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DOE OSTI2011

Effect of Energy Efficiency Standards on Natural Gas Prices

A primary justification for the establishment of energy efficiency standards for home appliances is the existence of information deficiencies and externalities in the market for appliances. For example, when a long-term homeowner purchases a new gas-fired water heater, she will maximize the value of her purchase by comparing the life-cycle cost of ownership of available units, including both total installed cost - purchase price plus installation costs - and operating cost in the calculus. Choice of the appliance with the lowest life-cycle costs leads to the most economically efficient balance between capital cost and fuel cost. However, if the purchaser's expected period of ownership is shorter than the useful life of the appliance, or the purchaser does not pay for the fuel used by the appliance, as is often the case with rental property, fuel cost will be external to her costs, biasing her decision toward spending less on fuel efficiency and resulting in the purchase of an appliance with greater than optimal fuel usage. By imposing an efficiency standard on appliances, less efficient appliances are made unavailable, precluding less efficient purchases and reducing fuel usage. The reduction in fuel demanded by residential users affects the total demand for such fuels as natural gas, for example. Reduced demand implies that residential customers are willing to purchase less gas at each price level. That is, the demand curve, labeled D 0 in Figure 1, shifts to the left to D 1 . If there is no change in the supply function, the supply curve will intersect the demand curve at a lower price. Residential demand is only one component of the total demand for natural gas. It is possible that total demand will decline very little if demand in other sectors increases substantially in response to a decline in the price. If demand does decrease, modeling studies generally confirm the intuition that reductions in demand for natural gas will result in reductions in its price as seen at the wellhead (Wiser 2007). The magnitude of the effect on price relative to the demand reduction, and the mechanism through which it occurs, is less well established. This report attempts to quantify the potential effects of reduced demand for natural gas in the residential sector, in response to the implementation of an energy efficiency standard for water heaters.

03 NATURAL GAS↗

DOE OSTI2025

RINO: Renormalization Group Invariance with No Labels

A common challenge with supervised machine learning (ML) in high energy physics (HEP) is the reliance on simulations for labeled data, which can often mismodel the underlying collision or detector response. To help mitigate this problem of domain shift, we propose RINO (Renormalization Group Invariance with No Labels), a self-supervised learning approach that can instead pretrain models directly on collision data, learning embeddings invariant to renormalization group flow scales. In this work, we pretrain a transformer-based model on jets originating from quantum chromodynamic (QCD) interactions from the JetClass dataset, emulating real QCD-dominated experimental data, and then finetune on the JetNet dataset -- emulating simulations -- for the task of identifying jets originating from top quark decays. RINO demonstrates improved generalization from the JetNet training data to JetClass data compared to supervised training on JetNet from scratch, demonstrating the potential for RINO pretraining on real collision data followed by fine-tuning on small, high-quality MC datasets, to improve the robustness of ML models in HEP.

Hao, Zichun [Caltech] (ORCID:0000000256244907)↗

DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as time series data shifts. In this research, a changepoint detection (CPD) algorithm that automatically detects data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window across the labeled data sets. Pending approval, we plan to release the labeled data sets for this research on NREL's DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package. By supplying the training sets and algorithm, we hope to encourage further development in this research space.

data shift↗

DOE OSTI2020

Distinguishing Potassium Channel Resting State Conformations in Live Cells with Environment-Sensitive Fluorescence

Ion channels are polymorphic membrane proteins whose high-resolution structures offer images of individual conformations, giving us starting points for identifying the complex and transient allosteric changes that give rise to channel physiology. Here, we report live-cell imaging of voltage-dependent structural changes of voltage-gated Kv2.1 channels using peptidyl tarantula toxins labeled with an environment-sensitive fluorophore, whose spectral shifts enable identification of voltage-dependent conformation changes in the resting voltage sensing domain (VSD) of the channel. We synthesize a new environment-sensitive, far-red fluorophore, julolidine phenoxazone (JP) azide, and conjugate it to tarantula toxin GxTX to characterize Kv2.1 VSD allostery during membrane depolarization. JP has an inherent response to the polarity of its immediate surroundings, offering site-specific structural insight into each channel conformation. Using voltage-clamp spectroscopy to collect emission spectra as a function of membrane potential, we find that they vary with toxin labeling site, the presence of Kv2 channels, and changes in membrane potential. With a high-affinity conjugate in which the fluorophore itself interacts closely with the channel, the emission shift midpoint is 50 mV more negative than the Kv2.1 gating current midpoint. This suggests that substantial conformational changes at the toxin-channel interface are associated with early gating charge transitions and these are not concerted with VSD motions at more depolarized potentials. These fluorescent probes enable study of conformational changes that can be correlated with electrophysiology, putting channel structures and models into a context of live-cell membranes and physiological states.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2014

Peptides derived from human galectin-3 N-terminal tail interact with its carbohydrate recognition domain in a phosphorylation-dependent manner

Highlights: •Galectin-3 is composed of a carbohydrate recognition domain and an N-terminal tail. •Synthetic peptides derived from the tail are shown to interact with the CRD. •This interaction is modulated by Ser- and Tyr-phosphorylation of the peptides. -- Abstract: Galectin-3 (Gal-3) is a multi-functional effector protein that functions in the cytoplasm and the nucleus, as well as extracellularly following non-classical secretion. Structurally, Gal-3 is unique among galectins with its carbohydrate recognition domain (CRD) attached to a rather long N-terminal tail composed mostly of collagen-like repeats (nine in the human protein) and terminating in a short non-collagenous terminal peptide sequence unique in this lectin family and not yet fully explored. Although several Ser and Tyr sites within the N-terminal tail can be phosphorylated, the physiological significance of this post-translational modification remains unclear. Here, we used a series of synthetic (phospho)peptides derived from the tail to assess phosphorylation-mediated interactions with {sup 15}N-labeled Gal-3 CRD. HSQC-derived chemical shift perturbations revealed selective interactions at the backface of the CRD that were attenuated by phosphorylation of Tyr 107 and Tyr 118, while phosphorylation of Ser 6 and Ser 12 was essential. Controls with sequence scrambling underscored inherent specificity. Our studies shed light on how phosphorylation of the N-terminal tail may impact on Gal-3 function and prompt further studies using phosphorylated full-length protein.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

Insight into small molecule binding to the neonatal Fc receptor by X-ray crystallography and 100 kHz magic-angle-spinning NMR

Aiming at the design of an allosteric modulator of the neonatal Fc receptor (FcRn)–Immunoglobulin G (IgG) interaction, we developed a new methodology including NMR fragment screening, X-ray crystallography, and magic-angle-spinning (MAS) NMR at 100 kHz after sedimentation, exploiting very fast spinning of the nondeuterated soluble 42 kDa receptor construct to obtain resolved proton-detected 2D and 3D NMR spectra. FcRn plays a crucial role in regulation of IgG and serum albumin catabolism. It is a clinically validated drug target for the treatment of autoimmune diseases caused by pathogenic antibodies via the inhibition of its interaction with IgG. We herein present the discovery of a small molecule that binds into a conserved cavity of the heterodimeric, extracellular domain composed of an α-chain and β2-microglobulin (β2m) (FcRnECD, 373 residues). X-ray crystallography was used alongside NMR at 100 kHz MAS with sedimented soluble protein to explore possibilities for refining the compound as an allosteric modulator. Proton-detected MAS NMR experiments on fully protonated [ 13 C, 15 N]-labeled FcRnECD yielded ligand-induced chemical-shift perturbations (CSPs) for residues in the binding pocket and allosteric changes close to the interface of the two receptor heterodimers present in the asymmetric unit as well as potentially in the albumin interaction site. X-ray structures with and without ligand suggest the need for an optimized ligand to displace the α-chain with respect to β2m, both of which participate in the FcRn ECD –IgG interaction site. Our investigation establishes a method to characterize structurally small molecule binding to nondeuterated large proteins by NMR, even in their glycosylated form, which may prove highly valuable for structure-based drug discovery campaigns.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2010

Conformational Exchange in a Membrane Transport Protein Is Altered in Protein Crystals

Successful macromolecular crystallography requires solution conditions that may alter the conformational sampling of a macromolecule. Here, site-directed spin labeling is used to examine a conformational equilibrium within BtuB, the Escherichia coli outer membrane transporter for vitamin B{sub 12}. Electron paramagnetic resonance (EPR) spectra from a spin label placed within the N-terminal energy coupling motif (Ton box) of BtuB indicate that this segment is in equilibrium between folded and unfolded forms. In bilayers, substrate binding shifts this equilibrium toward the unfolded form; however, EPR spectra from this same spin-labeled mutant indicate that this unfolding transition is blocked in protein crystals. Moreover, crystal structures of this spin-labeled mutant are consistent with the EPR result. When the free energy difference between substates is estimated from the EPR spectra, the crystal environment is found to alter this energy by 3 kcal/mol when compared to the bilayer state. Approximately half of this energy change is due to solutes or osmolytes in the crystallization buffer, and the remainder is contributed by the crystal lattice. These data provide a quantitative measure of how a conformational equilibrium in BtuB is modified in the crystal environment, and suggest that more-compact, less-hydrated substates will be favored in protein crystals.

36 MATERIALS SCIENCE↗

DOE OSTI2015

The generation of arbitrary order, non-classical, Gauss-type quadrature for transport applications

A method is presented, based upon the Stieltjes method (1884), for the determination of non-classical Gauss-type quadrature rules, and the associated sets of abscissae and weights. The method is then used to generate a number of quadrature sets, to arbitrary order, which are primarily aimed at deterministic transport calculations. The quadrature rules and sets detailed include arbitrary order reproductions of those presented by Abu-Shumays in [4,8] (known as the QR sets, but labelled QRA here), in addition to a number of new rules and associated sets; these are generated in a similar way, and we label them the QRS quadrature sets. The method presented here shifts the inherent difficulty (encountered by Abu-Shumays) associated with solving the non-linear moment equations, particular to the required quadrature rule, to one of the determination of non-classical weight functions and the subsequent calculation of various associated inner products. Once a quadrature rule has been written in a standard form, with an associated weight function having been identified, the calculation of the required inner products is achieved using specific variable transformations, in addition to the use of rapid, highly accurate quadrature suited to this purpose. The associated non-classical Gauss quadrature sets can then be determined, and this can be done to any order very rapidly. In this paper, instead of listing weights and abscissae for the different quadrature sets detailed (of which there are a number), the MATLAB code written to generate them is included as Appendix D. The accuracy and efficacy (in a transport setting) of the quadrature sets presented is not tested in this paper (although the accuracy of the QRA quadrature sets has been studied in [12,13]), but comparisons to tabulated results listed in [8] are made. When comparisons are made with one of the azimuthal QRA sets detailed in [8], the inherent difficulty in the method of generation, used there, becomes apparent, with the highest order tabulated sets showing unexpected anomalies. Although not in an actual transport setting, the accuracy of the sets presented here is assessed to some extent, by using them to approximate integrals (over an octant of the unit sphere) of various high order spherical harmonics. When this is done, errors in the tabulated QRA sets present themselves at the highest tabulated orders, whilst combinations of the new QRS quadrature sets offer some improvements in accuracy over the original QRA sets. Finally, in order to offer a quick, visual understanding of the various quadrature sets presented, when combined to give product sets for the purposes of integrating functions confined to the surface of a sphere, three-dimensional representations of points located on an octant of the unit sphere (as in [8,12]) are shown.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2019

Large Scale Unsupervised Domain Adaptation of Segmentation Networks with Adversarial Learning

Most current state-of-the-art methods for semantic segmentation on remote sensing imagery require large labeled data, which is scarcely available. Due to the distribution shifting phenomenon inherent in remote sensing imagery, the reuse of pre-trained models on new areas of interest rarely yield satisfactory results. In this paper, we approach this problem from an adversarial learning perspective toward unsupervised domain adaptation. The core concept is to infuse fully convolutional neural networks and adversarial networks for semantic segmentation assuming the structures in the scene and objects of interest are similar in two set of images. Models are trained on a source dataset where ground truth is available and adapted to new target dataset iteratively via a adversarial loss on unlabeled samples. We use two real large scale datasets to validate the framework: 1) cross city road extraction and 2) cross country building extraction. The preliminary results show the usefulness of considering adversarial learning for indirect re-use of the pre-trained models. Experimental validation suggests significant benefits over models without adaptation.

Deng, Xueqing↗

DOE OSTI2011

Conformational Exchange in a Membrane Transport Protein Is Altered in Protein Crystals

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Deep transfer operator learning for partial differential equations under conditional shift

Transfer learning enables the transfer of knowledge gained while learning to perform one task (source) to a related but different task (target), hence addressing the expense of data acquisition and labelling, potential computational power limitations and dataset distribution mismatches. Here, we propose a new transfer learning framework for task-specific learning (functional regression in partial differential equations) under conditional shift based on the deep operator network (DeepONet). Task-specific operator learning is accomplished by fine-tuning task-specific layers of the target DeepONet using a hybrid loss function that allows for the matching of individual target samples while also preserving the global properties of the conditional distribution of the target data. Inspired by conditional embedding operator theory, we minimize the statistical distance between labelled target data and the surrogate prediction on unlabelled target data by embedding conditional distributions onto a reproducing kernel Hilbert space. We demonstrate the advantages of our approach for various transfer learning scenarios involving nonlinear partial differential equations under diverse conditions due to shifts in the geometric domain and model dynamics. Our transfer learning framework enables fast and efficient learning of heterogeneous tasks despite considerable differences between the source and target domains.

42 ENGINEERING↗

DOE OSTI2019

Uniformly 15N-Labeled Recombinant Ricin A-chain as an Internal Retention Time Standard for Increased Confidence in Forensic Identification of Ricin by Untargeted Nanoflow Liquid Chromatography-Tandem Mass Spectrometry

Ricin, a toxic protein from the castor plant, is of forensic and biosecurity interest because of its high toxicity and common occurrence in crimes and attempted crimes. Qualitative methods to detect ricin are therefore needed. Untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) proteomics methods are well-suited because of their high speci-ficity. Specificity in LC-MS/MS comes from both the LC and MS components. However, modern untargeted proteomics methods often use nanoflow LC, which has less reproducible retention times than standard-flow LC, making it challenging to use retention time as a point of identification in a forensic assay. We address this challenge by using retention times rela-tive to a standard, namely uniformly 15N–labeled ricin A-chain produced recombinantly in a bacterial expression system. This material, added as an internal standard prior to trypsin digestion, produces a stable isotope labeled standard for every ricin tryptic peptide in the sample. We show that the MS signals for 15N and natural isotopic abundance ricin peptides are distinct, with mass shifts that correspond to the numbers of nitrogen atoms in each peptide or fragment. We also show that, as expected, labeled and unlabeled peptides coelute, with relative retention time differences of less than 0.2%.

ricin, mass spectrometry, isotope, nitrogen-15, re↗

DOE OSTI2025

A Thorough Characterization of the Tellurocyanate Anion

Tellurocyanate, [TeCN] − , is the heaviest group 16 congener of the cyanate anion, [OCN] − . Due to the relative instability of the C─Te bond, tellurocyanate chemistry has seen only scarce attention. Here, we present the facile synthesis and thorough characterization of [K@crypt-222][TeCN]. The anion is essentially linear with interatomic distances C─N = 1.150(6)Å and C─Te = 2.051(4)Å, thus approximating a C≡N triple bond and for C─Te a bond order between 1 and 2. Fully 13 C and 15 N labeled [Te 13 C 15 N] − allowed for the extraction of chemical shifts and all possible coupling constants ( 13 C = 77.8 ppm, 15 N = 285.7 ppm, 125 Te = −566 ppm, 1 J 13C-15N = 8 Hz, 1 J 13C-125Te = 748 Hz, 2 J1 5N-125Te = 55 Hz), which were also determined independently by quantum chemical calculations. In the series [ChCN] − (Ch = O─Te), [TeCN] − shows the strongest spin-orbit coupling (SOC) induced heavy-atom effect on the light-atom shielding (SO-HALA-effect). In contrast, 15 N shifts are also well described without considering relativistic effects and/or SOC. Negative-ion photoelectron spectroscopy was used to extract the electron affinity (EA = 3.034 eV) and spin-orbit splitting (3807 cm −1 ) of [TeCN] • . These values continue the trends of falling EA and rising SOC in the series [ChCN] • .

Bonding analysis↗

DOE OSTI2015

Efficient Total Chemical Synthesis of 13 C= 18 O Isotopomers of Human Insulin for Isotope-Edited FTIR

Isotope–edited two–dimensional Fourier transform infrared spectroscopy (2 D FTIR) can potentially provide a unique probe of protein structure and dynamics. However, general methods for the site–specific incorporation of stable 13 C= 18 O labels into the polypeptide backbone of the protein molecule have not yet been established. Here we describe, as a prototype for the incorporation of specific arrays of isotope labels, the total chemical synthesis—via a key ester insulin intermediate—of 97 % enriched [(1– 13 C= 18 O)Phe B24 ] human insulin: stable–isotope labeled at a single backbone amide carbonyl. The amino acid sequence as well as the positions of the disulfide bonds and the correctly folded structure were unambiguously confirmed by the X–ray crystal structure of the synthetic protein molecule. In vitro assays of the isotope labeled [(1– 13 C= 18 O)Phe B24 ] human insulin showed that it had full insulin receptor binding activity. Linear and 2 D IR spectra revealed a distinct red–shifted amide I carbonyl band peak at 1595 cm –1 resulting from the (1– 13 C= 18 O)Phe B24 backbone label. Furthermore, this work illustrates the utility of chemical synthesis to enable the application of advanced physical methods for the elucidation of the molecular basis of protein function.

2D IR spectra↗

DOE OSTI2024

Uncertainty-refined image segmentation under domain shift

Digital image segmentation is provided. The method comprises training a neural network for image segmentation with a labeled training dataset from a first domain, wherein a subset of nodes in the neural net are dropped out during training. The neural network receives image data from a second, different domain. A vector of N values that sum to 1 is calculated for each image element, wherein each value represents an image segmentation class. A label is assigned to each image element according to the class with the highest value in the vector. Multiple inferences are performed with active dropout layers for each image element, and an uncertainty value is generated for each image element. Uncertainty is resolved according to expected characteristics. The label of any image element with an uncertainty above a threshold is replaced with a new label corresponding to a segmentation class based on domain knowledge.

Martinez, Carianne↗

DOE OSTI2012

Unraveling the 13C NMR Chemical Shifts in Single-Walled Carbon Nanotubes: Dependence on Diameter and Electronic Structure

The atomic specificity afforded by nuclear magnetic resonance (NMR) spectroscopy could enable detailed mechanistic information about single-walled carbon nanotube (SWCNT) functionalization as well as the noncovalent molecular interactions that dictate ground-state charge transfer and separation by electronic structure and diameter. However, to date, the polydispersity present in as-synthesized SWCNT populations has obscured the dependence of the SWCNT {sup 13}C chemical shift on intrinsic parameters such as diameter and electronic structure, meaning that no information is gleaned for specific SWCNTs with unique chiral indices. In this article, we utilize a combination of {sup 13}C labeling and density gradient ultracentrifugation (DGU) to produce an array of {sup 13}C-labeled SWCNT populations with varying diameter, electronic structure, and chiral angle. We find that the SWCNT isotropic {sup 13}C chemical shift decreases systematically with increasing diameter for semiconducting SWCNTs, in agreement with recent theoretical predictions that have heretofore gone unaddressed. Furthermore, we find that the {sup 13}C chemical shifts for small diameter metallic and semiconducting SWCNTs differ significantly, and that the full-width of the isotropic peak for metallic SWCNTs is much larger than that of semiconducting nanotubes, irrespective of diameter.

14 SOLAR ENERGY↗

DOE OSTI2015

Detecting carbon uptake and cellular allocation by individual algae in multispecies assemblages: Tracking carbon into single algal cells

Algal species vary in carbon (C) need and uptake rates. Understanding differences in C uptake and cellular allocation among species from natural communities will bring new insight into many ecosystem process questions including how species changes will alter energy availability and C sequestration in aquatic ecosystems. A major limitation of current methods that measure algal C incorporation is the inability to separate the response of individual species from mixed-species assemblages. I used Fourier-transform infrared microspectroscopy to qualitatively measure inorganic 13 C isotope incorporation into individual algal cells in single species, two species, and natural phytoplankton assemblages. Lateral shifts in spectral peaks from 13 C treatments were observed in all species. Comparison of peaks associated with carbohydrates, proteins, and lipids allowed for the detection of which individuals took in C, and which macromolecules the C was used to make. For example, shifts in Spirogyra spectral peaks showed substantial C incorporation in carbohydrates. Further, shifts in peaks at 1160 cm -1 , 1108 cm -1 , 1080 cm -1 , 1048 cm -1 , and 1030 cm -1 suggested C was being allocated into cellulose. The natural phytoplankton assemblage demonstrated how C could be tracked into co-occurring species. A diatom had large shifts in protein and carbohydrate peaks, while a green alga and euglenoid had only a few shifts in protein related peaks. Fourier-transform infrared microspectroscopy is an established, label free method for measuring the chemical composition of algal cells. However, adding a label such as 13 C isotope can greatly expand the technique's capabilities by qualitatively tracking C movement between inorganic and organic states within single cells.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Cellulose Structural Polymorphism in Plant Primary Cell Walls Investigated by High-Field 2D Solid-State NMR Spectroscopy and Density Functional Theory Calculations

The native cellulose of bacterial, algal, and animal origins has been well studied structurally using X-ray and neutron diffraction and solid-state NMR spectroscopy, and is known to consist of varying proportions of two allomorphs, Iα and Iβ, which differ in hydrogen bonding, chain packing, and local conformation. In comparison, cellulose structure in plant primary cell walls is much less understood because plant cellulose has lower crystallinity and extensive interactions with matrix polysaccharides. Here we have combined two-dimensional magic-angle-spinning (MAS) solid-state nuclear magnetic resonance (solid-state NMR) spectroscopy at high magnetic fields with density functional theory (DFT) calculations to obtain detailed information about the structural polymorphism and spatial distributions of plant primary-wall cellulose. 2D 13 C– 13 C correlation spectra of uniformly 13 C-labeled cell walls of several model plants resolved seven sets of cellulose chemical shifts. Among these, five sets (denoted a–e) belong to cellulose in the interior of the microfibril while two sets (f and g) can be assigned to surface cellulose. Importantly, most of the interior cellulose 13 C chemical shifts differ significantly from the 13 C chemical shifts of the Iα and Iβ allomorphs, indicating that plant primary-wall cellulose has different conformations, packing, and hydrogen bonding from celluloses of other organisms. 2D 13 C– 13 C correlation experiments with long mixing times and with water polarization transfer revealed the spatial distributions and matrix-polysaccharide interactions of these cellulose structures. Celluloses f and g are well mixed chains on the microfibril surface, celluloses a and b are interior chains that are in molecular contact with the surface chains, while cellulose c resides in the core of the microfibril, outside spin diffusion contact with the surface. Interestingly, cellulose d, whose chemical shifts differ most significantly from those of bacterial, algal, and animal cellulose, interacts with hemicellulose, is poorly hydrated, and is targeted by the protein expansin during wall loosening. To obtain information about the C6 hydroxymethyl conformation of these plant celluloses, we carried out DFT calculations of 13 C chemical shifts, using the Iα and Iβ crystal structures as templates and varying the C5–C6 torsion angle. Comparison with the experimental chemical shifts suggests that all interior cellulose favor the tg conformation, but cellulose d also has a similar propensity to adopt the gt conformation. In conclusion, these results indicate that cellulose in plant primary cell walls, due to their interactions with matrix polysaccharides, and has polymorphic structures that are not a simple superposition of the Iα and Iβ allomorphs, thus distinguishing them from bacterial and animal celluloses.

59 BASIC BIOLOGICAL SCIENCES↗