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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Mercat

Mercat is a highly scalable property software package for robust of analysis of features in next-generation sequencing data and observed unique peptides from metaproteomic data. Mercat is offered in python 3.5/anaconda3, is multiparellel and is easily installed using bioconda/conda recipes. Mercat inputs include assembled contigs, raw sequence reads from any platform, and unique peptide files obtained from proteomics with feature abundance counts tables. Mercat is the only software available that allows for direct analysis of data properties without a data-dependent search tools such as BLAST or diamond for compositional analysis of whole community shotgun sequencing (e.g. metagenomes and/or metatranscriptomes) or metaproteomic data.

Source record↗
DOE OSTI2017

Mercat

Mercat is a highly scalable property software package for robust of analysis of features in next-generation sequencing data and observed unique peptides from metaproteomic data. Mercat is offered in python 3.5/anaconda3, is multiparellel and is easily installed using bioconda/conda recipes. Mercat inputs include assembled contigs, raw sequence reads from any platform, and unique peptide files obtained from proteomics with feature abundance counts tables. Mercat is the only software available that allows for direct analysis of data properties without a data-dependent search tools such as BLAST or diamond for compositional analysis of whole community shotgun sequencing (e.g. metagenomes and/or metatranscriptomes) or metaproteomic data.

Source record↗
DOE OSTI2023

Crystal structures of ‘ALternative Isoinformational ENgineered’ DNA in B-form

The first structural model of duplex DNA reported in 1953 by Watson & Crick presented the double helix in B-form, the form that genomic DNA exists in much of the time. Thus, artificial DNA seeking to mimic the properties of natural DNA should also be able to adopt B-form. Using a host–guest system in which Moloney murine leukemia virus reverse transcriptase serves as the host and DNA as the guests, we determined high-resolution crystal structures of three complexes including 5'-CTTBPPBBSSZZSAAG, 5'-CTTSSPBZPSZBBAAG and 5'-CTTZZPBSBSZPPAAG with 10 consecutive unnatural nucleobase pairs in B-form within self-complementary 16 bp duplex oligonucleotides. We refer to this ALternative Isoinformational ENgineered (ALIEN) genetic system containing two nucleobase pairs (P:Z, pairing 2-amino-imidazo-[1,2-a]-1,3,5-triazin-(8H)-4-one with 6-amino-5-nitro-(1H)-pyridin-2-one, and B:S, 6-amino-4-hydroxy-5-(1H)-purin-2-one with 3-methyl-6-amino-pyrimidin-2-one) as ALIEN DNA. We characterized both position- and sequence-specific helical, nucleobase pair and dinucleotide step parameters of P:Z and B:S pairs in the context of B-form DNA. We conclude that ALIEN DNA exhibits structural features that vary with sequence. Further, Z can participate in alternative stacking modes within a similar sequence context as captured in two different structures. This finding suggests that ALIEN DNA may have a larger repertoire of B-form structures than natural DNA.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Intensity of sample processing methods impacts wastewater SARS-CoV-2 whole genome amplicon sequencing outcomes

Wastewater SARS-CoV-2 surveillance has been deployed since the beginning of the COVID-19 pandemic to monitor the dynamics in virus burden in local communities. Genomic surveillance of SARS-CoV-2 in wastewater, particularly efforts aimed at whole genome sequencing for variant tracking and identification, are still challenging due to low target concentration, complex microbial and chemical background, and lack of robust nucleic acid recovery experimental procedures. The intrinsic sample limitations are inherent to wastewater and are thus unavoidable. Here, we use a statistical approach that couples correlation analyses to a random forest-based machine learning algorithm to evaluate potentially important factors associated with wastewater SARS-CoV-2 whole genome amplicon sequencing outcomes, with a specific focus on the breadth of genome coverage. We collected 182 composite and grab wastewater samples from the Chicago area between November 2020 to October 2021. Samples were processed using a mixture of processing methods reflecting different homogenization intensities (HA + Zymo beads, HA + glass beads, and Nanotrap), and were sequenced using one of the two library preparation kits (the Illumina COVIDseq kit and the QIAseq DIRECT kit). Technical factors evaluated using statistical and machine learning approaches include sample types, certain sample intrinsic features, and processing and sequencing methods. The results suggested that sample processing methods could be a predominant factor affecting sequencing outcomes, and library preparation kits was considered a minor factor. Finally, a synthetic SARS-CoV-2 RNA spike-in experiment was performed to validate the impact from processing methods and suggested that the intensity of the processing methods could lead to different RNA fragmentation

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Three-dimensional structure-guided evolution of a ribosome with tethered subunits

We report RNA-based macromolecular machines, such as the ribosome, have functional parts reliant on structural interactions spanning sequence-distant regions. These features limit evolutionary exploration of mutant libraries and confound three-dimensional structure-guided design. To address these challenges, we describe Evolink (evolution and linkage), a method that enables high-throughput evolution of sequence-distant regions in large macromolecular machines, and library design guided by computational RNA modeling to enable exploration of structurally stable designs. Using Evolink, we evolved a tethered ribosome with a 58% increased activity in orthogonal protein translation and a 97% improvement in doubling times in SQ171 cells compared to a previously developed tethered ribosome, and reveal new permissible sequences in a pair of ribosomal helices with previously explored biological function. The Evolink approach may enable enhanced engineering of macromolecular machines for new and improved functions for synthetic biology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Chemical synthesis rewriting of a bacterial genome to achieve design flexibility and biological functionality

Understanding how to program biological functions into artificial DNA sequences remains a key challenge in synthetic genomics. Here, we report the chemical synthesis and testing of Caulobacter ethensis-2.0 ( C. eth-2.0 ), a rewritten bacterial genome composed of the most fundamental functions of a bacterial cell. We rebuilt the essential genome of Caulobacter crescentus through the process of chemical synthesis rewriting and studied the genetic information content at the level of its essential genes. Within the 785,701-bp genome, we used sequence rewriting to reduce the number of encoded genetic features from 6,290 to 799. Overall, we introduced 133,313 base substitutions, resulting in the rewriting of 123,562 codons. We tested the biological functionality of the genome design in C. crescentus by transposon mutagenesis. Our analysis revealed that 432 essential genes of C. eth-2.0 , corresponding to 81.5% of the design, are equal in functionality to natural genes. These findings suggest that neither changing mRNA structure nor changing the codon context have significant influence on biological functionality of synthetic genomes. Discovery of 98 genes that lost their function identified essential genes with incorrect annotation, including a limited set of 27 genes where we uncovered noncoding control features embedded within protein-coding sequences. In sum, our results highlight the promise of chemical synthesis rewriting to decode fundamental genome functions and its utility toward the design of improved organisms for industrial purposes and health benefits.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Supplementary table, figures and DNA sequences of sorghum gene models SbiRTx430.01G455400 and SbiRTx.02G006600 that feature primers, gRNAs and indels created

In-context promoter bashing via genome editing is a route to identify and characterize critical regulatory regions that govern expression of genes of interest. The outcomes of in-context promoter bashing can be used to inform editing strategies to modulate the expression of selected gene models in a desired fashion. Here we employed in-context promoter bashing to characterize the proximal upstream regulatory regions of sorghum genes encoding phosphoenolpyruvate carboxykinase (Sb.PEPCK.BS, SbiTx430.01G455400) and alanine aminotransferase (SbiTx430.02G006600, SbAlaAT.BS), two proteins involved in the PCK C4 pathway. Characterized germinal edits within the targeted regions upstream of these two genes ranged in size from 138 bp up to 1790 bp. A 138 bp within the Sb.PEPCK.BS upstream region and a 1643 bp element within the Sb.AlaAT.BS upstream region were determined to be important for maintenance of transcription levels. No change in development or various physiological parameters was observed in characterized lineages carrying promoter edits. However, significant changes in seed reserves and a reduction in 100 seed weight were consistently observed, under both greenhouse and field environments, in plants carrying an edit in the promoter of Sb.PEPCK.BS gene were significantly reduced in transcript accumulation for this gene.

Quach, Truyen [Center for Plant Science Innovation↗
DOE OSTI2025

Role of Ribosomal Protein bS1 in Orthogonal mRNA Start Codon Selection

In many bacteria, the location of the mRNA start codon is determined by a short ribosome binding site sequence that base pairs with the 3'-end of 16S rRNA (rRNA) in the 30S subunit. Many groups have changed these short sequences, termed the Shine-Dalgarno (SD) sequence in the mRNA and the anti-Shine-Dalgarno (ASD) sequence in 16S rRNA, to create "orthogonal" ribosomes to enable the synthesis of orthogonal polymers in the presence of the endogenous translation machinery. However, orthogonal ribosomes are prone to SD-independent translation. Ribosomal protein bS1, which binds to the 30S ribosomal subunit, is thought to promote translation initiation by shuttling the mRNA to the ribosome. Thus, a better understanding of how the SD and bS1 contribute to start codon selection could help efforts to improve the orthogonality of ribosomes. Here, we engineered the Escherichia coli ribosome to prevent binding of bS1 to the 30S subunit and separate the activity of bS1 binding to the ribosome from the role of the mRNA SD sequence in start codon selection. We find that ribosomes lacking bS1 are slightly less active than wild-type ribosomes in vitro. Furthermore, orthogonal 30S subunits lacking bS1 do not have an improved orthogonality. Our findings suggest that mRNA features outside the SD sequence and independent of binding of bS1 to the ribosome likely contribute to start codon selection and the lack of orthogonality of present orthogonal ribosomes.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1994

(abstract) Synthesis of Speaker Facial Movements to Match Selected Speech Sequences

We are developing a system for synthesizing image sequences the simulate the facial motion of a speaker. To perform this synthesis, we are pursuing two major areas of effort. We are developing the necessary computer graphics technology to synthesize a realistic image sequence of a person speaking selected speech sequences. Next, we are developing a model that expresses the relation between spoken phonemes and face/mouth shape. A subject is video taped speaking an arbitrary text that contains expression of the full list of desired database phonemes. The subject is video taped from the front speaking normally, recording both audio and video detail simultaneously. Using the audio track, we identify the specific video frames on the tape relating to each spoken phoneme. From this range we digitize the video frame which represents the extreme of mouth motion/shape. Thus, we construct a database of images of face/mouth shape related to spoken phonemes. A selected audio speech sequence is recorded which is the basis for synthesizing a matching video sequence; the speaker need not be the same as used for constructing the database. The audio sequence is analyzed to determine the spoken phoneme sequence and the relative timing of the enunciation of those phonemes. Synthesizing an image sequence corresponding to the spoken phoneme sequence is accomplished using a graphics technique known as morphing. Image sequence keyframes necessary for this processing are based on the spoken phoneme sequence and timing. We have been successful in synthesizing the facial motion of a native English speaker for a small set of arbitrary speech segments. Our future work will focus on advancement of the face shape/phoneme model and independent control of facial features.

graphics audio digitizing morphing enunciation pho↗
DOE OSTI2017

Prospecting Biotechnologically-Relevant Monooxygenases from Cold Sediment Metagenomes: An In Silico Approach

The goal of this work was to identify sequences encoding monooxygenase biocatalysts with novel features by in silico mining an assembled metagenomic dataset of polar and subpolar marine sediments. The targeted enzyme sequences were Baeyer–Villiger and bacterial cytochrome P450 monooxygenases (CYP153). These enzymes have wide-ranging applications, from the synthesis of steroids, antibiotics, mycotoxins and pheromones to the synthesis of monomers for polymerization and anticancer precursors, due to their extraordinary enantio-, regio-, and chemo- selectivity that are valuable features for organic synthesis. Phylogenetic analyses were used to select the most divergent sequences affiliated to these enzyme families among the 264 putative monooxygenases recovered from the ~14 million protein-coding sequences in the assembled metagenome dataset. Three-dimensional structure modeling and docking analysis suggested features useful in biotechnological applications in five metagenomic sequences, such as wide substrate range, novel substrate specificity or regioselectivity. Further analysis revealed structural features associated with psychrophilic enzymes, such as broader substrate accessibility, larger catalytic pockets or low domain interactions, suggesting that they could be applied in biooxidations at room or low temperatures, saving costs inherent to energy consumption. This work allowed the identification of putative enzyme candidates with promising features from metagenomes, providing a suitable starting point for further developments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Sas20 is a highly flexible starch-binding protein in the Ruminococcus bromii cell-surface amylosome

Ruminococcus bromii is a keystone species in the human gut that has the rare ability to degrade dietary resistant starch (RS). This bacterium secretes a suite of starch-active proteins that work together within larger complexes called amylosomes that allow R. bromii to bind and degrade RS. Starch adherence system protein 20 (Sas20) is one of the more abundant proteins assembled within amylosomes, but little could be predicted about its molecular features based on amino acid sequence. Here, we performed a structure–function analysis of Sas20 and determined that it features two discrete starch-binding domains separated by a flexible linker. We show that Sas20 domain 1 contains an N-terminal β-sandwich followed by a cluster of α-helices, and the nonreducing end of maltooligosaccharides can be captured between these structural features. Furthermore, the crystal structure of a close homolog of Sas20 domain 2 revealed a unique bilobed starch-binding groove that targets the helical α1,4-linked glycan chains found in amorphous regions of amylopectin and crystalline regions of amylose. Affinity PAGE and isothermal titration calorimetry demonstrated that both domains bind maltoheptaose and soluble starch with relatively high affinity (K d ≤ 20 μM) but exhibit limited or no binding to cyclodextrins. Finally, small-angle X-ray scattering analysis of the individual and combined domains support that these structures are highly flexible, which may allow the protein to adopt conformations that enhance its starch-targeting efficiency. Taken together, we conclude that Sas20 binds distinct features within the starch granule, facilitating the ability of R. bromii to hydrolyze dietary RS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Physics-Informed Machine Learning Model for Ceramic Matrix Composite Creep

A physics-informed recurrent neural network (RNN) based surrogate model is developed to emulate the nonlinear, time-dependent constitutive behavior of ceramic matrix composites (CMCs) driven by matrix damage and constituent creep at the microscale. Physics-informed constraints are introduced into the surrogate model through regularization to ground the prediction in physics and improve its predictive capabilities. Training data is generated using the high-fidelity generalized method of cells (HFGMC) approach which calls appropriate creep and damage models for each of the constituents. This coupling permits simulating the nonlinear behavior of CMCs based on constituent response at the microscale along with microstructural features such as fiber and porosity volume fraction and fiber radius. The microscale repeating unit cell is loaded under creep fatigue conditions to replicate the material loading experienced in a turbine engine. Therefore, the RNN-based surrogate model is tasked with predicting, as a function of variable input stress sequence, temperature, and microstructural features, the resulting strain history response while satisfying physical constraints related to creep rate, isochoric inelastic deformation, and strain energy density. The trained surrogate model is shown to effectively match the strain history over quantified distributions of microstructural features and relevant loading regimes and temperatures. Neural network based surrogate models can offer efficient alternatives to running computationally intensive multiscale material models to simulate the nonlinear response of large structural models. Therefore, the presented work provides evidence towards the feasibility of developing, training, and running such models for CMCs with complex microstructures, nonlinear time-dependent material response, and under non-monotonic loading conditions.

ceramic matrix composites↗
DOE OSTI2019

Robust predictions of specialized metabolism genes through machine learning

Plant specialized metabolism (SM) enzymes produce lineage-specific metabolites with important ecological, evolutionary, and biotechnological implications. Using Arabidopsis thaliana as a model, we identified distinguishing characteristics of SM and GM (general metabolism, traditionally referred to as primary metabolism) genes through a detailed study of features including duplication pattern, sequence conservation, transcription, protein domain content, and gene network properties. Analysis of multiple sets of benchmark genes revealed that SM genes tend to be tandemly duplicated, coexpressed with their paralogs, narrowly expressed at lower levels, less conserved, and less well connected in gene networks relative to GM genes. Although the values of each of these features significantly differed between SM and GM genes, any single feature was ineffective at predicting SM from GM genes. Using machine learning methods to integrate all features, a prediction model was established with a true positive rate of 87% and a true negative rate of 71%. In addition, 86% of known SM genes not used to create the machine learning model were predicted. We also demonstrated that the model could be further improved when we distinguished between SM, GM, and junction genes responsible for reactions shared by SM and GM pathways, indicating that topological considerations may further improve the SM prediction model. Application of the prediction model led to the identification of 1,220 A. thaliana genes with previously unknown functions, each assigned a confidence measure called an SM score, providing a global estimate of SM gene content in a plant genome.

data integration↗
DOE OSTI2022

Poisson hurdle model-based method for clustering microbiome features

Abstract Motivation High-throughput sequencing technologies have greatly facilitated microbiome research and have generated a large volume of microbiome data with the potential to answer key questions regarding microbiome assembly, structure and function. Cluster analysis aims to group features that behave similarly across treatments, and such grouping helps to highlight the functional relationships among features and may provide biological insights into microbiome networks. However, clustering microbiome data are challenging due to the sparsity and high dimensionality. Results We propose a model-based clustering method based on Poisson hurdle models for sparse microbiome count data. We describe an expectation–maximization algorithm and a modified version using simulated annealing to conduct the cluster analysis. Moreover, we provide algorithms for initialization and choosing the number of clusters. Simulation results demonstrate that our proposed methods provide better clustering results than alternative methods under a variety of settings. We also apply the proposed method to a sorghum rhizosphere microbiome dataset that results in interesting biological findings. Availability and implementation R package is freely available for download at https://cran.r-project.org/package=PHclust. Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A Deep Learning Modeling Framework to Capture Mixing Patterns in Reactive-Transport Systems

Prediction and control of chemical mixing are vital for many scientific areas such as subsurface reactive transport, climate modeling, combustion, epidemiology, and pharmacology. Due to the complex nature of mixing in heterogeneous and anisotropic media, the mathematical models related to this phenomenon are not analytically tractable. Numerical simulations often provide a viable route to predict chemical mixing accurately. However, contemporary modeling approaches for mixing cannot utilize available spatial-temporal data to improve the accuracy of the future prediction and can be compute-intensive, especially when the spatial domain is large and for long-term temporal predictions. To address this knowledge gap, in this work we will present in this paper a deep learning (DL) modeling framework applied to predict the progress of chemical mixing under fast bimolecular reactions. This framework uses convolutional neural networks (CNN) for capturing spatial patterns and long short-term memory (LSTM) networks for forecasting temporal variations in mixing. By careful design of the framework—placement of non-negative constraint on the weights of the CNN and the selection of activation function, the framework ensures non-negativity of the chemical species at all spatial points and for all times. Our DL-based framework is fast, accurate, and requires minimal data for training. The time needed to obtain a forecast using the model is a fraction (≈ O(-6)) of the time needed to obtain the result using a high-fidelity simulation. To achieve an error of 10% (measured using the infinity norm) for capturing local-scale mixing features such as interfacial mixing, only 24% to 32% of the sequence data for model training is required. To achieve the same level of accuracy for capturing global-scale mixing features, the sequence data required for model training is 64% to 70% of the total spatial-temporal data. Hence, the proposed approach—a fast and accurate way to forecast long-time spatial-temporal mixing patterns in heterogeneous and anisotropic media—will be a valuable tool for modeling reactive-transport in a wide range of applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1978

Sequential High-Resolution Wind Profile Measurements

Tropospheric flow and lower stratospheric flow as measured by 94 sequences of high-resolution Jimsphere balloon data are presented and discussed. The 70 and 24 sequential series are presented for the Kennedy Space Center, Florida, and Point Mugu, California, areas, respectively. Supplemental data, consisting of the associative temperature profiles and surface and 200 mb synoptic maps, are also presented. The measurements are discussed relative to both the engineering and disciplinary areas. An initial subjective analysis of mesoscale features observed on some sequences is presented.

Johnson, D. L.↗
NASA NTRS1998

Growth dynamics and cytoskeleton organization during stem maturation and gravity-induced stem bending in Zea mays L

Characterization of gravitropic bending in the maize stem pulvinus, a tissue that functions specifically in gravity responses, demonstrates that the pulvinus is an ideal system for studying gravitropism. Gravistimulation during the second of three developmental phases of the pulvinus induces a gradient of cell elongation across the non-growing cells of the pulvinus, with the most elongation occurring on the lower side. This cell elongation is spatially and temporally separated from normal internodal cell elongation. The three characterized growth phases in the pulvinus correspond closely to a specialized developmental sequence in which structural features typical of cells not fully matured are retained while cell maturation occurs in surrounding internodal and nodal tissue. For example, the lignification of supporting tissue and rearrangement of transverse microtubules to oblique that occur in the internode when cell elongation ceases are delayed for up to 10 d in the adjacent cells of the pulvinus, and only occurs as a pulvinus loses its capacity to respond to gravistimulation. Gravistimulation does not modify this developmental sequence. Neither wall lignification nor rearrangement of transverse microtubules occurs in the rapidly elongating lower side or non-responsive upper side of the pulvinus until the pulvinus loses the capacity to bend further. Gravistimulation does, however, lead to the formation of putative pit fields within the expanding cells of the pulvinus.

Non-NASA Center↗