Design Status of the Capillary Brine Residual in Containment Water Recovery System

One of the goals of the AES Life Support System (LSS) Project is to achieve 98% water loop closure for long duration human exploration missions beyond low Earth orbit. To meet this objective, the AES LSS Project is developing technologies to recover water from wastewater brine; highly concentrated waste products generated from a primary water recovery system. The state of the art system used aboard the International Space Station (ISS) has the potential to recover up to 85% water from unine wastewater, leaving a significant amounts of water in the waste brine, the recovery of which is critical technology gap that must be filled in order to enable long duration human exploration. Recovering water from the urine wastewater brine is complicated by the concentration of solids as water is removed from the brine, and the concentration of the corrosive, toxic chemicals used to stabilize the urine which fouls and degrades water processing hardware, and poses a hazard to operators and crew. Brine Residual in Containment (BRIC) is focused on solids management through a process of "in-place" drying - the drying of brines within the container used for final disposal. Application of in-place drying has the potential to improve the safety and reliability of the system by reducing the exposure to curew and hardware to the problematic brine residual. Through a collaboration between the NASA Johnson Space Center and Portland Status University, a novel water recovery system was developed that utilizes containment geometry to support passive capillary flow and static phase separation allowing free surface evaporation to take place in a microgravity environment. A notional design for an ISS demonstration system was developed. This paper describes the testing performed to characterize the performance of the system as well as the status of the system level design

Dosing Regimen of Enrofloxacin Impacts Intestinal Pharmacokinetics and the Fecal Microbiota in Steers

Objective: The intestinal concentrations of antimicrobial drugs that select for resistance in fecal bacteria of cattle are poorly understood. Our objective was to associate active drug concentrations in the intestine of steers with changes in the resistance profile and composition of the fecal microbiome.Methods: Steers were administered either a single dose (12.5 mg/kg) or 3 multiple doses (5 mg/kg) of enrofloxacin subcutaneously every 24 h. Enrofloxacin and ciprofloxacin concentrations in intestinal fluid were measured over 96 h, and the abundance and MIC of E. coli in culture and the composition of the fecal microbiota by 16S rRNA gene sequencing were assessed over 192 h after initial treatment.Results: Active drug concentrations in the ileum and colon exceeded plasma and interstitial fluid concentrations, but were largely eliminated by 48 h after the last dose. The concentration of E. coli in the feces significantly decreased during peak drug concentrations, but returned to baseline by 96 h in both groups. The median MIC of E. coli isolates increased for 24 h in the single dose group, and for 48 h in the multiple dose group. The median MIC was higher in the multiple dose group when compared to the single dose group starting 12 h after the initial dose. The diversity of the fecal microbiota did not change in either treatment group, and taxa-specific changes were primarily seen in phyla commonly associated with the rumen.Conclusions: Both dosing regimens of enrofloxacin achieve high concentrations in the intestinal lumen, and the rapid elimination mitigates long-term impacts on fecal E. coli resistance and the microbiota

KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response.

Integrated, up-to-date data about SARS-CoV-2 and COVID-19 is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time-consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community vary drastically for different tasks; the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates heterogeneous biomedical data to produce knowledge graphs (KGs), and applied it to create a KG for COVID-19 response. This KG framework also can be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics

Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy.

MotivationMultiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.ResultsWe performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified.Availability and implementationDatasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.Supplementary informationSupplementary data are available at Bioinformatics online

Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy

Motivation Multiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia. Results We performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified. Availability and implementation Datasets and scripts for reproduction of results are available through: Https://nalab.stanford.edu/multiomics-pregnancy/

LSST: from Science Drivers to Reference Design and Anticipated Data Products

(Abridged) We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). A vast array of science will be enabled by a single wide-deep-fast sky survey, and LSST will have unique survey capability in the faint time domain. The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the Solar System, exploring the transient optical sky, and mapping the Milky Way. LSST will be a wide-field ground-based system sited at Cerro Pach\'{o}n in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg$^2$ field of view, and a 3.2 Gigapixel camera. The standard observing sequence will consist of pairs of 15-second exposures in a given field, with two such visits in each pointing in a given night. With these repeats, the LSST system is capable of imaging about 10,000 square degrees of sky in a single filter in three nights. The typical 5$\sigma$ point-source depth in a single visit in $r$ will be $\sim 24.5$ (AB). The project is in the construction phase and will begin regular survey operations by 2022. The survey area will be contained within 30,000 deg$^2$ with $\delta<+34.5^\circ$, and will be imaged multiple times in six bands, $ugrizy$, covering the wavelength range 320--1050 nm. About 90\% of the observing time will be devoted to a deep-wide-fast survey mode which will uniformly observe a 18,000 deg$^2$ region about 800 times (summed over all six bands) during the anticipated 10 years of operations, and yield a coadded map to $r\sim27.5$. The remaining 10\% of the observing time will be allocated to projects such as a Very Deep and Fast time domain survey. The goal is to make LSST data products, including a relational database of about 32 trillion observations of 40 billion objects, available to the public and scientists around the world.Comment: 57 pages, 32 color figures, version with high-resolution figures available from https://www.lsst.org/overvie

A Simple Standard for Sharing Ontological Mappings (SSSOM).

Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL: http://w3id.org/sssom/spec

Correlated Evolution of Nearby Residues in Drosophilid Proteins

Here we investigate the correlations between coding sequence substitutions as a function of their separation along the protein sequence. We consider both substitutions between the reference genomes of several Drosophilids as well as polymorphisms in a population sample of Zimbabwean Drosophila melanogaster. We find that amino acid substitutions are “clustered” along the protein sequence, that is, the frequency of additional substitutions is strongly enhanced within ≈10 residues of a first such substitution. No such clustering is observed for synonymous substitutions, supporting a “correlation length” associated with selection on proteins as the causative mechanism. Clustering is stronger between substitutions that arose in the same lineage than it is between substitutions that arose in different lineages. We consider several possible origins of clustering, concluding that epistasis (interactions between amino acids within a protein that affect function) and positional heterogeneity in the strength of purifying selection are primarily responsible. The role of epistasis is directly supported by the tendency of nearby substitutions that arose on the same lineage to preserve the total charge of the residues within the correlation length and by the preferential cosegregation of neighboring derived alleles in our population sample. We interpret the observed length scale of clustering as a statistical reflection of the functional locality (or modularity) of proteins: amino acids that are near each other on the protein backbone are more likely to contribute to, and collaborate toward, a common subfunction

Adaptive Value of Phenological Traits in Stressful Environments: Predictions Based on Seed Production and Laboratory Natural Selection

Phenological traits often show variation within and among natural populations of annual plants. Nevertheless, the adaptive value of post-anthesis traits is seldom tested. In this study, we estimated the adaptive values of pre- and post-anthesis traits in two stressful environments (water stress and interspecific competition), using the selfing annual species Arabidopsis thaliana. By estimating seed production and by performing laboratory natural selection (LNS), we assessed the strength and nature (directional, disruptive and stabilizing) of selection acting on phenological traits in A. thaliana under the two tested stress conditions, each with four intensities. Both the type of stress and its intensity affected the strength and nature of selection, as did genetic constraints among phenological traits. Under water stress, both experimental approaches demonstrated directional selection for a shorter life cycle, although bolting time imposes a genetic constraint on the length of the interval between bolting and anthesis. Under interspecific competition, results from the two experimental approaches showed discrepancies. Estimation of seed production predicted directional selection toward early pre-anthesis traits and long post-anthesis periods. In contrast, the LNS approach suggested neutrality for all phenological traits. This study opens questions on adaptation in complex natural environment where many selective pressures act simultaneously