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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Decision Framework for Classifying the State of Knowledge for Pharmaceutical Stability in Spaceflight

The currently approved medication formulary for exploration-class spaceflight missions presently exceeds 200pharmaceuticals. However, these medications' physical and chemical stability during long-term exposure to the spaceflight environment remains uncharacterized. A multi-disciplinary team of pharmacy and spaceflight subject matter experts (SMEs) collaborated to develop a decision framework that will prioritize medications for future stability research. This framework prioritizes the physical stability, drug quality, and safety of the active pharmaceutical ingredient of finished drug products selected for specific design reference mission (DRM) drug formularies. The United States Pharmacopeia (USP) defines drug stability as "the extent to which a drug product retains, within specified limits, and throughout its period of storage and use, the same properties and characteristics that it possessed at the time of its manufacture.1"The candidate medication is assessed by applying a sequence of drug agnostic questions to evaluate the limits of stability information pertinent to spaceflight. The answers to these questions ultimately lead to one of three characterizations: green (not prioritized for further research for this DRM), yellow (requires further literature review/additional studies), or red (not suitable for this DRM based on current knowledge). The decision framework will rely on several information resources to inform the ultimate decision, including the ExMC Pharmacy Information Database, published literature, FDA/drug manufacturer monographs, and USP. The results of this framework will be used to classify the state of knowledge for pharmaceutical stability for any specified DRM and guide future research efforts accordingly. This presentation will provide an overview of how this decision framework was developed, detail the challenges and limitations of the pathways, and discuss how researchers can apply the lessons learned from this project to future work

S Kurian↗
NASA NTRS2003

Analysis of Properties of the North Polar Layered Deposits: THEMIS Data in Context of MGS Data

One of the many questions of Martian exploration is to uncover the history of Mars, through analysis of the polar layered deposits (PLD). Martian polar ice caps hold most of the exposed water on the surface on Mars and yet their history and physical processed involved in their formation are unclear. We will attempt to contribute to our knowledge of the composition and stratigraphy of the polar deposits. In this work we present the latest imaging data acquired by the Mars Odyssey THermal EMission Imaging System (THEMIS) and place it into context of the Mars Global Surveyor (MGS) data. THEMIS provides capabilities for imaging in both thermal IR and visible color wavelengths. These observations are affected by atmospheric scattering and topography. The Mars Orbiter Laser Altimeter (MOLA) and Thermal Emission Spectrometer (TES) instruments on board of the MGS spacecraft can provide context information for THEMIS data. Of particular interest are Mars Orbiter Camera (MOC) images, which provide high resolution data. We are primarily interested in the seasonal evolution of ice cap temperatures during the first northern summer of THEMIS observations. Morphology, stratigraphy and composition of the layered deposits can be addressed by THEMIS VIS color images, along with MOC high resolution data and MOLA Digital Elevation Models (DEM). This work is intentionally descriptive. Based on the knowledge obtained by the orbiting spacecraft and described here, we will attempt to expose major directions for modeling and further understanding of of the physical processes involved in the formation of the polar layered terrain 2 Available data 2.1 THEMIS IR The THEMIS IR camera has 10 bands from 6 to 15 m. Due to to signal-to-noise restrictions the most useful band for polar observations is band 9 (12.57 m ). Band 10 (14.88 m ) data can be used for atmospheric calibration. An example of seasonal evolution observed by the THEMIS IR subsystem is shown in Figure 1. We have projected all IR images, covering a small area near 86N and 90E into a polar stereographic projection and then sampled time dimension in order to look at temperature evolution over the course of the summer. We are plotting averaged temperature data over two 1km2 regions of interest: layered material inside the trough and the surface of the residual ice cap. High resolution THEMIS IR data allows us to distinguish properties of bulk of layered terrain and ice. We were not able yet to distinguish properties of individual layers.

A B Ivanov↗
DOE OSTI2022

Employing Technology to Enable Remote Research Charrettes as a Method for Engaging Industry and Uncovering Best Practices: A Novel Approach for a Post-COVID-19 World

Methods to collect data in construction engineering and management (CEM) research are evolving, informed by recent technological advancements. One such method is research charrettes that allow effective interactions and knowledge sharing between expert industry practitioners and academic researchers, all colocated in a single venue, enabling rich data collection and live communication. A pivot point in technological evolution occurred with the COVID-19 pandemic, forcing a global shift to remote work. Hence, planned in-person research charrettes had to shift to remote sessions, relying on virtual conferencing platforms and online data collection mechanisms. Technology-enabled charrettes have allowed the authors to collect significantly richer data sets and ensure a more diverse representation of participants, while saving tremendous amounts of time. With the continuing emergence of technological applications, the world might not go back to functioning fully in person. The authors believe remote research charrettes (RRCs) will still be used in a post-COVID-19 world because of their superior performance. This paper builds on a previous publication that described traditional research charrettes as a method to enhance CEM research a decade ago; it offers a significantly updated and improved RRC method based on the knowledge gained from transitioning a dozen in-person charrettes into RRCs. It also presents performance comparisons between RRCs and traditional charrettes by quantifying metrics indicating how RRCs are more time-efficient and cost-saving, harness more participants from more diverse locations, and enable the collection of richer data sets and four times more industry comments and expert feedback. This paper also provides guidance on the integration of technology with traditional research charrettes, hence contributing to the CEM body of knowledge.

42 ENGINEERING↗
DOE OSTI2021

Deep Analysis Net with Causal Embedding for Coal-fired Power Plant Fault Detection and Diagnosis (DANCE4CFDD)

Fault detection and diagnosis is critical to power plant operation to ensure attaining high reliability while reducing operation cost. As more renewable power is introduced to the power grid, traditional fossil power plants take on the extra burden of excessive load cycling to compensate the generation variability from renewable power. Such load cycling will pose more reliability challenges to power plant operation. There are a number of challenges faced by today’s asset health management system in coal- fired (or gas) power plants: 1) high-dimensional nonlinear interaction among multiple time series measurements; 2) high measurement variance induced by operational conditions/modes; 3) variation among asset types and plant configurations; and 4) a small number of faulty events to learn from. To cope with these challenges, today’s fielded asset health management systems rely heavily on manual efforts from domain experts and hand-crafted features or rules based on domain knowledge. Despite its role in plant reliability, such a practice is costly and hinders its scalability and sustainability, particularly when a plant undergoes modifications. The objective of this project is to develop a novel end-to-end AI learning system that is trainable (i.e., the AI representation of a complex system behavior can be directly learned from properly labeled data) for accurate fault detection and root cause analysis. The ability to create a fault detection model directly from time series could alleviate the efforts associated with today’s asset management solution development. In the course of this project, we have achieved the following: Created an AI model development environment incorporating state-of-the-art neural network architectures for rapid model development and evaluation; Developed novel learning strategies for training of fault detection model; Developed special-purpose neural network architecture embedded with variable association graph aiming for better interpretability; Developed a learning strategy to leverage a small number of faulty events for enhanced fault detection capability; Conducted detailed experimental study based on public benchmark datasets and demonstrated the effectiveness of the proposed solution; and Validated the developed system with data from both a coal-fired plant boiler dynamic simulation model and real-world coal-fired power plant covering multiple asset and fault types. Overall, the project attained a technology readiness level of TRL 5 from TRL 2 at the beginning of the project.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2026

Validated Reactive Force Field Quantifies MXene Interfacial Properties, Mechanics, and Thermal Transport

MXenes combine rich surface chemistry, mechanical strength, and high conductivity for a multitude of emerging applications. Predictive modeling supports accelerated materials designs and has been limited by the absence of validated and transferable force fields. Here, we introduce an interpretable, reactive INTERFACE force field (IFF and IFF-R) for Ti 3 C 2 T x MXenes that is trained based on chemical knowledge and achieves quantitative agreement with experiments across lattice parameters (<0.5%), density (<0.2%), liquid contact angles, Raman spectra, and the in-plane elastic modulus (∼320 GPa). The models cover surface terminations from hydroxyl (−OH) to fluorine (−F) groups and are extensible to other chemistries. We introduce pH-resolved surface chemistry and identify dopamine adsorption mechanisms at MXene–aqueous interfaces supported by QCM-D and UV–Vis experiments. The data reveal coplanar and perpendicular binding modes and concentration-dependent multilayer assembly. We predict previously inaccessible properties, including termination-dependent cleavage energies, interlayer shear moduli and dynamic shear failure, nanoindentation and brittle fracture, anisotropic in-plane and out-of-plane thermal conductivities, including the role of defects. Agreement with available experimental data is consistently close and exceeds DFT accuracy across the benchmark properties examined. The IFF/IFF-R model is compatible with CHARMM, AMBER, OPLS, and CVFF force fields for simulations of MXenes with diverse surface terminations, electrolyte interfaces, biointerfaces, and polymer composites without additional parameters. Parameter sets, 3D models, and analysis scripts are provided for community use. The validated, reactive, and transferable IFF framework facilitates predictive design of MXene-based films, membranes, sensing interfaces, and composites.

MXene↗
NASA NTRS1988

Portable inference engine: An extended CLIPS for real-time production systems

The present C-Language Integrated Production System (CLIPS) architecture has not been optimized to deal with the constraints of real-time production systems. Matching in CLIPS is based on the Rete Net algorithm, whose assumption of working memory stability might fail to be satisfied in a system subject to real-time dataflow. Further, the CLIPS forward-chaining control mechanism with a predefined conflict resultion strategy may not effectively focus the system's attention on situation-dependent current priorties, or appropriately address different kinds of knowledge which might appear in a given application. Portable Inference Engine (PIE) is a production system architecture based on CLIPS which attempts to create a more general tool while addressing the problems of real-time expert systems. Features of the PIE design include a modular knowledge base, a modified Rete Net algorithm, a bi-directional control strategy, and multiple user-defined conflict resolution strategies. Problems associated with real-time applications are analyzed and an explanation is given for how the PIE architecture addresses these problems.

Le, Thach↗
DOE OSTI2025

Temporal Galactose‐Manganese Feeding in Fed‐Batch and Perfusion Bioreactors Modulates UDP‐Galactose Pools for Enhanced mAb Glycosylation Homogeneity

ABSTRACT Monoclonal antibodies (mAbs) represent a majority of biotherapeutics in the market today. These glycoproteins undergo posttranslational modifications, such as N‐linked glycosylation, that influence the structural & functional characteristics of the antibody. Glycosylation is a heterogenous posttranslational modification that may influence therapeutic glycoprotein stability and clinical efficacy, which is why it is often considered a critical quality attribute (CQA) of the mAb product. While much is known about the glycosylation pathways of Chinese Hamster Ovary (CHO) cells and how cell culture chemical modifiers may influence the N‐glycosylation profile of the final product, this knowledge is often based on the final cumulative glycan profile at the end of the batch process. Building a temporal understanding of N‐glycosylation and how mAb glycoform composition responds to real‐time changes in the biomanufacturing process will help build integrated process models that may allow for glycosylation control to produce a more homogenous product. Here, we look at the effect of specific nutrient feed media additives (e.g., galactose, manganese) and feeding times on the N‐glycosylation pathway to modulate N‐glycosylation of a Herceptin biosimilar mAb (i.e., Trastuzumab). We deploy the N‐GLYcanyzer process analytical technology (PAT) to monitor glycoforms in near real‐time for bench‐scale bioprocesses operated in both fed‐batch and perfusion modes to build an understanding of how temporal changes in mAb N‐glycosylation are dependent on specific media additives. We find that Trastuzumab terminal galactosylation is sensitive to media feeding times and intracellular nucleotide sugar pools. Temporal analysis reveals an increased desirable production of single and double galactose‐occupied glycoforms over time under glucose‐starved fed‐batch cultures. Comparable galactosylation profiles were also observed between fed‐batch (nutrient‐limited) and perfusion (non‐nutrient‐limited) bioprocess conditions. In summary, our results demonstrate the utility of real‐time monitoring of mAb glycoforms and feeding critical cell culture nutrients under fed‐batch and perfusion bioprocessing conditions to produce higher‐quality biologics.

Biotechnology & Applied Microbiology↗
DOE OSTI2020

Implications from Monitoring Gopher Tortoises at Two Spatial Scales

A problem that conservation biologists face is how to monitor species, given both resource limitations and the inherent challenges of assessing long-term demographic processes. We assessed gopher tortoise (Gopherus polyphemus) abundance at a landscape scale and at the scale of 3 local populations within the Conecuh National Forest (CNF).USA between 1991 and 2017. Landscape-level data were collected 26 from line transect distance sampling arranged uniformly across the CNF and collected during a single season (2011); data for local populations were generated from long-term mark-recapture of individuals at three sites selected based on prior knowledge of high density at each. At a landscape scale, we estimated 5,242 (95% CI = 3,538-7,768) tortoises occurred across the approximately 34,000-ha forest, yielding a density of 0.14-0.32 tortoises/ ha. These low densities across the landscape suggest that, on average, management activities across the property have not allowed tortoise populations to retain social structure needed for long-term persistence. The three local populations, however, contained 25-60 individuals and densities of 1.9–6.9 tortoises/ha. Over the study period, populations at two sites were stable and the third experienced significant population growth. Mean annual survival of individuals was 0.89 and invariant across size classes. Altogether, line transect distance sampling is important for assessing landscape-scale abundance of tortoises but may fail to detect local clusters of high-density sites important for population persistence. Our mark-recapture efforts at the local scale revealed that small populations on these high-density sites can exhibit long-term stability or growth even though they do not meet current established criteria for viability. Improved models that incorporate immigration and emigration and better reflect dynamics of peripheral populations would assist in determining how such populations best contribute to species recovery and regional conservation targets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Microstructural Evolution of Tantalum During Deformation and Subsequent Annealing

Microstructure-aware models are necessary to predict the behavior of material based on process knowledge or to extrapolate mechanical properties of materials to environmental conditions which are not easily reproduced in the laboratory, e.g., nuclear reactor environments. Elemental Ta provides a relatively simple BCC system in which to develop a microstructural understanding of deformation processes which can then be applied to more complicated BCC alloys. In situ neutron diffraction during compressive deformation and subsequent heat treatment have been used to monitor the evolution of microstructural features in Ta throughout simulated processing steps. Crystallographic texture and dislocation density are determined as a function of first plastic strain, then temperature. Lattice strains are determined and attributed to stresses at macroscopic, grain and dislocation length scales. The increase of the dislocation density through deformation and subsequent recovery during heat treatment is monitored through the changing diffraction line profile. Also, randomization of the texture is used as a signature of recrystallization. The recovery of dislocations through annihilation is not observed to depend on the initial dislocation density in the range studied here. In contrast, recrystallization is observed to depend strongly on the initially dislocation density.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Optical spectroscopy and modeling of uranium gas-phase oxidation: Progress and perspectives

We report that studies related to U gas-phase oxidation through plasma- and thermo-chemistry are important for many fields, including environmental monitoring, forensic analysis, debris analysis in a weapon detonation event, and nucleation physics. Recently, significant efforts have been made to understand the chemical pathways involved in the progression from U atoms to diatoms (UO) and polyatomic molecules (UxOy), employing optical spectroscopy tools and computational modeling. In many studies, laser ablation of U or a U-containing flow reactor are used as a highly resource-efficient, repeatable, tunable, and lab-scale testbed for studying gas-phase oxidation in U plasmas. The spectroscopic analysis of high-temperature gas-phase oxidation of U is challenging due to the congested U spectra, resolution limitations of instrumentation, and the numerous chemical reaction pathways possible. This article focuses on the current understanding and challenges related to studying U plasma chemistry, specifically U gas-phase oxidation and molecular formation, via optical spectroscopy of plasmas and associated computational and spectral modeling. The physical and chemical processes involved in the evolution from U atoms to U oxide molecules to nanoparticles and agglomerates (i.e., debris) are discussed in the context of optical spectroscopic studies. The article concludes by highlighting opportunities for future research efforts based on existing knowledge published in the literature.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2020

Phase transformation in boron under shock compression

Using first-principles molecular dynamics, we calculated the equation of state and shock Hugoniot of various boron phases. Here, we find a large mismatch between Hugoniots based on existing knowledge of the equilibrium phase diagram and those measured by shock experiments, which could be reconciled if the α-B 12 /β phases are significantly over-pressurized in boron under shock compression. Our results also indicate that there exist an anomaly and negative Clapeyron slope along the melting curve of boron at 100 GPa and 1500–3000 K. These results enable an in-depth understanding of matter under shock compression, in particular the significance of compression-rate dependence of phase transitions and kinetic effects in experimental measurements.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Photophysical Properties and Phase Behavior of Ultrawide Photovoltaic Bandgap Cesium–Lead-Based Triple Halide Perovskites

Metal halide perovskite films in the top cell of triple-junction tandems require bandgaps around 2.0 eV to achieve current matching, assuming that the middle absorbing layer is the commonly used FAPbI 3 composition and the bottom cell has a bandgap around 1.1 eV. Unfortunately, mixed organic/inorganic metal halide perovskites that have the necessary Br content to reach a bandgap of 2.0 eV segregate into iodine-rich and bromine-rich phases under illumination, limiting their obtainable voltage. Previous reports have shown improved photostability using either Cs-based inorganic compositions or Cl incorporation on the X-site. Here, we investigate the inorganic triple halide compositional space CsPb­(I 1–x–y Br y Cl x ) 3 where bandgaps near 2.0 eV are expected based on the knowledge that CsPbI2Br has a bandgap of 1.90 eV. Incorporation of Cl occurs readily for x ≤ 0.07–0.10 within perovskites with a Br content of 0.3 ≤ y ≤ 0.42. When x >0.1, X-ray diffraction and photoluminescence (PL) measurements indicate that multiple compositional phases form. We hypothesize that the variable sizes of the three halide ions are not supported within the rigid Cs lattice, resulting in the formation of multiple compositional phases. The photoluminescence quantum yield of the single-phase compositional space–CsPb­(I 1–x–y Br y Cl x ) 3 where x ≤ 0.07was typically 0.001–0.004%, most likely as a result of a high defect density, including mobile iodine species. PL light-soaking measurements of many perovskite compositions with bandgaps in the range of 1.89–2.05 eV demonstrate that phase segregation occurs when initial bandgaps are above 1.95 eV regardless of halide content: indicating further iodide oxidation and corresponding migration under illumination. The conclusion is that further compositional or additive engineering is necessary for the development of inorganic triple halide compositions that accomplish the elusive goal of fabricating high-quality and photostable 2.0 eV films for use in multijunction tandems.

Electrical conductivity↗
DOE OSTI2023

Octahedral Distortion and Excitonic Behavior of Cs 3 Bi 2 Br 9 Halide Perovskite at Low Temperature

The metal halide ionic octahedron, represented as [MX 6 ] $n$- (M = metal cation, X = halide anion), serves as the basic structural unit in halide perovskites and plays a crucial role in determining their optoelectronic and chemical properties. Thus, it is possible to correlate the responses of metal halide perovskites to various environmental stimuli with the dynamic behaviors of the [MX 6 ] $n$- octahedra. In this study, with the temperature-dependent single-crystal X ray diffraction (SCXRD) measurements on Cs 3 Bi 2 Br 9 2D halide perovskites, we can identify two classes of distortions through the lowering of temperature: intraoctahedral distortion, which is the off-centering of Bi 3+ cation within a [BiBr 6 ] 3– octahedron due to the Bi 3+ 6s 2 lone pair electrons, and interoctahedral distortion, which is the collective misalignments among the [BiBr 6 ] 3– building blocks. Free exciton (FE) and self-trapped exciton (STE) models are used to study the relationship between the distortion of octahedra in Cs 3 Bi 2 Br 9 and the corresponding changes in its optoelectronic properties, which transform from dominating blue emission above 100 K to red emission at 4 K. In conclusion, this work provides new insights into the excitonic behaviors of perovskites and suggests a possibility that we can design and rationalize the optical properties of halide perovskites by regulating the environmental stimuli based on the knowledge of behaviors of the individual [MX 6 ] $n$- building blocks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

High-efficiency stretchable light-emitting polymers from thermally activated delayed fluorescence

Stretchable light-emitting materials are the key components for realizing skin-like displays and optical biostimulation. All the stretchable emitters reported to date, to the best of our knowledge, have been based on electroluminescent polymers that only harness singlet excitons, limiting their theoretical quantum yield to 25%. Here we present a design concept for imparting stretchability onto electroluminescent polymers that can harness all the excitons through thermally activated delayed fluorescence, thereby reaching a near-unity theoretical quantum yield. We show that our design strategy of inserting flexible, linear units into a polymer backbone can substantially increase the mechanical stretchability without affecting the underlying electroluminescent processes. As a result, our synthesized polymer achieves a stretchability of 125%, with an external quantum efficiency of 10%. Additionally, we demonstrate a fully stretchable organic light-emitting diode, confirming that the proposed stretchable thermally activated delayed fluorescence polymers provide a path towards simultaneously achieving desirable electroluminescent and mechanical characteristics, including high efficiency, brightness, switching speed and stretchability as well as low driving voltage.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Excited-state structural dynamics of nickel complexes probed by optical and X-ray transient absorption spectroscopies: insights and implications

Excited states of nickel complexes undergo a variety of photochemical processes, such as charge transfer, ligation/deligation, and redox reactions, relevant to solar energy conversion and photocatalysis. The efficiencies of the aforementioned processes are closely coupled to the molecular structures in the ground and excited states. The conventional optical transient absorption spectroscopy has revealed important excited-state pathways and kinetics, but information regarding the metal center, in particular transient structural and electronic properties, remains limited. These deficiencies are addressed by X-ray transient absorption (XTA) spectroscopy, a detailed probe of 3d orbital occupancy, oxidation state and coordination geometry. The examples of excited-state structural dynamics of nickel porphyrin and nickel phthalocyanine have been described from our previous studies with highlights on the unique structural information obtained by XTA spectroscopy. Furthermore, we close by surveying prospective applications of XTA spectroscopy to active areas of Ni-based photocatalysis based on the knowledge gained from our previous studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Energy resolution of the LZ detector for high-energy electronic recoils

The LUX-ZEPLIN (LZ) detector is a dual-phase liquid xenon time projection chamber (TPC) installed at the Sanford Underground Research Facility (Lead, South Dakota) at a depth of 1478 meters. Although the main objective of LZ is the direct detection of dark matter, its low background environment allows for the search of other rare processes, such as the neutrinoless double beta decay of xenon isotopes 134 Xe and 136 Xe with the respective Q-values of 826 keV and 2458 keV. The sensitivity of the detector to these decays is directly determined by the energy resolution, which, in turn, is degraded by non-uniformities in detector response. In this work, we present a novel method to correct, in the data, the non-uniformity of the light collected by an array of photosensors in a scintillation detector. This method is based on the knowledge of the light response functions of individual photosensors. With these techniques, we report, at a very early phase of the detector operations, a state-of-the-art energy resolution (σ/μ) of (0.67 ± 0.01)% at 2614 keV for the fiducial volume of 5.6 tonnes of liquid xenon.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Optimal Management of Grid-Interactive Efficient Buildings via Safe Reinforcement Learning

Reinforcement learning (RL)-based methods have achieved significant success in managing grid-interactive efficient buildings (GEBs). However, RL does not carry intrinsic guarantees of constraint satisfaction, which may lead to severe safety consequences. Besides, in GEB control applications, most existing safe RL approaches rely only on the regularisation parameters in neural networks or penalty of rewards, which often encounter challenges with parameter tuning and lead to catastrophic constraint violations. To provide enforced safety guarantees in controlling GEBs, this paper designs a physics-inspired safe RL method whose decision-making is enhanced through safe interaction with the environment. Different energy resources in GEBs are optimally managed to minimize energy costs and maximize customer comfort. The proposed approach can achieve strict constraint guarantees based on prior knowledge of a set of developed hard steady-state rules. Simulations on the optimal management of GEBs, including heating, ventilation, and air conditioning (HVAC), solar photovoltaics, and energy storage systems, demonstrate the effectiveness of the proposed approach.

Huo, Xiang↗
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↗

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