Search NASA⌕ Search

SEARCH · Search NASA

Results for “Translation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Cooperative Research and Development Agreement With Georgetown University Report: National Institutes of Health, National Center for Advancing Translational Sciences Clinical and Translational Science Award

Oak Ridge National Laboratory (ORNL) is participating with Georgetown University (GU) as a subrecipient in response to the National Institutes of Health (NIH), National Center for Advancing Translational Sciences (NCATS) Clinical and Translational Science Award (CTSA) (U54 Clinical Trial Optional) funding opportunity announcement. This Cooperative Research and Development Agreement is put in to place to facilitate the development and implementation of clinical interventions that demonstrably improve human health is currently a complex, recursive, and inefficient process that leads to delays of years or decades before discoveries in biomedical research result in health benefits for patients and communities. NCATS conducts and supports research in the science of translation, to discover the mechanistic and operational principles of the intervention development and dissemination process, thereby providing the scientific foundation for improvements in translational efficiency that will accelerate the realization of interventions that improve human health. Under NCATS’ leadership, the CTSA Program supports a national network of medical research institutions called hubs. GU is the lead institution in one of the NIH hubs that was created as a result of a previous NIH CTSA. The missions of the GU have historically included the advancement of health through research in the clinical and biomedical sciences, the education of future leaders in medical and nursing practice and academia, and the provision of compassionate and scientifically competent patient care and service to the Washington, DC community and the nation. GU is the lead institution for the Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS), a multi-institutional partnership of medical research institutions forged from a desire to promote clinical research and translational science. Through multiple collaborations among these institutions, GHUCCTS is transforming clinical research and translational science in order to bring new scientific advances to health care. Oak Ridge National Laboratory is the Department of Energy's (DOE) largest science and energy laboratory. Managed since April 2000 by a partnership of the University of Tennessee and Battelle, ORNL was established in 1943 as a part of the secret Manhattan Project to pioneer a method for producing and separating plutonium. During the 1950s and 1960s, ORNL became an international center for the study of nuclear energy and related research in the physical and life sciences. With the creation of DOE in the 1970s, ORNL's mission broadened to include a variety of energy technologies and strategies. Today the laboratory supports the nation with a peacetime science and technology mission that is just as important as, but very different from, its role during the Manhattan Project. ORNL is home to the world's premier center for high performance supercomputing to enable scientific discovery. ORNL has extensive expertise in various areas of computer science that are uniquely situated to support GU. Additionally, ORNL’s leading computational user facilities present a unique opportunity to leverage the largest scale machines for open science in support of the stated mission of the NCATS CTSA. ORNL's partnership with GU will offer unparalleled opportunity in data analytics, deep-learning, artificial intelligence, and urban dynamics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

CRISPR-RNAa: targeted activation of translation using dCas13 fusions to translation initiation factors

Abstract Tools for synthetically controlling gene expression are a cornerstone of genetic engineering. CRISPRi and CRISPRa technologies have been applied extensively for programmable modulation of gene transcription, but there are few such tools for targeted modulation of protein translation rates. Here, we employ CRISPR-Cas13 as a programmable activator of translation. We develop a novel variant of the catalytically-deactivated Cas13d enzyme dCasRx by fusing it to translation initiation factor IF3. We demonstrate dCasRx-IF3’s ability to enhance expression 21.3-fold above dCasRx when both are targeted to the start of the 5′ untranslated region of mRNA encoding red fluorescent protein in Escherichia coli. Activation of translation is location-dependent, and we show dCasRx-IF3 represses translation when targeted to the ribosomal binding site, rather than enhancing it. We provide evidence that dCasRx-IF3 targeting enhances mRNA stability relative to dCasRx, providing mechanistic insights into how this new tool functions to enhance gene expression. We also demonstrate targeted upregulation of native LacZ 2.6-fold, showing dCasRx-IF3’s ability to enhance expression of endogenous genes. dCasRx-IF3 requires no additional host modification to influence gene expression. This work outlines a novel approach, CRISPR-RNAa, for post-transcriptional control of translation to activate gene expression.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Lost in translation: What we have learned from attributes that do not translate from Arabidopsis to other plants

Abstract Research in Arabidopsis thaliana has a powerful influence on our understanding of gene functions and pathways. However, not everything translates from Arabidopsis to crops and other plants. Here, a group of experts consider instances where translation has been lost and why such translation is not possible or is challenging. First, despite great efforts, floral dip transformation has not succeeded in other species outside Brassicaceae. Second, due to gene duplications and losses throughout evolution, it can be complex to establish which genes are orthologs of Arabidopsis genes. Third, during evolution Arabidopsis has lost arbuscular mycorrhizal symbiosis. Fourth, other plants have evolved specialized cell types that are not present in Arabidopsis. Fifth, similarly, C4 photosynthesis cannot be studied in Arabidopsis, which is a C3 plant. Sixth, many other plant species have larger genomes, which has given rise to innovations in transcriptional regulation that are not present in Arabidopsis. Seventh, phenotypes such as acclimation to water stress can be challenging to translate due to different measurement strategies. And eighth, while the circadian oscillator is conserved, there are important nuances in the roles of circadian regulators in crop plants. A key theme emerging across these vignettes is that even when translation is lost, insights can still be gained through comparison with Arabidopsis.

Biochemistry & Molecular Biology↗
DOE OSTI2020

Prediction of histone post-translational modifications using deep learning

Abstract Motivation Histone post-translational modifications (PTMs) are involved in a variety of essential regulatory processes in the cell, including transcription control. Recent studies have shown that histone PTMs can be accurately predicted from the knowledge of transcription factor binding or DNase hypersensitivity data. Similarly, it has been shown that one can predict PTMs from the underlying DNA primary sequence. Results In this study, we introduce a deep learning architecture called DeepPTM for predicting histone PTMs from transcription factor binding data and the primary DNA sequence. Extensive experimental results show that our deep learning model outperforms the prediction accuracy of the model proposed in Benveniste et al. (PNAS 2014) and DeepHistone (BMC Genomics 2019). The competitive advantage of our framework lies in the synergistic use of deep learning combined with an effective pre-processing step. Our classification framework has also enabled the discovery that the knowledge of a small subset of transcription factors (which are histone-PTM and cell-type-specific) can provide almost the same prediction accuracy that can be obtained using all the transcription factors data. Availabilityand implementation https://github.com/dDipankar/DeepPTM. Supplementary information Supplementary data are available at Bioinformatics online.

Baisya, Dipankar Ranjan (ORCID:0000000267847359)↗
DOE OSTI2022

Modeling District Heating and Cooling Systems with URBANopt, GeoJSON to Modelica Translator, and the Modelica Buildings Library

The URBANopt project has successfully leveraged OpenStudio/EnergyPlus to model buildings and electrical systems at an urban scale; however, URBANopt has lacked the ability to model district thermal energy systems until recently. This paper will present the modeling infrastructure that was developed specifically for the analysis of district heating and cooling systems, and how it is integrated into the existing URBANopt framework. The paper also discusses the development of new models added to the Modelica Buildings Library to model various district energy system components including loads, energy transfer stations (ETS), distribution networks, and central plants. The paper describes how different building loads can be modeled including time series, TEASER reduced-order models, or Spawn of EnergyPlus models. URBANopt District Energy Systems allows the user to switch between the various configurations.

district heating and cooling↗
DOE OSTI2022

Progress toward a universal biomedical data translator

Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based “Translator” system capable of integrating existing biomedical data sets and “translating” those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system’s architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

The Helix-Loop-Helix motif of human EIF3A regulates translation of proliferative cellular mRNAs

Improper regulation of translation initiation, a vital checkpoint of protein synthesis in the cell, has been linked to a number of cancers. Overexpression of protein subunits of eukaryotic translation initiation factor 3 (eIF3) is associated with increased translation of mRNAs involved in cell proliferation. In addition to playing a major role in general translation initiation by serving as a scaffold for the assembly of translation initiation complexes, eIF3 regulates translation of specific cellular mRNAs and viral RNAs. Mutations in the N-terminal Helix-Loop-Helix (HLH) RNA-binding motif of the EIF3A subunit interfere with Hepatitis C Virus Internal Ribosome Entry Site (IRES) mediated translation initiation in vitro . Here we show that the EIF3A HLH motif controls translation of a small set of cellular transcripts enriched in oncogenic mRNAs, including MYC . We demonstrate that the HLH motif of EIF3A acts specifically on the 5' UTR of MYC mRNA and modulates the function of EIF4A1 on select transcripts during translation initiation. In Ramos lymphoma cell lines, which are dependent on MYC overexpression, mutations in the HLH motif greatly reduce MYC expression, impede proliferation and sensitize cells to anti-cancer compounds. These results reveal the potential of the EIF3A HLH motif in eIF3 as a promising chemotherapeutic target.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Announcing the Biomedical Data Translator: Initial Public Release

ABSTRACT The growing availability of biomedical data offers vast potential to improve human health, but the complexity and lack of integration of these datasets often limit their utility. To address this, the Biomedical Data Translator Consortium has developed an open‐source knowledge graph–based system—Translator—designed to integrate, harmonize, and make inferences over diverse biomedical data sources. We announce here Translator's initial public release and provide an overview of its architecture, standards, user interface, and core features. Translator employs a scalable, federated, knowledge graph framework for the integration of clinical, genomic, pharmacological, and other biomedical knowledge sources, enabling query retrieval, inference, and hypothesis generation. Translator's user interface is designed to support the exploration of knowledge relationships and the generation of insights, without requiring deep technical expertise and gradually revealing more detailed evidence, provenance, and confidence information, as needed by a given user. To demonstrate Translator's application and impact, we highlight features of the user interface in the context of three real‐world use cases: suggesting potential therapeutics for patients with rare disease; explaining the mechanism of action of a pipeline drug; and screening and validating drug candidates in a model organism. We discuss strengths and limitations of reasoning within a largely federated system and the need for rich concept modeling and deep provenance tracking. Finally, we outline future directions for enhancing Translator's functionality and expanding its data sources. Translator represents a significant step forward in making complex biomedical knowledge more accessible and actionable, aiming to accelerate translational research and improve patient care.

Research & Experimental Medicine↗
DOE OSTI2025

Altering translation allows E. coli to overcome G-quadruplex stabilizers

G-quadruplex (G4) structures can form in guanine-rich DNA or RNA and have been found to modulate cellular processes, including replication, transcription, and translation. Many studies on the cellular roles of G4s have focused on eukaryotic systems, with far fewer probing bacterial G4s. Using a chemical-genetic approach, we identified genes in Escherichia coli that are important for growth in G4-stabilizing conditions. Reducing levels of translation elongation factor Tu or slowing translation initiation or elongation with kasugamycin, chloramphenicol, or spectinomycin suppress the effects of G4-stabilizing compounds. In contrast, reducing the expression of specific translation termination or ribosome recycling proteins is detrimental to growth in G4-stabilizing conditions. Proteomic and transcriptomic analyses reveal decreased protein and transcript levels, respectively, for ribosome assembly factors and proteins associated with translation in the presence of G4 stabilizer. Our results support a model in which reducing the rate of translation by altering translation initiation, translation elongation, or ribosome assembly can compensate for G4-related stress in E. coli.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

GMT: A deep learning approach to generalized multivariate translation for scientific data analysis and visualization

In scientific visualization, despite the significant advances of deep learning for data generation, researchers have not thoroughly investigated the issue of data translation. We present a new deep learning approach called generalized multivariate translation (GMT) for multivariate time-varying data analysis and visualization. Like V2V, GMT assumes a preprocessing step that selects suitable variables for translation. However, unlike V2V, which only handles one-to-one variable translation during training and inference, GMT enables one-to-many and many-to-many variable translation in the same framework. We leverage the recent StarGAN design from multi-domain image-to-image translation to achieve this generalization capability. We experiment with different loss functions and injection strategies to explore the best choices and leverage pre-training for performance improvement. We compare GMT with other state-of-the-art methods (i.e., Pix2Pix, V2V, StarGAN). Furthermore, the results demonstrate the overall advantage of GMT in translation quality and generalization ability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Unpaired image translation to mitigate domain shift in liquid argon time projection chamber detector responses

Deep learning algorithms often are developed and trained on a training dataset and deployed on test datasets. Any systematic difference between the training and a test dataset may severely degrade the final algorithm performance on the test dataset—what is known as the domain shift problem . This issue is prevalent in many scientific domains where algorithms are trained on simulated data but applied to real-world datasets. Typically, the domain shift problem is solved through various domain adaptation (DA) methods. However, these methods are often tailored for a specific downstream task, such as classification or semantic segmentation, and may not easily generalize to different tasks. This work explores the feasibility of using an alternative way to solve the domain shift problem that is not specific to any downstream algorithm. The proposed approach relies on modern Unpaired Image-to-Image (UI2I) translation techniques, designed to find translations between different image domains in a fully unsupervised fashion. In this study, the approach is applied to a domain shift problem commonly encountered in Liquid Argon Time Projection Chamber (LArTPC) detector research when seeking a way to translate samples between two differently distributed LArTPC detector datasets deterministically. This translation allows for mapping real-world data into the simulated data domain where the downstream algorithms can be run with much less domain-shift-related performance degradation. Conversely, using the translation from the simulated data to a real-world domain can increase the realism of the simulated dataset and reduce the magnitude of any systematic uncertainties. To evaluate the quality of the translations, we use both pixel-wise metrics and a downstream task to measure the effectiveness of UI2I methods for mitigating the domain shift problem. We adapted several popular UI2I translation algorithms to work on scientific data and demonstrated the viability of these techniques for solving the domain shift problem with LArTPC detector data. To facilitate further development of DA techniques for scientific datasets, the ‘Simple Liquid-Argon Track Samples’ dataset used in this study is also published.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

The HIGH CHLOROPHYLL FLUORESCENCE 244 homolog CrHCF244 is required for psbA (D1) translation in Chlamydomonas reinhardtii

Translation of psbA, the chloroplast gene that encodes the D1 subunit of PSII, is important for both PSII biogenesis and repair. The translation of psbA transcripts in the chloroplast is under the control of nuclear gene products. Using a forward genetic screen and whole-genome sequencing of the alga Chlamydomonas reinhardtii , we found a mutant defective in PSII activity and mapped the causative gene to be the homolog of Arabidopsis HIGH CHLOROPHYLL FLUORESCENCE 244 (HCF244) , namely CrHCF244 . We then demonstrated that CrHCF244 is required for psbA translation in the alga, consistent with the function of HCF244 in Arabidopsis, and found that AtHCF244 also partially complemented the algal mutant. These results experimentally support the functional conservation of the homologs in green algae and land plants. Intriguingly, the CrHCF244 mutant also exhibited a relatively high rate of suppressor mutants, pointing to the presence of alternative factor(s)/pathway(s) for D1 translational control. The establishment of CrHCF244 as a psbA translation factor in C. reinhardti i shows the similarities in psbA translation regulation in algae and plants. The future identification of the alternative factor(s) in this alga will provide insights on psbA translation in plants.

Arabidopsis↗
DOE OSTI2025

Effect of nozzle translation on a bounded vortex flow

A bounded vortex flow is generated by a nozzle that combines azimuthally tilted downward jets in a circular array and a central suction port. When the nozzle is directed toward a bottom surface (called the impingement surface), the flow develops a strong intake vortex spanning from the impingement surface to the suction port, which acts to generate strong shear stress on the impingement surface. This strong shear stress can aid in removing particles attached to the impingement surface or in enhancement of heat or mass transfer, where in typical applications, the nozzle would be translated along the impingement surface. This paper reports on a computational study of the effect of nozzle translation on the bounded vortex flow field. The nozzle translation induces a bending of the bounded vortex in the downstream flow direction, which becomes larger with increase in the nozzle translation speed. We identify an interesting oscillatory phenomenon by which the bounded vortex can resist the bending induced by the translational flow and rotate about a mean position downstream of the suction port. At sufficiently high translation speeds, the oscillatory process can no longer resist the downwash effect from nozzle translation and the vortex eventually detaches from the impingement surface, limiting the effectiveness of this flow device.

42 ENGINEERING↗
DOE OSTI2023

Dual incorporation of non-canonical amino acids enables production of post-translationally modified selenoproteins

Post-translational modifications (PTMs) can occur on almost all amino acids in eukaryotes as a key mechanism for regulating protein function. The ability to study the role of these modifications in various biological processes requires techniques to modify proteins site-specifically. One strategy for this is genetic code expansion (GCE) in bacteria. The low frequency of post-translational modifications in bacteria makes it a preferred host to study whether the presence of a post-translational modification influences a protein’s function. Genetic code expansion employs orthogonal translation systems engineered to incorporate a modified amino acid at a designated protein position. Selenoproteins, proteins containing selenocysteine, are also known to be post-translationally modified. Selenoproteins have essential roles in oxidative stress, immune response, cell maintenance, and skeletal muscle regeneration. Their complicated biosynthesis mechanism has been a hurdle in our understanding of selenoprotein functions. As technologies for selenocysteine insertion have recently improved, we wanted to create a genetic system that would allow the study of post-translational modifications in selenoproteins. By combining genetic code expansion techniques and selenocysteine insertion technologies, we were able to recode stop codons for insertion of N ε -acetyl- l -lysine and selenocysteine, respectively, into multiple proteins. The specificity of these amino acids for their assigned position and the simplicity of reverting the modified amino acid via mutagenesis of the codon sequence demonstrates the capacity of this method to study selenoproteins and the role of their post-translational modifications. Moreover, the evidence that Sec insertion technology can be combined with genetic code expansion tools further expands the chemical biology applications.

59 BASIC BIOLOGICAL SCIENCES↗