Search NASA⌕ Search

SEARCH · Search NASA

Results for “encoding”

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

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Fission-powered in-core thermoacoustic sensor

A thermoacoustic engine is operated within the core of a nuclear reactor to acoustically telemeter coolant temperature (frequency-encoded) and reactor power level (amplitude-encoded) outside the reactor, thus providing the values of these important parameters without external electrical power or wiring. We present data from two hydrophones in the coolant (far from the core) and an accelerometer attached to a structure outside the reactor. Furthermore, these signals have been detected even in the presence of substantial background noise generated by the reactor's fluid pumps.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Unsupervised machine learning discovery of structural units and transformation pathways from imaging data

We show that unsupervised machine learning can be used to learn chemical transformation pathways from observational Scanning Transmission Electron Microscopy (STEM) data. To enable this analysis, we assumed the existence of atoms, a discreteness of atomic classes, and the presence of an explicit relationship between the observed STEM contrast and the presence of atomic units. With only these postulates, we developed a machine learning method leveraging a rotationally invariant variational autoencoder (VAE) that can identify the existing molecular fragments observed within a material. The approach encodes the information contained in STEM image sequences using a small number of latent variables, allowing the exploration of chemical transformation pathways by tracing the evolution of atoms in the latent space of the system. The results suggest that atomically resolved STEM data can be used to derive fundamental physical and chemical mechanisms involved, by providing encodings of the observed structures that act as bottom-up equivalents of structural order parameters. The approach also demonstrates the potential of variational (i.e., Bayesian) methods in the physical sciences and will stimulate the development of more sophisticated ways to encode physical constraints in the encoder–decoder architectures and generative physical laws and causal relationships in the latent space of VAEs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

A nonpyrrolysine member of the widely distributed trimethylamine methyltransferase family is a glycine betaine methyltransferase

COG5598 comprises a large number of proteins related to MttB, the trimethylamine:corrinoid methyltransferase. MttB has a genetically encoded pyrrolysine residue proposed essential for catalysis. MttB is the only known trimethylamine methyltransferase, yet the great majority of members of COG5598 lack pyrrolysine, leaving the activity of these proteins an open question. Here, we describe the function of one of the nonpyrrolysine members of this large protein family. Three nonpyrrolysine MttB homologs are encoded in Desulfitobacterium hafniense, a Gram-positive strict anaerobe present in both the environment and human intestine. D. hafniense was found capable of growth on glycine betaine with electron acceptors such as nitrate or fumarate, producing dimethylglycine and CO 2 as products. Examination of the genome revealed genes for tetrahydrofolate-linked oxidation of a methyl group originating from a methylated corrinoid protein, but no obvious means to carry out corrinoid methylation with glycine betaine. DSY3156, encoding one of the nonpyrrolysine MttB homologs, was up-regulated during growth on glycine betaine. The recombinant DSY3156 protein converts glycine betaine and cob(I)alamin to dimethylglycine and methylcobalamin. To our knowledge, DSY3156 is the first glycine betaine:corrinoid methyltransferase described, and a designation of MtgB is proposed. Additionally, DSY3157, an adjacently encoded protein, was shown to be a methylcobalamin:tetrahydrofolate methyltransferase and is designated MtgA. Homologs of MtgB are widely distributed, especially in marine bacterioplankton and nitrogen-fixing plant symbionts. Lastly, they are also found in multiple members of the human microbiome, and may play a beneficial role in trimethylamine homeostasis, which in recent years has been directly tied to human cardiovascular health.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Key Questions for the Quantum Machine Learner to Ask Themselves

Within the last several years quantum machine learning (QML) has begun to mature; however, many open questions remain. Rather than review open questions, in this perspective piece I will discuss my view about how we should approach problems in QML. In particular I will list a series of questions that I think we should ask ourselves when developing quantum algorithms for machine learning. These questions focus on what the definition of quantum ML is, what is the proper quantum analogue of QML algorithms is, how one should compare QML to traditional ML and what fundamental limitations emerge when trying to build QML protocols. As an illustration of this process I also provide information theoretic arguments that show that amplitude encoding can require exponentially more queries to a quantum model to determine membership of a vector in a concept class than classical bit-encodings would require; however, if the correct analogue is chosen then both the quantum and classical complexities become polynomially equivalent. This example underscores the importance of asking ourselves the right questions when developing and benchmarking QML algorithms.

Wiebe, Nathan O.↗
DOE OSTI2023

Machine learning-based real-time kinetic profile reconstruction in DIII-D

Abstract Kinetic equilibrium reconstruction plays a vital role in the physical analysis of plasma stability and control in fusion tokamaks. However, the traditional approach is subjective and prone to human biases. To address this, the consistent automatic kinetic equilibrium reconstruction (CAKE) method was introduced, providing objective results. Nonetheless, its offline nature limits its application in real-time plasma control systems (PCSs). To address this limitation, we present RTCAKENN, a machine learning model that approximates 7 CAKE-level output profiles, namely pressure, inverse q , toroidal current density, electron temperature and density, carbon ion impurity temperature and rotation profiles, using real-time available inputs. The deep neural network consists of an encoder layer, where the scalars and interdependent inputs such as plasma boundary coordinates and motional Stark effect data are encoded using multi-layer perceptrons (MLPs), while profile inputs are encoded by 1D convolutional layers. The encoded data is passed through a MLP for latent feature extraction, before being decoded in the decoding layers, which consist of upsampling and convolutional layers. RTCAKENN has been implemented in the DIII-D PCS and our model achieves accuracy comparable to CAKE and surpasses existing real-time alternatives. Through clever dropout training, RTCAKENN exhibits robustness and can operate even in the absence of Thomson scattering data or charge exchange recombination data. It executes in under 8 ms in the real-time environment, enabling future application in real-time control and analysis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

The origin of aerobic methanotrophy within the Proteobacteria

ABSTRACT Aerobic methanotrophs play critical roles in the global carbon cycle, but despite their environmental ubiquity, they are phylogenetically restricted. Via bioinformatic analyses, it is shown that methanotrophy likely arose from methylotrophy from the lateral gene transfer of either of the two known forms of methane monooxygenase (particulate and soluble methane monooxygenases). Moreover, it appears that both known forms of pyrroloquinoline quinone-dependent methanol dehydrogenase (MeDH) found in methanotrophs—the calcium-containing Mxa-MeDH and the rare earth element-containing Xox-MeDH—were likely encoded in the genomes before the acquisition of the methane monooxygenases (MMOs), but that some methanotrophs subsequently received an additional copy of Xox-MeDH-encoding genes via lateral gene transfer. Further, data are presented that indicate the evolution of methanotrophy from methylotrophy not only required lateral transfer of genes encoding for methane monooxygenases, but also likely the pre-existence of a means of collecting copper. Given the emerging interest in valorizing methane via biological platforms, it is recommended that future strategies for heterologous expression of methane monooxygenase for conversion of methane to methanol also include cloning of genes encoding mechanism(s) of copper uptake, especially for expression of particulate methane monooxygenase.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

The Whole Genome Sequence of Sphingobium chlorophenolicum L-1: Insights into the Evolution of the Pentachlorophenol Degradation Pathway

Sphingobium chlorophenolicum Strain L-1 can mineralize the toxic pesticide pentachlorophenol (PCP). We have sequenced the genome of S. chlorophenolicum Strain L-1. The genome consists of a primary chromosome that encodes most of the genes for core processes, a secondary chromosome that encodes primarily genes that appear to be involved in environmental adaptation, and a small plasmid. The genes responsible for degradation of PCP are found on chromosome 2. We have compared the genomes of S. chlorophenolicum Strain L-1 and Sphingobium japonicum, a closely related Sphingomonad that degrades lindane. Our analysis suggests that the genes encoding the first three enzymes in the PCP degradation pathway were acquired via two different horizontal gene transfer events, and the genes encoding the final two enzymes in the pathway were acquired from the most recent common ancestor of these two bacteria.

3-DIOXYGENASE↗
DOE OSTI2014

chlB Requirement for Chlorophyll Biosynthesis under Short Photoperiod in Marchantia polymorpha L.

Chlorophylls (Chls) play pivotal roles in energy absorption and transduction and also in charge separation in reaction centers in all photosynthetic organisms. In Chl biosynthesis steps, only a step for the enzymatic reduction of protochlorophyllide (Pchlide) to chlorophyllide (Chlide) is mediated by both nuclear- and chloroplast-encoded genes in land plants. Many plants encode the genes for light-dependent Pchlide reductase (LPOR) and light-independent Pchlide reductase (DPOR) in the nucleus and chloroplast genome, respectively. During the diversification of land plants, the reduction step of Pchlide to Chlide has become solely dependent on LPOR, and the genes for DPOR have been lost from chloroplast genome. It remains unclear why DPOR persists in some land plants, how they were eliminated from chloroplast genomes during the diversification of land plants, and under what environmental conditions DPOR was required. We demonstrate that DPOR is functional in liverwort (Marchantia polymorpha L.) and plays an important role in Chl biosynthesis. Having established a plastid transformation system in liverwort, we disrupted chlB, which encodes a subunit of DPOR in the M. polymorpha chloroplast genome. Morphological and Chl content analysis of a chlB mutant grown under different photoperiods revealed that DPOR is particularly required for Chl biosynthesis under short-day conditions. Our findings suggest that an environmental condition in the form of photoperiod is an important factor that determines the loss or retention of chloroplast-encoded genes mediating Pchlide reduction to Chlide.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Machine-Learning Classification Suggests That Many Alphaproteobacterial Prophages May Instead Be Gene Transfer Agents

Abstract Many of the sequenced bacterial and archaeal genomes encode regions of viral provenance. Yet, not all of these regions encode bona fide viruses. Gene transfer agents (GTAs) are thought to be former viruses that are now maintained in genomes of some bacteria and archaea and are hypothesized to enable exchange of DNA within bacterial populations. In Alphaproteobacteria, genes homologous to the “head–tail” gene cluster that encodes structural components of the Rhodobacter capsulatus GTA (RcGTA) are found in many taxa, even if they are only distantly related to Rhodobacter capsulatus. Yet, in most genomes available in GenBank RcGTA-like genes have annotations of typical viral proteins, and therefore are not easily distinguished from their viral homologs without additional analyses. Here, we report a “support vector machine” classifier that quickly and accurately distinguishes RcGTA-like genes from their viral homologs by capturing the differences in the amino acid composition of the encoded proteins. Our open-source classifier is implemented in Python and can be used to scan homologs of the RcGTA genes in newly sequenced genomes. The classifier can also be trained to identify other types of GTAs, or even to detect other elements of viral ancestry. Using the classifier trained on a manually curated set of homologous viruses and GTAs, we detected RcGTA-like “head–tail” gene clusters in 57.5% of the 1,423 examined alphaproteobacterial genomes. We also demonstrated that more than half of the in silico prophage predictions are instead likely to be GTAs, suggesting that in many alphaproteobacterial genomes the RcGTA-like elements remain unrecognized.

Evolutionary Biology↗
DOE OSTI2023

Phosphate Limitation Responses in Marine Green Algae Are Linked to Reprogramming of the tRNA Epitranscriptome and Codon Usage Bias

Marine algae are central to global carbon fixation, and their productivity is dictated largely by resource availability. Reduced nutrient availability is predicted for vast oceanic regions as an outcome of climate change; however, there is much to learn regarding response mechanisms of the tiny picoplankton that thrive in these environments, especially eukaryotic phytoplankton. Here, we investigate responses of the picoeukaryote Micromonas commoda, a green alga found throughout subtropical and tropical oceans. Under shifting phosphate availability scenarios, transcriptomic analyses revealed altered expression of transfer RNA modification enzymes and biased codon usage of transcripts more abundant during phosphate-limiting versus phosphate-replete conditions, consistent with the role of transfer RNA modifications in regulating codon recognition. To associate the observed shift in the expression of the transfer RNA modification enzyme complement with the transfer RNAs encoded by M. commoda, we also determined the transfer RNA repertoire of this alga revealing potential targets of the modification enzymes. Codon usage bias was particularly pronounced in transcripts encoding proteins with direct roles in managing phosphate limitation and photosystem-associated proteins that have ill-characterized putative functions in “light stress.” The observed codon usage bias corresponds to a proposed stress response mechanism in which the interplay between stress-induced changes in transfer RNA modifications and skewed codon usage in certain essential response genes drives preferential translation of the encoded proteins. Collectively, we expose a potential underlying mechanism for achieving growth under enhanced nutrient limitation that extends beyond the catalog of up- or downregulated protein-encoding genes to the cell biological controls that underpin acclimation to changing environmental conditions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Paraburkholderia madseniana sp. nov., a phenolic acid-degrading bacterium isolated from acidic forest soil

RP11 T was isolated from forest soil following enrichment with 4-hydroxybenzoic acid. Cells of RP11 T are aerobic, non-sporulating, exhibit swimming motility, and are rods (0.8 µm by 1.4 µm) that often occur as diplobacillus or in short chains (3–4 cells). Optimal growth on minimal media containing 4-hydroxybenzoic acid (µ=0.216 hr -1 ) occurred at 30 °C, pH 6.5 or 7.0 and 0% salinity. Comparative chemotaxonomic, genomic and phylogenetic analyses revealed the isolate was distinct from its closest relative type strains identified as Paraburkholderia aspalathi LMG 27731 T , Paraburkholderia fungorum LMG 16225 T and Paraburkholderia caffeinilytica CF1 T . Strain RP11 T is genetically distinct from P. aspalathi , its closest relative, in terms of 16S rRNA gene sequence similarity (98.7%), genomic average nucleotide identity (94%) and in silico DNA–DNA hybridization (56.7%±2.8). The composition of fatty acids and substrate utilization pattern differentiated strain RP11 T from its closest relatives, including growth on phthalic acid. Strain RP11 T encoded the greatest number of aromatic degradation genes of all eleven closely related type strains and uniquely encoded a phthalic acid dioxygenase and paralog of the 3-hydroxybenzoate 4-monooxygenase. The only ubiquinone detected in strain RP11 T was Q-8, and the major cellular fatty acids were C 16:0 , 3OH-C 16:0 , C 17:0 cyclo, C 19:0 cyclo ω8c, and summed feature 8 (C 18:1 ω7c/ω6c). On the basis of this polyphasic approach, it was determined that strain RP11 T represents a novel species from the genus Paraburkholderia for which the name Paraburkholderia madseniana sp. nov. is proposed. The type strain is RP11 T (=DSM 110123 T =LMG 31517 T ).

Microbiology↗
DOE OSTI2024

Genomics and physiology of Catenibacillus, human gut bacteria capable of polyphenol C-deglycosylation and flavonoid degradation

The genusCatenibacillus(familyLachnospiraceae, phylumBacillota) includes only one cultivated species so far,Catenibacillus scindens,isolated from human faeces and capable of deglycosylating dietary polyphenols and degrading flavonoid aglycones. Another human intestinalCatenibacillusstrain not taxonomically resolved at that time was recently genome-sequenced. We analysed the genome of this novel isolate, designatedCatenibacillus decagia, and showed its ability to deglycosylateC-coupled flavone and xanthone glucosides andO-coupled flavonoid glycosides. Most of the resulting aglycones were further degraded to the corresponding phenolic acids. Including the recently sequenced genome ofC. scindensand ten faecal metagenome-assembled genomes assigned to the genusCatenibacillus, we performed a comparative genome analysis and searched for genes encoding potentialC-glycosidases and other polyphenol-converting enzymes. According to genome data and physiological characterization, the core metabolism ofCatenibacillusstrains is based on a fermentative lifestyle with butyrate production and hydrogen evolution. BothC. scindensandC. decagiaencode a flavonoidO-glycosidase, a flavone reductase, a flavanone/flavanonol-cleaving reductase and a phloretin hydrolase. Several gene clusters encode enzymes similar to those of the flavonoidC-deglycosylation system ofDoreastrain PUE (DgpBC), while separately located genes encode putative polyphenol-glucoside oxidases (DgpA) required forC-deglycosylation. The diversity ofdgpAanddgpBCgene clusters might explain the broadC-glycoside substrate spectrum ofC. scindensandC. decagia. The otherCatenibacillusgenomes encode only a few potential flavonoid-converting enzymes. Our results indicate that severalCatenibacillusspecies are well-equipped to deglycosylate and degrade dietary plant polyphenols and might inhabit a corresponding, specific niche in the gut.

Genetics & Heredity↗
DOE OSTI2011

Discovery and annotation of small proteins using genomics, proteomics and computational approaches

Small proteins (10 200 amino acids aa in length) encoded by short open reading frames (sORF) play important regulatory roles in various biological processes, including tumor progression, stress response, flowering, and hormone signaling. However, ab initio discovery of small proteins has been relatively overlooked. Recent advances in deep transcriptome sequencing make it possible to efficiently identify sORFs at the genome level. In this study, we obtained 2.6 million expressed sequence tag (EST) reads from Populus deltoides leaf transcriptome and reconstructed full-length transcripts from the EST sequences. We identified an initial set of 12,852 sORFs encoding proteins of 10 200 aa in length. Three computational approaches were then used to enrich for bona fide protein-coding sORFs from the initial sORF set: (1) codingpotential prediction, (2) evolutionary conservation between P. deltoides and other plant species, and (3) gene family clustering within P. deltoides. As a result, a high-confidence sORF candidate set containing 1469 genes was obtained. Analysis of the protein domains, non-protein-coding RNA motifs, sequence length distribution, and protein mass spectrometry data supported this high-confidence sORF set. In the high-confidence sORF candidate set, known protein domains were identified in 1282 genes (higher-confidence sORF candidate set), out of which 611 genes, designated as highest-confidence candidate sORF set, were supported by proteomics data. Of the 611 highest-confidence candidate sORF genes, 56 were new to the current Populus genome annotation. This study not only demonstrates that there are potential sORF candidates to be annotated in sequenced genomes, but also presents an efficient strategy for discovery of sORFs in species with no genome annotation yet available.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Adaptive variational ground state preparation for spin-1 models on qubit-based architectures

Here, we apply the adaptive variational quantum imaginary time evolution (AVQITE) method to prepare ground states of one-dimensional spin S = 1 models. We compare different spin-to-qubit encodings (standard binary, Gray, unary, and multiplet) with regard to the performance and quantum resource cost of the algorithm. Using state-vector simulations, we study two well-known spin-1 models: the Blume-Capel model of transverse-field Ising spins with single-ion anisotropy, and the XXZ model with single-ion anisotropy. We consider system sizes of up to 20 qubits, which corresponds to spin-1 chains up to length 10. We determine the dependence of the number of CNOT gates in the AVQITE state preparation circuit on the encoding, the initial state, and the choice of operator pool in the adaptive method. Independent of the choice of encoding, we find that the CNOT gate count scales cubically with the number of spins for the Blume-Capel model and quartically for the anisotropic XXZ model. However, the multiplet and Gray encodings present smaller prefactors in the scaling relations. These results provide useful insights for the implementation of AVQITE on quantum hardware.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Improving Schrödinger Equation Implementations with Gray Code for Adiabatic Quantum Computers

We reformulate the continuous-space Schrödinger equation in terms of spin Hamiltonians. For the kinetic energy operator, the critical concept facilitating the reduction in model complexity is the idea of position encoding. A binary encoding of position produces a spin-1/2 Heisenberg-like model and yields exponential improvement in space complexity when compared to classical computing. Encoding with a binary reflected Gray code (BRGC), and a Hamming-distance-2 Gray code (H2GC) reduces the model complexity down to the 𝑋⁢𝑍 and transverse Ising model, respectively. For 𝐴 qubits BRGC yields 2 𝐴 positions and is reduced to its 2-local form with O⁡(𝐴) ancillary qubits. H2GC yields 2 𝐴/2+1 positions with O⁡(𝐴 2 ) three-local penalty terms. We also identify the bijective mapping between diagonal unitaries and the Walsh series, producing the mapping of any real potential to a series of 𝑘 -local Ising models through the fast Walsh transform. Finally, in a finite volume, we provide some numerical evidence to support the claim that the total time needed for adiabatic evolution is protected by the infrared cutoff of the system. As a result, initial state preparation from a free-field wave function to an interacting system is expected to exhibit polynomial time complexity with volume and constant scaling with respect to lattice discretization for all encodings. For H2GC, if the evolution starts with the transverse Hamiltonian due to hardware restrictions, then penalties are dynamically introduced such that the low-lying spectrum reproduces the energy levels of the Laplacian. The adiabatic evolution of the penalty Hamiltonian is therefore sensitive to the ultraviolet scale. It is expected to exhibit polynomial time complexity with lattice discretization, or exponential time complexity with respect to the number of qubits given a fixed volume.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Variational Simulation of the Lipkin-Meshkov-Glick Model on a Neutral Atom Quantum Computer

We simulate the Lipkin-Meshkov-Glick model using the variational-quantum-eigensolver algorithm on a neutral atom quantum computer. We test the ground-state energy of spin systems with up to 15 spins. Two different encoding schemes are used: an individual spin encoding where each spin is represented by one qubit, and an efficient Gray code encoding scheme that only requires a number of qubits that scales with the logarithm of the number of spins. This more efficient encoding, together with zero-noise extrapolation techniques, is shown to improve the fidelity of the simulated energies with respect to exact solutions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

3D Deep Learning Joint Inversion of Active Seismic Full Waveform and Passive Seismic Traveltime Data for Reservoir Imaging and Uncertainty Quantification

Here, we present deep learning (DL) networks for three-dimensional (3D) joint inversion of active seismic full waveform and passive seismic traveltime data to image reservoirs and their properties and quantify imaging uncertainties. Active seismic full-waveform data can provide high-resolution monitoring images but are collected only intermittently because of their high acquisition cost. In contrast, passive seismic data can be gathered at relatively low cost between regular active surveys, although their imaging quality can be compromised by factors such as low signal-to-noise ratios and limited ray coverage of the target. Although these datasets are routinely acquired together at CO 2 storage sites, their combined inversion within a 3D DL framework has not been previously demonstrated. To our knowledge, this is the first study to address this gap, combining the strength of both data types. For efficient data storage and DL training with large 3D seismic datasets, we use a 3D data matrix in which a random number of passive seismic traveltime data are stored as parabolic envelopes using one-hot encoding and a 3D full-waveform data matrix in which multiple shot gathers are summed. Two network architectures are evaluated: a single-encoder U-Net for single-data type inversion and a dual-encoder U-Net for joint inversion of active and passive seismic data. We also evaluate the single-encoder U-Net for joint inversion by concatenating full-waveform data and traveltime data. We propose a systematic approach for selecting an optimal dropout rate that balances regularization during training and Monte Carlo dropout-based uncertainty quantification during prediction by examining the correlation coefficient between standard deviation and prediction error, along with the training misfit, across a range of dropout rates. 3D DL inversion experiments include five different network configurations, with evaluations under ideal, noisy and dropout-enabled conditions. Both model and data uncertainties are assessed, as well as their combined effects. Across all conditions, the networks consistently predict accurate CO 2 saturation models with low prediction errors, such as a structural similarity index of 0.993 and CO 2 difference of 1.1%. Uncertainty estimates show strong spatial correlation with prediction errors, confirming the effectiveness of the proposed dropout selection approach. The results demonstrate that our DL approach, utilizing compact data representations and appropriate uncertainty quantification, yields accurate subsurface images under various inversion conditions and provides valuable insights into the reliability of predictions.

Um, Evan Schankee [Lawrence Berkeley National Labo↗
DOE OSTI2025

Tonoplast Sucrose Transporter SUT4-Dependent Sugar Partitioning Modulates Phenological Transitions and Reproductive Success in Poplar

Climate uncertainty is intensifying the need for greater plasticity in carbohydrate reserve utilization to support winter survival and spring growth in woody perennials. In poplar, the single-copy SUT4, which encodes a tonoplast-localized sucrose transporter, and the SUT5/SUT6 genome duplicates, which encode plasma membrane-localized transporters, are expressed year-round, with SUT4 showing the highest expression during cool seasons. Given its role in vacuolar sucrose efflux and winter-predominant expression, SUT4 may play a key role in modulating seasonal carbohydrate dynamics. While SUT4-knockdown and knockout effects have been studied under greenhouse conditions, their impact under field conditions remains unexplored. Here, we report a field-based study comparing CRISPR knockout mutants of winter-expressed SUT4 and SUT5/SUT6 in Populus tremula x alba. We show that sut4, but not sut5/6, mutants exhibited earlier autumn leaf senescence, delayed spring bud flush, reduced stem growth, and altered sugar partitioning in winter xylem and bark relative to controls. After 2 years in the field, all genotypes flowered before leaf flush in early spring; however, sut4 mutants produced sterile ovules despite developing normal-looking catkins. Metabolic profiling revealed disrupted sucrose and raffinose dynamics in elongating sut4 catkins. This was accompanied by transcriptomic signatures of elevated stress and downregulation of proanthocyanidin biosynthesis and circadian clock genes. These findings highlight the critical role of SUT4 in coordinating sugar allocation, stress responses, and seasonal development in poplar.

09 BIOMASS FUELS↗