Seasonal cycles and variability of O3 and H2O in the UT/LMS during SPURT

Airborne high resolution in situ measurements of a large set of trace gases including ozone (O3) and total water (H2O) in the upper troposphere and the lowermost stratosphere (UT/LMS) have been performed above Europe within the SPURT project. SPURT provides an extensive data coverage of the UT/LMS in each season within the time period between November 2001 and July 2003. In the LMS a distinct spring maximum and autumn minimum is observed in O3, whereas its annual cycle in the UT is shifted by 2–3 months later towards the end of the year. The more variable H2O measurements reveal a maximum during summer and a minimum during autumn/winter with no phase shift between the two atmospheric compartments. For a comprehensive insight into trace gas composition and variability in the UT/LMS several statistical methods are applied using chemical, thermal and dynamical vertical coordinates. In particular, 2-dimensional probability distribution functions serve as a tool to transform localised aircraft data to a more comprehensive view of the probed atmospheric region. It appears that both trace gases, O3 and H2O, reveal the most compact arrangement and are best correlated in the view of potential vorticity (PV) and distance to the local tropopause, indicating an advanced mixing state on these surfaces. Thus, strong gradients of PV seem to act as a transport barrier both in the vertical and the horizontal direction. The alignment of trace gas isopleths reflects the existence of a year-round extra-tropical tropopause transition layer. The SPURT measurements reveal that this layer is mainly affected by stratospheric air during winter/spring and by tropospheric air during autumn/summer. Normalised mixing entropy values for O3 and H2O in the LMS appear to be maximal during spring and summer, respectively, indicating highest variability of these trace gases during the respective seasons.

Inferring animal husbandry strategies in coastal zones through stable isotope analysis: new evidence from the Flemish coastal plain (Belgium, 1st-15th century AD)

In a proof-of-concept study, Britton et al. (2008) demonstrated that the isotopic composition of halophytic plants can be traced in the skeletal tissues of their animal consumers. Here we apply the method to domestic herbivore remains (n = 303) from nine archaeological sites in or near the Flemish coastal plain (Belgium), where, prior to embankments, salt-marshes offered extensive pasture grounds for domestic herbivores. The sites span a period of ∼1500 years (Roman to late medieval period), during which the coastal landscape was progressively transformed from little managed wetlands to a fully embanked polder area. The bulk collagen data show variations between sites and over time, which are consistent with this historical framework and are interpreted as reflecting environmental change and differences in animal management in the coastal plain throughout the late Holocene. The study demonstrates the immense value of faunal stable isotope analysis for characterising coastal husbandry strategies beyond the means of traditional zooarchaeological techniques.

Reading the world through words: cultural themes in heritage Chinese language textbooks

This paper explores the social and cultural knowledge embedded in the textbooks for language and literacy education in a Chinese heritage language school, the Zhonguo School, in Montreal, Quebec, Canada. It examines how Chinese language arts textbooks introduce the child reader to cultural knowledge considered legitimate and valued in China as well as in Chinese diasporan communities. Furthermore, it looks at the construction of cultural knowledge in Chinese language textbooks in relation to the mainstream ideology to which immigrant children are exposed in and out of mainstream school classrooms. It looks at how the power relationship between legitimate cultural knowledge in majority and minority contexts is established and to what extent it affects language minority students' literacy practices in mainstream school and heritage language school contexts. Data sources are the Chinese textbooks used from kindergarten to Grade 5 in a Chinese heritage language school.

Deriving a sea surface temperature record suitable for climate change research from the along-track scanning radiometers

We describe the approach to be adopted for a major new initiative to derive a homogeneous record of sea surface temperature for 1991–2007 from the observations of the series of three along-track scanning radiometers (ATSRs). This initiative is called (A)RC: (Advanced) ATSR Re-analysis for Climate. The main objectives are to reduce regional biases in retrieved sea surface temperature (SST) to less than 0.1 K for all global oceans, while creating a very homogenous record that is stable in time to within 0.05 K decade−1, with maximum independence of the record from existing analyses of SST used in climate change research. If these stringent targets are achieved, this record will enable significantly improved estimates of surface temperature trends and variability of sufficient quality to advance questions of climate change attribution, climate sensitivity and historical reconstruction of surface temperature changes. The approach includes development of new, consistent estimators for SST for each of the ATSRs, and detailed analysis of overlap periods. Novel aspects of the approach include generation of multiple versions of the record using alternative channel sets and cloud detection techniques, to assess for the first time the effect of such choices. There will be extensive effort in quality control, validation and analysis of the impact on climate SST data sets. Evidence for the plausibility of the 0.1 K target for systematic error is reviewed, as is the need for alternative cloud screening methods in this context.

Soil food web properties explain ecosystem services across European land use systems

Intensive land use reduces the diversity and abundance of many soil biota, with consequences for the processes that they govern and the ecosystem services that these processes underpin. Relationships between soil biota and ecosystem processes have mostly been found in laboratory experiments and rarely are found in the field. Here, we quantified, across four countries of contrasting climatic and soil conditions in Europe, how differences in soil food web composition resulting from land use systems (intensive wheat rotation, extensive rotation, and permanent grassland) influence the functioning of soils and the ecosystem services that they deliver. Intensive wheat rotation consistently reduced the biomass of all components of the soil food web across all countries. Soil food web properties strongly and consistently predicted processes of C and N cycling across land use systems and geographic locations, and they were a better predictor of these processes than land use. Processes of carbon loss increased with soil food web properties that correlated with soil C content, such as earthworm biomass and fungal/bacterial energy channel ratio, and were greatest in permanent grassland. In contrast, processes of N cycling were explained by soil food web properties independent of land use, such as arbuscular mycorrhizal fungi and bacterial channel biomass. Our quantification of the contribution of soil organisms to processes of C and N cycling across land use systems and geographic locations shows that soil biota need to be included in C and N cycling models and highlights the need to map and conserve soil biodiversity across the world.

Gradient recovery in adaptive finite-element methods for parabolic problems

We derive energy-norm a posteriori error bounds, using gradient recovery (ZZ) estimators to control the spatial error, for fully discrete schemes for the linear heat equation. This appears to be the �rst completely rigorous derivation of ZZ estimators for fully discrete schemes for evolution problems, without any restrictive assumption on the timestep size. An essential tool for the analysis is the elliptic reconstruction technique.Our theoretical results are backed with extensive numerical experimentation aimed at (a) testing the practical sharpness and asymptotic behaviour of the error estimator against the error, and (b) deriving an adaptive method based on our estimators. An extra novelty provided is an implementation of a coarsening error "preindicator", with a complete implementation guide in ALBERTA in the appendix.

Zinc in plants

Zinc (Zn) is an essential component of thousands of proteins in plants, although it is toxic in excess. In this review, the dominant fluxes of Zn in the soil-root-shoot continuum are described, including Zn inputs to soils, the plant availability of soluble Zn(2+) at the root surface, and plant uptake and accumulation of Zn. Knowledge of these fluxes can inform agronomic and genetic strategies to address the widespread problem of Zn-limited crop growth. Substantial within-species genetic variation in Zn composition is being used to alleviate human dietary Zn deficiencies through biofortification. Intriguingly, a meta-analysis of data from an extensive literature survey indicates that a small proportion of the genetic variation in shoot Zn concentration can be attributed to evolutionary processes whose effects manifest above the family level. Remarkable insights into the evolutionary potential of plants to respond to elevated soil Zn have recently been made through detailed anatomical, physiological, chemical, genetic and molecular characterizations of the brassicaceous Zn hyperaccumulators Thlaspi caerulescens and Arabidopsis halleri.

Error, reproducibility and sensitivity: a pipeline for data processing of Agilent oligonucleotide expression arrays

Background: Expression microarrays are increasingly used to obtain large scale transcriptomic information on a wide range of biological samples. Nevertheless, there is still much debate on the best ways to process data, to design experiments and analyse the output. Furthermore, many of the more sophisticated mathematical approaches to data analysis in the literature remain inaccessible to much of the biological research community. In this study we examine ways of extracting and analysing a large data set obtained using the Agilent long oligonucleotide transcriptomics platform, applied to a set of human macrophage and dendritic cell samples. Results: We describe and validate a series of data extraction, transformation and normalisation steps which are implemented via a new R function. Analysis of replicate normalised reference data demonstrate that intrarray variability is small (only around 2 of the mean log signal), while interarray variability from replicate array measurements has a standard deviation (SD) of around 0.5 log(2) units (6 of mean). The common practise of working with ratios of Cy5/Cy3 signal offers little further improvement in terms of reducing error. Comparison to expression data obtained using Arabidopsis samples demonstrates that the large number of genes in each sample showing a low level of transcription reflect the real complexity of the cellular transcriptome. Multidimensional scaling is used to show that the processed data identifies an underlying structure which reflect some of the key biological variables which define the data set. This structure is robust, allowing reliable comparison of samples collected over a number of years and collected by a variety of operators. Conclusions: This study outlines a robust and easily implemented pipeline for extracting, transforming normalising and visualising transcriptomic array data from Agilent expression platform. The analysis is used to obtain quantitative estimates of the SD arising from experimental (non biological) intra- and interarray variability, and for a lower threshold for determining whether an individual gene is expressed. The study provides a reliable basis for further more extensive studies of the systems biology of eukaryotic cells.

Genetic analysis of potassium use efficiency in Brassica oleracea

Potassium (K) fertilizers are used in intensive and extensive agricultural systems to maximize production. However, there are both financial and environmental costs to K-fertilization. It is therefore important to optimize the efficiency with which K-fertilizers are used. Cultivating crops that acquire and/or utilize K more effectively can reduce the use of K-fertilizers. The aim of the present study was to determine the genetic factors affecting K utilization efficiency (KUtE), defined as the reciprocal of shoot K concentration (1/K(shoot)), and K acquisition efficiency (KUpE), defined as shoot K content, in Brassica oleracea. Genetic variation in K(shoot) was estimated using a structured diversity foundation set (DFS) of 376 accessions and in 74 commercial genotypes grown in glasshouse and field experiments that included phosphorus (P) supply as a treatment factor. Chromosomal quantitative trait loci (QTL) associated with K(shoot) and KUpE were identified using a genetic mapping population grown in the glasshouse and field. Putative QTL were tested using recurrent backcross substitution lines in the glasshouse. More than two-fold variation in K(shoot) was observed among DFS accessions grown in the glasshouse, a significant proportion of which could be attributed to genetic factors. Several QTL associated with K(shoot) were identified, which, despite a significant correlation in K(shoot) among genotypes grown in the glasshouse and field, differed between these two environments. A QTL associated with K(shoot) in glasshouse-grown plants (chromosome C7 at 62 center dot 2 cM) was confirmed using substitution lines. This QTL corresponds to a segment of arabidopsis chromosome 4 containing genes encoding the K(+) transporters AtKUP9, AtAKT2, AtKAT2 and AtTPK3. There is sufficient genetic variation in B. oleracea to breed for both KUtE and KUpE. However, as QTL associated with these traits differ between glasshouse and field environments, marker-assisted breeding programmes must consider carefully the conditions under which the crop will be grown.

Geographical variation in total and inorganic arsenic content of polished (white) rice

An extensive data set of total arsenic analysis for 901 polished (white) grain samples, originating from 10 countries from 4 continents, was compiled. The samples represented the baseline (i.e., not specifically collected from arsenic contaminated areas), and all were for market sale in major conurbations. Median total arsenic contents of rice varied 7-fold, with Egypt (0.04 mg/kg) and India (0.07 mg/kg) having the lowest arsenic content while the U.S. (0.25 mg/kg) and France (0.28 mg/kg) had the highest content. Global distribution of total arsenic in rice was modeled by weighting each country’s arsenic distribution by that country’s contribution to global production. A subset of 63 samples from Bangladesh, China, India, Italy, and the U.S. was analyzed for arsenic species. The relationship between inorganic arsenic content versus total arsenic content significantly differed among countries, with Bangladesh and India having the steepest slope in linear regression, and the U.S. having the shallowest slope. Using country-specific rice consumption data, daily intake of inorganic arsenic was estimated and the associated internal cancer risk was calculated using the U.S. Environmental Protection Agency (EPA) cancer slope. Median excess internal cancer risks posed by inorganic arsenic ranged 30-fold for the 5 countries examined, being 0.7 per 10,000 for Italians to 22 per 10,000 for Bangladeshis, when a 60 kg person was considered.

A modified PNN algorithm with optimal PD modeling using the orthogonal least squares method

In this paper a modified algorithm is suggested for developing polynomial neural network (PNN) models. Optimal partial description (PD) modeling is introduced at each layer of the PNN expansion, a task accomplished using the orthogonal least squares (OLS) method. Based on the initial PD models determined by the polynomial order and the number of PD inputs, OLS selects the most significant regressor terms reducing the output error variance. The method produces PNN models exhibiting a high level of accuracy and superior generalization capabilities. Additionally, parsimonious models are obtained comprising a considerably smaller number of parameters compared to the ones generated by means of the conventional PNN algorithm. Three benchmark examples are elaborated, including modeling of the gas furnace process as well as the iris and wine classification problems. Extensive simulation results and comparison with other methods in the literature, demonstrate the effectiveness of the suggested modeling approach.

Particle swarm optimisation assisted classification using elastic net prefiltering

A novel two-stage construction algorithm for linear-in-the-parameters classifier is proposed, aiming at noisy two-class classification problems. The purpose of the first stage is to produce a prefiltered signal that is used as the desired output for the second stage to construct a sparse linear-in-the-parameters classifier. For the first stage learning of generating the prefiltered signal, a two-level algorithm is introduced to maximise the model's generalisation capability, in which an elastic net model identification algorithm using singular value decomposition is employed at the lower level while the two regularisation parameters are selected by maximising the Bayesian evidence using a particle swarm optimization algorithm. Analysis is provided to demonstrate how “Occam's razor” is embodied in this approach. The second stage of sparse classifier construction is based on an orthogonal forward regression with the D-optimality algorithm. Extensive experimental results demonstrate that the proposed approach is effective and yields competitive results for noisy data sets.

A comparison of thiolated and disulfide‐crosslinked polyethylenimine for nonviral gene delivery

Branched polyethylenimine (25 kDa) is thiolated and compared with redox-sensitive crosslinked derivatives. Both polymers thiol contents are assessed; the thiolated polymers have 390–2300 mmol SH groups/mol, whereas the crosslinked polymers have lower thiol contents. Cytotoxicity assays show that both modified polymers give lower hemolysis than unmodified PEI. Increased thiol content increases gene transfer efficiency but also elevates cytotoxicity. Crosslinking improves plasmid DNA condensation and enhances transfection efficiency, but extensive crosslinking overstabilizes the polyplexes and decreases transfection, emphasizing the need to balance polyplex stabilization and unpacking. Thus, at low levels of crosslinking, 25 kDa PEI can be an efficient redox-sensitive carrier system.