NASA Advanced Explorations Systems: 2017 Advancements in Life Support Systems

The NASA Advanced Exploration Systems (AES) Life Support Systems (LSS) project strives to develop reliable, energy-efficient, and low-mass spacecraft systems to provide environmental control and life support systems (ECLSS) critical to enabling long duration human missions beyond low Earth orbit (LEO). Highly reliable, closed-loop life support systems are among the capabilities required for the longer duration human space exploration missions planned in the mid-2020s and beyond. The LSS Project is focused on four are-as-architecture and systems engineering for life support systems, environmental monitoring, air revitalization, and wastewater processing and water management. Starting with the International Space Station (ISS) LSS systems as a point of departure where applicable, the three-fold mission of the LSS Project is to address discrete LSS technology gaps, to improve the reliability of LSS systems, and to advance LSS systems toward integrated testing aboard the ISS. This paper is a follow on to the AES LSS development status reported in 2016 and provides additional details on the progress made since that paper was published with specific attention to the status of the Aerosol Sampler ISS Flight Experiment, the Spacecraft Atmosphere Monitor (SAM) Flight Experiment, the Brine Processor Assembly (BPA) Flight Experiment, the CO2 removal technology development tasks, and the work investigating the impacts of dormancy on LSS systems

NASA Advanced Explorations Systems: Advancements in Life Support Systems

The NASA Advanced Exploration Systems (AES) Life Support Systems (LSS) project strives to develop reliable, energy-efficient, and low-mass spacecraft systems to provide environmental control and life support systems (ECLSS) critical to enabling long duration human missions beyond low Earth orbit (LEO). Highly reliable, closed-loop life support systems are among the capabilities required for the longer duration human space exploration missions assessed by NASA's Habitability Architecture Team (HAT). The LSS project is focused on four areas: architecture and systems engineering for life support systems, environmental monitoring, air revitalization, and wastewater processing and water management. Starting with the international space station (ISS) LSS systems as a point of departure (where applicable), the mission of the LSS project is three-fold: 1. Address discrete LSS technology gaps 2. Improve the reliability of LSS systems 3. Advance LSS systems towards integrated testing on the ISS. This paper summarized the work being done in the four areas listed above to meet these objectives. Details will be given on the following focus areas: Systems Engineering and Architecture- With so many complex systems comprising life support in space, it is important to understand the overall system requirements to define life support system architectures for different space mission classes, ensure that all the components integrate well together and verify that testing is as representative of destination environments as possible. Environmental Monitoring- In an enclosed spacecraft that is constantly operating complex machinery for its own basic functionality as well as science experiments and technology demonstrations, it's possible for the environment to become compromised. While current environmental monitors aboard the ISS will alert crew members and mission control if there is an emergency, long-duration environmental monitoring cannot be done in-orbit as current methodologies rely largely on sending environmental samples back to Earth. The LSS project is developing onboard analysis capabilities that will replace the need to return air and water samples from space for ground analysis. Air Revitalization- The air revitalization task is comprised of work in carbon dioxide removal, oxygen generation and recovery and trace contamination and particulate control. The CO2 Removal and associated air drying development efforts under the LSS project are focused both on improving the current SOA technology on the ISS and assessing and examining the viability of other sorbents and technologies available in academia and industry. The Oxygen Generation and Recovery technology development area encompasses several sub-tasks in an effort to supply O2 to the crew at the required conditions, to recover O2 from metabolic CO2, and to recycle recovered O2 back to the cabin environment. Current state-of-the-art oxygen generation systems aboard space station are capable of generating or recovering approximately 40% of required oxygen; for exploration missions this percentage needs to be greatly increased. A spacecraft cabin trace contaminant and particulate control system serves to keep the environment below the spacecraft maximum allowable concentration (SMAC) for chemicals and particulates. Both passive (filters) and active (scrubbers) methods contribute to the overall TC & PC design. Work in the area of trace contamination and particulate control under the LSS project is focused on making improvements to the SOA TC & PC systems on ISS to improve performance and reduce consumables. Wastewater Processing and Water Management- A major goal of the LSS project is the development of water recovery systems to support long duration human exploration beyond LEO. Current space station wastewater processing and water management systems distill urine and wastewater to recover water from urine and humidity condensate in the spacecraft at a approximately 74% recovery rate. For longer, farther missions into deep space, that recovery rate must be greatly increased so that astronauts can journey for months without resupply cargo ships from Earth

NASA Advanced Explorations Systems: 2018 Advancements in Life Support Systems

The NASA Advanced Exploration Systems (AES) Life Support Systems (LSS) project strives to develop reliable, energy-efficient, and low-mass spacecraft systems to provide envi-ronmental control and life support systems (ECLSS) critical to enabling long duration human missions beyond low Earth orbit (LEO). Highly reliable, closed-loop life support systems are among the capabilities required for the longer duration human space exploration missions planned in the mid-2020s and beyond. The LSS Project is focused on three life support areas: air revitalization, wastewater processing/water management and environmental monitoring. Building upon the International Space Station (ISS) LSS systems (where applicable), the three-fold mission of the LSS Project is to address discrete LSS technology gaps, to improve the reliability of LSS systems, and to advance LSS systems toward integrated testing aboard the ISS. This paper is a follow on to the AES LSS development status reported in 2017 and provides additional details on the progress made since that publication with specific attention to the status of the Aerosol Sampler ISS Flight Experiment, the Spacecraft Atmosphere Monitor (SAM) Flight Experiment, the Brine Processor Assembly (BPA) Flight Experiment as well as the progress of the terrestrial development in air, water and environmental monitoring technologies

A support vector machine based method to distinguish long non-coding RNAs from protein coding transcripts

Background: In recent years, a rapidly increasing number of RNA transcripts has been generated by thousands of sequencing projects around the world, creating enormous volumes of transcript data to be analyzed. An important problem to be addressed when analyzing this data is distinguishing between long non-coding RNAs (lncRNAs) and protein coding transcripts (PCTs). Thus, we present a Support Vector Machine (SVM) based method to distinguish lncRNAs from PCTs, using features based on frequencies of nucleotide patterns and ORF lengths, in transcripts. Methods: The proposed method is based on SVM and uses the first ORF relative length and frequencies of nucleotide patterns selected by PCA as features. FASTA files were used as input to calculate all possible features. These features were divided in two sets: (i) 336 frequencies of nucleotide patterns; and (ii) 4 features derived from ORFs. PCA were applied to the first set to identify 6 groups of frequencies that could most contribute to the distinction. Twenty-four experiments using the 6 groups from the first set and the features from the second set where built to create the best model to distinguish lncRNAs from PCTs. Results: This method was trained and tested with human (Homo sapiens), mouse (Mus musculus) and zebrafish (Danio rerio) data, achieving 98.21%, 98.03% and 96.09%, accuracy, respectively. Our method was compared to other tools available in the literature (CPAT, CPC, iSeeRNA, lncRNApred, lncRScan-SVM and FEELnc), and showed an improvement in accuracy by ≈ 3.00%. In addition, to validate our model, the mouse data was classified with the human model, and vice-versa, achieving ≈ 97.80% accuracy in both cases, showing that the model is not overfit. The SVM models were validated with data from rat (Rattus norvegicus), pig (Sus scrofa) and fruit fly (Drosophila melanogaster), and obtained more than 84.00% accuracy in all these organisms. Our results also showed that 81.2% of human pseudogenes and 91.7% of mouse pseudogenes were classified as non-coding. Moreover, our method was capable of re-annotating two uncharacterized sequences of Swiss-Prot database with high probability of being lncRNAs. Finally, in order to use the method to annotate transcripts derived from RNA-seq, previously identified lncRNAs of human, gorilla (Gorilla gorilla) and rhesus macaque (Macaca mulatta) were analyzed, having successfully classified 98.62%, 80.8% and 91.9%, respectively. Conclusions: The SVM method proposed in this work presents high performance to distinguish lncRNAs from PCTs, as shown in the results. To build the model, besides using features known in the literature regarding ORFs, we used PCA to identify features among nucleotide pattern frequencies that contribute the most in distinguishing lncRNAs from PCTs, in reference data sets. Interestingly, models created with two evolutionary distant species could distinguish lncRNAs of even more distant species

Identification of the mitochondrial receptor complex in Saccharomyces cerevisiae

Mitochondrial protein import involves the recognition of preproteins by receptors and their subsequent translocation across the outer membrane. In Neurospora crassa, the two import receptors, MOM19 and MOM72, were found in a complex with the general insertion protein, GIP (formed by MOM7, MOM8, MOM30 and MOM38) and MOM22. We isolated a complex out of S. cerevisiae mitochondria consisting of MOM38/ISP42, the receptor MOM72, and five new yeast proteins, the putative equivalents of N. crassa MOM7, MOM8, MOM19, MOM22 and MOM30. A receptor complex isolated out of yeast cells transformed with N. crassa MOM19 contained the N. crassa master receptor in addition to the yeast proteins. This demonstrates that the yeast complex is functional, and provides strong evidence that we also have identified the yeast MOM19

Atomic carbon at redshift ~2.5

Using the IRAM 30m telescope we detected the lower fine structure line of neutral carbon towards three high--redshift sources: IRAS FSC10214 (z=2.3), SMMJ14011+0252 (z=2.5) and H1413+117 (Cloverleaf quasar, z=2.5). SMMJ14011+0252 is the first high--redshift, non--AGN source in which CI has been detected. The CI(1-0) line from FSC10214 is almost an order of magnitude weaker than previously claimed, while our detection in the Cloverleaf is in good agreement with earlier observations. The CI(1-0) linewidths are similar to the CO widths, indicating that both lines trace similar regions of molecular gas on galactic scales. Derived CI masses for all three objects are of order few 10^7 solar masses and the implied CI(1-0)/CO(3-2) line luminosity ratio is about 0.2. This number is similar to values found in local galaxies. We derive a CI abundance of 5x10^{-5} which implies significant metal enrichment of the cold molecular gas at redshifts 2.5 (age of the universe 2.7 Gyr). We conclude that the physical properties of systems at large lookback times are similar to today's starburst/AGN environments.Comment: 4 pages, 2 figures; accepted by A&

Anaesthesia Monitoring by Recurrence Quantification Analysis of EEG Data

Appropriate monitoring of the depth of anaesthesia is crucial to prevent deleterious effects of insufficient anaesthesia on surgical patients. Since cardiovascular parameters and motor response testing may fail to display awareness during surgery, attempts are made to utilise alterations in brain activity as reliable markers of the anaesthetic state. Here we present a novel, promising approach for anaesthesia monitoring, basing on recurrence quantification analysis (RQA) of EEG recordings. This nonlinear time series analysis technique separates consciousness from unconsciousness during both remifentanil/sevoflurane and remifentanil/propofol anaesthesia with an overall prediction probability of more than 85%, when applied to spontaneous one-channel EEG activity in surgical patients

Discovery of a Fifth Image of the Large Separation Gravitationally Lensed Quasar SDSS J1004+4112

We report the discovery of a fifth image in the large separation lensed quasar system SDSS J1004+4112. A faint point source located 0.2'' from the center of the brightest galaxy in the lensing cluster is detected in images taken with the Advanced Camera for Surveys (ACS) and the Near Infrared Camera and Multi-Object Spectrometer (NICMOS) on the Hubble Space Telescope. The flux ratio between the point source and the brightest lensed component in the ACS image is similar to that in the NICMOS image. The location and brightness of the point source are consistent with lens model predictions for a lensed image. We therefore conclude that the point source is likely to be a fifth image of the source quasar. In addition, the NICMOS image reveals the lensed host galaxy of the source quasar, which can strongly constrain the structure of the lensing critical curves and thereby the mass distribution of the lensing cluster.Comment: 5 pages, 5 figures, accepted for publication in PAS