PNNL-13501 - Pacific Northwest National Laboratory

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Analysis of High-Volume, Hyper-Dimensional Mixed Data: NMR Spectra and Confocal Image Ensembles Study Control Number: PN00009/1416 Don Simone Daly New research tools for advanced biological studies generate highly complex, large volume, and multi-dimensional data. Often, these datasets represent mixed media, such as spectra, picture images, and scalars. Project Description Scientific research often generates complex, large volume, or hyper-dimensional datasets. Analyzing complex, multi-dimensional datasets can be highly challenging. The object of this project was to provide the tools, techniques, and expertise to analyze ensembles of spectra and images as an ensemble. Making the step from characteristics of a single object (such as an NMR spectrum) to the characteristics of the ensemble (i.e., an NMR spectral time series) provides new opportunities to unravel complex associations. We investigated the tools, techniques, and expertise necessary for the analysis of mixed sets of NMR spectra and optical images. Results and Accomplishments The primary accomplishments of this project are 1) the introduction of a new perspective from which to view the collection and analysis of NMR and optical datasets, and 2) the development of new analysis methods made possible by this perspective. Our new perspective emphasizes the data ensemble over the individual measurements. The new methods are geared to the collection and analysis of the ensemble. We reexamined the NMR phenomenon and its subsequent measurement. Our examination produced a stochastic model of this spectral measurement that is true to the physics and includes the measuring uncertainties. Our model suggested a graphical method to view an NMR quadrature spectrum and an NMR spectral ensemble. Our stochastic model describes an NMR-quadrature freeinduction-decay measurement as a complex-valued time series. This model suggested that a phase-space graph plotting the real component of the measured time series against the imaginary component would provide not only information about the phenomenon under study, but also about the effects of measuring this phenomenon using NMR (Figure 1). Figure 1. Phase space representation of a complex-valued NMR free induction decay of a sample featuring two dominant NM environments A quick assessment of an ensemble of NMR measurements is gained by using the phase-space graph as a glyph to represent one NMR free induction decay on a graph of the ensemble (Figure 2). In this figure, the variation in the glyph over the underlying image hints at the varying spatial distribution of chemicals within the sample. The corner glyphs, where no signal is present, indicate that the measuring noise is random and uncorrelated between the two components of the complex-valued free induction decay measurements. Under this project, APEX, a set of spectral feature extraction algorithms developed for MALDI-mass spectrometry was extended to extract features from an NMR spectrum. The extended algorithms automatically extract objective, robust estimates of peak locations, heights, and other peak characteristics with associated standard errors. The collection of estimates for each spectrum was then analyzed further. We also explored the analysis of original and extracted spectral collections using spectral mixing models and spectral unmixing algorithms. Under a spectral mixing Statistics 445
Figure 2. Display of an NMR dataset with NMR free induction decay phase glyphs overlaid on MRI of an amphibian oocyte 446 FY 2000 <strong>Laboratory</strong> Directed Research and Development Annual Report model, each observed spectrum in a collection is modeled as a mixture of pure spectra which may or may not be known. The objectives of spectral unmixing are to 1) estimate the number of pure spectra, 2) estimate the pure spectra, and 3) estimate the contributions of each pure spectra to each observed spectrum. The spectral unmixing literature proposes a number of algorithms to estimate the parameters in the spectral mixing model. We found these algorithms to be less inadequate. We are presently developing new approaches, deriving new estimators, and developing new algorithms that will, hopefully, generate improved solutions. Ongoing studies being conducted will benefit from spectral enhancement and unmixing.
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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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expression systems for several year
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accumulation of protein products, i
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Fungal Conversion of Agricultural W
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Fungal Conversion of Waste Glucose/
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Summary and Conclusions We demonstr
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are shown in Figure 1(a) and 1(b),
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Anchorage-Independent Growth (% con
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selection of seedlings demonstratin
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NMR-Based Structural Genomics Micha
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Solution-State Structure and Fold-C
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Plant Root Exudates and Microbial G
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Summary and Conclusions • The gre
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Jones-Oliveira JB, JS Oliveira, HE
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• securely moves files to a targe
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Plenary invited lecture, “The Ele
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Figure 1. X3DGEN tetrahedral mesh o
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Figure 5a. OSO volume rendering of
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Development of Models of Cell-Signa
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SOS p 23 EGFR Professor Rodland at
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Oliveira JS, JB Jones-Oliveira, and
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Figure 2. Associating a dataset mar
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to 1) incorporate three-dimensional
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shown in Figure 1. Simulated result
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HostBuilder: A Tool for the Combina
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Invariant Discretization Methods fo
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Graphics, Inc., workstation. The co
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Mixed Hamiltonian Methods for Geoch
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guyanaite, and grimaldiite (see Fig
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in the Environmental Molecular Scie
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Simulation and Modeling of Electroc
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Summary and Conclusions We implemen
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Since the implementation of the gen
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asis of previous performance, perce
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Bioinformatics for High-Throughput
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Summary and Conclusions In previous
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User Cat - Supervisor (client) Anal
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The second goal was to research way
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Figure 1. A visualization technique
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and interactions. The flexibility o
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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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PNNL-13501 - Pacific Northwest National Laboratory

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