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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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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Root angle is controlled by EGT1 in cereal crops employing an antigravitropic mechanism

Root angle in crops represents a key trait for efficient capture of soil resources. Root angle is determined by competing gravitropic versus antigravitropic offset (AGO) mechanisms. Here we report a root angle regulatory gene termed ENHANCED GRAVITROPISM1 ( EGT1 ) that encodes a putative AGO component, whose loss-of-function enhances root gravitropism. Mutations in barley and wheat EGT1 genes confer a striking root phenotype, where every root class adopts a steeper growth angle. EGT1 encodes an F-box and Tubby domain-containing protein that is highly conserved across plant species. Haplotype analysis found that natural allelic variation at the barley EGT1 locus impacts root angle. Gravitropic assays indicated that Hvegt1 roots bend more rapidly than wild-type. Transcript profiling revealed Hvegt1 roots deregulate reactive oxygen species (ROS) homeostasis and cell wall-loosening enzymes and cofactors. ROS imaging shows that Hvegt1 root basal meristem and elongation zone tissues have reduced levels. Atomic force microscopy measurements detected elongating Hvegt1 root cortical cell walls are significantly less stiff than wild-type. In situ analysis identified HvEGT1 is expressed in elongating cortical and stele tissues, which are distinct from known root gravitropic perception and response tissues in the columella and epidermis, respectively. We propose that EGT1 controls root angle by regulating cell wall stiffness in elongating root cortical tissue, counteracting the gravitropic machinery’s known ability to bend the root via its outermost tissues. We conclude that root angle is controlled by EGT1 in cereal crops employing an antigravitropic mechanism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Providing biological context for GWAS results using eQTL regulatory and co‐expression networks in Populus

Summary Our study utilized genome‐wide association studies (GWAS) to link nucleotide variants to traits in Populus trichocarpa , a species with rapid linkage disequilibrium decay. The aim was to overcome the challenge of interpreting statistical associations at individual loci without sufficient biological context, which often leads to reliance solely on gene annotations from unrelated model organisms. We employed an integrative approach that included GWAS targeting multiple traits using three individual techniques for lignocellulose phenotyping, expression quantitative trait loci (eQTL) analysis to construct transcriptional regulatory networks around each candidate locus and co‐expression analysis to provide biological context for these networks, using lignocellulose biosynthesis in Populus trichocarpa as a case study. The research identified three candidate genes potentially involved in lignocellulose formation, including one previously recognized gene (Potri.005G116800/VND1, a critical regulator of secondary cell wall formation) and two genes (Potri.012G130000/AtSAP9 and Potri.004G202900/BIC1) with newly identified putative roles in lignocellulose biosynthesis. Our integrative approach offers a framework for providing biological context to loci associated with trait variation, facilitating the discovery of new genes and regulatory networks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Predicting ecosystem metaphenome from community metagenome: A grand challenge for environmental biology

Abstract Elucidating how an organism's characteristics emerge from its DNA sequence has been one of the great triumphs of biology. This triumph has cumulated in sophisticated computational models that successfully predict how an organism's detailed phenotype emerges from its specific genotype. Inspired by that effort's vision and empowered by its methodologies, a grand challenge is described here that aims to predict the biotic characteristics of an ecosystem, its metaphenome, from nucleic acid sequences of all the species in its community, its metagenome. Meeting this challenge would integrate rapidly advancing abilities of environmental nucleic acids (eDNA and eRNA) to identify organisms, their ecological interactions, and their evolutionary relationships with advances in mechanistic models of complex ecosystems. Addressing the challenge would help integrate ecology and evolutionary biology into a more unified and successfully predictive science that can better help describe and manage ecosystems and the services they provide to humanity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Glyceraldehyde-3-phosphate dehydrogenase homologs as bifunctional gatekeepers of metabolic segregation in Pseudomonas putida

Metabolically versatile Pseudomonas species can assimilate various glycolytic and gluconeogenic substrates. Simultaneous assimilation is known to segregate carbons from each substrate type into different metabolic pathways. However, the mechanisms of this metabolic segregation remain unresolved. Here, we investigate Pseudomonas putida KT2440 during processing of the sugar glucose through glycolysis versus the phenolic acid ferulate through gluconeogenesis. Metabolome profiling reveals up to twofold less tricarboxylic acid cycle metabolites but up to 10-fold higher metabolites of upper glycolysis, pentose-phosphate, and Entner–Doudoroff pathways in glucose-grown cells compared to ferulate-grown cells. After 13C-substrate switching, kinetic isotopic profiling captures rapid assimilation of new substrate carbons into initial catabolic pathways, but incorporation into downstream pathways is absent or incomplete. Proteomics identifies a 22-fold higher abundance of one homolog of glyceraldehyde-3-phosphate dehydrogenase (GAPDH, GapA) in cells fed on glucose relative to ferulate, while abundance of another homolog (GapB) remains unchanged. Growth phenotypes and quantitative metabolomics for single and double knockout mutants of these GAPDH homologs indicate only GapA involvement in glycolytic flux, which can be compensated by the Entner–Doudoroff pathway, and distinct preference of GapB with minimal role of GapA for gluconeogenic flux. Accordingly, growth of triple knockout mutant with deletion of gapA, gapB, and edd is possible only when glycolytic and gluconeogenic substrates are provided together to meet metabolic demands in a segregated fashion, but metabolic tradeoffs lead to slow growth. A mathematical, experimentally constrained, model of the GAPDH node shows that tuning of GapA and GapB concentrations enables transition between flux regimes for nutritional adaptability.

Zhou, Nanqing [Northwestern University, Evanston]↗
DOE OSTI2021

Fast and accurate metagenotyping of the human gut microbiome with GT-Pro

Single nucleotide polymorphisms (SNPs) in metagenomics are used to quantify population structure, track strains and identify genetic determinants of microbial phenotypes. However, existing alignment-based approaches for metagenomic SNP detection require high-performance computing and enough read coverage to distinguish SNPs from sequencing errors. To address these issues, we developed the GenoTyper for Prokaryotes (GT-Pro), a suite of methods to catalog SNPs from genomes and use unique k-mers to rapidly genotype these SNPs from metagenomes. Compared to methods that use read alignment, GT-Pro is more accurate and two orders of magnitude faster. Here, using high-quality genomes, we constructed a catalog of 104 million SNPs in 909 human gut species and used unique k-mers targeting this catalog to characterize the global population structure of gut microbes from 7,459 samples. GT-Pro enables fast and memory-efficient metagenotyping of millions of SNPs on a personal computer.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Transforming the study of organisms: Phenomic data models and knowledge bases

The rapidly decreasing cost of gene sequencing has resulted in a deluge of genomic data from across the tree of life; however, outside a few model organism databases, genomic data are limited in their scientific impact because they are not accompanied by computable phenomic data. The majority of phenomic data are contained in countless small, heterogeneous phenotypic data sets that are very difficult or impossible to integrate at scale because of variable formats, lack of digitization, and linguistic problems. One powerful solution is to represent phenotypic data using data models with precise, computable semantics, but adoption of semantic standards for representing phenotypic data has been slow, especially in biodiversity and ecology. Some phenotypic and trait data are available in a semantic language from knowledge bases, but these are often not interoperable. In this review, we will compare and contrast existing ontology and data models, focusing on nonhuman phenotypes and traits. We discuss barriers to integration of phenotypic data and make recommendations for developing an operationally useful, semantically interoperable phenotypic data ecosystem.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

FrESCO: Framework for Exploring Scalable Computational Oncology

The National Cancer Institute (NCI) monitors population level cancer trends as part of its Surveillance, Epidemiology, and End Results (SEER) program. This program consists of state or regional level cancer registries which collect, analyze, and annotate cancer pathology reports. From these annotated pathology reports, each individual registry aggregates cancer phenotype information from electronic health records. This data is then used to create summary statistics about cancer incidence and mortality to facilitate population health monitoring. Extracting phenotypic information from these reports is a labor intensive task, requiring specialized knowledge about the reports and cancer. Automating the information extraction process from cancer pathology reports has the potential to improve data quality by extracting information in a consistent manner across registries. It can also improve patient outcomes by reducing the time from diagnosis, enabling rapid case ascertainment for clinical trials. Here we present FrESCO, a modular deep-learning natural language processing (NLP) library initially designed for extracting pathology information from clinical text documents. This repository is not solely limited to clinical medical text, but may also be used by researchers just getting started with NLP methods and those looking for a robust solution for their classification problems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Deep Convolutional Neural Networks Exploit High-Spatial- and -Temporal-Resolution Aerial Imagery to Phenotype Key Traits in Miscanthus

Miscanthus is one of the most promising perennial crops for bioenergy production, with high yield potential and a low environmental footprint. The increasing interest in this crop requires accelerated selection and the development of new screening techniques. New analytical methods that are more accurate and less labor-intensive are needed to better characterize the effects of genetics and the environment on key traits under field conditions. We used persistent multispectral and photogrammetric UAV time-series imagery collected 10 times over the season, together with ground-truth data for thousands of Miscanthus genotypes, to determine the flowering time, culm length, and biomass yield traits. We compared the performance of convolutional neural network (CNN) architectures that used image data from single dates (2D-spatial) versus the integration of multiple dates by 3D-spatiotemporal architectures. The ability of UAV-based remote sensing to rapidly and non-destructively assess large-scale genetic variation in flowering time, height, and biomass production was improved through the use of 3D-spatiotemporal CNN architectures versus 2D-spatial CNN architectures. The performance gains of the best 3D-spatiotemporal analyses compared to the best 2D-spatial architectures manifested in up to 23% improvements in R2, 17% reductions in RMSE, and 20% reductions in MAE. The integration of photogrammetric and spectral features with 3D architectures was crucial to the improved assessment of all traits. In conclusion, our findings demonstrate that the integration of high-spatiotemporal-resolution UAV imagery with 3D-CNNs enables more accurate monitoring of the dynamics of key phenological and yield-related crop traits. This is especially valuable in highly productive, perennial grass crops such as Miscanthus, where in-field phenotyping is especially challenging and traditionally limits the rate of crop improvement through breeding.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Spatiotemporal 4D Whole-cell Modeling of a Minimal Autotroph Reveals Central Carbon Metabolism Regulated Locally by Protein Megacomplexes via Post-translational Modifications under Light Disturbance

Photosynthetic microorganisms rely on multiple pathways in central carbon metabolism to adapt to fluctuating light and energy availability across diel cycles. Mechanistic insight into the regulatory dynamics of this adaptation requires integrating processes spanning disparate timescales, from rapid redox-dependent post-translational modifications (PTMs) to slower changes in protein expression and metabolic pathway usage. To address this complexity beyond genome-based inference and traditional modeling, we develop a whole-cell four-dimensional (3D + time) model of the marine cyanobacterium Prochlorococcus marinus MED4 that explicitly represents the spatial organization of enzymatic and molecular processes in central carbon metabolism under light perturbation. We employ a perturbation-based research design to experimentally generate time-series, multi-omics measurements that provide molecular descriptors and cryo-ET derived 3D segmented volumes as constraints for this dynamic 4D framework. The integration of experiments and modeling across defined light regimes enables quantitative validation of system-level responses and forecasting under distinct light disturbances. We test the hypothesis that light-dependent redox PTMs regulating the structural assembly of a protein megacomplex, the “dark complex,” modulate metabolic flux at a conserved regulatory node of the Calvin–Benson cycle (CBC) in cyanobacteria. Our model shows that subcellular spatial organization buffers rapid light-induced changes in thylakoid reaction rates, which are followed by redox-PTM-mediated sequestration or release of CBC enzymes in the dark complex, ultimately impacting carbon fixation dynamics within carboxysomes. Comparison with an equivalently parameterized well-mixed stochastic model demonstrates that post-translational regulation not only buffers transcriptional noise and diffusion-driven fluctuations but also stabilizes phenotypic outcomes, underscoring the importance of spatial heterogeneity in phenotypic robustness. This ability to probe adaptive, spatiotemporally resolved mechanisms in photosynthetic machinery and central carbon metabolism addresses a critical gap in genotype-to-phenotype inference and expands modeling and design capabilities for understudied or genetically intractable autotrophs such as P. marinus MED4.

Johnson, Connah G.↗
DOE OSTI2023

INDETERMINATE1 -mediated expression of FT family genes is required for proper timing of flowering in Brachypodium distachyon

The transition to flowering is a major developmental switch in plants. In many temperate grasses, perception of indicators of seasonal change, such as changing day-length and temperature, leads to expression of FLOWERING LOCUS T1 ( FT1 ) and FT-Like ( FTL ) genes that are essential for promoting the transition to flowering. However, little is known about the upstream regulators of FT1 and FTL genes in temperate grasses. Here, we characterize the monocot-specific gene INDETERMINATE1 ( BdID1 ) in Brachypodium distachyon and demonstrate that BdID1 is a regulator of FT family genes. Mutations in ID1 impact the ability of the short-day (SD) vernalization, cold vernalization, and long-day (LD) photoperiod pathways to induce certain FTL genes. BdID1 is required for upregulation of FTL9 ( FT-LIKE9 ) expression by the SD vernalization pathway, and overexpression of FTL9 in an id1 background can partially restore the delayed flowering phenotype of id1 . We show that BdID1 binds in vitro to the promoter region of FTL genes suggesting that ID1 directly activates FTL expression. Transcriptome analysis shows that BdID1 is required for FT1 , FT2 , FTL12 , and FTL13 expression under inductive LD photoperiods, indicating that BdID1 is a regulator of the FT gene family. Moreover, overexpression of FT1 in the id1 background results in rapid flowering similar to overexpressing FT1 in the wild type, demonstrating that BdID1 is upstream of FT family genes. Interestingly, ID1 negatively regulates a previously uncharacterized FTL gene, FTL4, and we show that FTL4 is a repressor of flowering. Thus, BdID1 is critical for proper timing of flowering in temperate grasses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modeling

Remote sensing enables the rapid assessment of many traits that provide valuable information to plant breeders throughout the growing season to improve genetic gain. These traits are often extracted from remote sensing data on a row segment (rows within a plot) basis enabling the quantitative assessment of any row-wise subset of plants in a plot, rather than a few individual representative plants, as is commonly done in field-based phenotyping. Nevertheless, which rows to include in analysis is still a matter of debate. The objective of this experiment was to evaluate row selection and plot trimming in field trials conducted using four-row plots with remote sensing traits extracted from RGB (red-green-blue), LiDAR (light detection and ranging), and VNIR (visible near infrared) hyperspectral data. Uncrewed aerial vehicle flights were conducted throughout the growing seasons of 2018 to 2021 with data collected on three years of a sorghum experiment and two years of a maize experiment. Traits were extracted from each plot based on all four row segments (RS) (RS1234), inner rows (RS23), outer rows (RS14), and individual rows (RS1, RS2, RS3, and RS4). Plot end trimming of 40 cm was an additional factor tested. Repeatability and predictive modeling of end-season yield were used to evaluate performance of these methodologies. Plot trimming was never shown to result in significantly different outcomes from non-trimmed plots. Significant differences were often observed based on differences in row selection. Plots with more row segments were often favorable for increasing repeatability, and excluding outer rows improved predictive modeling. These results support long-standing principles of experimental design in agronomy and should be considered in breeding programs that incorporate remote sensing.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Lettuce ( Lactuca sativa ) productivity influenced by microbial inocula under nitrogen-limited conditions in aquaponics

The demand for food will outpace productivity of conventional agriculture due to projected growth of the human population, concomitant with shrinkage of arable land, increasing scarcity of freshwater, and a rapidly changing climate. While aquaponics has potential to sustainably supplement food production with minimal environmental impact, there is a need to better characterize the complex interplay between the various components (fish, plant, microbiome) of these systems to optimize scale up and productivity. Here, we investigated how the commonly-implemented practice of continued microbial community transfer from pre-existing systems might promote or impede productivity of aquaponics. Specifically, we monitored plant growth phenotypes, water chemistry, and microbiome composition of rhizospheres, biofilters, and fish feces over 61-days of lettuce (Lactuca sativa var. crispa) growth in nitrogen-limited aquaponic systems inoculated with bacteria that were either commercially sourced or originating from a pre-existing aquaponic system. Lettuce above- and below-ground growth were significantly reduced across replicates treated with a pre-existing aquaponic system inoculum when compared to replicates treated with a commercial inoculum. Reduced productivity was associated with enrichment in specific bacterial genera in plant roots, including Pseudomonas, following inoculum transfer from pre-existing systems. Increased productivity was associated with enrichment of nitrogen-fixing Rahnella in roots of plants treated with the commercial inoculum. Thus, we show that inoculation from a pre-existing system, rather than from a commercial inoculum, is associated with lower yields. Further work will be necessary to test the putative mechanisms involved.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Hyperspectral Reflectance-Based High Throughput Phenotyping to Assess Water-Use Efficiency in Cotton

Cotton is a pivotal global commodity underscored by its economic value and widespread use. In the face of climate change, breeding resilient cultivars for variable environmental conditions becomes increasingly essential. However, the process of phenotyping, crucial to breeding programs, is often viewed as a bottleneck due to the inefficiency of traditional, low-throughput methods. To address this limitation, this study utilizes hyperspectral remote sensing, a promising tool for assessing crucial crop traits across forty cotton varieties. The results from this study demonstrated the effectiveness of four vegetation indices (VIs) in evaluating these varieties for water-use efficiency (WUE). The prediction accuracy for WUE through VIs such as the simple ratio water index (SRWI) and normalized difference water index (NDWI) was higher (up to R2 = 0.66), enabling better detection of phenotypic variations (p < 0.05) among the varieties compared to physiological-related traits (from R2 = 0.21 to R2 = 0.42), with high repeatability and a low RMSE. These VIs also showed high Pearson correlations with WUE (up to r = 0.81) and yield-related traits (up to r = 0.63). We also selected high-performing varieties based on the VIs, WUE, and fiber quality traits. This study demonstrated that the hyperspectral-based proximal sensing approach helps rapidly assess the in-season performance of varieties for imperative traits and aids in precise breeding decisions.

Agriculture↗
DOE OSTI2025

A chromosome-level genome assembly of the varied leaved jewelflower, Streptanthus diversifolius, reveals a recent whole genome duplication

Abstract The Streptanthoid complex, a clade of primarily Streptanthus and Caulanthus species in the Thelypodieae (Brassicaceae) is an emerging model system for ecological and evolutionary studies. This complex spans the full range of the California Floristic Province including desert, foothill, and mountain environments. The ability of these related species to radiate into dramatically different environments makes them a desirable study subject for exploring how plant species expand their ranges and adapt to new environments over time. Ecological and evolutionary studies for this complex have revealed fascinating variation in serpentine soil adaptation, defense compounds, germination, flowering, and life history strategies. Until now a lack of publicly available genome assemblies has hindered the ability to relate these phenotypic observations to their underlying genetic and molecular mechanisms. To help remedy this situation, we present here a chromosome-level genome assembly and annotation of Streptanthus diversifolius, a member of the Streptanthoid Complex, developed using Illumina, Hi-C, and HiFi sequencing technologies. Construction of this assembly also provides further evidence to support the previously reported recent whole genome duplication unique to the Thelypodieae. This whole genome duplication may have provided individuals in the Streptanthoid Complex the genetic arsenal to rapidly radiate throughout the California Floristic Province and to occupy commonly inhospitable environments including serpentine soils.

Genetics & Heredity↗
DOE OSTI2021

RhizoVision Explorer: open-source software for root image analysis and measurement standardization

Abstract Roots are central to the function of natural and agricultural ecosystems by driving plant acquisition of soil resources and influencing the carbon cycle. Root characteristics like length, diameter and volume are critical to measure to understand plant and soil functions. RhizoVision Explorer is an open-source software designed to enable researchers interested in roots by providing an easy-to-use interface, fast image processing and reliable measurements. The default broken roots mode is intended for roots sampled from pots and soil cores, washed and typically scanned on a flatbed scanner, and provides measurements like length, diameter and volume. The optional whole root mode for complete root systems or root crowns provides additional measurements such as angles, root depth and convex hull. Both modes support providing measurements grouped by defined diameter ranges, the inclusion of multiple regions of interest and batch analysis. RhizoVision Explorer was successfully validated against ground truth data using a new copper wire image set. In comparison, the current reference software, the commercial WinRhizo™, drastically underestimated volume when wires of different diameters were in the same image. Additionally, measurements were compared with WinRhizo™ and IJ_Rhizo using a simulated root image set, showing general agreement in software measurements, except for root volume. Finally, scanned root image sets acquired in different labs for the crop, herbaceous and tree species were used to compare results from RhizoVision Explorer with WinRhizo™. The two software showed general agreement, except that WinRhizo™ substantially underestimated root volume relative to RhizoVision Explorer. In the current context of rapidly growing interest in root science, RhizoVision Explorer intends to become a reference software, improve the overall accuracy and replicability of root trait measurements and provide a foundation for collaborative improvement and reliable access to all.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A Rapid Method for Detecting Normal or Modified Plant and Algal Carbonic Anhydrase Activity Using Saccharomyces cerevisiae

In recent years, researchers have attempted to improve photosynthesis by introducing components from cyanobacterial and algal CO2-concentrating mechanisms (CCMs) into terrestrial C3 plants. For these attempts to succeed, we need to understand the CCM components in more detail, especially carbonic anhydrase (CA) and bicarbonate (HCO3−) transporters. Heterologous complementation systems capable of detecting carbonic anhydrase activity (i.e., catalysis of the pH-dependent interconversion between CO2 and HCO3−) or active HCO3− transport can be of great value in the process of introducing CCM components into terrestrial C3 plants. In this study, we generated a Saccharomyces cerevisiae CA knock-out (ΔNCE103 or ΔCA) that has a high-CO2-dependent phenotype (5% (v/v) CO2 in air). CAs produce HCO3− for anaplerotic pathways in S. cerevisiae; therefore, the unavailability of HCO3− for neutral lipid biosynthesis is a limitation for the growth of ΔCA in ambient levels of CO2 (0.04% (v/v) CO2 in air). ΔCA can be complemented for growth at ambient levels of CO2 by expressing a CA from human red blood cells. ΔCA was also successfully complemented for growth at ambient levels of CO2 through the expression of CAs from Chlamydomonas reinhardtii and Arabidopsis thaliana. The ΔCA strain is also useful for investigating the activity of modified CAs, allowing for quick screening of modified CAs before putting them into the plants. CA activity in the complemented ΔCA strains can be probed using the Wilbur–Anderson assay and by isotope exchange membrane-inlet mass spectrometry (MIMS). Other potential uses for this new ΔCA-based screening system are also discussed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer

Natural language processing (NLP), which aims to convert human language into expressions that can be analyzed by computers, is one of the most rapidly developing and widely used technologies in the field of artificial intelligence. Natural language processing algorithms convert unstructured free text data into structured data that can be extracted and analyzed at scale. In medicine, this unlocking of the rich, expressive data within clinical free text in electronic medical records will help untap the full potential of big data for research and clinical purposes. Recent major NLP algorithmic advances have significantly improved the performance of these algorithms, leading to a surge in academic and industry interest in developing tools to automate information extraction and phenotyping from clinical texts. Thus, these technologies are poised to transform medical research and alter clinical practices in the future. Radiation oncology stands to benefit from NLP algorithms if they are appropriately developed and deployed, as they may enable advances such as automated inclusion of radiation therapy details into cancer registries, discovery of novel insights about cancer care, and improved patient data curation and presentation at the point of care. However, challenges remain before the full value of NLP is realized, such as the plethora of jargon specific to radiation oncology, nonstandard nomenclature, a lack of publicly available labeled data for model development, and interoperability limitations between radiation oncology data silos. Successful development and implementation of high quality and high value NLP models for radiation oncology will require close collaboration between computer scientists and the radiation oncology community. Here, we present a primer on artificial intelligence algorithms in general and NLP algorithms in particular; provide guidance on how to assess the performance of such algorithms; review prior research on NLP algorithms for oncology; and describe future avenues for NLP in radiation oncology research and clinics.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

A chromosome-scale reference genome of trifoliate orange ( Poncirus trifoliata ) provides insights into disease resistance, cold tolerance and genome evolution in Citrus

Trifoliate orange (Poncirus trifoliata), a deciduous close relative of evergreen Citrus, has important traits for citrus production, including tolerance/resistance to citrus greening disease (Huanglongbing, HLB) and other major diseases, and cold tolerance. It has been one of the most important rootstocks, and one of the most valuable sources of resistance and tolerance genes for citrus. Here we present a high-quality, chromosomescale genome assembly of P. trifoliata. The 264.9-Mb assembly contains nine chromosomal pseudomolecules with 25 538 protein-coding genes, covering 97.2% of the estimated gene space. Comparative analyses of P. trifoliata and nine Citrus genomes revealed 605 species-specific genes and six rapidly evolving gene families in the P. trifoliata genome. Poncirus trifoliata has evolved specific adaptation in the C-repeat/ DREB binding factor (CBF)-dependent and CBF-independent cold signaling pathways to tolerate cold. We identified candidate genes within quantitative trait loci for HLB tolerance, and at the loci for resistance to citrus tristeza virus and citrus nematode. Genetic diversity analysis of Poncirus accessions and Poncirus/ Citrus hybrids shows a narrow genetic base in the US germplasm collection, and points to the importance of collecting and preserving more natural genetic variation. Two phenotypically divergent Poncirus accessions are found to be clonally related, supporting a previous conjecture that dwarf Flying Dragon originated as a mutant of a non-dwarfing type. The high-quality genome reveals features and evolutionary insights of Poncirus, and it will serve as a valuable resource for genetic, genomic and molecular research and manipulation in citrus.

54 ENVIRONMENTAL SCIENCES↗