM. Each experiment was a 4 × 4 Latin square design with 33-d periods, with measurements during d 29 to 33 while animals were housed in respiration chambers. Heifers fed MS at a heavier BW (experiment 1) emitted 20% less methane per unit of DM intake (yield) compared with GS (21.4 vs. 26.6, respectively). However, when repeated with heifers of a lower BW (experiment 2), methane yield did not differ between the 2 diets (26.6 g/kg of DM intake). Differences in heifer BW had no overall effect on methane emissions, except when expressed as grams per kilogram of digestible organic matter (OMD) intake (32.4 vs. 36.6, heavy vs. light heifers). Heavier heifers fed MS in experiment 1 had a greater DM intake (9.4 kg/d) and lower OMD (755 g/kg), but no difference in N utilization (31% of N intake) compared with heifers fed GS (7.9 kg/d and 799 g/kg, respectively). Tissue energy retention was nearly double for heifers fed MS compared with GS in experiment 1 (15 vs. 8% of energy intake, respectively). Heifers fed MS in experiment 2 had similar DM intake (7.2 kg/d) and retention of energy (5% of intake energy) and N (28% of N intake), compared with GS-fed heifers, but OMD was lower (741 vs. 765 g/kg, respectively). No effect of ELS was noted on any of the variables measured, irrespective of animal BW, and this was likely due to the relatively low amount of supplemental oil provided. Differences in heifer BW did not markedly influence dietary effects on methane emissions. Differences in methane yield were attributable to differences in dietary starch and fiber composition associated with forage type and source.

On the observability of turbulent transport rates by Argo: supporting evidence from an inversion experiment

Although estimation of turbulent transport parameters using inverse methods is not new, there is little evaluation of the method in the literature. Here, it is shown that extended observation of the broad scale hydrography by Argo provides a path to improved estimates of regional turbulent transport rates. Results from a 20 year ocean state estimate produced with the ECCO v4 non-linear inverse modeling framework provide supporting evidence. Turbulent transport parameter maps are estimated under the constraints of fitting the extensive collection of Argo profiles collected through 2011. The adjusted parameters dramatically reduce misfits to in situ profiles as compared with earlier ECCO solutions. They also yield a clear reduction in the model drift away from observations over multi-century long simulations, both for assimilated variables (temperature and salinity) and independent variables (bio-geochemical tracers). Despite the minimal constraints imposed specifically on the estimated parameters, their geography is physically plausible and exhibits close connections with the upper ocean ocean stratification as observed by Argo. The estimated parameter adjustments furthermore have first order impacts on upper-ocean stratification and mixed layer depths over 20 years. These results identify the constraint of fitting Argo profiles as an effective observational basis for regional turbulent transport rates. Uncertainties and further improvements of the method are discussed.

An ensemble of eddy-permitting global ocean reanalyses from the MyOcean project

A set of four eddy-permitting global ocean reanalyses produced in the framework of the MyOcean project have been compared over the altimetry period 1993–2011. The main differences among the reanalyses used here come from the data assimilation scheme implemented to control the ocean state by inserting reprocessed observations of sea surface temperature (SST), in situ temperature and salinity profiles, sea level anomaly and sea-ice concentration. A first objective of this work includes assessing the interannual variability and trends for a series of parameters, usually considered in the community as essential ocean variables: SST, sea surface salinity, temperature and salinity averaged over meaningful layers of the water column, sea level, transports across pre-defined sections, and sea ice parameters. The eddy-permitting nature of the global reanalyses allows also to estimate eddy kinetic energy. The results show that in general there is a good consistency between the different reanalyses. An intercomparison against experiments without data assimilation was done during the MyOcean project and we conclude that data assimilation is crucial for correctly simulating some quantities such as regional trends of sea level as well as the eddy kinetic energy. A second objective is to show that the ensemble mean of reanalyses can be evaluated as one single system regarding its reliability in reproducing the climate signals, where both variability and uncertainties are assessed through the ensemble spread and signal-to-noise ratio. The main advantage of having access to several reanalyses differing in the way data assimilation is performed is that it becomes possible to assess part of the total uncertainty. Given the fact that we use very similar ocean models and atmospheric forcing, we can conclude that the spread of the ensemble of reanalyses is mainly representative of our ability to gauge uncertainty in the assimilation methods. This uncertainty changes a lot from one ocean parameter to another, especially in global indices. However, despite several caveats in the design of the multi-system ensemble, the main conclusion from this study is that an eddy-permitting multi-system ensemble approach has become mature and our results provide a first step towards a systematic comparison of eddy-permitting global ocean reanalyses aimed at providing robust conclusions on the recent evolution of the oceanic state.

Evaluation of a modern-analogue methodology for reconstructing Australian palaeoclimate from pollen

Quantitative palaeoclimate reconstructions are widely used to evaluate climatemodel performance. Here, as part of an effort to provide such a data set for Australia, we examine the impact of analytical decisions and sampling assumptions on modern-analogue reconstructions using a continent-wide pollen data set. There is a high degree of correlation between temperature variables in the modern climate of Australia, but there is sufficient orthogonality in the variations of precipitation, summer and winter temperature and plant–available moisture to allow independent reconstructions of these four variables to be made. The method of analogue selection does not affect the reconstructions, although bootstrap resampling provides a more reliable technique for obtaining robust measures of uncertainty. The number of analogues used affects the quality of the reconstructions: the most robust reconstructions are obtained using 5 analogues. The quality of reconstructions based on post-1850 CE pollen samples differ little from those using samples from between 1450 and 1849 CE, showing that European post settlement modification of vegetation has no impact on the fidelity of the reconstructions although it substantially increases the availability of potential analogues. Reconstructions based on core top samples are more realistic than those using surface samples, but only using core top samples would substantially reduce the number of available analogues and therefore increases the uncertainty of the reconstructions. Spatial and/or temporal averaging of pollen assemblages prior to analysis negatively affects the subsequent reconstructions for some variables and increases the associated uncertainties. In addition, the quality of the reconstructions is affected by the degree of spatial smoothing of the original climate data, with the best reconstructions obtained using climate data froma 0.5° resolution grid, which corresponds to the typical size of the pollen catchment. This study provides a methodology that can be used to provide reliable palaeoclimate reconstructions for Australia, which will fill in a major gap in the data sets used to evaluate climate models.

Independent uncertainty estimates for coefficient based sea surface temperature retrieval from the Along-Trck Scanning Radiometer instruments

We establish a methodology for calculating uncertainties in sea surface temperature estimates from coefficient based satellite retrievals. The uncertainty estimates are derived independently of in-situ data. This enables validation of both the retrieved SSTs and their uncertainty estimate using in-situ data records. The total uncertainty budget is comprised of a number of components, arising from uncorrelated (eg. noise), locally systematic (eg. atmospheric), large scale systematic and sampling effects (for gridded products). The importance of distinguishing these components arises in propagating uncertainty across spatio-temporal scales. We apply the method to SST data retrieved from the Advanced Along Track Scanning Radiometer (AATSR) and validate the results for two different SST retrieval algorithms, both at a per pixel level and for gridded data. We find good agreement between our estimated uncertainties and validation data. This approach to calculating uncertainties in SST retrievals has a wider application to data from other instruments and retrieval of other geophysical variables.

Hydrodynamics with spin angular momentum from systematic coarse graining: a tutorial example

The derivation of time evolution equations for slow collective variables starting from a micro- scopic model system is demonstrated for the tutorial example of the classical, two-dimensional XY model. Projection operator techniques are used within a nonequilibrium thermodynamics framework together with molecular simulations in order to establish the building blocks of the hydrodynamics equations: Poisson brackets that determine the deterministic drift, the driving forces from the macroscopic free energy and the friction matrix. The approach is rather general and can be applied for deriving the equations of slow variables for a broad variety of systems.

Risks Associated With Tendinitis: Effects From Demographic, Socioeconomic, and Psychological Status Among Brazilian Workers

Background Self-reported tendinitis/tenosynovitis was evaluated by gender, age group, skin color, family income, and educational and psychological status. Methods The study was carried out in a representative sample of formally contracted Brazilian workers from a household survey. A total of 54,660 participants were included. Occupations were stratified according to estimated prevalences of self-reported injuries. Non-conditional logistic regression was performed, and all variables were analyzed in two occupational groups. Results The overall prevalence rate of tendinitis/tenosynovitis was 3.1%: 5.5% in high-prevalence occupations (n=10,726); and 2.5% in low-prevalence occupations (n=43,934). White female workers between the ages of 45 and 64 years and at a higher socioeconomic level were more likely to report tendinitis/tenosynovitis regardless of their occupational category. An adjusted OR = 3.59 [95% CI: 3.15-4.09] was found between tendinitis/tenosynovitis and psychological status. Conclusion Among formally contracted Brazilian workers, higher income can imply greater physical and psychological demands that, regardless of occupational stratum, increase the risk of tendinitis/tenosynovitis. Am. J. Ind. Med. 53:72-79, 2010. (C) 2009 Wiley-Liss, Inc.