Transcriptome analysis of grain development in hexaploid wheat

Background: Hexaploid wheat is one of the most important cereal crops for human nutrition. Molecular understanding of the biology of the developing grain will assist the improvement of yield and quality traits for different environments. High quality transcriptomics is a powerful method to increase this understanding. Results: The transcriptome of developing caryopses from hexaploid wheat ( Triticum aestivum, cv. Hereward) was determined using Affymetrix wheat GeneChip (R) oligonucleotide arrays which have probes for 55,052 transcripts. Of these, 14,550 showed significant differential regulation in the period between 6 and 42 days after anthesis ( daa). Large changes in transcript abundance were observed which were categorised into distinct phases of differentiation ( 6 - 10 daa), grain fill ( 12 - 21 daa) and desiccation/maturation ( 28 - 42 daa) and were associated with specific tissues and processes. A similar experiment on developing caryopses grown with dry and/or hot environmental treatments was also analysed, using the profiles established in the first experiment to show that most environmental treatment effects on transcription were due to acceleration of development, but that a few transcripts were specifically affected. Transcript abundance profiles in both experiments for nine selected known and putative wheat transcription factors were independently confirmed by real time RT-PCR. These expression profiles confirm or extend our knowledge of the roles of the known transcription factors and suggest roles for the unknown ones. Conclusion: This transcriptome data will provide a valuable resource for molecular studies on wheat grain. It has been demonstrated how it can be used to distinguish general developmental shifts from specific effects of treatments on gene expression and to diagnose the probable tissue specificity and role of transcription factors.

Drivers of fund performance: a panel data analysis

The principle aim of this research is to elucidate the factors driving the total rate of return of non-listed funds using a panel data analytical framework. In line with previous results, we find that core funds exhibit lower yet more stable returns than value-added and, in particular, opportunistic funds, both cross-sectionally and over time. After taking into account overall market exposure, as measured by weighted market returns, the excess returns of value-added and opportunity funds are likely to stem from: high leverage, high exposure to development, active asset management and investment in specialized property sectors. A random effects estimation of the panel data model largely confirms the findings obtained from the fixed effects model. Again, the country and sector property effect shows the strongest significance in explaining total returns. The stock market variable is negative which hints at switching effects between competing asset classes. For opportunity funds, on average, the returns attributable to gearing are three times higher than those for value added funds and over five times higher than for core funds. Overall, there is relatively strong evidence indicating that country and sector allocation, style, gearing and fund size combinations impact on the performance of unlisted real estate funds.

Global trends and factors associated with the illegal killing of elephants: a hierarchical bayesian analysis of carcass encounter data

Elephant poaching and the ivory trade remain high on the agenda at meetings of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). Well-informed debates require robust estimates of trends, the spatial distribution of poaching, and drivers of poaching. We present an analysis of trends and drivers of an indicator of elephant poaching of all elephant species. The site-based monitoring system known as Monitoring the Illegal Killing of Elephants (MIKE), set up by the 10th Conference of the Parties of CITES in 1997, produces carcass encounter data reported mainly by anti-poaching patrols. Data analyzed were site by year totals of 6,337 carcasses from 66 sites in Africa and Asia from 2002–2009. Analysis of these observational data is a serious challenge to traditional statistical methods because of the opportunistic and non-random nature of patrols, and the heterogeneity across sites. Adopting a Bayesian hierarchical modeling approach, we used the proportion of carcasses that were illegally killed (PIKE) as a poaching index, to estimate the trend and the effects of site- and country-level factors associated with poaching. Important drivers of illegal killing that emerged at country level were poor governance and low levels of human development, and at site level, forest cover and area of the site in regions where human population density is low. After a drop from 2002, PIKE remained fairly constant from 2003 until 2006, after which it increased until 2008. The results for 2009 indicate a decline. Sites with PIKE ranging from the lowest to the highest were identified. The results of the analysis provide a sound information base for scientific evidence-based decision making in the CITES process.

Analyzing the performance of non-listed real estate funds: an panel data analysis

The rapid growth of non-listed real estate funds over the last several years has contributed towards establishing this sector as a major investment vehicle for gaining exposure to commercial real estate. Academic research has not kept up with this development, however, as there are still only a few published studies on non-listed real estate funds. This paper aims to identify the factors driving the total return over a seven-year period. Influential factors tested in our analysis include the weighted underlying direct property returns in each country and sector as well as fund size, investment style gearing and the distribution yield. Furthermore, we analyze the interaction of non-listed real estate funds with the performance of the overall economy and that of competing asset classes and found that lagged GDP growth and stock market returns as well as contemporaneous government bond rates are significant and positive predictors of annual fund performance.

An experiment in the Assimilation of Data in Dynamical Analysis

A system for continuous data assimilation is presented and discussed. To simulate the dynamical development a channel version of a balanced barotropic model is used and geopotential (height) data are assimilated into the models computations as data become available. In the first experiment the updating is performed every 24th, 12th and 6th hours with a given network. The stations are distributed at random in 4 groups in order to simulate 4 areas with different density of stations. Optimum interpolation is performed for the difference between the forecast and the valid observations. The RMS-error of the analyses is reduced in time, and the error being smaller the more frequent the updating is performed. The updating every 6th hour yields an error in the analysis less than the RMS-error of the observation. In a second experiment the updating is performed by data from a moving satellite with a side-scan capability of about 15°. If the satellite data are analysed at every time step before they are introduced into the system the error of the analysis is reduced to a value below the RMS-error of the observation already after 24 hours and yields as a whole a better result than updating from a fixed network. If the satellite data are introduced without any modification the error of the analysis is reduced much slower and it takes about 4 days to reach a comparable result to the one where the data have been analysed.

Optimising the analysis of transcript data using high density oligonucleotide arrays and genomic DNA-based probe selection

Background: Affymetrix GeneChip arrays are widely used for transcriptomic studies in a diverse range of species. Each gene is represented on a GeneChip array by a probe- set, consisting of up to 16 probe-pairs. Signal intensities across probe- pairs within a probe-set vary in part due to different physical hybridisation characteristics of individual probes with their target labelled transcripts. We have previously developed a technique to study the transcriptomes of heterologous species based on hybridising genomic DNA (gDNA) to a GeneChip array designed for a different species, and subsequently using only those probes with good homology. Results: Here we have investigated the effects of hybridising homologous species gDNA to study the transcriptomes of species for which the arrays have been designed. Genomic DNA from Arabidopsis thaliana and rice (Oryza sativa) were hybridised to the Affymetrix Arabidopsis ATH1 and Rice Genome GeneChip arrays respectively. Probe selection based on gDNA hybridisation intensity increased the number of genes identified as significantly differentially expressed in two published studies of Arabidopsis development, and optimised the analysis of technical replicates obtained from pooled samples of RNA from rice. Conclusion: This mixed physical and bioinformatics approach can be used to optimise estimates of gene expression when using GeneChip arrays.