SEARCH · Engineering Papers

Results for “functioning”

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.

SourceResearch connectionFrom yearThrough year

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

At least 1,135 records · Page 63

All sources NASA NTRS DOE OSTI

DOE OSTI2013

The Performance of Density Functionals for Sulfate-Water Clusters

The performance of 24 density functionals, Hartree-Fock, and MP2 is assessed with respect to the energetics of 49 sulfate-water clusters with between three and six water molecules. Included among the density functionals are GGA, meta-GGA, hybrid GGA, hybrid meta-GGA, and double hybrid density functionals, as well as the LDA. Three types of dispersion corrections (VV10, XDM, and -D) are tested in conjunction with these functionals. The functionals are compared using the relative and binding energies of the sulfatewater clusters as the main criteria. It is discovered that a majority of current density functionals are unable to simultaneously capture the physics necessary to describe both the relative and binding energies of the anionic solvation clusters. The only density functionals that perform acceptably with respect to both measures are XYG3 and XYGJOS, primarily due to their balanced treatment of exchange and correlation. On the other hand, density functionals with exact exchange that lack nonlocal correlation tend to perform well only for the relative energies. However, there is evidence that hybrid density functionals that provide a more comprehensive treatment of local correlation through their dependence on the kinetic energy density and their ability to treat the inhomogeneities in the present system can partially resolve this issue. While dispersion correction functionals cannot replace the accuracy provided by MP2 correlation, it is shown that the proper combination of a hybrid GGA functional with a dispersion correction functional can lead to drastic improvements in the binding energies of the parent functional, while preserving its performance with respect to the relative energies.

Environmental Molecular Sciences Laboratory↗

DOE OSTI2024

Direct evidence of low work function on SrVO 3 cathode using thermionic electron emission microscopy and high-field ultraviolet photoemission spectroscopy

Perovskite SrVO 3 has recently been proposed as a novel electron emission cathode material. Density functional theory (DFT) calculations suggest multiple low work function surfaces, and recent experimental efforts have consistently demonstrated effective work functions of ~2.7 eV for polycrystalline samples, both results suggesting, but not directly confirming, that some fraction of even lower work function surface is present. In this work, thermionic electron emission microscopy (ThEEM) and high-field ultraviolet photoemission spectroscopy (UPS) are used to study the local work function distribution and measure the work function of a partially oriented- (110)-SrVO 3 perovskite oxide cathode surface. Our results show direct evidence of low work function patches of about 2.0 eV on the cathode surface, with a corresponding onset of observable thermionic emission at 750 °C. We hypothesize that, in our ThEEM and UPS experiments, the high applied electric field suppresses the patch field effect, enabling the direct measurement of local work functions. This measured work function of 2.0 eV is comparable to the previous DFT-calculated work function values of the SrVO-terminated (110) SrVO 3 surface (2.3 eV) and SrO-terminated (100) surface (1.9 eV). The measured 2.0 eV value is also much lower than the work function for the (001) LaB 6 single crystal cathode (~2.7 eV) and comparable to the effective work function of B-type dispenser cathodes (~2.1 eV). If SrVO 3 thermionic emitters can be engineered to access domains of this low 2.0 eV work function, they have the potential to significantly improve thermionic emitter-based technologies.

36 MATERIALS SCIENCE↗

DOE OSTI2016

WE-AB-202-04: Statistical Evaluation of Lung Function Using 4DCT Ventilation Imaging: Proton Therapy VS IMRT

Purpose: Variation in function of different lung regions has been ignored so far for conventional lung cancer treatment planning, which may lead to higher risk of radiation induced lung disease. 4DCT based lung ventilation imaging provides a novel yet convenient approach for lung functional imaging as 4DCT is taken as routine for lung cancer treatment. Our work aims to evaluate the impact of accounting for spatial heterogeneity in lung function using 4DCT based lung ventilation imaging for proton and IMRT plans. Methods: Six patients with advanced stage lung cancer of various tumor locations were retrospectively evaluated for the study. Proton and IMRT plans were designed following identical planning objective and constrains for each patient. Ventilation images were calculated from patients’ 4DCT using deformable image registration implemented by Velocity AI software based on Jacobian-metrics. Lung was delineated into two function level regions based on ventilation (low and high functional area). High functional region was defined as lung ventilation greater than 30%. Dose distribution and statistics in different lung function area was calculated for patients. Results: Variation in dosimetric statistics of different function lung region was observed between proton and IMRT plans. In all proton plans, high function lung regions receive lower maximum dose (100.2%–108.9%), compared with IMRT plans (106.4%–119.7%). Interestingly, three out of six proton plans gave higher mean dose by up to 2.2% than IMRT to high function lung region. Lower mean dose (lower by up to 14.1%) and maximum dose (lower by up to 9%) were observed in low function lung for proton plans. Conclusion: A systematic approach was developed to generate function lung ventilation imaging and use it to evaluate plans. This method hold great promise in function analysis of lung during planning. We are currently studying more subjects to evaluate this tool.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2009

Physiological Factors Contributing to Postflight Changes in Functional Performance

Astronauts experience alterations in multiple physiological systems due to exposure to the microgravity conditions of space flight. These physiological changes include sensorimotor disturbances, cardiovascular deconditioning and loss of muscle mass and strength. These changes might affect the ability of crewmembers to perform critical mission tasks immediately after landing on lunar and Martian surfaces. To date, changes in functional performance have not been systematically studied or correlated with physiological changes. To understand how changes in physiological function impact functional performance an interdisciplinary pre/postflight testing regimen (Functional Task Test, FTT) has been developed that systematically evaluates both astronaut postflight functional performance and related physiological changes. The overall objectives of the FTT are to: Develop a set of functional tasks that represent critical mission tasks for Constellation. Determine the ability to perform these tasks after flight. Identify the key physiological factors that contribute to functional decrements. Use this information to develop targeted countermeasures. The functional test battery was designed to address high priority tasks identified by the Constellation program as critical for mission success. The set of functional tests making up the FTT include the: 1) Seat Egress and Walk Test, 2) Ladder Climb Test, 3) Recovery from Fall/Stand Test, 4) Rock Translation Test, 5) Jump Down Test, 6) Torque Generation Test, and 7) Construction Activity Board Test. Corresponding physiological measures include assessments of postural and gait control, dynamic visual acuity, fine motor control, plasma volume, orthostatic intolerance, upper and lower body muscle strength, power, fatigue, control and neuromuscular drive. Crewmembers will perform both functional and physiological tests before and after short (Shuttle) and long-duration (ISS) space flight. Data will be collected on R+0 (Shuttle only), R+1, R+6 and R+30. Using a multivariate regression model we will identify which physiological systems contribute the most to impaired performance on each functional test. This will allow us to identify the physiological systems that play the largest role in decrement in functional performance. Using this information we can then design and implement countermeasures that specifically target the physiological systems most responsible for the altered functional performance associated with space flight.

Bloomberg, J. J.↗

DOE OSTI2018

The Lorenzo–Hartley’s function for fractional calculus and its applications pertaining to fractional order modelling of anomalous relaxation in dielectrics

Considering the applications and a great significance of the study pertaining to generalized functions in applied sciences, the author investigated the Lorenzo–Hartley’s function which is a generalization of classical functions widely used in fractional calculus. The Laplace transform pairs are derived and the requirements for the Lorenzo–Hartley’s function G{sub ν,μ,δ}(a,c,t) to be completely monotonic (for t>0) are investigated. The author has shown the applications of this generalized function to describe the relaxation models, particularly in dielectrics. The Lorenzo–Hartley’s function G{sub ν,μ,δ}(a,c,t) of a real variable t is considered to investigate a computable mathematical framework for standard Debye and non-Debye relaxation processes in dielectric materials. The non-negative spectral distribution function is obtained for the corresponding response function. It is also demonstrated that the classical models like Cole–Cole (C–C), Davidson–Cole (D–C), and Havriliak–Negami (H–N) for non-Debye relaxation and standard Debye relaxation are the particular cases of the Lorenzo–Hartley’s function. Some of the study cases are also worked out to visualize the effects of variations of parameters on the response function and corresponding spectral distribution function. A generalized and unified fractional relaxation differential equation which governs response functions for classical dielectrics models pertaining to non-Debye relaxation (C–C, D–C, and H–N) is also established in the present investigation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2014

Compact two-electron wave function for bond dissociation and Van der Waals interactions: A natural amplitude assessment

Electron correlations in molecules can be divided in short range dynamical correlations, long range Van der Waals type interactions, and near degeneracy static correlations. In this work, we analyze for a one-dimensional model of a two-electron system how these three types of correlations can be incorporated in a simple wave function of restricted functional form consisting of an orbital product multiplied by a single correlation function f (r{sub 12}) depending on the interelectronic distance r{sub 12}. Since the three types of correlations mentioned lead to different signatures in terms of the natural orbital (NO) amplitudes in two-electron systems, we make an analysis of the wave function in terms of the NO amplitudes for a model system of a diatomic molecule. In our numerical implementation, we fully optimize the orbitals and the correlation function on a spatial grid without restrictions on their functional form. Due to this particular form of the wave function, we can prove that none of the amplitudes vanishes and moreover that it displays a distinct sign pattern and a series of avoided crossings as a function of the bond distance in agreement with the exact solution. This shows that the wave function ansatz correctly incorporates the long range Van der Waals interactions. We further show that the approximate wave function gives an excellent binding curve and is able to describe static correlations. We show that in order to do this the correlation function f (r{sub 12}) needs to diverge for large r{sub 12} at large internuclear distances while for shorter bond distances it increases as a function of r{sub 12} to a maximum value after which it decays exponentially. We further give a physical interpretation of this behavior.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Floor and ceiling effects in the EORTC QLQ-C30 Physical Functioning subscale among patients with advanced or metastatic breast cancer

The European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire Core 30 Physical Functioning subscale is a widely used patient-reported outcome measure that quantifies cancer patients' physical functioning. Strong floor/ceiling effects can affect a scale's sensitivity to change. The aim of this study was to characterize floor/ceiling effects of the physical functioning domain in patients with advanced/metastatic breast cancer enrolled in commercial clinical trials and a community-based trial. The clinical trial cohort comprised patients from 5 registrational trials submitted to the Food and Drug Administration for review (2010-2017). The community cohort comprised a subgroup of patients from the Alliance Patient Reported Outcomes to Enhance Cancer Treatment (PRO-TECT) trial. The distribution of patient responses to Physical Functioning items and the summed score were assessed at the baseline and 3-month follow-up for both cohorts. Descriptive statistics were used to determine floor/ceiling effects at the item and scale levels. The clinical trial cohort and the community cohort consisted of 2407 and 178 patients, respectively. Twenty-four percent or more of the respondents reported “not at all” for having trouble/needing help with each Physical Functioning item across both cohorts and measurement time points. Fourteen to twenty percent of the patients scored perfectly (100 of 100) on the Physical Functioning subscale summary measure (where higher scores indicated better physical functioning) across both cohorts and time points. Minor floor effects and notable ceiling effects were found at the item and scale levels of the Physical Functioning subscale, regardless of cohort, and this creates some uncertainty about its ability to detect changes in physical functioning among high-functioning patients. In conclusion, investigators may consider adding additional high-functioning items from the EORTC's item library to more accurately describe the impact of anticancer treatment on patients' physical functioning.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

Inter-Procedural Resource Sharing in High Level Synthesis through Function Proxies

Modular design is becoming increasingly important in High Level Synthesis (HLS) flows. Current HLS flows generate hierarchical and modular designs that mimic the structure and call graph of input specification translating functions into modules. Function calls are translated instantiating the callee module in the data-path of its caller, allowing for resource sharing when the same function is called multiple times. However, if two different callers invoke the same function, current HLS flows cannot share the instance of the module between the two callers, even if they invoke the function in a mutually exclusive way. In this paper, we propose a methodology that enables sharing of (sub)modules across modules boundaries. Sharing is obtained through function proxies, which act as forwarders of function calls in the original specification to shared modules without adding performance penalties. Building on the concept of function proxies, we propose a methodology and the related components to perform HLS of function calls through function pointers, without requiring complete static knowledge of the alias set (point-to set). We show that module sharing through function proxies provides valuable area savings without significant impacts on the execution delays, and that our synthesis approach for function pointers enables dynamic polymorphism.

HLS, function proxy, FPGA, modular HLS,↗

DOE OSTI2024

Machine learning analysis of RB-TnSeq fitness data predicts functional gene modules in Pseudomonas putida KT2440

ABSTRACT There is growing interest in engineering Pseudomonas putida KT2440 as a microbial chassis for the conversion of renewable and waste-based feedstocks, and metabolic engineering of P. putida relies on the understanding of the functional relationships between genes. In this work, independent component analysis (ICA) was applied to a compendium of existing fitness data from randomly barcoded transposon insertion sequencing (RB-TnSeq) of P. putida KT2440 grown in 179 unique experimental conditions. ICA identified 84 independent groups of genes, which we call fModules (“functional modules”), where gene members displayed shared functional influence in a specific cellular process. This machine learning-based approach both successfully recapitulated previously characterized functional relationships and established hitherto unknown associations between genes. Selected gene members from fModules for hydroxycinnamate metabolism and stress resistance, acetyl coenzyme A assimilation, and nitrogen metabolism were validated with engineered mutants of P. putida . Additionally, functional gene clusters from ICA of RB-TnSeq data sets were compared with regulatory gene clusters from prior ICA of RNAseq data sets to draw connections between gene regulation and function. Because ICA profiles the functional role of several distinct gene networks simultaneously, it can reduce the time required to annotate gene function relative to manual curation of RB-TnSeq data sets. IMPORTANCE This study demonstrates a rapid, automated approach for elucidating functional modules within complex genetic networks. While Pseudomonas putida randomly barcoded transposon insertion sequencing data were used as a proof of concept, this approach is applicable to any organism with existing functional genomics data sets and may serve as a useful tool for many valuable applications, such as guiding metabolic engineering efforts in other microbes or understanding functional relationships between virulence-associated genes in pathogenic microbes. Furthermore, this work demonstrates that comparison of data obtained from independent component analysis of transcriptomics and gene fitness datasets can elucidate regulatory-functional relationships between genes, which may have utility in a variety of applications, such as metabolic modeling, strain engineering, or identification of antimicrobial drug targets.

09 BIOMASS FUELS↗

DOE OSTI2025

Blocky Selective Postpolymerization C–H Functionalization of Polyolefins

C–H functionalization of commodity polyolefins affords functional materials derived from a high-volume, low-cost resource. However, current postpolymerization modification strategies result in randomly distributed functionalization along the length of the polymer backbone, which has a negative impact on the crystallinity of the resultant polymers, and thus the thermomechanical properties. Here, we demonstrate an amidyl radical mediated C–H functionalization of polyolefins to access blocky microstructures, which exhibit a higher crystalline fraction, larger crystallite size, and improved mechanical properties compared to their randomly functionalized analogues. Taking inspiration from the site-selective C–H functionalization of small molecules, we leverage the steric protection provided by crystallites and target polymer functionalization to amorphous domains in a semicrystalline polyolefin gel. The beneficial outcomes of blocky functionalization are independent of the identity of the pendant functional group that is installed through functionalization. Here, the decoupling of functional group incorporation and crystallinity highlights the promise in accessing nonrandom microstructures through selective functionalization to circumvent traditional tradeoffs in postpolymerization modification, with potential impact in advanced materials and upcycling plastic waste.

Neidhart, Eliza K. [The University of North Caroli↗

DOE OSTI2025

Heterogeneous Mixtures of Dictionary Functions to Approximate Subspace Invariance in Koopman Operators: Why Deep Koopman Operators Work

Abstract Koopman operators model nonlinear dynamics as a linear dynamic system acting on a nonlinear function as the state. This nonstandard state is often called a Koopman observable and is usually approximated numerically by a superposition of functions drawn from a dictionary . In a widely used algorithm, extended dynamic mode decomposition (EDMD), the dictionary functions are drawn from a fixed class of functions. Deep learning combined with EDMD has been used to learn novel dictionary functions in an algorithm called deep dynamic mode decomposition (deepDMD). The learned representation both (1) accurately models and (2) scales well with the dimension of the original nonlinear system. In this paper, we analyze the learned dictionaries from deepDMD and explore the theoretical basis for their strong performance. We explore State-Inclusive Logistic Lifting (SILL) dictionary functions to approximate Koopman observables. Error analysis of these dictionary functions show they satisfy a property of subspace approximation, which we define as uniform finite approximate closure. Typically, a Koopman dictionary’s nonlinear functions are homogeneous. In this paper, we discover that structured mixing of heterogeneous dictionary functions drawn from different classes of nonlinear functions achieve the same accuracy and dimensional scaling as the deep-learning-based deepDMD algorithm Yeung et al. ( In: 2019 American Control Conference (ACC), 2019). We specifically show this by building a heterogeneous dictionary comprised of SILL functions and conjunctive radial basis functions (RBFs). This mixed dictionary achieves similar accuracy and dimensional scaling to deepDMD with an order of magnitude reduction in parameters, while maintaining geometric interpretability. These results strengthen the viability of dictionary-based Koopman models to solving high-dimensional nonlinear learning problems.

Johnson, Charles A.↗

DOE OSTI2013

The analytic solution for the power series expansion of Heun function

The Heun function generalizes all well-known special functions such as Spheroidal Wave, Lame, Mathieu, and hypergeometric {sub 2}F{sub 1}, {sub 1}F{sub 1} and {sub 0}F{sub 1} functions. Heun functions are applicable to diverse areas such as theory of black holes, lattice systems in statistical mechanics, solution of the Schrödinger equation of quantum mechanics, and addition of three quantum spins. In this paper I will apply three term recurrence formula (Y.S. Choun, (arXiv:1303.0806), 2013) to the power series expansion in closed forms of Heun function (infinite series and polynomial) including all higher terms of A{sub n}’s. Section 3 contains my analysis on applying the power series expansions of Heun function to a recent paper (R.S. Maier, Math. Comp. 33 (2007) 811–843). Due to space restriction final equations for the 192 Heun functions are not included in the paper, but feel free to contact me for the final solutions. Section 4 contains two additional examples using the power series expansions of Heun function. This paper is 3rd out of 10 in series “Special functions and three term recurrence formula (3TRF)”. See Section 5 for all the papers in the series. The previous paper in series deals with three term recurrence formula (3TRF). The next paper in the series describes the integral forms of Heun function and its asymptotic behaviors analytically. -- Highlights: •Power series expansion for infinite series of Heun function using 3 term rec. form. •Power series for polynomial which makes B{sub n} term terminated of Heun function. •Applicable to areas such as the Teukolsky equation in Kerr–Newman–de Sitter geometries.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2017

Evaluating the Toxicity Reduction With Computed Tomographic Ventilation Functional Avoidance Radiation Therapy

Purpose: Computed tomographic (CT) ventilation imaging is a new modality that uses 4-dimensional (4D) CT information to calculate lung ventilation. Although retrospective studies have reported on the reduction in dose to functional lung, no work to our knowledge has been published in which the dosimetric improvements have been translated to a reduction in the probability of pulmonary toxicity. Our work estimates the reduction in toxicity for CT ventilation–based functional avoidance planning. Methods and Materials: Seventy previously treated lung cancer patients who underwent 4DCT imaging were used for the study. CT ventilation maps were calculated with 4DCT deformable image registration and a density change–based algorithm. Pneumonitis was graded on the basis of imaging and clinical presentation. Maximum likelihood methods were used to generate normal tissue complication probability (NTCP) models predicting grade 2 or higher (2+) and grade 3+ pneumonitis as a function of dose (V5 Gy, V10 Gy, V20 Gy, V30 Gy, and mean dose) to functional lung. For 30 patients a functional plan was generated with the goal of reducing dose to the functional lung while meeting Radiation Therapy Oncology Group 0617 constraints. The NTCP models were applied to the functional plans and the clinically used plans to calculate toxicity reduction. Results: By the use of functional avoidance planning, absolute reductions in grade 2+ NTCP of 6.3%, 7.8%, and 4.8% were achieved based on the mean fV20 Gy, fV30 Gy, and mean dose to functional lung metrics, respectively. Absolute grade 3+ NTCP reductions of 3.6%, 4.8%, and 2.4% were achieved with fV20 Gy, fV30 Gy, and mean dose to functional lung. Maximum absolute reductions of 52.3% and 16.4% were seen for grade 2+ and grade 3+ pneumonitis for individual patients. Conclusion: Our study quantifies the possible toxicity reduction from CT ventilation–based functional avoidance planning. Reductions in grades 2+ and 3+ pneumonitis were 7.1% and 4.7% based on mean dose-function metrics, with reductions as high as 52.3% for individual patients. Our work provides seminal data for determining the potential toxicity benefit from incorporating CT ventilation into thoracic treatment planning.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2019

Determination of nanoparticle collision cross section distribution functions in low pressure plasma synthesis reactors via ion mobility spectrometry

Low pressure (< 20 Torr) non-thermal plasma flow tube reactors are often used for the synthesis of high purity nanoparticles (NPs) via nucleation and subsequent growth from vapor phase precursors. In spite of their utility, process monitoring (i.e. output NP size characterization) in such reactors is difficult, as there is a dearth of techniques available for online nanoparticle size distribution function measurement. In this study, we developed and applied an ion mobility spectrometry (IMS) system consisting of a low pressure differential mobility analyzer (LPDMA) and electrical detector to determine the collision cross section distribution functions of Si NPs synthesized in a radiofrequency non-thermal SiH4-Ar plasma operated at ~2 Torr (266 Pa). The collision cross section, roughly proportional to projected area, is a parameter quantifying the size and structure of nanometer scale species in the vapor phase. We introduce the collision cross section distribution function as a metric for potential use in online process monitoring in NP synthesis. Proper inversion of the collision cross section distribution function requires a priori knowledge of the LPDMA transfer function. We utilized a tandem differential mobility analyzer approach, coupled with a Twomey-Markwoski based scheme to determine LPDMA transfer functions. Subsequent application of these transfer functions in collision cross section distribution function determination showed that at the outlet of the non-thermal plasma flow tube reactor, both negatively and positively charged Si NPs persisted with nearly identical collision cross section distribution functions. NPs were found to have mode “mobility equivalent” diameters near 10 nm, and via TEM analysis were found to persist at the reactor outlet as small aggregates composed of ~5 nm diameter primary particles. Size distributions functions inferred from collision cross section measurements were compared to size distribution functions inferred from TEM images; excellent agreement was found between IMS and TEM for both mean mobility equivalent diameter and distribution function width. In total, this study shows that (1) IMS is a viable approach for process monitoring in non-thermal plasma NP flow tube synthesis systems, (2) although NPs are modestly aggregated at low-pressure plasma reactor outlets, the extent of aggregation is considerably less than observed in most atmospheric pressure and equilibrium NP synthesis systems (e.g. flames), and (3) after exiting the plasma reactor, the decharging of NPs from highly negatively charge states to a bipolar charge distribution likely drives aggregation on the plasma boundary.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2014

Experimental evaluation and basis function optimization of the spatially variant image-space PSF on the Ingenuity PET/MR scanner

Purpose: The Ingenuity time-of-flight (TF) PET/MR is a recently developed hybrid scanner combining the molecular imaging capabilities of PET with the excellent soft tissue contrast of MRI. It is becoming common practice to characterize the system's point spread function (PSF) and understand its variation under spatial transformations to guide clinical studies and potentially use it within resolution recovery image reconstruction algorithms. Furthermore, due to the system's utilization of overlapping and spherical symmetric Kaiser-Bessel basis functions during image reconstruction, its image space PSF and reconstructed spatial resolution could be affected by the selection of the basis function parameters. Hence, a detailed investigation into the multidimensional basis function parameter space is needed to evaluate the impact of these parameters on spatial resolution. Methods: Using an array of 12 × 7 printed point sources, along with a custom made phantom, and with the MR magnet on, the system's spatially variant image-based PSF was characterized in detail. Moreover, basis function parameters were systematically varied during reconstruction (list-mode TF OSEM) to evaluate their impact on the reconstructed resolution and the image space PSF. Following the spatial resolution optimization, phantom, and clinical studies were subsequently reconstructed using representative basis function parameters. Results: Based on the analysis and under standard basis function parameters, the axial and tangential components of the PSF were found to be almost invariant under spatial transformations (∼4 mm) while the radial component varied modestly from 4 to 6.7 mm. Using a systematic investigation into the basis function parameter space, the spatial resolution was found to degrade for basis functions with a large radius and small shape parameter. However, it was found that optimizing the spatial resolution in the reconstructed PET images, while having a good basis function superposition and keeping the image representation error to a minimum, is feasible, with the parameter combination range depending upon the scanner's intrinsic resolution characteristics. Conclusions: Using the printed point source array as a MR compatible methodology for experimentally measuring the scanner's PSF, the system's spatially variant resolution properties were successfully evaluated in image space. Overall the PET subsystem exhibits excellent resolution characteristics mainly due to the fact that the raw data are not under-sampled/rebinned, enabling the spatial resolution to be dictated by the scanner's intrinsic resolution and the image reconstruction parameters. Due to the impact of these parameters on the resolution properties of the reconstructed images, the image space PSF varies both under spatial transformations and due to basis function parameter selection. Nonetheless, for a range of basis function parameters, the image space PSF remains unaffected, with the range depending on the scanner's intrinsic resolution properties.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2014

SU-E-T-259: Particle Swarm Optimization in Radial Dose Function Fitting for a Novel Iodine-125 Seed

Purpose: To determine the coefficients of bi- and tri-exponential functions for the best fit of radial dose functions of the new iodine brachytherapy source: Iodine-125 Seed AgX-100. Methods: The particle swarm optimization (PSO) method was used to search for the coefficients of the biand tri-exponential functions that yield the best fit to data published for a few selected radial distances from the source. The coefficients were encoded into particles, and these particles move through the search space by following their local and global best-known positions. In each generation, particles were evaluated through their fitness function and their positions were changed through their velocities. This procedure was repeated until the convergence criterion was met or the maximum generation was reached. All best particles were found in less than 1,500 generations. Results: For the I-125 seed AgX-100 considered as a point source, the maximum deviation from the published data is less than 2.9% for bi-exponential fitting function and 0.2% for tri-exponential fitting function. For its line source, the maximum deviation is less than 1.1% for bi-exponential fitting function and 0.08% for tri-exponential fitting function. Conclusion: PSO is a powerful method in searching coefficients for bi-exponential and tri-exponential fitting functions. The bi- and tri-exponential models of Iodine-125 seed AgX-100 point and line sources obtained with PSO optimization provide accurate analytical forms of the radial dose function. The tri-exponential fitting function is more accurate than the bi-exponential function.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

TH-A-9A-04: Incorporating Liver Functionality in Radiation Therapy Treatment Planning

Purpose: Liver SBRT patients have both variable pretreatment liver function (e.g., due to degree of cirrhosis and/or prior treatments) and sensitivity to radiation, leading to high variability in potential liver toxicity with similar doses. This work aims to explicitly incorporate liver perfusion into treatment planning to redistribute dose to preserve well-functioning areas without compromising target coverage. Methods: Voxel-based liver perfusion, a measure of functionality, was computed from dynamic contrast-enhanced MRI. Two optimization models with different cost functions subject to the same dose constraints (e.g., minimum target EUD and maximum critical structure EUDs) were compared. The cost functions minimized were EUD (standard model) and functionality-weighted EUD (functional model) to the liver. The resulting treatment plans delivering the same target EUD were compared with respect to their DVHs, their dose wash difference, the average dose delivered to voxels of a particular perfusion level, and change in number of high-/low-functioning voxels receiving a particular dose. Two-dimensional synthetic and three-dimensional clinical examples were studied. Results: The DVHs of all structures of plans from each model were comparable. In contrast, in plans obtained with the functional model, the average dose delivered to high-/low-functioning voxels was lower/higher than in plans obtained with its standard counterpart. The number of high-/low-functioning voxels receiving high/low dose was lower in the plans that considered perfusion in the cost function than in the plans that did not. Redistribution of dose can be observed in the dose wash differences. Conclusion: Liver perfusion can be used during treatment planning potentially to minimize the risk of toxicity during liver SBRT, resulting in better global liver function. The functional model redistributes dose in the standard model from higher to lower functioning voxels, while achieving the same target EUD and satisfying dose limits to critical structures. This project is funded by MCubed and grant R01-CA132834.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

PanFP: Pangenome-based functional profiles for microbial communities

For decades there has been increasing interest in understanding the relationships between microbial communities and ecosystem functions. Current DNA sequencing technologies allows for the exploration of microbial communities in two principle ways: targeted rRNA gene surveys and shotgun metagenomics. For large study designs, it is often still prohibitively expensive to sequence metagenomes at both the breadth and depth necessary to statistically capture the true functional diversity of a community. Although rRNA gene surveys provide no direct evidence of function, they do provide a reasonable estimation of microbial diversity, while being a very cost effective way to screen samples of interest for later shotgun metagenomic analyses. However, there is a great deal of 16S rRNA gene survey data currently available from diverse environments, and thus a need for tools to infer functional composition of environmental samples based on 16S rRNA gene survey data. As a result, we present a computational method called pangenome based functional profiles (PanFP), which infers functional profiles of microbial communities from 16S rRNA gene survey data for Bacteria and Archaea. PanFP is based on pangenome reconstruction of a 16S rRNA gene operational taxonomic unit (OTU) from known genes and genomes pooled from the OTU s taxonomic lineage. From this lineage, we derive an OTU functional profile by weighting a pangenome s functional profile with the OTUs abundance observed in a given sample. We validated our method by comparing PanFP to the functional profiles obtained from the direct shotgun metagenomic measurement of 65 diverse communities via Spearman correlation coefficients. These correlations improved with increasing sequencing depth, within the range of 0.8 0.9 for the most deeply sequenced Human Microbiome Project mock community samples. PanFP is very similar in performance to another recently released tool, PICRUSt, for almost all of survey data analysed here. But, our method is unique in that any OTU building method can be used, as opposed to being limited to closed reference OTU picking strategies against specific reference sequence databases. In conclusion, we developed an automated computational method, which derives an inferred functional profile based on the 16S rRNA gene surveys of microbial communities. The inferred functional profile provides a cost effective way to study complex ecosystems through predicted comparative functional metagenomes and metadata analysis. All PanFP source code and additional documentation are freely available online at GitHub.

16S rRNA survey↗