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NASA NTRS2019

Towards the Development of Real-Time Normalized Burn Ratio (NBR) and Delta NBR Imagery from GOES-16/17 and S-NPP

Burned landscapes present difficult hydrologic forecasting challenges for National Weather Service Offices. Burned soils and landscapes can be conducive to the development of flash flooding and landslides from heavy precipitation events. The severity of the burn scar can be directly related to the risk for debris flows and flash flooding. Burned Area Reflectance Classification (BARC) map is generated to indicate the degree of burn severity, which is generated initially by high-resolution satellite imagery from sources such as Landsat, and later by labor-intensive efforts conducted at the burn scar by Burned Area Emergency Response (BAER) teams. The challenge for operational meteorologists is that these sources of information are not readily available in near real-time. Landsat imagery, for example, may only be available about once every eight days, and cloudy conditions can obstruct the observation of the burn scar during a single pass. BAER teams cannot conduct assessments until the wildfire has been at least 40 percent contained (up to 80 percent in some regions), and the process itself can take further days to weeks to complete depending on a number of factors. To help remedy this lapse in knowledge, NASA SPoRT has developed the generation of NBR imagery in the Advanced Weather Interactive Processing System (AWIPS) using data from the operational GOES 16 and 17 satellites and S-NPP. This presentation will discuss the development of the GOES- and SNPP-derived NBR and dNBR imagery and their initial evaluation by real-time decision makers.

Berndt, Emily↗

NASA NTRSDate not supplied

Comparing the Regional Variability of Emission Factors of Greenhouse Gases Over Different Landscape During FIREX-AQ Campaign

Biomass burning (wildfires, prescribed and agricultural burning) is one of the major sources of trace gases and particulate emissions and annual variability in growth rates. Biomass burning can impact local, regional, and global air quality, as well as climate. Measurements of emissions from biomass burning are crucial to a better understanding of how it influences and interacts with biogeochemical cycles. High resolution in-situ measurements were recorded onboard the NASA DC-8 aircraft during the FIREX-AQ (Fire Influence on Regional to Global Environments and Air Quality) airborne field campaign July-September, 2019, which was conducted over the continental U.S. Fire emission factors (EF) are essential input for emissions models used to develop biomass burning emission inventories. Here we present the Emission Ratio (ER), MCE (Modified Combustion Efficiency), and EF (Emission Factor) of CO2, CO, and CH4, which constitute the majority of carbon emitted from the wildland, prescribed, and agricultural fires. EFCO2, EFCO, and EFCH4 from the Wildland fires at Williams Flats, WA (primarily Douglas Fir, Ponderosa pine, wheatgrass: 50-75%), ranged from 1527 – 1820 g/kg (Avg. 1641±42), 6.5 – 174.1 g/kg (110.5±24.1), and 0.7 – 11.3 g/kg (6.2±1.9), respectively. EFs from the Arizona CASTLE fire, with somewhat different fuel sources (primarily Ponderosa pine, Douglas fir: 40-70%) ranged from 1266 – 1667 g/kg (1596±59), 99.5 – 344.5 g/kg (136.7±36.8), and 0.4 – 9.2 g/kg (7.2±1.7), respectively. Another primary driver of EFs is likely fire weather. Detailed variability of greenhouse gas EFs will be examined and presented in accordance with different fuels and fire conditions at burned areas, specifically within unique wildland and croplands, using the FCCS (Fuel Characteristic Classification System) 30m land cover identification and the Cropland Data Layer (CDL).

Biomass burning↗

NASA NTRSDate not supplied

Comparing the regional variability of emission factors of greenhouse gases over different landscape during FIREX-AQ campaign

Biomass burning↗

DOE OSTI2026

ORCHA: A performance portability system for extreme heterogeneity

Heterogeneity is the prevalent trend in the rapidly evolving high-performance computing (HPC) landscape in both hardware and application software. The diversity in hardware platforms, currently comprising various accelerators and a future possibility of specializable chiplets, poses a significant challenge for scientific software developers aiming to harness optimal performance across different computing platforms while maintaining the quality of solutions when their applications are simultaneously growing more complex. Code synthesis and code generation can provide mechanisms to mitigate this challenge. We have developed a divide and conquer approach where different aspects of performance are handled by different stand-alone tools that are interfaced with the application through generated code. This portability system, ORCHA, enables users to configure and orchestrate their computations among available resources on a platform by specifying a high-level recipe, thereby permitting a many-to-many paradigm where each recipe results in a different variant of the application. The core design goal is to let users decide the application’s hardware mapping and orchestration by editing only the high-level recipe—without modifying the maintained source code or binding the application to a particular runtime system. Tools in ORCHA distribution are: CG-Kit for translating the recipe into an execution graph; Milhoja to execute the graph by orchestrating data and task movement among hardware resources; and Macroprocessor that enables users to define their own code-shorthand for higher composability and easier management of code variants. Additionally, the design of ORCHA permits tools to work in a plug-and-play mode where the application can build and run without CG-Kit and Milhoja, and either tool can be swapped out for other tools with similar capabilities by modifying the code generation portion of ORCHA. In this paper, we describe the design of ORCHA and the role that code-generation plays in isolating applications from tools. We demonstrate the breadth of configurations ORCHA enables with a case study in which an application configuration is realized on three distinct hardware mappings—a GPU-centric, a CPU/GPU balanced, and a CPU/GPU concurrent layouts by using different recipes.

Lee, Youngjun↗

DOE OSTI2020

Review of Landscaping Equipment Vibration Risk Exposure, LLNL Site 200 (Various Locations), Whole Body Vibration Seated and Standing Mowers

WBV (Whole-Body Vibration) for operators of seated and standing lawn mowers was conducted during landscape activities from April 2019 through August 2019 at various locations around LLNL Site 200. Mowing is generally performed by each landscaper for 2-3 days every other week with a maximum duration of 3-5 hours each day. On mowing days, other tasks are performed for the remainder of the day that do not expose the worker to WBV such as irrigation, blowing, or sweeping. The mowing terrain at LLNL is variable with typical reported conditions such as gopher holes, exposed tree roots, quick turns, normal and high growth, weeds, and mushy/wet ground that can result in jolts to the mower operator. The Larson Davis HVM200 seat pad with tri-axial accelerometer was used for monitoring workers. For the seated mowers, the HVM200 seat pad with tri-axial accelerometer was placed on the seat between the user and seat pan of the mower. For standing mowers, the seat pad with tri-axial accelerometer was wrapped with Dycem (for seat pad protection) and placed directly on top of the mower deck and underneath the end-user’s right foot. Monitoring for WBV was conducted while operators used different types, makes, and models of mowers. A total of sixteen samples were collected from the operators of seated mower and 6 samples were collected from operators of standing mower. This initial data sampling was part of a discovery phase to identify if WBV exposure met the 2016 ACGIH TLVs (Threshold Limit Values). This initial data identified that workers operating the mowers do not exceed either the TLV or Action Level (AL) for Weighted Acceleration Levels (A(8)) but typically exceed the Vibration Dose Value (VDV) lower boundary for the standing mowers.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2008

Mars Mapping Technology Brings Main Street to Life

The Red Planet has long held a particular hold on the human psyche. From the Roman god of war to Orson Welles infamous Halloween broadcast, our nearest planetary neighbor has been viewed with curiosity, suspicion, and awe. Pictures of Mars from 1965 to the present reveal familiar landscapes while also challenging our perceptions and revising our understanding of the processes at work in planets. Frequent discoveries have forced significant revisions to previous theories. Although Mars shares many familiar features with Earth, such as mountains, plains, valleys, and polar ice, the conditions on Mars can vary wildly from those with which we are familiar. The apparently cold, rocky, and dusty wasteland seen through the eyes of spacecraft and Martian probes hints at a dynamic past of volcanic activity, cataclysmic meteors, and raging waters. New discoveries continue to revise our view of our next-door neighbor, and further exploration is now paving the way for a human sortie to the fourth stone from the Sun. NASA s Mars Exploration Program, a long-term effort of robotic exploration, utilizes wide-angle stereo cameras mounted on NASA s twin robot geologists, the Mars Exploration Rovers (MERs), launched in 2003. The rovers, named "Spirit" and "Opportunity," celebrated 4 Earth years of exploration on January 3, 2008, and have sent back a wealth of information on the terrain and composition of the Martian surface. Their marathon performance has far outlasted the intended 90 days of operation, and the two intrepid explorers promise more images and data.

Source record↗

NASA NTRS2009

The Long, Hard Journey: Expanding the Use of NASA Data and Models for Sustainable Development Planning Around the World

In 2007, the National Research Council's committee on Extending Observations and Research Results to Practical Applications recommended that NASA's Applied Science Program (ASP) directly engage with a broader community of users - not just federal agencies. Soon afterwards, scientists at the NASA Marshall Space Flight Center began discussions on a collaborative research project with EMBARQ - the World Resource Institute's Center for Sustainable Transport. The discussions initially focused on how best to utilize satellite observations and atmospheric models for assessing the impact of a proposed transportation project on land use and air quality. Discussions exposed the participants to a broad spectrum of science and policy challenges that these diverse organizations face on a routine basis. It brought into clear focus the need for an observation-modeling system that will allow a proactive approach towards development planning, and the fact that satellite systems do not always provide the spatial and temporal resolution useful for urban-scale applications, underscoring the need for earth system models to bridge this gap. Realizing the significant risk that unplanned urbanization and climate change pose to the social and functional stability of large cities, both organizations decided to expand the scope of their preliminary discussion to include water resources and agriculture. A pilot project, funded by NASA ASP, EMBARQ and Istanbul Technical University focused on quantifying the magnitude and extent of urbanization in Istanbul, and analyzed the combined effect of urbanization and projected climate change on local climate, air quality, and its consequent effects on agricultural productivity. Preliminary results show that Istanbul has undergone a significant amount of Land Use/Land Cover change over the past two decades. While some forested areas have been lost to urban-landscapes, urbanization has mostly occurred over former croplands due to the fact that in contrast to forested areas, croplands have flat terrain, making them a more attractive target for conversion to urban land. The reduction in land area for agricultural and increase in temperature has reduced agricultural production in the vicinity of the city. The United Nations expects the world population to reach 9.2 billion by 2050. Most of this increase will be absorbed by countries that are least able to sustain it. Rapid economic growth is likely to continue and will require massive infrastructure investments. If not properly managed, the magnitude and extent of these initiatives will overwhelm sensitive ecosystems around the world. Successful relationships such as the one described here are critically important if we are to stabilize the threatened ecosystems on which human systems ultimately rely. The challenges, needs, culture, and operating environment of development planning organizations are fundamentally different from a research organization. An appreciation of these differences is a prerequisite for any successful collaboration.

Khan, Maudood↗

DOE OSTI2025

Filling the Gaps: A Bayesian Mixture Model for Imputing Missing Soil Water Content Data

ABSTRACT Soil water content (SWC) data are central to evaluating how soil moisture varies over time and space and influences critical plant and ecosystem functions, especially in water‐limited drylands. However, sensors that record SWC at high frequencies often malfunction, leading to incomplete timeseries and limiting our understanding of dryland ecosystem dynamics. We developed an analytical approach to impute missing SWC data, which we tested at six eddy flux tower sites along an elevation gradient in the southwestern United States. We impute missing data as a mixture of linearly interpolated SWC between the observed endpoints of a missing data gap and SWC simulated by an ecosystem water balance model (SOILWAT2). Within a Bayesian framework, we allowed the relative utility (mixture weight) of each component (linearly interpolated vs. SOILWAT2) to vary by depth, site and gap characteristics. We explored “fixed” weights versus “dynamic” weights that vary as a function of cumulative precipitation, average temperature, and time since the start of the gap. Both models estimated missing SWC data well ( R 2 = 0.70–0.88 vs. 0.75–0.91 for fixed vs. dynamic weights, respectively), but the utility of linearly interpolated versus SOILWAT2 values depended on site and depth. SOILWAT2 was more useful for more arid sites, shallower depths, longer and warmer gaps and gaps that received greater precipitation. Overall, the mixture model reliably gap‐fills SWC, while lending insight into processes governing SWC dynamics. This approach to impute missing data could be adapted to accommodate more than two mixture components and other types of environmental timeseries.

Ogle, Kiona [School of Informatics, Computing, and↗

DOE OSTI2020

A Comprehensive Scientific Survey of Excipients Used in Currently Marketed, Therapeutic Biological Drug Products

The steady development of biotechnology-derived therapeutic biologics over the last few decades has generated drugs that are now standard medical treatments for a range of indications. While the development of protein products has surged in recent years, the formulation and delivery of these complex molecules have relied on drug-specific studies and, in some instances, data from non-proteinaceous drug products. The commonalities, trends, and gaps in excipient technologies used to support the development of therapeutic proteins largely remain unexplored due to the drug-specific nature of many formulations. Using a comprehensive and relational database approach, we aimed to provide a scientific survey of all approved or licensed biotechnology-derived drug products with the goal of providing evidence-based information on common attributes and trending features in protein product excipients. We examined 665 formulations, and 395 unique formulations based on having unique excipients within them, that supported 211 therapeutic proteins as of June 2020. We report the prevalence of each excipient class and excipient chemical used in eight different drug types including monoclonal antibodies, antibody conjugates, cytokines and growth factors, enzymes, polypeptide hormones, pulmonary surfactants, recombinant fusion proteins, and toxins. We also report the prevalence by excipient type among all therapeutic proteins, in the context of each drug’s recommended pH range, concentration ranges for excipients, and route of administration. The results of our analyses indicate certain excipients common to monoclonal antibodies, cytokines, and polypeptide hormones. We also report on excipients unique to protein drug products, such as amino acids, solubilizers, and lyoprotectants. Overall, our report summarizes the current landscape of excipients used in marketed biotechnology-derived therapeutic biologic products.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2022

Satellite-derived cyanobacteria frequency and magnitude in headwaters & near-dam reservoir surface waters of the Southern U.S.

Reservoirs are dominant features of the modern hydrologic landscape and provide vital services. However, the unique morphology of reservoirs can create suitable conditions for excessive algae growth and associated cyanobacteria blooms in shallow in-flow reservoir locations by providing warm water environments with relatively high nutrient inputs, deposition, and nutrient storage. Cyanobacteria harmful algal blooms (cyanoHAB) are costly water management issues and bloom recurrence is associated with economic costs and negative impacts to human, animal, and environmental health. As cyanoHAB occurrence varies substantially within different regions of a water body, understanding in-lake cyanoHAB spatial dynamics is essential to guide reservoir monitoring and mitigate potential public exposure to cyanotoxins. Cloud-based computational processing power and high temporal frequency of satellites enables advanced pixel-based spatial analysis of cyanoHAB frequency and quantitative assessment of reservoir headwater in-flows compared to near-dam surface waters of individual reservoirs. Additionally, extensive spatial coverage of satellite imagery allows for evaluation of spatial trends across many dozens of reservoir sites. Surface water cyanobacteria concentrations for sixty reservoirs in the southern U.S. were estimated using 300m resolution European Space Agency (ESA) Ocean and Land Colour Instrument (OLCI) satellite sensor for a five year period (May 2016–April 2021). Of the reservoirs studied, spatial analysis of OLCI data revealed 98% had more frequent cyanoHAB occurrence above the concentration of >100,000 cells/mL in headwaters compared to near-dam surface waters (P < 0.001). Headwaters exhibited greater seasonal variability with more frequent and higher magnitude cyanoHABs occurring mid-summer to fall. Examination of reservoirs identified extremely high concentration cyanobacteria events (>1,000,000 cells/mL) occurring in 70% of headwater locations while only 30% of near-dam locations exceeded this threshold. Wilcoxon signed-rank tests of cyanoHAB magnitudes using paired-observations (dates with observations in both a reservoir's headwater and near-dam locations) confirmed significantly higher concentrations in headwater versus near-dam locations (p < 0.001).

cyanobacteria↗

NASA NTRS2022

Satellite-derived Cyanobacteria Frequency and Magnitude in Headwaters & Near-dam Reservoir Surface Waters of the Southern U.S.

Reservoirs are dominant features of the modern hydrologic landscape and provide vital services. However, the unique morphology of reservoirs can create suitable conditions for excessive algae growth and associated cyanobacteria blooms in shallow in-flow reservoir locations by providing warm water environments with relatively high nutrient inputs, deposition, and nutrient storage. Cyanobacteria harmful algal blooms (cyanoHAB) are costly water management issues and bloom recurrence is associated with economic costs and negative impacts to human, animal, and environmental health. As cyanoHAB occurrence varies substantially within different regions of a water body, understanding in-lake cyanoHAB spatial dynamics is essential to guide reservoir monitoring and mitigate potential public exposure to cyanotoxins. Cloud-based computational processing power and high temporal frequency of satellites enables advanced pixel-based spatial analysis of cyanoHAB frequency and quantitative assessment of reservoir headwater in-flows compared to near-dam surface waters of individual reservoirs. Additionally, extensive spatial coverage of satellite imagery allows for evaluation of spatial trends across many dozens of reservoir sites. Surface water cyanobacteria concentrations for sixty reservoirs in the southern U.S. were estimated using 300 m resolution European Space Agency (ESA) Ocean and Land Colour Instrument (OLCI) satellite sensor for a five year period (May 2016–April 2021). Of the reservoirs studied, spatial analysis of OLCI data revealed 98% had more frequent cyanoHAB occurrence above the concentration of >100,000 cells/mL in headwaters compared to near-dam surface waters (P < 0.001). Headwaters exhibited greater seasonal variability with more frequent and higher magnitude cyanoHABs occurring mid-summer to fall. Examination of reservoirs identified extremely high concentration cyanobacteria events (>1,000,000 cells/mL) occurring in 70% of headwater locations while only 30% of near-dam locations exceeded this threshold. Wilcoxon signed-rank tests of cyanoHAB magnitudes using paired-observations (dates with observations in both a reservoir's headwater and near-dam locations) confirmed significantly higher concentrations in headwater versus near-dam locations (p < 0.001).

Amber R Ignatius↗

DOE OSTI2020

River bank erosion and accretion rates, and planform metrics of the East River, downstream of Gothic Colorado over intervals between 1955 and 2015

This dataset provides the tabular summary of analysis of an alluvial floodplain reach of the East River, downstream of Gothic, CO near Crested Butte. The measurements include bank erosion and accretion rates, channel width, bank curvature, and the aspect/orientation of the river banks. The measurements were derived from binary masks of the location of the river channel from aerial and satellite imagery collected on seven dates over a sixty-year timespan: 1955, 1973, 1983, 1990, 2001, 2011, and 2015. The masks were analyzed using the Spatially Continuous Riverbank Erosion and Accretion Measurements (SCREAM) software detailed in Rowland et al. 2016. The masks used in this analysis can be found an accompanying dataset (DOI: ). Rates of change along the East River were measured over a total of 8-time intervals: 1955-1973; 1955-2015; 1973-1983; 1983-1990; 1990-2001; 2001-2011; 2001-2015; and 2011-2015.In files with “summary” in the name, the data is provided at a pixel level, where each mapped bank pixel has an associated erosion or accretion value, a channel width, a curvature value, and an aspect each river and time period will have an individual file. Files with “Segments” in name provide data that is averaged along segments of the rivers. These segments are approximately 10 channel widths in length. In addition to erosion and accretion rates, the segment-based results include area measurements of erosion and accretion, islands, and channels. The number of islands is also included.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Role of social structure in establishment of an invasive large mammal after translocation

Abstract Background Data on the movement behavior of translocated wild pigs is needed to develop appropriate response strategies for containing and eliminating new source populations following translocation events. We conducted experimental trials to compare the home range establishment and space‐use metrics, including the number of days and distance traveled before becoming range residents, for wild pigs translocated with their social group and individually. Results We found wild pigs translocated with their social group made less extensive movements away from the release location and established a stable home range ~5 days faster than those translocated individually. We also examined how habitat quality impacted the home range sizes of translocated wild pigs and found wild pigs maintained larger ranges in areas with higher proportion of low‐quality habitat. Conclusion Collectively, our findings suggest translocations of invasive wild pigs have a greater probability of establishing a viable population near the release site when habitat quality is high and when released with members of their social unit compared to individuals moved independent of their social group or to low‐quality habitat. However, all wild pigs translocated in our study made extensive movements from their release location, highlighting the potential for single translocation events of either individuals or groups to have far‐reaching consequences within a much broader landscape beyond the location where they are released. These results highlight the challenges associated with containing populations in areas where illegal introduction of wild pigs occurs, and the need for rapid response once releases are identified. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Clontz, Lindsay M.↗

NASA NTRS2009

Sequential Imaging of Earth by Astronauts: 50 Years of Global Change

For nearly 50 years, astronauts have collected sequential imagery of the Earth. In fact, the collection of astronaut photography comprises one of the earliest sets of data (1961 to present) available to scientists to study the regional context of the Earth s surface and how it changes. While today s availability of global high resolution satellite imagery enables anyone with an internet connection to examine specific features on the Earth s surface with a regional context, historical satellite imagery adds another dimension (time) that provides researchers and students insight about the features and processes of a region. For example, one of the geographic areas with the longest length of record contained within the astronaut photography database is the lower Nile River. The database contains images that document the flooding of Lake Nasser (an analog to today s flooding behind China s Three Gorges Dam), the changing levels of Lake Nasser s water with multiyear cycles of flood and drought, the recent flooding and drying of the Toshka Lakes, as well as urban growth, changes in agriculture and coastal subsidence. The imagery database allows investigations using different time scales (hours to decades) and spatial scales (resolutions and fields of view) as variables. To continue the imagery collection, the astronauts on the International Space Station are trained to understand basic the Earth Sciences and look for and photograph major events such as tropical storms, landslides, and volcanic eruptions, and document landscapes undergoing change (e.g., coastal systems, cities, changing forest cover). We present examples of selected sequences of astronaut imagery that illustrate the interdependence of geological processes, climate cycles, human geography and development, and prompt additional questions about the underlying elements of change.

Evans, Cynthia A.↗

DOE OSTI2022

Vulnerability and resilience of urban energy ecosystems to extreme climate events: A systematic review and perspectives

We reviewed the present studies on the vulnerability and resilience of the energy ecosystem (most parts of the energy ecosystem), considering extreme climate events. This study revealed that the increased interactions formed during the transformation of the energy landscape into an ecosystem could notably increase the vulnerability of the energy infrastructure. Such complex ecosystem cannot be assessed using the present state of the art models used by the energy system modelers. Therefore, this study introduces a novel analogy known as the COVID analogy to understand the propagation of disruption within and beyond the energy ecosystem and organized the present state of the art based on the COVID analogy. The analogy helps to categorize the vulnerability of the energy infrastructure into three stages. The study revealed that although there are many publications covering the vulnerability and resilience of the energy infrastructure, considering extreme climate events, the majority are focused on the direct impact of extreme climate on the energy ecosystem. In addition, most of the studies do not consider the impact of future climate variations during this assessment. The propagation of disruptions was assessed mainly for wildfires and hurricanes. Further, there is a clear research gap in considering vulnerability assessment for interconnected energy infrastructure. Here, the transformation of energy systems into a complex ecosystem notably increases the complexity, making it difficult to assess vulnerability and resilience. A shift from a centralized to decentralized modeling architecture could be beneficial when considering the complexities brought by that transformation. Hybrid models consisting of both physical and data-driven machine learning techniques could also be beneficial in this context.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2016

DeepSAT: A Deep Learning Approach to Tree-Cover Delineation in 1-m NAIP Imagery for the Continental United States

High resolution tree cover classification maps are needed to increase the accuracy of current land ecosystem and climate model outputs. Limited studies are in place that demonstrates the state-of-the-art in deriving very high resolution (VHR) tree cover products. In addition, most methods heavily rely on commercial softwares that are difficult to scale given the region of study (e.g. continents to globe). Complexities in present approaches relate to (a) scalability of the algorithm, (b) large image data processing (compute and memory intensive), (c) computational cost, (d) massively parallel architecture, and (e) machine learning automation. In addition, VHR satellite datasets are of the order of terabytes and features extracted from these datasets are of the order of petabytes. In our present study, we have acquired the National Agriculture Imagery Program (NAIP) dataset for the Continental United States at a spatial resolution of 1-m. This data comes as image tiles (a total of quarter million image scenes with ~60 million pixels) and has a total size of ~65 terabytes for a single acquisition. Features extracted from the entire dataset would amount to ~8-10 petabytes. In our proposed approach, we have implemented a novel semi-automated machine learning algorithm rooted on the principles of "deep learning" to delineate the percentage of tree cover. Using the NASA Earth Exchange (NEX) initiative, we have developed an end-to-end architecture by integrating a segmentation module based on Statistical Region Merging, a classification algorithm using Deep Belief Network and a structured prediction algorithm using Conditional Random Fields to integrate the results from the segmentation and classification modules to create per-pixel class labels. The training process is scaled up using the power of GPUs and the prediction is scaled to quarter million NAIP tiles spanning the whole of Continental United States using the NEX HPC supercomputing cluster. An initial pilot over the state of California spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles has produced true positive rates of around 88 percent for fragmented forests and 74 percent for urban tree cover areas, with false positive rates lower than 2 percent for both landscapes.

Imagery↗

DOE OSTI2024

A modular and extensible CHARMM-compatible model for all-atom simulation of polypeptoids

Peptoids (N-substituted glycines) are a class of sequence-defined synthetic peptidomimetic polymers with applications including drug delivery, catalysis, and biomimicry. Classical molecular simulations have been used to predict and understand the conformational dynamics of single chains and their self-assembly into morphologies including sheets, tubes, spheres, and fibrils. The CGenFF-NTOID model based on the CHARMM General Force Field has demonstrated success in accurate all-atom molecular modeling of peptoid structure and thermodynamics. Extension of this force field to new peptoid side chains has historically required reparameterization of side chain bonded interactions against ab initio data. This fitting protocol improves the accuracy of the force field but is also burdensome and precludes modular extensibility of the model to arbitrary peptoid sequences. In this work, we develop and demonstrate a Modular Side Chain CGenFF-NTOID (MoSiC-CGenFF-NTOID) as an extension of CGenFF-NTOID employing a modular decomposition of the peptoid backbone and side chain parameterizations, wherein arbitrary side chains within the large family of substituted methyl groups (i.e., –CH 3 , –CH 2 R, –CHRR', and –CRR'R") are directly ported from CGenFF. We validate this approach against ab initio calculations and experimental data to develop a MoSiC-CGenFF-NTOID model for all 20 natural amino acid side chains along with 13 commonly used synthetic side chains and present an extensible paradigm to efficiently determine whether a novel side chain can be directly incorporated into the model or whether refitting of the CGenFF parameters is warranted. We make the model freely available to the community along with a tool to perform automated initial structure generation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Proteogenomic characterization of difficult-to-treat breast cancer with tumor cells enriched through laser microdissection

Abstract Background Breast cancer (BC) is the most commonly diagnosed cancer and the leading cause of cancer death among women globally. Despite advances, there is considerable variation in clinical outcomes for patients with non-luminal A tumors, classified as difficult-to-treat breast cancers (DTBC). This study aims to delineate the proteogenomic landscape of DTBC tumors compared to luminal A (LumA) tumors. Methods We retrospectively collected a total of 117 untreated primary breast tumor specimens, focusing on DTBC subtypes. Breast tumors were processed by laser microdissection (LMD) to enrich tumor cells. DNA, RNA, and protein were simultaneously extracted from each tumor preparation, followed by whole genome sequencing, paired-end RNA sequencing, global proteomics and phosphoproteomics. Differential feature analysis, pathway analysis and survival analysis were performed to better understand DTBC and investigate biomarkers. Results We observed distinct variations in gene mutations, structural variations, and chromosomal alterations between DTBC and LumA breast tumors. DTBC tumors predominantly had more mutations inTP53,PLXNB3, Zinc finger genes, and fewer mutations inSDC2,CDH1,PIK3CA,SVIL, andPTEN. Notably, Cytoband 1q21, which contains numerous cell proliferation-related genes, was significantly amplified in the DTBC tumors. LMD successfully minimized stromal components and increased RNA–protein concordance, as evidenced by stromal score comparisons and proteomic analysis. Distinct DTBC and LumA-enriched clusters were observed by proteomic and phosphoproteomic clustering analysis, some with survival differences. Phosphoproteomics identified two distinct phosphoproteomic profiles for high relapse-risk and low relapse-risk basal-like tumors, involving several genes known to be associated with breast cancer oncogenesis and progression, includingKIAA1522,DCK,FOXO3,MYO9B,ARID1A,EPRS,ZC3HAV1, andRBM14. Lastly, an integrated pathway analysis of multi-omics data highlighted a robust enrichment of proliferation pathways in DTBC tumors. Conclusions This study provides an integrated proteogenomic characterization of DTBC vs LumA with tumor cells enriched through laser microdissection. We identified many common features of DTBC tumors and the phosphopeptides that could serve as potential biomarkers for high/low relapse-risk basal-like BC and possibly guide treatment selections.

Oncology↗