Vehicle Systems Panel deliberations

The Vehicle Systems Panel addressed materials and structures technology issues related to launch and space vehicle systems not directly associated with the propulsion or entry systems. The Vehicle Systems Panel was comprised of two subpanels - Expendable Launch Vehicles & Cryotanks (ELVC) and Reusable Vehicles (RV). Tom Bales, LaRC, and Tom Modlin, JSC, chaired the expendable and reusable vehicles subpanels, respectively, and co-chaired the Vehicle Systems Panel. The following four papers are discussed in this section: (1) Net Section components for Weldalite Cryogenic Tanks, by Don Bolstad; (2) Build-up Structures for Cryogenic Tanks and Dry Bay Structural Applications, by Barry Lisagor; (3) Composite Materials Program, by Robert Van Siclen; (4) Shuttle Technology (and M&S Lessons Learned), by Stan Greenberg

Sperm storage and mating in the deep-sea squid Taningia danae Joubin, 1931 (Oegopsida:Octopoteuthidae)

Spermatangium implantation is reported in the large oceanic squid Taningia danae, based on ten mated females from the stomachs of sperm whales. Implanted spermatangia were located in the mantle, head and neck (on both sides) or above the nuchal cartilage, under the neck collar and were often associated with incisions. These cuts ranged from 30 to 65 mm in length and were probably made by males, using the beak or arm hooks. This is the first time wounds facilitating spermatangium storage have been observed in the internal muscle layers (rather than external, as observed in some other species of squid). The implications of these observations for the mating behavior of the rarely encountered squid T. danae are discussed

Trends in marine survival of Atlantic salmon populations in eastern Canada

Declines in wild Atlantic salmon (Salmo salar) abundance throughout the north Atlantic are primarily attributed to decreases in survival at sea. However, comparing trends in marine survival among populations is challenging as data on both migrating smolts and returning adults are sparse and models are difficult to parameterize due to their varied life histories. We fit a hierarchical Bayesian maturity schedule model to data from seven populations in eastern Canada to estimate numbers of out-migrating smolts, survival in the first and second year at sea, and the proportion returning after 1 year. Trends in survival at sea were not consistent among populations; we observe positive, negative, and no correlations in these, suggesting that large-scale patterns of changes in marine survival are not necessarily representative for individual populations. Variation in return abundances was mostly explained by marine survival in the first winter at sea in all but one population. However, variation in the other components were not negligible and their relative importance differed among populations. If salmon populations do not respond in a uniform manner to changing environmental conditions throughout their range, future research initiatives should explore why.publishedVersio

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 log2 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

Probe set algorithms: is there a rational best bet?

Affymetrix microarrays have become a standard experimental platform for studies of mRNA expression profiling. Their success is due, in part, to the multiple oligonucleotide features (probes) against each transcript (probe set). This multiple testing allows for more robust background assessments and gene expression measures, and has permitted the development of many computational methods to translate image data into a single normalized "signal" for mRNA transcript abundance. There are now many probe set algorithms that have been developed, with a gradual movement away from chip-by-chip methods (MAS5), to project-based model-fitting methods (dCHIP, RMA, others). Data interpretation is often profoundly changed by choice of algorithm, with disoriented biologists questioning what the "accurate" interpretation of their experiment is. Here, we summarize the debate concerning probe set algorithms. We provide examples of how changes in mismatch weight, normalizations, and construction of expression ratios each dramatically change data interpretation. All interpretations can be considered as computationally appropriate, but with varying biological credibility. We also illustrate the performance of two new hybrid algorithms (PLIER, GC-RMA) relative to more traditional algorithms (dCHIP, MAS5, Probe Profiler PCA, RMA) using an interactive power analysis tool. PLIER appears superior to other algorithms in avoiding false positives with poorly performing probe sets. Based on our interpretation of the literature, and examples presented here, we suggest that the variability in performance of probe set algorithms is more dependent upon assumptions regarding "background", than on calculations of "signal". We argue that "background" is an enormously complex variable that can only be vaguely quantified, and thus the "best" probe set algorithm will vary from project to project

Normalized Affymetrix expression data are biased by G-quadruplex formation

Probes with runs of four or more guanines (G-stacks) in their sequences can exhibit a level of hybridization that is unrelated to the expression levels of the mRNA that they are intended to measure. This is most likely caused by the formation of G-quadruplexes, where inter-probe guanines form Hoogsteen hydrogen bonds, which probes with G-stacks are capable of forming. We demonstrate that for a specific microarray data set using the Human HG-U133A Affymetrix GeneChip and RMA normalization there is significant bias in the expression levels, the fold change and the correlations between expression levels. These effects grow more pronounced as the number of G-stack probes in a probe set increases. Approximately 14 of the probe sets are directly affected. The analysis was repeated for a number of other normalization pipelines and two, FARMS and PLIER, minimized the bias to some extent. We estimate that ∼15 of the data sets deposited in the GEO database are susceptible to the effect. The inclusion of G-stack probes in the affected data sets can bias key parameters used in the selection and clustering of genes. The elimination of these probes from any analysis in such affected data sets outweighs the increase of noise in the signal. © 2011 The Author(s)

Canopy nitrogen, carbon assimilation, and albedo in temperate and boreal forests: Functional relations and potential climate feedbacks

The availability of nitrogen represents a key constraint on carbon cycling in terrestrial ecosystems, and it is largely in this capacity that the role of N in the Earth\u27s climate system has been considered. Despite this, few studies have included continuous variation in plant N status as a driver of broad-scale carbon cycle analyses. This is partly because of uncertainties in how leaf-level physiological relationships scale to whole ecosystems and because methods for regional to continental detection of plant N concentrations have yet to be developed. Here, we show that ecosystem CO2 uptake capacity in temperate and boreal forests scales directly with whole-canopy N concentrations, mirroring a leaf-level trend that has been observed for woody plants worldwide. We further show that both CO2 uptake capacity and canopy N concentration are strongly and positively correlated with shortwave surface albedo. These results suggest that N plays an additional, and overlooked, role in the climate system via its influence on vegetation reflectivity and shortwave surface energy exchange. We also demonstrate that much of the spatial variation in canopy N can be detected by using broad-band satellite sensors, offering a means through which these findings can be applied toward improved application of coupled carbon cycle–climate models

Variation in hybrid gene expression: Implications for the evolution of genetic incompatibilities in interbreeding species

Interbreeding species often produce low-fitness hybrids due to genetic incompatibilities between parental genomes. Whether these incompatibilities reflect fixed allelic differences between hybridizing species, or, alternatively, standing variants that segregate within them, remains unknown for many natural systems. Yet, evaluating these alternatives is important for understanding the origins and nature of species boundaries. We examined these alternatives using spadefoot toads (genus Spea), which naturally hybridize. Specifically, we contrasted patterns of gene expression in hybrids relative to pure-species types in experimentally produced tadpoles from allopatric parents versus those from sympatric parents. We evaluated the prediction that segregating variation should result in gene expression differences between hybrids derived from sympatric parents versus hybrids derived from allopatric parents, and found that 24% of the transcriptome showed such differences. Our results further suggest that gene expression in hybrids has evolved in sympatry owing to evolutionary pressures associated with ongoing hybridization. Although we did not measure hybrid incompatibilities directly, we discuss the implications of our findings for understanding the nature of hybrid incompatibilities, how they might vary across populations over time, and the resulting effects on the evolutionary maintenance - or breakdown - of reproductive barriers between species

Modelling the spatial distribution of DEM Error

Assessment of a DEM’s quality is usually undertaken by deriving a measure of DEM accuracy – how close the DEM’s elevation values are to the true elevation. Measures such as Root Mean Squared Error and standard deviation of the error are frequently used. These measures summarise elevation errors in a DEM as a single value. A more detailed description of DEM accuracy would allow better understanding of DEM quality and the consequent uncertainty associated with using DEMs in analytical applications. The research presented addresses the limitations of using a single root mean squared error (RMSE) value to represent the uncertainty associated with a DEM by developing a new technique for creating a spatially distributed model of DEM quality – an accuracy surface. The technique is based on the hypothesis that the distribution and scale of elevation error within a DEM are at least partly related to morphometric characteristics of the terrain. The technique involves generating a set of terrain parameters to characterise terrain morphometry and developing regression models to define the relationship between DEM error and morphometric character. The regression models form the basis for creating standard deviation surfaces to represent DEM accuracy. The hypothesis is shown to be true and reliable accuracy surfaces are successfully created. These accuracy surfaces provide more detailed information about DEM accuracy than a single global estimate of RMSE