PNNL-13501 - Pacific Northwest National Laboratory

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determining the combinatorial state and transition network invariants that characterize both the flow and conditional regulation of information flow in the signaling reaction network. In addition, we will investigate the algebraic symmetries of these nets in terms of a path algebra representation. These algebraic representations will be used to identify the existence of operational signal processing symmetry group invariants in terms of process semiorder relations. These symmetry invariants are shown to correspond to the state and transition invariants and will be used to obtain sets of optimal paths that indicate observably reachable states with optimal molecular signaling bandwith. We will also explore the use of incidence matrix representations of circuits, and both path and relational algebraic representations of network path configurations to computationally establish the existence of hypergraph isomorphisms between different signaling paths. Additionally, we will explore the use of reverse Bayesian inference (abductive inference) methods to identify and define the characteristics of hidden states. We conjecture that the multi-dimensionality of molecular communications in a pathway can be modeled by using a dimension-matched hierarchy of scale-invariant Petri nets. Petri nets are stochastically extensible as an application for modeling the microscopic reactive diffusive transport kinetics of signal pathways within and between cells. We will use the discrete (hidden) Markov nature of timed stochastic Petri networks to discretely represent the continuous Markov state transition models of diffusive molecular transport systems. In turn, the associated sets of state transition probability measures will be used to define the molecular analogs of classical information theory (as developed by Shannon and Weaver) that underly the emerging theory of molecular communications. Coupled Rate Equations Cell signaling is a complex phenomena, yet it is such a fundamentally important part of cellular behavior that the basic principles of cell signaling are the same in essentially all organisms. The response of a cell to its environment is governed by cell-signaling mechanisms. Figure 1 illustrates a network of signaling pathways that activate the mitogen-activated-protein kinase (MAPK) in response to ligand binding to the epidermal-growth-factor receptor (EGFR). Co-activation of phospholipase C-γ1 (PLCγ) modifies MAPK signal characteristics by interaction between modules K and H mediated by protein kinase C (PKC). A feedback loop connects H and K through module E (cf. Figure 1). Figure 2 shows a Petri 118 FY 2000 <strong>Laboratory</strong> Directed Research and Development Annual Report net representation of component A of the MAPKactivating network. F PLC g IPS DAG PKC Ca K E AA PLA 2 Ca DAG EGFR Figure 1. A network of signaling pathways associated with MAPK activation Even though kinetic data are sparse, Iyengar and coworkers at the Mount Sinai School of Medicine maintain a database of kinetic parameters for modules found in well-studied pathways. Signaling networks, like that shown in Figure 1, assembled from these modules have emergent properties, like bistatic activation profiles, that may be biologically significant. We will use Petri net representations to evaluate the bistability of the network shown in Figure 1. Nonhomogeneous Kinetic Models SHC + GRB + SOS RAS RAF + MEK + MAPK 1.2 Localization of reactants is an important mechanism by which cells regulate signaling processes. In collaboration with Professor Leslie Loew at the University of Connecticut Health Center (UCHC), we are including spatial inhomogeneity in kinetic models of signaling pathways. We will expand the capabilities of the problem-solving environment being developed in Dr. Loew’s laboratory through links to NWGrid and NWPhys, the <strong>Laboratory</strong>’s grid generation and physics solver codes, respectively. More efficient computational methods and extended functionality are required for the applications of virtual cell technology that we envision. For example, changes in cell morphology are often a part of signaling dynamics. We are also applying spatial inhomogeneous kinetic models in our collaboration with A Nucleus H
SOS p 23 EGFR Professor Rodland at Oregon Health Science University to investigate the signaling pathways that connect the extra-cellular calcium-sensing receptor (CaR) to cell proliferation. Like skin fibroblast, normal cells of the ovarian surface layer respond to elevated calcium by proliferation and return to a quiescent state when the extra-cellular calcium concentration is reduced. Nonhomogenous chemical kinetics is required to model this system because signaling involves the release of calcium from the endoplasmic reticulum that is not homogeneously distributed in the cell. Results and Accomplishments EGF p 1 t 2 p 2 We have initiated the development of a Petri network model of cell signaling. The Petri net model transforms the molecular signal into a linear algebra representation of a systems process model. The linear algebra representation defines the stoichiometric system of t 1 F p 3 t 3 EGF: EGFR t 4 p 4 EGF: EGFR_Internal t5 t t 7 9 SHC : EGF: EGFR EGF: EGFR : SHC-P SHC SHC -P p5 p8 p9 p6 t 6 t 11 SHC : P'ase(1) p10 t 12 t 8 t 13 t 14 p 7 P'ase(1) t 10 t 15 p11 P'ase(1) : SHC-P t 16 t 35 t 36 t 41 SOS : Erk-PP p 27 SOS : P'ase(2) p29 t 42 p 25 Erk-PP t 37 t 38 t 17 t 18 t 43 t 44 p 26 H P'ase(2) Erk-PP : SOS* p28 GDP : Ras t 39 t 40 p 12 t19 t21 GDP : Ras : SHC -P : SOS : GRB2 p13 p16 p17 p14 t 20 t 25 GDP : Ras : RasGAP p18 t 26 p30 P'ase(2) : SOS* t 23 SHC-P : SOS : GRB2 : GTP : Ras t22 t24 t 27 t 28 p 15 RasGAP SHC -P : SOS : GRB2 t 29 p19 RasGAP : GTP : Ras t 30 H SOS : GRB2 p20 Figure 2. This is a Petri net of the EGFR pathway, SOS. Linkage to other components in the network shown in Figure 1 are indicated by arrows labeled F and H. t 45 t 46 GTP : Ras SOS* : GRB2 energy-mass balanced chemical equations. The Petri network model provides us with a representation of the molecular signal that enables us to mathematically predict the behavior of a molecular communications pathway when it is no longer in an equilibrium state. This model will direct us to the eventual construction of the molecular information analog of Shannon’s laws of information and communication theory. We will also have an opportunity to examine the orders of both physical and computational complexity associated with reliable molecular communications within and between cells. Accomplishments include the following. 1. We have shown that cell-signaling pathways are represented by sets of chemical kinetic rate-reaction graphs, where the reacting species themselves are generated from the products of multiple subreaction cascades of subspecies. In particular, we examined several subnetworks of the EGFR signaling network t31 t 32 GRB2 p21 Computational Science and Engineering 119 t 33 t34 p 22 SOS* p 24
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Laboratory Directed Research and De
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Laboratory Directed Research and De
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Computational Science and Engineeri
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Policy and Management Sciences Asse
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Introduction Department of Energy O
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5. After reviews by the Technical a
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• The Technical Council peer revi
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methods applicable to combustion, s
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Summary Each national laboratory mu
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Study Control Number: PN0004/1411 A
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Table 1. Positive and negative ions
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m/z Arbitrary Units Figure 1. Mass
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introduce low-volatility compounds
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arrangement, but the charge is reta
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two liquid chromatographic gradient
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Integrated Microfabricated Devices
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Matrix Assisted Laser Desorption/Io
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Molecular Beam Synthesis and Charac
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temperature distribution of the des
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expected to result in significant a
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Study Control Number: PN99069/1397
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Seuss DT and KA Prather. 1999. “M
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Analysis of Receptor-Modulated Ion
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laboratory (Lu and Xie 1997; Lu et
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Automated DNA Fingerprinting Microa
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Comparison of 4 Xanthomonas Isolate
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Approach Hematopoietic (bone marrow
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Anti-Human lgG Human lgG Protein G
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Characterization of Global Gene Reg
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PCR products for immobilization on
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identify novel proteins of importan
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Immunoprecipitaton of tyrosinephosp
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Coupled NMR and Confocal Microscopy
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In the optical microscopy image, th
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Development and Applications of a M
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Figure 3. Hepa-1 cells undergoing a
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cellular processing (such as post-t
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8. Initial demonstration of an off-
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Figure 4. A filtered version of Fig
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Development of Modeling Tools for J
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Figure 2. Schematic of experimental
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Integrated Modeling and Simulation
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(a) (b) (c) (d) Figure 1. Results o
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Thurman DA. August 2000. Collaborat
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MARC Input initial m esh and result
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(Johnson 1999; Colligan 1999; Gould
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Modeling of High-Velocity Forming f
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Global divergence error 1 10 -14 0
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that users were unlikely to appreci
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Earth System Science
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the lateral extent of the plume so
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Biogeochemical Processes and Microb
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Analyses of populations of viable a
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The first task was to establish fiv
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purged for 2 minutes. The gas sampl
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developing code architecture to min
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Berkowitz, CM. June 2000. “Atmosp
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environmental monitoring, 2) portab
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corresponded to a current density o
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Enhancing Emissions Pre-Processing
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Environmental Fate and Effects of D
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nitrogen and unpredictable eutrophi
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Figure 1. Ordinary kriging of 2 m d
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precipitation of calcite occurred i
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Interrelationships Between Processe
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phenanthrene resistant fraction con
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injection of an immiscible gas phas
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still some key kinetic issues that
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Application of a Crop Model The res
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The Nature, Occurrence, and Origin
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Particle number concentration, 1/cm
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geometry optimized AlOOH and FeOOH
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allowable parameters using thermody
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TCE + 3H + + 3Fe 2+ Fe)=> acetylene
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Use of Shear-Thinning Fluids for In
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concentration of 0.5% AMX was the m
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Roberts AL, LA Totten, WA Arnold, D
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tetra-scale computing, envisioned a
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Energy Technology and Management
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Figure 2. Setup for the second expe
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phase. The algorithms will be teste
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[Pb-212]/[Pb-212] final 100 10 1 0.
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Plasma Particle Dielectric Fiber El
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(species, sub-species, and sex), sp
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Figure 3. Relative risk of bone and
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Development of a Preliminary Physio
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Development of a Virtual Respirator
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Figure 3. Use of the chest cavity a
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Examination of the Use of Diazolumi
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Indoor Air Pathways to Nuclear, Che
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Source Building Release Observed re
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To illustrate the potential impact
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Non-Invasive Biological Monitoring
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Materials Science and Technology
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Figure 1. (a) - (c) Successive magn
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Component Fabrication and Assembly
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Development of a Low-Cost Photoelec
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elaxations, indicating the adequacy
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Promising chemical durability, as m
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Study Control Number : PN98028/1274
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High Power Density Solid Oxide Fuel
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Hybrid Plasma Process for Vacuum De
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Matson DW, PM Martin, WD Bennett, J
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We showed the proof-of-principle ap
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Bandgap Energy (eV) 2.9 2.85 2.8 2.
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Ultrathin Electrolyte Test Results
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Detailed Investigations on Hydrogen
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identified low energy step structur
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Development of Novel Photo-Catalyst
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-2 -1 Energy (eV) 0 1 2 Cu2O SrTiO3
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Here, we perform adsorption and des
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exceed those in the all-metal devic
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Intensity (a.u.) 0 100 200 300 400
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integrated voltage (V) 0.10 0.08 0.
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The first step was to implement thi
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Spontaneous Formation of Cu2O Nano-
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Actinide Chemistry at the Aqueous-M
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Figure 5. Close-up atomic force mic
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to 300°C and 400°C. Unfortunately
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Summary and Conclusions Transmutati
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Advanced Calibration/Registration T
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Figure 1a. Violet filter Figure 1b.
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Autonomous Ultrasonic Probe for Pro
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containing nitrous oxide, an optica
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Chemical Detection Using Cavity Enh
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Figure 3 is a color rendered simula
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appropriate study has been performe
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Exploitation of Conventional Chemic
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Figures 3 and 4 illustrate the impa
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enefits of using a modified spread-
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Single Channel Signal 0.0025 0.0020
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Therefore, in order to extract the
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A next-generation technology is nee
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Wind Dir. Figure 2. Negative ion co
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Volumetric Imaging of Materials by
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amplitude and peak frequency change
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Figure 3. 13 C NMR spectrum of corn
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Approach Two flow loops, one that o
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Modification of Ion Exchange Resins
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Permanganate Treatment for the Deco
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minimized by medium exchange. After
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Figure 1. Thermogravimetric analysi
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Validation and Scale-Up of Monosacc
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Concentration (g/100g solution) Con
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Analysis of High-Volume, Hyper-Dime
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ecent figure of merit values. Shoul
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Probabilistic Methods Development f
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a) Traditional PCA-based process co
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Acronyms and Abbreviations 2-D two-
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PNNL-13501 - Pacific Northwest National Laboratory

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