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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

An automated liquid jet for fluorescence dosimetry and microsecond radiolytic labeling of proteins

X-ray radiolytic labeling uses broadband X-rays for in situ hydroxyl radical labeling to map protein interactions and conformation. High flux density beams are essential to overcome radical scavengers. However, conventional sample delivery environments, such as capillary flow, limit the use of a fully unattenuated focused broadband beam. An alternative is to use a liquid jet, and we have previously demonstrated that use of this form of sample delivery can increase labeling by tenfold at an unfocused X-ray source. Here we report the first use of a liquid jet for automated inline quantitative fluorescence dosage characterization and sample exposure at a high flux density microfocused synchrotron beamline. Our approach enables exposure times in single-digit microseconds while retaining a high level of side-chain labeling. This development significantly boosts the method’s overall effectiveness and efficiency, generates high-quality data, and opens up the arena for high throughput and ultrafast time-resolved in situ hydroxyl radical labeling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Photosynthetic Antenna Research Center (PARC) (Final Report)

The Photosynthetic Antenna Research Center (PARC) was funded for five years (2009-2014) with the initial group of Energy Frontier Research Centers (EFRCs) by the U.S. Department of Energy. The PARC grant was then renewed for four more years (2014-2018). PARC was focused on basic scientific research aimed at elucidating the fundamental principles that underlie light harvesting and energy funneling in natural photosynthetic antennas and translating those principles into construction of biohybrid and bioinspired architectures to form the basis for next-generation systems for solar-energy conversion. PARC carried out high quality fundamental science on the path towards ambitious, visionary goals. The resources, infrastructure, and melding of scientific subdisciplines, techniques, and experiences made possible in PARC resulted in high risk but high reward and high impact research, enabling conceptual breakthroughs in light harvesting. PARC’s broad global vision was addressed through specific research and educational activities, and the legacy of intellectual and technical capacity that it built.

14 SOLAR ENERGY↗
DOE OSTI2021

Improving deep learning-based protein distance prediction in CASP14

Abstract Motivation Accurate prediction of residue–residue distances is important for protein structure prediction. We developed several protein distance predictors based on a deep learning distance prediction method and blindly tested them in the 14th Critical Assessment of Protein Structure Prediction (CASP14). The prediction method uses deep residual neural networks with the channel-wise attention mechanism to classify the distance between every two residues into multiple distance intervals. The input features for the deep learning method include co-evolutionary features as well as other sequence-based features derived from multiple sequence alignments (MSAs). Three alignment methods are used with multiple protein sequence/profile databases to generate MSAs for input feature generation. Based on different configurations and training strategies of the deep learning method, five MULTICOM distance predictors were created to participate in the CASP14 experiment. Results Benchmarked on 37 hard CASP14 domains, the best performing MULTICOM predictor is ranked 5th out of 30 automated CASP14 distance prediction servers in terms of precision of top L/5 long-range contact predictions [i.e. classifying distances between two residues into two categories: in contact (<8 Angstrom) and not in contact otherwise] and performs better than the best CASP13 distance prediction method. The best performing MULTICOM predictor is also ranked 6th among automated server predictors in classifying inter-residue distances into 10 distance intervals defined by CASP14 according to the precision of distance classification. The results show that the quality and depth of MSAs depend on alignment methods and sequence databases and have a significant impact on the accuracy of distance prediction. Using larger training datasets and multiple complementary features improves prediction accuracy. However, the number of effective sequences in MSAs is only a weak indicator of the quality of MSAs and the accuracy of predicted distance maps. In contrast, there is a strong correlation between the accuracy of contact/distance predictions and the average probability of the predicted contacts, which can therefore be more effectively used to estimate the confidence of distance predictions and select predicted distance maps. Availability and implementation The software package, source code and data of DeepDist2 are freely available at https://github.com/multicom-toolbox/deepdist and https://zenodo.org/record/4712084#.YIIM13VKhQM. Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A foundation model for atomistic materials chemistry

Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model-and its qualitative and at times quantitative accuracy-on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces, and even the dynamics of a small protein. The model can be applied out of the box as a starting or "foundation" model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users obtain reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step toward democratizing the revolution in atomic-scale modeling that has been brought about by ML force fields.

Batatia, Ilyes↗
DOE OSTI2024

A multi-ancestry genetic study of pain intensity in 598,339 veterans

Chronic pain is a common problem, with more than one-fifth of adult Americans reporting pain daily or on most days. It adversely affects the quality of life and imposes substantial personal and economic costs. Efforts to treat chronic pain using opioids had a central role in precipitating the opioid crisis. Despite an estimated heritability of 25–50%, the genetic architecture of chronic pain is not well-characterized, in part because studies have largely been limited to samples of European ancestry. To help address this knowledge gap, we conducted a cross-ancestry meta-analysis of pain intensity in 598,339 participants in the Million Veteran Program, which identified 126 independent genetic loci, 69 of which are new. Pain intensity was genetically correlated with other pain phenotypes, level of substance use and substance use disorders, other psychiatric traits, education level and cognitive traits. Integration of the genome-wide association studies findings with functional genomics data shows enrichment for putatively causal genes (n = 142) and proteins (n = 14) expressed in brain tissues, specifically in GABAergic neurons. Drug repurposing analysis identified anticonvulsants, β-blockers and calcium-channel blockers, among other drug groups, as having potential analgesic effects. Our results provide insights into key molecular contributors to the experience of pain and highlight attractive drug targets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Improved joint X-ray and neutron refinement procedure in Phenix

Neutron diffraction is one of the three crystallographic techniques (X-ray, neutron and electron diffraction) used to determine the atomic structures of molecules. Its particular strengths derive from the fact that H (and D) atoms are strong neutron scatterers, meaning that their positions, and thus protonation states, can be derived from crystallographic maps. However, because of technical limitations and experimental obstacles, the quality of neutron diffraction data is typically much poorer (completeness, resolution and signal to noise) than that of X-ray diffraction data for the same sample. Further, refinement is more complex as it usually requires additional parameters to describe the H (and D) atoms. The increase in the number of parameters may be mitigated by using the `riding hydrogen' refinement strategy, in which the positions of H atoms without a rotational degree of freedom are inferred from their neighboring heavy atoms. However, this does not address the issues related to poor data quality. Therefore, neutron structure determination often relies on the presence of an X-ray data set for joint X-ray and neutron (XN) refinement. In this approach, the X-ray data serve to compensate for the deficiencies of the neutron diffraction data by refining one model simultaneously against the X-ray and neutron data sets. To be applicable, it is assumed that both data sets are highly isomorphous, and preferably collected from the same crystals and at the same temperature. However, the approach has a number of limitations that are discussed in this work by comparing four separately re-refined neutron models. To address the limitations, a new method for joint XN refinement is introduced that optimizes two different models against the different data sets. This approach is tested using neutron models and data deposited in the Protein Data Bank. The efficacy of refining models with H atoms as riding or as individual atoms is also investigated.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Developing Cost Effective Biological Removal Technology for Selenium and Nitrate from Flue Gas Desulfurization Wastewater from an Existing Power Facility

Funding from this project supported new research in environmental biotechnology at WVSU Energy and Environmental Science Institute (WVSUEESI) to develop biological treatment techniques for FGD wastewater using photosynthetic organisms algae and plants. We tested the possibility of using algae and plants in treating FGD wastewater. Results suggested that algae and plants could able to perform normal photosynthesis under low concentrations of selenium. However, higher concentrations of selenium are lethal on photosynthesis. The total protein data indicated that FGD wastewater induced the accumulation of complete proteins in the duckweeds compared to control plants. It appears that nitrate, nitrates, and other compounds in FGD wastewater promote total protein accumulation. The FGD wastewater treatment has triggered Triacylglycerols (TAG) and carbohydrate accumulation in duckweed. We studied the differential gene expression using RNA-Sequencing data. Gene expression analysis revealed that glycolysis, fatty acid, triacylglycerol biosynthesis, and starch degradation were up-regulated; on the contrary, gluconeogenesis, photosynthesis, and starch biosynthesis genes were down-regulated by FGD wastewater treatment. We confirmed the expression of some of these genes using q-RT PCR analysis. We demonstrated that when FGD gypsum was blended with the coal mine soil to grow the bioenergy crop, Camelina, it could grow and produce seed. Moreover, Camelina is a non-edible oilseed crop and has heavy metal sequestering properties, making it the right reclamation choice. Besides, this project provided the opportunity for WVSU undergraduates to integrate hands-on research and education. Funding from this grant helped us to enhance student hands-on experience in water quality and environmental science and participation in STEM research and education.

09 BIOMASS FUELS↗
DOE OSTI2023

The exceptional form and function of the giant bacterium Ca. Epulopiscium viviparus revolves around its sodium motive force

Epulopiscium spp. are the largest known heterotrophic bacteria; a large cigar-shaped individual is a million times the volume of Escherichia coli. To better understand the metabolic potential and relationship of Epulopiscium sp. type B with its host Naso tonganus, we generated a high-quality draft genome from a population of cells taken from a single fish. We propose the name Candidatus Epulopiscium viviparus to describe populations of this best-characterized Epulopiscium species. Metabolic reconstruction reveals more than 5% of the genome codes for carbohydrate active enzymes, which likely degrade recalcitrant host-diet algal polysaccharides into substrates that may be fermented to acetate, the most abundant short-chain fatty acid in the intestinal tract. Moreover, transcriptome analyses and the concentration of sodium ions in the host intestinal tract suggest that the use of a sodium motive force (SMF) to drive ATP synthesis and flagellar rotation is integral to symbiont metabolism and cellular biology. In natural populations, genes encoding both F-type and V-type ATPases and SMF generation via oxaloacetate decarboxylation are among the most highly expressed, suggesting that ATPases synthesize ATP and balance ion concentrations across the cell membrane. High expression of these and other integral membrane proteins may allow for the growth of its extensive intracellular membrane system. Further, complementary metabolism between microbe and host is implied with the potential provision of nitrogen and B vitamins to reinforce this nutritional symbiosis. The few features shared by all bacterial behemoths include extreme polyploidy, polyphosphate synthesis, and thus far, they have all resisted cultivation in the lab.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Chloroplast Stress Signals: Control of Retrograde Signaling, Chloroplast Turn-Over, and Cell Fate Decisions

Chloroplasts (photosynthetic plastids) are semiautonomous organelles that contain their own small genomes. The proteomes of chloroplasts, however, are a mixture of plastid and nuclear-encoded proteins. Chloroplasts perform photosynthesis, which is prone to damaging the organelles, leading to the production of reactive oxygen species (ROS) that damage the cell under environmental stresses. Thus, for the cell to maintain proper chloroplast function, efficient photosynthesis, and avoid ROS damage, it relies on complex crosstalk between the chloroplast, the nucleus, other organelles within the cell, and the cytoplasm in between. This communication involves retrograde signals from chloroplasts to control nuclear gene expression, programmed cell death (PCD), and chloroplast degradation. Here we review these signals with an emphasis on the roles of the ROS singlet oxygen ( 1 O 2 ) and plastid gene expression. We cover (1) recent work on understanding how multiple 1 O 2 signaling pathways can be initiated within stressed chloroplasts, (2) how individualized post-translational regulatory systems allow chloroplasts to control their proteomes and degradation, and (3) how chloroplast signals ultimately control cell fate decisions, such as PCD, senescence, and vacuole-mediated degradation of chloroplasts (chloroplast quality control). Overall, this chapter discusses how chloroplasts can act as environmental sensors for the cell and allow plants to acclimate to stress and thrive in dynamic environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Crystal Diffraction Prediction and Partiality estimation using Gaussian basis functions

A small dataset with good data quality, useful as a benchmark. There exists a two-fold indexing ambiguity, so when processing with CrystFEL you will need to do: ambigator -o disambiguated.stream -y m-3 -w m-3m -j 72 ambiguous.stream Please check the README file for more information about the dataset. ----- Begin unit cell ----- CrystFEL unit cell file version 1.0 lattice_type = cubic centering = I a = 103.40 A b = 103.40 A c = 103.40 A al = 90.00 deg be = 90.00 deg ga = 90.00 deg ; Please note: this is the target unit cell. ; The actual unit cells produced by indexing depend on many other factors. ----- End unit cell -----

Cydia pomonella granulovirus hull protein↗
DOE OSTI2020

What are the current limits on determination of protonation state using neutron macromolecular crystallography?

The rate of deposition of models determined by neutron diffraction, or a hybrid approach that combines X-ray and neutron diffraction, has increased in recent years. The benefit of neutron diffraction is that hydrogen atom (H) positions are detectable, allowing for the determination of protonation state and water molecule orientation. This study analyses all neutron models deposited in the Protein Data Bank to date, focusing on protonation state and properties of H (or deuterium, D) atoms as well as the details of water molecules. In particular, clashes and hydrogen bonds involving H or D atoms are investigated. As water molecules are typically the least reproducible part of a structural model, their positions in neutron models were compared to those in homologous high-resolution X-ray structures. For models determined by joint refinement against X-ray and neutron data, the water structure comparison was also carried out for models re-refined against the X-ray data alone. The homologues have generally fewer conserved water molecules where X-ray only was used and the positions of equivalent waters vary more than in the case of the hybrid X-ray model. As neutron diffraction data are generally less complete than X-ray data, the influence of neutron data completeness on nuclear density maps was also analyzed. Here, we observe and discuss systematic map quality deterioration as result of data incompleteness.

Hydrogen bond↗
DOE OSTI2020

MONACO: accurate biological network alignment through optimal neighborhood matching between focal nodes

Motivation: Alignment of protein–protein interaction networks can be used for the unsupervised prediction of functional modules, such as protein complexes and signaling pathways, that are conserved across different species. To date, various algorithms have been proposed for biological network alignment, many of which attempt to incorporate topological similarity between the networks into the alignment process with the goal of constructing accurate and biologically meaningful alignments. Especially, random walk models have been shown to be effective for quantifying the global topological relatedness between nodes that belong to different networks by diffusing node-level similarity along the interaction edges. However, these schemes are not ideal for capturing the local topological similarity between nodes. Results: Here, we propose MONACO, a novel and versatile network alignment algorithm that finds highly accurate pairwise and multiple network alignments through the iterative optimal matching of ‘local’ neighborhoods around focal nodes. Extensive performance assessment based on real networks as well as synthetic networks, for which the ground truth is known, demonstrates that MONACO clearly and consistently outperforms all other state-of-the-art network alignment algorithms that we have tested, in terms of accuracy, coherence and topological quality of the aligned network regions. Furthermore, despite the sharply enhanced alignment accuracy, MONACO remains computationally efficient and it scales well with increasing size and number of networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Multiple Wheat Genomes Reveal Novel Gli-2 Sublocus Location and Variation of Celiac Disease Epitopes in Duplicated α-Gliadin Genes

The seed protein α-gliadin is a major component of wheat flour and causes gluten-related diseases. However, due to the complexity of this multigene family with a genome structure composed of dozens of copies derived from tandem and genome duplications, little was known about the variation between accessions, and thus little effort has been made to explicitly target α-gliadin for bread wheat breeding. Here, we analyzed genomic variation in α-gliadins across 11 recently published chromosome-scale assemblies of hexaploid wheat, with validation using long-read data. We unexpectedly found that the Gli-B2 locus is not a single contiguous locus but is composed of two subloci, suggesting the possibility of recombination between the two during breeding. We confirmed that the number of immunogenic epitopes among 11 accessions varied. The D subgenome of a European spelt line also contained epitopes, in agreement with its hybridization history. Evolutionary analysis identified amino acid sites under diversifying selection, suggesting their functional importance. The analysis opens the way for improved grain quality and safety through wheat breeding.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

SAXS Assistant: Automated SAXS analysis for structural discovery in biologics and polymeric nanoparticles

Small-angle x-ray scattering (SAXS) is a powerful technique for assessing macromolecular structure. High-throughput SAXS is limited by the time-consuming and, at times, subjective nature of SAXS data interpretation. Here, we present SAXS Assistant, a Python-based script that streamlines SAXS data analysis to extract features for machine learning (ML) and key structural parameters, including the Guinier radius of gyration (R g ), pair distance distribution function (PDDF)-derived R g , maximum particle dimension (D max ), and Kratky plots. The script builds upon BioXTAS RAW and validates reliability via Guinier/PDDF R g agreement, an important indicator of well-measured data sets. For assistance in D max estimation, a multilayer perceptron regressor was trained with 1940 data files from the Small Angle Scattering Biological Data Bank. The model achieved a test set performance R 2 = 0.90 and mean absolute error = 11.7 Å. Training exclusively with experimental data translates analyses from researchers, including experts in the field, to the ML model, which helps assess D max estimations from PDDF. Gaussian mixture model clustering was implemented to classify profiles into structural classes based on entries in the Small Angle Scattering Biological Data Bank. Users may therefore assess the similarity between experimental samples and known biomolecular shapes within the mapped repository entries. This probabilistic clustering aids in quantifying information from Kratky and generating shape-descriptive features. SAXS Assistant accelerates SAXS data analysis through enforced quality control, ML-ready outputs, and flags for low-confidence results. In addition to providing the ability to analyze large data sets at high throughput, this tool is versatile and may serve researchers in both biological and synthetic polymer research fields.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Multi-Trait Regressor Stacking Increased Genomic Prediction Accuracy of Sorghum Grain Composition

Genomic prediction has enabled plant breeders to estimate breeding values of unobserved genotypes and environments. The use of genomic prediction will be extremely valuable for compositional traits for which phenotyping is labor-intensive and destructive for most accurate results. We studied the potential of Bayesian multi-output regressor stacking (BMORS) model in improving prediction performance over single trait single environment (STSE) models using a grain sorghum diversity panel (GSDP) and a biparental recombinant inbred lines (RILs) population. A total of five highly correlated grain composition traits—amylose, fat, gross energy, protein and starch, with genomic heritability ranging from 0.24 to 0.59 in the GSDP and 0.69 to 0.83 in the RILs were studied. Average prediction accuracies from the STSE model were within a range of 0.4 to 0.6 for all traits across both populations except amylose (0.25) in the GSDP. Prediction accuracy for BMORS increased by 41% and 32% on average over STSE in the GSDP and RILs, respectively. Prediction of whole environments by training with remaining environments in BMORS resulted in moderate to high prediction accuracy. Our results show regression stacking methods such as BMORS have potential to accurately predict unobserved individuals and environments, and implementation of such models can accelerate genetic gain.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Psb27, a photosystem II assembly protein, enables quenching of excess light energy during its participation in the PSII lifecycle

Photosystem II (PSII), the enzyme responsible for oxidizing water into molecular oxygen, undergoes a complex lifecycle during which multiple assembly proteins transiently bind to and depart from PSII assembly intermediate complexes. Psb27 is one such protein. It associates with the CP43 chlorophyll-binding subunit of PSII to form a Psb27-PSII sub-complex that constitutes 7–10% of the total PSII pool. Psb27 remains bound to PSII assembly intermediates and dissociates prior to the formation of fully functional PSII. In this study, we compared a series of Psb27 mutant strains in the cyanobacterium Synechocystis sp. PCC 6803 with varied expression levels of Psb27: wild type (WT); psb27 genetic deletion (Del27), genetically complemented psb27 (Com27); and over-expressed Psb27 (OE27). The Del27 strain demonstrated decreased non-photochemical fluorescence quenching, while the OE27 strain showed increased non-photochemical quenching and tolerance to fluctuating light conditions. Multiple flashes and fluorescence decay analysis indicated that OE27 has the least affected maximum PSII quantum yield of the mutants. OE27 also displayed a minimal impact on the half-life of the fast component of QA – reoxidation over multiple flashes, indicating robust PSII function. Further, we propose that the close association between Psb27 and CP43, and the absence of a fully functional manganese cluster in the Psb27-PSII complex create a PSII sub-population that dissipates excitation energy prior to its recruitment into the functional PSII pool. Efficient energy dissipation prevents damage to this pre-PSII pool and allows for efficient PSII repair and maturation. Participation of Psb27 in the PSII life cycle ensures high-quality PSII assembly.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Linking crop traits to transcriptome differences in a progeny population of tetraploid potato

Background: Potato is the third most consumed crop in the world. Breeding for traits such as yield, product quality and pathogen resistance are main priorities. Identifying molecular signatures of these and other important traits is important in future breeding efforts. In this study, a progeny population from a cross between a breeding line, SW93–1015, and a cultivar, Désirée, was studied by trait analysis and RNA-seq in order to develop understanding of segregating traits at the molecular level and identify transcripts with expressional correlation to these traits. Transcript markers with predictive value for field performance applicable under controlled environments would be of great value for plant breeding.Results: A total of 34 progeny lines from SW93–1015 and Désirée were phenotyped for 17 different traits in a field in Nordic climate conditions and controlled climate settings. A master transcriptome was constructed with all 34 progeny lines and the parents through a de novo assembly of RNA-seq reads. Gene expression data obtained in a controlled environment from the 34 lines was correlated to traits by different similarity indices, including Pearson and Spearman, as well as DUO, which calculates the co-occurrence between high and low values for gene expression and trait. Our study linked transcripts to traits such as yield, growth rate, high laying tubers, late and tuber blight, tuber greening and early flowering. We found several transcripts associated to late blight resistance and transcripts encoding receptors were associated to Dickeya solani susceptibility. Transcript levels of a UBX-domain protein was negatively associated to yield and a GLABRA2 expression modulator was negatively associated to growth rate.Conclusion: In our study, we identify 100’s of transcripts, putatively linked based on expression with 17 traits of potato, representing both well-known and novel associations. This approach can be used to link the transcriptome to traits. We explore the possibility of associating the level of transcript expression from controlled, optimal environments to traits in a progeny population with different methods introducing the application of DUO for the first time on transcriptome data. We verify the expression pattern for five of the putative transcript markers in another progeny population.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Pixel-Registered Multimodal Synchrotron XRF and FTIR Microscopies Reveal Salinity Stress Response Mechanisms in Pistachio

Background: Salinity is a major abiotic stress that negatively affects nearly all plant species at all stages of growth. Drought and poor-quality irrigation cause high soil salinity and salt accumulation via evaporation, reducing crop productivity. Despite its critical importance, the spatial localization of salt ions and associated biochemical changes within plants experiencing high salinity remains largely unknown. In this study, we developed a multimodal imaging pipeline to understand the impact of salinity on the pistachio rootstock UCB-1 (Pistacia atlantica x Pistacia integerrima). We directly link biochemical fingerprints in stem tissue architecture with salt ion localization to provide insights into the strategies pistachio uses to tolerate salinity. Results: We observed that Pistacia spp. exposed to high salt conditions accumulated Ca, Si, Cl, Al and Mg as hotspots within the pith, compared to the control (of which only Ca and Al co-locate). In contrast, there was a decrease in K between the control and salinity treatment. Hotspots of amide I and II were present in the cortex and pith of the salinity treated sample. Additionally, the salinity treatment resulted in an increased abundance of pectin and carbohydrates within the pith compared to the control, and the abundance of esters/carboxylic acid was greater in the salinity treatment. Conclusions: We determined that Cl and K, S and P, and biochemical components polysaccharide and pectin, esters and carboxylic acid, amide I and cellulose are the strongest drivers of salinity- treatment induced variability. In the cortex and phloem/xylem, a negative K-Ca correlation decreases in the salinity treatment. Several hotspots of elements and amide I (proteins) appear under salinity treatment, particularly in the cortex, suggesting an increase in the production of stress-related proteins (in response to high Cl) and/or structural proteins (i.e. Ca). Together, these results indicate that pistachio responds to salinity through ion compartmentalization coupled with a targeted biochemical adjustment, rather than a broadscale tissue-wide response. Overall, these novel, spatially resolved pixel-registered multimodal imaging data provide an enabling platform to understand the mechanisms of salinity tolerance in Pistacia spp and can be broadly applied to studying stress-related phenotype response in various plant tissues.

FTIR spectromicroscopy↗