When the time is ripe.

The diverse effects of the plant hormone ethylene on development and growth are shaped by the actions of a master regulator of transcription, EIN3

Draft Genome Sequence of Rhizobium rhizogenes Strain ATCC 15834.

Here, we present the draft genome of Rhizobium rhizogenes strain ATCC 15834. The genome contains 7,070,307 bp in 43 scaffolds. R. rhizogenes, also known as Agrobacterium rhizogenes, is a plant pathogen that causes hairy root disease. This hairy root induction has been used in biotechnology for the generation of transgenic root cultures

Error, bias, and long-branch attraction in data for two chloroplast photosystem genes in seed plants

Sequences of two chloroplast photosystem genes, psaA and psbB, together comprising about 3,500 bp, were obtained for all five major groups of extant seed plants and several outgroups among other vascular plants. Strongly supported, but significantly conflicting, phylogenetic signals were obtained in parsimony analyses from partitions of the data into first and second codon positions versus third positions. In the former, both genes agreed on a monophyletic gymnosperms, with Gnetales closely related to certain conifers. In the latter, Gnetales are inferred to be the sister group of all other seed plants, with gymnosperms paraphyletic. None of the data supported the modern ‘‘anthophyte hypothesis,’’ which places Gnetales as the sister group of flowering plants. A series of simulation studies were undertaken to examine the error rate for parsimony inference. Three kinds of errors were examined: random error, systematic bias (both properties of finite data sets), and statistical inconsistency owing to long-branch attraction (an asymptotic property). Parsimony reconstructions were extremely biased for third-position data for psbB. Regardless of the true underlying tree, a tree in which Gnetales are sister to all other seed plants was likely to be reconstructed for these data. None of the combinations of genes or partitions permits the anthophyte tree to be reconstructed with high probability. Simulations of progressively larger data sets indicate the existence of long-branch attraction (statistical inconsistency) for third-position psbB data if either the anthophyte tree or the gymnosperm tree is correct. This is also true for the anthophyte tree using either psaA third positions or psbB first and second positions. A factor contributing to bias and inconsistency is extremely short branches at the base of the seed plant radiation, coupled with extremely high rates in Gnetales and nonseed plant outgroups. M. J. Sanderson,* M. F. Wojciechowski,*† J.-M. Hu,* T. Sher Khan,* and S. G. Brad

Care-experienced Young People Accessing Higher Education in Ireland

While there has been considerable policy attention given to educational disadvantage in the Irish context in recent years, evidence on the educational experiences, attainment, and progression of young people with experience of living in alternative care settings (e.g. foster care, residential care) remains limited. International literature suggests that young people with such ‘care-experience’ typically have lower attainment and progress to higher education at lower rates than their majority population peers. This brief paper focuses on one of these issues, the question of how care-experienced young people in Ireland fare in accessing opportunities in higher education. It presents some very preliminary evidence from an initial analysis of a small data set related to care-experienced applicants to the Higher Education Access Route (HEAR) programme, a higher education access scheme that offers places on a reduced-points basis to school leavers under the age of 23 from socio-economically disadvantaged backgrounds. The findings highlight a number of features of the experience of this group in accessing higher education. In our conclusion, we argue that there is an urgent need to collect, and draw on, data related to the educational attainment and progress of both children in care and those who have left care in Ireland in order to effectively inform policy and practice and to demonstrate a commitment to understanding and addressing this issue

Translational regulation contributes to the elevated CO2 response in two Solanum species.

Understanding the impact of elevated CO2 (eCO2 ) in global agriculture is important given climate change projections. Breeding climate-resilient crops depends on genetic variation within naturally varying populations. The effect of genetic variation in response to eCO2 is poorly understood, especially in crop species. We describe the different ways in which Solanum lycopersicum and its wild relative S. pennellii respond to eCO2 , from cell anatomy, to the transcriptome, and metabolome. We further validate the importance of translational regulation as a potential mechanism for plants to adaptively respond to rising levels of atmospheric CO2

Reconstructing Spatiotemporal Gene Expression Data from Partial Observations

Developmental transcriptional networks in plants and animals operate in both space and time. To understand these transcriptional networks it is essential to obtain whole-genome expression data at high spatiotemporal resolution. Substantial amounts of spatial and temporal microarray expression data previously have been obtained for the Arabidopsis root; however, these two dimensions of data have not been integrated thoroughly. Complicating this integration is the fact that these data are heterogeneous and incomplete, with observed expression levels representing complex spatial or temporal mixtures. Given these partial observations, we present a novel method for reconstructing integrated high resolution spatiotemporal data. Our method is based on a new iterative algorithm for finding approximate roots to systems of bilinear equations.Comment: 19 pages, 4 figure

Detecting separate time scales in genetic expression data.

BACKGROUND: Biological processes occur on a vast range of time scales, and many of them occur concurrently. As a result, system-wide measurements of gene expression have the potential to capture many of these processes simultaneously. The challenge however, is to separate these processes and time scales in the data. In many cases the number of processes and their time scales is unknown. This issue is particularly relevant to developmental biologists, who are interested in processes such as growth, segmentation and differentiation, which can all take place simultaneously, but on different time scales. RESULTS: We introduce a flexible and statistically rigorous method for detecting different time scales in time-series gene expression data, by identifying expression patterns that are temporally shifted between replicate datasets. We apply our approach to a Saccharomyces cerevisiae cell-cycle dataset and an Arabidopsis thaliana root developmental dataset. In both datasets our method successfully detects processes operating on several different time scales. Furthermore we show that many of these time scales can be associated with particular biological functions. CONCLUSIONS: The spatiotemporal modules identified by our method suggest the presence of multiple biological processes, acting at distinct time scales in both the Arabidopsis root and yeast. Using similar large-scale expression datasets, the identification of biological processes acting at multiple time scales in many organisms is now possible.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are