Search NASA⌕ Search

SEARCH · Search NASA

Results for “Human Performance Modeling”

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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

U.S. Department of Energy: National Virtual Biotechnology Laboratory (Technical Report)

With funding from the CARES Act, the U.S Department of Energy (DOE) established the National Virtual Biotechnology Laboratory (NVBL) in March 2020 to address key challenges associated with the COVID-19 crisis. The NVBL brought together the broad scientific and technical expertise and resources of DOE’s 17 national laboratories to help tackle medical supply shortages, discover potential drugs to fight the virus, develop and validate COVID-19 testing methods, model disease spread and impact across the nation, and understand virus transport in buildings and the environment. National laboratory resources leveraged for this effort include a suite of world-leading user facilities broadly available to the research community, such as light and neutron sources, nanoscale science research centers, sequencing and biocharacterization facilities, and high-performance computing facilities. As part of the NVBL framework, DOE rapidly assembled five project teams to (1) identify new targets for medical therapeutics; (2) develop innovations in testing capabilities; (3) provide epidemiological and logistical support; (4) understand viral fate and transport in the environment; and (5) address supply chain bottlenecks by harnessing extensive additive manufacturing capabilities. Each research team was charged with defining high-impact projects that could be completed in a 6-month sprint while coordinating their developments with academia, other government agencies, and the private sector. Within months, NVBL teams used DOE’s high-performance computers and light and neutron sources to identify promising candidates for antibodies and antivirals that universities and drug companies are now evaluating. NVBL researchers also developed new diagnostic targets and sample collection approaches, and supported efforts by the U.S. Food and Drug Administration, Centers for Disease Control and Prevention, and U.S. Department of Defense to establish national guidelines used in administering millions of tests. Researchers used artificial intelligence and high-performance computing to produce near-real-time data analysis to forecast disease transmission, stress on public health infrastructure, and economic impact, which supported decision-makers at the local, state, and national levels. To minimize virus uptake and protect human health, NVBL teams studied how to control indoor virus movement. Researchers also produced innovations in materials and advanced manufacturing that mitigated shortages in test kits and personal protective equipment, creating nearly 1,000 new jobs. Through its NVBL framework, DOE has contributed significantly to the nation’s COVID response, demonstrating in only a few months the critical impact of its national laboratories. NVBL’s accomplishments demonstrate not only the powerful resource represented by DOE’s national laboratories working together to meet national needs, but also the effectiveness of the integrated NVBL framework for rapidly responding to emergencies with research and development solutions. Going forward, the NVBL is poised to apply the unique capabilities and expertise of the national laboratory complex to future national and international emergencies, both natural and engineered. Through this framework, DOE will continue to be an integral component of agency-wide efforts to prepare for and respond to biorisks and other crises. This technical report describes the goals, progress, and results of NVBL’s five project teams—Molecular Design for COVID-19 Therapeutics, COVID-19 Testing, Epidemiological Modeling, Viral Fate and Transport, and Materials and Manufacturing of Critical Supplies—and lists each team’s publications and research output.

42 ENGINEERING↗
DOE OSTI2015

SU-E-T-97: An Analysis of Knowledge Based Planning for Stereotactic Body Radiation Therapy of the Spine

Purpose: Creating high quality SBRT treatment plans for the spine is often tedious and time consuming. In addition, the quality of treatment plans can vary greatly between treatment facilities due to inconsistencies in planning methods. This study investigates the performance of knowledge-based planning (KBP) for spine SBRT. Methods: Treatment plans were created for 28 spine SBRT patients. Each case was planned to meet strict dose objectives and guidelines. After physician and physicist approval, the plans were added to a custom model in a KBP system (RapidPlan, Varian Eclipse v13.5). The model was then trained to be able to predict estimated DVHs and provide starting objective functions for future patients based on both generated and manual objectives. To validate the model, ten additional spine SBRT cases were planned manually as well as using the model objectives. Plans were compared based on planning time and quality (ability to meet the plan objectives, including dose metrics and conformity). Results: The average dose to the spinal cord and the cord PRV differed between the validation and control plans by <0.25% demonstrating iso-toxicity. Six out of 10 validation plans met all dose objectives without the need for modifications, and overall, target dose coverage was increased by about 4.8%. If the validation plans did not meet the dose requirements initially, only 1–2 iterations of modifying the planning parameters were required before an acceptable plan was achieved. While manually created plans usually required 30 minutes to 3 hours to create, KBP can be used to create similar quality plans in 15–20 minutes. Conclusion: KBP for spinal tumors has shown to greatly decrease the amount of time required to achieve high quality treatment plans with minimal human intervention and could feasibly be used to standardize plan quality between institutions. Supported by Varian Medical Systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

All-polymer nanocomposites having superior strength, toughness and ultralow energy dissipation

Toughening polymers has attracted significant interest. Traditionally, polymer toughness is enhanced by constructing polymer networks or introducing sacrificial bonds into the chains between crosslink points. These strategies, though, introduce pronounced energy dissipation and associated heat, both of which are undesirable under long-term cyclic loading, for example at the interface of implants in the human body. By incorporating single-chain nanoparticles (SCNPs) into linear polymer chains to generate all-polymer nanocomposites (APNCs), we have been able to achieve high strength, high toughness with low energy dissipation. Using a combination of simulation and experimental results, we are advancing a “SCNPs effect” where tightly cross-linked SCNPs produce a modulus contrast to achieve strengthening and toughening. Benefitting from the soft interface, the penetrable and deformable SCNPs cause the surrounding polymer chains to move in concert, significantly reducing the interfacial friction to achieve low energy dissipation. The intramolecular cross-linking of the SCNPs and adhesion between the SCNPs and polymer matrix are critical for realizing such high-performance systems. Based on a Gaussian regression model and back propagation (BP) neural network, the mechanical strength can be predicted and is supported by simulations. Finally, the APNC concept described can be applied to elastomers and gels, broadening its utilization in high-cycle and low-dissipation applications, like soft robots, flexible sensors and cartilage replacements, and artificial heart valves.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Ex vivo to in vivo model of malignant peripheral nerve sheath tumors for precision oncology

Malignant peripheral nerve sheath tumors (MPNST) are aggressive soft tissue sarcomas that often develop in patients with neurofibromatosis type 1 (NF1). To address the critical need for novel therapeutics in MPNST, we aimed to establish an ex vivo 3D platform that accurately captured the genomic diversity of MPNST and could be utilized in a medium-throughput manner for drug screening studies to be validated in vivo using patient-derived xenografts (PDX). Genomic analysis was performed on all PDX-tumor pairs. Selected PDX were harvested for assembly into 3D microtissues. Based on prior work in our labs, we evaluated drugs (trabectedin, olaparib, and mirdametinib) ex vivo and in vivo. For 3D microtissue studies, cell viability was the endpoint as assessed by Zeiss Axio Observer. For PDX drug studies, tumor volume was measured twice weekly. Bulk RNA sequencing was performed to identify pathways enriched in cells. We developed 13 NF1-associated MPNST-PDX and identified mutations or structural abnormalities in NF1 (100%), SUZ12 (85%), EED (15%), TP53 (15%), CDKN2A (85%), and chromosome 8 gain (77%). We successfully assembled PDX into 3D microtissues, categorized as robust (>90% viability at 48 h), good (>50%), or unusable (<50%). We evaluated drug response to “robust” or “good” microtissues, namely MN-2, JH-2-002, JH-2-079-c, and WU-225. Drug response ex vivo predicted drug response in vivo, and enhanced drug effects were observed in select models. These data support the successful establishment of a novel 3D platform for drug discovery and MPNST biology exploration in a system representative of the human condition.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Development of models for predicting Torsade de Pointes cardiac arrhythmias using perceptron neural networks

Background: Blockage of some ion channels and in particular, the hERG (human Ether-a’-go-go-Related Gene) cardiac potassium channel delays cardiac repolarization and can induce arrhythmia. In some cases it leads to a potentially life-threatening arrhythmia known as Torsade de Pointes (TdP). Therefore recognizing drugs with TdP risk is essential. Candidate drugs that are determined not to cause cardiac ion channel blockage are more likely to pass successfully through clinical phases II and III trials (and preclinical work) and not be withdrawn even later from the marketplace due to cardiotoxic effects. The objective of the present study is to develop an SAR (Structure-Activity Relationship) model that can be used as an early screen for torsadogenic (causing TdP arrhythmias) potential in drug candidates. The method is performed using descriptors comprised of atomic NMR chemical shifts (13C and 15N NMR) and corresponding interatomic distances which are combined into a 3D abstract space matrix. The method is called 3D-SDAR (3-dimensional spectral data-activity relationship) and can be interrogated to identify molecular features responsible for the activity, which can in turn yield simplified hERG toxicophores. A dataset of 55 hERG potassium channel inhibitors collected from Kramer et al. consisting of 32 drugs with TdP risk and 23 with no TdP risk was used for training the 3D-SDAR model. Results: An artificial neural network (ANN) with multilayer perceptron was used to define collinearities among the independent 3D-SDAR features. A composite model from 200 random iterations with 25% of the molecules in each case yielded the following figures of merit: training, 99.2%; internal test sets, 66.7%; external (blind validation) test set, 68. 4%. In the external test set, 70.3% of positive TdP drugs were correctly predicted. Moreover, toxicophores were generated from TdP drugs. Conclusion: A 3D-SDAR was successfully used to build a predictive model for drug-induced torsadogenic and non-torsadogenic drugs based on 55 compounds. The model was tested in 38 external drugs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Synthetic β-sheets mimicking fibrillar and oligomeric structures for evaluation of spectral X-ray scattering technique for biomarker quantification

Archetypical cross-β spines sharpen the boundary between functional and pathological proteins including β-amyloid, tau, α-synuclein and transthyretin are linked to many debilitating human neurodegenerative and non-neurodegenerative amyloidoses. An increased focus on development of pathogenic β-sheet specific fluid and imaging structural biomarkers and conformation-specific monoclonal antibodies in targeted therapies has been recently observed. Identification and quantification of pathogenic oligomers remain challenging for existing neuroimaging modalities. We propose two artificial β-sheets which can mimic the nanoscopic structural characteristics of pathogenic oligomers and fibrils for evaluating the performance of a label free, X-ray based biomarker detection and quantification technique. Highly similar structure with elliptical cross-section and parallel cross-β motif is observed among recombinant α-synuclein fibril, Aβ-42 fibril and artificial β-sheet fibrils. We then use these β-sheet models to assess the performance of spectral small angle X-ray scattering (sSAXS) technique for detecting β-sheet structures. sSAXS showed quantitatively accurate detection of antiparallel, cross-β artificial oligomers from a tissue mimicking environment and significant distinction between different oligomer packing densities such as diffuse and dense packings. The proposed synthetic β-sheet models mimicked the nanoscopic structural characteristics of β-sheets of fibrillar and oligomeric states of Aβ and α-synuclein based on the ATR-FTIR and SAXS data. The tunability of β-sheet proportions and shapes of structural motifs, and the low-cost of these β-sheet models can become useful test materials for evaluating β-sheet or amyloid specific biomarkers in a wide range of neurological diseases. By using the proposed synthetic β-sheet models, our study indicates that the sSAXS has potential to evaluate different stages of β-sheet-enriched structures including oligomers of pathogenic proteins.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Improved Throughput and Analysis of Scratch Test Results via Automation and Machine Learning

A data analysis automation interface that incorporates machine learning (ML) has been developed to improve productivity, efficiency, and consistency in identifying and defining critical load values (or other values associated with optically identifiable characteristics) of a coating when a scratch test is performed. In this specific program, the machine learning component of the program has been trained to identify the Critical Load 2 (L C2 ) value by analyzing images of the scratch tracks created in each test. An optical examination of the scratch by a human operator is currently used to determine where this value occurs. However, the vagueness of the standard has led to varying interpretations and nonuniform usage by different operators at different laboratories where the test is implemented, resulting in multiple definitions of the desired parameter. Using a standard set of training and validation images to create the dataset, the critical load can be identified consistently amongst different laboratories using the automation interface without requiring the training of human operators. When the model was used in conjunction with an instrument manufacturer's scratch test software, the model produced accurate and repeatable results and defined L C2 values in as little as half of the time compared to a human operator. When combined with a program that automates other aspects of the scratch testing process usually conducted by a human operator, scratch testing and analysis can occur with little to no intervention from a human beyond initial setup and frees them to complete other work in the lab.

36 MATERIALS SCIENCE↗
DOE OSTI2015

TECA: Petascale pattern recognition for climate science

Climate Change is one of the most pressing challenges facing humanity in the 21st century. Climate simulations provide us with a unique opportunity to examine effects of anthropogenic emissions. Highresolution climate simulations produce “Big Data”: contemporary climate archives are ≈ 5PB in size and we expect future archives to measure on the order of Exa-Bytes. In this work, we present the successful application of TECA (Toolkit for Extreme Climate Analysis) framework, for extracting extreme weather patterns such as Tropical Cyclones, Atmospheric Rivers and Extra-Tropical Cyclones from TB-sized simulation datasets. TECA has been run at full-scale on Cray XE6 and IBM BG/Q systems, and has reduced the runtime for pattern detection tasks from years to hours. TECA has been utilized to evaluate the performance of various computational models in reproducing the statistics of extreme weather events, and for characterizing the change in frequency of storm systems in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Computational multiphysics modeling of radioactive aerosol deposition in diverse human respiratory tract geometries

The evaluation of aerosol exposure relies on generic mathematical models that assume uniform particle deposition profiles over the human respiratory tract and do not account for subject-specific characteristics. Here we introduce a hybrid-automated computational workflow that generates personalized particle deposition profiles in 3D reconstructed human airways from computed tomography scans using Computational Fluid and Particle Dynamics simulations. This is the first large-scale study to consider realistic airways variability, where 380 lower and 40 upper human respiratory tract 3D geometries are reconstructed and parameterized. The data is clustered into nine groups using random forest regression. Computational fluid and particle dynamics simulations are conducted on these representative geometries using a realistic heavy-breathing respiratory cycle and radioactive iodine-131 as a source term. Monte Carlo radiation transport simulations are performed to obtain detailed energy deposition maps. Our findings emphasize the importance of personalized studies, as minor respiratory tract variations notably influence deposition patterns rather than global parameters of the lower airways, observing more than 30% variance in the mass deposition fraction.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

The Enzymatic and Structural Basis for Inhibition of Echinococcus granulosus Thioredoxin Glutathione Reductase by Gold(I)

Aims: New drugs are needed to treat flatworm infections that cause severe human diseases such as schistosomiasis. The unique flatworm enzyme thioredoxin glutathione reductase (TGR), structurally different from the human enzyme, is a key drug target. Structural studies of the flatworm Echinococcus granulosus TGR, free and complexed with AuI-MPO, a novel gold inhibitor, together with inhibition assays were performed. Results: AuI-MPO is a potent TGR inhibitor that achieves 75% inhibition at a 1:1 TGR:Au ratio and efficiently kills E. granulosus in vitro. The structures revealed salient insights: (i) unique monomer–monomer interactions, (ii) distinct binding sites for thioredoxin and the glutaredoxin (Grx) domain, (iii) a single glutathione disulfide reduction site in the Grx domain, (iv) rotation of the Grx domain toward the Sec-containing redox active site, and (v) a single gold atom bound to Cys519 and Cys573 in the AuI-TGR complex. Structural modeling suggests that these residues are involved in the stabilization of the Sec-containing C-terminus. Consistently, Cys→Ser mutations in these residues decreased TGR activities. Mass spectroscopy confirmed these cysteines are the primary binding site. Innovation: The identification of a primary site for gold binding and the structural model provide a basis for gold compound optimization through scaffold adjustments. Conclusions: The structural study revealed that TGR functions are achieved not only through a mobile Sec-containing redox center but also by rotation of the Grx domain and distinct binding sites for Grx domain and thioredoxin. The conserved Cys519 and Cys573 residues targeted by gold assist catalysis through stabilization of the Sec-containing redox center. Antioxid. Redox Signal. 27, 1491–1504.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Automated prediction of lattice parameters from X-ray powder diffraction patterns

A key step in the analysis of powder X-ray diffraction (PXRD) data is the accurate determination of unit-cell lattice parameters. This step often requires significant human intervention and is a bottleneck that hinders efforts towards automated analysis. This work develops a series of one-dimensional convolutional neural networks (1D-CNNs) trained to provide lattice parameter estimates for each crystal system. A mean absolute percentage error of approximately 10% is achieved for each crystal system, which corresponds to a 100- to 1000-fold reduction in lattice parameter search space volume. The models learn from nearly one million crystal structures contained within the Inorganic Crystal Structure Database and the Cambridge Structural Database and, due to the nature of these two complimentary databases, the models generalize well across chemistries. A key component of this work is a systematic analysis of the effect of different realistic experimental non-idealities on model performance. It is found that the addition of impurity phases, baseline noise and peak broadening present the greatest challenges to learning, while zero-offset error and random intensity modulations have little effect. However, appropriate data modification schemes can be used to bolster model performance and yield reasonable predictions, even for data which simulate realistic experimental non-idealities. In order to obtain accurate results, a new approach is introduced which uses the initial machine learning estimates with existing iterative whole-pattern refinement schemes to tackle automated unit-cell solution.

42 ENGINEERING↗
DOE OSTI2019

RNA-seq latent featurizer using center loss cost function

Predictive modeling of patient tumor growth response to drug treatment is severely limited by a lack of experimental data for training. Combining experimental data from several studies is an attractive approach, but presents two problems: batch effects and limited data for individual drugs and cell lines. Batch effects are caused by systematic procedural differences among studies, which causes systematic experimental outcome differences. Directly using these experimental results as features for machine learning commonly causes problems when training on one study and testing on another. This severely limits a model's ability to perform well on new experiments. Even after combining studies, predicting outcomes on new patient tumors remains an open challenge. We propose a semi-supervised, autoencoder-based, machine learning procedure, which learns a smaller set of gene expression features that are robust to batch effects using background information on a cell line or tissue's tumor type. We implemented this reduced feature representation and show that the new feature space clusters strongly according to tumor type. This experiment is carried out across multiple studies: CCLE, CTRP, gCSI, GDSC, NCI60, and patient derived tumors. We hypothesize that using a batch effect resistant feature set across studies will improve prediction performance. Genome Data Commons (GDC) gene expression profiles for publicly available human tissues and cell lines from NCI60 and CCLE were processed using the semi-supervised learning procedure. Our autoencoder repurposes the 'center loss' (CL) cost function of Wen et. al. to learn a more generalized set of features using the cell line or tissue's tumor type.

He, Stewart↗
DOE OSTI2016

Radiosensitization by PARP inhibition to proton beam irradiation in cancer cells

The poly(ADP-ribose) polymerase (PARP)-1 regulates DNA damage responses and promotes base excision repair. PARP inhibitors have been shown to enhance the cytotoxicity of ionizing radiation in various cancer cells and animal models. We have demonstrated that the PARP inhibitor (PARPi) AZD2281 is also an effective radiosensitizer for carbon-ion radiation; thus, we speculated that the PARPi could be applied to a wide therapeutic range of linear energy transfer (LET) radiation as a radiosensitizer. Institutes for biological experiments using proton beam are limited worldwide. This study was performed as a cooperative research at heavy ion medical accelerator in Chiba (HIMAC) in National Institute of Radiological Sciences. HIMAC can generate various ion beams; this enabled us to compare the radiosensitization effect of the PARPi on cells subjected to proton and carbon-ion beams from the same beam line. After physical optimization of proton beam irradiation, the radiosensitization effect of the PARPi was assessed in the human lung cancer cell line, A549, and the pancreatic cancer cell line, MIA PaCa-2. The effect of the PARPi, AZD2281, on radiosensitization to Bragg peak was more significant than that to entrance region. The PARPi increased the number of phosphorylated H2AX (γ-H2AX) foci and enhanced G2/M arrest after proton beam irradiation. This result supports our hypothesis that a PARPi could be applied to a wide therapeutic range of LET radiation by blocking the DNA repair response. - Highlights: • Effective radiosensitizers for particle radiation therapy have not been reported. • PARP inhibitor treatment radiosensitized after proton beam irradiation. • The sensitization at Bragg peak was greater than that at entrance region. • DSB induction and G2/M arrest is involved in the sensitization mechanism.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Compartmentalized microchannel array for high-throughput analysis of single cell polarized growth and dynamics

Here, interrogating polarized growth is technologically challenging due to extensive cellular branching and uncontrollable environmental conditions in conventional assays. Here we present a robust and high-performance microfluidic system that enables observations of polarized growth with enhanced temporal and spatial control over prolonged periods. The system has built-in tunability and versatility to accommodate a variety of science applications requiring precisely controlled environments. Using the model filamentous fungus, Neurospora crassa , this microfluidic system enabled direct visualization and analysis of cellular heterogeneity in a clonal fungal cell population, nuclear distribution and dynamics at the subhyphal level, and quantitative dynamics of gene expression with single hyphal compartment resolution in response to carbon source starvation and exchange experiments. Although the microfluidic device is demonstrated on filamentous fungi, our technology is immediately extensible to a wide array of other biosystems that exhibit similar polarized cell growth with applications ranging from bioenergy production to human health.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Study of potential utility of new radiopharmaceuticals based on technetium-99m labeled derivative of glucose

Purpose: to study the potential utility of 1-thio-D-glucose labeled with {sup 99m}Tc for cancer imaging in laboratory animals. Materials and method: the study was carried out in cell cultures of normal CHO (Chinese hamster ovary cells CHO) and malignant tissues MCF-7 (human breast adenocarcinoma MCF-7). To evaluate the uptake of {sup 99m}Tc-1-thio-D-glucose in normal and tumor tissue cells, 25 MBq of 1-thio-D-glucose labeled with {sup 99m}Tc was added to the vials with 3 million cells and incubated for 30 min at room temperature. After centrifugation of the vials with cells, the supernatant was removed. The radioactivity in vials with normal and tumor cells was then measured. In addition, the study included 40 mice of C57B1/6j lines with tumor lesion of the right femur. For neoplastic lesions, Lewis lung carcinoma model was used. Following anesthesia, mice were injected intravenously with 25 MBq of {sup 99m}Tc-1-thio-D-glucose. Planar scintigraphy was performed 15 minutes later in a matrix of 512x512 pixels for 5 min. Results: when measuring the radioactivity of normal and malignant cells after incubation with {sup 99m}Tc-1-thio-D-glucose, it was found that the radioactivity of malignant cells was higher than that of normal cells. The mean values of radioactivity levels in normal and malignant cells were 0.3 ± 0.15 MBq and 1.07 ± 0.6 MBq, respectively. All examined animals had increased accumulation of {sup 99m}Tc-1-thio-D-glucose at the tumor site. The accumulation of {sup 99m}Tc-1-thio-D-glucose in the tumor was on average twice as high as compared to the symmetric region. Conclusion: The present study demonstrated that {sup 99m}Tc-1-thio-D-glucose is a prospective radiopharmaceutical for cancer visualization. In addition, high accumulation of {sup 99m}Tc-1-thio-D-glucose in the culture of cancer cells and in tumor tissue of animals demonstrates tumor tropism of the radiopharmaceutical.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Russian anthropogenic black carbon: Emission reconstruction and Arctic black carbon simulation

Development of reliable source emission inventories is needed to advance the understanding of the origin of Arctic haze using chemical transport modeling. This paper develops a regional anthropogenic black carbon (BC) emission inventory for the Russian Federation, the largest country by land area in the Arctic Council. Activity data from combination of local Russia information and international resources, emission factors based on either Russian documents or adjusted values for local conditions, and other emission source data are used to approximate the BC emissions. Emissions are gridded at a resolution of 0.1° × 0.1° and developed into a monthly temporal profile. Total anthropogenic BC emission of Russia in 2010 is estimated to be around 224 Gg. Gas flaring, a commonly ignored black carbon source, contributes a significant fraction of 36.2% to Russia's total anthropogenic BC emissions. Other sectors, i.e., residential, transportation, industry, and power plants, contribute 25.0%, 20.3%, 13.1%, and 5.4%, respectively. Three major BC hot spot regions are identified: the European part of Russia, the southern central part of Russia where human population densities are relatively high, and the Urals Federal District where Russia's major oil and gas fields are located but with sparse human population. BC simulations are conducted using the hemispheric version of Community Multi-scale Air Quality Model with emission inputs from a global emission database EDGAR (Emissions Database for Global Atmospheric Research)-HTAPv2 (Hemispheric Transport of Air Pollution) and EDGAR-HTAPv2 with its Russian part replaced by the newly developed Russian BC emissions, respectively. The simulation using the new Russian BC emission inventory could improve 30–65% of absorption aerosol optical depth measured at the AERONET sites in Russia throughout the whole year as compared to that using the default HTAPv2 emissions. At the four ground monitoring sites (Zeppelin, Barrow, Alert, and Tiksi) in the Arctic Circle, surface BC simulations are improved the most during the Arctic haze periods (October–March). The poor performance of Arctic BC simulations in previous studies may be partly ascribed to the Russian BC emissions built on out-of-date and/or missing information, which could result in biases to both emission rates and the spatial distribution of emissions. Finally, this study highlights that the impact of Russian emissions on the Arctic haze has likely been underestimated, and its role in the Arctic climate system needs to be reassessed. The Russian black carbon emission source data generated in this study can be obtained via http://abci.ornl.gov/download.shtml or http://acs.engr.utk.edu/Data.php.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Prevalence and scalable control of localized networks

The ability to control network dynamics is essential for ensuring desirable functionality of many technological, biological, and social systems. Such systems often consist of a large number of network elements, and controlling large-scale networks remains challenging because the computation and communication requirements increase prohibitively fast with network size. Here, we introduce a notion of network locality that can be exploited to make the control of networks scalable, even when the dynamics are nonlinear. We show that network locality is captured by an information metric and is almost universally observed across real and model networks. In localized networks, the optimal control actions and system responses are both shown to be necessarily concentrated in small neighborhoods induced by the information metric. This allows us to develop localized algorithms for determining network controllability and optimizing the placement of driver nodes. This also allows us to develop a localized algorithm for designing local feedback controllers that approach the performance of the corresponding best global controllers, while incurring a computational cost orders-of-magnitude lower. Here, we validate the locality, performance, and efficiency of the algorithms in Kuramoto oscillator networks, as well as three large empirical networks: synchronization dynamics in the Eastern US power grid, epidemic spreading mediated by the global air-transportation network, and Alzheimer’s disease dynamics in a human brain network. Taken together, our results establish that large networks can be controlled with computation and communication costs comparable to those for small networks.

42 ENGINEERING↗
DOE OSTI2019

Using the concordance of in vitro and in vivo data to evaluate extrapolation assumptions

Linking in vitro bioactivity and in vivo toxicity on a dose basis enables the use of highthroughput in vitro assays as an alternative to traditional animal studies. In this study, we evaluated assumptions in the use of a high-throughput, physiologically based toxicokinetic (PBTK) model to relate in vitro bioactivity and rat in vivo toxicity data. The fraction unbound in plasma (fup) and intrinsic hepatic clearance (Clint) were measured for rats (for 67 and 77 chemicals, respectively), combined with fup and Clint literature data for 97 chemicals, and incorporated in the PBTK model. Of these chemicals, 84 had corresponding in vitro ToxCast bioactivity data and in vivo toxicity data. For each possible comparison of in vitro and in vivo endpoint, the concordance between the in vivo and in vitro data was evaluated by a regression analysis. For a base set of assumptions, the PBTK results were more frequently better associated than either the results from a “random” model parameterization or direct comparison of the “untransformed” values of AC50 and dose (performed best in 51%, 28%, and 21% of cases, respectively). We also investigated several assumptions in the application of PBTK for IVIVE, including clearance and internal dose selection. One of the better assumptions sets–restrictive clearance and comparing free in vivo venous plasma concentration with free in vitro concentration–outperformed the random and untransformed results in 71% of the in vitro-in vivo endpoint comparisons. These results demonstrate that applying PBTK improves our ability to observe the association between in vitro bioactivity and in vivo toxicity data in general. This suggests that potency values from in vitro screening should be transformed using in vitro-in vivo extrapolation (IVIVE) to build potentially better machine learning and other statistical models for predicting in vivo toxicity in humans

59 BASIC BIOLOGICAL SCIENCES↗