2020 NASA Technology Taxonomy

This document is an update (new photos used) of the PDF version of the 2020 NASA Technology Taxonomy that will be available to download on the OCT Public Website. The updated 2020 NASA Technology Taxonomy, or "technology dictionary", uses a technology discipline based approach that realigns like-technologies independent of their application within the NASA mission portfolio. This tool is meant to serve as a common technology discipline-based communication tool across the agency and with its partners in other government agencies, academia, industry, and across the world

Introducing deep learning -based methods into the variant calling analysis pipeline

Biological interpretation of the genetic variation enhances our understanding of normal and pathological phenotypes, and may lead to the development of new therapeutics. However, it is heavily dependent on the genomic data analysis, which might be inaccurate due to the various sequencing errors and inconsistencies caused by these errors. Modern analysis pipelines already utilize heuristic and statistical techniques, but the rate of falsely identified mutations remains high and variable, particular sequencing technology, settings and variant type. Recently, several tools based on deep neural networks have been published. The neural networks are supposed to find motifs in the data that were not previously seen. The performance of these novel tools is assessed in terms of precision and recall, as well as computational efficiency. Following the established best practices in both variant detection and benchmarking, the discussed tools demonstrate accuracy metrics and computational efficiency that spur further discussion

GCAT|Genomes for life: a prospective cohort study of the genomes of Catalonia

PURPOSE: The prevalence of chronic non-communicable diseases (NCDs) is increasing worldwide. NCDs are the leading cause of both morbidity and mortality, and it is estimated that by 2030, they will be responsible for 80% of deaths across the world. The Genomes for Life (GCAT) project is a long-term prospective cohort study that was designed to integrate and assess the role of epidemiological, genomic and epigenomic factors in the development of major chronic diseases in Catalonia, a north-east region of Spain. PARTICIPANTS: At the end of 2017, the GCAT Study will have recruited 20 000 participants aged 40-65 years. Participants who agreed to take part in the study completed a self-administered computer-driven questionnaire, and underwent blood pressure, cardiac frequency and anthropometry measurements. For each participant, blood plasma, blood serum and white blood cells are collected at baseline. The GCAT Study has access to the electronic health records of the Catalan Public Healthcare System. Participants will be followed biannually at least 20 years after recruitment. FINDINGS TO DATE: Among all GCAT participants, 59.2% are women and 83.3% of the cohort identified themselves as Caucasian/white. More than half of the participants have higher education levels, 72.2% are current workers and 42.1% are classified as overweight (body mass index ≥25 and <30 kg/m2). We have genotyped 5459 participants, of which 5000 have metabolome data. Further, the whole genome of 808 participants will be sequenced by the end of 2017. FUTURE PLANS: The first follow-up study started in December 2017 and will end by March 2018. Residences of all subjects will be geocoded during the following year. Several genomic analyses are ongoing, and metabolomic and genomic integrations will be performed to identify underlying genetic variants, as well as environmental factors that influence metabolites

Utilizing plant genetic resources for pre-breeding of water-efficient sorghum: genetics of the limited transpiration trait

Includes bibliographical references.2022 Fall.Shifting precipitation patterns driven by the changing climate threaten productivity of dryland agricultural systems. Increasing the efficiency of water use by crops grown in dryland regions, such as sorghum (Sorghum bicolor), is a target for plant breeding to address this issue. c variants conferring efficient water use in sorghum may be found within collections of plant genetic resources (PGR). However, tropical sorghum PGR require adaptation to the target temperate environment to begin the pre-breeding trait discovery process. The landmark Sorghum Conversion Program unlocked diverse sorghum genetics for temperate breeding by adapting tropical African lines to temperate height and maturity standards. In the U.S. Sorghum Belt, spanning South Dakota to central Texas, the limited transpiration (LT) trait could provide growers a 5% yield increase in water-limited conditions with high vapor pressure deficit (VPD) according to crop modeling. To transfer the LT trait into commercial breeding programs, an elite donor line must be developed. Characterizing the genetic architecture of LT informs markers and breeding strategy for development of an elite donor. To characterize the genetic architecture of LT, two biparental recombinant inbred line (RIL) mapping families were developed from crossing putative LT parents SC979 and BTx2752 by putative non-LT parent RTx430. For this study, the families were grown together as a mapping population in three locations (continental-humid eastern Kansas, semi-arid western Kansas, and semi-arid Colorado) in one year. The families were phenotyped for the LT trait using UAS- collected thermal imaging and canopy temperature as a proxy. The families were initially designed with the goal of controlling phenotypic covariates of canopy temperature associated with height and flowering time, like neighbor-shading and artifactual temperature inflation related to panicle imaging. To test whether the family design controlled for height and flowering time covariates, the populations were phenotyped for both traits. High broad-sense heritability (H2) > 0.86 for all traits and families across locations indicates that the traits are not fixed. However, phenotypic distributions reveal that most lines are within an agronomically-relevant range that limits confounding covariates. Using DArTseq-LD genotyping data, GWAS analyses of height and flowering time reveal putatively significant marker-trait associations (MTA) with known loci underlying height and maturity in sorghum. These results collectively indicate that, while genetic variation for height and flowering exist in the LT mapping families, the resulting phenotypes are homogeneous enough to be suitable for LT genetic mapping. To test hypotheses on the monogenic, oligogenic, or polygenic architecture of the LT trait, canopy temperature data collected by the UAS-thermal imaging missions was used. Non-zero H2 of canopy temperature in most location-timepoints indicates genetic variation is present for LT in the population. Continuous phenotypic distributions imply a quantitative architecture. GWAS analyses revealed moderate marker-trait association peaks visible within timepoints and across locations, indicating oligogenic architecture of LT. Some of those peaks also colocalize with sorghum homologs of aquaporin genes in Arabidopsis thaliana, suggesting that aquaporin variation could be a molecular basis underlying the trait. These results provide a basis for marker-assisted selection in developing an LT donor line

An Overview of the Use of Neural Networks for Data Mining Tasks

In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks

A Hybrid Multi-Robot Control Architecture

Multi-robot systems provide system redundancy and enhanced capability versus single robot systems. Implementations of these systems are varied, each with specific design approaches geared towards an application domain. Some traditional single robot control architectures have been expanded for multi-robot systems, but these expansions predominantly focus on the addition of communication capabilities. Both design approaches are application specific and limit the generalizability of the system. This work presents a redesign of a common single robot architecture in order to provide a more sophisticated multi-robot system. The single robot architecture chosen for application is the Three Layer Architecture (TLA). The primary strength of TLA is in the ability to perform both reactive and deliberative decision making, enabling the robot to be both sophisticated and perform well in stochastic environments. The redesign of this architecture includes incorporation of the Unified Behavior Framework (UBF) into the controller layer and an addition of a sequencer-like layer (called a Coordinator) to accommodate the multi-robot system. These combine to provide a robust, independent, and taskable individual architecture along with improved cooperation and collaboration capabilities, in turn reducing communication overhead versus many traditional approaches. This multi-robot systems architecture is demonstrated on the RoboCup Soccer Simulator showing its ability to perform well in a dynamic environment where communication constraints are high