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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Explainable Neural Architecture Search (XNAS)

Code for the paper Learning Interpretable Models Through Multi-Objective Neural Architecture Search by Zachariah Carmichael, Tim Moon, and Sam Ade Jacobs. Monumental advances in deep learning have led to unprecedented achievements across a multitude of domains. While the performance of deep neural networks is indubitable, the architectural design and interpretability of such models are nontrivial. Research has been introduced to automate the design of neural network architectures through neural architecture search (NAS). Recent progress has made these methods more pragmatic by exploiting distributed computation and novel optimization algorithms. However, there is little work in optimizing architectures for interpretability. To this end, we propose a multiobjective distributed NAS framework that optimizes for both task performance and introspection. We leverage the non-dominated sorting genetic algorithm (NSGA-II) and explainable AI (XAI) techniques to reward architectures that can be better comprehended by humans. The framework is evaluated on several image classification datasets. We demonstrate that jointly optimizing for introspection ability and task error leads to more disentangled architectures that perform within tolerable error.

Carmichael, ZachariahJ↗
DOE OSTI2024

Self-Supervised Cloud Classification

Abstract Low-level marine clouds play a pivotal role in Earth’s weather and climate through their interactions with radiation, heat and moisture transport, and the hydrological cycle. These interactions depend on a range of dynamical and microphysical processes that result in a broad diversity of cloud types and spatial structures, and a comprehensive understanding of cloud morphology is critical for continued improvement of our atmospheric modeling and prediction capabilities moving forward. Deep learning has recently accelerated our ability to study clouds using satellite remote sensing, and machine learning classifiers have enabled detailed studies of cloud morphology. A major limitation of deep learning approaches to this problem, however, is the large number of hand-labeled samples that are required for training. This work applies a recently developed self-supervised learning scheme to train a deep convolutional neural network (CNN) to map marine cloud imagery to vector embeddings that capture information about mesoscale cloud morphology and can be used for satellite image classification. The model is evaluated against existing cloud classification datasets and several use cases are demonstrated, including training cloud classifiers with very few labeled samples, interrogation of the CNN’s learned internal feature representations, cross-instrument application, and resilience against sensor calibration drift and changing scene brightness. The self-supervised approach learns meaningful internal representations of cloud structures and achieves comparable classification accuracy to supervised deep learning methods without the expense of creating large hand-annotated training datasets. Significance Statement Marine clouds heavily influence Earth’s weather and climate, and improved understanding of marine clouds is required to improve our atmospheric modeling capabilities and physical understanding of the atmosphere. Recently, deep learning has emerged as a powerful research tool that can be used to identify and study specific marine cloud types in the vast number of images collected by Earth-observing satellites. While powerful, these approaches require hand-labeling of training data, which is prohibitively time intensive. This study evaluates a recently developed self-supervised deep learning method that does not require human-labeled training data for processing images of clouds. We show that the trained algorithm performs competitively with algorithms trained on hand-labeled data for image classification tasks. We also discuss potential downstream uses and demonstrate some exciting features of the approach including application to multiple satellite instruments, resilience against changing image brightness, and its learned internal representations of cloud types. The self-supervised technique removes one of the major hurdles for applying deep learning to very large atmospheric datasets.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

A Briefing on Metrics and Risks for Autonomous Decision-Making in Aerospace Applications

Significant technology advances will enable future aerospace systems to safely and reliably make decisions autonomously, or without human interaction. The decision-making may result in actions that enable an aircraft or spacecraft in an off-nominal state or with slightly degraded components to achieve mission performance and safety goals while reducing or avoiding damage to the aircraft or spacecraft. Some key technology enablers for autonomous decision-making include: a continuous state awareness through the maturation of the prognostics health management field, novel sensor development, and the considerable gains made in computation power and data processing bandwidth versus system size. Sophisticated algorithms and physics based models coupled with these technological advances allow reliable assessment of a system, subsystem, or components. Decisions that balance mission objectives and constraints with remaining useful life predictions can be made autonomously to maintain safety requirements, optimal performance, and ensure mission objectives. This autonomous approach to decision-making will come with new risks and benefits, some of which will be examined in this paper. To start, an account of previous work to categorize or quantify autonomy in aerospace systems will be presented. In addition, a survey of perceived risks in autonomous decision-making in the context of piloted aircraft and remotely piloted or completely autonomous unmanned autonomous systems (UAS) will be presented based on interviews that were conducted with individuals from industry, academia, and government.

Frost, Susan↗
NASA NTRS2013

Modeling Complex Cross-Systems Software Interfaces Using SysML

The complex flight and ground systems for NASA human space exploration are designed, built, operated and managed as separate programs and projects. However, each system relies on one or more of the other systems in order to accomplish specific mission objectives, creating a complex, tightly coupled architecture. Thus, there is a fundamental need to understand how each system interacts with the other. To determine if a model-based system engineering approach could be utilized to assist with understanding the complex system interactions, the NASA Engineering and Safety Center (NESC) sponsored a task to develop an approach for performing cross-system behavior modeling. This paper presents the results of applying Model Based Systems Engineering (MBSE) principles using the System Modeling Language (SysML) to define cross-system behaviors and how they map to crosssystem software interfaces documented in system-level Interface Control Documents (ICDs).

Earth orbit↗
NASA NTRSDate not supplied

Small Satellite-sized Hypersonic Inflatable Aerodynamic Decelerators for Interplanetary Science Missions

To make the most of ridesharing opportunities, small satellite (SmallSat) mission designers endeavor to pack as much payload into a SmallSat-class form factor as possible. The mass and volume constraints of this smaller vehicle class present a challenge for interplanetary mission sets that require a means of achieving orbit insertion at their destination of interest. For a fully propulsive orbit insertion design, this may translate to the propellant mass being a significant fraction of the overall vehicle mass and prolonged insertion time. Aerocapture is a single quick maneuver that can significantly reduce the required propellant mass for orbit insertion. Because aerocapture uses a planet’s atmosphere to achieve the necessary change in velocity, a protective aeroshell is needed. The constraints imposed on secondary payloads render traditional rigid aeroshells mass and space prohibitive for the SmallSat class of vehicles; thus, warranting consideration of deployable designs that can be stowed compactly until needed for atmospheric entry. The Hypersonic Inflatable Aerodynamic Decelerator (HIAD) is a deployable aeroshell that leverages inflatable toroids to achieve the large drag area needed for aerodynamic deceleration. While the technology is currently being analyzed for Mars human-scale missions, it has the potential applicability for interplanetary SmallSat-scale missions as well. This paper highlights a study conducted during an internship at NASA Langley Research Center to investigate the feasibility of using a scaled-down HIAD design in SmallSat aerocapture missions. Several scaling methodologies are investigated including use of parametric models and direct computer-aided design (CAD) model scaling. Candidate HIAD configurations that conform to secondary payload adapter requirements are identified. The Program to Optimize Simulated Trajectories II (POST2) is utilized to conduct orbit insertion performance and trajectory sensitivity studies using the candidate configurations at Earth, Venus, and Mars. The results of the study indicate that multiple SmallSat-sized HIAD designs, targeting a range of SmallSat payload classes, are feasible for planetary aerocapture missions to Mars and Venus as well as Earth-based aerocapture missions.

Shelly C. Mann↗
NASA NTRS1997

Image discrimination models predict detection in fixed but not random noise

By means of a two-interval forced-choice procedure, contrast detection thresholds for an aircraft positioned on a simulated airport runway scene were measured with fixed and random white-noise masks. The term fixed noise refers to a constant, or unchanging, noise pattern for each stimulus presentation. The random noise was either the same or different in the two intervals. Contrary to simple image discrimination model predictions, the same random noise condition produced greater masking than the fixed noise. This suggests that observers seem unable to hold a new noisy image for comparison. Also, performance appeared limited by internal process variability rather than by external noise variability, since similar masking was obtained for both random noise types.

NASA Center ARC↗
DOE OSTI2025

Airport Ground Support Equipment Infrastructure & Logistics Electrification Assessment Tool: 2025 Data Development, Modeling and Analysis for DFW

The aviation industry is increasingly turning to modernize freight facilities by integrating electric Ground Support Equipment (eGSE) to enhance operational efficiency of freight facility moving vehicles and equipment. Airports worldwide are adopting eGSE to streamline cargo movement, reduce fuel and maintenance costs, and improve logistics coordination.1 North America, with its advanced aviation infrastructure, leads this transition, leveraging Internet of things (IoT)-enabled automation and zero emission technologies to boost reliability and reduce human errors.2 Electrification of freight facility moving vehicles and equipment boosts turnaround times, improves equipment reliability, and optimizes logistics coordination, giving operators a competitive advantage. With rising fuel price volatility and the pressure to meet stringent performance benchmarks, airports are focusing on cost-effective, scalable solutions for long-term financial and operational gains. To further accelerate electrification, airports are integrating Zero Emission Vehicles (ZEVs) into rental car fleets and deploying electric baggage carts, requiring strategic investments in charging infrastructure. 3 The shift, however, presents challenges, such as limited technical expertise, high capital costs, and complex procurement processes. By forging strategic partnerships, leveraging advanced technologies, and optimizing infrastructure investments, airports can create a resilient, future-ready ecosystem that enhances the movement of people and goods through electrification-driven efficiency. Supported by the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO), this electrification effort provides a scalable, cost-effective solution to improve airport freight operations. Through targeted investments and innovation, airports enhance efficiency, reduce costs, and meet performance benchmarks while advancing toward a resilient, electrified future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Breaking the barrier of human-annotated training data for machine learning-aided plant research using aerial imagery

Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by (i) the need for a large amount of human-annotated training data for each context in which the tool is used and (ii) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training data is generally costly and time-consuming. This study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building. A case study was performed to compare ML approaches that examine images collected by an uncrewed aerial vehicle to determine the presence/absence of panicles (i.e. “heading”) across thousands of field plots containing genetically diverse breeding populations of 2 Miscanthus species. Automated analysis of aerial imagery enabled the identification of heading approximately 9 times faster than in-field visual inspection by humans. Leveraging an Efficiently Supervised Generative Adversarial Network (ESGAN) learning strategy reduced the requirement for human-annotated data by 1 to 2 orders of magnitude compared to traditional, fully supervised learning approaches. The ESGAN model learned the salient features of the data set by using thousands of unlabeled images to inform the discriminative ability of a classifier so that it required minimal human-labeled training data. This method can accelerate the phenotyping of heading date as a measure of flowering time in Miscanthus across diverse contexts (e.g. in multistate trials) and opens avenues to promote the broad adoption of ML tools.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

pixelvar79/ESGAN-Flowering-Detection-paper

Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by (i) the need for a large amount of human-annotated training data for each context in which the tool is used and (ii) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training data is generally costly and time-consuming. This study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building. A case study was performed to compare ML approaches that examine images collected by an uncrewed aerial vehicle to determine the presence/absence of panicles (i.e. “heading”) across thousands of field plots containing genetically diverse breeding populations of 2 Miscanthus species. Automated analysis of aerial imagery enabled the identification of heading approximately 9 times faster than in-field visual inspection by humans. Leveraging an Efficiently Supervised Generative Adversarial Network (ESGAN) learning strategy reduced the requirement for human-annotated data by 1 to 2 orders of magnitude compared to traditional, fully supervised learning approaches. The ESGAN model learned the salient features of the data set by using thousands of unlabeled images to inform the discriminative ability of a classifier so that it required minimal human-labeled training data. This method can accelerate the phenotyping of heading date as a measure of flowering time in Miscanthus across diverse contexts (e.g. in multistate trials) and opens avenues to promote the broad adoption of ML tools.

Varela, Sebastian↗
DOE OSTI2018

A New Black Carbon Sensor for Dense Air Quality Monitoring Networks

Low-cost air pollution sensors are emerging and increasingly being deployed in densely distributed wireless networks that provide more spatial resolution than is typical in traditional monitoring of ambient air quality. However, a low-cost option to measure black carbon (BC)—a major component of particulate matter pollution associated with adverse human health risks—is missing. This paper presents a new BC sensor designed to fill this gap, the Aerosol Black Carbon Detector (ABCD), which incorporates a compact weatherproof enclosure, solar-powered rechargeable battery, and cellular communication to enable long-term, remote operation. This paper also demonstrates a data processing methodology that reduces the ABCD’s sensitivity to ambient temperature fluctuations, and therefore improves measurement performance in unconditioned operating environments (e.g., outdoors). A fleet of over 100 ABCDs was operated outdoors in collocation with a commercial BC instrument (Magee Scientific, Model AE33) housed inside a regulatory air quality monitoring station. The measurement performance of the 105 ABCDs is comparable to the AE33. The fleet-average precision and accuracy, expressed in terms of mean absolute percentage error, are 9.2 ± 0.8% (relative to the fleet average data) and 24.6 ± 0.9% (relative to the AE33 data), respectively (fleet-average ± 90% confidence interval).

47 OTHER INSTRUMENTATION↗
NASA NTRS1982

Does man always close the loop in trying to pilot a large ship?

Control of a slowly responding complex dynamic system such as a large supertanker poses special problems for the human. Experienced ship's crew members maneuvered a simulated ship in real time. The simulation was performed using a graphics display run by a DPD 11/40 minicomputer. The maneuvers consisted of piloting a large ship along a narrow waterway. Preliminary results suggest that the human operates as a feedforward controller utilizing the preview information for 'gross changes' in heading, and as a feedback controller for correcting small deviations. The instruments displayed on the deck do not seem to be helpful in all situations. In fact, evidence seems to indicate that some of the displays might even interfere with the pilot's attempt to form a good internal model.

Arnott, D. R.↗
NASA NTRS1988

Visual field influence on manual roll and pitch stabilization

Human control performance in nulling perceived tilt angles was investigated for combinations of pseudo-random vestibular disturbances and different waveforms of low frequency wide visual field motions. For both roll and pitch axes, subjects tilted the trainer in which they were seated in the direction of field rotation. This visual bias was much stronger for pitch backwards with upward field rotation. Frequency response analysis showed the dominance of visual cues at low frequencies (below 0.06 Hz) and the reliance on vestibular information in the high frequency range for both axes. Models suggest that operator balancing responses at high frequencies are mainly processed by the semicircular canals rather than the otolith organs. The results also suggest that the subject tends to rely less on the otolith organs for pitch perception than for roll.

Huang, J.-K.↗
NASA NTRS2004

Alssat Development Status and Its Applications in Trade Studies

The development of the Advanced Life Support (ALS) Sizing Analysis Tool (ALSSAT) using Microsoft® Excel was initiated by the Crew and Thermal Systems Division (CTSD) of Johnson Space Center (JSC) in 1997 to support the ALS and Exploration Offices in Environmental Control and Life Support System (ECLSS) design and studies. It aids the user in performing detailed sizing of the ECLSS based on suggested default values or user inputs for different combinations of the ALS regenerative system technologies (Ref. 1, 2). This analysis tool will assist the user in performing ECLSS preliminary design and trade studies as well as system optimization efficiently and economically. Since ALSSAT's latest publication in ICES 2001 (Ref. 1) describing the development of ALSSAT with its Air Revitalization Subsystem (ARS), Water Management Subsystem (WMS), and Biomass Subsystem (Biomass) mass balance sheets, ALSSAT has been expanded to include mass balance and sizing models for the remaining three ALS subsystems, namely, the Solid Waste Management Subsystem (SWMS), the Food Management Subsystem (FMS), and the Thermal Control Subsystem (TCS). The external interfaces, including the Extravehicular Activities (EVA) and Human Accommodations (HA), were implemented into ALSSAT in 2002. The overall mass balance sheet, which integrates the six ALS subsystems and the external interfaces applicable to the ECLSS, was also developed. In 2003, ALSSAT was upgraded to include the consideration of redundancy and contingency options in the ECLSS, as well as more ALS regenerative technology selections. ALSSAT has been used for the Metric Calculation for FY02 and FY03 (Ref. 3). Several trade studies were conducted in 2003. The analytical results will be presented in this paper.

Yeh, H. Y. (Jannivine)↗
NASA NTRSDate not supplied

Human placenta-derived stromal cells mitigate select immune responses to simulated weightlessness

Space flight presents unique risks to human health. Microgravity and social isolation are important space flight stressors which have vast impact on multiple systems such as the musculoskeletal, immune and central nervous system (CNS). To explore candidate countermeasures against tissue decrements caused by spaceflight, we performed a pilot study with PLacental-eXpanded stromal (PLX) cells. PLX cells secrete factors that modulate the immune system, but do not trigger host immune responses, enabling treatment in subjects that have intact immune systems. These cells had previous success in treating critical limb ischemia and acute radiation syndrome in murine models. In this study, 4-month-old female mice underwent 30 days of hindlimb unloading (HU) in single housing conditions to simulate long-term weightlessness and isolation. HU and normally ambulating controls were administered with PLX cells or sham-injected with Plasmalyte. We hypothesized that administration of PLX cells will mitigate some of the effects caused by prolonged HU. We found that treatment with PLX cells was well tolerated as indicated by body weights, circulating corticosterone levels and immune organ response to injection of the cells. HU led to the expected decreases in soleus muscle weights and bone loss. We did not observe any protective properties of the PLX cells in the musculoskeletal system but found that the cell treatment partially protected from a subset of HU-induced immune and CNS changes. In the hippocampus, protein levels of 9 of 44 cytokines were downregulated by HU. PLX administration mitigated changes in expression of 8 of these cytokines, including IL-2, IL-6 and MCP-1. To our knowledge, this is the first report on the protective effects of PLX-PAD against HU-induced changes in the cytokine milieu of the CNS. Our findings warrant further studies, which includes testing in actual spaceflight.

Microgravity↗
NASA NTRSDate not supplied

Application of a Detached Eddy Simulation Approach with Finite-Rate Chemistry to Mars-Relevant Retropropulsion Operating Environments

Human-scale Mars vehicles will require retropropulsion for descent and landing, replacing heritage supersonic parachute systems with an extended phase of powered flight. Due to the limitations of terrestrial testing in Mars-relevant conditions, design and analysis will increasingly rely on computational modeling and simulation. This paper provides an overview of a computational campaign investigating the aerodynamics of a Mars lander concept along various points on a powered descent trajectory including supersonic, transonic, and subsonic conditions using finite-rate chemistry. Simulations using unstructured grids containing billions of elements are performed at scale using thousands of Graphics Processing Units, enabling run-times of a few days for each simulation presented. At each freestream condition, significant minor species concentrations are observed external to the nozzles in the large mixing region upstream of the vehicle. While the flowfields are highly non-stationary in all cases, the mean integrated forces and moments on the vehicle remain small in comparison to the deceleration provided through retropropulsion.

Ashley Korzun↗
NASA NTRSDate not supplied

Verification of the Generalized Aerospace Simulation in Simulink

NASA uses six-degrees-of-freedom (6-DOF) simulations tools to design, test, develop Guidance Navigation and Control (GN&C) software, and certify vehicle performance prior to flight. Therefore, it is critical that the 6-DOF tools used for vehicle design and certification are validated. The focus of this work is the validation of the NASA Marshall Space Flight Center 6-DOF “GeneraLized Aerospace Simulation in Simulink” (GLASS) framework tool. The GLASS tool framework is currently used to support NASA GN&C insight for the Human Landing System (HLS) project, simulating vehicle dynamics during lunar descent and ascent. The GLASS framework utilizes the off-the-shelf Mathworks (R) Simscape (TM) Multibody (TM) toolbox to model vehicle multi-body dynamics. NASA’s Engineering and Safety Center (NESC) provides a set of 6-DOF simulation verification “check cases” that are available to any user needing to verify 6-DOF tools. The check cases contain seventeen atmospheric and twenty-six orbital test scenarios are provided to validate equations of motion, environmental models (e.g., atmosphere, gravitation, and geodesy) and tool propagators. This paper compares GLASS 6-DOF simulation results against the NESC check cases’ results via simulation-to-simulation comparisons. The comparison demonstrates that GLASS simulation results are “in family” with the outputs of the applicable NASA NESC check-cases and verify the GLASS core framework dynamics and the implementation of the check case scenario models.

6-Dof↗
NASA NTRSDate not supplied

IMPACT 1.0—Task Impairment: A Novel Approach for Assessing Impairment during Exploration-Class Missions

Exploration-class and International Space Station (ISS) missions have significantly different levels of associated medical risk for crewmembers. The Integrated Medical Model (IMM), the probabilistic risk assessment tool used for ISS medical trade-space analyses, uses a Functional Impairment (FI) metric to determine quality time lost should a crewmember be afflicted with a medical condition. While IMM-based FI has been successful for ISS operations, it has limitations when applied to exploration-class missions. As the National Aeronautics and Space Administration (NASA) looks ahead to Gateway, Artemis, and Martian missions, a novel, dynamic, and mission-appropriate impairment paradigm is necessary for accurate contingency planning. This paradigm, Task Impairment (TI), fulfills that need by utilizing mission-specific tasks. TI will allow future capability for a loss of mission metric, previously not available with IMM. TI serves as a replacement metric for FI within IMPACT, the next-generation trade-space analysis tool suite created to replace the IMM. IMPACT significantly increases the fidelity of probabilistic risk assessment for exploration-class missions. The Human Exploration of Mars Preliminary List of Crew Tasks (MTL, a source of 1100+ exploration-class mission-specific tasks for crewmembers) was used to calculate TI. Using the Task List, 18 individual Human System Categories were identified as being required to perform each task (e.g., Cardiopulmonary, Cognitive, etc.). Each of the 1200+ tasks were mapped to the Human System categories listed in the Task List. The total number of tasks in each category were tallied to determine how many tasks required each Human System. Lastly, each of the 120 medical conditions from the IMPACT condition list were mapped to the Human System categories to complete the TI calculation. NASA subject matter experts across five medical specialties established consensus for the mapping of each condition. The resulting total tasks impaired by each condition were used to calculate discrete TI values. This process was repeated for each of the four severity/resource utilization variants of each condition, and for each of the three phases of clinical care. Through this process, discrete TI values were calculated for each of the 120 IMPACT medical conditions and their variants. The Task Impairment metric provides higher fidelity, dynamic utility, and quicker analysis of medical impairment compared to the previous Functional Impairment metric used for the Integrated Medical Model. TI will be used for higher fidelity medical impairment analysis and contingency planning during Lunar, Martian, and other long-term exploration-class missions.

William L Fernandez↗
DOE OSTI2019

Jiangsu Nanjing – Lawrence Berkeley Life Sciences Center for Translational Medicine (CRADA AWD00000392)

Project 1: Our previous work demonstrated that the CES biomarker can be an approximate measure for the levels of chromosomal instability (CIN) in cancer, and that the CES (for Centromere and kinetochore gene Expression Score) biomarker effectively predicts which cancer patients benefit from adjuvant chemotherapies in lung and other cancer types. Such a utility can potentially reduce overtreatment that is common in lung cancer treatment. We determined performance of the multiplex Quantigene RNA assay platform for measuring mRNA levels of CES genes using clinical tumor samples. These experiments determined sensitivity, specificity and detection limit of 12 CES genes and 5 control genes using clinical tumor samples with paired normal tissue samples. We determined how sample concentration and storage conditions affect measurement sensitivity and specificity, and whether sample storage over time affects result reproducibility. Importantly, we confirmed that early stage lung cancer CES values are significantly higher than those of paired normal tissues in all cases. This result greatly extended our bioinformatic data mining by confirming that we can measure CES values directly from patient tumor tissue, and that tumors have higher CES values than normal tissues. Unfortunately, our CRADA partner in China did not finish measuring CES expression levels for a larger group of ~100 tumor samples, due to the long period time required for human subject review in China. This delay prevented us from generating enough additional data to generate a significant statistical model. We finished analyzing all the data acquired during the CRADA, and are trying to work collaboratively with the Shanghai University Lung Cancer Hospital to complete the analysis of more tumor samples. Although no publications have resulted from this research, the findings are crucial for ongoing efforts to obtain funding to develop the technology to address problems in deciding on appropriate treatment regimens for individual patients, in clinical settings. Project 2: The overarching milestones of CRADA Project 2 were to investigate the role of FAM83 family members in cancer progression. Two publications resulted from this work which are highlighted below. The first major goal of the CRADA was to broadly assess the role of FAM83 family oncogenes in human cancer using a multi-omic bioinformatic approach (Snijders et al 2017 Mol. Oncol 11). The second major goal was to specifically assess the role of FAM83D in a cancer progression and metastasis (Yin et al 2019 Cell Oncol; in revision). Role of FAM83 family oncogenes in human cancers Recent studies from our laboratory as well as other groups have demonstrated that FAM83 family members have oncogenic properties and have significantly elevated expression levels in multiple human tumor types, including breast cancers (BC). We performed an integrative multi-omics bio-informatic analysis of the role of FAM83 family oncogenes in human cancers. Our findings were published in the journal Molecular Oncology (Snijders et al; Mol Oncol. 2017 Feb;11(2):167-179). Highlights from this manuscript are: • Transcriptional levels of FAM83 family members are frequently and dramatically elevated across tumor types in human • DNA copy number increase appears to be a potential mechanism for increased expression of FAM83 genes • FAM83 family gene signature is associated with poor survival across human tumor types • FAM83 gene alterations are significantly correlated with TP53, PIK3CA and Ecadherin mutations. In conclusion, this analysis of FAM83 family members opened up a new horizon for further understanding the significance of FAM83 genes in cancer and clinical applications in diagnosis, prognosis and therapy. Role of FAM83D in human non-small cell lung cancer (NSCLC) metastasis and its impact on chemotherapy. Lung cancer is one of the most common malignant tumors in the world and non-small cell lung cancer (NSCLC) accounts for 80~85% of all diagnosed lung cancer cases, one of the deadliest cancers worldwide. FAM83D has been proposed as an oncoprotein in several types of human cancer, yet its role and molecular mechanism in human non-small cell lung cancer (NSCLC) metastasis and its impact on chemotherapy are poorly understood. We evaluated the expression of FAM83D in NSCLC cells and cancerous tissues and its prognostic significance, then investigated the effect of FAM83D on tumorigenic phenotypes such as cell proliferation, EMT and invasive capability, and finally assessed impact of FAM83D on NSCLC chemo-drug resistance. The results of this study are currently in revision at the journal Cellular Oncology. Highlights from this manuscript are: • FAM83D expression is elevated in NSCLC cells and tissues and associated with poor prognosis • FAM83D promotes NSCLC cell proliferation • FAM83D induces EMT in NSCLC cells • FAM83D promotes NSCLC cell migration and invasion • FAM83D promotes EMT and invasive capability of NSCLC cells by regulating AKT/mTOR pathway • FAM83D silencing increases cisplatin sensitivity of NSCLC cells In summary, this second study demonstrated for the first time that FAM83D promoted EMT and metastasis of NSCLC cells through AKT/mTOR signal pathway as well as increased the sensitivity of NSCLC to cisplatin. This study may provide potential targets for advanced NSCLC treatment and personalized therapy.

59 BASIC BIOLOGICAL SCIENCES↗