Development and Certification of a System for the Controlled Deployment of Micro-Satellite Payloads from the International Space Station

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Solidifying Small Satellite Access to Orbit via the International Space Station (ISS): Cyclops' Deployment of the Lonestar SmallSat from the ISS

On January 29, 2016, the Space Station Integrated Kinetic Launcher for Orbital Payload Systems (SSIKLOPS), known as "Cyclops" to the International Space Station (ISS) community, deployed Lonestar from the ISS. The deployment of Lonestar, a collaboration between Texas A&M University and the University of Texas at Austin, continued to showcase the simplicity and reliability of the Cyclops deployment system. Cyclops, a NASA-developed, dedicated 10-100 kg class ISS SmallSat deployment system, utilizes the Japanese airlock and robotic systems to seamlessly insert SmallSats into orbit. This paper will illustrate Cyclops' successful deployment of Lonestar from the ISS as well as outline its concept of operations, interfaces, requirements, and processes

Paving the Way for Small Satellite Access to Orbit: Cyclops’ Deployment of SpinSat, the Largest Satellite ever Deployed from the International Space Station

The Space Station Integrated Kinetic Launcher for Orbital Payload Systems (SSIKLOPS), known as “Cyclops” to the International Space Station (ISS) community, successfully deployed the largest satellite ever (SpinSat) from the ISS on November 28, 2014. Cyclops, a collaboration between the NASA ISS Program, NASA Johnson Space Center Engineering, and Department of Defense Space Test Program (DoD STP) communities, is a dedicated 10-100 kg class ISS small satellite deployment system. This paper will showcase the successful deployment of SpinSat from the ISS. It will also outline the concept of operations, interfaces, requirements, and processes for satellites to utilize the Cyclops satellite deployment system

Fundamental research questions in subterranean biology

Five decades ago, a landmark paper inSciencetitledThe Cave Environmentheralded caves as ideal natural experimental laboratories in which to develop and address general questions in geology, ecology, biogeography, and evolutionary biology. Although the 'caves as laboratory' paradigm has since been advocated by subterranean biologists, there are few examples of studies that successfully translated their results into general principles. The contemporary era of big data, modelling tools, and revolutionary advances in genetics and (meta)genomics provides an opportunity to revisit unresolved questions and challenges, as well as examine promising new avenues of research in subterranean biology. Accordingly, we have developed a roadmap to guide future research endeavours in subterranean biology by adapting a well-established methodology of 'horizon scanning' to identify the highest priority research questions across six subject areas. Based on the expert opinion of 30 scientists from around the globe with complementary expertise and of different academic ages, we assembled an initial list of 258 fundamental questions concentrating on macroecology and microbial ecology, adaptation, evolution, and conservation. Subsequently, through online surveys, 130 subterranean biologists with various backgrounds assisted us in reducing our list to 50 top-priority questions. These research questions are broad in scope and ready to be addressed in the next decade. We believe this exercise will stimulate research towards a deeper understanding of subterranean biology and foster hypothesis-driven studies likely to resonate broadly from the traditional boundaries of this field.Peer reviewe

Methods for Bayesian inversion of seismic data

The purpose of Bayesian seismic inversion is to combine information derived from seismic data and prior geological knowledge to determine a posterior probability distribution over parameters describing the elastic and geological properties of the subsurface. Typically the subsurface is modelled by a cellular grid model containing thousands or millions of cells within which these parameters are to be determined. Thus such inversions are computationally expensive due to the size of the parameter space (being proportional to the number of grid cells) over which the posterior is to be determined. Therefore, in practice approximations to Bayesian seismic inversion must be considered. A particular, existing approximate workflow is described in this thesis: the so-called two-stage inversion method explicitly splits the inversion problem into elastic and geological inversion stages. These two stages sequentially estimate the elastic parameters given the seismic data, and then the geological parameters given the elastic parameter estimates, respectively. In this thesis a number of methodologies are developed which enhance the accuracy of this approximate workflow. To reduce computational cost, existing elastic inversion methods often incorporate only simplified prior information about the elastic parameters. Thus a method is introduced which transforms such results, obtained using prior information specified using only two-point geostatistics, into new estimates containing sophisticated multi-point geostatistical prior information. The method uses a so-called deep neural network, trained using only synthetic instances (or `examples') of these two estimates, to apply this transformation. The method is shown to improve the resolution and accuracy (by comparison to well measurements) of elastic parameter estimates determined for a real hydrocarbon reservoir. It has been shown previously that so-called mixture density network (MDN) inversion can be used to solve geological inversion analytically (and thus very rapidly and efficiently) but only under certain assumptions about the geological prior distribution. A so-called prior replacement operation is developed here, which can be used to relax these requirements. It permits the efficient MDN method to be incorporated into general stochastic geological inversion methods which are free from the restrictive assumptions. Such methods rely on the use of Markov-chain Monte-Carlo (MCMC) sampling, which estimate the posterior (over the geological parameters) by producing a correlated chain of samples from it. It is shown that this approach can yield biased estimates of the posterior. Thus an alternative method which obtains a set of non-correlated samples from the posterior is developed, avoiding the possibility of bias in the estimate. The new method was tested on a synthetic geological inversion problem; its results compared favourably to those of Gibbs sampling (a MCMC method) on the same problem, which exhibited very significant bias. The geological prior information used in seismic inversion can be derived from real images which bear similarity to the geology anticipated within the target region of the subsurface. Such so-called training images are not always available from which this information (in the form of geostatistics) may be extracted. In this case appropriate training images may be generated by geological experts. However, this process can be costly and difficult. Thus an elicitation method (based on a genetic algorithm) is developed here which obtains the appropriate geostatistics reliably and directly from a geological expert, without the need for training images. 12 experts were asked to use the algorithm (individually) to determine the appropriate geostatistics for a physical (target) geological image. The majority of the experts were able to obtain a set of geostatistics which were consistent with the true (measured) statistics of the target image

Characterization of an Orphan Diterpenoid Biosynthetic Operon from Salinispora arenicola

While more commonly associated with plants than microbes, diterpenoid natural products have been reported to have profound effects in marine microbe–microbe interactions. Intriguingly, the genome of the marine bacterium Salinispora arenicola CNS-205 contains a putative diterpenoid biosynthetic operon, terp1. Here recombinant expression studies are reported, indicating that this three-gene operon leads to the production of isopimara-8,15-dien-19-ol (4). Although 4 is not observed in pure cultures of S. arenicola, it is plausible that the terp1 operon is only expressed under certain physiologically relevant conditions such as in the presence of other marine organisms

Deep Sequencing the Transcriptome Reveals Seasonal Adaptive Mechanisms in a Hibernating Mammal

Mammalian hibernation is a complex phenotype involving metabolic rate reduction, bradycardia, profound hypothermia, and a reliance on stored fat that allows the animal to survive for months without food in a state of suspended animation. To determine the genes responsible for this phenotype in the thirteen-lined ground squirrel (Ictidomys tridecemlineatus) we used the Roche 454 platform to sequence mRNA isolated at six points throughout the year from three key tissues: heart, skeletal muscle, and white adipose tissue (WAT). Deep sequencing generated approximately 3.7 million cDNA reads from 18 samples (6 time points ×3 tissues) with a mean read length of 335 bases. Of these, 3,125,337 reads were assembled into 140,703 contigs. Approximately 90% of all sequences were matched to proteins in the human UniProt database. The total number of distinct human proteins matched by ground squirrel transcripts was 13,637 for heart, 12,496 for skeletal muscle, and 14,351 for WAT. Extensive mitochondrial RNA sequences enabled a novel approach of using the transcriptome to construct the complete mitochondrial genome for I. tridecemlineatus. Seasonal and activity-specific changes in mRNA levels that met our stringent false discovery rate cutoff (1.0×10−11) were used to identify patterns of gene expression involving various aspects of the hibernation phenotype. Among these patterns are differentially expressed genes encoding heart proteins AT1A1, NAC1 and RYR2 controlling ion transport required for contraction and relaxation at low body temperatures. Abundant RNAs in skeletal muscle coding ubiquitin pathway proteins ASB2, UBC and DDB1 peak in October, suggesting an increase in muscle proteolysis. Finally, genes in WAT that encode proteins involved in lipogenesis (ACOD, FABP4) are highly expressed in August, but gradually decline in expression during the seasonal transition to lipolysis

Solidifying Small Satellite Access to Orbit via the International Space Station (ISS): Cyclops\u27 Deployment of the Lonestar SmallSat from the ISS

On January 29, 2016, the Space Station Integrated Kinetic Launcher for Orbital Payload Systems (SSIKLOPS), known as Cyclops to the International Space Station (ISS) community, deployed Lonestar from the ISS. The deployment of Lonestar, a collaboration between Texas A&M University and the University of Texas at Austin, continued to showcase the simplicity and reliability of the Cyclops deployment system. Cyclops, a NASA-developed, dedicated 10-100 kg class ISS SmallSat deployment system, utilizes the Japanese airlock and robotic systems to seamlessly insert SmallSats into orbit. This paper will illustrate Cyclops\u27 successful deployment of Lonestar from the ISS as well as outline its concept of operations, interfaces, requirements, and processes

Nasal DNA methylation differentiates severe from non‐severe asthma in African‐American children

BackgroundAsthma is highly heterogeneous, and severity evaluation is key to asthma management. DNA methylation (DNAm) contributes to asthma pathogenesis. This study aimed to identify nasal epithelial DNAm differences between severe and nonsevere asthmatic children and evaluate the impact of environmental exposures.MethodsThirty-three nonsevere and 22 severe asthmatic African American children were included in an epigenome-wide association study. Genome-wide nasal epithelial DNAm and gene expression were measured. CpG sites associated with asthma severity and environmental exposures and predictive of severe asthma were identified. DNAm was correlated with gene expression. Enrichment for transcription factor (TF) binding sites or histone modifications surrounding DNAm differences were determined.ResultsWe identified 816 differentially methylated CpG positions (DMPs) and 10 differentially methylated regions (DMRs) associated with asthma severity. Three DMPs exhibited discriminatory ability for severe asthma. Intriguingly, six DMPs were simultaneously associated with asthma, allergic asthma, total IgE, environmental IgE, and FeNO in an independent cohort of children. Twenty-seven DMPs were associated with traffic-related air pollution or secondhand smoke. DNAm at 22 DMPs was altered by diesel particles or allergen in human bronchial epithelial cells. DNAm levels at 39 DMPs were correlated with mRNA expression. Proximal to 816 DMPs, three histone marks and several TFs involved in asthma pathogenesis were enriched.ConclusionsSignificant differences in nasal epithelial DNAm were observed between nonsevere and severe asthma in African American children, a subset of which may be useful to predict disease severity. These CpG sites are subjected to the influences of environmental exposures and may regulate gene expression