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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

PathTracer Comprehensively Identifies Hypoxia-Induced Dormancy Adaptations in Mycobacterium tuberculosis

Mining large-scale data to discover biologically relevant information remains a challenge despite the rapid development of bioinformatics tools. Here, we have developed a new tool, PathTracer, to identify biologically relevant information flows by mining genome-wide protein–protein interaction networks following integration of gene expression data. PathTracer successfully mines interactions between genes and traces the most perturbed paths of perceived activities under the conditions of the study. Here, we further demonstrated the utility of this tool by identifying adaptation mechanisms of hypoxia-induced dormancy in Mycobacterium tuberculosis (Mtb).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

HtPIP: High-throughput phage isolation platform increases diversity and reduces isolation time using multiple bacteria

Bacteriophages are ubiquitous in nature, but relatively few have been isolated and characterized compared to the number of bacterial strains. Phage biotechnology applications benefit from a diverse library of isolated phages to kill or transfer genetic material to a bacterium of interest. However, scaling up phage discovery for diverse bacterial hosts can be time-consuming and costly. Here, we developed an approach to capture novel phages for multiple bacterial strains in parallel from an environmental sample using commercially available 0.2-μM filter plates. Using this High-throughput Phage Isolation Platform (HtPIP), 12 novel phages were isolated spanning 9 diverse bacterial host genera. Eleven of the isolated phages define new phage species, with nine also defining new genera. The HtPIP was used to discover both DNA and RNA phages, including a Tectiviridae infecting Pseudomonas putida mt-2 and a Leviviricetes infecting a Microbacterium isolate, which represents the first cultured RNA phage infecting a host outside of Proteobacteria. Using a metagenomic approach, we demonstrate that the HtPIP captures a higher proportion of novel phages compared to traditional low-throughput methods.

High-throughput↗
DOE OSTI2022

Data for Efficient Delivery of a DNA Aptamer-Based Biosensor into Plant Cells for Glucose Sensing through Thiol-Mediated Uptake

DNA aptamers have been widely used as biosensors for detecting a variety of targets. Despite decades of success, they have not been applied to monitor any targets in plants, even though plants are a major platform for providing oxygen, food, and sustainable products ranging from energy fuels to chemicals, and high-value products such as pharmaceuticals. A major barrier to progress is a lack of efficient methods to deliver DNA into plant cells. We herein report a thiol-mediated uptake method that more efficiently delivers DNA into Arabidopsis and tobacco leaf cells than another state-of-the-art method, DNA nanostructures. Such a method allowed efficient delivery of a glucose DNA aptamer sensor into Arabidopsis for sensing glucose. This demonstration opens a new avenue to apply DNA aptamer sensors for functional studies of various targets, including metabolites, plant hormones, metal ions, and proteins in plants for a better understanding of the biodistribution and regulation of these species and their functions.

Conversion↗
DOE OSTI2020

Multitask Recommender Systems for Cancer Drug Response

The problem we are currently trying to address is that there are many types of cancer drugs and many types of cancers and there is not always experimental data for a specific cancer type and cancer drug interaction. While there is a large possible set of feasible drug and cancer combinations, testing each pair is not realistic due to the high monetary cost of cell-based assays. Thus, this leaves researchers with a difficult choice of what drugs they should test on specific cancer types. This issue is known as the cold-start problem. Our focus is on developing recommender systems capable of addressing the cold-start problem as it relates to interaction between cancer types and cancer drugs. One of the most effective ways to address the cold-start problem is through large data analysis, however due to the cost prohibitive nature of cancer research the largest available data set size is the Genomics of Drug Sensitivity in Cancer with 494,973 genomic associations. To achieve optimal model performance on the cold-start problem, it is advantageous to employ multitask algorithms that are capable of transferring information between cancer datasets. The aim of this report is to draw from adaptations and state of the art developments in both algorithms for recommender systems and multitask learning to model the interaction between cancer cell lines and cancer drugs. Cancer cell lines are defined by the US National Cancer Institute as "cancer cells that keep dividing and growing over time, under certain conditions in a laboratory". This paper will focus on evaluating the performance of Neural Collaborative Filtering and Gaussian Processes, as well as their multitask adaptations, on cancer datasets from CCLE, NCI60, GDSC and CTRP. These methods will be evaluated on model performance in regression prediction but also in interpretability.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Subsampling and DNA pooling can increase gains through genomic selection in switchgrass

Genomic selection (GS) can accelerate breeding cycles in perennial crops such as the bioenergy grass switchgrass (Panicum virgatum L.). The sequencing costs of GS can be reduced by pooling DNA samples in the training population (TP), only sequencing TP phenotypic outliers, or pooling candidate population (CP) samples. These strategies were simulated for two traits (spring vigor and anthesis date) in three breeding populations. Sequencing only the outlier 50% of the TP phenotype distribution resulted in a penalty of <5% of the predictive ability, measured using cross-validation. Predictive ability also decreased when sequencing progressively fewer TP DNA pools, but TPs constructed from only two phenotypically contrasting DNA samples retained a mean of >80% predictive ability relative to individual TP sequencing. Novel group testing methods allowed greater than one CP individual to be screened per sequenced DNA sample but resulted in a predictive ability penalty. To determine the impact of reduced sequencing, genetic gain was calculated for seven GS scenarios with variable sequencing budgets. Reduced TP sequencing and most CP pooling methods were superior to individual sequence-based GS when sequencing resources were restricted (2,000 DNA samples per 5-yr cycle). Only one scenario was superior to individual sequencing when sequencing budgets were large (8,000 DNA samples per 5-yr cycle). This study highlights multiple routes for reduced sequencing costs in GS.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS)

Genome-scale stoichiometric modeling of metabolism has become a standard systems biology tool for modeling cellular physiology and growth. Extensions of this approach are emerging as a valuable avenue for predicting, understanding and designing microbial communities. Computation of microbial ecosystems in time and space (COMETS) extends dynamic flux balance analysis to generate simulations of multiple microbial species in molecularly complex and spatially structured environments. Here we describe how to best use and apply the most recent version of COMETS, which incorporates a more accurate biophysical model of microbial biomass expansion upon growth, evolutionary dynamics and extracellular enzyme activity modules. In addition to a command-line option, COMETS includes user-friendly Python and MATLAB interfaces compatible with the well-established COBRA models and methods, as well as comprehensive documentation and tutorials. Overall, this protocol provides a detailed guideline for installing, testing and applying COMETS to different scenarios, generating simulations that take from a few minutes to several days to run, with broad applicability to microbial communities across biomes and scales.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Tag you're it: Application of stable isotope labeling and LC-MS to identify the precursors of specialized metabolites in plants

Untargeted liquid chromatography/mass spectrometry (LC-MS) can contribute a comprehensive and unbiased picture of the metabolic space of plants. These data can be used to quantify natural metabolite variation for genome wide association studies, to compare global metabolic responses from environmental or genetic perturbations, and to identify previously undescribed metabolites in Nature. A major limitation with untargeted metabolomics is the classification and identification of the thousands of metabolite features that can be detected in a single analytical run. Isotopic labeling improves the informational value of these datasets by categorizing metabolites as being derived from specific upstream precursors and/or to known metabolic pathways. When a 13 C-labeled precursor is fed to either a plant or tissue, the downstream metabolites produced from it have a higher m/z value than the molecules in the pre-existing pool, generating an m/z peak pair that can be specifically identified within the MS data. In this paper, we outline methods and principles to consider when supplementing untargeted MS data with isotopic labeling, including how to choose the appropriate isotopic label, grow and feed plant tissues to maximize label uptake and incorporation into derivatives, optimize LC-MS methods, and interpret the resulting labeling data. Although the focus here is on annotation of amino acid-derived metabolites using LC-MS, we anticipate that the methods are generally adaptable to other precursors, plant species, and chromatographic approaches.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Quantum-parallel vectorized data encodings and computations on trapped-ion and transmon QPUs

Compact data representations in quantum systems are crucial for the development of quantum algorithms for data analysis. In this study, we present two innovative data encoding techniques, known as QCrank and QBArt, which exhibit significant quantum parallelism via uniformly controlled rotation gates. The QCrank method encodes a series of real-valued data as rotations on data qubits, resulting in increased storage capacity. On the other hand, QBArt directly incorporates a binary representation of the data within the computational basis, requiring fewer quantum measurements and enabling well-established arithmetic operations on binary data. We showcase various applications of the proposed encoding methods for various data types. Notably, we demonstrate quantum algorithms for tasks such as DNA pattern matching, Hamming weight computation, complex value conjugation, and the retrieval of a binary image with 384 pixels, all executed on the Quantinuum trapped-ion QPU. Furthermore, we employ several cloud-accessible QPUs, including those from IBMQ and IonQ, to conduct supplementary benchmarking experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

scPlantAnnotate: an accurate and robust transformer-based model for plant cell type annotation

Accurate cell type annotation remains a major bottleneck in plant single-cell RNA sequencing (scRNA-seq), where existing tools are often adapted from animal studies and perform sub-optimally on plant data. The lack of plant-specific computational frameworks limits the construction of plant cell atlases and downstream biological discovery. We develop and evaluate scPlantAnnotate, a Transformer-based reference annotation framework tailored for plant scRNA-seq data, and benchmark it against state-of-the-art deep learning and conventional methods across multiple plant species. Species-specific scPlantAnnotate models were trained using curated datasets from Arabidopsis thaliana, Zea mays, Oryza sativa, and Glycine max. We compared scPlantAnnotate with leading baselines under both standard random-split evaluation and a more stringent leave-one-dataset-out setting, which tests robustness to completely unseen datasets and tissue types. scPlantAnnotate consistently outperforms existing approaches across all four species under random-split evaluation. In the leave-one-dataset-out setting for A. thaliana, where performance drops markedly for all methods due to strong batch effects and dataset heterogeneity, scPlantAnnotate nonetheless achieves the highest Accuracy, Macro-F1, Balanced Accuracy, and Macro-AUROC on average and ranks first on most held-out datasets. These results demonstrate improved robustness to dataset shifts, a critical yet underexplored challenge in plant scRNA-seq analysis. A freely accessible web server enables users to annotate their own datasets using pretrained models. scPlantAnnotate provides a plant-specific, Transformer-based framework for single-cell annotation that delivers state-of-the-art performance and enhanced robustness to unseen datasets. By addressing limitations of existing tools and enabling scalable reference-based annotation, scPlantAnnotate supports the development of comprehensive plant cell atlases and facilitates broader use of single-cell genomics in plant biology.

Bioinformatics↗
DOE OSTI2022

Spacer2PAM: A computational framework to guide experimental determination of functional CRISPR-Cas system PAM sequences

Abstract RNA-guided nucleases from CRISPR-Cas systems expand opportunities for precise, targeted genome modification. Endogenous CRISPR-Cas systems in many prokaryotes are attractive to circumvent expression, functionality, and unintended activity hurdles posed by heterologous CRISPR-Cas effectors. However, each CRISPR-Cas system recognizes a unique set of protospacer adjacent motifs (PAMs), which requires identification by extensive screening of randomized DNA libraries. This challenge hinders development of endogenous CRISPR-Cas systems, especially those based on multi-protein effectors and in organisms that are slow-growing or have transformation idiosyncrasies. To address this challenge, we present Spacer2PAM, an easy-to-use, easy-to-interpret R package built to predict and guide experimental determination of functional PAM sequences for any CRISPR-Cas system given its corresponding CRISPR array as input. Spacer2PAM can be used in a ‘Quick’ method to generate a single PAM prediction or in a ‘Comprehensive’ method to inform targeted PAM libraries small enough to screen in difficult to transform organisms. We demonstrate Spacer2PAM by predicting PAM sequences for industrially relevant organisms and experimentally identifying seven PAM sequences that mediate interference from the Spacer2PAM-informed PAM library for the type I-B CRISPR-Cas system from Clostridium autoethanogenum. We anticipate that Spacer2PAM will facilitate the use of endogenous CRISPR-Cas systems for industrial biotechnology and synthetic biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Ecosystems and Networks Integrated with Genes and Molecular Assemblies (ENIGMA). Final report

The goal of the Stahl lab is to use a combination of isotopic, genomic, and activity-based analyses in the field, of selected field isolates, and defined synthetic communities to identify sources and sinks of different nitrogen species in the field, focusing on the FRC at the ORNL. The most general research objective is to characterize variants in the pathway for denitrification among field isolates and synthetic microbial assemblies as a foundation for predicting the biotic and abiotic controls of nitrogen and carbon transformations in the field. Specifically, we have focused on characterizing environmental controls of nitrous oxide emissions, an important greenhouse gas and ozone depleting molecule. Studies have mapped the flux of nitrous oxide and controlling genetic elements within the FRC subsurface, indicating the importance of different enzyme variants in nitrous oxide production and consumption in controlling surface emissions. We have developed methods to quantify and catalog the isotopic signatures imparted on nitrous oxide by different nitrous oxide reductase variants in isolated organisms and model synthetic communities. We have developed methods to quantify and catalog the nitrous oxide affinity of field relevant isolates and their biomass yield when respiring nitrous oxide on different carbon substrates. We continue to expand the collection of field-relevant microbial isolates within ENIGMA by focused enrichment and isolation from field material. These objectives have been facilitated by the development of new technologies, including: 1) the development of a multiplexed automated optical density measurement system for quantifying growth kinetics of microorganisms dependent on gaseous substrates in monoculture or gas exchange in synthetic communities and 2) the development of a microcalorimetric analysis format to quantify the impact of environmental stress on microbial maintenance energy. Since field relevant processes are mediated by assemblies of organisms interacting with each other and their local environment, we have also collaborated with other ENIGMA investigators on the assembly and characterization of synthetic communities. These studies have evaluated the role of organisms lacking a complete pathway for denitrification in the control of field processes, measuring process rates of assemblies of organisms that individually lack the full denitrification pathway but mediate the complete denitrification when growing together. These efforts have so far resulted in the publication of 18 peer reviewed papers ranging from ecological and physiological drivers of pathway optimization to field-scale process characterization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Improved high-throughput screening technique to rapidly isolate Chlamydomonas transformants expressing recombinant proteins

Abstract The single-celled eukaryotic green alga Chlamydomonas reinhardtii has long been a model system for developing genetic tools for algae, and is also considered a potential platform for the production of high-value recombinant proteins. Identifying transformants with high levels of recombinant protein expression has been a challenge in this organism, as random integration of transgenes into the nuclear genome leads to low frequency of cell lines with high gene expression. Here, we describe the design of an optimized vector for the expression of recombinant proteins in Chlamydomonas , that when transformed and screened using a dual antibiotic selection, followed by screening using fluorescence activated cell sorting (FACS), permits rapid identification and isolation of microalgal transformants with high expression of a recombinant protein. This process greatly reduces the time required for the screening process, and can produce large populations of recombinant algae transformants with between 60 and 100% of cells producing the recombinant protein of interest, in as little as 3 weeks, that can then be used for whole population sequencing or individual clone analysis. Utilizing this new vector and high-throughput screening (HTS) process resulted in an order of magnitude improvement over existing methods, which normally produced under 1% of algae transformants expressing the protein of interest. This process can be applied to other algal strains and recombinant proteins to enhance screening efficiency, thereby speeding up the discovery and development of algal-derived recombinant protein products. Key points • A protein expression vector using double-antibiotic resistance genes was designed • Double antibiotic selection causes fewer colonies with more positive for phenotype • Coupling the new vector with FACS improves microalgal screening efficiency > 60%

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A k-mer based approach for classifying viruses without taxonomy identifies viral associations in human autism and plant microbiomes

Viruses are an underrepresented taxa in the study and identification of microbiome constituents; however, they play an essential role in health, microbiome regulation, and transfer of genetic material. Only a few thousand viruses have been isolated, sequenced, and assigned a taxonomy, which limits the ability to identify and quantify viruses in the microbiome. Additionally, the vast diversity of viruses represents a challenge for classification, not only in constructing a viral taxonomy, but also in identifying similarities between a virus’ genotype and its phenotype. However, the diversity of viral sequences can be leveraged to classify their sequences in metagenomic and metatranscriptomic samples, even if they do not have a taxonomy. To identify and quantify viruses in transcriptomic and genomic samples, we developed a dynamic programming algorithm for creating a classification tree out of 715,672 metagenome viruses. To create the classification tree, we clustered proportional similarity scores generated from the k-mer profiles of each of the metagenome viruses to create a database of metagenomic viruses. The resulting Kraken2 database of the metagenomic viruses can be found here: https://www.osti.gov/biblio/1615774 and is compatible with Kraken2. We then integrated the viral classification database with databases created with genomes from NCBI for use with ParaKraken (a parallelized version of Kraken provided in Supplemental Zip 1), a metagenomic/transcriptomic classifier. To illustrate the breadth of our utility for classifying metagenome viruses, we analyzed data from a plant metagenome study identifying genotypic and compartment specific differences between two Populus genotypes in three different compartments. We also identified a significant increase in abundance of eight viral sequences in post mortem brains in a human metatranscriptome study comparing Autism Spectrum Disorder patients and controls. We also show the potential accuracy for classifying viruses by utilizing both the JGI and NCBI viral databases to identify the uniqueness of viral sequences. Finally, we validate the accuracy of viral classification with NCBI databases containing viruses with taxonomy to identify pathogenic viruses in known COVID-19 and cassava brown streak virus infection samples. Our method represents the compulsory first step in better understanding the role of viruses in the microbiome by allowing for a more complete identification of sequences without taxonomy. Better classification of viruses will improve identifying associations between viruses and their hosts as well as viruses and other microbiome members. Despite the lack of taxonomy, this database of metagenomic viruses can be used with any tool that utilizes a taxonomy, such as Kraken, for accurate classification of viruses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Creation of a High-Yield AAV Vector Production Platform in Suspension Cells Using a Design-of-Experiment Approach

Recombinant adeno-associated virus (rAAV) vectors are a leading gene delivery platform, but vector manufacturing remains a challenge. New methods are needed to increase rAAV yields and reduce costs. Past efforts to improve rAAV production have focused on optimizing a single variable at a time, but this approach does not account for the interactions of multiple factors that contribute to vector generation. Here, we utilized a design-of-experiment (DOE) methodology to optimize rAAV production in a HEK293T suspension cell system. We simultaneously varied the transgene, packaging, and helper plasmid ratios, the total DNA concentration, and the cell density to systematically evaluate the impact of each variable across 52 conditions. The results revealed a unique set of parameters with a lower concentration of transgene plasmid, a higher concentration of packaging plasmid, and a higher cell density than previously described protocols. Using this DOE-optimized protocol, we achieved unpurified yields approaching 3 × 10 14 viral genomes (VGs)/L of cell culture. Additionally, we incorporated polyethylene glycol (PEG)-based virus precipitation, pH-mediated protein removal, and affinity chromatography to our downstream processing, enabling average purified yields of >1 × 10 14 VGs/L for rAAV-EGFPs across 13 serotypes and capsid variants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Purification and expression of a novel bacteriocin, JUQZ-1, against Pseudomonas syringae pv. Actinidiae (PSA), secreted by Brevibacillus laterosporus Wq-1, isolated from the rhizosphere soil of healthy kiwifruit

Kiwifruit canker, caused by Pseudomonas syringae pv. actinidiae (PSA), has led to significant losses in the kiwifruit industry each year. Due to the drug resistance feature of PSA, biological control is currently the most promising method. Developing biocontrol bacteria against PSA could help solve the issue of drug resistance generated during the chemical control of PSA to a certain extent. In this research, a Wq-1 strain that demonstrated excellent inhibitory activity against PSA was isolated from the rhizosphere soil of healthy kiwifruit. Based on the morphological characteristics and phylogenetic analysis of the 16S rRNA gene sequence, the isolated strain was identified as Brevibacillus laterosporus Wq-1. Bacteriostatic proteins were isolated from the cell-free culture filtrate of strain Wq-1 and were found to have a molecular weight of approximately 12 kDa, as determined by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). Liquid chromatography–tandem mass spectrometry (LC–MS/MS) detection revealed that there were several peptides in the target band that were consistent with protein 01021 in the genome. The gene of the 01021 protein was cloned into the plasmid pPICZa, and the recombinant bacteriocin was successfully expressed using the Pichia pastoris X33 expression system. The recombinant protein 01021 effectively inhibited the growth of PSA. This is the first report of the protein’s antimicrobial activity, distinguishing it from previously identified bacteriocins. Therefore, we named this bacteriocin JUQZ-1. In addition, our results showed that the protein JUQZ-1 not only exhibited a broad bacteriostatic spectrum but also high thermal and pH stability suitable for harsh environmental conditions., JUQZ-1, a protein with antimicrobial properties and strong environmental tolerance, may serve as a promising alternative to antibiotics.

Shuai, Yang↗
DOE OSTI2022

Objective Phenotyping of Root System Architecture Using Image Augmentation and Machine Learning in Alfalfa (Medicago sativa L.)

Active breeding programs specifically for root system architecture (RSA) phenotypes remain rare; however, breeding for branch and taproot types in the perennial crop alfalfa is ongoing. Phenotyping in this and other crops for active RSA breeding has mostly used visual scoring of specific traits or subjective classification into different root types. While image-based methods have been developed, translation to applied breeding is limited. This research is aimed at developing and comparing image-based RSA phenotyping methods using machine and deep learning algorithms for objective classification of 617 root images from mature alfalfa plants collected from the field to support the ongoing breeding efforts. Our results show that unsupervised machine learning tends to incorrectly classify roots into a normal distribution with most lines predicted as the intermediate root type. Encouragingly, random forest and TensorFlow-based neural networks can classify the root types into branch-type, taproot-type, and an intermediate taproot-branch type with 86% accuracy. With image augmentation, the prediction accuracy was improved to 97%. Coupling the predicted root type with its prediction probability will give breeders a confidence level for better decisions to advance the best and exclude the worst lines from their breeding program. This machine and deep learning approach enables accurate classification of the RSA phenotypes for genomic breeding of climate-resilient alfalfa.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Microbial Taxonomy Run Amok

DNA sequencing has led to an explosion in discovery of microbial phylogenetic novelty, especially that represented by uncultivated taxa, to which the traditional system of prokaryotic taxonomy has not adapted. A lack of expansion of the International Code of Nomenclature of Prokaryotes (ICNP, 'the Code') to effectively capture this information has created a 'wild west' situation where names are published or appear in popular reference databases without further verification or validation. The rapid propagation of variant and questionable naming methods has led to widespread confusion and undermines prior accomplishments. Furthermore, we exemplify inconsistencies that have arisen from this practice and endanger the interoperability of scientific findings. The immediate solution to this problem is to develop and adopt universal best practices that are accepted by expert researchers, major publishers, the International Committee on Systematics of Prokaryotes (ICSP), and international microbiological societies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Robust High-Throughput Phenotyping with Deep Segmentation Enabled by a Web-Based Annotator

The abilities of plant biologists and breeders to characterize the genetic basis of physiological traits are limited by their abilities to obtain quantitative data representing precise details of trait variation, and particularly to collect this data at a high-throughput scale with low cost. Although deep learning methods have demonstrated unprecedented potential to automate plant phenotyping, these methods commonly rely on large training sets that can be time-consuming to generate. Intelligent algorithms have therefore been proposed to enhance the productivity of these annotations and reduce human efforts. We propose a high-throughput phenotyping system which features a Graphical User Interface (GUI) and a novel interactive segmentation algorithm: Semantic-Guided Interactive Object Segmentation (SGIOS). By providing a user-friendly interface and intelligent assistance with annotation, this system offers potential to streamline and accelerate the generation of training sets, reducing the effort required by the user. Our evaluation shows that our proposed SGIOS model requires fewer user inputs compared to the state-of-art models for interactive segmentation. As a case study of the use of the GUI applied for genetic discovery in plants, we present an example of results from a preliminary genome-wide association study (GWAS) of in planta regeneration in Populus trichocarpa (poplar). We further demonstrate that the inclusion of a semantic prior map with SGIOS can accelerate the training process for future GWAS, using a sample of a dataset extracted from a poplar GWAS of in vitro regeneration. The capabilities of our phenotyping system surpass those of unassisted humans to rapidly and precisely phenotype our traits of interest. The scalability of this system enables large-scale phenomic screens that would otherwise be time-prohibitive, thereby providing increased power for GWAS, mutant screens, and other studies relying on large sample sizes to characterize the genetic basis of trait variation. Our user-friendly system can be used by researchers lacking a computational background, thus helping to democratize the use of deep segmentation as a tool for plant phenotyping.

54 ENVIRONMENTAL SCIENCES↗