Quantitative Predictive Models for Systemic Toxicity (SOT)

EPA Science Inventory

Models to identify systemic and specific target organ toxicity were developed to help transition the field of toxicology towards computational models. By leveraging multiple data sources to incorporate read-across and machine learning approaches, a quantitative model of systemic ...

LDEF data: Comparisons with existing models

NASA Technical Reports Server (NTRS)

Coombs, Cassandra R.; Watts, Alan J.; Wagner, John D.; Atkinson, Dale R.

1993-01-01

The relationship between the observed cratering impact damage on the Long Duration Exposure Facility (LDEF) versus the existing models for both the natural environment of micrometeoroids and the man-made debris was investigated. Experimental data was provided by several LDEF Principal Investigators, Meteoroid and Debris Special Investigation Group (M&D SIG) members, and by the Kennedy Space Center Analysis Team (KSC A-Team) members. These data were collected from various aluminum materials around the LDEF satellite. A PC (personal computer) computer program, SPENV, was written which incorporates the existing models of the Low Earth Orbit (LEO) environment. This program calculates the expected number of impacts per unit area as functions of altitude, orbital inclination, time in orbit, and direction of the spacecraft surface relative to the velocity vector, for both micrometeoroids and man-made debris. Since both particle models are couched in terms of impact fluxes versus impactor particle size, and much of the LDEF data is in the form of crater production rates, scaling laws have been used to relate the two. Also many hydrodynamic impact computer simulations were conducted, using CTH, of various impact events, that identified certain modes of response, including simple metallic target cratering, perforations and delamination effects of coatings.

LDEF data: Comparisons with existing models

NASA Astrophysics Data System (ADS)

Coombs, Cassandra R.; Watts, Alan J.; Wagner, John D.; Atkinson, Dale R.

1993-04-01

The relationship between the observed cratering impact damage on the Long Duration Exposure Facility (LDEF) versus the existing models for both the natural environment of micrometeoroids and the man-made debris was investigated. Experimental data was provided by several LDEF Principal Investigators, Meteoroid and Debris Special Investigation Group (M&D SIG) members, and by the Kennedy Space Center Analysis Team (KSC A-Team) members. These data were collected from various aluminum materials around the LDEF satellite. A PC (personal computer) computer program, SPENV, was written which incorporates the existing models of the Low Earth Orbit (LEO) environment. This program calculates the expected number of impacts per unit area as functions of altitude, orbital inclination, time in orbit, and direction of the spacecraft surface relative to the velocity vector, for both micrometeoroids and man-made debris. Since both particle models are couched in terms of impact fluxes versus impactor particle size, and much of the LDEF data is in the form of crater production rates, scaling laws have been used to relate the two. Also many hydrodynamic impact computer simulations were conducted, using CTH, of various impact events, that identified certain modes of response, including simple metallic target cratering, perforations and delamination effects of coatings.

Gene Level Meta-Analysis of Quantitative Traits by Functional Linear Models.

PubMed

Fan, Ruzong; Wang, Yifan; Boehnke, Michael; Chen, Wei; Li, Yun; Ren, Haobo; Lobach, Iryna; Xiong, Momiao

2015-08-01

Meta-analysis of genetic data must account for differences among studies including study designs, markers genotyped, and covariates. The effects of genetic variants may differ from population to population, i.e., heterogeneity. Thus, meta-analysis of combining data of multiple studies is difficult. Novel statistical methods for meta-analysis are needed. In this article, functional linear models are developed for meta-analyses that connect genetic data to quantitative traits, adjusting for covariates. The models can be used to analyze rare variants, common variants, or a combination of the two. Both likelihood-ratio test (LRT) and F-distributed statistics are introduced to test association between quantitative traits and multiple variants in one genetic region. Extensive simulations are performed to evaluate empirical type I error rates and power performance of the proposed tests. The proposed LRT and F-distributed statistics control the type I error very well and have higher power than the existing methods of the meta-analysis sequence kernel association test (MetaSKAT). We analyze four blood lipid levels in data from a meta-analysis of eight European studies. The proposed methods detect more significant associations than MetaSKAT and the P-values of the proposed LRT and F-distributed statistics are usually much smaller than those of MetaSKAT. The functional linear models and related test statistics can be useful in whole-genome and whole-exome association studies. Copyright © 2015 by the Genetics Society of America.

Modeling with Young Students--Quantitative and Qualitative.

ERIC Educational Resources Information Center

Bliss, Joan; Ogborn, Jon; Boohan, Richard; Brosnan, Tim; Mellar, Harvey; Sakonidis, Babis

1999-01-01

A project created tasks and tools to investigate quality and nature of 11- to 14-year-old pupils' reasoning with quantitative and qualitative computer-based modeling tools. Tasks and tools were used in two innovative modes of learning: expressive, where pupils created their own models, and exploratory, where pupils investigated an expert's model.…

Multicriteria decision model for retrofitting existing buildings

NASA Astrophysics Data System (ADS)

Bostenaru Dan, B.

2003-04-01

In this paper a model to decide which buildings from an urban area should be retrofitted is presented. The model has been cast into existing ones by choosing the decision rule, criterion weighting and decision support system types most suitable for the spatial problem of reducing earthquake risk in urban areas, considering existing spatial multiatributive and multiobjective decision methods and especially collaborative issues. Due to the participative character of the group decision problem "retrofitting existing buildings" the decision making model is based on interactivity. Buildings have been modeled following the criteria of spatial decision support systems. This includes identifying the corresponding spatial elements of buildings according to the information needs of actors from different sphaeres like architects, construction engineers and economists. The decision model aims to facilitate collaboration between this actors. The way of setting priorities interactivelly will be shown, by detailing the two phases: judgemental and computational, in this case site analysis, collection and evaluation of the unmodified data and converting survey data to information with computational methods using additional expert support. Buildings have been divided into spatial elements which are characteristic for the survey, present typical damages in case of an earthquake and are decisive for a better seismic behaviour in case of retrofitting. The paper describes the architectural and engineering characteristics as well as the structural damage for constuctions of different building ages on the example of building types in Bucharest, Romania in compressible and interdependent charts, based on field observation, reports from the 1977 earthquake and detailed studies made by the author together with a local engineer for the EERI Web Housing Encyclopedia. On this base criteria for setting priorities flow into the expert information contained in the system.

A quantitative speciation model for the adsorption of organic pollutants on activated carbon.

PubMed

Grivé, M; García, D; Domènech, C; Richard, L; Rojo, I; Martínez, X; Rovira, M

2013-01-01

Granular activated carbon (GAC) is commonly used as adsorbent in water treatment plants given its high capacity for retaining organic pollutants in aqueous phase. The current knowledge on GAC behaviour is essentially empirical, and no quantitative description of the chemical relationships between GAC surface groups and pollutants has been proposed. In this paper, we describe a quantitative model for the adsorption of atrazine onto GAC surface. The model is based on results of potentiometric titrations and three types of adsorption experiments which have been carried out in order to determine the nature and distribution of the functional groups on the GAC surface, and evaluate the adsorption characteristics of GAC towards atrazine. Potentiometric titrations have indicated the existence of at least two different families of chemical groups on the GAC surface, including phenolic- and benzoic-type surface groups. Adsorption experiments with atrazine have been satisfactorily modelled with the geochemical code PhreeqC, assuming that atrazine is sorbed onto the GAC surface in equilibrium (log Ks = 5.1 ± 0.5). Independent thermodynamic calculations suggest a possible adsorption of atrazine on a benzoic derivative. The present work opens a new approach for improving the adsorption capabilities of GAC towards organic pollutants by modifying its chemical properties.

Quantitative model validation of manipulative robot systems

NASA Astrophysics Data System (ADS)

Kartowisastro, Iman Herwidiana

This thesis is concerned with applying the distortion quantitative validation technique to a robot manipulative system with revolute joints. Using the distortion technique to validate a model quantitatively, the model parameter uncertainties are taken into account in assessing the faithfulness of the model and this approach is relatively more objective than the commonly visual comparison method. The industrial robot is represented by the TQ MA2000 robot arm. Details of the mathematical derivation of the distortion technique are given which explains the required distortion of the constant parameters within the model and the assessment of model adequacy. Due to the complexity of a robot model, only the first three degrees of freedom are considered where all links are assumed rigid. The modelling involves the Newton-Euler approach to obtain the dynamics model, and the Denavit-Hartenberg convention is used throughout the work. The conventional feedback control system is used in developing the model. The system behavior to parameter changes is investigated as some parameters are redundant. This work is important so that the most important parameters to be distorted can be selected and this leads to a new term called the fundamental parameters. The transfer function approach has been chosen to validate an industrial robot quantitatively against the measured data due to its practicality. Initially, the assessment of the model fidelity criterion indicated that the model was not capable of explaining the transient record in term of the model parameter uncertainties. Further investigations led to significant improvements of the model and better understanding of the model properties. After several improvements in the model, the fidelity criterion obtained was almost satisfied. Although the fidelity criterion is slightly less than unity, it has been shown that the distortion technique can be applied in a robot manipulative system. Using the validated model, the importance of

Nonparametric modeling of longitudinal covariance structure in functional mapping of quantitative trait loci.

PubMed

Yap, John Stephen; Fan, Jianqing; Wu, Rongling

2009-12-01

Estimation of the covariance structure of longitudinal processes is a fundamental prerequisite for the practical deployment of functional mapping designed to study the genetic regulation and network of quantitative variation in dynamic complex traits. We present a nonparametric approach for estimating the covariance structure of a quantitative trait measured repeatedly at a series of time points. Specifically, we adopt Huang et al.'s (2006, Biometrika 93, 85-98) approach of invoking the modified Cholesky decomposition and converting the problem into modeling a sequence of regressions of responses. A regularized covariance estimator is obtained using a normal penalized likelihood with an L(2) penalty. This approach, embedded within a mixture likelihood framework, leads to enhanced accuracy, precision, and flexibility of functional mapping while preserving its biological relevance. Simulation studies are performed to reveal the statistical properties and advantages of the proposed method. A real example from a mouse genome project is analyzed to illustrate the utilization of the methodology. The new method will provide a useful tool for genome-wide scanning for the existence and distribution of quantitative trait loci underlying a dynamic trait important to agriculture, biology, and health sciences.

Unbiased Quantitative Models of Protein Translation Derived from Ribosome Profiling Data

PubMed Central

Gritsenko, Alexey A.; Hulsman, Marc; Reinders, Marcel J. T.; de Ridder, Dick

2015-01-01

Translation of RNA to protein is a core process for any living organism. While for some steps of this process the effect on protein production is understood, a holistic understanding of translation still remains elusive. In silico modelling is a promising approach for elucidating the process of protein synthesis. Although a number of computational models of the process have been proposed, their application is limited by the assumptions they make. Ribosome profiling (RP), a relatively new sequencing-based technique capable of recording snapshots of the locations of actively translating ribosomes, is a promising source of information for deriving unbiased data-driven translation models. However, quantitative analysis of RP data is challenging due to high measurement variance and the inability to discriminate between the number of ribosomes measured on a gene and their speed of translation. We propose a solution in the form of a novel multi-scale interpretation of RP data that allows for deriving models with translation dynamics extracted from the snapshots. We demonstrate the usefulness of this approach by simultaneously determining for the first time per-codon translation elongation and per-gene translation initiation rates of Saccharomyces cerevisiae from RP data for two versions of the Totally Asymmetric Exclusion Process (TASEP) model of translation. We do this in an unbiased fashion, by fitting the models using only RP data with a novel optimization scheme based on Monte Carlo simulation to keep the problem tractable. The fitted models match the data significantly better than existing models and their predictions show better agreement with several independent protein abundance datasets than existing models. Results additionally indicate that the tRNA pool adaptation hypothesis is incomplete, with evidence suggesting that tRNA post-transcriptional modifications and codon context may play a role in determining codon elongation rates. PMID:26275099

Unbiased Quantitative Models of Protein Translation Derived from Ribosome Profiling Data.

PubMed

Gritsenko, Alexey A; Hulsman, Marc; Reinders, Marcel J T; de Ridder, Dick

2015-08-01

Translation of RNA to protein is a core process for any living organism. While for some steps of this process the effect on protein production is understood, a holistic understanding of translation still remains elusive. In silico modelling is a promising approach for elucidating the process of protein synthesis. Although a number of computational models of the process have been proposed, their application is limited by the assumptions they make. Ribosome profiling (RP), a relatively new sequencing-based technique capable of recording snapshots of the locations of actively translating ribosomes, is a promising source of information for deriving unbiased data-driven translation models. However, quantitative analysis of RP data is challenging due to high measurement variance and the inability to discriminate between the number of ribosomes measured on a gene and their speed of translation. We propose a solution in the form of a novel multi-scale interpretation of RP data that allows for deriving models with translation dynamics extracted from the snapshots. We demonstrate the usefulness of this approach by simultaneously determining for the first time per-codon translation elongation and per-gene translation initiation rates of Saccharomyces cerevisiae from RP data for two versions of the Totally Asymmetric Exclusion Process (TASEP) model of translation. We do this in an unbiased fashion, by fitting the models using only RP data with a novel optimization scheme based on Monte Carlo simulation to keep the problem tractable. The fitted models match the data significantly better than existing models and their predictions show better agreement with several independent protein abundance datasets than existing models. Results additionally indicate that the tRNA pool adaptation hypothesis is incomplete, with evidence suggesting that tRNA post-transcriptional modifications and codon context may play a role in determining codon elongation rates.

What Are We Doing When We Translate from Quantitative Models?

PubMed Central

Critchfield, Thomas S; Reed, Derek D

2009-01-01

Although quantitative analysis (in which behavior principles are defined in terms of equations) has become common in basic behavior analysis, translational efforts often examine everyday events through the lens of narrative versions of laboratory-derived principles. This approach to translation, although useful, is incomplete because equations may convey concepts that are difficult to capture in words. To support this point, we provide a nontechnical introduction to selected aspects of quantitative analysis; consider some issues that translational investigators (and, potentially, practitioners) confront when attempting to translate from quantitative models; and discuss examples of relevant translational studies. We conclude that, where behavior-science translation is concerned, the quantitative features of quantitative models cannot be ignored without sacrificing conceptual precision, scientific and practical insights, and the capacity of the basic and applied wings of behavior analysis to communicate effectively. PMID:22478533

6 Principles for Quantitative Reasoning and Modeling

ERIC Educational Resources Information Center

Weber, Eric; Ellis, Amy; Kulow, Torrey; Ozgur, Zekiye

2014-01-01

Encouraging students to reason with quantitative relationships can help them develop, understand, and explore mathematical models of real-world phenomena. Through two examples--modeling the motion of a speeding car and the growth of a Jactus plant--this article describes how teachers can use six practical tips to help students develop quantitative…

Quantitative modeling of soil genesis processes

NASA Technical Reports Server (NTRS)

Levine, E. R.; Knox, R. G.; Kerber, A. G.

1992-01-01

For fine spatial scale simulation, a model is being developed to predict changes in properties over short-, meso-, and long-term time scales within horizons of a given soil profile. Processes that control these changes can be grouped into five major process clusters: (1) abiotic chemical reactions; (2) activities of organisms; (3) energy balance and water phase transitions; (4) hydrologic flows; and (5) particle redistribution. Landscape modeling of soil development is possible using digitized soil maps associated with quantitative soil attribute data in a geographic information system (GIS) framework to which simulation models are applied.

Quantitative risk assessment system (QRAS)

NASA Technical Reports Server (NTRS)

Tan, Zhibin (Inventor); Mosleh, Ali (Inventor); Weinstock, Robert M (Inventor); Smidts, Carol S (Inventor); Chang, Yung-Hsien (Inventor); Groen, Francisco J (Inventor); Swaminathan, Sankaran (Inventor)

2001-01-01

A quantitative risk assessment system (QRAS) builds a risk model of a system for which risk of failure is being assessed, then analyzes the risk of the system corresponding to the risk model. The QRAS performs sensitivity analysis of the risk model by altering fundamental components and quantifications built into the risk model, then re-analyzes the risk of the system using the modifications. More particularly, the risk model is built by building a hierarchy, creating a mission timeline, quantifying failure modes, and building/editing event sequence diagrams. Multiplicities, dependencies, and redundancies of the system are included in the risk model. For analysis runs, a fixed baseline is first constructed and stored. This baseline contains the lowest level scenarios, preserved in event tree structure. The analysis runs, at any level of the hierarchy and below, access this baseline for risk quantitative computation as well as ranking of particular risks. A standalone Tool Box capability exists, allowing the user to store application programs within QRAS.

Testing process predictions of models of risky choice: a quantitative model comparison approach

PubMed Central

Pachur, Thorsten; Hertwig, Ralph; Gigerenzer, Gerd; Brandstätter, Eduard

2013-01-01

This article presents a quantitative model comparison contrasting the process predictions of two prominent views on risky choice. One view assumes a trade-off between probabilities and outcomes (or non-linear functions thereof) and the separate evaluation of risky options (expectation models). Another view assumes that risky choice is based on comparative evaluation, limited search, aspiration levels, and the forgoing of trade-offs (heuristic models). We derived quantitative process predictions for a generic expectation model and for a specific heuristic model, namely the priority heuristic (Brandstätter et al., 2006), and tested them in two experiments. The focus was on two key features of the cognitive process: acquisition frequencies (i.e., how frequently individual reasons are looked up) and direction of search (i.e., gamble-wise vs. reason-wise). In Experiment 1, the priority heuristic predicted direction of search better than the expectation model (although neither model predicted the acquisition process perfectly); acquisition frequencies, however, were inconsistent with both models. Additional analyses revealed that these frequencies were primarily a function of what Rubinstein (1988) called “similarity.” In Experiment 2, the quantitative model comparison approach showed that people seemed to rely more on the priority heuristic in difficult problems, but to make more trade-offs in easy problems. This finding suggests that risky choice may be based on a mental toolbox of strategies. PMID:24151472

Building a Database for a Quantitative Model

NASA Technical Reports Server (NTRS)

Kahn, C. Joseph; Kleinhammer, Roger

2014-01-01

A database can greatly benefit a quantitative analysis. The defining characteristic of a quantitative risk, or reliability, model is the use of failure estimate data. Models can easily contain a thousand Basic Events, relying on hundreds of individual data sources. Obviously, entering so much data by hand will eventually lead to errors. Not so obviously entering data this way does not aid linking the Basic Events to the data sources. The best way to organize large amounts of data on a computer is with a database. But a model does not require a large, enterprise-level database with dedicated developers and administrators. A database built in Excel can be quite sufficient. A simple spreadsheet database can link every Basic Event to the individual data source selected for them. This database can also contain the manipulations appropriate for how the data is used in the model. These manipulations include stressing factors based on use and maintenance cycles, dormancy, unique failure modes, the modeling of multiple items as a single "Super component" Basic Event, and Bayesian Updating based on flight and testing experience. A simple, unique metadata field in both the model and database provides a link from any Basic Event in the model to its data source and all relevant calculations. The credibility for the entire model often rests on the credibility and traceability of the data.

Integrated Environmental Modeling: Quantitative Microbial Risk Assessment

EPA Science Inventory

The presentation discusses the need for microbial assessments and presents a road map associated with quantitative microbial risk assessments, through an integrated environmental modeling approach. A brief introduction and the strengths of the current knowledge are illustrated. W...

An evidential reasoning extension to quantitative model-based failure diagnosis

NASA Technical Reports Server (NTRS)

Gertler, Janos J.; Anderson, Kenneth C.

1992-01-01

The detection and diagnosis of failures in physical systems characterized by continuous-time operation are studied. A quantitative diagnostic methodology has been developed that utilizes the mathematical model of the physical system. On the basis of the latter, diagnostic models are derived each of which comprises a set of orthogonal parity equations. To improve the robustness of the algorithm, several models may be used in parallel, providing potentially incomplete and/or conflicting inferences. Dempster's rule of combination is used to integrate evidence from the different models. The basic probability measures are assigned utilizing quantitative information extracted from the mathematical model and from online computation performed therewith.

Refining the quantitative pathway of the Pathways to Mathematics model.

PubMed

Sowinski, Carla; LeFevre, Jo-Anne; Skwarchuk, Sheri-Lynn; Kamawar, Deepthi; Bisanz, Jeffrey; Smith-Chant, Brenda

2015-03-01

In the current study, we adopted the Pathways to Mathematics model of LeFevre et al. (2010). In this model, there are three cognitive domains--labeled as the quantitative, linguistic, and working memory pathways--that make unique contributions to children's mathematical development. We attempted to refine the quantitative pathway by combining children's (N=141 in Grades 2 and 3) subitizing, counting, and symbolic magnitude comparison skills using principal components analysis. The quantitative pathway was examined in relation to dependent numerical measures (backward counting, arithmetic fluency, calculation, and number system knowledge) and a dependent reading measure, while simultaneously accounting for linguistic and working memory skills. Analyses controlled for processing speed, parental education, and gender. We hypothesized that the quantitative, linguistic, and working memory pathways would account for unique variance in the numerical outcomes; this was the case for backward counting and arithmetic fluency. However, only the quantitative and linguistic pathways (not working memory) accounted for unique variance in calculation and number system knowledge. Not surprisingly, only the linguistic pathway accounted for unique variance in the reading measure. These findings suggest that the relative contributions of quantitative, linguistic, and working memory skills vary depending on the specific cognitive task. Copyright © 2014 Elsevier Inc. All rights reserved.

Modeling conflict : research methods, quantitative modeling, and lessons learned.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rexroth, Paul E.; Malczynski, Leonard A.; Hendrickson, Gerald A.

2004-09-01

This study investigates the factors that lead countries into conflict. Specifically, political, social and economic factors may offer insight as to how prone a country (or set of countries) may be for inter-country or intra-country conflict. Largely methodological in scope, this study examines the literature for quantitative models that address or attempt to model conflict both in the past, and for future insight. The analysis concentrates specifically on the system dynamics paradigm, not the political science mainstream approaches of econometrics and game theory. The application of this paradigm builds upon the most sophisticated attempt at modeling conflict as a resultmore » of system level interactions. This study presents the modeling efforts built on limited data and working literature paradigms, and recommendations for future attempts at modeling conflict.« less

A cost-effectiveness comparison of existing and Landsat-aided snow water content estimation systems

NASA Technical Reports Server (NTRS)

Sharp, J. M.; Thomas, R. W.

1975-01-01

This study describes how Landsat imagery can be cost-effectively employed to augment an operational hydrologic model. Attention is directed toward the estimation of snow water content, a major predictor variable in the volumetric runoff forecasting model presently used by the California Department of Water Resources. A stratified double sampling scheme is supplemented with qualitative and quantitative analyses of existing operations to develop a comparison between the existing and satellite-aided approaches to snow water content estimation. Results show a decided advantage for the Landsat-aided approach.

The Impact of School Climate on Student Achievement in the Middle Schools of the Commonwealth of Virginia: A Quantitative Analysis of Existing Data

ERIC Educational Resources Information Center

Bergren, David Alexander

2014-01-01

This quantitative study was designed to be an analysis of the relationship between school climate and student achievement through the creation of an index of climate-factors (SES, discipline, attendance, and school size) for which publicly available data existed. The index that was formed served as a proxy measure of climate; it was analyzed…

Quantitative model analysis with diverse biological data: applications in developmental pattern formation.

PubMed

Pargett, Michael; Umulis, David M

2013-07-15

Mathematical modeling of transcription factor and signaling networks is widely used to understand if and how a mechanism works, and to infer regulatory interactions that produce a model consistent with the observed data. Both of these approaches to modeling are informed by experimental data, however, much of the data available or even acquirable are not quantitative. Data that is not strictly quantitative cannot be used by classical, quantitative, model-based analyses that measure a difference between the measured observation and the model prediction for that observation. To bridge the model-to-data gap, a variety of techniques have been developed to measure model "fitness" and provide numerical values that can subsequently be used in model optimization or model inference studies. Here, we discuss a selection of traditional and novel techniques to transform data of varied quality and enable quantitative comparison with mathematical models. This review is intended to both inform the use of these model analysis methods, focused on parameter estimation, and to help guide the choice of method to use for a given study based on the type of data available. Applying techniques such as normalization or optimal scaling may significantly improve the utility of current biological data in model-based study and allow greater integration between disparate types of data. Copyright © 2013 Elsevier Inc. All rights reserved.

Interpretation of protein quantitation using the Bradford assay: comparison with two calculation models.

PubMed

Ku, Hyung-Keun; Lim, Hyuk-Min; Oh, Kyong-Hwa; Yang, Hyo-Jin; Jeong, Ji-Seon; Kim, Sook-Kyung

2013-03-01

The Bradford assay is a simple method for protein quantitation, but variation in the results between proteins is a matter of concern. In this study, we compared and normalized quantitative values from two models for protein quantitation, where the residues in the protein that bind to anionic Coomassie Brilliant Blue G-250 comprise either Arg and Lys (Method 1, M1) or Arg, Lys, and His (Method 2, M2). Use of the M2 model yielded much more consistent quantitation values compared with use of the M1 model, which exhibited marked overestimations against protein standards. Copyright © 2012 Elsevier Inc. All rights reserved.

Incorporation of Electrical Systems Models Into an Existing Thermodynamic Cycle Code

NASA Technical Reports Server (NTRS)

Freeh, Josh

2003-01-01

Integration of entire system includes: Fuel cells, motors, propulsors, thermal/power management, compressors, etc. Use of existing, pre-developed NPSS capabilities includes: 1) Optimization tools; 2) Gas turbine models for hybrid systems; 3) Increased interplay between subsystems; 4) Off-design modeling capabilities; 5) Altitude effects; and 6) Existing transient modeling architecture. Other factors inclde: 1) Easier transfer between users and groups of users; 2) General aerospace industry acceptance and familiarity; and 3) Flexible analysis tool that can also be used for ground power applications.

Facilities Management of Existing School Buildings: Two Models.

ERIC Educational Resources Information Center

Building Technology, Inc., Silver Spring, MD.

While all school districts are responsible for the management of their existing buildings, they often approach the task in different ways. This document presents two models that offer ways a school district administration, regardless of size, may introduce activities into its ongoing management process that will lead to improvements in earthquake…

Asynchronous adaptive time step in quantitative cellular automata modeling

PubMed Central

Zhu, Hao; Pang, Peter YH; Sun, Yan; Dhar, Pawan

2004-01-01

Background The behaviors of cells in metazoans are context dependent, thus large-scale multi-cellular modeling is often necessary, for which cellular automata are natural candidates. Two related issues are involved in cellular automata based multi-cellular modeling: how to introduce differential equation based quantitative computing to precisely describe cellular activity, and upon it, how to solve the heavy time consumption issue in simulation. Results Based on a modified, language based cellular automata system we extended that allows ordinary differential equations in models, we introduce a method implementing asynchronous adaptive time step in simulation that can considerably improve efficiency yet without a significant sacrifice of accuracy. An average speedup rate of 4–5 is achieved in the given example. Conclusions Strategies for reducing time consumption in simulation are indispensable for large-scale, quantitative multi-cellular models, because even a small 100 × 100 × 100 tissue slab contains one million cells. Distributed and adaptive time step is a practical solution in cellular automata environment. PMID:15222901

Numerical modeling of flow focusing: Quantitative characterization of the flow regimes

NASA Astrophysics Data System (ADS)

Mamet, V.; Namy, P.; Dedulle, J.-M.

2017-09-01

Among droplet generation technologies, the flow focusing technique is a major process due to its control, stability, and reproducibility. In this process, one fluid (the continuous phase) interacts with another one (the dispersed phase) to create small droplets. Experimental assays in the literature on gas-liquid flow focusing have shown that different jet regimes can be obtained depending on the operating conditions. However, the underlying physical phenomena remain unclear, especially mechanical interactions between the fluids and the oscillation phenomenon of the liquid. In this paper, based on published studies, a numerical diphasic model has been developed to take into consideration the mechanical interaction between phases, using the Cahn-Hilliard method to monitor the interface. Depending on the liquid/gas inputs and the geometrical parameters, various regimes can be obtained, from a steady state regime to an unsteady one with liquid oscillation. In the dispersed phase, the model enables us to compute the evolution of fluid flow, both in space (size of the recirculation zone) and in time (period of oscillation). The transition between unsteady and stationary regimes is assessed in relation to liquid and gas dimensionless numbers, showing the existence of critical thresholds. This model successfully highlights, qualitatively and quantitatively, the influence of the geometry of the nozzle, in particular, its inner diameter.

The Mapping Model: A Cognitive Theory of Quantitative Estimation

ERIC Educational Resources Information Center

von Helversen, Bettina; Rieskamp, Jorg

2008-01-01

How do people make quantitative estimations, such as estimating a car's selling price? Traditionally, linear-regression-type models have been used to answer this question. These models assume that people weight and integrate all information available to estimate a criterion. The authors propose an alternative cognitive theory for quantitative…

Review of existing terrestrial bioaccumulation models and terrestrial bioaccumulation modeling needs for organic chemicals

EPA Science Inventory

Protocols for terrestrial bioaccumulation assessments are far less-developed than for aquatic systems. This manuscript reviews modeling approaches that can be used to assess the terrestrial bioaccumulation potential of commercial organic chemicals. Models exist for plant, inver...

Incorporating Learning Theory into Existing Systems Engineering Models

DTIC Science & Technology

2013-09-01

3. Social  Cognition 22 Table 1. Classification of learning theories Behaviorism Cognitivism Constructivism Connectivism...Introdution to design of large scale systems. New York: Mcgraw-Hill. Grusec. J. (1992). Social learning theory and development psychology: The... LEARNING THEORY INTO EXISTING SYSTEMS ENGINEERING MODELS by Valentine Leo September 2013 Thesis Advisor: Gary O. Langford Co-Advisor

Quantitative and predictive model of kinetic regulation by E. coli TPP riboswitches

PubMed Central

Guedich, Sondés; Puffer-Enders, Barbara; Baltzinger, Mireille; Hoffmann, Guillaume; Da Veiga, Cyrielle; Jossinet, Fabrice; Thore, Stéphane; Bec, Guillaume; Ennifar, Eric; Burnouf, Dominique; Dumas, Philippe

2016-01-01

ABSTRACT Riboswitches are non-coding elements upstream or downstream of mRNAs that, upon binding of a specific ligand, regulate transcription and/or translation initiation in bacteria, or alternative splicing in plants and fungi. We have studied thiamine pyrophosphate (TPP) riboswitches regulating translation of thiM operon and transcription and translation of thiC operon in E. coli, and that of THIC in the plant A. thaliana. For all, we ascertained an induced-fit mechanism involving initial binding of the TPP followed by a conformational change leading to a higher-affinity complex. The experimental values obtained for all kinetic and thermodynamic parameters of TPP binding imply that the regulation by A. thaliana riboswitch is governed by mass-action law, whereas it is of kinetic nature for the two bacterial riboswitches. Kinetic regulation requires that the RNA polymerase pauses after synthesis of each riboswitch aptamer to leave time for TPP binding, but only when its concentration is sufficient. A quantitative model of regulation highlighted how the pausing time has to be linked to the kinetic rates of initial TPP binding to obtain an ON/OFF switch in the correct concentration range of TPP. We verified the existence of these pauses and the model prediction on their duration. Our analysis also led to quantitative estimates of the respective efficiency of kinetic and thermodynamic regulations, which shows that kinetically regulated riboswitches react more sharply to concentration variation of their ligand than thermodynamically regulated riboswitches. This rationalizes the interest of kinetic regulation and confirms empirical observations that were obtained by numerical simulations. PMID:26932506

Quantitative and predictive model of kinetic regulation by E. coli TPP riboswitches.

PubMed

Guedich, Sondés; Puffer-Enders, Barbara; Baltzinger, Mireille; Hoffmann, Guillaume; Da Veiga, Cyrielle; Jossinet, Fabrice; Thore, Stéphane; Bec, Guillaume; Ennifar, Eric; Burnouf, Dominique; Dumas, Philippe

2016-01-01

Riboswitches are non-coding elements upstream or downstream of mRNAs that, upon binding of a specific ligand, regulate transcription and/or translation initiation in bacteria, or alternative splicing in plants and fungi. We have studied thiamine pyrophosphate (TPP) riboswitches regulating translation of thiM operon and transcription and translation of thiC operon in E. coli, and that of THIC in the plant A. thaliana. For all, we ascertained an induced-fit mechanism involving initial binding of the TPP followed by a conformational change leading to a higher-affinity complex. The experimental values obtained for all kinetic and thermodynamic parameters of TPP binding imply that the regulation by A. thaliana riboswitch is governed by mass-action law, whereas it is of kinetic nature for the two bacterial riboswitches. Kinetic regulation requires that the RNA polymerase pauses after synthesis of each riboswitch aptamer to leave time for TPP binding, but only when its concentration is sufficient. A quantitative model of regulation highlighted how the pausing time has to be linked to the kinetic rates of initial TPP binding to obtain an ON/OFF switch in the correct concentration range of TPP. We verified the existence of these pauses and the model prediction on their duration. Our analysis also led to quantitative estimates of the respective efficiency of kinetic and thermodynamic regulations, which shows that kinetically regulated riboswitches react more sharply to concentration variation of their ligand than thermodynamically regulated riboswitches. This rationalizes the interest of kinetic regulation and confirms empirical observations that were obtained by numerical simulations.

Quantitative trait nucleotide analysis using Bayesian model selection.

PubMed

Blangero, John; Goring, Harald H H; Kent, Jack W; Williams, Jeff T; Peterson, Charles P; Almasy, Laura; Dyer, Thomas D

2005-10-01

Although much attention has been given to statistical genetic methods for the initial localization and fine mapping of quantitative trait loci (QTLs), little methodological work has been done to date on the problem of statistically identifying the most likely functional polymorphisms using sequence data. In this paper we provide a general statistical genetic framework, called Bayesian quantitative trait nucleotide (BQTN) analysis, for assessing the likely functional status of genetic variants. The approach requires the initial enumeration of all genetic variants in a set of resequenced individuals. These polymorphisms are then typed in a large number of individuals (potentially in families), and marker variation is related to quantitative phenotypic variation using Bayesian model selection and averaging. For each sequence variant a posterior probability of effect is obtained and can be used to prioritize additional molecular functional experiments. An example of this quantitative nucleotide analysis is provided using the GAW12 simulated data. The results show that the BQTN method may be useful for choosing the most likely functional variants within a gene (or set of genes). We also include instructions on how to use our computer program, SOLAR, for association analysis and BQTN analysis.

Pre-existing periodontitis exacerbates experimental arthritis in a mouse model.

PubMed

Cantley, Melissa D; Haynes, David R; Marino, Victor; Bartold, P Mark

2011-06-01

Previous studies have shown a higher incidence of alveolar bone loss in patients with rheumatoid arthritis (RA) and that patients with periodontitis are at a greater risk of developing RA. The aim of this study was to develop an animal model to assess the relationship between pre-existing periodontitis and experimental arthritis (EA). Periodontitis was first induced in mice by oral gavage with Porphyromonas gingivalis followed by EA using the collagen antibody-induced arthritis model. These animals were compared with animals with periodontitis alone, EA alone and no disease (controls). Visual changes in paw swelling were assessed to determine clinical development of EA. Alveolar bone and joint changes were assessed using micro-CT, histological analyses and immunohistochemistry. Serum levels of C-reactive protein were used to monitor systemic inflammation. Mice with pre-existing periodontitis developed more severe arthritis, which developed at a faster rate. Mice with periodontitis only also showed evidence of loss of bone within the radiocarpal joint. There was also evidence of alveolar bone loss in mice with EA alone. The results of this study indicate that pre-existing periodontitis exacerbated experimental arthritis in a mouse model. © 2011 John Wiley & Sons A/S.

Existence of periodic solutions in a model of respiratory syncytial virus RSV

NASA Astrophysics Data System (ADS)

Arenas, Abraham J.; González, Gilberto; Jódar, Lucas

2008-08-01

In this paper we study the existence of a positive periodic solutions for nested models of respiratory syncytial virus RSV, by using a continuation theorem based on coincidence degree theory. Conditions for the existence of periodic solutions in the model are given. Numerical simulations related to the transmission of respiratory syncytial virus in Madrid and Rio Janeiro are included.

General Methods for Evolutionary Quantitative Genetic Inference from Generalized Mixed Models.

PubMed

de Villemereuil, Pierre; Schielzeth, Holger; Nakagawa, Shinichi; Morrissey, Michael

2016-11-01

Methods for inference and interpretation of evolutionary quantitative genetic parameters, and for prediction of the response to selection, are best developed for traits with normal distributions. Many traits of evolutionary interest, including many life history and behavioral traits, have inherently nonnormal distributions. The generalized linear mixed model (GLMM) framework has become a widely used tool for estimating quantitative genetic parameters for nonnormal traits. However, whereas GLMMs provide inference on a statistically convenient latent scale, it is often desirable to express quantitative genetic parameters on the scale upon which traits are measured. The parameters of fitted GLMMs, despite being on a latent scale, fully determine all quantities of potential interest on the scale on which traits are expressed. We provide expressions for deriving each of such quantities, including population means, phenotypic (co)variances, variance components including additive genetic (co)variances, and parameters such as heritability. We demonstrate that fixed effects have a strong impact on those parameters and show how to deal with this by averaging or integrating over fixed effects. The expressions require integration of quantities determined by the link function, over distributions of latent values. In general cases, the required integrals must be solved numerically, but efficient methods are available and we provide an implementation in an R package, QGglmm. We show that known formulas for quantities such as heritability of traits with binomial and Poisson distributions are special cases of our expressions. Additionally, we show how fitted GLMM can be incorporated into existing methods for predicting evolutionary trajectories. We demonstrate the accuracy of the resulting method for evolutionary prediction by simulation and apply our approach to data from a wild pedigreed vertebrate population. Copyright © 2016 de Villemereuil et al.

Quantitative modelling in cognitive ergonomics: predicting signals passed at danger.

PubMed

Moray, Neville; Groeger, John; Stanton, Neville

2017-02-01

This paper shows how to combine field observations, experimental data and mathematical modelling to produce quantitative explanations and predictions of complex events in human-machine interaction. As an example, we consider a major railway accident. In 1999, a commuter train passed a red signal near Ladbroke Grove, UK, into the path of an express. We use the Public Inquiry Report, 'black box' data, and accident and engineering reports to construct a case history of the accident. We show how to combine field data with mathematical modelling to estimate the probability that the driver observed and identified the state of the signals, and checked their status. Our methodology can explain the SPAD ('Signal Passed At Danger'), generate recommendations about signal design and placement and provide quantitative guidance for the design of safer railway systems' speed limits and the location of signals. Practitioner Summary: Detailed ergonomic analysis of railway signals and rail infrastructure reveals problems of signal identification at this location. A record of driver eye movements measures attention, from which a quantitative model for out signal placement and permitted speeds can be derived. The paper is an example of how to combine field data, basic research and mathematical modelling to solve ergonomic design problems.

Existing and Required Modeling Capabilities for Evaluating ATM Systems and Concepts

NASA Technical Reports Server (NTRS)

Odoni, Amedeo R.; Bowman, Jeremy; Delahaye, Daniel; Deyst, John J.; Feron, Eric; Hansman, R. John; Khan, Kashif; Kuchar, James K.; Pujet, Nicolas; Simpson, Robert W.

1997-01-01

ATM systems throughout the world are entering a period of major transition and change. The combination of important technological developments and of the globalization of the air transportation industry has necessitated a reexamination of some of the fundamental premises of existing Air Traffic Management (ATM) concepts. New ATM concepts have to be examined, concepts that may place more emphasis on: strategic traffic management; planning and control; partial decentralization of decision-making; and added reliance on the aircraft to carry out strategic ATM plans, with ground controllers confined primarily to a monitoring and supervisory role. 'Free Flight' is a case in point. In order to study, evaluate and validate such new concepts, the ATM community will have to rely heavily on models and computer-based tools/utilities, covering a wide range of issues and metrics related to safety, capacity and efficiency. The state of the art in such modeling support is adequate in some respects, but clearly deficient in others. It is the objective of this study to assist in: (1) assessing the strengths and weaknesses of existing fast-time models and tools for the study of ATM systems and concepts and (2) identifying and prioritizing the requirements for the development of additional modeling capabilities in the near future. A three-stage process has been followed to this purpose: 1. Through the analysis of two case studies involving future ATM system scenarios, as well as through expert assessment, modeling capabilities and supporting tools needed for testing and validating future ATM systems and concepts were identified and described. 2. Existing fast-time ATM models and support tools were reviewed and assessed with regard to the degree to which they offer the capabilities identified under Step 1. 3 . The findings of 1 and 2 were combined to draw conclusions about (1) the best capabilities currently existing, (2) the types of concept testing and validation that can be carried

Quantitative interpretation of Great Lakes remote sensing data

NASA Technical Reports Server (NTRS)

Shook, D. F.; Salzman, J.; Svehla, R. A.; Gedney, R. T.

1980-01-01

The paper discusses the quantitative interpretation of Great Lakes remote sensing water quality data. Remote sensing using color information must take into account (1) the existence of many different organic and inorganic species throughout the Great Lakes, (2) the occurrence of a mixture of species in most locations, and (3) spatial variations in types and concentration of species. The radiative transfer model provides a potential method for an orderly analysis of remote sensing data and a physical basis for developing quantitative algorithms. Predictions and field measurements of volume reflectances are presented which show the advantage of using a radiative transfer model. Spectral absorptance and backscattering coefficients for two inorganic sediments are reported.

Performance Theories for Sentence Coding: Some Quantitative Models

ERIC Educational Resources Information Center

Aaronson, Doris; And Others

1977-01-01

This study deals with the patterns of word-by-word reading times over a sentence when the subject must code the linguistic information sufficiently for immediate verbatim recall. A class of quantitative models is considered that would account for reading times at phrase breaks. (Author/RM)

Detection of Prostate Cancer: Quantitative Multiparametric MR Imaging Models Developed Using Registered Correlative Histopathology.

PubMed

Metzger, Gregory J; Kalavagunta, Chaitanya; Spilseth, Benjamin; Bolan, Patrick J; Li, Xiufeng; Hutter, Diane; Nam, Jung W; Johnson, Andrew D; Henriksen, Jonathan C; Moench, Laura; Konety, Badrinath; Warlick, Christopher A; Schmechel, Stephen C; Koopmeiners, Joseph S

2016-06-01

Purpose To develop multiparametric magnetic resonance (MR) imaging models to generate a quantitative, user-independent, voxel-wise composite biomarker score (CBS) for detection of prostate cancer by using coregistered correlative histopathologic results, and to compare performance of CBS-based detection with that of single quantitative MR imaging parameters. Materials and Methods Institutional review board approval and informed consent were obtained. Patients with a diagnosis of prostate cancer underwent multiparametric MR imaging before surgery for treatment. All MR imaging voxels in the prostate were classified as cancer or noncancer on the basis of coregistered histopathologic data. Predictive models were developed by using more than one quantitative MR imaging parameter to generate CBS maps. Model development and evaluation of quantitative MR imaging parameters and CBS were performed separately for the peripheral zone and the whole gland. Model accuracy was evaluated by using the area under the receiver operating characteristic curve (AUC), and confidence intervals were calculated with the bootstrap procedure. The improvement in classification accuracy was evaluated by comparing the AUC for the multiparametric model and the single best-performing quantitative MR imaging parameter at the individual level and in aggregate. Results Quantitative T2, apparent diffusion coefficient (ADC), volume transfer constant (K(trans)), reflux rate constant (kep), and area under the gadolinium concentration curve at 90 seconds (AUGC90) were significantly different between cancer and noncancer voxels (P < .001), with ADC showing the best accuracy (peripheral zone AUC, 0.82; whole gland AUC, 0.74). Four-parameter models demonstrated the best performance in both the peripheral zone (AUC, 0.85; P = .010 vs ADC alone) and whole gland (AUC, 0.77; P = .043 vs ADC alone). Individual-level analysis showed statistically significant improvement in AUC in 82% (23 of 28) and 71% (24 of 34

Quantitative assessment model for gastric cancer screening

PubMed Central

Chen, Kun; Yu, Wei-Ping; Song, Liang; Zhu, Yi-Min

2005-01-01

AIM: To set up a mathematic model for gastric cancer screening and to evaluate its function in mass screening for gastric cancer. METHODS: A case control study was carried on in 66 patients and 198 normal people, then the risk and protective factors of gastric cancer were determined, including heavy manual work, foods such as small yellow-fin tuna, dried small shrimps, squills, crabs, mothers suffering from gastric diseases, spouse alive, use of refrigerators and hot food, etc. According to some principles and methods of probability and fuzzy mathematics, a quantitative assessment model was established as follows: first, we selected some factors significant in statistics, and calculated weight coefficient for each one by two different methods; second, population space was divided into gastric cancer fuzzy subset and non gastric cancer fuzzy subset, then a mathematic model for each subset was established, we got a mathematic expression of attribute degree (AD). RESULTS: Based on the data of 63 patients and 693 normal people, AD of each subject was calculated. Considering the sensitivity and specificity, the thresholds of AD values calculated were configured with 0.20 and 0.17, respectively. According to these thresholds, the sensitivity and specificity of the quantitative model were about 69% and 63%. Moreover, statistical test showed that the identification outcomes of these two different calculation methods were identical (P>0.05). CONCLUSION: The validity of this method is satisfactory. It is convenient, feasible, economic and can be used to determine individual and population risks of gastric cancer. PMID:15655813

BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models

PubMed Central

2010-01-01

Background Quantitative models of biochemical and cellular systems are used to answer a variety of questions in the biological sciences. The number of published quantitative models is growing steadily thanks to increasing interest in the use of models as well as the development of improved software systems and the availability of better, cheaper computer hardware. To maximise the benefits of this growing body of models, the field needs centralised model repositories that will encourage, facilitate and promote model dissemination and reuse. Ideally, the models stored in these repositories should be extensively tested and encoded in community-supported and standardised formats. In addition, the models and their components should be cross-referenced with other resources in order to allow their unambiguous identification. Description BioModels Database http://www.ebi.ac.uk/biomodels/ is aimed at addressing exactly these needs. It is a freely-accessible online resource for storing, viewing, retrieving, and analysing published, peer-reviewed quantitative models of biochemical and cellular systems. The structure and behaviour of each simulation model distributed by BioModels Database are thoroughly checked; in addition, model elements are annotated with terms from controlled vocabularies as well as linked to relevant data resources. Models can be examined online or downloaded in various formats. Reaction network diagrams generated from the models are also available in several formats. BioModels Database also provides features such as online simulation and the extraction of components from large scale models into smaller submodels. Finally, the system provides a range of web services that external software systems can use to access up-to-date data from the database. Conclusions BioModels Database has become a recognised reference resource for systems biology. It is being used by the community in a variety of ways; for example, it is used to benchmark different simulation

Quantitative metal magnetic memory reliability modeling for welded joints

NASA Astrophysics Data System (ADS)

Xing, Haiyan; Dang, Yongbin; Wang, Ben; Leng, Jiancheng

2016-03-01

Metal magnetic memory(MMM) testing has been widely used to detect welded joints. However, load levels, environmental magnetic field, and measurement noises make the MMM data dispersive and bring difficulty to quantitative evaluation. In order to promote the development of quantitative MMM reliability assessment, a new MMM model is presented for welded joints. Steel Q235 welded specimens are tested along the longitudinal and horizontal lines by TSC-2M-8 instrument in the tensile fatigue experiments. The X-ray testing is carried out synchronously to verify the MMM results. It is found that MMM testing can detect the hidden crack earlier than X-ray testing. Moreover, the MMM gradient vector sum K vs is sensitive to the damage degree, especially at early and hidden damage stages. Considering the dispersion of MMM data, the K vs statistical law is investigated, which shows that K vs obeys Gaussian distribution. So K vs is the suitable MMM parameter to establish reliability model of welded joints. At last, the original quantitative MMM reliability model is first presented based on the improved stress strength interference theory. It is shown that the reliability degree R gradually decreases with the decreasing of the residual life ratio T, and the maximal error between prediction reliability degree R 1 and verification reliability degree R 2 is 9.15%. This presented method provides a novel tool of reliability testing and evaluating in practical engineering for welded joints.

A Quantitative Cost Effectiveness Model for Web-Supported Academic Instruction

ERIC Educational Resources Information Center

Cohen, Anat; Nachmias, Rafi

2006-01-01

This paper describes a quantitative cost effectiveness model for Web-supported academic instruction. The model was designed for Web-supported instruction (rather than distance learning only) characterizing most of the traditional higher education institutions. It is based on empirical data (Web logs) of students' and instructors' usage…

Marginal iodide deficiency and thyroid function: dose-response analysis for quantitative pharmacokinetic modeling.

PubMed

Gilbert, M E; McLanahan, E D; Hedge, J; Crofton, K M; Fisher, J W; Valentín-Blasini, L; Blount, B C

2011-04-28

Severe iodine deficiency (ID) results in adverse health outcomes and remains a benchmark for understanding the effects of developmental hypothyroidism. The implications of marginal ID, however, remain less well known. The current study examined the relationship between graded levels of ID in rats and serum thyroid hormones, thyroid iodine content, and urinary iodide excretion. The goals of this study were to provide parametric and dose-response information for development of a quantitative model of the thyroid axis. Female Long Evans rats were fed casein-based diets containing varying iodine (I) concentrations for 8 weeks. Diets were created by adding 975, 200, 125, 25, or 0 μg/kg I to the base diet (~25 μg I/kg chow) to produce 5 nominal I levels, ranging from excess (basal+added I, Treatment 1: 1000 μg I/kg chow) to deficient (Treatment 5: 25 μg I/kg chow). Food intake and body weight were monitored throughout and on 2 consecutive days each week over the 8-week exposure period, animals were placed in metabolism cages to capture urine. Food, water intake, and body weight gain did not differ among treatment groups. Serum T4 was dose-dependently reduced relative to Treatment 1 with significant declines (19 and 48%) at the two lowest I groups, and no significant changes in serum T3 or TSH were detected. Increases in thyroid weight and decreases in thyroidal and urinary iodide content were observed as a function of decreasing I in the diet. Data were compared with predictions from a recently published biologically based dose-response (BBDR) model for ID. Relative to model predictions, female Long Evans rats under the conditions of this study appeared more resilient to low I intake. These results challenge existing models and provide essential information for development of quantitative BBDR models for ID during pregnancy and lactation. Published by Elsevier Ireland Ltd.

Designing automation for human use: empirical studies and quantitative models.

PubMed

Parasuraman, R

2000-07-01

An emerging knowledge base of human performance research can provide guidelines for designing automation that can be used effectively by human operators of complex systems. Which functions should be automated and to what extent in a given system? A model for types and levels of automation that provides a framework and an objective basis for making such choices is described. The human performance consequences of particular types and levels of automation constitute primary evaluative criteria for automation design when using the model. Four human performance areas are considered--mental workload, situation awareness, complacency and skill degradation. Secondary evaluative criteria include such factors as automation reliability, the risks of decision/action consequences and the ease of systems integration. In addition to this qualitative approach, quantitative models can inform design. Several computational and formal models of human interaction with automation that have been proposed by various researchers are reviewed. An important future research need is the integration of qualitative and quantitative approaches. Application of these models provides an objective basis for designing automation for effective human use.

A two-factor error model for quantitative steganalysis

NASA Astrophysics Data System (ADS)

Böhme, Rainer; Ker, Andrew D.

2006-02-01

Quantitative steganalysis refers to the exercise not only of detecting the presence of hidden stego messages in carrier objects, but also of estimating the secret message length. This problem is well studied, with many detectors proposed but only a sparse analysis of errors in the estimators. A deep understanding of the error model, however, is a fundamental requirement for the assessment and comparison of different detection methods. This paper presents a rationale for a two-factor model for sources of error in quantitative steganalysis, and shows evidence from a dedicated large-scale nested experimental set-up with a total of more than 200 million attacks. Apart from general findings about the distribution functions found in both classes of errors, their respective weight is determined, and implications for statistical hypothesis tests in benchmarking scenarios or regression analyses are demonstrated. The results are based on a rigorous comparison of five different detection methods under many different external conditions, such as size of the carrier, previous JPEG compression, and colour channel selection. We include analyses demonstrating the effects of local variance and cover saturation on the different sources of error, as well as presenting the case for a relative bias model for between-image error.

A Quantitative Model of Expert Transcription Typing

DTIC Science & Technology

1993-03-08

side of pure psychology, several researchers have argued that transcription typing is a particularly good activity for the study of human skilled...phenomenon with a quantitative METT prediction. The first, quick and dirty analysis gives a good prediction of the copy span, in fact, it is even...typing, it should be demonstrated that the mechanism of the model does not get in the way of good predictions. If situations occur where the entire

Impact of implementation choices on quantitative predictions of cell-based computational models

NASA Astrophysics Data System (ADS)

Kursawe, Jochen; Baker, Ruth E.; Fletcher, Alexander G.

2017-09-01

'Cell-based' models provide a powerful computational tool for studying the mechanisms underlying the growth and dynamics of biological tissues in health and disease. An increasing amount of quantitative data with cellular resolution has paved the way for the quantitative parameterisation and validation of such models. However, the numerical implementation of cell-based models remains challenging, and little work has been done to understand to what extent implementation choices may influence model predictions. Here, we consider the numerical implementation of a popular class of cell-based models called vertex models, which are often used to study epithelial tissues. In two-dimensional vertex models, a tissue is approximated as a tessellation of polygons and the vertices of these polygons move due to mechanical forces originating from the cells. Such models have been used extensively to study the mechanical regulation of tissue topology in the literature. Here, we analyse how the model predictions may be affected by numerical parameters, such as the size of the time step, and non-physical model parameters, such as length thresholds for cell rearrangement. We find that vertex positions and summary statistics are sensitive to several of these implementation parameters. For example, the predicted tissue size decreases with decreasing cell cycle durations, and cell rearrangement may be suppressed by large time steps. These findings are counter-intuitive and illustrate that model predictions need to be thoroughly analysed and implementation details carefully considered when applying cell-based computational models in a quantitative setting.

Quantitative Systems Pharmacology: A Case for Disease Models.

PubMed

Musante, C J; Ramanujan, S; Schmidt, B J; Ghobrial, O G; Lu, J; Heatherington, A C

2017-01-01

Quantitative systems pharmacology (QSP) has emerged as an innovative approach in model-informed drug discovery and development, supporting program decisions from exploratory research through late-stage clinical trials. In this commentary, we discuss the unique value of disease-scale "platform" QSP models that are amenable to reuse and repurposing to support diverse clinical decisions in ways distinct from other pharmacometrics strategies. © 2016 The Authors Clinical Pharmacology & Therapeutics published by Wiley Periodicals, Inc. on behalf of The American Society for Clinical Pharmacology and Therapeutics.

Improved accuracy in quantitative laser-induced breakdown spectroscopy using sub-models

USGS Publications Warehouse

Anderson, Ryan; Clegg, Samuel M.; Frydenvang, Jens; Wiens, Roger C.; McLennan, Scott M.; Morris, Richard V.; Ehlmann, Bethany L.; Dyar, M. Darby

2017-01-01

Accurate quantitative analysis of diverse geologic materials is one of the primary challenges faced by the Laser-Induced Breakdown Spectroscopy (LIBS)-based ChemCam instrument on the Mars Science Laboratory (MSL) rover. The SuperCam instrument on the Mars 2020 rover, as well as other LIBS instruments developed for geochemical analysis on Earth or other planets, will face the same challenge. Consequently, part of the ChemCam science team has focused on the development of improved multivariate analysis calibrations methods. Developing a single regression model capable of accurately determining the composition of very different target materials is difficult because the response of an element’s emission lines in LIBS spectra can vary with the concentration of other elements. We demonstrate a conceptually simple “sub-model” method for improving the accuracy of quantitative LIBS analysis of diverse target materials. The method is based on training several regression models on sets of targets with limited composition ranges and then “blending” these “sub-models” into a single final result. Tests of the sub-model method show improvement in test set root mean squared error of prediction (RMSEP) for almost all cases. The sub-model method, using partial least squares regression (PLS), is being used as part of the current ChemCam quantitative calibration, but the sub-model method is applicable to any multivariate regression method and may yield similar improvements.

An Integrated Qualitative and Quantitative Biochemical Model Learning Framework Using Evolutionary Strategy and Simulated Annealing.

PubMed

Wu, Zujian; Pang, Wei; Coghill, George M

2015-01-01

Both qualitative and quantitative model learning frameworks for biochemical systems have been studied in computational systems biology. In this research, after introducing two forms of pre-defined component patterns to represent biochemical models, we propose an integrative qualitative and quantitative modelling framework for inferring biochemical systems. In the proposed framework, interactions between reactants in the candidate models for a target biochemical system are evolved and eventually identified by the application of a qualitative model learning approach with an evolution strategy. Kinetic rates of the models generated from qualitative model learning are then further optimised by employing a quantitative approach with simulated annealing. Experimental results indicate that our proposed integrative framework is feasible to learn the relationships between biochemical reactants qualitatively and to make the model replicate the behaviours of the target system by optimising the kinetic rates quantitatively. Moreover, potential reactants of a target biochemical system can be discovered by hypothesising complex reactants in the synthetic models. Based on the biochemical models learned from the proposed framework, biologists can further perform experimental study in wet laboratory. In this way, natural biochemical systems can be better understood.

The linearized multistage model and the future of quantitative risk assessment.

PubMed

Crump, K S

1996-10-01

The linearized multistage (LMS) model has for over 15 years been the default dose-response model used by the U.S. Environmental Protection Agency (USEPA) and other federal and state regulatory agencies in the United States for calculating quantitative estimates of low-dose carcinogenic risks from animal data. The LMS model is in essence a flexible statistical model that can describe both linear and non-linear dose-response patterns, and that produces an upper confidence bound on the linear low-dose slope of the dose-response curve. Unlike its namesake, the Armitage-Doll multistage model, the parameters of the LMS do not correspond to actual physiological phenomena. Thus the LMS is 'biological' only to the extent that the true biological dose response is linear at low dose and that low-dose slope is reflected in the experimental data. If the true dose response is non-linear the LMS upper bound may overestimate the true risk by many orders of magnitude. However, competing low-dose extrapolation models, including those derived from 'biologically-based models' that are capable of incorporating additional biological information, have not shown evidence to date of being able to produce quantitative estimates of low-dose risks that are any more accurate than those obtained from the LMS model. Further, even if these attempts were successful, the extent to which more accurate estimates of low-dose risks in a test animal species would translate into improved estimates of human risk is questionable. Thus, it does not appear possible at present to develop a quantitative approach that would be generally applicable and that would offer significant improvements upon the crude bounding estimates of the type provided by the LMS model. Draft USEPA guidelines for cancer risk assessment incorporate an approach similar to the LMS for carcinogens having a linear mode of action. However, under these guidelines quantitative estimates of low-dose risks would not be developed for

Multicomponent quantitative spectroscopic analysis without reference substances based on ICA modelling.

PubMed

Monakhova, Yulia B; Mushtakova, Svetlana P

2017-05-01

A fast and reliable spectroscopic method for multicomponent quantitative analysis of targeted compounds with overlapping signals in complex mixtures has been established. The innovative analytical approach is based on the preliminary chemometric extraction of qualitative and quantitative information from UV-vis and IR spectral profiles of a calibration system using independent component analysis (ICA). Using this quantitative model and ICA resolution results of spectral profiling of "unknown" model mixtures, the absolute analyte concentrations in multicomponent mixtures and authentic samples were then calculated without reference solutions. Good recoveries generally between 95% and 105% were obtained. The method can be applied to any spectroscopic data that obey the Beer-Lambert-Bouguer law. The proposed method was tested on analysis of vitamins and caffeine in energy drinks and aromatic hydrocarbons in motor fuel with 10% error. The results demonstrated that the proposed method is a promising tool for rapid simultaneous multicomponent analysis in the case of spectral overlap and the absence/inaccessibility of reference materials.

A Quantitative Human Spacecraft Design Evaluation Model for Assessing Crew Accommodation and Utilization

NASA Astrophysics Data System (ADS)

Fanchiang, Christine

Crew performance, including both accommodation and utilization factors, is an integral part of every human spaceflight mission from commercial space tourism, to the demanding journey to Mars and beyond. Spacecraft were historically built by engineers and technologists trying to adapt the vehicle into cutting edge rocketry with the assumption that the astronauts could be trained and will adapt to the design. By and large, that is still the current state of the art. It is recognized, however, that poor human-machine design integration can lead to catastrophic and deadly mishaps. The premise of this work relies on the idea that if an accurate predictive model exists to forecast crew performance issues as a result of spacecraft design and operations, it can help designers and managers make better decisions throughout the design process, and ensure that the crewmembers are well-integrated with the system from the very start. The result should be a high-quality, user-friendly spacecraft that optimizes the utilization of the crew while keeping them alive, healthy, and happy during the course of the mission. Therefore, the goal of this work was to develop an integrative framework to quantitatively evaluate a spacecraft design from the crew performance perspective. The approach presented here is done at a very fundamental level starting with identifying and defining basic terminology, and then builds up important axioms of human spaceflight that lay the foundation for how such a framework can be developed. With the framework established, a methodology for characterizing the outcome using a mathematical model was developed by pulling from existing metrics and data collected on human performance in space. Representative test scenarios were run to show what information could be garnered and how it could be applied as a useful, understandable metric for future spacecraft design. While the model is the primary tangible product from this research, the more interesting outcome of

Quantitative Characterization of Spurious Gibbs Waves in 45 CMIP5 Models

NASA Astrophysics Data System (ADS)

Geil, K. L.; Zeng, X.

2014-12-01

Gibbs oscillations appear in global climate models when representing fields, such as orography, that contain discontinuities or sharp gradients. It has been known for decades that the oscillations are associated with the transformation of the truncated spectral representation of a field to physical space and that the oscillations can also be present in global models that do not use spectral methods. The spurious oscillations are potentially detrimental to model simulations (e.g., over ocean) and this work provides a quantitative characterization of the Gibbs oscillations that appear across the Coupled Model Intercomparison Project Phase 5 (CMIP5) models. An ocean transect running through the South Pacific High toward the Andes is used to characterize the oscillations in ten different variables. These oscillations are found to be stationary and hence are not caused by (physical) waves in the atmosphere. We quantify the oscillation amplitude using the root mean square difference (RMSD) between the transect of a variable and its running mean (rather than the constant mean across the transect). We also compute the RMSD to interannual variability (IAV) ratio, which provides a relative measure of the oscillation amplitude. Of the variables examined, the largest RMSD values exist in the surface pressure field of spectral models, while the smallest RMSD values within the surface pressure field come from models that use finite difference (FD) techniques. Many spectral models have a surface pressure RMSD that is 2 to 15 times greater than IAV over the transect and an RMSD:IAV ratio greater than one for many other variables including surface temperature, incoming shortwave radiation at the surface, incoming longwave radiation at the surface, and total cloud fraction. In general, the FD models out-perform the spectral models, but not all the spectral models have large amplitude oscillations and there are a few FD models where the oscillations do appear. Finally, we present a

A quantitative model of optimal data selection in Wason's selection task.

PubMed

Hattori, Masasi

2002-10-01

The optimal data selection model proposed by Oaksford and Chater (1994) successfully formalized Wason's selection task (Wason, 1966). The model, however, involved some questionable assumptions and was also not sufficient as a model of the task because it could not provide quantitative predictions of the card selection frequencies. In this paper, the model was revised to provide quantitative fits to the data. The model can predict the selection frequencies of cards based on a selection tendency function (STF), or conversely, it enables the estimation of subjective probabilities from data. Past experimental data were first re-analysed based on the model. In Experiment 1, the superiority of the revised model was shown. However, when the relationship between antecedent and consequent was forced to deviate from the biconditional form, the model was not supported. In Experiment 2, it was shown that sufficient emphasis on probabilistic information can affect participants' performance. A detailed experimental method to sort participants by probabilistic strategies was introduced. Here, the model was supported by a subgroup of participants who used the probabilistic strategy. Finally, the results were discussed from the viewpoint of adaptive rationality.

Teaching Note--"By Any Means Necessary!" Infusing Socioeconomic Justice Content into Quantitative Research Course Work

ERIC Educational Resources Information Center

Slayter, Elspeth M.

2017-01-01

Existing research suggests a majority of faculty include social justice content in research courses but not through the use of existing quantitative data for in-class activities that foster mastery of data analysis and interpretation and curiosity about social justice-related topics. By modeling data-driven dialogue and the deconstruction of…

Quantitative modeling of reservoir-triggered seismicity

NASA Astrophysics Data System (ADS)

Hainzl, S.; Catalli, F.; Dahm, T.; Heinicke, J.; Woith, H.

2017-12-01

Reservoir-triggered seismicity might occur as the response to the crustal stress caused by the poroelastic response to the weight of the water volume and fluid diffusion. Several cases of high correlations have been found in the past decades. However, crustal stresses might be altered by many other processes such as continuous tectonic stressing and coseismic stress changes. Because reservoir-triggered stresses decay quickly with distance, even tidal or rainfall-triggered stresses might be of similar size at depth. To account for simultaneous stress sources in a physically meaningful way, we apply a seismicity model based on calculated stress changes in the crust and laboratory-derived friction laws. Based on the observed seismicity, the model parameters can be determined by maximum likelihood method. The model leads to quantitative predictions of the variations of seismicity rate in space and time which can be used for hypothesis testing and forecasting. For case studies in Talala (India), Val d'Agri (Italy) and Novy Kostel (Czech Republic), we show the comparison of predicted and observed seismicity, demonstrating the potential and limitations of the approach.

Improved accuracy in quantitative laser-induced breakdown spectroscopy using sub-models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anderson, Ryan B.; Clegg, Samuel M.; Frydenvang, Jens

We report that accurate quantitative analysis of diverse geologic materials is one of the primary challenges faced by the Laser-Induced Breakdown Spectroscopy (LIBS)-based ChemCam instrument on the Mars Science Laboratory (MSL) rover. The SuperCam instrument on the Mars 2020 rover, as well as other LIBS instruments developed for geochemical analysis on Earth or other planets, will face the same challenge. Consequently, part of the ChemCam science team has focused on the development of improved multivariate analysis calibrations methods. Developing a single regression model capable of accurately determining the composition of very different target materials is difficult because the response ofmore » an element’s emission lines in LIBS spectra can vary with the concentration of other elements. We demonstrate a conceptually simple “submodel” method for improving the accuracy of quantitative LIBS analysis of diverse target materials. The method is based on training several regression models on sets of targets with limited composition ranges and then “blending” these “sub-models” into a single final result. Tests of the sub-model method show improvement in test set root mean squared error of prediction (RMSEP) for almost all cases. Lastly, the sub-model method, using partial least squares regression (PLS), is being used as part of the current ChemCam quantitative calibration, but the sub-model method is applicable to any multivariate regression method and may yield similar improvements.« less

Improved accuracy in quantitative laser-induced breakdown spectroscopy using sub-models

DOE PAGES

Anderson, Ryan B.; Clegg, Samuel M.; Frydenvang, Jens; ...

2016-12-15

We report that accurate quantitative analysis of diverse geologic materials is one of the primary challenges faced by the Laser-Induced Breakdown Spectroscopy (LIBS)-based ChemCam instrument on the Mars Science Laboratory (MSL) rover. The SuperCam instrument on the Mars 2020 rover, as well as other LIBS instruments developed for geochemical analysis on Earth or other planets, will face the same challenge. Consequently, part of the ChemCam science team has focused on the development of improved multivariate analysis calibrations methods. Developing a single regression model capable of accurately determining the composition of very different target materials is difficult because the response ofmore » an element’s emission lines in LIBS spectra can vary with the concentration of other elements. We demonstrate a conceptually simple “submodel” method for improving the accuracy of quantitative LIBS analysis of diverse target materials. The method is based on training several regression models on sets of targets with limited composition ranges and then “blending” these “sub-models” into a single final result. Tests of the sub-model method show improvement in test set root mean squared error of prediction (RMSEP) for almost all cases. Lastly, the sub-model method, using partial least squares regression (PLS), is being used as part of the current ChemCam quantitative calibration, but the sub-model method is applicable to any multivariate regression method and may yield similar improvements.« less

Quantitative Systems Pharmacology: A Case for Disease Models

PubMed Central

Ramanujan, S; Schmidt, BJ; Ghobrial, OG; Lu, J; Heatherington, AC

2016-01-01

Quantitative systems pharmacology (QSP) has emerged as an innovative approach in model‐informed drug discovery and development, supporting program decisions from exploratory research through late‐stage clinical trials. In this commentary, we discuss the unique value of disease‐scale “platform” QSP models that are amenable to reuse and repurposing to support diverse clinical decisions in ways distinct from other pharmacometrics strategies. PMID:27709613

Pharmacokinetic modeling of gentamicin in treatment of infective endocarditis: Model development and validation of existing models.

PubMed

Gomes, Anna; van der Wijk, Lars; Proost, Johannes H; Sinha, Bhanu; Touw, Daan J

2017-01-01

Gentamicin shows large variations in half-life and volume of distribution (Vd) within and between individuals. Thus, monitoring and accurately predicting serum levels are required to optimize effectiveness and minimize toxicity. Currently, two population pharmacokinetic models are applied for predicting gentamicin doses in adults. For endocarditis patients the optimal model is unknown. We aimed at: 1) creating an optimal model for endocarditis patients; and 2) assessing whether the endocarditis and existing models can accurately predict serum levels. We performed a retrospective observational two-cohort study: one cohort to parameterize the endocarditis model by iterative two-stage Bayesian analysis, and a second cohort to validate and compare all three models. The Akaike Information Criterion and the weighted sum of squares of the residuals divided by the degrees of freedom were used to select the endocarditis model. Median Prediction Error (MDPE) and Median Absolute Prediction Error (MDAPE) were used to test all models with the validation dataset. We built the endocarditis model based on data from the modeling cohort (65 patients) with a fixed 0.277 L/h/70kg metabolic clearance, 0.698 (±0.358) renal clearance as fraction of creatinine clearance, and Vd 0.312 (±0.076) L/kg corrected lean body mass. External validation with data from 14 validation cohort patients showed a similar predictive power of the endocarditis model (MDPE -1.77%, MDAPE 4.68%) as compared to the intensive-care (MDPE -1.33%, MDAPE 4.37%) and standard (MDPE -0.90%, MDAPE 4.82%) models. All models acceptably predicted pharmacokinetic parameters for gentamicin in endocarditis patients. However, these patients appear to have an increased Vd, similar to intensive care patients. Vd mainly determines the height of peak serum levels, which in turn correlate with bactericidal activity. In order to maintain simplicity, we advise to use the existing intensive-care model in clinical practice to avoid

Pharmacokinetic modeling of gentamicin in treatment of infective endocarditis: Model development and validation of existing models

PubMed Central

van der Wijk, Lars; Proost, Johannes H.; Sinha, Bhanu; Touw, Daan J.

2017-01-01

Gentamicin shows large variations in half-life and volume of distribution (Vd) within and between individuals. Thus, monitoring and accurately predicting serum levels are required to optimize effectiveness and minimize toxicity. Currently, two population pharmacokinetic models are applied for predicting gentamicin doses in adults. For endocarditis patients the optimal model is unknown. We aimed at: 1) creating an optimal model for endocarditis patients; and 2) assessing whether the endocarditis and existing models can accurately predict serum levels. We performed a retrospective observational two-cohort study: one cohort to parameterize the endocarditis model by iterative two-stage Bayesian analysis, and a second cohort to validate and compare all three models. The Akaike Information Criterion and the weighted sum of squares of the residuals divided by the degrees of freedom were used to select the endocarditis model. Median Prediction Error (MDPE) and Median Absolute Prediction Error (MDAPE) were used to test all models with the validation dataset. We built the endocarditis model based on data from the modeling cohort (65 patients) with a fixed 0.277 L/h/70kg metabolic clearance, 0.698 (±0.358) renal clearance as fraction of creatinine clearance, and Vd 0.312 (±0.076) L/kg corrected lean body mass. External validation with data from 14 validation cohort patients showed a similar predictive power of the endocarditis model (MDPE -1.77%, MDAPE 4.68%) as compared to the intensive-care (MDPE -1.33%, MDAPE 4.37%) and standard (MDPE -0.90%, MDAPE 4.82%) models. All models acceptably predicted pharmacokinetic parameters for gentamicin in endocarditis patients. However, these patients appear to have an increased Vd, similar to intensive care patients. Vd mainly determines the height of peak serum levels, which in turn correlate with bactericidal activity. In order to maintain simplicity, we advise to use the existing intensive-care model in clinical practice to avoid

Quantitative determination of wool in textile by near-infrared spectroscopy and multivariate models.

PubMed

Chen, Hui; Tan, Chao; Lin, Zan

2018-08-05

The wool content in textiles is a key quality index and the corresponding quantitative analysis takes an important position due to common adulterations in both raw and finished textiles. Conventional methods are maybe complicated, destructive, time-consuming, environment-unfriendly. Developing a quick, easy-to-use and green alternative method is interesting. The work focuses on exploring the feasibility of combining near-infrared (NIR) spectroscopy and several partial least squares (PLS)-based algorithms and elastic component regression (ECR) algorithms for measuring wool content in textile. A total of 108 cloth samples with wool content ranging from 0% to 100% (w/w) were collected and all the compositions are really existent in the market. The dataset was divided equally into the training and test sets for developing and validating calibration models. When using local PLS, the original spectrum axis was split into 20 sub-intervals. No obvious difference of performance can be seen for the local PLS models. The ECR model is comparable or superior to the other models due its flexibility, i.e., being transition state from PCR to PLS. It seems that ECR combined with NIR technique may be a potential method for determining wool content in textile products. In addition, it might have regulatory advantages to avoid time-consuming and environmental-unfriendly chemical analysis. Copyright © 2018 Elsevier B.V. All rights reserved.

A Systematic Quantitative-Qualitative Model: How To Evaluate Professional Services

ERIC Educational Resources Information Center

Yoda, Koji

1973-01-01

The proposed evaluation model provides for the assignment of relative weights to each criterion, and establishes a weighting system for calculating a quantitative-qualitative raw score for each service activity of a faculty member being reviewed. (Author)

Human judgment vs. quantitative models for the management of ecological resources.

PubMed

Holden, Matthew H; Ellner, Stephen P

2016-07-01

Despite major advances in quantitative approaches to natural resource management, there has been resistance to using these tools in the actual practice of managing ecological populations. Given a managed system and a set of assumptions, translated into a model, optimization methods can be used to solve for the most cost-effective management actions. However, when the underlying assumptions are not met, such methods can potentially lead to decisions that harm the environment and economy. Managers who develop decisions based on past experience and judgment, without the aid of mathematical models, can potentially learn about the system and develop flexible management strategies. However, these strategies are often based on subjective criteria and equally invalid and often unstated assumptions. Given the drawbacks of both methods, it is unclear whether simple quantitative models improve environmental decision making over expert opinion. In this study, we explore how well students, using their experience and judgment, manage simulated fishery populations in an online computer game and compare their management outcomes to the performance of model-based decisions. We consider harvest decisions generated using four different quantitative models: (1) the model used to produce the simulated population dynamics observed in the game, with the values of all parameters known (as a control), (2) the same model, but with unknown parameter values that must be estimated during the game from observed data, (3) models that are structurally different from those used to simulate the population dynamics, and (4) a model that ignores age structure. Humans on average performed much worse than the models in cases 1-3, but in a small minority of scenarios, models produced worse outcomes than those resulting from students making decisions based on experience and judgment. When the models ignored age structure, they generated poorly performing management decisions, but still outperformed

Comparison of blood flow models and acquisitions for quantitative myocardial perfusion estimation from dynamic CT

NASA Astrophysics Data System (ADS)

Bindschadler, Michael; Modgil, Dimple; Branch, Kelley R.; La Riviere, Patrick J.; Alessio, Adam M.

2014-04-01

Myocardial blood flow (MBF) can be estimated from dynamic contrast enhanced (DCE) cardiac CT acquisitions, leading to quantitative assessment of regional perfusion. The need for low radiation dose and the lack of consensus on MBF estimation methods motivates this study to refine the selection of acquisition protocols and models for CT-derived MBF. DCE cardiac CT acquisitions were simulated for a range of flow states (MBF = 0.5, 1, 2, 3 ml (min g)-1, cardiac output = 3, 5, 8 L min-1). Patient kinetics were generated by a mathematical model of iodine exchange incorporating numerous physiological features including heterogenenous microvascular flow, permeability and capillary contrast gradients. CT acquisitions were simulated for multiple realizations of realistic x-ray flux levels. CT acquisitions that reduce radiation exposure were implemented by varying both temporal sampling (1, 2, and 3 s sampling intervals) and tube currents (140, 70, and 25 mAs). For all acquisitions, we compared three quantitative MBF estimation methods (two-compartment model, an axially-distributed model, and the adiabatic approximation to the tissue homogeneous model) and a qualitative slope-based method. In total, over 11 000 time attenuation curves were used to evaluate MBF estimation in multiple patient and imaging scenarios. After iodine-based beam hardening correction, the slope method consistently underestimated flow by on average 47.5% and the quantitative models provided estimates with less than 6.5% average bias and increasing variance with increasing dose reductions. The three quantitative models performed equally well, offering estimates with essentially identical root mean squared error (RMSE) for matched acquisitions. MBF estimates using the qualitative slope method were inferior in terms of bias and RMSE compared to the quantitative methods. MBF estimate error was equal at matched dose reductions for all quantitative methods and range of techniques evaluated. This suggests that

Models of Quantitative Estimations: Rule-Based and Exemplar-Based Processes Compared

ERIC Educational Resources Information Center

von Helversen, Bettina; Rieskamp, Jorg

2009-01-01

The cognitive processes underlying quantitative estimations vary. Past research has identified task-contingent changes between rule-based and exemplar-based processes (P. Juslin, L. Karlsson, & H. Olsson, 2008). B. von Helversen and J. Rieskamp (2008), however, proposed a simple rule-based model--the mapping model--that outperformed the…

Real Patient and its Virtual Twin: Application of Quantitative Systems Toxicology Modelling in the Cardiac Safety Assessment of Citalopram.

PubMed

Patel, Nikunjkumar; Wiśniowska, Barbara; Jamei, Masoud; Polak, Sebastian

2017-11-27

A quantitative systems toxicology (QST) model for citalopram was established to simulate, in silico, a 'virtual twin' of a real patient to predict the occurrence of cardiotoxic events previously reported in patients under various clinical conditions. The QST model considers the effects of citalopram and its most notable electrophysiologically active primary (desmethylcitalopram) and secondary (didesmethylcitalopram) metabolites, on cardiac electrophysiology. The in vitro cardiac ion channel current inhibition data was coupled with the biophysically detailed model of human cardiac electrophysiology to investigate the impact of (i) the inhibition of multiple ion currents (I Kr , I Ks , I CaL ); (ii) the inclusion of metabolites in the QST model; and (iii) unbound or total plasma as the operating drug concentration, in predicting clinically observed QT prolongation. The inclusion of multiple ion channel current inhibition and metabolites in the simulation with unbound plasma citalopram concentration provided the lowest prediction error. The predictive performance of the model was verified with three additional therapeutic and supra-therapeutic drug exposure clinical cases. The results indicate that considering only the hERG ion channel inhibition of only the parent drug is potentially misleading, and the inclusion of active metabolite data and the influence of other ion channel currents should be considered to improve the prediction of potential cardiac toxicity. Mechanistic modelling can help bridge the gaps existing in the quantitative translation from preclinical cardiac safety assessment to clinical toxicology. Moreover, this study shows that the QST models, in combination with appropriate drug and systems parameters, can pave the way towards personalised safety assessment.

Quantitative Structure--Activity Relationship Modeling of Rat Acute Toxicity by Oral Exposure

EPA Science Inventory

Background: Few Quantitative Structure-Activity Relationship (QSAR) studies have successfully modeled large, diverse rodent toxicity endpoints. Objective: In this study, a combinatorial QSAR approach has been employed for the creation of robust and predictive models of acute toxi...

Global Quantitative Modeling of Chromatin Factor Interactions

PubMed Central

Zhou, Jian; Troyanskaya, Olga G.

2014-01-01

Chromatin is the driver of gene regulation, yet understanding the molecular interactions underlying chromatin factor combinatorial patterns (or the “chromatin codes”) remains a fundamental challenge in chromatin biology. Here we developed a global modeling framework that leverages chromatin profiling data to produce a systems-level view of the macromolecular complex of chromatin. Our model ultilizes maximum entropy modeling with regularization-based structure learning to statistically dissect dependencies between chromatin factors and produce an accurate probability distribution of chromatin code. Our unsupervised quantitative model, trained on genome-wide chromatin profiles of 73 histone marks and chromatin proteins from modENCODE, enabled making various data-driven inferences about chromatin profiles and interactions. We provided a highly accurate predictor of chromatin factor pairwise interactions validated by known experimental evidence, and for the first time enabled higher-order interaction prediction. Our predictions can thus help guide future experimental studies. The model can also serve as an inference engine for predicting unknown chromatin profiles — we demonstrated that with this approach we can leverage data from well-characterized cell types to help understand less-studied cell type or conditions. PMID:24675896

Training of Existing Workers: Issues, Incentives and Models. Support Document

ERIC Educational Resources Information Center

Mawer, Giselle; Jackson, Elaine

2005-01-01

This document was produced by the authors based on their research for the report, "Training of Existing Workers: Issues, Incentives and Models," (ED495138) and is an added resource for further information. This support document is divided into the following sections: (1) The Retail Industry--A Snapshot; (2) Case Studies--Hardware, Retail…

A quantitative model of application slow-down in multi-resource shared systems

DOE PAGES

Lim, Seung-Hwan; Kim, Youngjae

2016-12-26

Scheduling multiple jobs onto a platform enhances system utilization by sharing resources. The benefits from higher resource utilization include reduced cost to construct, operate, and maintain a system, which often include energy consumption. Maximizing these benefits comes at a price-resource contention among jobs increases job completion time. In this study, we analyze slow-downs of jobs due to contention for multiple resources in a system; referred to as dilation factor. We observe that multiple-resource contention creates non-linear dilation factors of jobs. From this observation, we establish a general quantitative model for dilation factors of jobs in multi-resource systems. A job ismore » characterized by a vector-valued loading statistics and dilation factors of a job set are given by a quadratic function of their loading vectors. We demonstrate how to systematically characterize a job, maintain the data structure to calculate the dilation factor (loading matrix), and calculate the dilation factor of each job. We validate the accuracy of the model with multiple processes running on a native Linux server, virtualized servers, and with multiple MapReduce workloads co-scheduled in a cluster. Evaluation with measured data shows that the D-factor model has an error margin of less than 16%. We extended the D-factor model to capture the slow-down of applications when multiple identical resources exist such as multi-core environments and multi-disks environments. Finally, validation results of the extended D-factor model with HPC checkpoint applications on the parallel file systems show that D-factor accurately captures the slow down of concurrent applications in such environments.« less

A quantitative model of application slow-down in multi-resource shared systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lim, Seung-Hwan; Kim, Youngjae

Scheduling multiple jobs onto a platform enhances system utilization by sharing resources. The benefits from higher resource utilization include reduced cost to construct, operate, and maintain a system, which often include energy consumption. Maximizing these benefits comes at a price-resource contention among jobs increases job completion time. In this study, we analyze slow-downs of jobs due to contention for multiple resources in a system; referred to as dilation factor. We observe that multiple-resource contention creates non-linear dilation factors of jobs. From this observation, we establish a general quantitative model for dilation factors of jobs in multi-resource systems. A job ismore » characterized by a vector-valued loading statistics and dilation factors of a job set are given by a quadratic function of their loading vectors. We demonstrate how to systematically characterize a job, maintain the data structure to calculate the dilation factor (loading matrix), and calculate the dilation factor of each job. We validate the accuracy of the model with multiple processes running on a native Linux server, virtualized servers, and with multiple MapReduce workloads co-scheduled in a cluster. Evaluation with measured data shows that the D-factor model has an error margin of less than 16%. We extended the D-factor model to capture the slow-down of applications when multiple identical resources exist such as multi-core environments and multi-disks environments. Finally, validation results of the extended D-factor model with HPC checkpoint applications on the parallel file systems show that D-factor accurately captures the slow down of concurrent applications in such environments.« less

Selecting Models for Measuring Change When True Experimental Conditions Do Not Exist.

ERIC Educational Resources Information Center

Fortune, Jim C.; Hutson, Barbara A.

1984-01-01

Measuring change when true experimental conditions do not exist is a difficult process. This article reviews the artifacts of change measurement in evaluations and quasi-experimental designs, delineates considerations in choosing a model to measure change under nonideal conditions, and suggests ways to organize models to facilitate selection.…

Quantitative phenomenological model of the BOLD contrast mechanism

NASA Astrophysics Data System (ADS)

Dickson, John D.; Ash, Tom W. J.; Williams, Guy B.; Sukstanskii, Alexander L.; Ansorge, Richard E.; Yablonskiy, Dmitriy A.

2011-09-01

Different theoretical models of the BOLD contrast mechanism are used for many applications including BOLD quantification (qBOLD) and vessel size imaging, both in health and disease. Each model simplifies the system under consideration, making approximations about the structure of the blood vessel network and diffusion of water molecules through inhomogeneities in the magnetic field created by deoxyhemoglobin-containing blood vessels. In this study, Monte-Carlo methods are used to simulate the BOLD MR signal generated by diffusing water molecules in the presence of long, cylindrical blood vessels. Using these simulations we introduce a new, phenomenological model that is far more accurate over a range of blood oxygenation levels and blood vessel radii than existing models. This model could be used to extract physiological parameters of the blood vessel network from experimental data in BOLD-based experiments. We use our model to establish ranges of validity for the existing analytical models of Yablonskiy and Haacke, Kiselev and Posse, Sukstanskii and Yablonskiy (extended to the case of arbitrary time in the spin echo sequence) and Bauer et al. (extended to the case of randomly oriented cylinders). Although these models are shown to be accurate in the limits of diffusion under which they were derived, none of them is accurate for the whole physiological range of blood vessels radii and blood oxygenation levels. We also show the extent of systematic errors that are introduced due to the approximations of these models when used for BOLD signal quantification.

Quantitative 3D investigation of Neuronal network in mouse spinal cord model

NASA Astrophysics Data System (ADS)

Bukreeva, I.; Campi, G.; Fratini, M.; Spanò, R.; Bucci, D.; Battaglia, G.; Giove, F.; Bravin, A.; Uccelli, A.; Venturi, C.; Mastrogiacomo, M.; Cedola, A.

2017-01-01

The investigation of the neuronal network in mouse spinal cord models represents the basis for the research on neurodegenerative diseases. In this framework, the quantitative analysis of the single elements in different districts is a crucial task. However, conventional 3D imaging techniques do not have enough spatial resolution and contrast to allow for a quantitative investigation of the neuronal network. Exploiting the high coherence and the high flux of synchrotron sources, X-ray Phase-Contrast multiscale-Tomography allows for the 3D investigation of the neuronal microanatomy without any aggressive sample preparation or sectioning. We investigated healthy-mouse neuronal architecture by imaging the 3D distribution of the neuronal-network with a spatial resolution of 640 nm. The high quality of the obtained images enables a quantitative study of the neuronal structure on a subject-by-subject basis. We developed and applied a spatial statistical analysis on the motor neurons to obtain quantitative information on their 3D arrangement in the healthy-mice spinal cord. Then, we compared the obtained results with a mouse model of multiple sclerosis. Our approach paves the way to the creation of a “database” for the characterization of the neuronal network main features for a comparative investigation of neurodegenerative diseases and therapies.

Neuroergonomics: Quantitative Modeling of Individual, Shared, and Team Neurodynamic Information.

PubMed

Stevens, Ronald H; Galloway, Trysha L; Willemsen-Dunlap, Ann

2018-06-01

The aim of this study was to use the same quantitative measure and scale to directly compare the neurodynamic information/organizations of individual team members with those of the team. Team processes are difficult to separate from those of individual team members due to the lack of quantitative measures that can be applied to both process sets. Second-by-second symbolic representations were created of each team member's electroencephalographic power, and quantitative estimates of their neurodynamic organizations were calculated from the Shannon entropy of the symbolic data streams. The information in the neurodynamic data streams of health care ( n = 24), submarine navigation ( n = 12), and high school problem-solving ( n = 13) dyads was separated into the information of each team member, the information shared by team members, and the overall team information. Most of the team information was the sum of each individual's neurodynamic information. The remaining team information was shared among the team members. This shared information averaged ~15% of the individual information, with momentary levels of 1% to 80%. Continuous quantitative estimates can be made from the shared, individual, and team neurodynamic information about the contributions of different team members to the overall neurodynamic organization of a team and the neurodynamic interdependencies among the team members. Information models provide a generalizable quantitative method for separating a team's neurodynamic organization into that of individual team members and that shared among team members.

Framework for a Quantitative Systemic Toxicity Model (FutureToxII)

EPA Science Inventory

EPA’s ToxCast program profiles the bioactivity of chemicals in a diverse set of ~700 high throughput screening (HTS) assays. In collaboration with L’Oreal, a quantitative model of systemic toxicity was developed using no effect levels (NEL) from ToxRefDB for 633 chemicals with HT...

A quantitative risk-based model for reasoning over critical system properties

NASA Technical Reports Server (NTRS)

Feather, M. S.

2002-01-01

This position paper suggests the use of a quantitative risk-based model to help support reeasoning and decision making that spans many of the critical properties such as security, safety, survivability, fault tolerance, and real-time.

Predictive value of EEG in postanoxic encephalopathy: A quantitative model-based approach.

PubMed

Efthymiou, Evdokia; Renzel, Roland; Baumann, Christian R; Poryazova, Rositsa; Imbach, Lukas L

2017-10-01

The majority of comatose patients after cardiac arrest do not regain consciousness due to severe postanoxic encephalopathy. Early and accurate outcome prediction is therefore essential in determining further therapeutic interventions. The electroencephalogram is a standardized and commonly available tool used to estimate prognosis in postanoxic patients. The identification of pathological EEG patterns with poor prognosis relies however primarily on visual EEG scoring by experts. We introduced a model-based approach of EEG analysis (state space model) that allows for an objective and quantitative description of spectral EEG variability. We retrospectively analyzed standard EEG recordings in 83 comatose patients after cardiac arrest between 2005 and 2013 in the intensive care unit of the University Hospital Zürich. Neurological outcome was assessed one month after cardiac arrest using the Cerebral Performance Category. For a dynamic and quantitative EEG analysis, we implemented a model-based approach (state space analysis) to quantify EEG background variability independent from visual scoring of EEG epochs. Spectral variability was compared between groups and correlated with clinical outcome parameters and visual EEG patterns. Quantitative assessment of spectral EEG variability (state space velocity) revealed significant differences between patients with poor and good outcome after cardiac arrest: Lower mean velocity in temporal electrodes (T4 and T5) was significantly associated with poor prognostic outcome (p<0.005) and correlated with independently identified visual EEG patterns such as generalized periodic discharges (p<0.02). Receiver operating characteristic (ROC) analysis confirmed the predictive value of lower state space velocity for poor clinical outcome after cardiac arrest (AUC 80.8, 70% sensitivity, 15% false positive rate). Model-based quantitative EEG analysis (state space analysis) provides a novel, complementary marker for prognosis in postanoxic

Tip-Enhanced Raman Voltammetry: Coverage Dependence and Quantitative Modeling.

PubMed

Mattei, Michael; Kang, Gyeongwon; Goubert, Guillaume; Chulhai, Dhabih V; Schatz, George C; Jensen, Lasse; Van Duyne, Richard P

2017-01-11

Electrochemical atomic force microscopy tip-enhanced Raman spectroscopy (EC-AFM-TERS) was employed for the first time to observe nanoscale spatial variations in the formal potential, E 0' , of a surface-bound redox couple. TERS cyclic voltammograms (TERS CVs) of single Nile Blue (NB) molecules were acquired at different locations spaced 5-10 nm apart on an indium tin oxide (ITO) electrode. Analysis of TERS CVs at different coverages was used to verify the observation of single-molecule electrochemistry. The resulting TERS CVs were fit to the Laviron model for surface-bound electroactive species to quantitatively extract the formal potential E 0' at each spatial location. Histograms of single-molecule E 0' at each coverage indicate that the electrochemical behavior of the cationic oxidized species is less sensitive to local environment than the neutral reduced species. This information is not accessible using purely electrochemical methods or ensemble spectroelectrochemical measurements. We anticipate that quantitative modeling and measurement of site-specific electrochemistry with EC-AFM-TERS will have a profound impact on our understanding of the role of nanoscale electrode heterogeneity in applications such as electrocatalysis, biological electron transfer, and energy production and storage.

[A quantitative risk assessment model of salmonella on carcass in poultry slaughterhouse].

PubMed

Zhang, Yu; Chen, Yuzhen; Hu, Chunguang; Zhang, Huaning; Bi, Zhenwang; Bi, Zhenqiang

2015-05-01

To construct a quantitative risk assessment model of salmonella on carcass in poultry slaughterhouse and to find out effective interventions to reduce salmonella contamination. We constructed a modular process risk model (MPRM) from evisceration to chilling in Excel Sheet using the data of the process parameters in poultry and the Salmomella concentration surveillance of Jinan in 2012. The MPRM was simulated by @ risk software. The concentration of salmonella on carcass after chilling was 1.96MPN/g which was calculated by model. The sensitive analysis indicated that the correlation coefficient of the concentration of salmonella after defeathering and in chilling pool were 0.84 and 0.34,which were the primary factors to the concentration of salmonella on carcass after chilling. The study provided a quantitative assessment model structure for salmonella on carcass in poultry slaughterhouse. The risk manager could control the contamination of salmonella on carcass after chilling by reducing the concentration of salmonella after defeathering and in chilling pool.

Lack of quantitative training among early-career ecologists: a survey of the problem and potential solutions

PubMed Central

Ezard, Thomas H.G.; Jørgensen, Peter S.; Zimmerman, Naupaka; Chamberlain, Scott; Salguero-Gómez, Roberto; Curran, Timothy J.; Poisot, Timothée

2014-01-01

Proficiency in mathematics and statistics is essential to modern ecological science, yet few studies have assessed the level of quantitative training received by ecologists. To do so, we conducted an online survey. The 937 respondents were mostly early-career scientists who studied biology as undergraduates. We found a clear self-perceived lack of quantitative training: 75% were not satisfied with their understanding of mathematical models; 75% felt that the level of mathematics was “too low” in their ecology classes; 90% wanted more mathematics classes for ecologists; and 95% more statistics classes. Respondents thought that 30% of classes in ecology-related degrees should be focused on quantitative disciplines, which is likely higher than for most existing programs. The main suggestion to improve quantitative training was to relate theoretical and statistical modeling to applied ecological problems. Improving quantitative training will require dedicated, quantitative classes for ecology-related degrees that contain good mathematical and statistical practice. PMID:24688862

Existence of Torsional Solitons in a Beam Model of Suspension Bridge

NASA Astrophysics Data System (ADS)

Benci, Vieri; Fortunato, Donato; Gazzola, Filippo

2017-11-01

This paper studies the existence of solitons, namely stable solitary waves, in an idealized suspension bridge. The bridge is modeled as an unbounded degenerate plate, that is, a central beam with cross sections, and displays two degrees of freedom: the vertical displacement of the beam and the torsional angles of the cross sections. Under fairly general assumptions, we prove the existence of solitons. Under the additional assumption of large tension in the sustaining cables, we prove that these solitons have a nontrivial torsional component. This appears relevant for security since several suspension bridges collapsed due to torsional oscillations.

A quantitative model to assess Social Responsibility in Environmental Science and Technology.

PubMed

Valcárcel, M; Lucena, R

2014-01-01

The awareness of the impact of human activities in society and environment is known as "Social Responsibility" (SR). It has been a topic of growing interest in many enterprises since the fifties of the past Century, and its implementation/assessment is nowadays supported by international standards. There is a tendency to amplify its scope of application to other areas of the human activities, such as Research, Development and Innovation (R + D + I). In this paper, a model of quantitative assessment of Social Responsibility in Environmental Science and Technology (SR EST) is described in detail. This model is based on well established written standards as the EFQM Excellence model and the ISO 26000:2010 Guidance on SR. The definition of five hierarchies of indicators, the transformation of qualitative information into quantitative data and the dual procedure of self-evaluation and external evaluation are the milestones of the proposed model, which can be applied to Environmental Research Centres and institutions. In addition, a simplified model that facilitates its implementation is presented in the article. © 2013 Elsevier B.V. All rights reserved.

Functional linear models for association analysis of quantitative traits.

PubMed

Fan, Ruzong; Wang, Yifan; Mills, James L; Wilson, Alexander F; Bailey-Wilson, Joan E; Xiong, Momiao

2013-11-01

Functional linear models are developed in this paper for testing associations between quantitative traits and genetic variants, which can be rare variants or common variants or the combination of the two. By treating multiple genetic variants of an individual in a human population as a realization of a stochastic process, the genome of an individual in a chromosome region is a continuum of sequence data rather than discrete observations. The genome of an individual is viewed as a stochastic function that contains both linkage and linkage disequilibrium (LD) information of the genetic markers. By using techniques of functional data analysis, both fixed and mixed effect functional linear models are built to test the association between quantitative traits and genetic variants adjusting for covariates. After extensive simulation analysis, it is shown that the F-distributed tests of the proposed fixed effect functional linear models have higher power than that of sequence kernel association test (SKAT) and its optimal unified test (SKAT-O) for three scenarios in most cases: (1) the causal variants are all rare, (2) the causal variants are both rare and common, and (3) the causal variants are common. The superior performance of the fixed effect functional linear models is most likely due to its optimal utilization of both genetic linkage and LD information of multiple genetic variants in a genome and similarity among different individuals, while SKAT and SKAT-O only model the similarities and pairwise LD but do not model linkage and higher order LD information sufficiently. In addition, the proposed fixed effect models generate accurate type I error rates in simulation studies. We also show that the functional kernel score tests of the proposed mixed effect functional linear models are preferable in candidate gene analysis and small sample problems. The methods are applied to analyze three biochemical traits in data from the Trinity Students Study. © 2013 WILEY

Quantitative prediction of drug side effects based on drug-related features.

PubMed

Niu, Yanqing; Zhang, Wen

2017-09-01

Unexpected side effects of drugs are great concern in the drug development, and the identification of side effects is an important task. Recently, machine learning methods are proposed to predict the presence or absence of interested side effects for drugs, but it is difficult to make the accurate prediction for all of them. In this paper, we transform side effect profiles of drugs as their quantitative scores, by summing up their side effects with weights. The quantitative scores may measure the dangers of drugs, and thus help to compare the risk of different drugs. Here, we attempt to predict quantitative scores of drugs, namely the quantitative prediction. Specifically, we explore a variety of drug-related features and evaluate their discriminative powers for the quantitative prediction. Then, we consider several feature combination strategies (direct combination, average scoring ensemble combination) to integrate three informative features: chemical substructures, targets, and treatment indications. Finally, the average scoring ensemble model which produces the better performances is used as the final quantitative prediction model. Since weights for side effects are empirical values, we randomly generate different weights in the simulation experiments. The experimental results show that the quantitative method is robust to different weights, and produces satisfying results. Although other state-of-the-art methods cannot make the quantitative prediction directly, the prediction results can be transformed as the quantitative scores. By indirect comparison, the proposed method produces much better results than benchmark methods in the quantitative prediction. In conclusion, the proposed method is promising for the quantitative prediction of side effects, which may work cooperatively with existing state-of-the-art methods to reveal dangers of drugs.

The use of mode of action information in risk assessment: quantitative key events/dose-response framework for modeling the dose-response for key events.

PubMed

Simon, Ted W; Simons, S Stoney; Preston, R Julian; Boobis, Alan R; Cohen, Samuel M; Doerrer, Nancy G; Fenner-Crisp, Penelope A; McMullin, Tami S; McQueen, Charlene A; Rowlands, J Craig

2014-08-01

The HESI RISK21 project formed the Dose-Response/Mode-of-Action Subteam to develop strategies for using all available data (in vitro, in vivo, and in silico) to advance the next-generation of chemical risk assessments. A goal of the Subteam is to enhance the existing Mode of Action/Human Relevance Framework and Key Events/Dose Response Framework (KEDRF) to make the best use of quantitative dose-response and timing information for Key Events (KEs). The resulting Quantitative Key Events/Dose-Response Framework (Q-KEDRF) provides a structured quantitative approach for systematic examination of the dose-response and timing of KEs resulting from a dose of a bioactive agent that causes a potential adverse outcome. Two concepts are described as aids to increasing the understanding of mode of action-Associative Events and Modulating Factors. These concepts are illustrated in two case studies; 1) cholinesterase inhibition by the pesticide chlorpyrifos, which illustrates the necessity of considering quantitative dose-response information when assessing the effect of a Modulating Factor, that is, enzyme polymorphisms in humans, and 2) estrogen-induced uterotrophic responses in rodents, which demonstrate how quantitative dose-response modeling for KE, the understanding of temporal relationships between KEs and a counterfactual examination of hypothesized KEs can determine whether they are Associative Events or true KEs.

Methodologies for Quantitative Systems Pharmacology (QSP) Models: Design and Estimation.

PubMed

Ribba, B; Grimm, H P; Agoram, B; Davies, M R; Gadkar, K; Niederer, S; van Riel, N; Timmis, J; van der Graaf, P H

2017-08-01

With the increased interest in the application of quantitative systems pharmacology (QSP) models within medicine research and development, there is an increasing need to formalize model development and verification aspects. In February 2016, a workshop was held at Roche Pharma Research and Early Development to focus discussions on two critical methodological aspects of QSP model development: optimal structural granularity and parameter estimation. We here report in a perspective article a summary of presentations and discussions. © 2017 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

76 FR 28819 - NUREG/CR-XXXX, Development of Quantitative Software Reliability Models for Digital Protection...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-18

... NUCLEAR REGULATORY COMMISSION [NRC-2011-0109] NUREG/CR-XXXX, Development of Quantitative Software..., ``Development of Quantitative Software Reliability Models for Digital Protection Systems of Nuclear Power Plants... of Risk Analysis, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission...

Current Challenges in the First Principle Quantitative Modelling of the Lower Hybrid Current Drive in Tokamaks

NASA Astrophysics Data System (ADS)

Peysson, Y.; Bonoli, P. T.; Chen, J.; Garofalo, A.; Hillairet, J.; Li, M.; Qian, J.; Shiraiwa, S.; Decker, J.; Ding, B. J.; Ekedahl, A.; Goniche, M.; Zhai, X.

2017-10-01

The Lower Hybrid (LH) wave is widely used in existing tokamaks for tailoring current density profile or extending pulse duration to steady-state regimes. Its high efficiency makes it particularly attractive for a fusion reactor, leading to consider it for this purpose in ITER tokamak. Nevertheless, if basics of the LH wave in tokamak plasma are well known, quantitative modeling of experimental observations based on first principles remains a highly challenging exercise, despite considerable numerical efforts achieved so far. In this context, a rigorous methodology must be carried out in the simulations to identify the minimum number of physical mechanisms that must be considered to reproduce experimental shot to shot observations and also scalings (density, power spectrum). Based on recent simulations carried out for EAST, Alcator C-Mod and Tore Supra tokamaks, the state of the art in LH modeling is reviewed. The capability of fast electron bremsstrahlung, internal inductance li and LH driven current at zero loop voltage to constrain all together LH simulations is discussed, as well as the needs of further improvements (diagnostics, codes, LH model), for robust interpretative and predictive simulations.

Quantitative photoacoustic elasticity and viscosity imaging for cirrhosis detection

NASA Astrophysics Data System (ADS)

Wang, Qian; Shi, Yujiao; Yang, Fen; Yang, Sihua

2018-05-01

Elasticity and viscosity assessments are essential for understanding and characterizing the physiological and pathological states of tissue. In this work, by establishing a photoacoustic (PA) shear wave model, an approach for quantitative PA elasticity imaging based on measurement of the rise time of the thermoelastic displacement was developed. Thus, using an existing PA viscoelasticity imaging method that features a phase delay measurement, quantitative PA elasticity imaging and viscosity imaging can be obtained in a simultaneous manner. The method was tested and validated by imaging viscoelastic agar phantoms prepared at different agar concentrations, and the imaging data were in good agreement with rheometry results. Ex vivo experiments on liver pathological models demonstrated the capability for cirrhosis detection, and the results were consistent with the corresponding histological results. This method expands the scope of conventional PA imaging and has potential to become an important alternative imaging modality.

Dissecting Embryonic Stem Cell Self-Renewal and Differentiation Commitment from Quantitative Models.

PubMed

Hu, Rong; Dai, Xianhua; Dai, Zhiming; Xiang, Qian; Cai, Yanning

2016-10-01

To model quantitatively embryonic stem cell (ESC) self-renewal and differentiation by computational approaches, we developed a unified mathematical model for gene expression involved in cell fate choices. Our quantitative model comprised ESC master regulators and lineage-specific pivotal genes. It took the factors of multiple pathways as input and computed expression as a function of intrinsic transcription factors, extrinsic cues, epigenetic modifications, and antagonism between ESC master regulators and lineage-specific pivotal genes. In the model, the differential equations of expression of genes involved in cell fate choices from regulation relationship were established according to the transcription and degradation rates. We applied this model to the Murine ESC self-renewal and differentiation commitment and found that it modeled the expression patterns with good accuracy. Our model analysis revealed that Murine ESC was an attractor state in culture and differentiation was predominantly caused by antagonism between ESC master regulators and lineage-specific pivotal genes. Moreover, antagonism among lineages played a critical role in lineage reprogramming. Our results also uncovered that the ordered expression alteration of ESC master regulators over time had a central role in ESC differentiation fates. Our computational framework was generally applicable to most cell-type maintenance and lineage reprogramming.

A quantitative quantum chemical model of the Dewar-Knott color rule for cationic diarylmethanes

NASA Astrophysics Data System (ADS)

Olsen, Seth

2012-04-01

We document the quantitative manifestation of the Dewar-Knott color rule in a four-electron, three-orbital state-averaged complete active space self-consistent field (SA-CASSCF) model of a series of bridge-substituted cationic diarylmethanes. We show that the lowest excitation energies calculated using multireference perturbation theory based on the model are linearly correlated with the development of hole density in an orbital localized on the bridge, and the depletion of pair density in the same orbital. We quantitatively express the correlation in the form of a generalized Hammett equation.

Wires in the soup: quantitative models of cell signaling

PubMed Central

Cheong, Raymond; Levchenko, Andre

2014-01-01

Living cells are capable of extracting information from their environments and mounting appropriate responses to a variety of associated challenges. The underlying signal transduction networks enabling this can be quite complex, necessitating for their unraveling by sophisticated computational modeling coupled with precise experimentation. Although we are still at the beginning of this process, some recent examples of integrative analysis of cell signaling are very encouraging. This review highlights the case of the NF-κB pathway in order to illustrate how a quantitative model of a signaling pathway can be gradually constructed through continuous experimental validation, and what lessons one might learn from such exercises. PMID:18291655

Preparing systems engineering and computing science students in disciplined methods, quantitative, and advanced statistical techniques to improve process performance

NASA Astrophysics Data System (ADS)

McCray, Wilmon Wil L., Jr.

The research was prompted by a need to conduct a study that assesses process improvement, quality management and analytical techniques taught to students in U.S. colleges and universities undergraduate and graduate systems engineering and the computing science discipline (e.g., software engineering, computer science, and information technology) degree programs during their academic training that can be applied to quantitatively manage processes for performance. Everyone involved in executing repeatable processes in the software and systems development lifecycle processes needs to become familiar with the concepts of quantitative management, statistical thinking, process improvement methods and how they relate to process-performance. Organizations are starting to embrace the de facto Software Engineering Institute (SEI) Capability Maturity Model Integration (CMMI RTM) Models as process improvement frameworks to improve business processes performance. High maturity process areas in the CMMI model imply the use of analytical, statistical, quantitative management techniques, and process performance modeling to identify and eliminate sources of variation, continually improve process-performance; reduce cost and predict future outcomes. The research study identifies and provides a detail discussion of the gap analysis findings of process improvement and quantitative analysis techniques taught in U.S. universities systems engineering and computing science degree programs, gaps that exist in the literature, and a comparison analysis which identifies the gaps that exist between the SEI's "healthy ingredients " of a process performance model and courses taught in U.S. universities degree program. The research also heightens awareness that academicians have conducted little research on applicable statistics and quantitative techniques that can be used to demonstrate high maturity as implied in the CMMI models. The research also includes a Monte Carlo simulation optimization

The quantitative modelling of human spatial habitability

NASA Technical Reports Server (NTRS)

Wise, J. A.

1985-01-01

A model for the quantitative assessment of human spatial habitability is presented in the space station context. The visual aspect assesses how interior spaces appear to the inhabitants. This aspect concerns criteria such as sensed spaciousness and the affective (emotional) connotations of settings' appearances. The kinesthetic aspect evaluates the available space in terms of its suitability to accommodate human movement patterns, as well as the postural and anthrometric changes due to microgravity. Finally, social logic concerns how the volume and geometry of available space either affirms or contravenes established social and organizational expectations for spatial arrangements. Here, the criteria include privacy, status, social power, and proxemics (the uses of space as a medium of social communication).

General quantitative genetic methods for comparative biology: phylogenies, taxonomies and multi-trait models for continuous and categorical characters.

PubMed

Hadfield, J D; Nakagawa, S

2010-03-01

Although many of the statistical techniques used in comparative biology were originally developed in quantitative genetics, subsequent development of comparative techniques has progressed in relative isolation. Consequently, many of the new and planned developments in comparative analysis already have well-tested solutions in quantitative genetics. In this paper, we take three recent publications that develop phylogenetic meta-analysis, either implicitly or explicitly, and show how they can be considered as quantitative genetic models. We highlight some of the difficulties with the proposed solutions, and demonstrate that standard quantitative genetic theory and software offer solutions. We also show how results from Bayesian quantitative genetics can be used to create efficient Markov chain Monte Carlo algorithms for phylogenetic mixed models, thereby extending their generality to non-Gaussian data. Of particular utility is the development of multinomial models for analysing the evolution of discrete traits, and the development of multi-trait models in which traits can follow different distributions. Meta-analyses often include a nonrandom collection of species for which the full phylogenetic tree has only been partly resolved. Using missing data theory, we show how the presented models can be used to correct for nonrandom sampling and show how taxonomies and phylogenies can be combined to give a flexible framework with which to model dependence.

Evaluating habitat for black-footed ferrets: Revision of an existing model

USGS Publications Warehouse

Biggins, Dean E.; Lockhart, J. Michael; Godbey, Jerry L.

2006-01-01

Black-footed ferrets (Mustela nigripes) are highly dependent on prairie dogs (Cynomys spp.) as prey, and prairie dog colonies are the only known habitats that sustain black-footed ferret populations. An existing model used extensively for evaluating black-footed ferret reintroduction habitat defined complexes by interconnecting colonies with 7-km line segments. Although the 7-km complex remains a useful construct, we propose additional, smaller-scale evaluations that consider 1.5-km subcomplexes. The original model estimated the carrying capacity of complexes based on energy requirements of ferrets and density estimates of their prairie dog prey. Recent data have supported earlier contentions of intraspecific competition and intrasexual territorial behavior in ferrets. We suggest a revised model that retains the fixed linear relationship of the existing model when prairie dog densities are <18/ha and uses a curvilinear relationship that reflects increasing effects of ferret territoriality when there are 18–42 prairie dogs per hectare. We discuss possible effects of colony size and shape, interacting with territoriality, as justification for the exclusion of territorial influences if a prairie dog colony supports only a single female ferret. We also present data to support continued use of active prairie dog burrow densities as indices suitable for broad-scale estimates of prairie dog density. Calculation of percent of complexes that are occupied by prairie dog colonies was recommended as part of the original habitat evaluation process. That attribute has been largely ignored, resulting in rating anomalies.

[Influence of sample surface roughness on mathematical model of NIR quantitative analysis of wood density].

PubMed

Huang, An-Min; Fei, Ben-Hua; Jiang, Ze-Hui; Hse, Chung-Yun

2007-09-01

Near infrared spectroscopy is widely used as a quantitative method, and the main multivariate techniques consist of regression methods used to build prediction models, however, the accuracy of analysis results will be affected by many factors. In the present paper, the influence of different sample roughness on the mathematical model of NIR quantitative analysis of wood density was studied. The result of experiments showed that if the roughness of predicted samples was consistent with that of calibrated samples, the result was good, otherwise the error would be much higher. The roughness-mixed model was more flexible and adaptable to different sample roughness. The prediction ability of the roughness-mixed model was much better than that of the single-roughness model.

A Quantitative Model of Early Atherosclerotic Plaques Parameterized Using In Vitro Experiments.

PubMed

Thon, Moritz P; Ford, Hugh Z; Gee, Michael W; Myerscough, Mary R

2018-01-01

There are a growing number of studies that model immunological processes in the artery wall that lead to the development of atherosclerotic plaques. However, few of these models use parameters that are obtained from experimental data even though data-driven models are vital if mathematical models are to become clinically relevant. We present the development and analysis of a quantitative mathematical model for the coupled inflammatory, lipid and macrophage dynamics in early atherosclerotic plaques. Our modeling approach is similar to the biologists' experimental approach where the bigger picture of atherosclerosis is put together from many smaller observations and findings from in vitro experiments. We first develop a series of three simpler submodels which are least-squares fitted to various in vitro experimental results from the literature. Subsequently, we use these three submodels to construct a quantitative model of the development of early atherosclerotic plaques. We perform a local sensitivity analysis of the model with respect to its parameters that identifies critical parameters and processes. Further, we present a systematic analysis of the long-term outcome of the model which produces a characterization of the stability of model plaques based on the rates of recruitment of low-density lipoproteins, high-density lipoproteins and macrophages. The analysis of the model suggests that further experimental work quantifying the different fates of macrophages as a function of cholesterol load and the balance between free cholesterol and cholesterol ester inside macrophages may give valuable insight into long-term atherosclerotic plaque outcomes. This model is an important step toward models applicable in a clinical setting.

Modeling logistic performance in quantitative microbial risk assessment.

PubMed

Rijgersberg, Hajo; Tromp, Seth; Jacxsens, Liesbeth; Uyttendaele, Mieke

2010-01-01

In quantitative microbial risk assessment (QMRA), food safety in the food chain is modeled and simulated. In general, prevalences, concentrations, and numbers of microorganisms in media are investigated in the different steps from farm to fork. The underlying rates and conditions (such as storage times, temperatures, gas conditions, and their distributions) are determined. However, the logistic chain with its queues (storages, shelves) and mechanisms for ordering products is usually not taken into account. As a consequence, storage times-mutually dependent in successive steps in the chain-cannot be described adequately. This may have a great impact on the tails of risk distributions. Because food safety risks are generally very small, it is crucial to model the tails of (underlying) distributions as accurately as possible. Logistic performance can be modeled by describing the underlying planning and scheduling mechanisms in discrete-event modeling. This is common practice in operations research, specifically in supply chain management. In this article, we present the application of discrete-event modeling in the context of a QMRA for Listeria monocytogenes in fresh-cut iceberg lettuce. We show the potential value of discrete-event modeling in QMRA by calculating logistic interventions (modifications in the logistic chain) and determining their significance with respect to food safety.

Quantitative and Functional Requirements for Bioluminescent Cancer Models.

PubMed

Feys, Lynn; Descamps, Benedicte; Vanhove, Christian; Vermeulen, Stefan; Vandesompele, J O; Vanderheyden, Katrien; Messens, Kathy; Bracke, Marc; De Wever, Olivier

2016-01-01

Bioluminescent cancer models are widely used but detailed quantification of the luciferase signal and functional comparison with a non-transfected control cell line are generally lacking. In the present study, we provide quantitative and functional tests for luciferase-transfected cells. We quantified the luciferase expression in BLM and HCT8/E11 transfected cancer cells, and examined the effect of long-term luciferin exposure. The present study also investigated functional differences between parental and transfected cancer cells. Our results showed that quantification of different single-cell-derived populations are superior with droplet digital polymerase chain reaction. Quantification of luciferase protein level and luciferase bioluminescent activity is only useful when there is a significant difference in copy number. Continuous exposure of cell cultures to luciferin leads to inhibitory effects on mitochondrial activity, cell growth and bioluminescence. These inhibitory effects correlate with luciferase copy number. Cell culture and mouse xenograft assays showed no significant functional differences between luciferase-transfected and parental cells. Luciferase-transfected cells should be validated by quantitative and functional assays before starting large-scale experiments. Copyright © 2016 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

Existence, uniqueness and positivity of solutions for BGK models for mixtures

NASA Astrophysics Data System (ADS)

Klingenberg, C.; Pirner, M.

2018-01-01

We consider kinetic models for a multi component gas mixture without chemical reactions. In the literature, one can find two types of BGK models in order to describe gas mixtures. One type has a sum of BGK type interaction terms in the relaxation operator, for example the model described by Klingenberg, Pirner and Puppo [20] which contains well-known models of physicists and engineers for example Hamel [16] and Gross and Krook [15] as special cases. The other type contains only one collision term on the right-hand side, for example the well-known model of Andries, Aoki and Perthame [1]. For each of these two models [20] and [1], we prove existence, uniqueness and positivity of solutions in the first part of the paper. In the second part, we use the first model [20] in order to determine an unknown function in the energy exchange of the macroscopic equations for gas mixtures described by Dellacherie [11].

Stepwise kinetic equilibrium models of quantitative polymerase chain reaction.

PubMed

Cobbs, Gary

2012-08-16

Numerous models for use in interpreting quantitative PCR (qPCR) data are present in recent literature. The most commonly used models assume the amplification in qPCR is exponential and fit an exponential model with a constant rate of increase to a select part of the curve. Kinetic theory may be used to model the annealing phase and does not assume constant efficiency of amplification. Mechanistic models describing the annealing phase with kinetic theory offer the most potential for accurate interpretation of qPCR data. Even so, they have not been thoroughly investigated and are rarely used for interpretation of qPCR data. New results for kinetic modeling of qPCR are presented. Two models are presented in which the efficiency of amplification is based on equilibrium solutions for the annealing phase of the qPCR process. Model 1 assumes annealing of complementary targets strands and annealing of target and primers are both reversible reactions and reach a dynamic equilibrium. Model 2 assumes all annealing reactions are nonreversible and equilibrium is static. Both models include the effect of primer concentration during the annealing phase. Analytic formulae are given for the equilibrium values of all single and double stranded molecules at the end of the annealing step. The equilibrium values are then used in a stepwise method to describe the whole qPCR process. Rate constants of kinetic models are the same for solutions that are identical except for possibly having different initial target concentrations. Analysis of qPCR curves from such solutions are thus analyzed by simultaneous non-linear curve fitting with the same rate constant values applying to all curves and each curve having a unique value for initial target concentration. The models were fit to two data sets for which the true initial target concentrations are known. Both models give better fit to observed qPCR data than other kinetic models present in the literature. They also give better estimates of

Rotorcraft control system design for uncertain vehicle dynamics using quantitative feedback theory

NASA Technical Reports Server (NTRS)

Hess, R. A.

1994-01-01

Quantitative Feedback Theory describes a frequency-domain technique for the design of multi-input, multi-output control systems which must meet time or frequency domain performance criteria when specified uncertainty exists in the linear description of the vehicle dynamics. This theory is applied to the design of the longitudinal flight control system for a linear model of the BO-105C rotorcraft. Uncertainty in the vehicle model is due to the variation in the vehicle dynamics over a range of airspeeds from 0-100 kts. For purposes of exposition, the vehicle description contains no rotor or actuator dynamics. The design example indicates the manner in which significant uncertainty exists in the vehicle model. The advantage of using a sequential loop closure technique to reduce the cost of feedback is demonstrated by example.

From Existence to Essence: A Conceptual Model for an Appalachian Studies Curriculum.

ERIC Educational Resources Information Center

Best, Billy F.

Comprised of 4 chapters, this dissertation explores the existential premise "existence precedes essence" as applicable to development of a conceptual model for an Appalachian studies curriculum. Entitled "Personal Considerations: Pedagogy of a Hillbilly", the 1st chapter details the conflicts between the Appalachian institution…

Quantitative computational models of molecular self-assembly in systems biology

PubMed Central

Thomas, Marcus; Schwartz, Russell

2017-01-01

Molecular self-assembly is the dominant form of chemical reaction in living systems, yet efforts at systems biology modeling are only beginning to appreciate the need for and challenges to accurate quantitative modeling of self-assembly. Self-assembly reactions are essential to nearly every important process in cell and molecular biology and handling them is thus a necessary step in building comprehensive models of complex cellular systems. They present exceptional challenges, however, to standard methods for simulating complex systems. While the general systems biology world is just beginning to deal with these challenges, there is an extensive literature dealing with them for more specialized self-assembly modeling. This review will examine the challenges of self-assembly modeling, nascent efforts to deal with these challenges in the systems modeling community, and some of the solutions offered in prior work on self-assembly specifically. The review concludes with some consideration of the likely role of self-assembly in the future of complex biological system models more generally. PMID:28535149

Quantitative computational models of molecular self-assembly in systems biology.

PubMed

Thomas, Marcus; Schwartz, Russell

2017-05-23

Molecular self-assembly is the dominant form of chemical reaction in living systems, yet efforts at systems biology modeling are only beginning to appreciate the need for and challenges to accurate quantitative modeling of self-assembly. Self-assembly reactions are essential to nearly every important process in cell and molecular biology and handling them is thus a necessary step in building comprehensive models of complex cellular systems. They present exceptional challenges, however, to standard methods for simulating complex systems. While the general systems biology world is just beginning to deal with these challenges, there is an extensive literature dealing with them for more specialized self-assembly modeling. This review will examine the challenges of self-assembly modeling, nascent efforts to deal with these challenges in the systems modeling community, and some of the solutions offered in prior work on self-assembly specifically. The review concludes with some consideration of the likely role of self-assembly in the future of complex biological system models more generally.

Corequisite Model: An Effective Strategy for Remediation in Freshmen Level Quantitative Reasoning Course

ERIC Educational Resources Information Center

Kashyap, Upasana; Mathew, Santhosh

2017-01-01

The purpose of this study was to compare students' performances in a freshmen level quantitative reasoning course (QR) under three different instructional models. A cohort of 155 freshmen students was placed in one of the three models: needing a prerequisite course, corequisite (students enroll simultaneously in QR course and a course that…

Human eyeball model reconstruction and quantitative analysis.

PubMed

Xing, Qi; Wei, Qi

2014-01-01

Determining shape of the eyeball is important to diagnose eyeball disease like myopia. In this paper, we present an automatic approach to precisely reconstruct three dimensional geometric shape of eyeball from MR Images. The model development pipeline involved image segmentation, registration, B-Spline surface fitting and subdivision surface fitting, neither of which required manual interaction. From the high resolution resultant models, geometric characteristics of the eyeball can be accurately quantified and analyzed. In addition to the eight metrics commonly used by existing studies, we proposed two novel metrics, Gaussian Curvature Analysis and Sphere Distance Deviation, to quantify the cornea shape and the whole eyeball surface respectively. The experiment results showed that the reconstructed eyeball models accurately represent the complex morphology of the eye. The ten metrics parameterize the eyeball among different subjects, which can potentially be used for eye disease diagnosis.

The Quantitative Preparation of Future Geoscience Graduate Students

NASA Astrophysics Data System (ADS)

Manduca, C. A.; Hancock, G. S.

2006-12-01

Modern geoscience is a highly quantitative science. In February, a small group of faculty and graduate students from across the country met to discuss the quantitative preparation of geoscience majors for graduate school. The group included ten faculty supervising graduate students in quantitative areas spanning the earth, atmosphere, and ocean sciences; five current graduate students in these areas; and five faculty teaching undergraduate students in the spectrum of institutions preparing students for graduate work. Discussion focused in four key ares: Are incoming graduate students adequately prepared for the quantitative aspects of graduate geoscience programs? What are the essential quantitative skills are that are required for success in graduate school? What are perceived as the important courses to prepare students for the quantitative aspects of graduate school? What programs/resources would be valuable in helping faculty/departments improve the quantitative preparation of students? The participants concluded that strengthening the quantitative preparation of undergraduate geoscience majors would increase their opportunities in graduate school. While specifics differed amongst disciplines, a special importance was placed on developing the ability to use quantitative skills to solve geoscience problems. This requires the ability to pose problems so they can be addressed quantitatively, understand the relationship between quantitative concepts and physical representations, visualize mathematics, test the reasonableness of quantitative results, creatively move forward from existing models/techniques/approaches, and move between quantitative and verbal descriptions. A list of important quantitative competencies desirable in incoming graduate students includes mechanical skills in basic mathematics, functions, multi-variate analysis, statistics and calculus, as well as skills in logical analysis and the ability to learn independently in quantitative ways

Establishment of quantitative retention-activity model by optimized microemulsion liquid chromatography.

PubMed

Xu, Liyuan; Gao, Haoshi; Li, Liangxing; Li, Yinnong; Wang, Liuyun; Gao, Chongkai; Li, Ning

2016-12-23

The effective permeability coefficient is of theoretical and practical importance in evaluation of the bioavailability of drug candidates. However, most methods currently used to measure this coefficient are expensive and time-consuming. In this paper, we addressed these problems by proposing a new measurement method which is based on the microemulsion liquid chromatography. First, the parallel artificial membrane permeability assays model was used to determine the effective permeability of drug so that quantitative retention-activity relationships could be established, which were used to optimize the microemulsion liquid chromatography. The most effective microemulsion system used a mobile phase of 6.0% (w/w) Brij35, 6.6% (w/w) butanol, 0.8% (w/w) octanol, and 86.6% (w/w) phosphate buffer (pH 7.4). Next, support vector machine and back-propagation neural networks are employed to develop a quantitative retention-activity relationships model associated with the optimal microemulsion system, and used to improve the prediction ability. Finally, an adequate correlation between experimental value and predicted value is computed to verify the performance of the optimal model. The results indicate that the microemulsion liquid chromatography can serve as a possible alternative to the PAMPA method for determination of high-throughput permeability and simulation of biological processes. Copyright © 2016. Published by Elsevier B.V.

Quantitative petri net model of gene regulated metabolic networks in the cell.

PubMed

Chen, Ming; Hofestädt, Ralf

2011-01-01

A method to exploit hybrid Petri nets (HPN) for quantitatively modeling and simulating gene regulated metabolic networks is demonstrated. A global kinetic modeling strategy and Petri net modeling algorithm are applied to perform the bioprocess functioning and model analysis. With the model, the interrelations between pathway analysis and metabolic control mechanism are outlined. Diagrammatical results of the dynamics of metabolites are simulated and observed by implementing a HPN tool, Visual Object Net ++. An explanation of the observed behavior of the urea cycle is proposed to indicate possibilities for metabolic engineering and medical care. Finally, the perspective of Petri nets on modeling and simulation of metabolic networks is discussed.

Numerical Modelling of Extended Leak-Off Test with a Pre-Existing Fracture

NASA Astrophysics Data System (ADS)

Lavrov, A.; Larsen, I.; Bauer, A.

2016-04-01

Extended leak-off test (XLOT) is one of the few techniques available for stress measurements in oil and gas wells. Interpretation of the test is often difficult since the results depend on a multitude of factors, including the presence of natural or drilling-induced fractures in the near-well area. Coupled numerical modelling of XLOT has been performed to investigate the pressure behaviour during the flowback phase as well as the effect of a pre-existing fracture on the test results in a low-permeability formation. Essential features of XLOT known from field measurements are captured by the model, including the saw-tooth shape of the pressure vs injected volume curve, and the change of slope in the pressure vs time curve during flowback used by operators as an indicator of the bottomhole pressure reaching the minimum in situ stress. Simulations with a pre-existing fracture running from the borehole wall in the radial direction have revealed that the results of XLOT are quite sensitive to the orientation of the pre-existing fracture. In particular, the fracture initiation pressure and the formation breakdown pressure increase steadily with decreasing angle between the fracture and the minimum in situ stress. Our findings seem to invalidate the use of the fracture initiation pressure and the formation breakdown pressure for stress measurements or rock strength evaluation purposes.

A Team Mental Model Perspective of Pre-Quantitative Risk

NASA Technical Reports Server (NTRS)

Cooper, Lynne P.

2011-01-01

This study was conducted to better understand how teams conceptualize risk before it can be quantified, and the processes by which a team forms a shared mental model of this pre-quantitative risk. Using an extreme case, this study analyzes seven months of team meeting transcripts, covering the entire lifetime of the team. Through an analysis of team discussions, a rich and varied structural model of risk emerges that goes significantly beyond classical representations of risk as the product of a negative consequence and a probability. In addition to those two fundamental components, the team conceptualization includes the ability to influence outcomes and probabilities, networks of goals, interaction effects, and qualitative judgments about the acceptability of risk, all affected by associated uncertainties. In moving from individual to team mental models, team members employ a number of strategies to gain group recognition of risks and to resolve or accept differences.

Quantitative validation of an air-coupled ultrasonic probe model by Interferometric laser tomography

NASA Astrophysics Data System (ADS)

Revel, G. M.; Pandarese, G.; Cavuto, A.

2012-06-01

The present paper describes the quantitative validation of a finite element (FE) model of the ultrasound beam generated by an air coupled non-contact ultrasound transducer. The model boundary conditions are given by vibration velocities measured by laser vibrometry on the probe membrane. The proposed validation method is based on the comparison between the simulated 3D pressure field and the pressure data measured with interferometric laser tomography technique. The model details and the experimental techniques are described in paper. The analysis of results shows the effectiveness of the proposed approach and the possibility to quantitatively assess and predict the generated acoustic pressure field, with maximum discrepancies in the order of 20% due to uncertainty effects. This step is important for determining in complex problems the real applicability of air-coupled probes and for the simulation of the whole inspection procedure, also when the component is designed, so as to virtually verify its inspectability.

Quantitative Testing of Bedrock Incision Models, Clearwater River, WA

NASA Astrophysics Data System (ADS)

Tomkin, J. H.; Brandon, M.; Pazzaglia, F.; Barbour, J.; Willet, S.

2001-12-01

The topographic evolution of many active orogens is dominated by the process of bedrock channel incision. Several incision models based around the detachment limited shear-stress model (or stream power model) which employs an area (A) and slope (S) power law (E = K Sn Am) have been proposed to explain this process. They require quantitative assessment. We evaluate the proposed incision models by comparing their predictions with observations obtained from a river in a tectonically active mountain range: the Clearwater River in northwestern Washington State. Previous work on river terraces along the Clearwater have provided long-term incision rates for the river, and in conjunction with previous fission track studies it has also been determined that there is a long-term balance between river incision and rock uplift. This steady-state incision rate data allows us, through the use of inversion methods and statistical tests, to determine the applicability of the different incision models for the Clearwater. None of the models successfully explain the observations. This conclusion particularly applies to the commonly used detachment limited shear-stress model (or stream power model), which has a physically implausible best fit solution and systematic residuals for all the predicted combinations of m and n.

Quantitative modeling of multiscale neural activity

NASA Astrophysics Data System (ADS)

Robinson, Peter A.; Rennie, Christopher J.

2007-01-01

The electrical activity of the brain has been observed for over a century and is widely used to probe brain function and disorders, chiefly through the electroencephalogram (EEG) recorded by electrodes on the scalp. However, the connections between physiology and EEGs have been chiefly qualitative until recently, and most uses of the EEG have been based on phenomenological correlations. A quantitative mean-field model of brain electrical activity is described that spans the range of physiological and anatomical scales from microscopic synapses to the whole brain. Its parameters measure quantities such as synaptic strengths, signal delays, cellular time constants, and neural ranges, and are all constrained by independent physiological measurements. Application of standard techniques from wave physics allows successful predictions to be made of a wide range of EEG phenomena, including time series and spectra, evoked responses to stimuli, dependence on arousal state, seizure dynamics, and relationships to functional magnetic resonance imaging (fMRI). Fitting to experimental data also enables physiological parameters to be infered, giving a new noninvasive window into brain function, especially when referenced to a standardized database of subjects. Modifications of the core model to treat mm-scale patchy interconnections in the visual cortex are also described, and it is shown that resulting waves obey the Schroedinger equation. This opens the possibility of classical cortical analogs of quantum phenomena.

Using integrated environmental modeling to automate a process-based Quantitative Microbial Risk Assessment

EPA Science Inventory

Integrated Environmental Modeling (IEM) organizes multidisciplinary knowledge that explains and predicts environmental-system response to stressors. A Quantitative Microbial Risk Assessment (QMRA) is an approach integrating a range of disparate data (fate/transport, exposure, an...

Existence of standard models of conic fibrations over non-algebraically-closed fields

DOE Office of Scientific and Technical Information (OSTI.GOV)

Avilov, A A

2014-12-31

We prove an analogue of Sarkisov's theorem on the existence of a standard model of a conic fibration over an algebraically closed field of characteristic different from two for three-dimensional conic fibrations over an arbitrary field of characteristic zero with an action of a finite group. Bibliography: 16 titles.

A Quantitative Geochemical Target for Modeling the Formation of the Earth and Moon

NASA Technical Reports Server (NTRS)

Boyce, Jeremy W.; Barnes, Jessica J.; McCubbin, Francis M.

2017-01-01

The past decade has been one of geochemical, isotopic, and computational advances that are bringing the laboratory measurements and computational modeling neighborhoods of the Earth-Moon community to ever closer proximity. We are now however in the position to become even better neighbors: modelers can generate testable hypthotheses for geochemists; and geochemists can provide quantitive targets for modelers. Here we present a robust example of the latter based on Cl isotope measurements of mare basalts.

Computational modeling approaches to quantitative structure-binding kinetics relationships in drug discovery.

PubMed

De Benedetti, Pier G; Fanelli, Francesca

2018-03-21

Simple comparative correlation analyses and quantitative structure-kinetics relationship (QSKR) models highlight the interplay of kinetic rates and binding affinity as an essential feature in drug design and discovery. The choice of the molecular series, and their structural variations, used in QSKR modeling is fundamental to understanding the mechanistic implications of ligand and/or drug-target binding and/or unbinding processes. Here, we discuss the implications of linear correlations between kinetic rates and binding affinity constants and the relevance of the computational approaches to QSKR modeling. Copyright © 2018 Elsevier Ltd. All rights reserved.

Conversion of IVA Human Computer Model to EVA Use and Evaluation and Comparison of the Result to Existing EVA Models

NASA Technical Reports Server (NTRS)

Hamilton, George S.; Williams, Jermaine C.

1998-01-01

This paper describes the methods, rationale, and comparative results of the conversion of an intravehicular (IVA) 3D human computer model (HCM) to extravehicular (EVA) use and compares the converted model to an existing model on another computer platform. The task of accurately modeling a spacesuited human figure in software is daunting: the suit restricts the human's joint range of motion (ROM) and does not have joints collocated with human joints. The modeling of the variety of materials needed to construct a space suit (e. g. metal bearings, rigid fiberglass torso, flexible cloth limbs and rubber coated gloves) attached to a human figure is currently out of reach of desktop computer hardware and software. Therefore a simplified approach was taken. The HCM's body parts were enlarged and the joint ROM was restricted to match the existing spacesuit model. This basic approach could be used to model other restrictive environments in industry such as chemical or fire protective clothing. In summary, the approach provides a moderate fidelity, usable tool which will run on current notebook computers.

The Quantitative-MFG Test: A Linear Mixed Effect Model to Detect Maternal-Offspring Gene Interactions.

PubMed

Clark, Michelle M; Blangero, John; Dyer, Thomas D; Sobel, Eric M; Sinsheimer, Janet S

2016-01-01

Maternal-offspring gene interactions, aka maternal-fetal genotype (MFG) incompatibilities, are neglected in complex diseases and quantitative trait studies. They are implicated in birth to adult onset diseases but there are limited ways to investigate their influence on quantitative traits. We present the quantitative-MFG (QMFG) test, a linear mixed model where maternal and offspring genotypes are fixed effects and residual correlations between family members are random effects. The QMFG handles families of any size, common or general scenarios of MFG incompatibility, and additional covariates. We develop likelihood ratio tests (LRTs) and rapid score tests and show they provide correct inference. In addition, the LRT's alternative model provides unbiased parameter estimates. We show that testing the association of SNPs by fitting a standard model, which only considers the offspring genotypes, has very low power or can lead to incorrect conclusions. We also show that offspring genetic effects are missed if the MFG modeling assumptions are too restrictive. With genome-wide association study data from the San Antonio Family Heart Study, we demonstrate that the QMFG score test is an effective and rapid screening tool. The QMFG test therefore has important potential to identify pathways of complex diseases for which the genetic etiology remains to be discovered. © 2015 John Wiley & Sons Ltd/University College London.

Three models intercomparison for Quantitative Precipitation Forecast over Calabria

NASA Astrophysics Data System (ADS)

Federico, S.; Avolio, E.; Bellecci, C.; Colacino, M.; Lavagnini, A.; Accadia, C.; Mariani, S.; Casaioli, M.

2004-11-01

In the framework of the National Project “Sviluppo di distretti industriali per le Osservazioni della Terra” (Development of Industrial Districts for Earth Observations) funded by MIUR (Ministero dell'Università e della Ricerca Scientifica --Italian Ministry of the University and Scientific Research) two operational mesoscale models were set-up for Calabria, the southernmost tip of the Italian peninsula. Models are RAMS (Regional Atmospheric Modeling System) and MM5 (Mesoscale Modeling 5) that are run every day at Crati scrl to produce weather forecast over Calabria (http://www.crati.it). This paper reports model intercomparison for Quantitative Precipitation Forecast evaluated for a 20 month period from 1th October 2000 to 31th May 2002. In addition to RAMS and MM5 outputs, QBOLAM rainfall fields are available for the period selected and included in the comparison. This model runs operationally at “Agenzia per la Protezione dell'Ambiente e per i Servizi Tecnici”. Forecasts are verified comparing models outputs with raingauge data recorded by the regional meteorological network, which has 75 raingauges. Large-scale forcing is the same for all models considered and differences are due to physical/numerical parameterizations and horizontal resolutions. QPFs show differences between models. Largest differences are for BIA compared to the other considered scores. Performances decrease with increasing forecast time for RAMS and MM5, whilst QBOLAM scores better for second day forecast.

Comparison of semi-quantitative and quantitative dynamic contrast-enhanced MRI evaluations of vertebral marrow perfusion in a rat osteoporosis model.

PubMed

Zhu, Jingqi; Xiong, Zuogang; Zhang, Jiulong; Qiu, Yuyou; Hua, Ting; Tang, Guangyu

2017-11-14

This study aims to investigate the technical feasibility of semi-quantitative and quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in the assessment of longitudinal changes of marrow perfusion in a rat osteoporosis model, using bone mineral density (BMD) measured by micro-computed tomography (micro-CT) and histopathology as the gold standards. Fifty rats were randomly assigned to the control group (n=25) and ovariectomy (OVX) group whose bilateral ovaries were excised (n=25). Semi-quantitative and quantitative DCE-MRI, micro-CT, and histopathological examinations were performed on lumbar vertebrae at baseline and 3, 6, 9, and 12 weeks after operation. The differences between the two groups in terms of semi-quantitative DCE-MRI parameter (maximum enhancement, E max ), quantitative DCE-MRI parameters (volume transfer constant, K trans ; interstitial volume, V e ; and efflux rate constant, K ep ), micro-CT parameter (BMD), and histopathological parameter (microvessel density, MVD) were compared at each of the time points using an independent-sample t test. The differences in these parameters between baseline and other time points in each group were assessed via Bonferroni's multiple comparison test. A Pearson correlation analysis was applied to assess the relationships between DCE-MRI, micro-CT, and histopathological parameters. In the OVX group, the E max values decreased significantly compared with those of the control group at weeks 6 and 9 (p=0.003 and 0.004, respectively). The K trans values decreased significantly compared with those of the control group from week 3 (p<0.05). However, the V e values decreased significantly only at week 9 (p=0.032), and no difference in the K ep was found between two groups. The BMD values of the OVX group decreased significantly compared with those of the control group from week 3 (p<0.05). Transmission electron microscopy showed tighter gaps between vascular endothelial cells with swollen mitochondria

Using integrated environmental modeling to automate a process-based Quantitative Microbial Risk Assessment

USDA-ARS?s Scientific Manuscript database

Integrated Environmental Modeling (IEM) organizes multidisciplinary knowledge that explains and predicts environmental-system response to stressors. A Quantitative Microbial Risk Assessment (QMRA) is an approach integrating a range of disparate data (fate/transport, exposure, and human health effect...

Analysis of genetic effects of nuclear-cytoplasmic interaction on quantitative traits: genetic model for diploid plants.

PubMed

Han, Lide; Yang, Jian; Zhu, Jun

2007-06-01

A genetic model was proposed for simultaneously analyzing genetic effects of nuclear, cytoplasm, and nuclear-cytoplasmic interaction (NCI) as well as their genotype by environment (GE) interaction for quantitative traits of diploid plants. In the model, the NCI effects were further partitioned into additive and dominance nuclear-cytoplasmic interaction components. Mixed linear model approaches were used for statistical analysis. On the basis of diallel cross designs, Monte Carlo simulations showed that the genetic model was robust for estimating variance components under several situations without specific effects. Random genetic effects were predicted by an adjusted unbiased prediction (AUP) method. Data on four quantitative traits (boll number, lint percentage, fiber length, and micronaire) in Upland cotton (Gossypium hirsutum L.) were analyzed as a worked example to show the effectiveness of the model.

Quantitative Primary Tumor Indocyanine Green Measurements Predict Osteosarcoma Metastatic Lung Burden in a Mouse Model.

PubMed

Fourman, Mitchell S; Mahjoub, Adel; Mandell, Jon B; Yu, Shibing; Tebbets, Jessica C; Crasto, Jared A; Alexander, Peter E; Weiss, Kurt R

2018-03-01

demonstrate a novel methodology for quantifying primary and metastatic OS in a previously validated, immunocompetent, orthotopic mouse model. Quantitative fluorescence of the primary tumor with ICG angiography is linearly related to metastatic burden, a relationship that does not exist with respect to clinical tumor size. This highlights the potential utility of ICG near-infrared fluorescence imaging as a valuable preclinical proof-of-concept modality. Future experimental work will use this model to evaluate the efficacy of novel OS small molecule inhibitors. Given the histologic localization of ICG to only the tumor bed, we envision the clinical use of ICG angiography as an intraoperative margin and tumor detector. Such a tool may be used as an alternative to intraoperative histology to confirm negative primary tumor margins or as a valuable tool for debulking surgeries in vulnerable anatomic locations. Because we have demonstrated the successful preservation of ICG in frozen tumor samples, future work will focus on blinded validation of this modality in observational human trials, comparing the ICG fluorescence of harvested tissue samples with fresh frozen pathology.

Quantitative stress measurement of elastic deformation using mechanoluminescent sensor: An intensity ratio model

NASA Astrophysics Data System (ADS)

Cai, Tao; Guo, Songtao; Li, Yongzeng; Peng, Di; Zhao, Xiaofeng; Liu, Yingzheng

2018-04-01

The mechanoluminescent (ML) sensor is a newly developed non-invasive technique for stress/strain measurement. However, its application has been mostly restricted to qualitative measurement due to the lack of a well-defined relationship between ML intensity and stress. To achieve accurate stress measurement, an intensity ratio model was proposed in this study to establish a quantitative relationship between the stress condition and its ML intensity in elastic deformation. To verify the proposed model, experiments were carried out on a ML measurement system using resin samples mixed with the sensor material SrAl2O4:Eu2+, Dy3+. The ML intensity ratio was found to be dependent on the applied stress and strain rate, and the relationship acquired from the experimental results agreed well with the proposed model. The current study provided a physical explanation for the relationship between ML intensity and its stress condition. The proposed model was applicable in various SrAl2O4:Eu2+, Dy3+-based ML measurement in elastic deformation, and could provide a useful reference for quantitative stress measurement using the ML sensor in general.

The Role of Introductory Geosciences in Students' Quantitative Literacy

NASA Astrophysics Data System (ADS)

Wenner, J. M.; Manduca, C.; Baer, E. M.

2006-12-01

into existing introductory geoscience courses. In addition, participants at the workshop (http://serc.carleton.edu/quantskills/workshop06/index.html) submitted and modified more than 20 activities and model courses (with syllabi) designed to use best practices for helping introductory geoscience students to become quantitatively literate. We present insights from the workshop and other sources for a framework that can aid in increasing quantitative literacy of students from a variety of backgrounds in the introductory geoscience classroom.

Common data model for natural language processing based on two existing standard information models: CDA+GrAF.

PubMed

Meystre, Stéphane M; Lee, Sanghoon; Jung, Chai Young; Chevrier, Raphaël D

2012-08-01

An increasing need for collaboration and resources sharing in the Natural Language Processing (NLP) research and development community motivates efforts to create and share a common data model and a common terminology for all information annotated and extracted from clinical text. We have combined two existing standards: the HL7 Clinical Document Architecture (CDA), and the ISO Graph Annotation Format (GrAF; in development), to develop such a data model entitled "CDA+GrAF". We experimented with several methods to combine these existing standards, and eventually selected a method wrapping separate CDA and GrAF parts in a common standoff annotation (i.e., separate from the annotated text) XML document. Two use cases, clinical document sections, and the 2010 i2b2/VA NLP Challenge (i.e., problems, tests, and treatments, with their assertions and relations), were used to create examples of such standoff annotation documents, and were successfully validated with the XML schemata provided with both standards. We developed a tool to automatically translate annotation documents from the 2010 i2b2/VA NLP Challenge format to GrAF, and automatically generated 50 annotation documents using this tool, all successfully validated. Finally, we adapted the XSL stylesheet provided with HL7 CDA to allow viewing annotation XML documents in a web browser, and plan to adapt existing tools for translating annotation documents between CDA+GrAF and the UIMA and GATE frameworks. This common data model may ease directly comparing NLP tools and applications, combining their output, transforming and "translating" annotations between different NLP applications, and eventually "plug-and-play" of different modules in NLP applications. Copyright © 2011 Elsevier Inc. All rights reserved.

A sampling framework for incorporating quantitative mass spectrometry data in protein interaction analysis.

PubMed

Tucker, George; Loh, Po-Ru; Berger, Bonnie

2013-10-04

Comprehensive protein-protein interaction (PPI) maps are a powerful resource for uncovering the molecular basis of genetic interactions and providing mechanistic insights. Over the past decade, high-throughput experimental techniques have been developed to generate PPI maps at proteome scale, first using yeast two-hybrid approaches and more recently via affinity purification combined with mass spectrometry (AP-MS). Unfortunately, data from both protocols are prone to both high false positive and false negative rates. To address these issues, many methods have been developed to post-process raw PPI data. However, with few exceptions, these methods only analyze binary experimental data (in which each potential interaction tested is deemed either observed or unobserved), neglecting quantitative information available from AP-MS such as spectral counts. We propose a novel method for incorporating quantitative information from AP-MS data into existing PPI inference methods that analyze binary interaction data. Our approach introduces a probabilistic framework that models the statistical noise inherent in observations of co-purifications. Using a sampling-based approach, we model the uncertainty of interactions with low spectral counts by generating an ensemble of possible alternative experimental outcomes. We then apply the existing method of choice to each alternative outcome and aggregate results over the ensemble. We validate our approach on three recent AP-MS data sets and demonstrate performance comparable to or better than state-of-the-art methods. Additionally, we provide an in-depth discussion comparing the theoretical bases of existing approaches and identify common aspects that may be key to their performance. Our sampling framework extends the existing body of work on PPI analysis using binary interaction data to apply to the richer quantitative data now commonly available through AP-MS assays. This framework is quite general, and many enhancements are likely

Applying quantitative adiposity feature analysis models to predict benefit of bevacizumab-based chemotherapy in ovarian cancer patients

NASA Astrophysics Data System (ADS)

Wang, Yunzhi; Qiu, Yuchen; Thai, Theresa; More, Kathleen; Ding, Kai; Liu, Hong; Zheng, Bin

2016-03-01

How to rationally identify epithelial ovarian cancer (EOC) patients who will benefit from bevacizumab or other antiangiogenic therapies is a critical issue in EOC treatments. The motivation of this study is to quantitatively measure adiposity features from CT images and investigate the feasibility of predicting potential benefit of EOC patients with or without receiving bevacizumab-based chemotherapy treatment using multivariate statistical models built based on quantitative adiposity image features. A dataset involving CT images from 59 advanced EOC patients were included. Among them, 32 patients received maintenance bevacizumab after primary chemotherapy and the remaining 27 patients did not. We developed a computer-aided detection (CAD) scheme to automatically segment subcutaneous fat areas (VFA) and visceral fat areas (SFA) and then extracted 7 adiposity-related quantitative features. Three multivariate data analysis models (linear regression, logistic regression and Cox proportional hazards regression) were performed respectively to investigate the potential association between the model-generated prediction results and the patients' progression-free survival (PFS) and overall survival (OS). The results show that using all 3 statistical models, a statistically significant association was detected between the model-generated results and both of the two clinical outcomes in the group of patients receiving maintenance bevacizumab (p<0.01), while there were no significant association for both PFS and OS in the group of patients without receiving maintenance bevacizumab. Therefore, this study demonstrated the feasibility of using quantitative adiposity-related CT image features based statistical prediction models to generate a new clinical marker and predict the clinical outcome of EOC patients receiving maintenance bevacizumab-based chemotherapy.

Quantitative structure-property relationship (QSPR) modeling of drug-loaded polymeric micelles via genetic function approximation.

PubMed

Wu, Wensheng; Zhang, Canyang; Lin, Wenjing; Chen, Quan; Guo, Xindong; Qian, Yu; Zhang, Lijuan

2015-01-01

Self-assembled nano-micelles of amphiphilic polymers represent a novel anticancer drug delivery system. However, their full clinical utilization remains challenging because the quantitative structure-property relationship (QSPR) between the polymer structure and the efficacy of micelles as a drug carrier is poorly understood. Here, we developed a series of QSPR models to account for the drug loading capacity of polymeric micelles using the genetic function approximation (GFA) algorithm. These models were further evaluated by internal and external validation and a Y-randomization test in terms of stability and generalization, yielding an optimization model that is applicable to an expanded materials regime. As confirmed by experimental data, the relationship between microstructure and drug loading capacity can be well-simulated, suggesting that our models are readily applicable to the quantitative evaluation of the drug-loading capacity of polymeric micelles. Our work may offer a pathway to the design of formulation experiments.

Survival Prediction in Pancreatic Ductal Adenocarcinoma by Quantitative Computed Tomography Image Analysis.

PubMed

Attiyeh, Marc A; Chakraborty, Jayasree; Doussot, Alexandre; Langdon-Embry, Liana; Mainarich, Shiana; Gönen, Mithat; Balachandran, Vinod P; D'Angelica, Michael I; DeMatteo, Ronald P; Jarnagin, William R; Kingham, T Peter; Allen, Peter J; Simpson, Amber L; Do, Richard K

2018-04-01

Pancreatic cancer is a highly lethal cancer with no established a priori markers of survival. Existing nomograms rely mainly on post-resection data and are of limited utility in directing surgical management. This study investigated the use of quantitative computed tomography (CT) features to preoperatively assess survival for pancreatic ductal adenocarcinoma (PDAC) patients. A prospectively maintained database identified consecutive chemotherapy-naive patients with CT angiography and resected PDAC between 2009 and 2012. Variation in CT enhancement patterns was extracted from the tumor region using texture analysis, a quantitative image analysis tool previously described in the literature. Two continuous survival models were constructed, with 70% of the data (training set) using Cox regression, first based only on preoperative serum cancer antigen (CA) 19-9 levels and image features (model A), and then on CA19-9, image features, and the Brennan score (composite pathology score; model B). The remaining 30% of the data (test set) were reserved for independent validation. A total of 161 patients were included in the analysis. Training and test sets contained 113 and 48 patients, respectively. Quantitative image features combined with CA19-9 achieved a c-index of 0.69 [integrated Brier score (IBS) 0.224] on the test data, while combining CA19-9, imaging, and the Brennan score achieved a c-index of 0.74 (IBS 0.200) on the test data. We present two continuous survival prediction models for resected PDAC patients. Quantitative analysis of CT texture features is associated with overall survival. Further work includes applying the model to an external dataset to increase the sample size for training and to determine its applicability.

A Quantitative Approach to Scar Analysis

PubMed Central

Khorasani, Hooman; Zheng, Zhong; Nguyen, Calvin; Zara, Janette; Zhang, Xinli; Wang, Joyce; Ting, Kang; Soo, Chia

2011-01-01

Analysis of collagen architecture is essential to wound healing research. However, to date no consistent methodologies exist for quantitatively assessing dermal collagen architecture in scars. In this study, we developed a standardized approach for quantitative analysis of scar collagen morphology by confocal microscopy using fractal dimension and lacunarity analysis. Full-thickness wounds were created on adult mice, closed by primary intention, and harvested at 14 days after wounding for morphometrics and standard Fourier transform-based scar analysis as well as fractal dimension and lacunarity analysis. In addition, transmission electron microscopy was used to evaluate collagen ultrastructure. We demonstrated that fractal dimension and lacunarity analysis were superior to Fourier transform analysis in discriminating scar versus unwounded tissue in a wild-type mouse model. To fully test the robustness of this scar analysis approach, a fibromodulin-null mouse model that heals with increased scar was also used. Fractal dimension and lacunarity analysis effectively discriminated unwounded fibromodulin-null versus wild-type skin as well as healing fibromodulin-null versus wild-type wounds, whereas Fourier transform analysis failed to do so. Furthermore, fractal dimension and lacunarity data also correlated well with transmission electron microscopy collagen ultrastructure analysis, adding to their validity. These results demonstrate that fractal dimension and lacunarity are more sensitive than Fourier transform analysis for quantification of scar morphology. PMID:21281794

Quantitative determination of Auramine O by terahertz spectroscopy with 2DCOS-PLSR model

NASA Astrophysics Data System (ADS)

Zhang, Huo; Li, Zhi; Chen, Tao; Qin, Binyi

2017-09-01

Residues of harmful dyes such as Auramine O (AO) in herb and food products threaten the health of people. So, fast and sensitive detection techniques of the residues are needed. As a powerful tool for substance detection, terahertz (THz) spectroscopy was used for the quantitative determination of AO by combining with an improved partial least-squares regression (PLSR) model in this paper. Absorbance of herbal samples with different concentrations was obtained by THz-TDS in the band between 0.2THz and 1.6THz. We applied two-dimensional correlation spectroscopy (2DCOS) to improve the PLSR model. This method highlighted the spectral differences of different concentrations, provided a clear criterion of the input interval selection, and improved the accuracy of detection result. The experimental result indicated that the combination of the THz spectroscopy and 2DCOS-PLSR is an excellent quantitative analysis method.

DOSIMETRY MODELING OF INHALED FORMALDEHYDE: BINNING NASAL FLUX PREDICTIONS FOR QUANTITATIVE RISK ASSESSMENT

EPA Science Inventory

Dosimetry Modeling of Inhaled Formaldehyde: Binning Nasal Flux Predictions for Quantitative Risk Assessment. Kimbell, J.S., Overton, J.H., Subramaniam, R.P., Schlosser, P.M., Morgan, K.T., Conolly, R.B., and Miller, F.J. (2001). Toxicol. Sci. 000, 000:000.

Interspecies e...

Quantitative Finance

NASA Astrophysics Data System (ADS)

James, Jessica

2017-01-01

Quantitative finance is a field that has risen to prominence over the last few decades. It encompasses the complex models and calculations that value financial contracts, particularly those which reference events in the future, and apply probabilities to these events. While adding greatly to the flexibility of the market available to corporations and investors, it has also been blamed for worsening the impact of financial crises. But what exactly does quantitative finance encompass, and where did these ideas and models originate? We show that the mathematics behind finance and behind games of chance have tracked each other closely over the centuries and that many well-known physicists and mathematicians have contributed to the field.

Thermodynamic Modeling of a Solid Oxide Fuel Cell to Couple with an Existing Gas Turbine Engine Model

NASA Technical Reports Server (NTRS)

Brinson, Thomas E.; Kopasakis, George

2004-01-01

The Controls and Dynamics Technology Branch at NASA Glenn Research Center are interested in combining a solid oxide fuel cell (SOFC) to operate in conjunction with a gas turbine engine. A detailed engine model currently exists in the Matlab/Simulink environment. The idea is to incorporate a SOFC model within the turbine engine simulation and observe the hybrid system's performance. The fuel cell will be heated to its appropriate operating condition by the engine s combustor. Once the fuel cell is operating at its steady-state temperature, the gas burner will back down slowly until the engine is fully operating on the hot gases exhausted from the SOFC. The SOFC code is based on a steady-state model developed by The U.S. Department of Energy (DOE). In its current form, the DOE SOFC model exists in Microsoft Excel and uses Visual Basics to create an I-V (current-voltage) profile. For the project's application, the main issue with this model is that the gas path flow and fuel flow temperatures are used as input parameters instead of outputs. The objective is to create a SOFC model based on the DOE model that inputs the fuel cells flow rates and outputs temperature of the flow streams; therefore, creating a temperature profile as a function of fuel flow rate. This will be done by applying the First Law of Thermodynamics for a flow system to the fuel cell. Validation of this model will be done in two procedures. First, for a given flow rate the exit stream temperature will be calculated and compared to DOE SOFC temperature as a point comparison. Next, an I-V curve and temperature curve will be generated where the I-V curve will be compared with the DOE SOFC I-V curve. Matching I-V curves will suggest validation of the temperature curve because voltage is a function of temperature. Once the temperature profile is created and validated, the model will then be placed into the turbine engine simulation for system analysis.

Using Integrated Environmental Modeling to Automate a Process-Based Quantitative Microbial Risk Assessment (presentation)

EPA Science Inventory

Integrated Environmental Modeling (IEM) organizes multidisciplinary knowledge that explains and predicts environmental-system response to stressors. A Quantitative Microbial Risk Assessment (QMRA) is an approach integrating a range of disparate data (fate/transport, exposure, and...

A new quantitative model of ecological compensation based on ecosystem capital in Zhejiang Province, China*

PubMed Central

Jin, Yan; Huang, Jing-feng; Peng, Dai-liang

2009-01-01

Ecological compensation is becoming one of key and multidiscipline issues in the field of resources and environmental management. Considering the change relation between gross domestic product (GDP) and ecological capital (EC) based on remote sensing estimation, we construct a new quantitative estimate model for ecological compensation, using county as study unit, and determine standard value so as to evaluate ecological compensation from 2001 to 2004 in Zhejiang Province, China. Spatial differences of the ecological compensation were significant among all the counties or districts. This model fills up the gap in the field of quantitative evaluation of regional ecological compensation and provides a feasible way to reconcile the conflicts among benefits in the economic, social, and ecological sectors. PMID:19353749

A new quantitative model of ecological compensation based on ecosystem capital in Zhejiang Province, China.

PubMed

Jin, Yan; Huang, Jing-feng; Peng, Dai-liang

2009-04-01

Ecological compensation is becoming one of key and multidiscipline issues in the field of resources and environmental management. Considering the change relation between gross domestic product (GDP) and ecological capital (EC) based on remote sensing estimation, we construct a new quantitative estimate model for ecological compensation, using county as study unit, and determine standard value so as to evaluate ecological compensation from 2001 to 2004 in Zhejiang Province, China. Spatial differences of the ecological compensation were significant among all the counties or districts. This model fills up the gap in the field of quantitative evaluation of regional ecological compensation and provides a feasible way to reconcile the conflicts among benefits in the economic, social, and ecological sectors.

Quantitative Structure-Property Relationship (QSPR) Modeling of Drug-Loaded Polymeric Micelles via Genetic Function Approximation

PubMed Central

Lin, Wenjing; Chen, Quan; Guo, Xindong; Qian, Yu; Zhang, Lijuan

2015-01-01

Self-assembled nano-micelles of amphiphilic polymers represent a novel anticancer drug delivery system. However, their full clinical utilization remains challenging because the quantitative structure-property relationship (QSPR) between the polymer structure and the efficacy of micelles as a drug carrier is poorly understood. Here, we developed a series of QSPR models to account for the drug loading capacity of polymeric micelles using the genetic function approximation (GFA) algorithm. These models were further evaluated by internal and external validation and a Y-randomization test in terms of stability and generalization, yielding an optimization model that is applicable to an expanded materials regime. As confirmed by experimental data, the relationship between microstructure and drug loading capacity can be well-simulated, suggesting that our models are readily applicable to the quantitative evaluation of the drug-loading capacity of polymeric micelles. Our work may offer a pathway to the design of formulation experiments. PMID:25780923

Mentoring for junior medical faculty: Existing models and suggestions for low-resource settings.

PubMed

Menon, Vikas; Muraleedharan, Aparna; Bhat, Ballambhattu Vishnu

2016-02-01

Globally, there is increasing recognition about the positive benefits and impact of mentoring on faculty retention rates, career satisfaction and scholarly output. However, emphasis on research and practice of mentoring is comparatively meagre in low and middle income countries. In this commentary, we critically examine two existing models of mentorship for medical faculty and offer few suggestions for an integrated hybrid model that can be adapted for use in low resource settings. Copyright © 2016 Elsevier B.V. All rights reserved.

Fusing Quantitative Requirements Analysis with Model-based Systems Engineering

NASA Technical Reports Server (NTRS)

Cornford, Steven L.; Feather, Martin S.; Heron, Vance A.; Jenkins, J. Steven

2006-01-01

A vision is presented for fusing quantitative requirements analysis with model-based systems engineering. This vision draws upon and combines emergent themes in the engineering milieu. "Requirements engineering" provides means to explicitly represent requirements (both functional and non-functional) as constraints and preferences on acceptable solutions, and emphasizes early-lifecycle review, analysis and verification of design and development plans. "Design by shopping" emphasizes revealing the space of options available from which to choose (without presuming that all selection criteria have previously been elicited), and provides means to make understandable the range of choices and their ramifications. "Model-based engineering" emphasizes the goal of utilizing a formal representation of all aspects of system design, from development through operations, and provides powerful tool suites that support the practical application of these principles. A first step prototype towards this vision is described, embodying the key capabilities. Illustrations, implications, further challenges and opportunities are outlined.

Quantitative model for the blood pressure-lowering interaction of valsartan and amlodipine.

PubMed

Heo, Young-A; Holford, Nick; Kim, Yukyung; Son, Mijeong; Park, Kyungsoo

2016-12-01

The objective of this study was to develop a population pharmacokinetic (PK) and pharmacodynamic (PD) model to quantitatively describe the antihypertensive effect of combined therapy with amlodipine and valsartan. PK modelling was used with data collected from 48 healthy volunteers receiving a single dose of combined formulation of 10 mg amlodipine and 160 mg valsartan. Systolic (SBP) and diastolic blood pressure (DBP) were recorded during combined administration. SBP and DBP data for each drug alone were gathered from the literature. PKPD models of each drug and for combined administration were built with NONMEM 7.3. A two-compartment model with zero order absorption best described the PK data of both drugs. Amlodipine and valsartan monotherapy effects on SBP and DBP were best described by an I max model with an effect compartment delay. Combined therapy was described using a proportional interaction term as follows: (D 1  + D 2 ) +ALPHA×(D 1 × D 2 ). D 1 and D 2 are the predicted drug effects of amlodipine and valsartan monotherapy respectively. ALPHA is the interaction term for combined therapy. Quantitative estimates of ALPHA were -0.171 (95% CI: -0.218, -0.143) for SBP and -0.0312 (95% CI: -0.07739, -0.00283) for DBP. These infra-additive interaction terms for both SBP and DBP were consistent with literature results for combined administration of drugs in these classes. PKPD models for SBP and DBP successfully described the time course of the antihypertensive effects of amlodipine and valsartan. An infra-additive interaction between amlodipine and valsartan when used in combined administration was confirmed and quantified. © 2016 The British Pharmacological Society.

Quantitative model for the blood pressure‐lowering interaction of valsartan and amlodipine

PubMed Central

Heo, Young‐A; Holford, Nick; Kim, Yukyung; Son, Mijeong

2016-01-01

Aims The objective of this study was to develop a population pharmacokinetic (PK) and pharmacodynamic (PD) model to quantitatively describe the antihypertensive effect of combined therapy with amlodipine and valsartan. Methods PK modelling was used with data collected from 48 healthy volunteers receiving a single dose of combined formulation of 10 mg amlodipine and 160 mg valsartan. Systolic (SBP) and diastolic blood pressure (DBP) were recorded during combined administration. SBP and DBP data for each drug alone were gathered from the literature. PKPD models of each drug and for combined administration were built with NONMEM 7.3. Results A two‐compartment model with zero order absorption best described the PK data of both drugs. Amlodipine and valsartan monotherapy effects on SBP and DBP were best described by an I max model with an effect compartment delay. Combined therapy was described using a proportional interaction term as follows: (D1 + D2) +ALPHA×(D1 × D2). D1 and D2 are the predicted drug effects of amlodipine and valsartan monotherapy respectively. ALPHA is the interaction term for combined therapy. Quantitative estimates of ALPHA were −0.171 (95% CI: −0.218, −0.143) for SBP and −0.0312 (95% CI: −0.07739, −0.00283) for DBP. These infra‐additive interaction terms for both SBP and DBP were consistent with literature results for combined administration of drugs in these classes. Conclusion PKPD models for SBP and DBP successfully described the time course of the antihypertensive effects of amlodipine and valsartan. An infra‐additive interaction between amlodipine and valsartan when used in combined administration was confirmed and quantified. PMID:27504853

Tannin structural elucidation and quantitative ³¹P NMR analysis. 1. Model compounds.

PubMed

Melone, Federica; Saladino, Raffaele; Lange, Heiko; Crestini, Claudia

2013-10-02

Tannins and flavonoids are secondary metabolites of plants that display a wide array of biological activities. This peculiarity is related to the inhibition of extracellular enzymes that occurs through the complexation of peptides by tannins. Not only the nature of these interactions, but more fundamentally also the structure of these heterogeneous polyphenolic molecules are not completely clear. This first paper describes the development of a new analytical method for the structural characterization of tannins on the basis of tannin model compounds employing an in situ labeling of all labile H groups (aliphatic OH, phenolic OH, and carboxylic acids) with a phosphorus reagent. The ³¹P NMR analysis of ³¹P-labeled samples allowed the unprecedented quantitative and qualitative structural characterization of hydrolyzable tannins, proanthocyanidins, and catechin tannin model compounds, forming the foundations for the quantitative structural elucidation of a variety of actual tannin samples described in part 2 of this series.

Synthetic cannabinoids: In silico prediction of the cannabinoid receptor 1 affinity by a quantitative structure-activity relationship model.

PubMed

Paulke, Alexander; Proschak, Ewgenij; Sommer, Kai; Achenbach, Janosch; Wunder, Cora; Toennes, Stefan W

2016-03-14

The number of new synthetic psychoactive compounds increase steadily. Among the group of these psychoactive compounds, the synthetic cannabinoids (SCBs) are most popular and serve as a substitute of herbal cannabis. More than 600 of these substances already exist. For some SCBs the in vitro cannabinoid receptor 1 (CB1) affinity is known, but for the majority it is unknown. A quantitative structure-activity relationship (QSAR) model was developed, which allows the determination of the SCBs affinity to CB1 (expressed as binding constant (Ki)) without reference substances. The chemically advance template search descriptor was used for vector representation of the compound structures. The similarity between two molecules was calculated using the Feature-Pair Distribution Similarity. The Ki values were calculated using the Inverse Distance Weighting method. The prediction model was validated using a cross validation procedure. The predicted Ki values of some new SCBs were in a range between 20 (considerably higher affinity to CB1 than THC) to 468 (considerably lower affinity to CB1 than THC). The present QSAR model can serve as a simple, fast and cheap tool to get a first hint of the biological activity of new synthetic cannabinoids or of other new psychoactive compounds. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

1, 2, 3, 4: infusing quantitative literacy into introductory biology.

PubMed

Speth, Elena Bray; Momsen, Jennifer L; Moyerbrailean, Gregory A; Ebert-May, Diane; Long, Tammy M; Wyse, Sara; Linton, Debra

2010-01-01

Biology of the twenty-first century is an increasingly quantitative science. Undergraduate biology education therefore needs to provide opportunities for students to develop fluency in the tools and language of quantitative disciplines. Quantitative literacy (QL) is important for future scientists as well as for citizens, who need to interpret numeric information and data-based claims regarding nearly every aspect of daily life. To address the need for QL in biology education, we incorporated quantitative concepts throughout a semester-long introductory biology course at a large research university. Early in the course, we assessed the quantitative skills that students bring to the introductory biology classroom and found that students had difficulties in performing simple calculations, representing data graphically, and articulating data-driven arguments. In response to students' learning needs, we infused the course with quantitative concepts aligned with the existing course content and learning objectives. The effectiveness of this approach is demonstrated by significant improvement in the quality of students' graphical representations of biological data. Infusing QL in introductory biology presents challenges. Our study, however, supports the conclusion that it is feasible in the context of an existing course, consistent with the goals of college biology education, and promotes students' development of important quantitative skills.

1, 2, 3, 4: Infusing Quantitative Literacy into Introductory Biology

PubMed Central

Momsen, Jennifer L.; Moyerbrailean, Gregory A.; Ebert-May, Diane; Long, Tammy M.; Wyse, Sara; Linton, Debra

2010-01-01

Biology of the twenty-first century is an increasingly quantitative science. Undergraduate biology education therefore needs to provide opportunities for students to develop fluency in the tools and language of quantitative disciplines. Quantitative literacy (QL) is important for future scientists as well as for citizens, who need to interpret numeric information and data-based claims regarding nearly every aspect of daily life. To address the need for QL in biology education, we incorporated quantitative concepts throughout a semester-long introductory biology course at a large research university. Early in the course, we assessed the quantitative skills that students bring to the introductory biology classroom and found that students had difficulties in performing simple calculations, representing data graphically, and articulating data-driven arguments. In response to students' learning needs, we infused the course with quantitative concepts aligned with the existing course content and learning objectives. The effectiveness of this approach is demonstrated by significant improvement in the quality of students' graphical representations of biological data. Infusing QL in introductory biology presents challenges. Our study, however, supports the conclusion that it is feasible in the context of an existing course, consistent with the goals of college biology education, and promotes students' development of important quantitative skills. PMID:20810965

A Second-Generation Device for Automated Training and Quantitative Behavior Analyses of Molecularly-Tractable Model Organisms

PubMed Central

Blackiston, Douglas; Shomrat, Tal; Nicolas, Cindy L.; Granata, Christopher; Levin, Michael

2010-01-01

A deep understanding of cognitive processes requires functional, quantitative analyses of the steps leading from genetics and the development of nervous system structure to behavior. Molecularly-tractable model systems such as Xenopus laevis and planaria offer an unprecedented opportunity to dissect the mechanisms determining the complex structure of the brain and CNS. A standardized platform that facilitated quantitative analysis of behavior would make a significant impact on evolutionary ethology, neuropharmacology, and cognitive science. While some animal tracking systems exist, the available systems do not allow automated training (feedback to individual subjects in real time, which is necessary for operant conditioning assays). The lack of standardization in the field, and the numerous technical challenges that face the development of a versatile system with the necessary capabilities, comprise a significant barrier keeping molecular developmental biology labs from integrating behavior analysis endpoints into their pharmacological and genetic perturbations. Here we report the development of a second-generation system that is a highly flexible, powerful machine vision and environmental control platform. In order to enable multidisciplinary studies aimed at understanding the roles of genes in brain function and behavior, and aid other laboratories that do not have the facilities to undergo complex engineering development, we describe the device and the problems that it overcomes. We also present sample data using frog tadpoles and flatworms to illustrate its use. Having solved significant engineering challenges in its construction, the resulting design is a relatively inexpensive instrument of wide relevance for several fields, and will accelerate interdisciplinary discovery in pharmacology, neurobiology, regenerative medicine, and cognitive science. PMID:21179424

Electromagnetic braking: A simple quantitative model

NASA Astrophysics Data System (ADS)

Levin, Yan; da Silveira, Fernando L.; Rizzato, Felipe B.

2006-09-01

A calculation is presented that quantitatively accounts for the terminal velocity of a cylindrical magnet falling through a long copper or aluminum pipe. The experiment and the theory are a dramatic illustration of Faraday's and Lenz's laws.

Quantitation of active pharmaceutical ingredients and excipients in powder blends using designed multivariate calibration models by near-infrared spectroscopy.

PubMed

Li, Weiyong; Worosila, Gregory D

2005-05-13

This research note demonstrates the simultaneous quantitation of a pharmaceutical active ingredient and three excipients in a simulated powder blend containing acetaminophen, Prosolv and Crospovidone. An experimental design approach was used in generating a 5-level (%, w/w) calibration sample set that included 125 samples. The samples were prepared by weighing suitable amount of powders into separate 20-mL scintillation vials and were mixed manually. Partial least squares (PLS) regression was used in calibration model development. The models generated accurate results for quantitation of Crospovidone (at 5%, w/w) and magnesium stearate (at 0.5%, w/w). Further testing of the models demonstrated that the 2-level models were as effective as the 5-level ones, which reduced the calibration sample number to 50. The models had a small bias for quantitation of acetaminophen (at 30%, w/w) and Prosolv (at 64.5%, w/w) in the blend. The implication of the bias is discussed.

Quantitative Model of Systemic Toxicity Using ToxCast and ToxRefDB (SOT)

EPA Science Inventory

EPA’s ToxCast program profiles the bioactivity of chemicals in a diverse set of ~700 high throughput screening (HTS) assays. In collaboration with L’Oreal, a quantitative model of systemic toxicity was developed using no effect levels (NEL) from ToxRefDB for 633 chemicals with HT...

3D Numerical Modeling of the Propagation of Hydraulic Fracture at Its Intersection with Natural (Pre-existing) Fracture

NASA Astrophysics Data System (ADS)

Dehghan, Ali Naghi; Goshtasbi, Kamran; Ahangari, Kaveh; Jin, Yan; Bahmani, Aram

2017-02-01

A variety of 3D numerical models were developed based on hydraulic fracture experiments to simulate the propagation of hydraulic fracture at its intersection with natural (pre-existing) fracture. Since the interaction between hydraulic and pre-existing fractures is a key condition that causes complex fracture patterns, the extended finite element method was employed in ABAQUS software to simulate the problem. The propagation of hydraulic fracture in a fractured medium was modeled in two horizontal differential stresses (Δ σ) of 5e6 and 10e6 Pa considering different strike and dip angles of pre-existing fracture. The rate of energy release was calculated in the directions of hydraulic and pre-existing fractures (G_{{frac}} /G_{{rock}}) at their intersection point to determine the fracture behavior. Opening and crossing were two dominant fracture behaviors during the hydraulic and pre-existing fracture interaction at low and high differential stress conditions, respectively. The results of numerical studies were compared with those of experimental models, showing a good agreement between the two to validate the accuracy of the models. Besides the horizontal differential stress, strike and dip angles of the natural (pre-existing) fracture, the key finding of this research was the significant effect of the energy release rate on the propagation behavior of the hydraulic fracture. This effect was more prominent under the influence of strike and dip angles, as well as differential stress. The obtained results can be used to predict and interpret the generation of complex hydraulic fracture patterns in field conditions.

Bovine meat versus pork in Toxoplasma gondii transmission in Italy: A quantitative risk assessment model.

PubMed

Belluco, Simone; Patuzzi, Ilaria; Ricci, Antonia

2018-03-23

Toxoplasma gondii is a widespread zoonotic parasite with a high seroprevalence in the human population and the ability to infect almost all warm blooded animals. Humans can acquire toxoplasmosis from different transmission routes and food plays a critical role. Within the food category, meat is of utmost importance, as it may contain bradyzoites inside tissue cysts, which can potentially cause infection after ingestion if parasites are not inactivated through freezing or cooking before consumption. In Italy, the most commonly consumed meat-producing animal species are bovines and pigs. However, T. gondii prevalence and consumption habits for meat of these animal species are very different. There is debate within the scientific community concerning which of these animal species is the main source of meat-derived human toxoplasmosis. The aim of this work was to build a quantitative risk assessment model to estimate the yearly probability of acquiring toxoplasmosis infection due to consumption of bovine meat and pork (excluding cured products) in Italy, taking into account the different eating habits. The model was fitted with data obtained from the literature regarding: bradyzoite concentrations, portion size, dose-response relation, prevalence of T. gondii in bovines and swine, meat consumption and meat preparation habits. Alternative handling scenarios were considered. The model estimated the risk per year of acquiring T. gondii infection in Italy from bovine and swine meat to be 0.034% and 0.019%, respectively. Results suggest that, due to existing eating habits, bovine meat can be a not negligible source of toxoplasmosis in Italy. Copyright © 2017. Published by Elsevier B.V.

In vivo quantitative bioluminescence tomography using heterogeneous and homogeneous mouse models.

PubMed

Liu, Junting; Wang, Yabin; Qu, Xiaochao; Li, Xiangsi; Ma, Xiaopeng; Han, Runqiang; Hu, Zhenhua; Chen, Xueli; Sun, Dongdong; Zhang, Rongqing; Chen, Duofang; Chen, Dan; Chen, Xiaoyuan; Liang, Jimin; Cao, Feng; Tian, Jie

2010-06-07

Bioluminescence tomography (BLT) is a new optical molecular imaging modality, which can monitor both physiological and pathological processes by using bioluminescent light-emitting probes in small living animal. Especially, this technology possesses great potential in drug development, early detection, and therapy monitoring in preclinical settings. In the present study, we developed a dual modality BLT prototype system with Micro-computed tomography (MicroCT) registration approach, and improved the quantitative reconstruction algorithm based on adaptive hp finite element method (hp-FEM). Detailed comparisons of source reconstruction between the heterogeneous and homogeneous mouse models were performed. The models include mice with implanted luminescence source and tumor-bearing mice with firefly luciferase report gene. Our data suggest that the reconstruction based on heterogeneous mouse model is more accurate in localization and quantification than the homogeneous mouse model with appropriate optical parameters and that BLT allows super-early tumor detection in vivo based on tomographic reconstruction of heterogeneous mouse model signal.

Flow assignment model for quantitative analysis of diverting bulk freight from road to railway

PubMed Central

Liu, Chang; Wang, Jiaxi; Xiao, Jie; Liu, Siqi; Wu, Jianping; Li, Jian

2017-01-01

Since railway transport possesses the advantage of high volume and low carbon emissions, diverting some freight from road to railway will help reduce the negative environmental impacts associated with transport. This paper develops a flow assignment model for quantitative analysis of diverting truck freight to railway. First, a general network which considers road transportation, railway transportation, handling and transferring is established according to all the steps in the whole transportation process. Then general functions which embody the factors which the shippers will pay attention to when choosing mode and path are formulated. The general functions contain the congestion cost on road, the capacity constraints of railways and freight stations. Based on the general network and general cost function, a user equilibrium flow assignment model is developed to simulate the flow distribution on the general network under the condition that all shippers choose transportation mode and path independently. Since the model is nonlinear and challenging, we adopt a method that uses tangent lines to constitute envelope curve to linearize it. Finally, a numerical example is presented to test the model and show the method of making quantitative analysis of bulk freight modal shift between road and railway. PMID:28771536

A practical model for pressure probe system response estimation (with review of existing models)

NASA Astrophysics Data System (ADS)

Hall, B. F.; Povey, T.

2018-04-01

The accurate estimation of the unsteady response (bandwidth) of pneumatic pressure probe systems (probe, line and transducer volume) is a common practical problem encountered in the design of aerodynamic experiments. Understanding the bandwidth of the probe system is necessary to capture unsteady flow features accurately. Where traversing probes are used, the desired traverse speed and spatial gradients in the flow dictate the minimum probe system bandwidth required to resolve the flow. Existing approaches for bandwidth estimation are either complex or inaccurate in implementation, so probes are often designed based on experience. Where probe system bandwidth is characterized, it is often done experimentally, requiring careful experimental set-up and analysis. There is a need for a relatively simple but accurate model for estimation of probe system bandwidth. A new model is presented for the accurate estimation of pressure probe bandwidth for simple probes commonly used in wind tunnel environments; experimental validation is provided. An additional, simple graphical method for air is included for convenience.

Quantitative Hydraulic Models Of Early Land Plants Provide Insight Into Middle Paleozoic Terrestrial Paleoenvironmental Conditions

NASA Astrophysics Data System (ADS)

Wilson, J. P.; Fischer, W. W.

2010-12-01

Fossil plants provide useful proxies of Earth’s climate because plants are closely connected, through physiology and morphology, to the environments in which they lived. Recent advances in quantitative hydraulic models of plant water transport provide new insight into the history of climate by allowing fossils to speak directly to environmental conditions based on preserved internal anatomy. We report results of a quantitative hydraulic model applied to one of the earliest terrestrial plants preserved in three dimensions, the ~396 million-year-old vascular plant Asteroxylon mackei. This model combines equations describing the rate of fluid flow through plant tissues with detailed observations of plant anatomy; this allows quantitative estimates of two critical aspects of plant function. First and foremost, results from these models quantify the supply of water to evaporative surfaces; second, results describe the ability of plant vascular systems to resist tensile damage from extreme environmental events, such as drought or frost. This approach permits quantitative comparisons of functional aspects of Asteroxylon with other extinct and extant plants, informs the quality of plant-based environmental proxies, and provides concrete data that can be input into climate models. Results indicate that despite their small size, water transport cells in Asteroxylon could supply a large volume of water to the plant's leaves--even greater than cells from some later-evolved seed plants. The smallest Asteroxylon tracheids have conductivities exceeding 0.015 m^2 / MPa * s, whereas Paleozoic conifer tracheids do not reach this threshold until they are three times wider. However, this increase in conductivity came at the cost of little to no adaptations for transport safety, placing the plant’s vegetative organs in jeopardy during drought events. Analysis of the thickness-to-span ratio of Asteroxylon’s tracheids suggests that environmental conditions of reduced relative

AUTOMATED ANALYSIS OF QUANTITATIVE IMAGE DATA USING ISOMORPHIC FUNCTIONAL MIXED MODELS, WITH APPLICATION TO PROTEOMICS DATA.

PubMed

Morris, Jeffrey S; Baladandayuthapani, Veerabhadran; Herrick, Richard C; Sanna, Pietro; Gutstein, Howard

2011-01-01

Image data are increasingly encountered and are of growing importance in many areas of science. Much of these data are quantitative image data, which are characterized by intensities that represent some measurement of interest in the scanned images. The data typically consist of multiple images on the same domain and the goal of the research is to combine the quantitative information across images to make inference about populations or interventions. In this paper, we present a unified analysis framework for the analysis of quantitative image data using a Bayesian functional mixed model approach. This framework is flexible enough to handle complex, irregular images with many local features, and can model the simultaneous effects of multiple factors on the image intensities and account for the correlation between images induced by the design. We introduce a general isomorphic modeling approach to fitting the functional mixed model, of which the wavelet-based functional mixed model is one special case. With suitable modeling choices, this approach leads to efficient calculations and can result in flexible modeling and adaptive smoothing of the salient features in the data. The proposed method has the following advantages: it can be run automatically, it produces inferential plots indicating which regions of the image are associated with each factor, it simultaneously considers the practical and statistical significance of findings, and it controls the false discovery rate. Although the method we present is general and can be applied to quantitative image data from any application, in this paper we focus on image-based proteomic data. We apply our method to an animal study investigating the effects of opiate addiction on the brain proteome. Our image-based functional mixed model approach finds results that are missed with conventional spot-based analysis approaches. In particular, we find that the significant regions of the image identified by the proposed method

IWGT report on quantitative approaches to genotoxicity risk ...

EPA Pesticide Factsheets

This is the second of two reports from the International Workshops on Genotoxicity Testing (IWGT) Working Group on Quantitative Approaches to Genetic Toxicology Risk Assessment (the QWG). The first report summarized the discussions and recommendations of the QWG related to the need for quantitative dose–response analysis of genetic toxicology data, the existence and appropriate evaluation of threshold responses, and methods to analyze exposure-response relationships and derive points of departure (PoDs) from which acceptable exposure levels could be determined. This report summarizes the QWG discussions and recommendations regarding appropriate approaches to evaluate exposure-related risks of genotoxic damage, including extrapolation below identified PoDs and across test systems and species. Recommendations include the selection of appropriate genetic endpoints and target tissues, uncertainty factors and extrapolation methods to be considered, the importance and use of information on mode of action, toxicokinetics, metabolism, and exposure biomarkers when using quantitative exposure-response data to determine acceptable exposure levels in human populations or to assess the risk associated with known or anticipated exposures. The empirical relationship between genetic damage (mutation and chromosomal aberration) and cancer in animal models was also examined. It was concluded that there is a general correlation between cancer induction and mutagenic and/or clast

Quantitative risk stratification in Markov chains with limiting conditional distributions.

PubMed

Chan, David C; Pollett, Philip K; Weinstein, Milton C

2009-01-01

Many clinical decisions require patient risk stratification. The authors introduce the concept of limiting conditional distributions, which describe the equilibrium proportion of surviving patients occupying each disease state in a Markov chain with death. Such distributions can quantitatively describe risk stratification. The authors first establish conditions for the existence of a positive limiting conditional distribution in a general Markov chain and describe a framework for risk stratification using the limiting conditional distribution. They then apply their framework to a clinical example of a treatment indicated for high-risk patients, first to infer the risk of patients selected for treatment in clinical trials and then to predict the outcomes of expanding treatment to other populations of risk. For the general chain, a positive limiting conditional distribution exists only if patients in the earliest state have the lowest combined risk of progression or death. The authors show that in their general framework, outcomes and population risk are interchangeable. For the clinical example, they estimate that previous clinical trials have selected the upper quintile of patient risk for this treatment, but they also show that expanded treatment would weakly dominate this degree of targeted treatment, and universal treatment may be cost-effective. Limiting conditional distributions exist in most Markov models of progressive diseases and are well suited to represent risk stratification quantitatively. This framework can characterize patient risk in clinical trials and predict outcomes for other populations of risk.

Quantifying Zika: Advancing the Epidemiology of Zika With Quantitative Models.

PubMed

Keegan, Lindsay T; Lessler, Justin; Johansson, Michael A

2017-12-16

When Zika virus (ZIKV) emerged in the Americas, little was known about its biology, pathogenesis, and transmission potential, and the scope of the epidemic was largely hidden, owing to generally mild infections and no established surveillance systems. Surges in congenital defects and Guillain-Barré syndrome alerted the world to the danger of ZIKV. In the context of limited data, quantitative models were critical in reducing uncertainties and guiding the global ZIKV response. Here, we review some of the models used to assess the risk of ZIKV-associated severe outcomes, the potential speed and size of ZIKV epidemics, and the geographic distribution of ZIKV risk. These models provide important insights and highlight significant unresolved questions related to ZIKV and other emerging pathogens. Published by Oxford University Press for the Infectious Diseases Society of America 2017. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Inference of quantitative models of bacterial promoters from time-series reporter gene data.

PubMed

Stefan, Diana; Pinel, Corinne; Pinhal, Stéphane; Cinquemani, Eugenio; Geiselmann, Johannes; de Jong, Hidde

2015-01-01

The inference of regulatory interactions and quantitative models of gene regulation from time-series transcriptomics data has been extensively studied and applied to a range of problems in drug discovery, cancer research, and biotechnology. The application of existing methods is commonly based on implicit assumptions on the biological processes under study. First, the measurements of mRNA abundance obtained in transcriptomics experiments are taken to be representative of protein concentrations. Second, the observed changes in gene expression are assumed to be solely due to transcription factors and other specific regulators, while changes in the activity of the gene expression machinery and other global physiological effects are neglected. While convenient in practice, these assumptions are often not valid and bias the reverse engineering process. Here we systematically investigate, using a combination of models and experiments, the importance of this bias and possible corrections. We measure in real time and in vivo the activity of genes involved in the FliA-FlgM module of the E. coli motility network. From these data, we estimate protein concentrations and global physiological effects by means of kinetic models of gene expression. Our results indicate that correcting for the bias of commonly-made assumptions improves the quality of the models inferred from the data. Moreover, we show by simulation that these improvements are expected to be even stronger for systems in which protein concentrations have longer half-lives and the activity of the gene expression machinery varies more strongly across conditions than in the FliA-FlgM module. The approach proposed in this study is broadly applicable when using time-series transcriptome data to learn about the structure and dynamics of regulatory networks. In the case of the FliA-FlgM module, our results demonstrate the importance of global physiological effects and the active regulation of FliA and FlgM half-lives for

A quantitative validated model reveals two phases of transcriptional regulation for the gap gene giant in Drosophila.

PubMed

Hoermann, Astrid; Cicin-Sain, Damjan; Jaeger, Johannes

2016-03-15

Understanding eukaryotic transcriptional regulation and its role in development and pattern formation is one of the big challenges in biology today. Most attempts at tackling this problem either focus on the molecular details of transcription factor binding, or aim at genome-wide prediction of expression patterns from sequence through bioinformatics and mathematical modelling. Here we bridge the gap between these two complementary approaches by providing an integrative model of cis-regulatory elements governing the expression of the gap gene giant (gt) in the blastoderm embryo of Drosophila melanogaster. We use a reverse-engineering method, where mathematical models are fit to quantitative spatio-temporal reporter gene expression data to infer the regulatory mechanisms underlying gt expression in its anterior and posterior domains. These models are validated through prediction of gene expression in mutant backgrounds. A detailed analysis of our data and models reveals that gt is regulated by domain-specific CREs at early stages, while a late element drives expression in both the anterior and the posterior domains. Initial gt expression depends exclusively on inputs from maternal factors. Later, gap gene cross-repression and gt auto-activation become increasingly important. We show that auto-regulation creates a positive feedback, which mediates the transition from early to late stages of regulation. We confirm the existence and role of gt auto-activation through targeted mutagenesis of Gt transcription factor binding sites. In summary, our analysis provides a comprehensive picture of spatio-temporal gene regulation by different interacting enhancer elements for an important developmental regulator. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

On the Existence and Uniqueness of JML Estimates for the Partial Credit Model

ERIC Educational Resources Information Center

Bertoli-Barsotti, Lucio

2005-01-01

A necessary and sufficient condition is given in this paper for the existence and uniqueness of the maximum likelihood (the so-called joint maximum likelihood) estimate of the parameters of the Partial Credit Model. This condition is stated in terms of a structural property of the pattern of the data matrix that can be easily verified on the basis…

Appreciating the difference between design-based and model-based sampling strategies in quantitative morphology of the nervous system.

PubMed

Geuna, S

2000-11-20

Quantitative morphology of the nervous system has undergone great developments over recent years, and several new technical procedures have been devised and applied successfully to neuromorphological research. However, a lively debate has arisen on some issues, and a great deal of confusion appears to exist that is definitely responsible for the slow spread of the new techniques among scientists. One such element of confusion is related to uncertainty about the meaning, implications, and advantages of the design-based sampling strategy that characterize the new techniques. In this article, to help remove this uncertainty, morphoquantitative methods are described and contrasted on the basis of the inferential paradigm of the sampling strategy: design-based vs model-based. Moreover, some recommendations are made to help scientists judge the appropriateness of a method used for a given study in relation to its specific goals. Finally, the use of the term stereology to label, more or less expressly, only some methods is critically discussed. Copyright 2000 Wiley-Liss, Inc.

Quantitative comparison between crowd models for evacuation planning and evaluation

NASA Astrophysics Data System (ADS)

Viswanathan, Vaisagh; Lee, Chong Eu; Lees, Michael Harold; Cheong, Siew Ann; Sloot, Peter M. A.

2014-02-01

Crowd simulation is rapidly becoming a standard tool for evacuation planning and evaluation. However, the many crowd models in the literature are structurally different, and few have been rigorously calibrated against real-world egress data, especially in emergency situations. In this paper we describe a procedure to quantitatively compare different crowd models or between models and real-world data. We simulated three models: (1) the lattice gas model, (2) the social force model, and (3) the RVO2 model, and obtained the distributions of six observables: (1) evacuation time, (2) zoned evacuation time, (3) passage density, (4) total distance traveled, (5) inconvenience, and (6) flow rate. We then used the DISTATIS procedure to compute the compromise matrix of statistical distances between the three models. Projecting the three models onto the first two principal components of the compromise matrix, we find the lattice gas and RVO2 models are similar in terms of the evacuation time, passage density, and flow rates, whereas the social force and RVO2 models are similar in terms of the total distance traveled. Most importantly, we find that the zoned evacuation times of the three models to be very different from each other. Thus we propose to use this variable, if it can be measured, as the key test between different models, and also between models and the real world. Finally, we compared the model flow rates against the flow rate of an emergency evacuation during the May 2008 Sichuan earthquake, and found the social force model agrees best with this real data.

Existence and characterization of optimal control in mathematics model of diabetics population

NASA Astrophysics Data System (ADS)

Permatasari, A. H.; Tjahjana, R. H.; Udjiani, T.

2018-03-01

Diabetes is a chronic disease with a huge burden affecting individuals and the whole society. In this paper, we constructed the optimal control mathematical model by applying a strategy to control the development of diabetic population. The constructed mathematical model considers the dynamics of disabled people due to diabetes. Moreover, an optimal control approach is proposed in order to reduce the burden of pre-diabetes. Implementation of control is done by preventing the pre-diabetes develop into diabetics with and without complications. The existence of optimal control and characterization of optimal control is discussed in this paper. Optimal control is characterized by applying the Pontryagin minimum principle. The results indicate that there is an optimal control in optimization problem in mathematics model of diabetic population. The effect of the optimal control variable (prevention) is strongly affected by the number of healthy people.

Quantitative gene-gene and gene-environment mapping for leaf shape variation using tree-based models.

PubMed

Fu, Guifang; Dai, Xiaotian; Symanzik, Jürgen; Bushman, Shaun

2017-01-01

Leaf shape traits have long been a focus of many disciplines, but the complex genetic and environmental interactive mechanisms regulating leaf shape variation have not yet been investigated in detail. The question of the respective roles of genes and environment and how they interact to modulate leaf shape is a thorny evolutionary problem, and sophisticated methodology is needed to address it. In this study, we investigated a framework-level approach that inputs shape image photographs and genetic and environmental data, and then outputs the relative importance ranks of all variables after integrating shape feature extraction, dimension reduction, and tree-based statistical models. The power of the proposed framework was confirmed by simulation and a Populus szechuanica var. tibetica data set. This new methodology resulted in the detection of novel shape characteristics, and also confirmed some previous findings. The quantitative modeling of a combination of polygenetic, plastic, epistatic, and gene-environment interactive effects, as investigated in this study, will improve the discernment of quantitative leaf shape characteristics, and the methods are ready to be applied to other leaf morphology data sets. Unlike the majority of approaches in the quantitative leaf shape literature, this framework-level approach is data-driven, without assuming any pre-known shape attributes, landmarks, or model structures. © 2016 The Authors. New Phytologist © 2016 New Phytologist Trust.

Quantitative rubber sheet models of gravitation wells using Spandex

NASA Astrophysics Data System (ADS)

White, Gary

2008-04-01

Long a staple of introductory treatments of general relativity, the rubber sheet model exhibits Wheeler's concise summary---``Matter tells space-time how to curve and space-time tells matter how to move''---very nicely. But what of the quantitative aspects of the rubber sheet model: how far can the analogy be pushed? We show^1 that when a mass M is suspended from the center of an otherwise unstretched elastic sheet affixed to a circular boundary it exhibits a distortion far from the center given by h = A*(M*r^2)^1/3 . Here, as might be expected, h and r are the vertical and axial distances from the center, but this result is not the expected logarithmic form of 2-D solutions to LaPlace's equation (the stretched drumhead). This surprise has a natural explanation and is confirmed experimentally with Spandex as the medium, and its consequences for general rubber sheet models are pursued. ^1``The shape of `the Spandex' and orbits upon its surface'', American Journal of Physics, 70, 48-52 (2002), G. D. White and M. Walker. See also the comment by Don S. Lemons and T. C. Lipscombe, also in AJP, 70, 1056-1058 (2002).

Quantitative Modelling of Trace Elements in Hard Coal.

PubMed

Smoliński, Adam; Howaniec, Natalia

2016-01-01

The significance of coal in the world economy remains unquestionable for decades. It is also expected to be the dominant fossil fuel in the foreseeable future. The increased awareness of sustainable development reflected in the relevant regulations implies, however, the need for the development and implementation of clean coal technologies on the one hand, and adequate analytical tools on the other. The paper presents the application of the quantitative Partial Least Squares method in modeling the concentrations of trace elements (As, Ba, Cd, Co, Cr, Cu, Mn, Ni, Pb, Rb, Sr, V and Zn) in hard coal based on the physical and chemical parameters of coal, and coal ash components. The study was focused on trace elements potentially hazardous to the environment when emitted from coal processing systems. The studied data included 24 parameters determined for 132 coal samples provided by 17 coal mines of the Upper Silesian Coal Basin, Poland. Since the data set contained outliers, the construction of robust Partial Least Squares models for contaminated data set and the correct identification of outlying objects based on the robust scales were required. These enabled the development of the correct Partial Least Squares models, characterized by good fit and prediction abilities. The root mean square error was below 10% for all except for one the final Partial Least Squares models constructed, and the prediction error (root mean square error of cross-validation) exceeded 10% only for three models constructed. The study is of both cognitive and applicative importance. It presents the unique application of the chemometric methods of data exploration in modeling the content of trace elements in coal. In this way it contributes to the development of useful tools of coal quality assessment.

Quantitative Modelling of Trace Elements in Hard Coal

PubMed Central

Smoliński, Adam; Howaniec, Natalia

2016-01-01

The significance of coal in the world economy remains unquestionable for decades. It is also expected to be the dominant fossil fuel in the foreseeable future. The increased awareness of sustainable development reflected in the relevant regulations implies, however, the need for the development and implementation of clean coal technologies on the one hand, and adequate analytical tools on the other. The paper presents the application of the quantitative Partial Least Squares method in modeling the concentrations of trace elements (As, Ba, Cd, Co, Cr, Cu, Mn, Ni, Pb, Rb, Sr, V and Zn) in hard coal based on the physical and chemical parameters of coal, and coal ash components. The study was focused on trace elements potentially hazardous to the environment when emitted from coal processing systems. The studied data included 24 parameters determined for 132 coal samples provided by 17 coal mines of the Upper Silesian Coal Basin, Poland. Since the data set contained outliers, the construction of robust Partial Least Squares models for contaminated data set and the correct identification of outlying objects based on the robust scales were required. These enabled the development of the correct Partial Least Squares models, characterized by good fit and prediction abilities. The root mean square error was below 10% for all except for one the final Partial Least Squares models constructed, and the prediction error (root mean square error of cross–validation) exceeded 10% only for three models constructed. The study is of both cognitive and applicative importance. It presents the unique application of the chemometric methods of data exploration in modeling the content of trace elements in coal. In this way it contributes to the development of useful tools of coal quality assessment. PMID:27438794

Monitoring with Trackers Based on Semi-Quantitative Models

NASA Technical Reports Server (NTRS)

Kuipers, Benjamin

1997-01-01

In three years of NASA-sponsored research preceding this project, we successfully developed a technology for: (1) building qualitative and semi-quantitative models from libraries of model-fragments, (2) simulating these models to predict future behaviors with the guarantee that all possible behaviors are covered, (3) assimilating observations into behaviors, shrinking uncertainty so that incorrect models are eventually refuted and correct models make stronger predictions for the future. In our object-oriented framework, a tracker is an object which embodies the hypothesis that the available observation stream is consistent with a particular behavior of a particular model. The tracker maintains its own status (consistent, superceded, or refuted), and answers questions about its explanation for past observations and its predictions for the future. In the MIMIC approach to monitoring of continuous systems, a number of trackers are active in parallel, representing alternate hypotheses about the behavior of a system. This approach is motivated by the need to avoid 'system accidents' [Perrow, 1985] due to operator fixation on a single hypothesis, as for example at Three Mile Island. As we began to address these issues, we focused on three major research directions that we planned to pursue over a three-year project: (1) tractable qualitative simulation, (2) semiquantitative inference, and (3) tracking set management. Unfortunately, funding limitations made it impossible to continue past year one. Nonetheless, we made major progress in the first two of these areas. Progress in the third area as slower because the graduate student working on that aspect of the project decided to leave school and take a job in industry. I enclosed a set of abstract of selected papers on the work describe below. Several papers that draw on the research supported during this period appeared in print after the grant period ended.

The importance of topography-controlled sub-grid process heterogeneity and semi-quantitative prior constraints in distributed hydrological models

NASA Astrophysics Data System (ADS)

Nijzink, Remko C.; Samaniego, Luis; Mai, Juliane; Kumar, Rohini; Thober, Stephan; Zink, Matthias; Schäfer, David; Savenije, Hubert H. G.; Hrachowitz, Markus

2016-03-01

Heterogeneity of landscape features like terrain, soil, and vegetation properties affects the partitioning of water and energy. However, it remains unclear to what extent an explicit representation of this heterogeneity at the sub-grid scale of distributed hydrological models can improve the hydrological consistency and the robustness of such models. In this study, hydrological process complexity arising from sub-grid topography heterogeneity was incorporated into the distributed mesoscale Hydrologic Model (mHM). Seven study catchments across Europe were used to test whether (1) the incorporation of additional sub-grid variability on the basis of landscape-derived response units improves model internal dynamics, (2) the application of semi-quantitative, expert-knowledge-based model constraints reduces model uncertainty, and whether (3) the combined use of sub-grid response units and model constraints improves the spatial transferability of the model. Unconstrained and constrained versions of both the original mHM and mHMtopo, which allows for topography-based sub-grid heterogeneity, were calibrated for each catchment individually following a multi-objective calibration strategy. In addition, four of the study catchments were simultaneously calibrated and their feasible parameter sets were transferred to the remaining three receiver catchments. In a post-calibration evaluation procedure the probabilities of model and transferability improvement, when accounting for sub-grid variability and/or applying expert-knowledge-based model constraints, were assessed on the basis of a set of hydrological signatures. In terms of the Euclidian distance to the optimal model, used as an overall measure of model performance with respect to the individual signatures, the model improvement achieved by introducing sub-grid heterogeneity to mHM in mHMtopo was on average 13 %. The addition of semi-quantitative constraints to mHM and mHMtopo resulted in improvements of 13 and 19

Experimentally validated quantitative linear model for the device physics of elastomeric microfluidic valves

NASA Astrophysics Data System (ADS)

Kartalov, Emil P.; Scherer, Axel; Quake, Stephen R.; Taylor, Clive R.; Anderson, W. French

2007-03-01

A systematic experimental study and theoretical modeling of the device physics of polydimethylsiloxane "pushdown" microfluidic valves are presented. The phase space is charted by 1587 dimension combinations and encompasses 45-295μm lateral dimensions, 16-39μm membrane thickness, and 1-28psi closing pressure. Three linear models are developed and tested against the empirical data, and then combined into a fourth-power-polynomial superposition. The experimentally validated final model offers a useful quantitative prediction for a valve's properties as a function of its dimensions. Typical valves (80-150μm width) are shown to behave like thin springs.

Quantitative structure-activity relationship (QSAR) for insecticides: development of predictive in vivo insecticide activity models.

PubMed

Naik, P K; Singh, T; Singh, H

2009-07-01

Quantitative structure-activity relationship (QSAR) analyses were performed independently on data sets belonging to two groups of insecticides, namely the organophosphates and carbamates. Several types of descriptors including topological, spatial, thermodynamic, information content, lead likeness and E-state indices were used to derive quantitative relationships between insecticide activities and structural properties of chemicals. A systematic search approach based on missing value, zero value, simple correlation and multi-collinearity tests as well as the use of a genetic algorithm allowed the optimal selection of the descriptors used to generate the models. The QSAR models developed for both organophosphate and carbamate groups revealed good predictability with r(2) values of 0.949 and 0.838 as well as [image omitted] values of 0.890 and 0.765, respectively. In addition, a linear correlation was observed between the predicted and experimental LD(50) values for the test set data with r(2) of 0.871 and 0.788 for both the organophosphate and carbamate groups, indicating that the prediction accuracy of the QSAR models was acceptable. The models were also tested successfully from external validation criteria. QSAR models developed in this study should help further design of novel potent insecticides.

Efficient Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models.

PubMed

Allen, R J; Rieger, T R; Musante, C J

2016-03-01

Quantitative systems pharmacology models mechanistically describe a biological system and the effect of drug treatment on system behavior. Because these models rarely are identifiable from the available data, the uncertainty in physiological parameters may be sampled to create alternative parameterizations of the model, sometimes termed "virtual patients." In order to reproduce the statistics of a clinical population, virtual patients are often weighted to form a virtual population that reflects the baseline characteristics of the clinical cohort. Here we introduce a novel technique to efficiently generate virtual patients and, from this ensemble, demonstrate how to select a virtual population that matches the observed data without the need for weighting. This approach improves confidence in model predictions by mitigating the risk that spurious virtual patients become overrepresented in virtual populations.

Efficient Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models

PubMed Central

Rieger, TR; Musante, CJ

2016-01-01

Quantitative systems pharmacology models mechanistically describe a biological system and the effect of drug treatment on system behavior. Because these models rarely are identifiable from the available data, the uncertainty in physiological parameters may be sampled to create alternative parameterizations of the model, sometimes termed “virtual patients.” In order to reproduce the statistics of a clinical population, virtual patients are often weighted to form a virtual population that reflects the baseline characteristics of the clinical cohort. Here we introduce a novel technique to efficiently generate virtual patients and, from this ensemble, demonstrate how to select a virtual population that matches the observed data without the need for weighting. This approach improves confidence in model predictions by mitigating the risk that spurious virtual patients become overrepresented in virtual populations. PMID:27069777

SOME USES OF MODELS OF QUANTITATIVE GENETIC SELECTION IN SOCIAL SCIENCE.

PubMed

Weight, Michael D; Harpending, Henry

2017-01-01

The theory of selection of quantitative traits is widely used in evolutionary biology, agriculture and other related fields. The fundamental model known as the breeder's equation is simple, robust over short time scales, and it is often possible to estimate plausible parameters. In this paper it is suggested that the results of this model provide useful yardsticks for the description of social traits and the evaluation of transmission models. The differences on a standard personality test between samples of Old Order Amish and Indiana rural young men from the same county and the decline of homicide in Medieval Europe are used as illustrative examples of the overall approach. It is shown that the decline of homicide is unremarkable under a threshold model while the differences between rural Amish and non-Amish young men are too large to be a plausible outcome of simple genetic selection in which assortative mating by affiliation is equivalent to truncation selection.

Facilitating arrhythmia simulation: the method of quantitative cellular automata modeling and parallel running

PubMed Central

Zhu, Hao; Sun, Yan; Rajagopal, Gunaretnam; Mondry, Adrian; Dhar, Pawan

2004-01-01

Background Many arrhythmias are triggered by abnormal electrical activity at the ionic channel and cell level, and then evolve spatio-temporally within the heart. To understand arrhythmias better and to diagnose them more precisely by their ECG waveforms, a whole-heart model is required to explore the association between the massively parallel activities at the channel/cell level and the integrative electrophysiological phenomena at organ level. Methods We have developed a method to build large-scale electrophysiological models by using extended cellular automata, and to run such models on a cluster of shared memory machines. We describe here the method, including the extension of a language-based cellular automaton to implement quantitative computing, the building of a whole-heart model with Visible Human Project data, the parallelization of the model on a cluster of shared memory computers with OpenMP and MPI hybrid programming, and a simulation algorithm that links cellular activity with the ECG. Results We demonstrate that electrical activities at channel, cell, and organ levels can be traced and captured conveniently in our extended cellular automaton system. Examples of some ECG waveforms simulated with a 2-D slice are given to support the ECG simulation algorithm. A performance evaluation of the 3-D model on a four-node cluster is also given. Conclusions Quantitative multicellular modeling with extended cellular automata is a highly efficient and widely applicable method to weave experimental data at different levels into computational models. This process can be used to investigate complex and collective biological activities that can be described neither by their governing differentiation equations nor by discrete parallel computation. Transparent cluster computing is a convenient and effective method to make time-consuming simulation feasible. Arrhythmias, as a typical case, can be effectively simulated with the methods described. PMID:15339335

Compilation of a near-infrared library for the construction of quantitative models of amoxicillin and potassium clavulanate oral dosage forms

NASA Astrophysics Data System (ADS)

Zou, Wen-bo; Chong, Xiao-meng; Wang, Yan; Hu, Chang-qin

2018-05-01

The accuracy of NIR quantitative models depends on calibration samples with concentration variability. Conventional sample collecting methods have some shortcomings especially the time-consuming which remains a bottleneck in the application of NIR models for Process Analytical Technology (PAT) control. A study was performed to solve the problem of sample selection collection for construction of NIR quantitative models. Amoxicillin and potassium clavulanate oral dosage forms were used as examples. The aim was to find a normal approach to rapidly construct NIR quantitative models using an NIR spectral library based on the idea of a universal model [2021]. The NIR spectral library of amoxicillin and potassium clavulanate oral dosage forms was defined and consisted of spectra of 377 batches of samples produced by 26 domestic pharmaceutical companies, including tablets, dispersible tablets, chewable tablets, oral suspensions, and granules. The correlation coefficient (rT) was used to indicate the similarities of the spectra. The samples’ calibration sets were selected from a spectral library according to the median rT of the samples to be analyzed. The rT of the samples selected was close to the median rT. The difference in rT of those samples was 1.0% to 1.5%. We concluded that sample selection is not a problem when constructing NIR quantitative models using a spectral library versus conventional methods of determining universal models. The sample spectra with a suitable concentration range in the NIR models were collected quickly. In addition, the models constructed through this method were more easily targeted.

Optofluidic time-stretch quantitative phase microscopy.

PubMed

Guo, Baoshan; Lei, Cheng; Wu, Yi; Kobayashi, Hirofumi; Ito, Takuro; Yalikun, Yaxiaer; Lee, Sangwook; Isozaki, Akihiro; Li, Ming; Jiang, Yiyue; Yasumoto, Atsushi; Di Carlo, Dino; Tanaka, Yo; Yatomi, Yutaka; Ozeki, Yasuyuki; Goda, Keisuke

2018-03-01

Innovations in optical microscopy have opened new windows onto scientific research, industrial quality control, and medical practice over the last few decades. One of such innovations is optofluidic time-stretch quantitative phase microscopy - an emerging method for high-throughput quantitative phase imaging that builds on the interference between temporally stretched signal and reference pulses by using dispersive properties of light in both spatial and temporal domains in an interferometric configuration on a microfluidic platform. It achieves the continuous acquisition of both intensity and phase images with a high throughput of more than 10,000 particles or cells per second by overcoming speed limitations that exist in conventional quantitative phase imaging methods. Applications enabled by such capabilities are versatile and include characterization of cancer cells and microalgal cultures. In this paper, we review the principles and applications of optofluidic time-stretch quantitative phase microscopy and discuss its future perspective. Copyright © 2017 Elsevier Inc. All rights reserved.

Downscaling SSPs in Bangladesh - Integrating Science, Modelling and Stakeholders Through Qualitative and Quantitative Scenarios

NASA Astrophysics Data System (ADS)

Allan, A.; Barbour, E.; Salehin, M.; Hutton, C.; Lázár, A. N.; Nicholls, R. J.; Rahman, M. M.

2015-12-01

A downscaled scenario development process was adopted in the context of a project seeking to understand relationships between ecosystem services and human well-being in the Ganges-Brahmaputra delta. The aim was to link the concerns and priorities of relevant stakeholders with the integrated biophysical and poverty models used in the project. A 2-stage process was used to facilitate the connection between stakeholders concerns and available modelling capacity: the first to qualitatively describe what the future might look like in 2050; the second to translate these qualitative descriptions into the quantitative form required by the numerical models. An extended, modified SSP approach was adopted, with stakeholders downscaling issues identified through interviews as being priorities for the southwest of Bangladesh. Detailed qualitative futures were produced, before modellable elements were quantified in conjunction with an expert stakeholder cadre. Stakeholder input, using the methods adopted here, allows the top-down focus of the RCPs to be aligned with the bottom-up approach needed to make the SSPs appropriate at the more local scale, and also facilitates the translation of qualitative narrative scenarios into a quantitative form that lends itself to incorporation of biophysical and socio-economic indicators. The presentation will describe the downscaling process in detail, and conclude with findings regarding the importance of stakeholder involvement (and logistical considerations), balancing model capacity with expectations and recommendations on SSP refinement at local levels.

Quantitative structure-property relationship modeling of remote liposome loading of drugs.

PubMed

Cern, Ahuva; Golbraikh, Alexander; Sedykh, Aleck; Tropsha, Alexander; Barenholz, Yechezkel; Goldblum, Amiram

2012-06-10

Remote loading of liposomes by trans-membrane gradients is used to achieve therapeutically efficacious intra-liposome concentrations of drugs. We have developed Quantitative Structure Property Relationship (QSPR) models of remote liposome loading for a data set including 60 drugs studied in 366 loading experiments internally or elsewhere. Both experimental conditions and computed chemical descriptors were employed as independent variables to predict the initial drug/lipid ratio (D/L) required to achieve high loading efficiency. Both binary (to distinguish high vs. low initial D/L) and continuous (to predict real D/L values) models were generated using advanced machine learning approaches and 5-fold external validation. The external prediction accuracy for binary models was as high as 91-96%; for continuous models the mean coefficient R(2) for regression between predicted versus observed values was 0.76-0.79. We conclude that QSPR models can be used to identify candidate drugs expected to have high remote loading capacity while simultaneously optimizing the design of formulation experiments. Copyright © 2011 Elsevier B.V. All rights reserved.

A quantitative dynamic systems model of health-related quality of life among older adults

PubMed Central

Roppolo, Mattia; Kunnen, E Saskia; van Geert, Paul L; Mulasso, Anna; Rabaglietti, Emanuela

2015-01-01

Health-related quality of life (HRQOL) is a person-centered concept. The analysis of HRQOL is highly relevant in the aged population, which is generally suffering from health decline. Starting from a conceptual dynamic systems model that describes the development of HRQOL in individuals over time, this study aims to develop and test a quantitative dynamic systems model, in order to reveal the possible dynamic trends of HRQOL among older adults. The model is tested in different ways: first, with a calibration procedure to test whether the model produces theoretically plausible results, and second, with a preliminary validation procedure using empirical data of 194 older adults. This first validation tested the prediction that given a particular starting point (first empirical data point), the model will generate dynamic trajectories that lead to the observed endpoint (second empirical data point). The analyses reveal that the quantitative model produces theoretically plausible trajectories, thus providing support for the calibration procedure. Furthermore, the analyses of validation show a good fit between empirical and simulated data. In fact, no differences were found in the comparison between empirical and simulated final data for the same subgroup of participants, whereas the comparison between different subgroups of people resulted in significant differences. These data provide an initial basis of evidence for the dynamic nature of HRQOL during the aging process. Therefore, these data may give new theoretical and applied insights into the study of HRQOL and its development with time in the aging population. PMID:26604722

Novel mathematic models for quantitative transitivity of quality-markers in extraction process of the Buyanghuanwu decoction.

PubMed

Zhang, Yu-Tian; Xiao, Mei-Feng; Deng, Kai-Wen; Yang, Yan-Tao; Zhou, Yi-Qun; Zhou, Jin; He, Fu-Yuan; Liu, Wen-Long

2018-06-01

Nowadays, to research and formulate an efficiency extraction system for Chinese herbal medicine, scientists have always been facing a great challenge for quality management, so that the transitivity of Q-markers in quantitative analysis of TCM was proposed by Prof. Liu recently. In order to improve the quality of extraction from raw medicinal materials for clinical preparations, a series of integrated mathematic models for transitivity of Q-markers in quantitative analysis of TCM were established. Buyanghuanwu decoction (BYHWD) was a commonly TCMs prescription, which was used to prevent and treat the ischemic heart and brain diseases. In this paper, we selected BYHWD as an extraction experimental subject to study the quantitative transitivity of TCM. Based on theory of Fick's Rule and Noyes-Whitney equation, novel kinetic models were established for extraction of active components. Meanwhile, fitting out kinetic equations of extracted models and then calculating the inherent parameters in material piece and Q-marker quantitative transfer coefficients, which were considered as indexes to evaluate transitivity of Q-markers in quantitative analysis of the extraction process of BYHWD. HPLC was applied to screen and analyze the potential Q-markers in the extraction process. Fick's Rule and Noyes-Whitney equation were adopted for mathematically modeling extraction process. Kinetic parameters were fitted and calculated by the Statistical Program for Social Sciences 20.0 software. The transferable efficiency was described and evaluated by potential Q-markers transfer trajectory via transitivity availability AUC, extraction ratio P, and decomposition ratio D respectively. The Q-marker was identified with AUC, P, D. Astragaloside IV, laetrile, paeoniflorin, and ferulic acid were studied as potential Q-markers from BYHWD. The relative technologic parameters were presented by mathematic models, which could adequately illustrate the inherent properties of raw materials

Building Quantitative Hydrologic Storylines from Process-based Models for Managing Water Resources in the U.S. Under Climate-changed Futures

NASA Astrophysics Data System (ADS)

Arnold, J.; Gutmann, E. D.; Clark, M. P.; Nijssen, B.; Vano, J. A.; Addor, N.; Wood, A.; Newman, A. J.; Mizukami, N.; Brekke, L. D.; Rasmussen, R.; Mendoza, P. A.

2016-12-01

Climate change narratives for water-resource applications must represent the change signals contextualized by hydroclimatic process variability and uncertainty at multiple scales. Building narratives of plausible change includes assessing uncertainties across GCM structure, internal climate variability, climate downscaling methods, and hydrologic models. Work with this linked modeling chain has dealt mostly with GCM sampling directed separately to either model fidelity (does the model correctly reproduce the physical processes in the world?) or sensitivity (of different model responses to CO2 forcings) or diversity (of model type, structure, and complexity). This leaves unaddressed any interactions among those measures and with other components in the modeling chain used to identify water-resource vulnerabilities to specific climate threats. However, time-sensitive, real-world vulnerability studies typically cannot accommodate a full uncertainty ensemble across the whole modeling chain, so a gap has opened between current scientific knowledge and most routine applications for climate-changed hydrology. To close that gap, the US Army Corps of Engineers, the Bureau of Reclamation, and the National Center for Atmospheric Research are working on techniques to subsample uncertainties objectively across modeling chain components and to integrate results into quantitative hydrologic storylines of climate-changed futures. Importantly, these quantitative storylines are not drawn from a small sample of models or components. Rather, they stem from the more comprehensive characterization of the full uncertainty space for each component. Equally important from the perspective of water-resource practitioners, these quantitative hydrologic storylines are anchored in actual design and operations decisions potentially affected by climate change. This talk will describe part of our work characterizing variability and uncertainty across modeling chain components and their

Quantitative Decision Support Requires Quantitative User Guidance

NASA Astrophysics Data System (ADS)

Smith, L. A.

2009-12-01

Is it conceivable that models run on 2007 computer hardware could provide robust and credible probabilistic information for decision support and user guidance at the ZIP code level for sub-daily meteorological events in 2060? In 2090? Retrospectively, how informative would output from today’s models have proven in 2003? or the 1930’s? Consultancies in the United Kingdom, including the Met Office, are offering services to “future-proof” their customers from climate change. How is a US or European based user or policy maker to determine the extent to which exciting new Bayesian methods are relevant here? or when a commercial supplier is vastly overselling the insights of today’s climate science? How are policy makers and academic economists to make the closely related decisions facing them? How can we communicate deep uncertainty in the future at small length-scales without undermining the firm foundation established by climate science regarding global trends? Three distinct aspects of the communication of the uses of climate model output targeting users and policy makers, as well as other specialist adaptation scientists, are discussed. First, a brief scientific evaluation of the length and time scales at which climate model output is likely to become uninformative is provided, including a note on the applicability the latest Bayesian methodology to current state-of-the-art general circulation models output. Second, a critical evaluation of the language often employed in communication of climate model output, a language which accurately states that models are “better”, have “improved” and now “include” and “simulate” relevant meteorological processed, without clearly identifying where the current information is thought to be uninformative and misleads, both for the current climate and as a function of the state of the (each) climate simulation. And thirdly, a general approach for evaluating the relevance of quantitative climate model output

Quantitative model of the growth of floodplains by vertical accretion

USGS Publications Warehouse

Moody, J.A.; Troutman, B.M.

2000-01-01

A simple one-dimensional model is developed to quantitatively predict the change in elevation, over a period of decades, for vertically accreting floodplains. This unsteady model approximates the monotonic growth of a floodplain as an incremental but constant increase of net sediment deposition per flood for those floods of a partial duration series that exceed a threshold discharge corresponding to the elevation of the floodplain. Sediment deposition from each flood increases the elevation of the floodplain and consequently the magnitude of the threshold discharge resulting in a decrease in the number of floods and growth rate of the floodplain. Floodplain growth curves predicted by this model are compared to empirical growth curves based on dendrochronology and to direct field measurements at five floodplain sites. The model was used to predict the value of net sediment deposition per flood which best fits (in a least squares sense) the empirical and field measurements; these values fall within the range of independent estimates of the net sediment deposition per flood based on empirical equations. These empirical equations permit the application of the model to estimate of floodplain growth for other floodplains throughout the world which do not have detailed data of sediment deposition during individual floods. Copyright (C) 2000 John Wiley and Sons, Ltd.

Quantitative model of super-Arrhenian behavior in glass forming materials

NASA Astrophysics Data System (ADS)

Caruthers, J. M.; Medvedev, G. A.

2018-05-01

The key feature of glass forming liquids is the super-Arrhenian temperature dependence of the mobility, where the mobility can increase by ten orders of magnitude or more as the temperature is decreased if crystallization does not intervene. A fundamental description of the super-Arrhenian behavior has been developed; specifically, the logarithm of the relaxation time is a linear function of 1 /U¯x , where U¯x is the independently determined excess molar internal energy and B is a material constant. This one-parameter mobility model quantitatively describes data for 21 glass forming materials, which are all the materials where there are sufficient experimental data for analysis. The effect of pressure on the loga mobility is also described using the same U¯x(T ,p ) function determined from the difference between the liquid and crystalline internal energies. It is also shown that B is well correlated with the heat of fusion. The prediction of the B /U¯x model is compared to the Adam and Gibbs 1 /T S¯x model, where the B /U¯x model is significantly better in unifying the full complement of mobility data. The implications of the B /U¯x model for the development of a fundamental description of glass are discussed.

Quantitative Acoustic Model for Adhesion Evaluation of Pmma/silicon Film Structures

NASA Astrophysics Data System (ADS)

Ju, H. S.; Tittmann, B. R.

2010-02-01

A Poly-methyl-methacrylate (PMMA) film on a silicon substrate is a main structure for photolithography in semiconductor manufacturing processes. This paper presents a potential of scanning acoustic microscopy (SAM) for nondestructive evaluation of the PMMA/Si film structure, whose adhesion failure is commonly encountered during the fabrication and post-fabrication processes. A physical model employing a partial discontinuity in displacement is developed for rigorously quantitative evaluation of the interfacial weakness. The model is implanted to the matrix method for the surface acoustic wave (SAW) propagation in anisotropic media. Our results show that variations in the SAW velocity and reflectance are predicted to show their sensitivity to the adhesion condition. Experimental results by the v(z) technique and SAW velocity reconstruction verify the prediction.

Quantitative Microbial Risk Assessment Tutorial: Installation of Software for Watershed Modeling in Support of QMRA

EPA Science Inventory

This tutorial provides instructions for accessing, retrieving, and downloading the following software to install on a host computer in support of Quantitative Microbial Risk Assessment (QMRA) modeling:• SDMProjectBuilder (which includes the Microbial Source Module as part...

Spatiotemporal microbiota dynamics from quantitative in vitro and in silico models of the gut

NASA Astrophysics Data System (ADS)

Hwa, Terence

The human gut harbors a dynamic microbial community whose composition bears great importance for the health of the host. Here, we investigate how colonic physiology impacts bacterial growth behaviors, which ultimately dictate the gut microbiota composition. Combining measurements of bacterial growth physiology with analysis of published data on human physiology into a quantitative modeling framework, we show how hydrodynamic forces in the colon, in concert with other physiological factors, determine the abundances of the major bacterial phyla in the gut. Our model quantitatively explains the observed variation of microbiota composition among healthy adults, and predicts colonic water absorption (manifested as stool consistency) and nutrient intake to be two key factors determining this composition. The model further reveals that both factors, which have been identified in recent correlative studies, exert their effects through the same mechanism: changes in colonic pH that differentially affect the growth of different bacteria. Our findings show that a predictive and mechanistic understanding of microbial ecology in the human gut is possible, and offer the hope for the rational design of intervention strategies to actively control the microbiota. This work is supported by the Bill and Melinda Gates Foundation.

A study about the existence of the leverage effect in stochastic volatility models

NASA Astrophysics Data System (ADS)

Florescu, Ionuţ; Pãsãricã, Cristian Gabriel

2009-02-01

The empirical relationship between the return of an asset and the volatility of the asset has been well documented in the financial literature. Named the leverage effect or sometimes risk-premium effect, it is observed in real data that, when the return of the asset decreases, the volatility increases and vice versa. Consequently, it is important to demonstrate that any formulated model for the asset price is capable of generating this effect observed in practice. Furthermore, we need to understand the conditions on the parameters present in the model that guarantee the apparition of the leverage effect. In this paper we analyze two general specifications of stochastic volatility models and their capability of generating the perceived leverage effect. We derive conditions for the apparition of leverage effect in both of these stochastic volatility models. We exemplify using stochastic volatility models used in practice and we explicitly state the conditions for the existence of the leverage effect in these examples.

Quantitative dual-probe microdialysis: mathematical model and analysis.

PubMed

Chen, Kevin C; Höistad, Malin; Kehr, Jan; Fuxe, Kjell; Nicholson, Charles

2002-04-01

Steady-state microdialysis is a widely used technique to monitor the concentration changes and distributions of substances in tissues. To obtain more information about brain tissue properties from microdialysis, a dual-probe approach was applied to infuse and sample the radiotracer, [3H]mannitol, simultaneously both in agar gel and in the rat striatum. Because the molecules released by one probe and collected by the other must diffuse through the interstitial space, the concentration profile exhibits dynamic behavior that permits the assessment of the diffusion characteristics in the brain extracellular space and the clearance characteristics. In this paper a mathematical model for dual-probe microdialysis was developed to study brain interstitial diffusion and clearance processes. Theoretical expressions for the spatial distribution of the infused tracer in the brain extracellular space and the temporal concentration at the probe outlet were derived. A fitting program was developed using the simplex algorithm, which finds local minima of the standard deviations between experiments and theory by adjusting the relevant parameters. The theoretical curves accurately fitted the experimental data and generated realistic diffusion parameters, implying that the mathematical model is capable of predicting the interstitial diffusion behavior of [3H]mannitol and that it will be a valuable quantitative tool in dual-probe microdialysis.

Combining existing numerical models with data assimilation using weighted least-squares finite element methods.

PubMed

Rajaraman, Prathish K; Manteuffel, T A; Belohlavek, M; Heys, Jeffrey J

2017-01-01

A new approach has been developed for combining and enhancing the results from an existing computational fluid dynamics model with experimental data using the weighted least-squares finite element method (WLSFEM). Development of the approach was motivated by the existence of both limited experimental blood velocity in the left ventricle and inexact numerical models of the same flow. Limitations of the experimental data include measurement noise and having data only along a two-dimensional plane. Most numerical modeling approaches do not provide the flexibility to assimilate noisy experimental data. We previously developed an approach that could assimilate experimental data into the process of numerically solving the Navier-Stokes equations, but the approach was limited because it required the use of specific finite element methods for solving all model equations and did not support alternative numerical approximation methods. The new approach presented here allows virtually any numerical method to be used for approximately solving the Navier-Stokes equations, and then the WLSFEM is used to combine the experimental data with the numerical solution of the model equations in a final step. The approach dynamically adjusts the influence of the experimental data on the numerical solution so that more accurate data are more closely matched by the final solution and less accurate data are not closely matched. The new approach is demonstrated on different test problems and provides significantly reduced computational costs compared with many previous methods for data assimilation. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Target-based drug discovery for [Formula: see text]-globin disorders: drug target prediction using quantitative modeling with hybrid functional Petri nets.

PubMed

Mehraei, Mani; Bashirov, Rza; Tüzmen, Şükrü

2016-10-01

Recent molecular studies provide important clues into treatment of [Formula: see text]-thalassemia, sickle-cell anaemia and other [Formula: see text]-globin disorders revealing that increased production of fetal hemoglobin, that is normally suppressed in adulthood, can ameliorate the severity of these diseases. In this paper, we present a novel approach for drug prediction for [Formula: see text]-globin disorders. Our approach is centered upon quantitative modeling of interactions in human fetal-to-adult hemoglobin switch network using hybrid functional Petri nets. In accordance with the reverse pharmacology approach, we pose a hypothesis regarding modulation of specific protein targets that induce [Formula: see text]-globin and consequently fetal hemoglobin. Comparison of simulation results for the proposed strategy with the ones obtained for already existing drugs shows that our strategy is the optimal as it leads to highest level of [Formula: see text]-globin induction and thereby has potential beneficial therapeutic effects on [Formula: see text]-globin disorders. Simulation results enable verification of model coherence demonstrating that it is consistent with qPCR data available for known strategies and/or drugs.

Using Qualitative Hazard Analysis to Guide Quantitative Safety Analysis

NASA Technical Reports Server (NTRS)

Shortle, J. F.; Allocco, M.

2005-01-01

Quantitative methods can be beneficial in many types of safety investigations. However, there are many difficulties in using quantitative m ethods. Far example, there may be little relevant data available. This paper proposes a framework for using quantitative hazard analysis to prioritize hazard scenarios most suitable for quantitative mziysis. The framework first categorizes hazard scenarios by severity and likelihood. We then propose another metric "modeling difficulty" that desc ribes the complexity in modeling a given hazard scenario quantitatively. The combined metrics of severity, likelihood, and modeling difficu lty help to prioritize hazard scenarios for which quantitative analys is should be applied. We have applied this methodology to proposed concepts of operations for reduced wake separation for airplane operatio ns at closely spaced parallel runways.

Interfacial Mechanisms of Water Vapor Sorption into Cellulose Nanofibril Films as Revealed by Quantitative Models.

PubMed

Hakalahti, Minna; Faustini, Marco; Boissière, Cédric; Kontturi, Eero; Tammelin, Tekla

2017-09-11

Humidity is an efficient instrument for facilitating changes in local architectures of two-dimensional surfaces assembled from nanoscaled biomaterials. Here, complementary surface-sensitive methods are used to collect explicit and precise experimental evidence on the water vapor sorption into (2,2,6,6-tetramethylpiperidin-1-yl)oxyl (TEMPO) oxidized cellulose nanofibril (CNF) thin film over the relative humidity (RH) range from 0 to 97%. Changes in thickness and mass of the film due to water vapor uptake are tracked using spectroscopic ellipsometry and quartz crystal microbalance with dissipation monitoring, respectively. Experimental data is evaluated by the quantitative Langmuir/Flory-Huggins/clustering model and the Brunauer-Emmett-Teller model. The isotherms coupled with the quantitative models unveil distinct regions of predominant sorption modes: specific sorption of water molecules below 10% RH, multilayer build-up between 10 to 75% RH, and clustering of water molecules above 75% RH. The study reveals the sorption mechanisms underlying the well-known water uptake behavior of TEMPO oxidized CNF directly at the gas-solid interface.

Analytic proof of the existence of the Lorenz attractor in the extended Lorenz model

NASA Astrophysics Data System (ADS)

Ovsyannikov, I. I.; Turaev, D. V.

2017-01-01

We give an analytic (free of computer assistance) proof of the existence of a classical Lorenz attractor for an open set of parameter values of the Lorenz model in the form of Yudovich-Morioka-Shimizu. The proof is based on detection of a homoclinic butterfly with a zero saddle value and rigorous verification of one of the Shilnikov criteria for the birth of the Lorenz attractor; we also supply a proof for this criterion. The results are applied in order to give an analytic proof for the existence of a robust, pseudohyperbolic strange attractor (the so-called discrete Lorenz attractor) for an open set of parameter values in a 4-parameter family of 3D Henon-like diffeomorphisms.

Toward Accurate and Quantitative Comparative Metagenomics

PubMed Central

Nayfach, Stephen; Pollard, Katherine S.

2016-01-01

Shotgun metagenomics and computational analysis are used to compare the taxonomic and functional profiles of microbial communities. Leveraging this approach to understand roles of microbes in human biology and other environments requires quantitative data summaries whose values are comparable across samples and studies. Comparability is currently hampered by the use of abundance statistics that do not estimate a meaningful parameter of the microbial community and biases introduced by experimental protocols and data-cleaning approaches. Addressing these challenges, along with improving study design, data access, metadata standardization, and analysis tools, will enable accurate comparative metagenomics. We envision a future in which microbiome studies are replicable and new metagenomes are easily and rapidly integrated with existing data. Only then can the potential of metagenomics for predictive ecological modeling, well-powered association studies, and effective microbiome medicine be fully realized. PMID:27565341

A quantitative trait locus mixture model that avoids spurious LOD score peaks.

PubMed

Feenstra, Bjarke; Skovgaard, Ib M

2004-06-01

In standard interval mapping of quantitative trait loci (QTL), the QTL effect is described by a normal mixture model. At any given location in the genome, the evidence of a putative QTL is measured by the likelihood ratio of the mixture model compared to a single normal distribution (the LOD score). This approach can occasionally produce spurious LOD score peaks in regions of low genotype information (e.g., widely spaced markers), especially if the phenotype distribution deviates markedly from a normal distribution. Such peaks are not indicative of a QTL effect; rather, they are caused by the fact that a mixture of normals always produces a better fit than a single normal distribution. In this study, a mixture model for QTL mapping that avoids the problems of such spurious LOD score peaks is presented.

Properties predictive modeling through the concept of a hybrid interphase existing between phases in contact

NASA Astrophysics Data System (ADS)

Portan, D. V.; Papanicolaou, G. C.

2018-02-01

From practical point of view, predictive modeling based on the physics of composite material behavior is wealth generating; by guiding material system selection and process choices, by cutting down on experimentation and associated costs; and by speeding up the time frame from the research stage to the market place. The presence of areas with different properties and the existence of an interphase between them have a pronounced influence on the behavior of a composite system. The Viscoelastic Hybrid Interphase Model (VHIM), considers the existence of a non-homogeneous viscoelastic and anisotropic interphase having properties depended on the degree of adhesion between the two phases in contact. The model applies for any physical/mechanical property (e.g. mechanical, thermal, electrical and/or biomechanical). Knowing the interphasial variation of a specific property one can predict the corresponding macroscopic behavior of the composite. Moreover, the model acts as an algorithm and a two-way approach can be used: (i) phases in contact may be chosen to get the desired properties of the final composite system or (ii) the initial phases in contact determine the final behavior of the composite system, that can be approximately predicted. The VHIM has been proven, amongst others, to be extremely useful in biomaterial designing for improved contact with human tissues.

Melanoma screening: Informing public health policy with quantitative modelling.

PubMed

Gilmore, Stephen

2017-01-01

Australia and New Zealand share the highest incidence rates of melanoma worldwide. Despite the substantial increase in public and physician awareness of melanoma in Australia over the last 30 years-as a result of the introduction of publicly funded mass media campaigns that began in the early 1980s -mortality has steadily increased during this period. This increased mortality has led investigators to question the relative merits of primary versus secondary prevention; that is, sensible sun exposure practices versus early detection. Increased melanoma vigilance on the part of the public and among physicians has resulted in large increases in public health expenditure, primarily from screening costs and increased rates of office surgery. Has this attempt at secondary prevention been effective? Unfortunately epidemiologic studies addressing the causal relationship between the level of secondary prevention and mortality are prohibitively difficult to implement-it is currently unknown whether increased melanoma surveillance reduces mortality, and if so, whether such an approach is cost-effective. Here I address the issue of secondary prevention of melanoma with respect to incidence and mortality (and cost per life saved) by developing a Markov model of melanoma epidemiology based on Australian incidence and mortality data. The advantages of developing a methodology that can determine constraint-based surveillance outcomes are twofold: first, it can address the issue of effectiveness; and second, it can quantify the trade-off between cost and utilisation of medical resources on one hand, and reduced morbidity and lives saved on the other. With respect to melanoma, implementing the model facilitates the quantitative determination of the relative effectiveness and trade-offs associated with different levels of secondary and tertiary prevention, both retrospectively and prospectively. For example, I show that the surveillance enhancement that began in 1982 has resulted in

Quantitative Evaluation Method of Each Generation Margin for Power System Planning

NASA Astrophysics Data System (ADS)

Su, Su; Tanaka, Kazuyuki

As the power system deregulation advances, the competition among the power companies becomes heated, and they seek more efficient system planning using existing facilities. Therefore, an efficient system planning method has been expected. This paper proposes a quantitative evaluation method for the (N-1) generation margin considering the overload and the voltage stability restriction. Concerning the generation margin related with the overload, a fast solution method without the recalculation of the (N-1) Y-matrix is proposed. Referred to the voltage stability, this paper proposes an efficient method to search the stability limit. The IEEE30 model system which is composed of 6 generators and 14 load nodes is employed to validate the proposed method. According to the results, the proposed method can reduce the computational cost for the generation margin related with the overload under the (N-1) condition, and specify the value quantitatively.

An overview of quantitative approaches in Gestalt perception.

PubMed

Jäkel, Frank; Singh, Manish; Wichmann, Felix A; Herzog, Michael H

2016-09-01

Gestalt psychology is often criticized as lacking quantitative measurements and precise mathematical models. While this is true of the early Gestalt school, today there are many quantitative approaches in Gestalt perception and the special issue of Vision Research "Quantitative Approaches in Gestalt Perception" showcases the current state-of-the-art. In this article we give an overview of these current approaches. For example, ideal observer models are one of the standard quantitative tools in vision research and there is a clear trend to try and apply this tool to Gestalt perception and thereby integrate Gestalt perception into mainstream vision research. More generally, Bayesian models, long popular in other areas of vision research, are increasingly being employed to model perceptual grouping as well. Thus, although experimental and theoretical approaches to Gestalt perception remain quite diverse, we are hopeful that these quantitative trends will pave the way for a unified theory. Copyright © 2016 Elsevier Ltd. All rights reserved.

Quantitative genetic models of sexual selection by male choice.

PubMed

Nakahashi, Wataru

2008-09-01

There are many examples of male mate choice for female traits that tend to be associated with high fertility. I develop quantitative genetic models of a female trait and a male preference to show when such a male preference can evolve. I find that a disagreement between the fertility maximum and the viability maximum of the female trait is necessary for directional male preference (preference for extreme female trait values) to evolve. Moreover, when there is a shortage of available male partners or variance in male nongenetic quality, strong male preference can evolve. Furthermore, I also show that males evolve to exhibit a stronger preference for females that are more feminine (less resemblance to males) than the average female when there is a sexual dimorphism caused by fertility selection which acts only on females.

Quantitative Modeling of Human-Environment Interactions in Preindustrial Time

NASA Astrophysics Data System (ADS)

Sommer, Philipp S.; Kaplan, Jed O.

2017-04-01

Quantifying human-environment interactions and anthropogenic influences on the environment prior to the Industrial revolution is essential for understanding the current state of the earth system. This is particularly true for the terrestrial biosphere, but marine ecosystems and even climate were likely modified by human activities centuries to millennia ago. Direct observations are however very sparse in space and time, especially as one considers prehistory. Numerical models are therefore essential to produce a continuous picture of human-environment interactions in the past. Agent-based approaches, while widely applied to quantifying human influence on the environment in localized studies, are unsuitable for global spatial domains and Holocene timescales because of computational demands and large parameter uncertainty. Here we outline a new paradigm for the quantitative modeling of human-environment interactions in preindustrial time that is adapted to the global Holocene. Rather than attempting to simulate agency directly, the model is informed by a suite of characteristics describing those things about society that cannot be predicted on the basis of environment, e.g., diet, presence of agriculture, or range of animals exploited. These categorical data are combined with the properties of the physical environment in coupled human-environment model. The model is, at its core, a dynamic global vegetation model with a module for simulating crop growth that is adapted for preindustrial agriculture. This allows us to simulate yield and calories for feeding both humans and their domesticated animals. We couple this basic caloric availability with a simple demographic model to calculate potential population, and, constrained by labor requirements and land limitations, we create scenarios of land use and land cover on a moderate-resolution grid. We further implement a feedback loop where anthropogenic activities lead to changes in the properties of the physical

Probabilistic quantitative microbial risk assessment model of norovirus from wastewater irrigated vegetables in Ghana using genome copies and fecal indicator ratio conversion for estimating exposure dose.

PubMed

Owusu-Ansah, Emmanuel de-Graft Johnson; Sampson, Angelina; Amponsah, Samuel K; Abaidoo, Robert C; Dalsgaard, Anders; Hald, Tine

2017-12-01

The need to replace the commonly applied fecal indicator conversions ratio (an assumption of 1:10 -5 virus to fecal indicator organism) in Quantitative Microbial Risk Assessment (QMRA) with models based on quantitative data on the virus of interest has gained prominence due to the different physical and environmental factors that might influence the reliability of using indicator organisms in microbial risk assessment. The challenges facing analytical studies on virus enumeration (genome copies or particles) have contributed to the already existing lack of data in QMRA modelling. This study attempts to fit a QMRA model to genome copies of norovirus data. The model estimates the risk of norovirus infection from the intake of vegetables irrigated with wastewater from different sources. The results were compared to the results of a corresponding model using the fecal indicator conversion ratio to estimate the norovirus count. In all scenarios of using different water sources, the application of the fecal indicator conversion ratio underestimated the norovirus disease burden, measured by the Disability Adjusted Life Years (DALYs), when compared to results using the genome copies norovirus data. In some cases the difference was >2 orders of magnitude. All scenarios using genome copies met the 10 -4 DALY per person per year for consumption of vegetables irrigated with wastewater, although these results are considered to be highly conservative risk estimates. The fecal indicator conversion ratio model of stream-water and drain-water sources of wastewater achieved the 10 -6 DALY per person per year threshold, which tends to indicate an underestimation of health risk when compared to using genome copies for estimating the dose. Copyright © 2017 Elsevier B.V. All rights reserved.

Quantitative measurement of eyestrain on 3D stereoscopic display considering the eye foveation model and edge information.

PubMed

Heo, Hwan; Lee, Won Oh; Shin, Kwang Yong; Park, Kang Ryoung

2014-05-15

We propose a new method for measuring the degree of eyestrain on 3D stereoscopic displays using a glasses-type of eye tracking device. Our study is novel in the following four ways: first, the circular area where a user's gaze position exists is defined based on the calculated gaze position and gaze estimation error. Within this circular area, the position where edge strength is maximized can be detected, and we determine this position as the gaze position that has a higher probability of being the correct one. Based on this gaze point, the eye foveation model is defined. Second, we quantitatively evaluate the correlation between the degree of eyestrain and the causal factors of visual fatigue, such as the degree of change of stereoscopic disparity (CSD), stereoscopic disparity (SD), frame cancellation effect (FCE), and edge component (EC) of the 3D stereoscopic display using the eye foveation model. Third, by comparing the eyestrain in conventional 3D video and experimental 3D sample video, we analyze the characteristics of eyestrain according to various factors and types of 3D video. Fourth, by comparing the eyestrain with or without the compensation of eye saccades movement in 3D video, we analyze the characteristics of eyestrain according to the types of eye movements in 3D video. Experimental results show that the degree of CSD causes more eyestrain than other factors.

Machine learning-based kinetic modeling: a robust and reproducible solution for quantitative analysis of dynamic PET data

NASA Astrophysics Data System (ADS)

Pan, Leyun; Cheng, Caixia; Haberkorn, Uwe; Dimitrakopoulou-Strauss, Antonia

2017-05-01

A variety of compartment models are used for the quantitative analysis of dynamic positron emission tomography (PET) data. Traditionally, these models use an iterative fitting (IF) method to find the least squares between the measured and calculated values over time, which may encounter some problems such as the overfitting of model parameters and a lack of reproducibility, especially when handling noisy data or error data. In this paper, a machine learning (ML) based kinetic modeling method is introduced, which can fully utilize a historical reference database to build a moderate kinetic model directly dealing with noisy data but not trying to smooth the noise in the image. Also, due to the database, the presented method is capable of automatically adjusting the models using a multi-thread grid parameter searching technique. Furthermore, a candidate competition concept is proposed to combine the advantages of the ML and IF modeling methods, which could find a balance between fitting to historical data and to the unseen target curve. The machine learning based method provides a robust and reproducible solution that is user-independent for VOI-based and pixel-wise quantitative analysis of dynamic PET data.

Machine learning-based kinetic modeling: a robust and reproducible solution for quantitative analysis of dynamic PET data.

PubMed

Pan, Leyun; Cheng, Caixia; Haberkorn, Uwe; Dimitrakopoulou-Strauss, Antonia

2017-05-07

A variety of compartment models are used for the quantitative analysis of dynamic positron emission tomography (PET) data. Traditionally, these models use an iterative fitting (IF) method to find the least squares between the measured and calculated values over time, which may encounter some problems such as the overfitting of model parameters and a lack of reproducibility, especially when handling noisy data or error data. In this paper, a machine learning (ML) based kinetic modeling method is introduced, which can fully utilize a historical reference database to build a moderate kinetic model directly dealing with noisy data but not trying to smooth the noise in the image. Also, due to the database, the presented method is capable of automatically adjusting the models using a multi-thread grid parameter searching technique. Furthermore, a candidate competition concept is proposed to combine the advantages of the ML and IF modeling methods, which could find a balance between fitting to historical data and to the unseen target curve. The machine learning based method provides a robust and reproducible solution that is user-independent for VOI-based and pixel-wise quantitative analysis of dynamic PET data.

A Quantitative Model of Keyhole Instability Induced Porosity in Laser Welding of Titanium Alloy

NASA Astrophysics Data System (ADS)

Pang, Shengyong; Chen, Weidong; Wang, Wen

2014-06-01

Quantitative prediction of the porosity defects in deep penetration laser welding has generally been considered as a very challenging task. In this study, a quantitative model of porosity defects induced by keyhole instability in partial penetration CO2 laser welding of a titanium alloy is proposed. The three-dimensional keyhole instability, weld pool dynamics, and pore formation are determined by direct numerical simulation, and the results are compared to prior experimental results. It is shown that the simulated keyhole depth fluctuations could represent the variation trends in the number and average size of pores for the studied process conditions. Moreover, it is found that it is possible to use the predicted keyhole depth fluctuations as a quantitative measure of the average size of porosity. The results also suggest that due to the shadowing effect of keyhole wall humps, the rapid cooling of the surface of the keyhole tip before keyhole collapse could lead to a substantial decrease in vapor pressure inside the keyhole tip, which is suggested to be the mechanism by which shielding gas enters into the porosity.

Querying quantitative logic models (Q2LM) to study intracellular signaling networks and cell-cytokine interactions.

PubMed

Morris, Melody K; Shriver, Zachary; Sasisekharan, Ram; Lauffenburger, Douglas A

2012-03-01

Mathematical models have substantially improved our ability to predict the response of a complex biological system to perturbation, but their use is typically limited by difficulties in specifying model topology and parameter values. Additionally, incorporating entities across different biological scales ranging from molecular to organismal in the same model is not trivial. Here, we present a framework called "querying quantitative logic models" (Q2LM) for building and asking questions of constrained fuzzy logic (cFL) models. cFL is a recently developed modeling formalism that uses logic gates to describe influences among entities, with transfer functions to describe quantitative dependencies. Q2LM does not rely on dedicated data to train the parameters of the transfer functions, and it permits straight-forward incorporation of entities at multiple biological scales. The Q2LM framework can be employed to ask questions such as: Which therapeutic perturbations accomplish a designated goal, and under what environmental conditions will these perturbations be effective? We demonstrate the utility of this framework for generating testable hypotheses in two examples: (i) a intracellular signaling network model; and (ii) a model for pharmacokinetics and pharmacodynamics of cell-cytokine interactions; in the latter, we validate hypotheses concerning molecular design of granulocyte colony stimulating factor. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Evaluation of New Zealand's high-seas bottom trawl closures using predictive habitat models and quantitative risk assessment.

PubMed

Penney, Andrew J; Guinotte, John M

2013-01-01

United Nations General Assembly Resolution 61/105 on sustainable fisheries (UNGA 2007) establishes three difficult questions for participants in high-seas bottom fisheries to answer: 1) Where are vulnerable marine systems (VMEs) likely to occur?; 2) What is the likelihood of fisheries interaction with these VMEs?; and 3) What might qualify as adequate conservation and management measures to prevent significant adverse impacts? This paper develops an approach to answering these questions for bottom trawling activities in the Convention Area of the South Pacific Regional Fisheries Management Organisation (SPRFMO) within a quantitative risk assessment and cost : benefit analysis framework. The predicted distribution of deep-sea corals from habitat suitability models is used to answer the first question. Distribution of historical bottom trawl effort is used to answer the second, with estimates of seabed areas swept by bottom trawlers being used to develop discounting factors for reduced biodiversity in previously fished areas. These are used in a quantitative ecological risk assessment approach to guide spatial protection planning to address the third question. The coral VME likelihood (average, discounted, predicted coral habitat suitability) of existing spatial closures implemented by New Zealand within the SPRFMO area is evaluated. Historical catch is used as a measure of cost to industry in a cost : benefit analysis of alternative spatial closure scenarios. Results indicate that current closures within the New Zealand SPRFMO area bottom trawl footprint are suboptimal for protection of VMEs. Examples of alternative trawl closure scenarios are provided to illustrate how the approach could be used to optimise protection of VMEs under chosen management objectives, balancing protection of VMEs against economic loss to commercial fishers from closure of historically fished areas.

Influence of mom and dad: quantitative genetic models for maternal effects and genomic imprinting.

PubMed

Santure, Anna W; Spencer, Hamish G

2006-08-01

The expression of an imprinted gene is dependent on the sex of the parent it was inherited from, and as a result reciprocal heterozygotes may display different phenotypes. In contrast, maternal genetic terms arise when the phenotype of an offspring is influenced by the phenotype of its mother beyond the direct inheritance of alleles. Both maternal effects and imprinting may contribute to resemblance between offspring of the same mother. We demonstrate that two standard quantitative genetic models for deriving breeding values, population variances and covariances between relatives, are not equivalent when maternal genetic effects and imprinting are acting. Maternal and imprinting effects introduce both sex-dependent and generation-dependent effects that result in differences in the way additive and dominance effects are defined for the two approaches. We use a simple example to demonstrate that both imprinting and maternal genetic effects add extra terms to covariances between relatives and that model misspecification may over- or underestimate true covariances or lead to extremely variable parameter estimation. Thus, an understanding of various forms of parental effects is essential in correctly estimating quantitative genetic variance components.

Quantitative Reasoning in Environmental Science: A Learning Progression

ERIC Educational Resources Information Center

Mayes, Robert Lee; Forrester, Jennifer Harris; Christus, Jennifer Schuttlefield; Peterson, Franziska Isabel; Bonilla, Rachel; Yestness, Nissa

2014-01-01

The ability of middle and high school students to reason quantitatively within the context of environmental science was investigated. A quantitative reasoning (QR) learning progression was created with three progress variables: quantification act, quantitative interpretation, and quantitative modeling. An iterative research design was used as it…

Stroke onset time estimation from multispectral quantitative magnetic resonance imaging in a rat model of focal permanent cerebral ischemia.

PubMed

McGarry, Bryony L; Rogers, Harriet J; Knight, Michael J; Jokivarsi, Kimmo T; Sierra, Alejandra; Gröhn, Olli Hj; Kauppinen, Risto A

2016-08-01

Quantitative T2 relaxation magnetic resonance imaging allows estimation of stroke onset time. We aimed to examine the accuracy of quantitative T1 and quantitative T2 relaxation times alone and in combination to provide estimates of stroke onset time in a rat model of permanent focal cerebral ischemia and map the spatial distribution of elevated quantitative T1 and quantitative T2 to assess tissue status. Permanent middle cerebral artery occlusion was induced in Wistar rats. Animals were scanned at 9.4T for quantitative T1, quantitative T2, and Trace of Diffusion Tensor (Dav) up to 4 h post-middle cerebral artery occlusion. Time courses of differentials of quantitative T1 and quantitative T2 in ischemic and non-ischemic contralateral brain tissue (ΔT1, ΔT2) and volumes of tissue with elevated T1 and T2 relaxation times (f1, f2) were determined. TTC staining was used to highlight permanent ischemic damage. ΔT1, ΔT2, f1, f2, and the volume of tissue with both elevated quantitative T1 and quantitative T2 (V(Overlap)) increased with time post-middle cerebral artery occlusion allowing stroke onset time to be estimated. V(Overlap) provided the most accurate estimate with an uncertainty of ±25 min. At all times-points regions with elevated relaxation times were smaller than areas with Dav defined ischemia. Stroke onset time can be determined by quantitative T1 and quantitative T2 relaxation times and tissue volumes. Combining quantitative T1 and quantitative T2 provides the most accurate estimate and potentially identifies irreversibly damaged brain tissue. © 2016 World Stroke Organization.

Fuzzy object modeling

NASA Astrophysics Data System (ADS)

Udupa, Jayaram K.; Odhner, Dewey; Falcao, Alexandre X.; Ciesielski, Krzysztof C.; Miranda, Paulo A. V.; Vaideeswaran, Pavithra; Mishra, Shipra; Grevera, George J.; Saboury, Babak; Torigian, Drew A.

2011-03-01

To make Quantitative Radiology (QR) a reality in routine clinical practice, computerized automatic anatomy recognition (AAR) becomes essential. As part of this larger goal, we present in this paper a novel fuzzy strategy for building bodywide group-wise anatomic models. They have the potential to handle uncertainties and variability in anatomy naturally and to be integrated with the fuzzy connectedness framework for image segmentation. Our approach is to build a family of models, called the Virtual Quantitative Human, representing normal adult subjects at a chosen resolution of the population variables (gender, age). Models are represented hierarchically, the descendents representing organs contained in parent organs. Based on an index of fuzziness of the models, 32 thorax data sets, and 10 organs defined in them, we found that the hierarchical approach to modeling can effectively handle the non-linear relationships in position, scale, and orientation that exist among organs in different patients.

A quantitative analysis to objectively appraise drought indicators and model drought impacts

NASA Astrophysics Data System (ADS)

Bachmair, S.; Svensson, C.; Hannaford, J.; Barker, L. J.; Stahl, K.

2016-07-01

coverage. The predictions also provided insights into the EDII, in particular highlighting drought events where missing impact reports may reflect a lack of recording rather than true absence of impacts. Overall, the presented quantitative framework proved to be a useful tool for evaluating drought indicators, and to model impact occurrence. In summary, this study demonstrates the information gain for drought monitoring and early warning through impact data collection and analysis. It highlights the important role that quantitative analysis with impact data can have in providing "ground truth" for drought indicators, alongside more traditional stakeholder-led approaches.

Quantitative modeling of the reaction/diffusion kinetics of two-chemistry photopolymers

NASA Astrophysics Data System (ADS)

Kowalski, Benjamin Andrew

Optically driven diffusion in photopolymers is an appealing material platform for a broad range of applications, in which the recorded refractive index patterns serve either as images (e.g. data storage, display holography) or as optical elements (e.g. custom GRIN components, integrated optical devices). A quantitative understanding of the reaction/diffusion kinetics is difficult to obtain directly, but is nevertheless necessary in order to fully exploit the wide array of design freedoms in these materials. A general strategy for characterizing these kinetics is proposed, in which key processes are decoupled and independently measured. This strategy enables prediction of a material's potential refractive index change, solely on the basis of its chemical components. The degree to which a material does not reach this potential reveals the fraction of monomer that has participated in unwanted reactions, reducing spatial resolution and dynamic range. This approach is demonstrated for a model material similar to commercial media, achieving quantitative predictions of index response over three orders of exposure dose (~1 to ~103 mJ cm-2) and three orders of feature size (0.35 to 500 microns). The resulting insights enable guided, rational design of new material formulations with demonstrated performance improvement.

Frequency domain modeling and dynamic characteristics evaluation of existing wind turbine systems

NASA Astrophysics Data System (ADS)

Chiang, Chih-Hung; Yu, Chih-Peng

2016-04-01

It is quite well accepted that frequency domain procedures are suitable for the design and dynamic analysis of wind turbine structures, especially for floating offshore wind turbines, since random wind loads and wave induced motions are most likely simulated in the frequency domain. This paper presents specific applications of an effective frequency domain scheme to the linear analysis of wind turbine structures in which a 1-D spectral element was developed based on the axially-loaded member. The solution schemes are summarized for the spectral analyses of the tower, the blades, and the combined system with selected frequency-dependent coupling effect from foundation-structure interactions. Numerical examples demonstrate that the modal frequencies obtained using spectral-element models are in good agreement with those found in the literature. A 5-element mono-pile model results in less than 0.3% deviation from an existing 160-element model. It is preliminarily concluded that the proposed scheme is relatively efficient in performing quick verification for test data obtained from the on-site vibration measurement using the microwave interferometer.

A quantitative trait locus mixture model that avoids spurious LOD score peaks.

PubMed Central

Feenstra, Bjarke; Skovgaard, Ib M

2004-01-01

In standard interval mapping of quantitative trait loci (QTL), the QTL effect is described by a normal mixture model. At any given location in the genome, the evidence of a putative QTL is measured by the likelihood ratio of the mixture model compared to a single normal distribution (the LOD score). This approach can occasionally produce spurious LOD score peaks in regions of low genotype information (e.g., widely spaced markers), especially if the phenotype distribution deviates markedly from a normal distribution. Such peaks are not indicative of a QTL effect; rather, they are caused by the fact that a mixture of normals always produces a better fit than a single normal distribution. In this study, a mixture model for QTL mapping that avoids the problems of such spurious LOD score peaks is presented. PMID:15238544

Initial Description of a Quantitative, Cross-Species (Chimpanzee-Human) Social Responsiveness Measure

ERIC Educational Resources Information Center

Marrus, Natasha; Faughn, Carley; Shuman, Jeremy; Petersen, Steve E.; Constantino, John N.; Povinelli, Daniel J.; Pruett, John R., Jr.

2011-01-01

Objective: Comparative studies of social responsiveness, an ability that is impaired in autism spectrum disorders, can inform our understanding of both autism and the cognitive architecture of social behavior. Because there is no existing quantitative measure of social responsiveness in chimpanzees, we generated a quantitative, cross-species…

An Assessment of the Quantitative Literacy of Undergraduate Students

ERIC Educational Resources Information Center

Wilkins, Jesse L. M.

2016-01-01

Quantitative literacy (QLT) represents an underlying higher-order construct that accounts for a person's willingness to engage in quantitative situations in everyday life. The purpose of this study is to retest the construct validity of a model of quantitative literacy (Wilkins, 2010). In this model, QLT represents a second-order factor that…

Three-dimensional modeling and quantitative analysis of gap junction distributions in cardiac tissue.

PubMed

Lackey, Daniel P; Carruth, Eric D; Lasher, Richard A; Boenisch, Jan; Sachse, Frank B; Hitchcock, Robert W

2011-11-01

Gap junctions play a fundamental role in intercellular communication in cardiac tissue. Various types of heart disease including hypertrophy and ischemia are associated with alterations of the spatial arrangement of gap junctions. Previous studies applied two-dimensional optical and electron-microscopy to visualize gap junction arrangements. In normal cardiomyocytes, gap junctions were primarily found at cell ends, but can be found also in more central regions. In this study, we extended these approaches toward three-dimensional reconstruction of gap junction distributions based on high-resolution scanning confocal microscopy and image processing. We developed methods for quantitative characterization of gap junction distributions based on analysis of intensity profiles along the principal axes of myocytes. The analyses characterized gap junction polarization at cell ends and higher-order statistical image moments of intensity profiles. The methodology was tested in rat ventricular myocardium. Our analysis yielded novel quantitative data on gap junction distributions. In particular, the analysis demonstrated that the distributions exhibit significant variability with respect to polarization, skewness, and kurtosis. We suggest that this methodology provides a quantitative alternative to current approaches based on visual inspection, with applications in particular in characterization of engineered and diseased myocardium. Furthermore, we propose that these data provide improved input for computational modeling of cardiac conduction.

Thai student existing understanding about the solar system model and the motion of the stars

NASA Astrophysics Data System (ADS)

Anantasook, Sakanan; Yuenyong, Chokchai

2018-01-01

The paper examined Thai student existing understanding about the solar system model and the motion of the stars. The participants included 141 Grade 9 students in four different schools of the Surin province, Thailand. Methodology regarded interpretive paradigm. The tool of interpretation included the Student Celestial Motion Conception Questionnaire (SCMCQ) and informal interview. Given understandings in the SCMCQ were read through and categorized according to students' understandings. Then, students were further probed as informal interview. Students' understandings in each category were counted and percentages computed. Finally, students' understandings across four different schools were compared and contrasted using the percentage of student responses in each category. The findings revealed that most students understand about Sun-Moon-Earth (SME) system and solar system model as well, they can use scientific explanations to explain the celestial objects in solar system and how they orbiting. Unfortunately, most of students (more than 70%) never know about the Polaris, the North Star, and 90.1% of them never know about the ecliptic, and probably also the 12 zodiac constellations. These existing understanding suggested some ideas of teaching and learning about solar system model and the motion of the stars. The paper, then, discussed some learning activities to enhance students to further construct meaning about solar system model and the motion of the stars.

Quantitative Genetic Modeling of the Parental Care Hypothesis for the Evolution of Endothermy

PubMed Central

Bacigalupe, Leonardo D.; Moore, Allen J.; Nespolo, Roberto F.; Rezende, Enrico L.; Bozinovic, Francisco

2017-01-01

There are two heuristic explanations proposed for the evolution of endothermy in vertebrates: a correlated response to selection for stable body temperatures, or as a correlated response to increased activity. Parental care has been suggested as a major driving force in this context given its impact on the parents' activity levels and energy budgets, and in the offspring's growth rates due to food provisioning and controlled incubation temperature. This results in a complex scenario involving multiple traits and transgenerational fitness benefits that can be hard to disentangle, quantify and ultimately test. Here we demonstrate how standard quantitative genetic models of maternal effects can be applied to study the evolution of endothermy, focusing on the interplay between daily energy expenditure (DEE) of the mother and growth rates of the offspring. Our model shows that maternal effects can dramatically exacerbate evolutionary responses to selection in comparison to regular univariate models (breeder's equation). This effect would emerge from indirect selection mediated by maternal effects concomitantly with a positive genetic covariance between DEE and growth rates. The multivariate nature of selection, which could favor a higher DEE, higher growth rates or both, might partly explain how high turnover rates were continuously favored in a self-reinforcing process. Overall, our quantitative genetic analysis provides support for the parental care hypothesis for the evolution of endothermy. We contend that much has to be gained from quantifying maternal and developmental effects on metabolic and thermoregulatory variation during adulthood. PMID:29311952

Whole-brain ex-vivo quantitative MRI of the cuprizone mouse model

PubMed Central

Hurley, Samuel A.; Vernon, Anthony C.; Torres, Joel; Dell’Acqua, Flavio; Williams, Steve C.R.; Cash, Diana

2016-01-01

Myelin is a critical component of the nervous system and a major contributor to contrast in Magnetic Resonance (MR) images. However, the precise contribution of myelination to multiple MR modalities is still under debate. The cuprizone mouse is a well-established model of demyelination that has been used in several MR studies, but these have often imaged only a single slice and analysed a small region of interest in the corpus callosum. We imaged and analyzed the whole brain of the cuprizone mouse ex-vivo using high-resolution quantitative MR methods (multi-component relaxometry, Diffusion Tensor Imaging (DTI) and morphometry) and found changes in multiple regions, including the corpus callosum, cerebellum, thalamus and hippocampus. The presence of inflammation, confirmed with histology, presents difficulties in isolating the sensitivity and specificity of these MR methods to demyelination using this model. PMID:27833805

A network-based approach for semi-quantitative knowledge mining and its application to yield variability

NASA Astrophysics Data System (ADS)

Schauberger, Bernhard; Rolinski, Susanne; Müller, Christoph

2016-12-01

Variability of crop yields is detrimental for food security. Under climate change its amplitude is likely to increase, thus it is essential to understand the underlying causes and mechanisms. Crop models are the primary tool to project future changes in crop yields under climate change. A systematic overview of drivers and mechanisms of crop yield variability (YV) can thus inform crop model development and facilitate improved understanding of climate change impacts on crop yields. Yet there is a vast body of literature on crop physiology and YV, which makes a prioritization of mechanisms for implementation in models challenging. Therefore this paper takes on a novel approach to systematically mine and organize existing knowledge from the literature. The aim is to identify important mechanisms lacking in models, which can help to set priorities in model improvement. We structure knowledge from the literature in a semi-quantitative network. This network consists of complex interactions between growing conditions, plant physiology and crop yield. We utilize the resulting network structure to assign relative importance to causes of YV and related plant physiological processes. As expected, our findings confirm existing knowledge, in particular on the dominant role of temperature and precipitation, but also highlight other important drivers of YV. More importantly, our method allows for identifying the relevant physiological processes that transmit variability in growing conditions to variability in yield. We can identify explicit targets for the improvement of crop models. The network can additionally guide model development by outlining complex interactions between processes and by easily retrieving quantitative information for each of the 350 interactions. We show the validity of our network method as a structured, consistent and scalable dictionary of literature. The method can easily be applied to many other research fields.

A toy model to investigate the existence of excitons in the ground state of strongly-correlated semiconductor

NASA Astrophysics Data System (ADS)

Karima, H. R.; Majidi, M. A.

2018-04-01

Excitons, quasiparticles associated with bound states between an electron and a hole and are typically created when photons with a suitable energy are absorbed in a solid-state material. We propose to study a possible emergence of excitons, created not by photon absorption but the effect of strong electronic correlations. This study is motivated by a recent experimental study of a substrate material SrTiO3 (STO) that reveals strong exitonic signals in its optical conductivity. Here we conjecture that some excitons may already exist in the ground state as a result of the electronic correlations before the additional excitons being created later by photon absorption. To investigate the existence of excitons in the ground state, we propose to study a simple 4-energy-level model that mimics a situation in strongly-correlated semiconductors. The four levels are divided into two groups, lower and upper groups separated by an energy gap, Eg , mimicking the valence and the conduction bands, respectively. Further, we incorporate repulsive Coulomb interactions between the electrons. The model is then solved by exact diagonalization method. Our result shows that the toy model can demonstrate band gap widening or narrowing and the existence of exciton in the ground state depending on interaction parameter values.

Satellite Contributions to the Quantitative Characterization of Biomass Burning for Climate Modeling

NASA Technical Reports Server (NTRS)

Ichoku, Charles; Kahn, Ralph; Chin, Mian

2012-01-01

Characterization of biomass burning from space has been the subject of an extensive body of literature published over the last few decades. Given the importance of this topic, we review how satellite observations contribute toward improving the representation of biomass burning quantitatively in climate and air-quality modeling and assessment. Satellite observations related to biomass burning may be classified into five broad categories: (i) active fire location and energy release, (ii) burned areas and burn severity, (iii) smoke plume physical disposition, (iv) aerosol distribution and particle properties, and (v) trace gas concentrations. Each of these categories involves multiple parameters used in characterizing specific aspects of the biomass-burning phenomenon. Some of the parameters are merely qualitative, whereas others are quantitative, although all are essential for improving the scientific understanding of the overall distribution (both spatial and temporal) and impacts of biomass burning. Some of the qualitative satellite datasets, such as fire locations, aerosol index, and gas estimates have fairly long-term records. They date back as far as the 1970s, following the launches of the DMSP, Landsat, NOAA, and Nimbus series of earth observation satellites. Although there were additional satellite launches in the 1980s and 1990s, space-based retrieval of quantitative biomass burning data products began in earnest following the launch of Terra in December 1999. Starting in 2000, fire radiative power, aerosol optical thickness and particle properties over land, smoke plume injection height and profile, and essential trace gas concentrations at improved resolutions became available. The 2000s also saw a large list of other new satellite launches, including Aqua, Aura, Envisat, Parasol, and CALIPSO, carrying a host of sophisticated instruments providing high quality measurements of parameters related to biomass burning and other phenomena. These improved data

Quantitative Structure – Property Relationship Modeling of Remote Liposome Loading Of Drugs

PubMed Central

Cern, Ahuva; Golbraikh, Alexander; Sedykh, Aleck; Tropsha, Alexander; Barenholz, Yechezkel; Goldblum, Amiram

2012-01-01

Remote loading of liposomes by trans-membrane gradients is used to achieve therapeutically efficacious intra-liposome concentrations of drugs. We have developed Quantitative Structure Property Relationship (QSPR) models of remote liposome loading for a dataset including 60 drugs studied in 366 loading experiments internally or elsewhere. Both experimental conditions and computed chemical descriptors were employed as independent variables to predict the initial drug/lipid ratio (D/L) required to achieve high loading efficiency. Both binary (to distinguish high vs. low initial D/L) and continuous (to predict real D/L values) models were generated using advanced machine learning approaches and five-fold external validation. The external prediction accuracy for binary models was as high as 91–96%; for continuous models the mean coefficient R2 for regression between predicted versus observed values was 0.76–0.79. We conclude that QSPR models can be used to identify candidate drugs expected to have high remote loading capacity while simultaneously optimizing the design of formulation experiments. PMID:22154932

Mechanism of variable structural colour in the neon tetra: quantitative evaluation of the Venetian blind model.

PubMed

Yoshioka, S; Matsuhana, B; Tanaka, S; Inouye, Y; Oshima, N; Kinoshita, S

2011-01-06

The structural colour of the neon tetra is distinguishable from those of, e.g., butterfly wings and bird feathers, because it can change in response to the light intensity of the surrounding environment. This fact clearly indicates the variability of the colour-producing microstructures. It has been known that an iridophore of the neon tetra contains a few stacks of periodically arranged light-reflecting platelets, which can cause multilayer optical interference phenomena. As a mechanism of the colour variability, the Venetian blind model has been proposed, in which the light-reflecting platelets are assumed to be tilted during colour change, resulting in a variation in the spacing between the platelets. In order to quantitatively evaluate the validity of this model, we have performed a detailed optical study of a single stack of platelets inside an iridophore. In particular, we have prepared a new optical system that can simultaneously measure both the spectrum and direction of the reflected light, which are expected to be closely related to each other in the Venetian blind model. The experimental results and detailed analysis are found to quantitatively verify the model.

Toward Accurate and Quantitative Comparative Metagenomics.

PubMed

Nayfach, Stephen; Pollard, Katherine S

2016-08-25

Shotgun metagenomics and computational analysis are used to compare the taxonomic and functional profiles of microbial communities. Leveraging this approach to understand roles of microbes in human biology and other environments requires quantitative data summaries whose values are comparable across samples and studies. Comparability is currently hampered by the use of abundance statistics that do not estimate a meaningful parameter of the microbial community and biases introduced by experimental protocols and data-cleaning approaches. Addressing these challenges, along with improving study design, data access, metadata standardization, and analysis tools, will enable accurate comparative metagenomics. We envision a future in which microbiome studies are replicable and new metagenomes are easily and rapidly integrated with existing data. Only then can the potential of metagenomics for predictive ecological modeling, well-powered association studies, and effective microbiome medicine be fully realized. Copyright © 2016 Elsevier Inc. All rights reserved.

Existence of global weak solution for a reduced gravity two and a half layer model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guo, Zhenhua, E-mail: zhenhua.guo.math@gmail.com; Li, Zilai, E-mail: lizilai0917@163.com; Yao, Lei, E-mail: yaolei1056@hotmail.com

2013-12-15

We investigate the existence of global weak solution to a reduced gravity two and a half layer model in one-dimensional bounded spatial domain or periodic domain. Also, we show that any possible vacuum state has to vanish within finite time, then the weak solution becomes a unique strong one.

Existence and global attractivity of unique positive periodic solution for a model of hematopoiesis

NASA Astrophysics Data System (ADS)

Liu, Guirong; Yan, Jurang; Zhang, Fengqin

2007-10-01

In this paper, we consider the generalized model of hematopoiesis By using a fixed point theorem, some criteria are established for the existence of the unique positive [omega]-periodic solution of the above equation. In particular, we not only give the conclusion of convergence of xk to , where {xk} is a successive sequence, but also show that is a global attractor of all other positive solutions.

[Influence of Spectral Pre-Processing on PLS Quantitative Model of Detecting Cu in Navel Orange by LIBS].

PubMed

Li, Wen-bing; Yao, Lin-tao; Liu, Mu-hua; Huang, Lin; Yao, Ming-yin; Chen, Tian-bing; He, Xiu-wen; Yang, Ping; Hu, Hui-qin; Nie, Jiang-hui

2015-05-01

Cu in navel orange was detected rapidly by laser-induced breakdown spectroscopy (LIBS) combined with partial least squares (PLS) for quantitative analysis, then the effect on the detection accuracy of the model with different spectral data ptetreatment methods was explored. Spectral data for the 52 Gannan navel orange samples were pretreated by different data smoothing, mean centralized and standard normal variable transform. Then 319~338 nm wavelength section containing characteristic spectral lines of Cu was selected to build PLS models, the main evaluation indexes of models such as regression coefficient (r), root mean square error of cross validation (RMSECV) and the root mean square error of prediction (RMSEP) were compared and analyzed. Three indicators of PLS model after 13 points smoothing and processing of the mean center were found reaching 0. 992 8, 3. 43 and 3. 4 respectively, the average relative error of prediction model is only 5. 55%, and in one word, the quality of calibration and prediction of this model are the best results. The results show that selecting the appropriate data pre-processing method, the prediction accuracy of PLS quantitative model of fruits and vegetables detected by LIBS can be improved effectively, providing a new method for fast and accurate detection of fruits and vegetables by LIBS.

Quantitative Agent Based Model of Opinion Dynamics: Polish Elections of 2015

PubMed Central

Sobkowicz, Pawel

2016-01-01

We present results of an abstract, agent based model of opinion dynamics simulations based on the emotion/information/opinion (E/I/O) approach, applied to a strongly polarized society, corresponding to the Polish political scene between 2005 and 2015. Under certain conditions the model leads to metastable coexistence of two subcommunities of comparable size (supporting the corresponding opinions)—which corresponds to the bipartisan split found in Poland. Spurred by the recent breakdown of this political duopoly, which occurred in 2015, we present a model extension that describes both the long term coexistence of the two opposing opinions and a rapid, transitory change due to the appearance of a third party alternative. We provide quantitative comparison of the model with the results of polls and elections in Poland, testing the assumptions related to the modeled processes and the parameters used in the simulations. It is shown, that when the propaganda messages of the two incumbent parties differ in emotional tone, the political status quo may be unstable. The asymmetry of the emotions within the support bases of the two parties allows one of them to be ‘invaded’ by a newcomer third party very quickly, while the second remains immune to such invasion. PMID:27171226

Quantitative Modeling of Entangled Polymer Rheology: Experiments, Tube Models and Slip-Link Simulations

NASA Astrophysics Data System (ADS)

Desai, Priyanka Subhash

Rheology properties are sensitive indicators of molecular structure and dynamics. The relationship between rheology and polymer dynamics is captured in the constitutive model, which, if accurate and robust, would greatly aid molecular design and polymer processing. This dissertation is thus focused on building accurate and quantitative constitutive models that can help predict linear and non-linear viscoelasticity. In this work, we have used a multi-pronged approach based on the tube theory, coarse-grained slip-link simulations, and advanced polymeric synthetic and characterization techniques, to confront some of the outstanding problems in entangled polymer rheology. First, we modified simple tube based constitutive equations in extensional rheology and developed functional forms to test the effect of Kuhn segment alignment on a) tube diameter enlargement and b) monomeric friction reduction between subchains. We, then, used these functional forms to model extensional viscosity data for polystyrene (PS) melts and solutions. We demonstrated that the idea of reduction in segmental friction due to Kuhn alignment is successful in explaining the qualitative difference between melts and solutions in extension as revealed by recent experiments on PS. Second, we compiled literature data and used it to develop a universal tube model parameter set and prescribed their values and uncertainties for 1,4-PBd by comparing linear viscoelastic G' and G" mastercurves for 1,4-PBds of various branching architectures. The high frequency transition region of the mastercurves superposed very well for all the 1,4-PBds irrespective of their molecular weight and architecture, indicating universality in high frequency behavior. Therefore, all three parameters of the tube model were extracted from this high frequency transition region alone. Third, we compared predictions of two versions of the tube model, Hierarchical model and BoB model against linear viscoelastic data of blends of 1,4-PBd

Quantitative skills as a graduate learning outcome of university science degree programmes: student performance explored through theplanned-enacted-experiencedcurriculum model

NASA Astrophysics Data System (ADS)

Matthews, Kelly E.; Adams, Peter; Goos, Merrilyn

2016-07-01

Application of mathematical and statistical thinking and reasoning, typically referred to as quantitative skills, is essential for university bioscience students. First, this study developed an assessment task intended to gauge graduating students' quantitative skills. The Quantitative Skills Assessment of Science Students (QSASS) was the result, which examined 10 mathematical and statistical sub-topics. Second, the study established an evidential baseline of students' quantitative skills performance and confidence levels by piloting the QSASS with 187 final-year biosciences students at a research-intensive university. The study is framed within the planned-enacted-experienced curriculum model and contributes to science reform efforts focused on enhancing the quantitative skills of university graduates, particularly in the biosciences. The results found, on average, weak performance and low confidence on the QSASS, suggesting divergence between academics' intentions and students' experiences of learning quantitative skills. Implications for curriculum design and future studies are discussed.

Development of an Experimental Model of Diabetes Co-Existing with Metabolic Syndrome in Rats.

PubMed

Suman, Rajesh Kumar; Ray Mohanty, Ipseeta; Borde, Manjusha K; Maheshwari, Ujwala; Deshmukh, Y A

2016-01-01

Background. The incidence of metabolic syndrome co-existing with diabetes mellitus is on the rise globally. Objective. The present study was designed to develop a unique animal model that will mimic the pathological features seen in individuals with diabetes and metabolic syndrome, suitable for pharmacological screening of drugs. Materials and Methods. A combination of High-Fat Diet (HFD) and low dose of streptozotocin (STZ) at 30, 35, and 40 mg/kg was used to induce metabolic syndrome in the setting of diabetes mellitus in Wistar rats. Results. The 40 mg/kg STZ produced sustained hyperglycemia and the dose was thus selected for the study to induce diabetes mellitus. Various components of metabolic syndrome such as dyslipidemia {(increased triglyceride, total cholesterol, LDL cholesterol, and decreased HDL cholesterol)}, diabetes mellitus (blood glucose, HbA1c, serum insulin, and C-peptide), and hypertension {systolic blood pressure} were mimicked in the developed model of metabolic syndrome co-existing with diabetes mellitus. In addition to significant cardiac injury, atherogenic index, inflammation (hs-CRP), decline in hepatic and renal function were observed in the HF-DC group when compared to NC group rats. The histopathological assessment confirmed presence of edema, necrosis, and inflammation in heart, pancreas, liver, and kidney of HF-DC group as compared to NC. Conclusion. The present study has developed a unique rodent model of metabolic syndrome, with diabetes as an essential component.

Quantitative modeling and optimization of magnetic tweezers.

PubMed

Lipfert, Jan; Hao, Xiaomin; Dekker, Nynke H

2009-06-17

Magnetic tweezers are a powerful tool to manipulate single DNA or RNA molecules and to study nucleic acid-protein interactions in real time. Here, we have modeled the magnetic fields of permanent magnets in magnetic tweezers and computed the forces exerted on superparamagnetic beads from first principles. For simple, symmetric geometries the magnetic fields can be calculated semianalytically using the Biot-Savart law. For complicated geometries and in the presence of an iron yoke, we employ a finite-element three-dimensional PDE solver to numerically solve the magnetostatic problem. The theoretical predictions are in quantitative agreement with direct Hall-probe measurements of the magnetic field and with measurements of the force exerted on DNA-tethered beads. Using these predictive theories, we systematically explore the effects of magnet alignment, magnet spacing, magnet size, and of adding an iron yoke to the magnets on the forces that can be exerted on tethered particles. We find that the optimal configuration for maximal stretching forces is a vertically aligned pair of magnets, with a minimal gap between the magnets and minimal flow cell thickness. Following these principles, we present a configuration that allows one to apply > or = 40 pN stretching forces on approximately 1-microm tethered beads.

Quantitative Modeling and Optimization of Magnetic Tweezers

PubMed Central

Lipfert, Jan; Hao, Xiaomin; Dekker, Nynke H.

2009-01-01

Abstract Magnetic tweezers are a powerful tool to manipulate single DNA or RNA molecules and to study nucleic acid-protein interactions in real time. Here, we have modeled the magnetic fields of permanent magnets in magnetic tweezers and computed the forces exerted on superparamagnetic beads from first principles. For simple, symmetric geometries the magnetic fields can be calculated semianalytically using the Biot-Savart law. For complicated geometries and in the presence of an iron yoke, we employ a finite-element three-dimensional PDE solver to numerically solve the magnetostatic problem. The theoretical predictions are in quantitative agreement with direct Hall-probe measurements of the magnetic field and with measurements of the force exerted on DNA-tethered beads. Using these predictive theories, we systematically explore the effects of magnet alignment, magnet spacing, magnet size, and of adding an iron yoke to the magnets on the forces that can be exerted on tethered particles. We find that the optimal configuration for maximal stretching forces is a vertically aligned pair of magnets, with a minimal gap between the magnets and minimal flow cell thickness. Following these principles, we present a configuration that allows one to apply ≥40 pN stretching forces on ≈1-μm tethered beads. PMID:19527664

Global existence of the three-dimensional viscous quantum magnetohydrodynamic model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Jianwei, E-mail: yangjianwei@ncwu.edu.cn; Ju, Qiangchang, E-mail: qiangchang-ju@yahoo.com

2014-08-15

The global-in-time existence of weak solutions to the viscous quantum Magnetohydrodynamic equations in a three-dimensional torus with large data is proved. The global existence of weak solutions to the viscous quantum Magnetohydrodynamic equations is shown by using the Faedo-Galerkin method and weak compactness techniques.

A two-locus model of spatially varying stabilizing or directional selection on a quantitative trait

PubMed Central

Geroldinger, Ludwig; Bürger, Reinhard

2014-01-01

The consequences of spatially varying, stabilizing or directional selection on a quantitative trait in a subdivided population are studied. A deterministic two-locus two-deme model is employed to explore the effects of migration, the degree of divergent selection, and the genetic architecture, i.e., the recombination rate and ratio of locus effects, on the maintenance of genetic variation. The possible equilibrium configurations are determined as functions of the migration rate. They depend crucially on the strength of divergent selection and the genetic architecture. The maximum migration rates are investigated below which a stable fully polymorphic equilibrium or a stable single-locus polymorphism can exist. Under stabilizing selection, but with different optima in the demes, strong recombination may facilitate the maintenance of polymorphism. However usually, and in particular with directional selection in opposite direction, the critical migration rates are maximized by a concentrated genetic architecture, i.e., by a major locus and a tightly linked minor one. Thus, complementing previous work on the evolution of genetic architectures in subdivided populations subject to diversifying selection, it is shown that concentrated architectures may aid the maintenance of polymorphism. Conditions are obtained when this is the case. Finally, the dependence of the phenotypic variance, linkage disequilibrium, and various measures of local adaptation and differentiation on the parameters is elaborated. PMID:24726489

A two-locus model of spatially varying stabilizing or directional selection on a quantitative trait.

PubMed

Geroldinger, Ludwig; Bürger, Reinhard

2014-06-01

The consequences of spatially varying, stabilizing or directional selection on a quantitative trait in a subdivided population are studied. A deterministic two-locus two-deme model is employed to explore the effects of migration, the degree of divergent selection, and the genetic architecture, i.e., the recombination rate and ratio of locus effects, on the maintenance of genetic variation. The possible equilibrium configurations are determined as functions of the migration rate. They depend crucially on the strength of divergent selection and the genetic architecture. The maximum migration rates are investigated below which a stable fully polymorphic equilibrium or a stable single-locus polymorphism can exist. Under stabilizing selection, but with different optima in the demes, strong recombination may facilitate the maintenance of polymorphism. However usually, and in particular with directional selection in opposite direction, the critical migration rates are maximized by a concentrated genetic architecture, i.e., by a major locus and a tightly linked minor one. Thus, complementing previous work on the evolution of genetic architectures in subdivided populations subject to diversifying selection, it is shown that concentrated architectures may aid the maintenance of polymorphism. Conditions are obtained when this is the case. Finally, the dependence of the phenotypic variance, linkage disequilibrium, and various measures of local adaptation and differentiation on the parameters is elaborated. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Final Report for Dynamic Models for Causal Analysis of Panel Data. Models for Change in Quantitative Variables, Part II Scholastic Models. Part II, Chapter 4.

ERIC Educational Resources Information Center

Hannan, Michael T.

This document is part of a series of chapters described in SO 011 759. Stochastic models for the sociological analysis of change and the change process in quantitative variables are presented. The author lays groundwork for the statistical treatment of simple stochastic differential equations (SDEs) and discusses some of the continuities of…

Platform-independent and Label-free Quantitation of Proteomic Data Using MS1 Extracted Ion Chromatograms in Skyline

PubMed Central

Schilling, Birgit; Rardin, Matthew J.; MacLean, Brendan X.; Zawadzka, Anna M.; Frewen, Barbara E.; Cusack, Michael P.; Sorensen, Dylan J.; Bereman, Michael S.; Jing, Enxuan; Wu, Christine C.; Verdin, Eric; Kahn, C. Ronald; MacCoss, Michael J.; Gibson, Bradford W.

2012-01-01

Despite advances in metabolic and postmetabolic labeling methods for quantitative proteomics, there remains a need for improved label-free approaches. This need is particularly pressing for workflows that incorporate affinity enrichment at the peptide level, where isobaric chemical labels such as isobaric tags for relative and absolute quantitation and tandem mass tags may prove problematic or where stable isotope labeling with amino acids in cell culture labeling cannot be readily applied. Skyline is a freely available, open source software tool for quantitative data processing and proteomic analysis. We expanded the capabilities of Skyline to process ion intensity chromatograms of peptide analytes from full scan mass spectral data (MS1) acquired during HPLC MS/MS proteomic experiments. Moreover, unlike existing programs, Skyline MS1 filtering can be used with mass spectrometers from four major vendors, which allows results to be compared directly across laboratories. The new quantitative and graphical tools now available in Skyline specifically support interrogation of multiple acquisitions for MS1 filtering, including visual inspection of peak picking and both automated and manual integration, key features often lacking in existing software. In addition, Skyline MS1 filtering displays retention time indicators from underlying MS/MS data contained within the spectral library to ensure proper peak selection. The modular structure of Skyline also provides well defined, customizable data reports and thus allows users to directly connect to existing statistical programs for post hoc data analysis. To demonstrate the utility of the MS1 filtering approach, we have carried out experiments on several MS platforms and have specifically examined the performance of this method to quantify two important post-translational modifications: acetylation and phosphorylation, in peptide-centric affinity workflows of increasing complexity using mouse and human models. PMID:22454539

Multiplicative effects model with internal standard in mobile phase for quantitative liquid chromatography-mass spectrometry.

PubMed

Song, Mi; Chen, Zeng-Ping; Chen, Yao; Jin, Jing-Wen

2014-07-01

Liquid chromatography-mass spectrometry assays suffer from signal instability caused by the gradual fouling of the ion source, vacuum instability, aging of the ion multiplier, etc. To address this issue, in this contribution, an internal standard was added into the mobile phase. The internal standard was therefore ionized and detected together with the analytes of interest by the mass spectrometer to ensure that variations in measurement conditions and/or instrument have similar effects on the signal contributions of both the analytes of interest and the internal standard. Subsequently, based on the unique strategy of adding internal standard in mobile phase, a multiplicative effects model was developed for quantitative LC-MS assays and tested on a proof of concept model system: the determination of amino acids in water by LC-MS. The experimental results demonstrated that the proposed method could efficiently mitigate the detrimental effects of continuous signal variation, and achieved quantitative results with average relative predictive error values in the range of 8.0-15.0%, which were much more accurate than the corresponding results of conventional internal standard method based on the peak height ratio and partial least squares method (their average relative predictive error values were as high as 66.3% and 64.8%, respectively). Therefore, it is expected that the proposed method can be developed and extended in quantitative LC-MS analysis of more complex systems. Copyright © 2014 Elsevier B.V. All rights reserved.

Evaluation of New Zealand’s High-Seas Bottom Trawl Closures Using Predictive Habitat Models and Quantitative Risk Assessment

PubMed Central

Penney, Andrew J.; Guinotte, John M.

2013-01-01

United Nations General Assembly Resolution 61/105 on sustainable fisheries (UNGA 2007) establishes three difficult questions for participants in high-seas bottom fisheries to answer: 1) Where are vulnerable marine systems (VMEs) likely to occur?; 2) What is the likelihood of fisheries interaction with these VMEs?; and 3) What might qualify as adequate conservation and management measures to prevent significant adverse impacts? This paper develops an approach to answering these questions for bottom trawling activities in the Convention Area of the South Pacific Regional Fisheries Management Organisation (SPRFMO) within a quantitative risk assessment and cost : benefit analysis framework. The predicted distribution of deep-sea corals from habitat suitability models is used to answer the first question. Distribution of historical bottom trawl effort is used to answer the second, with estimates of seabed areas swept by bottom trawlers being used to develop discounting factors for reduced biodiversity in previously fished areas. These are used in a quantitative ecological risk assessment approach to guide spatial protection planning to address the third question. The coral VME likelihood (average, discounted, predicted coral habitat suitability) of existing spatial closures implemented by New Zealand within the SPRFMO area is evaluated. Historical catch is used as a measure of cost to industry in a cost : benefit analysis of alternative spatial closure scenarios. Results indicate that current closures within the New Zealand SPRFMO area bottom trawl footprint are suboptimal for protection of VMEs. Examples of alternative trawl closure scenarios are provided to illustrate how the approach could be used to optimise protection of VMEs under chosen management objectives, balancing protection of VMEs against economic loss to commercial fishers from closure of historically fished areas. PMID:24358162

Study on quantitative risk assessment model of the third party damage for natural gas pipelines based on fuzzy comprehensive assessment

NASA Astrophysics Data System (ADS)

Qiu, Zeyang; Liang, Wei; Wang, Xue; Lin, Yang; Zhang, Meng

2017-05-01

As an important part of national energy supply system, transmission pipelines for natural gas are possible to cause serious environmental pollution, life and property loss in case of accident. The third party damage is one of the most significant causes for natural gas pipeline system accidents, and it is very important to establish an effective quantitative risk assessment model of the third party damage for reducing the number of gas pipelines operation accidents. Against the third party damage accident has the characteristics such as diversity, complexity and uncertainty, this paper establishes a quantitative risk assessment model of the third party damage based on Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation (FCE). Firstly, risk sources of third party damage should be identified exactly, and the weight of factors could be determined via improved AHP, finally the importance of each factor is calculated by fuzzy comprehensive evaluation model. The results show that the quantitative risk assessment model is suitable for the third party damage of natural gas pipelines and improvement measures could be put forward to avoid accidents based on the importance of each factor.

Quantitative Measurement of Eyestrain on 3D Stereoscopic Display Considering the Eye Foveation Model and Edge Information

PubMed Central

Heo, Hwan; Lee, Won Oh; Shin, Kwang Yong; Park, Kang Ryoung

2014-01-01

We propose a new method for measuring the degree of eyestrain on 3D stereoscopic displays using a glasses-type of eye tracking device. Our study is novel in the following four ways: first, the circular area where a user's gaze position exists is defined based on the calculated gaze position and gaze estimation error. Within this circular area, the position where edge strength is maximized can be detected, and we determine this position as the gaze position that has a higher probability of being the correct one. Based on this gaze point, the eye foveation model is defined. Second, we quantitatively evaluate the correlation between the degree of eyestrain and the causal factors of visual fatigue, such as the degree of change of stereoscopic disparity (CSD), stereoscopic disparity (SD), frame cancellation effect (FCE), and edge component (EC) of the 3D stereoscopic display using the eye foveation model. Third, by comparing the eyestrain in conventional 3D video and experimental 3D sample video, we analyze the characteristics of eyestrain according to various factors and types of 3D video. Fourth, by comparing the eyestrain with or without the compensation of eye saccades movement in 3D video, we analyze the characteristics of eyestrain according to the types of eye movements in 3D video. Experimental results show that the degree of CSD causes more eyestrain than other factors. PMID:24834910

Quantitative Microbial Risk Assessment and Infectious Disease Transmission Modeling of Waterborne Enteric Pathogens.

PubMed

Brouwer, Andrew F; Masters, Nina B; Eisenberg, Joseph N S

2018-04-20

Waterborne enteric pathogens remain a global health threat. Increasingly, quantitative microbial risk assessment (QMRA) and infectious disease transmission modeling (IDTM) are used to assess waterborne pathogen risks and evaluate mitigation. These modeling efforts, however, have largely been conducted independently for different purposes and in different settings. In this review, we examine the settings where each modeling strategy is employed. QMRA research has focused on food contamination and recreational water in high-income countries (HICs) and drinking water and wastewater in low- and middle-income countries (LMICs). IDTM research has focused on large outbreaks (predominately LMICs) and vaccine-preventable diseases (LMICs and HICs). Human ecology determines the niches that pathogens exploit, leading researchers to focus on different risk assessment research strategies in different settings. To enhance risk modeling, QMRA and IDTM approaches should be integrated to include dynamics of pathogens in the environment and pathogen transmission through populations.

Quantitative CMMI Assessment for Offshoring through the Analysis of Project Management Repositories

NASA Astrophysics Data System (ADS)

Sunetnanta, Thanwadee; Nobprapai, Ni-On; Gotel, Olly

The nature of distributed teams and the existence of multiple sites in offshore software development projects pose a challenging setting for software process improvement. Often, the improvement and appraisal of software processes is achieved through a turnkey solution where best practices are imposed or transferred from a company’s headquarters to its offshore units. In so doing, successful project health checks and monitoring for quality on software processes requires strong project management skills, well-built onshore-offshore coordination, and often needs regular onsite visits by software process improvement consultants from the headquarters’ team. This paper focuses on software process improvement as guided by the Capability Maturity Model Integration (CMMI) and proposes a model to evaluate the status of such improvement efforts in the context of distributed multi-site projects without some of this overhead. The paper discusses the application of quantitative CMMI assessment through the collection and analysis of project data gathered directly from project repositories to facilitate CMMI implementation and reduce the cost of such implementation for offshore-outsourced software development projects. We exemplify this approach to quantitative CMMI assessment through the analysis of project management data and discuss the future directions of this work in progress.

Conditional Toxicity Value (CTV) Predictor: An In Silico Approach for Generating Quantitative Risk Estimates for Chemicals.

PubMed

Wignall, Jessica A; Muratov, Eugene; Sedykh, Alexander; Guyton, Kathryn Z; Tropsha, Alexander; Rusyn, Ivan; Chiu, Weihsueh A

2018-05-01

Human health assessments synthesize human, animal, and mechanistic data to produce toxicity values that are key inputs to risk-based decision making. Traditional assessments are data-, time-, and resource-intensive, and they cannot be developed for most environmental chemicals owing to a lack of appropriate data. As recommended by the National Research Council, we propose a solution for predicting toxicity values for data-poor chemicals through development of quantitative structure-activity relationship (QSAR) models. We used a comprehensive database of chemicals with existing regulatory toxicity values from U.S. federal and state agencies to develop quantitative QSAR models. We compared QSAR-based model predictions to those based on high-throughput screening (HTS) assays. QSAR models for noncancer threshold-based values and cancer slope factors had cross-validation-based Q 2 of 0.25-0.45, mean model errors of 0.70-1.11 log 10 units, and applicability domains covering >80% of environmental chemicals. Toxicity values predicted from QSAR models developed in this study were more accurate and precise than those based on HTS assays or mean-based predictions. A publicly accessible web interface to make predictions for any chemical of interest is available at http://toxvalue.org. An in silico tool that can predict toxicity values with an uncertainty of an order of magnitude or less can be used to quickly and quantitatively assess risks of environmental chemicals when traditional toxicity data or human health assessments are unavailable. This tool can fill a critical gap in the risk assessment and management of data-poor chemicals. https://doi.org/10.1289/EHP2998.

Pleiotropy Analysis of Quantitative Traits at Gene Level by Multivariate Functional Linear Models

PubMed Central

Wang, Yifan; Liu, Aiyi; Mills, James L.; Boehnke, Michael; Wilson, Alexander F.; Bailey-Wilson, Joan E.; Xiong, Momiao; Wu, Colin O.; Fan, Ruzong

2015-01-01

In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai–Bartlett trace, Hotelling–Lawley trace, and Wilks’s Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case. PMID:25809955

Pleiotropy analysis of quantitative traits at gene level by multivariate functional linear models.

PubMed

Wang, Yifan; Liu, Aiyi; Mills, James L; Boehnke, Michael; Wilson, Alexander F; Bailey-Wilson, Joan E; Xiong, Momiao; Wu, Colin O; Fan, Ruzong

2015-05-01

In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case. © 2015 WILEY PERIODICALS, INC.

A review of quantitative structure-property relationships for the fate of ionizable organic chemicals in water matrices and identification of knowledge gaps.

PubMed

Nolte, Tom M; Ragas, Ad M J

2017-03-22

Many organic chemicals are ionizable by nature. After use and release into the environment, various fate processes determine their concentrations, and hence exposure to aquatic organisms. In the absence of suitable data, such fate processes can be estimated using Quantitative Structure-Property Relationships (QSPRs). In this review we compiled available QSPRs from the open literature and assessed their applicability towards ionizable organic chemicals. Using quantitative and qualitative criteria we selected the 'best' QSPRs for sorption, (a)biotic degradation, and bioconcentration. The results indicate that many suitable QSPRs exist, but some critical knowledge gaps remain. Specifically, future focus should be directed towards the development of QSPR models for biodegradation in wastewater and sediment systems, direct photolysis and reaction with singlet oxygen, as well as additional reactive intermediates. Adequate QSPRs for bioconcentration in fish exist, but more accurate assessments can be achieved using pharmacologically based toxicokinetic (PBTK) models. No adequate QSPRs exist for bioconcentration in non-fish species. Due to the high variability of chemical and biological species as well as environmental conditions in QSPR datasets, accurate predictions for specific systems and inter-dataset conversions are problematic, for which standardization is needed. For all QSPR endpoints, additional data requirements involve supplementing the current chemical space covered and accurately characterizing the test systems used.

Quantitative systems toxicology

PubMed Central

Bloomingdale, Peter; Housand, Conrad; Apgar, Joshua F.; Millard, Bjorn L.; Mager, Donald E.; Burke, John M.; Shah, Dhaval K.

2017-01-01

The overarching goal of modern drug development is to optimize therapeutic benefits while minimizing adverse effects. However, inadequate efficacy and safety concerns remain to be the major causes of drug attrition in clinical development. For the past 80 years, toxicity testing has consisted of evaluating the adverse effects of drugs in animals to predict human health risks. The U.S. Environmental Protection Agency recognized the need to develop innovative toxicity testing strategies and asked the National Research Council to develop a long-range vision and strategy for toxicity testing in the 21st century. The vision aims to reduce the use of animals and drug development costs through the integration of computational modeling and in vitro experimental methods that evaluates the perturbation of toxicity-related pathways. Towards this vision, collaborative quantitative systems pharmacology and toxicology modeling endeavors (QSP/QST) have been initiated amongst numerous organizations worldwide. In this article, we discuss how quantitative structure-activity relationship (QSAR), network-based, and pharmacokinetic/pharmacodynamic modeling approaches can be integrated into the framework of QST models. Additionally, we review the application of QST models to predict cardiotoxicity and hepatotoxicity of drugs throughout their development. Cell and organ specific QST models are likely to become an essential component of modern toxicity testing, and provides a solid foundation towards determining individualized therapeutic windows to improve patient safety. PMID:29308440

Quantitative Microbial Risk Assessment Tutorial Installation of Software for Watershed Modeling in Support of QMRA - Updated 2017

EPA Science Inventory

This tutorial provides instructions for accessing, retrieving, and downloading the following software to install on a host computer in support of Quantitative Microbial Risk Assessment (QMRA) modeling: • QMRA Installation • SDMProjectBuilder (which includes the Microbial ...

Downscaling SSPs in the GBM Delta - Integrating Science, Modelling and Stakeholders Through Qualitative and Quantitative Scenarios

NASA Astrophysics Data System (ADS)

Allan, Andrew; Barbour, Emily; Salehin, Mashfiqus; Munsur Rahman, Md.; Hutton, Craig; Lazar, Attila

2016-04-01

A downscaled scenario development process was adopted in the context of a project seeking to understand relationships between ecosystem services and human well-being in the Ganges-Brahmaputra delta. The aim was to link the concerns and priorities of relevant stakeholders with the integrated biophysical and poverty models used in the project. A 2-stage process was used to facilitate the connection between stakeholders concerns and available modelling capacity: the first to qualitatively describe what the future might look like in 2050; the second to translate these qualitative descriptions into the quantitative form required by the numerical models. An extended, modified SSP approach was adopted, with stakeholders downscaling issues identified through interviews as being priorities for the southwest of Bangladesh. Detailed qualitative futures were produced, before modellable elements were quantified in conjunction with an expert stakeholder cadre. Stakeholder input, using the methods adopted here, allows the top-down focus of the RCPs to be aligned with the bottom-up approach needed to make the SSPs appropriate at the more local scale, and also facilitates the translation of qualitative narrative scenarios into a quantitative form that lends itself to incorporation of biophysical and socio-economic indicators. The presentation will describe the downscaling process in detail, and conclude with findings regarding the importance of stakeholder involvement (and logistical considerations), balancing model capacity with expectations and recommendations on SSP refinement at local levels.

Molecular Modeling on Berberine Derivatives toward BuChE: An Integrated Study with Quantitative Structure-Activity Relationships Models, Molecular Docking, and Molecular Dynamics Simulations.

PubMed

Fang, Jiansong; Pang, Xiaocong; Wu, Ping; Yan, Rong; Gao, Li; Li, Chao; Lian, Wenwen; Wang, Qi; Liu, Ai-lin; Du, Guan-hua

2016-05-01

A dataset of 67 berberine derivatives for the inhibition of butyrylcholinesterase (BuChE) was studied based on the combination of quantitative structure-activity relationships models, molecular docking, and molecular dynamics methods. First, a series of berberine derivatives were reported, and their inhibitory activities toward butyrylcholinesterase (BuChE) were evaluated. By 2D- quantitative structure-activity relationships studies, the best model built by partial least-square had a conventional correlation coefficient of the training set (R(2)) of 0.883, a cross-validation correlation coefficient (Qcv2) of 0.777, and a conventional correlation coefficient of the test set (Rpred2) of 0.775. The model was also confirmed by Y-randomization examination. In addition, the molecular docking and molecular dynamics simulation were performed to better elucidate the inhibitory mechanism of three typical berberine derivatives (berberine, C2, and C55) toward BuChE. The predicted binding free energy results were consistent with the experimental data and showed that the van der Waals energy term (ΔEvdw) difference played the most important role in differentiating the activity among the three inhibitors (berberine, C2, and C55). The developed quantitative structure-activity relationships models provide details on the fine relationship linking structure and activity and offer clues for structural modifications, and the molecular simulation helps to understand the inhibitory mechanism of the three typical inhibitors. In conclusion, the results of this study provide useful clues for new drug design and discovery of BuChE inhibitors from berberine derivatives. © 2015 John Wiley & Sons A/S.

Toward Quantitative Small Animal Pinhole SPECT: Assessment of Quantitation Accuracy Prior to Image Compensations

PubMed Central

Chen, Chia-Lin; Wang, Yuchuan; Lee, Jason J. S.; Tsui, Benjamin M. W.

2011-01-01

Purpose We assessed the quantitation accuracy of small animal pinhole single photon emission computed tomography (SPECT) under the current preclinical settings, where image compensations are not routinely applied. Procedures The effects of several common image-degrading factors and imaging parameters on quantitation accuracy were evaluated using Monte-Carlo simulation methods. Typical preclinical imaging configurations were modeled, and quantitative analyses were performed based on image reconstructions without compensating for attenuation, scatter, and limited system resolution. Results Using mouse-sized phantom studies as examples, attenuation effects alone degraded quantitation accuracy by up to −18% (Tc-99m or In-111) or −41% (I-125). The inclusion of scatter effects changed the above numbers to −12% (Tc-99m or In-111) and −21% (I-125), respectively, indicating the significance of scatter in quantitative I-125 imaging. Region-of-interest (ROI) definitions have greater impacts on regional quantitation accuracy for small sphere sources as compared to attenuation and scatter effects. For the same ROI, SPECT acquisitions using pinhole apertures of different sizes could significantly affect the outcome, whereas the use of different radii-of-rotation yielded negligible differences in quantitation accuracy for the imaging configurations simulated. Conclusions We have systematically quantified the influence of several factors affecting the quantitation accuracy of small animal pinhole SPECT. In order to consistently achieve accurate quantitation within 5% of the truth, comprehensive image compensation methods are needed. PMID:19048346

Evaluating Attitudes, Skill, and Performance in a Learning-Enhanced Quantitative Methods Course: A Structural Modeling Approach.

ERIC Educational Resources Information Center

Harlow, Lisa L.; Burkholder, Gary J.; Morrow, Jennifer A.

2002-01-01

Used a structural modeling approach to evaluate relations among attitudes, initial skills, and performance in a Quantitative Methods course that involved students in active learning. Results largely confirmed hypotheses offering support for educational reform efforts that propose actively involving students in the learning process, especially in…

Quantitative optical scanning tests of complex microcircuits

NASA Technical Reports Server (NTRS)

Erickson, J. J.

1980-01-01

An approach for the development of the optical scanner as a screening inspection instrument for microcircuits involves comparing the quantitative differences in photoresponse images and then correlating them with electrical parameter differences in test devices. The existing optical scanner was modified so that the photoresponse data could be recorded and subsequently digitized. A method was devised for applying digital image processing techniques to the digitized photoresponse data in order to quantitatively compare the data. Electrical tests were performed and photoresponse images were recorded before and following life test intervals on two groups of test devices. Correlations were made between differences or changes in the electrical parameters of the test devices.

AutoQSAR: an automated machine learning tool for best-practice quantitative structure-activity relationship modeling.

PubMed

Dixon, Steven L; Duan, Jianxin; Smith, Ethan; Von Bargen, Christopher D; Sherman, Woody; Repasky, Matthew P

2016-10-01

We introduce AutoQSAR, an automated machine-learning application to build, validate and deploy quantitative structure-activity relationship (QSAR) models. The process of descriptor generation, feature selection and the creation of a large number of QSAR models has been automated into a single workflow within AutoQSAR. The models are built using a variety of machine-learning methods, and each model is scored using a novel approach. Effectiveness of the method is demonstrated through comparison with literature QSAR models using identical datasets for six end points: protein-ligand binding affinity, solubility, blood-brain barrier permeability, carcinogenicity, mutagenicity and bioaccumulation in fish. AutoQSAR demonstrates similar or better predictive performance as compared with published results for four of the six endpoints while requiring minimal human time and expertise.

Physically based estimation of soil water retention from textural data: General framework, new models, and streamlined existing models

USGS Publications Warehouse

Nimmo, J.R.; Herkelrath, W.N.; Laguna, Luna A.M.

2007-01-01

Numerous models are in widespread use for the estimation of soil water retention from more easily measured textural data. Improved models are needed for better prediction and wider applicability. We developed a basic framework from which new and existing models can be derived to facilitate improvements. Starting from the assumption that every particle has a characteristic dimension R associated uniquely with a matric pressure ?? and that the form of the ??-R relation is the defining characteristic of each model, this framework leads to particular models by specification of geometric relationships between pores and particles. Typical assumptions are that particles are spheres, pores are cylinders with volume equal to the associated particle volume times the void ratio, and that the capillary inverse proportionality between radius and matric pressure is valid. Examples include fixed-pore-shape and fixed-pore-length models. We also developed alternative versions of the model of Arya and Paris that eliminate its interval-size dependence and other problems. The alternative models are calculable by direct application of algebraic formulas rather than manipulation of data tables and intermediate results, and they easily combine with other models (e.g., incorporating structural effects) that are formulated on a continuous basis. Additionally, we developed a family of models based on the same pore geometry as the widely used unsaturated hydraulic conductivity model of Mualem. Predictions of measurements for different suitable media show that some of the models provide consistently good results and can be chosen based on ease of calculations and other factors. ?? Soil Science Society of America. All rights reserved.

Training of Existing Workers: Issues, Incentives and Models

ERIC Educational Resources Information Center

Mawer, Giselle; Jackson, Elaine

2005-01-01

This report presents issues associated with incentives for training existing workers in small to medium-sized firms, identified through a small sample of case studies from the retail, manufacturing, and building and construction industries. While the majority of employers recognise workforce skill levels are fundamental to the success of the…

Concepts and challenges in quantitative pharmacology and model-based drug development.

PubMed

Zhang, Liping; Pfister, Marc; Meibohm, Bernd

2008-12-01

Model-based drug development (MBDD) has been recognized as a concept to improve the efficiency of drug development. The acceptance of MBDD from regulatory agencies, industry, and academia has been growing, yet today's drug development practice is still distinctly distant from MBDD. This manuscript is aimed at clarifying the concept of MBDD and proposing practical approaches for implementing MBDD in the pharmaceutical industry. The following concepts are defined and distinguished: PK-PD modeling, exposure-response modeling, pharmacometrics, quantitative pharmacology, and MBDD. MBDD is viewed as a paradigm and a mindset in which models constitute the instruments and aims of drug development efforts. MBDD covers the whole spectrum of the drug development process instead of being limited to a certain type of modeling technique or application area. The implementation of MBDD requires pharmaceutical companies to foster innovation and make changes at three levels: (1) to establish mindsets that are willing to get acquainted with MBDD, (2) to align processes that are adaptive to the requirements of MBDD, and (3) to create a closely collaborating organization in which all members play a role in MBDD. Pharmaceutical companies that are able to embrace the changes MBDD poses will likely be able to improve their success rate in drug development, and the beneficiaries will ultimately be the patients in need.

Quantitative structure-activation barrier relationship modeling for Diels-Alder ligations utilizing quantum chemical structural descriptors.

PubMed

Nandi, Sisir; Monesi, Alessandro; Drgan, Viktor; Merzel, Franci; Novič, Marjana

2013-10-30

In the present study, we show the correlation of quantum chemical structural descriptors with the activation barriers of the Diels-Alder ligations. A set of 72 non-catalysed Diels-Alder reactions were subjected to quantitative structure-activation barrier relationship (QSABR) under the framework of theoretical quantum chemical descriptors calculated solely from the structures of diene and dienophile reactants. Experimental activation barrier data were obtained from literature. Descriptors were computed using Hartree-Fock theory using 6-31G(d) basis set as implemented in Gaussian 09 software. Variable selection and model development were carried out by stepwise multiple linear regression methodology. Predictive performance of the quantitative structure-activation barrier relationship (QSABR) model was assessed by training and test set concept and by calculating leave-one-out cross-validated Q2 and predictive R2 values. The QSABR model can explain and predict 86.5% and 80% of the variances, respectively, in the activation energy barrier training data. Alternatively, a neural network model based on back propagation of errors was developed to assess the nonlinearity of the sought correlations between theoretical descriptors and experimental reaction barriers. A reasonable predictability for the activation barrier of the test set reactions was obtained, which enabled an exploration and interpretation of the significant variables responsible for Diels-Alder interaction between dienes and dienophiles. Thus, studies in the direction of QSABR modelling that provide efficient and fast prediction of activation barriers of the Diels-Alder reactions turn out to be a meaningful alternative to transition state theory based computation.

Quantitative laser diagnostic and modeling study of C2 and CH chemistry in combustion.

PubMed

Köhler, Markus; Brockhinke, Andreas; Braun-Unkhoff, Marina; Kohse-Höinghaus, Katharina

2010-04-15

Quantitative concentration measurements of CH and C(2) have been performed in laminar, premixed, flat flames of propene and cyclopentene with varying stoichiometry. A combination of cavity ring-down (CRD) spectroscopy and laser-induced fluorescence (LIF) was used to enable sensitive detection of these species with high spatial resolution. Previously, CH and C(2) chemistry had been studied, predominantly in methane flames, to understand potential correlations of their formation and consumption. For flames of larger hydrocarbon fuels, however, quantitative information on these small intermediates is scarce, especially under fuel-rich conditions. Also, the combustion chemistry of C(2) in particular has not been studied in detail, and although it has often been observed, its role in potential build-up reactions of higher hydrocarbon species is not well understood. The quantitative measurements performed here are the first to detect both species with good spatial resolution and high sensitivity in the same experiment in flames of C(3) and C(5) fuels. The experimental profiles were compared with results of combustion modeling to reveal details of the formation and consumption of these important combustion molecules, and the investigation was devoted to assist the further understanding of the role of C(2) and of its potential chemical interdependences with CH and other small radicals.

[Quantitative risk model for verocytotoxigenic Escherichia coli cross-contamination during homemade hamburger preparation].

PubMed

Signorini, M L; Frizzo, L S

2009-01-01

The objective of this study was to develop a quantitative risk model for verocytotoxigenic Escherichia coil (VTEC) cross-contamination during hamburger preparation at home. Published scientific information about the disease was considered for the elaboration of the model, which included a number of routines performed during food preparation in kitchens. The associated probabilities of bacterial transference between food items and kitchen utensils which best described each stage of the process were incorporated into the model by using @Risk software. Handling raw meat before preparing ready-to-eat foods (Odds ratio, OR, 6.57), as well as hand (OR = 12.02) and cutting board (OR = 5.02) washing habits were the major risk factors of VTEC cross-contamination from meat to vegetables. The information provided by this model should be considered when designing public information campaigns on hemolytic uremic syndrome risk directed to food handlers, in order to stress the importance of the above mentioned factors in disease transmission.

Development and evaluation of a model-based downscatter compensation method for quantitative I-131 SPECT

PubMed Central

Song, Na; Du, Yong; He, Bin; Frey, Eric C.

2011-01-01

Purpose: The radionuclide 131I has found widespread use in targeted radionuclide therapy (TRT), partly due to the fact that it emits photons that can be imaged to perform treatment planning or posttherapy dose verification as well as beta rays that are suitable for therapy. In both the treatment planning and dose verification applications, it is necessary to estimate the activity distribution in organs or tumors at several time points. In vivo estimates of the 131I activity distribution at each time point can be obtained from quantitative single-photon emission computed tomography (QSPECT) images and organ activity estimates can be obtained either from QSPECT images or quantification of planar projection data. However, in addition to the photon used for imaging, 131I decay results in emission of a number of other higher-energy photons with significant abundances. These higher-energy photons can scatter in the body, collimator, or detector and be counted in the 364 keV photopeak energy window, resulting in reduced image contrast and degraded quantitative accuracy; these photons are referred to as downscatter. The goal of this study was to develop and evaluate a model-based downscatter compensation method specifically designed for the compensation of high-energy photons emitted by 131I and detected in the imaging energy window. Methods: In the evaluation study, we used a Monte Carlo simulation (MCS) code that had previously been validated for other radionuclides. Thus, in preparation for the evaluation study, we first validated the code for 131I imaging simulation by comparison with experimental data. Next, we assessed the accuracy of the downscatter model by comparing downscatter estimates with MCS results. Finally, we combined the downscatter model with iterative reconstruction-based compensation for attenuation (A) and scatter (S) and the full (D) collimator-detector response of the 364 keV photons to form a comprehensive compensation method. We evaluated this

Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome.

PubMed

Davatzikos, Christos; Rathore, Saima; Bakas, Spyridon; Pati, Sarthak; Bergman, Mark; Kalarot, Ratheesh; Sridharan, Patmaa; Gastounioti, Aimilia; Jahani, Nariman; Cohen, Eric; Akbari, Hamed; Tunc, Birkan; Doshi, Jimit; Parker, Drew; Hsieh, Michael; Sotiras, Aristeidis; Li, Hongming; Ou, Yangming; Doot, Robert K; Bilello, Michel; Fan, Yong; Shinohara, Russell T; Yushkevich, Paul; Verma, Ragini; Kontos, Despina

2018-01-01

The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. First, image analysis algorithms are used to extract comprehensive panels of diverse and complementary features, such as multiparametric intensity histogram distributions, texture, shape, kinetics, connectomics, and spatial patterns. At the second level, these quantitative imaging signatures are fed into multivariate machine learning models to produce diagnostic, prognostic, and predictive biomarkers. Results from clinical studies in three areas are shown: (i) computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome, and treatment planning; (ii) prediction of treatment response for breast and lung cancer, and (iii) risk assessment for breast cancer.

Genetic variation maintained in multilocus models of additive quantitative traits under stabilizing selection.

PubMed Central

Bürger, R; Gimelfarb, A

1999-01-01

Stabilizing selection for an intermediate optimum is generally considered to deplete genetic variation in quantitative traits. However, conflicting results from various types of models have been obtained. While classical analyses assuming a large number of independent additive loci with individually small effects indicated that no genetic variation is preserved under stabilizing selection, several analyses of two-locus models showed the contrary. We perform a complete analysis of a generalization of Wright's two-locus quadratic-optimum model and investigate numerically the ability of quadratic stabilizing selection to maintain genetic variation in additive quantitative traits controlled by up to five loci. A statistical approach is employed by choosing randomly 4000 parameter sets (allelic effects, recombination rates, and strength of selection) for a given number of loci. For each parameter set we iterate the recursion equations that describe the dynamics of gamete frequencies starting from 20 randomly chosen initial conditions until an equilibrium is reached, record the quantities of interest, and calculate their corresponding mean values. As the number of loci increases from two to five, the fraction of the genome expected to be polymorphic declines surprisingly rapidly, and the loci that are polymorphic increasingly are those with small effects on the trait. As a result, the genetic variance expected to be maintained under stabilizing selection decreases very rapidly with increased number of loci. The equilibrium structure expected under stabilizing selection on an additive trait differs markedly from that expected under selection with no constraints on genotypic fitness values. The expected genetic variance, the expected polymorphic fraction of the genome, as well as other quantities of interest, are only weakly dependent on the selection intensity and the level of recombination. PMID:10353920

Integrating landslide and liquefaction hazard and loss estimates with existing USGS real-time earthquake information products

USGS Publications Warehouse

Allstadt, Kate E.; Thompson, Eric M.; Hearne, Mike; Nowicki Jessee, M. Anna; Zhu, J.; Wald, David J.; Tanyas, Hakan

2017-01-01

The U.S. Geological Survey (USGS) has made significant progress toward the rapid estimation of shaking and shakingrelated losses through their Did You Feel It? (DYFI), ShakeMap, ShakeCast, and PAGER products. However, quantitative estimates of the extent and severity of secondary hazards (e.g., landsliding, liquefaction) are not currently included in scenarios and real-time post-earthquake products despite their significant contributions to hazard and losses for many events worldwide. We are currently running parallel global statistical models for landslides and liquefaction developed with our collaborators in testing mode, but much work remains in order to operationalize these systems. We are expanding our efforts in this area by not only improving the existing statistical models, but also by (1) exploring more sophisticated, physics-based models where feasible; (2) incorporating uncertainties; and (3) identifying and undertaking research and product development to provide useful landslide and liquefaction estimates and their uncertainties. Although our existing models use standard predictor variables that are accessible globally or regionally, including peak ground motions, topographic slope, and distance to water bodies, we continue to explore readily available proxies for rock and soil strength as well as other susceptibility terms. This work is based on the foundation of an expanding, openly available, case-history database we are compiling along with historical ShakeMaps for each event. The expected outcome of our efforts is a robust set of real-time secondary hazards products that meet the needs of a wide variety of earthquake information users. We describe the available datasets and models, developments currently underway, and anticipated products. 

Quantitative ultrasound molecular imaging by modeling the binding kinetics of targeted contrast agent

NASA Astrophysics Data System (ADS)

Turco, Simona; Tardy, Isabelle; Frinking, Peter; Wijkstra, Hessel; Mischi, Massimo

2017-03-01

Ultrasound molecular imaging (USMI) is an emerging technique to monitor diseases at the molecular level by the use of novel targeted ultrasound contrast agents (tUCA). These consist of microbubbles functionalized with targeting ligands with high-affinity for molecular markers of specific disease processes, such as cancer-related angiogenesis. Among the molecular markers of angiogenesis, the vascular endothelial growth factor receptor 2 (VEGFR2) is recognized to play a major role. In response, the clinical-grade tUCA BR55 was recently developed, consisting of VEGFR2-targeting microbubbles which can flow through the entire circulation and accumulate where VEGFR2 is over-expressed, thus causing selective enhancement in areas of active angiogenesis. Discrimination between bound and free microbubbles is crucial to assess cancer angiogenesis. Currently, this is done non-quantitatively by looking at the late enhancement, about 10 min after injection, or by calculation of the differential targeted enhancement, requiring the application of a high-pressure ultrasound (US) burst to destroy all the microbubbles in the acoustic field and isolate the signal coming only from bound microbubbles. In this work, we propose a novel method based on mathematical modeling of the binding kinetics during the tUCA first pass, thus reducing the acquisition time and with no need for a destructive US burst. Fitting time-intensity curves measured with USMI by the proposed model enables the assessment of cancer angiogenesis at both the vascular and molecular levels. This is achieved by estimation of quantitative parameters related to the microvascular architecture and microbubble binding. The proposed method was tested in 11 prostate-tumor bearing rats by performing USMI after injection of BR55, and showed good agreement with current USMI methods. The novel information provided by the proposed method, possibly combined with the current non-quantitative methods, may bring deeper insight into

Quantitative evaluation of mucosal vascular contrast in narrow band imaging using Monte Carlo modeling

NASA Astrophysics Data System (ADS)

Le, Du; Wang, Quanzeng; Ramella-Roman, Jessica; Pfefer, Joshua

2012-06-01

Narrow-band imaging (NBI) is a spectrally-selective reflectance imaging technique for enhanced visualization of superficial vasculature. Prior clinical studies have indicated NBI's potential for detection of vasculature abnormalities associated with gastrointestinal mucosal neoplasia. While the basic mechanisms behind the increased vessel contrast - hemoglobin absorption and tissue scattering - are known, a quantitative understanding of the effect of tissue and device parameters has not been achieved. In this investigation, we developed and implemented a numerical model of light propagation that simulates NBI reflectance distributions. This was accomplished by incorporating mucosal tissue layers and vessel-like structures in a voxel-based Monte Carlo algorithm. Epithelial and mucosal layers as well as blood vessels were defined using wavelength-specific optical properties. The model was implemented to calculate reflectance distributions and vessel contrast values as a function of vessel depth (0.05 to 0.50 mm) and diameter (0.01 to 0.10 mm). These relationships were determined for NBI wavelengths of 410 nm and 540 nm, as well as broadband illumination common to standard endoscopic imaging. The effects of illumination bandwidth on vessel contrast were also simulated. Our results provide a quantitative analysis of the effect of absorption and scattering on vessel contrast. Additional insights and potential approaches for improving NBI system contrast are discussed.

Lentivirus-mediated platelet gene therapy of murine hemophilia A with pre-existing anti-FVIII immunity

PubMed Central

Kuether, E. L.; Schroeder, J. A.; Fahs, S. A.; Cooley, B. C.; Chen, Y.; Montgomery, R. R.; Wilcox, D. A.; Shi, Q.

2012-01-01

Summary Background The development of inhibitory antibodies, referred to as inhibitors, against exogenous FVIII in a significant subset of patients with hemophilia A remains a persistent challenge to the efficacy of protein replacement therapy. Our previous studies using the transgenic approach provided proof-of-principle that platelet-specific expression could be successful for treating hemophilia A in the presence of inhibitory antibodies. Objective To investigate a clinically translatable approach for platelet gene therapy of hemophilia A with pre-existing inhibitors. Methods Platelet-FVIII expression in pre-immunized FVIIInull mice was introduced by transplantation of lentivirus-transduced bone marrow or enriched hematopoietic stem cells. FVIII expression was determined by a chromogenic assay. The transgene copy number per cell was quantitated by real time PCR. Inhibitor titer was measured by Bethesda assay. Phenotypic correction was assessed by the tail clipping assay and an electrolytic-induced venous injury model. Integration sites were analyzed by LAM-PCR. Results Therapeutic levels of platelet-FVIII expression were sustained long-term without evoking an anti-FVIII memory response in the transduced pre-immunized recipients. The tail clip survival test and the electrolytic injury model confirmed that hemostasis was improved in the treated animals. Sequential bone marrow transplants showed sustained platelet-FVIII expression resulting in phenotypic correction in pre-immunized secondary and tertiary recipients. Conclusions Lentivirus-mediated platelet-specific gene transfer improves hemostasis in hemophilic A mice with pre-existing inhibitors, indicating that this approach may be a promising strategy for gene therapy of hemophilia A even in the high-risk setting of pre-existing inhibitory antibodies. PMID:22632092

Comparison of Dynamic Contrast Enhanced MRI and Quantitative SPECT in a Rat Glioma Model

PubMed Central

Skinner, Jack T.; Yankeelov, Thomas E.; Peterson, Todd E.; Does, Mark D.

2012-01-01

Pharmacokinetic modeling of dynamic contrast enhanced (DCE)-MRI data provides measures of the extracellular volume fraction (ve) and the volume transfer constant (Ktrans) in a given tissue. These parameter estimates may be biased, however, by confounding issues such as contrast agent and tissue water dynamics, or assumptions of vascularization and perfusion made by the commonly used model. In contrast to MRI, radiotracer imaging with SPECT is insensitive to water dynamics. A quantitative dual-isotope SPECT technique was developed to obtain an estimate of ve in a rat glioma model for comparison to the corresponding estimates obtained using DCE-MRI with a vascular input function (VIF) and reference region model (RR). Both DCE-MRI methods produced consistently larger estimates of ve in comparison to the SPECT estimates, and several experimental sources were postulated to contribute to these differences. PMID:22991315

Quantitative workflow based on NN for weighting criteria in landfill suitability mapping

NASA Astrophysics Data System (ADS)

Abujayyab, Sohaib K. M.; Ahamad, Mohd Sanusi S.; Yahya, Ahmad Shukri; Ahmad, Siti Zubaidah; Alkhasawneh, Mutasem Sh.; Aziz, Hamidi Abdul

2017-10-01

Our study aims to introduce a new quantitative workflow that integrates neural networks (NNs) and multi criteria decision analysis (MCDA). Existing MCDA workflows reveal a number of drawbacks, because of the reliance on human knowledge in the weighting stage. Thus, new workflow presented to form suitability maps at the regional scale for solid waste planning based on NNs. A feed-forward neural network employed in the workflow. A total of 34 criteria were pre-processed to establish the input dataset for NN modelling. The final learned network used to acquire the weights of the criteria. Accuracies of 95.2% and 93.2% achieved for the training dataset and testing dataset, respectively. The workflow was found to be capable of reducing human interference to generate highly reliable maps. The proposed workflow reveals the applicability of NN in generating landfill suitability maps and the feasibility of integrating them with existing MCDA workflows.

Quantitative oxygen concentration imaging in toluene atmospheres using Dual Imaging with Modeling Evaluation

NASA Astrophysics Data System (ADS)

Ehn, Andreas; Jonsson, Malin; Johansson, Olof; Aldén, Marcus; Bood, Joakim

2013-01-01

Fluorescence lifetimes of toluene as a function of oxygen concentration in toluene/nitrogen/oxygen mixtures have been measured at room temperature using picosecond-laser excitation of the S1-S0 transition at 266 nm. The data satisfy the Stern-Volmer relation with high accuracy, providing an updated value of the Stern-Volmer slope. A newly developed fluorescence lifetime imaging scheme, called Dual Imaging with Modeling Evaluation (DIME), is evaluated and successfully demonstrated for quantitative oxygen concentration imaging in toluene-seeded O2/N2 gas mixtures.

Quantitative oxygen concentration imaging in toluene atmospheres using Dual Imaging with Modeling Evaluation

NASA Astrophysics Data System (ADS)

Ehn, Andreas; Jonsson, Malin; Johansson, Olof; Aldén, Marcus; Bood, Joakim

2012-12-01

Fluorescence lifetimes of toluene as a function of oxygen concentration in toluene/nitrogen/oxygen mixtures have been measured at room temperature using picosecond-laser excitation of the S1-S0 transition at 266 nm. The data satisfy the Stern-Volmer relation with high accuracy, providing an updated value of the Stern-Volmer slope. A newly developed fluorescence lifetime imaging scheme, called Dual Imaging with Modeling Evaluation (DIME), is evaluated and successfully demonstrated for quantitative oxygen concentration imaging in toluene-seeded O2/N2 gas mixtures.

Quantitative Rheological Model Selection

NASA Astrophysics Data System (ADS)

Freund, Jonathan; Ewoldt, Randy

2014-11-01

The more parameters in a rheological the better it will reproduce available data, though this does not mean that it is necessarily a better justified model. Good fits are only part of model selection. We employ a Bayesian inference approach that quantifies model suitability by balancing closeness to data against both the number of model parameters and their a priori uncertainty. The penalty depends upon prior-to-calibration expectation of the viable range of values that model parameters might take, which we discuss as an essential aspect of the selection criterion. Models that are physically grounded are usually accompanied by tighter physical constraints on their respective parameters. The analysis reflects a basic principle: models grounded in physics can be expected to enjoy greater generality and perform better away from where they are calibrated. In contrast, purely empirical models can provide comparable fits, but the model selection framework penalizes their a priori uncertainty. We demonstrate the approach by selecting the best-justified number of modes in a Multi-mode Maxwell description of PVA-Borax. We also quantify relative merits of the Maxwell model relative to powerlaw fits and purely empirical fits for PVA-Borax, a viscoelastic liquid, and gluten.

Quantitative analysis of intra-Golgi transport shows intercisternal exchange for all cargo

PubMed Central

Dmitrieff, Serge; Rao, Madan; Sens, Pierre

2013-01-01

The mechanisms controlling the transport of proteins through the Golgi stack of mammalian and plant cells is the subject of intense debate, with two models, cisternal progression and intercisternal exchange, emerging as major contenders. A variety of transport experiments have claimed support for each of these models. We reevaluate these experiments using a single quantitative coarse-grained framework of intra-Golgi transport that accounts for both transport models and their many variants. Our analysis makes a definitive case for the existence of intercisternal exchange both for small membrane proteins and large protein complexes––this implies that membrane structures larger than the typical protein-coated vesicles must be involved in transport. Notwithstanding, we find that current observations on protein transport cannot rule out cisternal progression as contributing significantly to the transport process. To discriminate between the different models of intra-Golgi transport, we suggest experiments and an analysis based on our extended theoretical framework that compare the dynamics of transiting and resident proteins. PMID:24019488

Humanization of pediatric care in the world: focus and review of existing models and measurement tools.

PubMed

Tripodi, Marina; Siano, Maria Anna; Mandato, Claudia; De Anseris, Anna Giulia Elena; Quitadamo, Paolo; Guercio Nuzio, Salvatore; Viggiano, Claudia; Fasolino, Francesco; Bellopede, Annalisa; Annunziata, Maria; Massa, Grazia; Pepe, Francesco Maria; De Chiara, Maria; Siani, Paolo; Vajro, Pietro

2017-08-30

The term "humanization" indicates the process by which people try to make something more human and civilized, more in line with what is believed to be the human nature. The humanization of care is an important and not yet a well-defined issue which includes a wide range of aspects related to the approach to the patient and care modalities. In pediatrics, the humanization concept is even vaguer due to the dual involvement of both the child and his/her family and by the existence of multiple proposed models. The present study aims to analyze the main existing humanization models regarding pediatric care, and the tools for assessing its grade. The main Humanization care programs have been elaborated and developed both in America (Brazil, USA) and Europe. The North American and European models specifically concern pediatric care, while the model developed in Brazil is part of a broader program aimed at all age groups. The first emphasis is on the importance of the family in child care, the second emphasis is on the child's right to be a leader, to be heard and to be able to express its opinion on the program's own care. Several tools have been created and used to evaluate humanization of care programs and related aspects. None, however, had been mutually compared. The major models of humanization care and the related assessment tools here reviewed highlight the urgent need for a more unifying approach, which may help in realizing health care programs closer to the young patient's and his/her family needs.

Quantitative structure-property relationship modeling of Grätzel solar cell dyes.

PubMed

Venkatraman, Vishwesh; Åstrand, Per-Olof; Alsberg, Bjørn Kåre

2014-01-30

With fossil fuel reserves on the decline, there is increasing focus on the design and development of low-cost organic photovoltaic devices, in particular, dye-sensitized solar cells (DSSCs). The power conversion efficiency (PCE) of a DSSC is heavily influenced by the chemical structure of the dye. However, as far as we know, no predictive quantitative structure-property relationship models for DSSCs with PCE as one of the response variables have been reported. Thus, we report for the first time the successful application of comparative molecular field analysis (CoMFA) and vibrational frequency-based eigenvalue (EVA) descriptors to model molecular structure-photovoltaic performance relationships for a set of 40 coumarin derivatives. The results show that the models obtained provide statistically robust predictions of important photovoltaic parameters such as PCE, the open-circuit voltage (V(OC)), short-circuit current (J(SC)) and the peak absorption wavelength λ(max). Some of our findings based on the analysis of the models are in accordance with those reported in the literature. These structure-property relationships can be applied to the rational structural design and evaluation of new photovoltaic materials. Copyright © 2013 Wiley Periodicals, Inc.

Load-Flow in Multiphase Distribution Networks: Existence, Uniqueness, Non-Singularity, and Linear Models

DOE PAGES

Bernstein, Andrey; Wang, Cong; Dall'Anese, Emiliano; ...

2018-01-01

This paper considers unbalanced multiphase distribution systems with generic topology and different load models, and extends the Z-bus iterative load-flow algorithm based on a fixed-point interpretation of the AC load-flow equations. Explicit conditions for existence and uniqueness of load-flow solutions are presented. These conditions also guarantee convergence of the load-flow algorithm to the unique solution. The proposed methodology is applicable to generic systems featuring (i) wye connections; (ii) ungrounded delta connections; (iii) a combination of wye-connected and delta-connected sources/loads; and, (iv) a combination of line-to-line and line-to-grounded-neutral devices at the secondary of distribution transformers. Further, a sufficient condition for themore » non-singularity of the load-flow Jacobian is proposed. Finally, linear load-flow models are derived, and their approximation accuracy is analyzed. Theoretical results are corroborated through experiments on IEEE test feeders.« less

Load-Flow in Multiphase Distribution Networks: Existence, Uniqueness, Non-Singularity, and Linear Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bernstein, Andrey; Wang, Cong; Dall'Anese, Emiliano

This paper considers unbalanced multiphase distribution systems with generic topology and different load models, and extends the Z-bus iterative load-flow algorithm based on a fixed-point interpretation of the AC load-flow equations. Explicit conditions for existence and uniqueness of load-flow solutions are presented. These conditions also guarantee convergence of the load-flow algorithm to the unique solution. The proposed methodology is applicable to generic systems featuring (i) wye connections; (ii) ungrounded delta connections; (iii) a combination of wye-connected and delta-connected sources/loads; and, (iv) a combination of line-to-line and line-to-grounded-neutral devices at the secondary of distribution transformers. Further, a sufficient condition for themore » non-singularity of the load-flow Jacobian is proposed. Finally, linear load-flow models are derived, and their approximation accuracy is analyzed. Theoretical results are corroborated through experiments on IEEE test feeders.« less

A novel image-based quantitative method for the characterization of NETosis

PubMed Central

Zhao, Wenpu; Fogg, Darin K.; Kaplan, Mariana J.

2015-01-01

NETosis is a newly recognized mechanism of programmed neutrophil death. It is characterized by a stepwise progression of chromatin decondensation, membrane rupture, and release of bactericidal DNA-based structures called neutrophil extracellular traps (NETs). Conventional ‘suicidal’ NETosis has been described in pathogenic models of systemic autoimmune disorders. Recent in vivo studies suggest that a process of ‘vital’ NETosis also exists, in which chromatin is condensed and membrane integrity is preserved. Techniques to assess ‘suicidal’ or ‘vital’ NET formation in a specific, quantitative, rapid and semiautomated way have been lacking, hindering the characterization of this process. Here we have developed a new method to simultaneously assess both ‘suicidal’ and ‘vital’ NETosis, using high-speed multi-spectral imaging coupled to morphometric image analysis, to quantify spontaneous NET formation observed ex-vivo or stimulus-induced NET formation triggered in vitro. Use of imaging flow cytometry allows automated, quantitative and rapid analysis of subcellular morphology and texture, and introduces the potential for further investigation using NETosis as a biomarker in pre-clinical and clinical studies. PMID:26003624

Development and validation of open-source software for DNA mixture interpretation based on a quantitative continuous model

PubMed Central

Manabe, Sho; Morimoto, Chie; Hamano, Yuya; Fujimoto, Shuntaro

2017-01-01

In criminal investigations, forensic scientists need to evaluate DNA mixtures. The estimation of the number of contributors and evaluation of the contribution of a person of interest (POI) from these samples are challenging. In this study, we developed a new open-source software “Kongoh” for interpreting DNA mixture based on a quantitative continuous model. The model uses quantitative information of peak heights in the DNA profile and considers the effect of artifacts and allelic drop-out. By using this software, the likelihoods of 1–4 persons’ contributions are calculated, and the most optimal number of contributors is automatically determined; this differs from other open-source software. Therefore, we can eliminate the need to manually determine the number of contributors before the analysis. Kongoh also considers allele- or locus-specific effects of biological parameters based on the experimental data. We then validated Kongoh by calculating the likelihood ratio (LR) of a POI’s contribution in true contributors and non-contributors by using 2–4 person mixtures analyzed through a 15 short tandem repeat typing system. Most LR values obtained from Kongoh during true-contributor testing strongly supported the POI’s contribution even for small amounts or degraded DNA samples. Kongoh correctly rejected a false hypothesis in the non-contributor testing, generated reproducible LR values, and demonstrated higher accuracy of the estimated number of contributors than another software based on the quantitative continuous model. Therefore, Kongoh is useful in accurately interpreting DNA evidence like mixtures and small amounts or degraded DNA samples. PMID:29149210

Development and validation of open-source software for DNA mixture interpretation based on a quantitative continuous model.

PubMed

Manabe, Sho; Morimoto, Chie; Hamano, Yuya; Fujimoto, Shuntaro; Tamaki, Keiji

2017-01-01

In criminal investigations, forensic scientists need to evaluate DNA mixtures. The estimation of the number of contributors and evaluation of the contribution of a person of interest (POI) from these samples are challenging. In this study, we developed a new open-source software "Kongoh" for interpreting DNA mixture based on a quantitative continuous model. The model uses quantitative information of peak heights in the DNA profile and considers the effect of artifacts and allelic drop-out. By using this software, the likelihoods of 1-4 persons' contributions are calculated, and the most optimal number of contributors is automatically determined; this differs from other open-source software. Therefore, we can eliminate the need to manually determine the number of contributors before the analysis. Kongoh also considers allele- or locus-specific effects of biological parameters based on the experimental data. We then validated Kongoh by calculating the likelihood ratio (LR) of a POI's contribution in true contributors and non-contributors by using 2-4 person mixtures analyzed through a 15 short tandem repeat typing system. Most LR values obtained from Kongoh during true-contributor testing strongly supported the POI's contribution even for small amounts or degraded DNA samples. Kongoh correctly rejected a false hypothesis in the non-contributor testing, generated reproducible LR values, and demonstrated higher accuracy of the estimated number of contributors than another software based on the quantitative continuous model. Therefore, Kongoh is useful in accurately interpreting DNA evidence like mixtures and small amounts or degraded DNA samples.

Quantitative assessment of changes in landslide risk using a regional scale run-out model

NASA Astrophysics Data System (ADS)

Hussin, Haydar; Chen, Lixia; Ciurean, Roxana; van Westen, Cees; Reichenbach, Paola; Sterlacchini, Simone

2015-04-01

The risk of landslide hazard continuously changes in time and space and is rarely a static or constant phenomena in an affected area. However one of the main challenges of quantitatively assessing changes in landslide risk is the availability of multi-temporal data for the different components of risk. Furthermore, a truly "quantitative" landslide risk analysis requires the modeling of the landslide intensity (e.g. flow depth, velocities or impact pressures) affecting the elements at risk. Such a quantitative approach is often lacking in medium to regional scale studies in the scientific literature or is left out altogether. In this research we modelled the temporal and spatial changes of debris flow risk in a narrow alpine valley in the North Eastern Italian Alps. The debris flow inventory from 1996 to 2011 and multi-temporal digital elevation models (DEMs) were used to assess the susceptibility of debris flow triggering areas and to simulate debris flow run-out using the Flow-R regional scale model. In order to determine debris flow intensities, we used a linear relationship that was found between back calibrated physically based Flo-2D simulations (local scale models of five debris flows from 2003) and the probability values of the Flow-R software. This gave us the possibility to assign flow depth to a total of 10 separate classes on a regional scale. Debris flow vulnerability curves from the literature and one curve specifically for our case study area were used to determine the damage for different material and building types associated with the elements at risk. The building values were obtained from the Italian Revenue Agency (Agenzia delle Entrate) and were classified per cadastral zone according to the Real Estate Observatory data (Osservatorio del Mercato Immobiliare, Agenzia Entrate - OMI). The minimum and maximum market value for each building was obtained by multiplying the corresponding land-use value (€/msq) with building area and number of floors

Dynamic inundation mapping of Hurricane Harvey flooding in the Houston metro area using hyper-resolution modeling and quantitative image reanalysis

NASA Astrophysics Data System (ADS)

Noh, S. J.; Lee, J. H.; Lee, S.; Zhang, Y.; Seo, D. J.

2017-12-01

Hurricane Harvey was one of the most extreme weather events in Texas history and left significant damages in the Houston and adjoining coastal areas. To understand better the relative impact to urban flooding of extreme amount and spatial extent of rainfall, unique geography, land use and storm surge, high-resolution water modeling is necessary such that natural and man-made components are fully resolved. In this presentation, we reconstruct spatiotemporal evolution of inundation during Hurricane Harvey using hyper-resolution modeling and quantitative image reanalysis. The two-dimensional urban flood model used is based on dynamic wave approximation and 10 m-resolution terrain data, and is forced by the radar-based multisensor quantitative precipitation estimates. The model domain includes Buffalo, Brays, Greens and White Oak Bayous in Houston. The model is simulated using hybrid parallel computing. To evaluate dynamic inundation mapping, we combine various qualitative crowdsourced images and video footages with LiDAR-based terrain data.

A Quantitative Model for the Prediction of Sooting Tendency from Molecular Structure

DOE Office of Scientific and Technical Information (OSTI.GOV)

St. John, Peter C.; Kairys, Paul; Das, Dhrubajyoti D.

Particulate matter emissions negatively affect public health and global climate, yet newer fuel-efficient gasoline direct injection engines tend to produce more soot than their port-fuel injection counterparts. Fortunately, the search for sustainable biomass-based fuel blendstocks provides an opportunity to develop fuels that suppress soot formation in more efficient engine designs. However, as emissions tests are experimentally cumbersome and the search space for potential bioblendstocks is vast, new techniques are needed to estimate the sooting tendency of a diverse range of compounds. In this study, we develop a quantitative structure-activity relationship (QSAR) model of sooting tendency based on the experimental yieldmore » sooting index (YSI), which ranks molecules on a scale from n-hexane, 0, to benzene, 100. The model includes a rigorously defined applicability domain, and the predictive performance is checked using both internal and external validation. Model predictions for compounds in the external test set had a median absolute error of ~3 YSI units. An investigation of compounds that are poorly predicted by the model lends new insight into the complex mechanisms governing soot formation. Predictive models of soot formation can therefore be expected to play an increasingly important role in the screening and development of next-generation biofuels.« less

A Quantitative Model for the Prediction of Sooting Tendency from Molecular Structure

DOE PAGES

St. John, Peter C.; Kairys, Paul; Das, Dhrubajyoti D.; ...

2017-07-24

Particulate matter emissions negatively affect public health and global climate, yet newer fuel-efficient gasoline direct injection engines tend to produce more soot than their port-fuel injection counterparts. Fortunately, the search for sustainable biomass-based fuel blendstocks provides an opportunity to develop fuels that suppress soot formation in more efficient engine designs. However, as emissions tests are experimentally cumbersome and the search space for potential bioblendstocks is vast, new techniques are needed to estimate the sooting tendency of a diverse range of compounds. In this study, we develop a quantitative structure-activity relationship (QSAR) model of sooting tendency based on the experimental yieldmore » sooting index (YSI), which ranks molecules on a scale from n-hexane, 0, to benzene, 100. The model includes a rigorously defined applicability domain, and the predictive performance is checked using both internal and external validation. Model predictions for compounds in the external test set had a median absolute error of ~3 YSI units. An investigation of compounds that are poorly predicted by the model lends new insight into the complex mechanisms governing soot formation. Predictive models of soot formation can therefore be expected to play an increasingly important role in the screening and development of next-generation biofuels.« less

A functional-structural model of rice linking quantitative genetic information with morphological development and physiological processes.

PubMed

Xu, Lifeng; Henke, Michael; Zhu, Jun; Kurth, Winfried; Buck-Sorlin, Gerhard

2011-04-01

Although quantitative trait loci (QTL) analysis of yield-related traits for rice has developed rapidly, crop models using genotype information have been proposed only relatively recently. As a first step towards a generic genotype-phenotype model, we present here a three-dimensional functional-structural plant model (FSPM) of rice, in which some model parameters are controlled by functions describing the effect of main-effect and epistatic QTLs. The model simulates the growth and development of rice based on selected ecophysiological processes, such as photosynthesis (source process) and organ formation, growth and extension (sink processes). It was devised using GroIMP, an interactive modelling platform based on the Relational Growth Grammar formalism (RGG). RGG rules describe the course of organ initiation and extension resulting in final morphology. The link between the phenotype (as represented by the simulated rice plant) and the QTL genotype was implemented via a data interface between the rice FSPM and the QTLNetwork software, which computes predictions of QTLs from map data and measured trait data. Using plant height and grain yield, it is shown how QTL information for a given trait can be used in an FSPM, computing and visualizing the phenotypes of different lines of a mapping population. Furthermore, we demonstrate how modification of a particular trait feeds back on the entire plant phenotype via the physiological processes considered. We linked a rice FSPM to a quantitative genetic model, thereby employing QTL information to refine model parameters and visualizing the dynamics of development of the entire phenotype as a result of ecophysiological processes, including the trait(s) for which genetic information is available. Possibilities for further extension of the model, for example for the purposes of ideotype breeding, are discussed.

A functional–structural model of rice linking quantitative genetic information with morphological development and physiological processes

PubMed Central

Xu, Lifeng; Henke, Michael; Zhu, Jun; Kurth, Winfried; Buck-Sorlin, Gerhard

2011-01-01

Background and Aims Although quantitative trait loci (QTL) analysis of yield-related traits for rice has developed rapidly, crop models using genotype information have been proposed only relatively recently. As a first step towards a generic genotype–phenotype model, we present here a three-dimensional functional–structural plant model (FSPM) of rice, in which some model parameters are controlled by functions describing the effect of main-effect and epistatic QTLs. Methods The model simulates the growth and development of rice based on selected ecophysiological processes, such as photosynthesis (source process) and organ formation, growth and extension (sink processes). It was devised using GroIMP, an interactive modelling platform based on the Relational Growth Grammar formalism (RGG). RGG rules describe the course of organ initiation and extension resulting in final morphology. The link between the phenotype (as represented by the simulated rice plant) and the QTL genotype was implemented via a data interface between the rice FSPM and the QTLNetwork software, which computes predictions of QTLs from map data and measured trait data. Key Results Using plant height and grain yield, it is shown how QTL information for a given trait can be used in an FSPM, computing and visualizing the phenotypes of different lines of a mapping population. Furthermore, we demonstrate how modification of a particular trait feeds back on the entire plant phenotype via the physiological processes considered. Conclusions We linked a rice FSPM to a quantitative genetic model, thereby employing QTL information to refine model parameters and visualizing the dynamics of development of the entire phenotype as a result of ecophysiological processes, including the trait(s) for which genetic information is available. Possibilities for further extension of the model, for example for the purposes of ideotype breeding, are discussed. PMID:21247905

Further evaluation of quantitative structure--activity relationship models for the prediction of the skin sensitization potency of selected fragrance allergens.

PubMed

Patlewicz, Grace Y; Basketter, David A; Pease, Camilla K Smith; Wilson, Karen; Wright, Zoe M; Roberts, David W; Bernard, Guillaume; Arnau, Elena Giménez; Lepoittevin, Jean-Pierre

2004-02-01

Fragrance substances represent a very diverse group of chemicals; a proportion of them are associated with the ability to cause allergic reactions in the skin. Efforts to find substitute materials are hindered by the need to undertake animal testing for determining both skin sensitization hazard and potency. One strategy to avoid such testing is through an understanding of the relationships between chemical structure and skin sensitization, so-called structure-activity relationships. In recent work, we evaluated 2 groups of fragrance chemicals -- saturated aldehydes and alpha,beta-unsaturated aldehydes. Simple quantitative structure-activity relationship (QSAR) models relating the EC3 values [derived from the local lymph node assay (LLNA)] to physicochemical properties were developed for both sets of aldehydes. In the current study, we evaluated an additional group of carbonyl-containing compounds to test the predictive power of the developed QSARs and to extend their scope. The QSAR models were used to predict EC3 values of 10 newly selected compounds. Local lymph node assay data generated for these compounds demonstrated that the original QSARs were fairly accurate, but still required improvement. Development of these QSAR models has provided us with a better understanding of the potential mechanisms of action for aldehydes, and hence how to avoid or limit allergy. Knowledge generated from this work is being incorporated into new/improved rules for sensitization in the expert toxicity prediction system, deductive estimation of risk from existing knowledge (DEREK).

Quantitative analysis of breast cancer diagnosis using a probabilistic modelling approach.

PubMed

Liu, Shuo; Zeng, Jinshu; Gong, Huizhou; Yang, Hongqin; Zhai, Jia; Cao, Yi; Liu, Junxiu; Luo, Yuling; Li, Yuhua; Maguire, Liam; Ding, Xuemei

2018-01-01

Breast cancer is the most prevalent cancer in women in most countries of the world. Many computer-aided diagnostic methods have been proposed, but there are few studies on quantitative discovery of probabilistic dependencies among breast cancer data features and identification of the contribution of each feature to breast cancer diagnosis. This study aims to fill this void by utilizing a Bayesian network (BN) modelling approach. A K2 learning algorithm and statistical computation methods are used to construct BN structure and assess the obtained BN model. The data used in this study were collected from a clinical ultrasound dataset derived from a Chinese local hospital and a fine-needle aspiration cytology (FNAC) dataset from UCI machine learning repository. Our study suggested that, in terms of ultrasound data, cell shape is the most significant feature for breast cancer diagnosis, and the resistance index presents a strong probabilistic dependency on blood signals. With respect to FNAC data, bare nuclei are the most important discriminating feature of malignant and benign breast tumours, and uniformity of both cell size and cell shape are tightly interdependent. The BN modelling approach can support clinicians in making diagnostic decisions based on the significant features identified by the model, especially when some other features are missing for specific patients. The approach is also applicable to other healthcare data analytics and data modelling for disease diagnosis. Copyright © 2017 Elsevier Ltd. All rights reserved.

How fast is fisheries-induced evolution? Quantitative analysis of modelling and empirical studies

PubMed Central

Audzijonyte, Asta; Kuparinen, Anna; Fulton, Elizabeth A

2013-01-01

A number of theoretical models, experimental studies and time-series studies of wild fish have explored the presence and magnitude of fisheries-induced evolution (FIE). While most studies agree that FIE is likely to be happening in many fished stocks, there are disagreements about its rates and implications for stock viability. To address these disagreements in a quantitative manner, we conducted a meta-analysis of FIE rates reported in theoretical and empirical studies. We discovered that rates of phenotypic change observed in wild fish are about four times higher than the evolutionary rates reported in modelling studies, but correlation between the rate of change and instantaneous fishing mortality (F) was very similar in the two types of studies. Mixed-model analyses showed that in the modelling studies traits associated with reproductive investment and growth evolved slower than rates related to maturation. In empirical observations age-at-maturation was changing faster than other life-history traits. We also found that, despite different assumption and modelling approaches, rates of evolution for a given F value reported in 10 of 13 modelling studies were not significantly different. PMID:23789026

Comprehensive Quantitative Analysis on Privacy Leak Behavior

PubMed Central

Fan, Lejun; Wang, Yuanzhuo; Jin, Xiaolong; Li, Jingyuan; Cheng, Xueqi; Jin, Shuyuan

2013-01-01

Privacy information is prone to be leaked by illegal software providers with various motivations. Privacy leak behavior has thus become an important research issue of cyber security. However, existing approaches can only qualitatively analyze privacy leak behavior of software applications. No quantitative approach, to the best of our knowledge, has been developed in the open literature. To fill this gap, in this paper we propose for the first time four quantitative metrics, namely, possibility, severity, crypticity, and manipulability, for privacy leak behavior analysis based on Privacy Petri Net (PPN). In order to compare the privacy leak behavior among different software, we further propose a comprehensive metric, namely, overall leak degree, based on these four metrics. Finally, we validate the effectiveness of the proposed approach using real-world software applications. The experimental results demonstrate that our approach can quantitatively analyze the privacy leak behaviors of various software types and reveal their characteristics from different aspects. PMID:24066046

Comprehensive quantitative analysis on privacy leak behavior.

PubMed

Fan, Lejun; Wang, Yuanzhuo; Jin, Xiaolong; Li, Jingyuan; Cheng, Xueqi; Jin, Shuyuan

2013-01-01

Privacy information is prone to be leaked by illegal software providers with various motivations. Privacy leak behavior has thus become an important research issue of cyber security. However, existing approaches can only qualitatively analyze privacy leak behavior of software applications. No quantitative approach, to the best of our knowledge, has been developed in the open literature. To fill this gap, in this paper we propose for the first time four quantitative metrics, namely, possibility, severity, crypticity, and manipulability, for privacy leak behavior analysis based on Privacy Petri Net (PPN). In order to compare the privacy leak behavior among different software, we further propose a comprehensive metric, namely, overall leak degree, based on these four metrics. Finally, we validate the effectiveness of the proposed approach using real-world software applications. The experimental results demonstrate that our approach can quantitatively analyze the privacy leak behaviors of various software types and reveal their characteristics from different aspects.

Quantitative Literacy: Geosciences and Beyond

NASA Astrophysics Data System (ADS)

Richardson, R. M.; McCallum, W. G.

2002-12-01

Quantitative literacy seems like such a natural for the geosciences, right? The field has gone from its origin as a largely descriptive discipline to one where it is hard to imagine failing to bring a full range of mathematical tools to the solution of geological problems. Although there are many definitions of quantitative literacy, we have proposed one that is analogous to the UNESCO definition of conventional literacy: "A quantitatively literate person is one who, with understanding, can both read and represent quantitative information arising in his or her everyday life." Central to this definition is the concept that a curriculum for quantitative literacy must go beyond the basic ability to "read and write" mathematics and develop conceptual understanding. It is also critical that a curriculum for quantitative literacy be engaged with a context, be it everyday life, humanities, geoscience or other sciences, business, engineering, or technology. Thus, our definition works both within and outside the sciences. What role do geoscience faculty have in helping students become quantitatively literate? Is it our role, or that of the mathematicians? How does quantitative literacy vary between different scientific and engineering fields? Or between science and nonscience fields? We will argue that successful quantitative literacy curricula must be an across-the-curriculum responsibility. We will share examples of how quantitative literacy can be developed within a geoscience curriculum, beginning with introductory classes for nonmajors (using the Mauna Loa CO2 data set) through graduate courses in inverse theory (using singular value decomposition). We will highlight six approaches to across-the curriculum efforts from national models: collaboration between mathematics and other faculty; gateway testing; intensive instructional support; workshops for nonmathematics faculty; quantitative reasoning requirement; and individual initiative by nonmathematics faculty.

Transcriptome discovery in non-model wild fish species for the development of quantitative transcript abundance assays

USGS Publications Warehouse

Hahn, Cassidy M.; Iwanowicz, Luke R.; Cornman, Robert S.; Mazik, Patricia M.; Blazer, Vicki S.

2016-01-01

Environmental studies increasingly identify the presence of both contaminants of emerging concern (CECs) and legacy contaminants in aquatic environments; however, the biological effects of these compounds on resident fishes remain largely unknown. High throughput methodologies were employed to establish partial transcriptomes for three wild-caught, non-model fish species; smallmouth bass (Micropterus dolomieu), white sucker (Catostomus commersonii) and brown bullhead (Ameiurus nebulosus). Sequences from these transcriptome databases were utilized in the development of a custom nCounter CodeSet that allowed for direct multiplexed measurement of 50 transcript abundance endpoints in liver tissue. Sequence information was also utilized in the development of quantitative real-time PCR (qPCR) primers. Cross-species hybridization allowed the smallmouth bass nCounter CodeSet to be used for quantitative transcript abundance analysis of an additional non-model species, largemouth bass (Micropterus salmoides). We validated the nCounter analysis data system with qPCR for a subset of genes and confirmed concordant results. Changes in transcript abundance biomarkers between sexes and seasons were evaluated to provide baseline data on transcript modulation for each species of interest.

Novel Uses of In Vitro Data to Develop Quantitative Biological Activity Relationship Models for in Vivo Carcinogenicity Prediction.

PubMed

Pradeep, Prachi; Povinelli, Richard J; Merrill, Stephen J; Bozdag, Serdar; Sem, Daniel S

2015-04-01

The availability of large in vitro datasets enables better insight into the mode of action of chemicals and better identification of potential mechanism(s) of toxicity. Several studies have shown that not all in vitro assays can contribute as equal predictors of in vivo carcinogenicity for development of hybrid Quantitative Structure Activity Relationship (QSAR) models. We propose two novel approaches for the use of mechanistically relevant in vitro assay data in the identification of relevant biological descriptors and development of Quantitative Biological Activity Relationship (QBAR) models for carcinogenicity prediction. We demonstrate that in vitro assay data can be used to develop QBAR models for in vivo carcinogenicity prediction via two case studies corroborated with firm scientific rationale. The case studies demonstrate the similarities between QBAR and QSAR modeling in: (i) the selection of relevant descriptors to be used in the machine learning algorithm, and (ii) the development of a computational model that maps chemical or biological descriptors to a toxic endpoint. The results of both the case studies show: (i) improved accuracy and sensitivity which is especially desirable under regulatory requirements, and (ii) overall adherence with the OECD/REACH guidelines. Such mechanism based models can be used along with QSAR models for prediction of mechanistically complex toxic endpoints. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Advanced quantitative measurement methodology in physics education research

NASA Astrophysics Data System (ADS)

Wang, Jing

parts. The first part involves the comparison between item response theory (IRT) and classical test theory (CTT). The two theories both provide test item statistics for educational inferences and decisions. The two theories are both applied to Force Concept Inventory data obtained from students enrolled in The Ohio State University. Effort was made to examine the similarity and difference between the two theories, and the possible explanation to the difference. The study suggests that item response theory is more sensitive to the context and conceptual features of the test items than classical test theory. The IRT parameters provide a better measure than CTT parameters for the educational audience to investigate item features. The second part of the dissertation is on the measure of association for binary data. In quantitative assessment, binary data is often encountered because of its simplicity. The current popular measures of association fail under some extremely unbalanced conditions. However, the occurrence of these conditions is not rare in educational data. Two popular association measures, the Pearson's correlation and the tetrachoric correlation are examined. A new method, model based association is introduced, and an educational testing constraint is discussed. The existing popular methods are compared with the model based association measure with and without the constraint. Connections between the value of association and the context and conceptual features of questions are discussed in detail. Results show that all the methods have their advantages and disadvantages. Special attention to the test and data conditions is necessary. The last part of the dissertation is focused on exploratory factor analysis (EFA). The theoretical advantages of EFA are discussed. Typical misunderstanding and misusage of EFA are explored. The EFA is performed on Lawson's Classroom Test of Scientific Reasoning (LCTSR), a widely used assessment on scientific reasoning skills. The

Spiraling between qualitative and quantitative data on women's health behaviors: a double helix model for mixed methods.

PubMed

Mendlinger, Sheryl; Cwikel, Julie

2008-02-01

A double helix spiral model is presented which demonstrates how to combine qualitative and quantitative methods of inquiry in an interactive fashion over time. Using findings on women's health behaviors (e.g., menstruation, breast-feeding, coping strategies), we show how qualitative and quantitative methods highlight the theory of knowledge acquisition in women's health decisions. A rich data set of 48 semistructured, in-depth ethnographic interviews with mother-daughter dyads from six ethnic groups (Israeli, European, North African, Former Soviet Union [FSU], American/Canadian, and Ethiopian), plus seven focus groups, provided the qualitative sources for analysis. This data set formed the basis of research questions used in a quantitative telephone survey of 302 Israeli women from the ages of 25 to 42 from four ethnic groups. We employed multiple cycles of data analysis from both data sets to produce a more detailed and multidimensional picture of women's health behavior decisions through a spiraling process.

Toxicity challenges in environmental chemicals: Prediction of human plasma protein binding through quantitative structure-activity relationship (QSAR) models

EPA Science Inventory

The present study explores the merit of utilizing available pharmaceutical data to construct a quantitative structure-activity relationship (QSAR) for prediction of the fraction of a chemical unbound to plasma protein (Fub) in environmentally relevant compounds. Independent model...

A cascading failure model for analyzing railway accident causation

NASA Astrophysics Data System (ADS)

Liu, Jin-Tao; Li, Ke-Ping

2018-01-01

In this paper, a new cascading failure model is proposed for quantitatively analyzing the railway accident causation. In the model, the loads of nodes are redistributed according to the strength of the causal relationships between the nodes. By analyzing the actual situation of the existing prevention measures, a critical threshold of the load parameter in the model is obtained. To verify the effectiveness of the proposed cascading model, simulation experiments of a train collision accident are performed. The results show that the cascading failure model can describe the cascading process of the railway accident more accurately than the previous models, and can quantitatively analyze the sensitivities and the influence of the causes. In conclusion, this model can assist us to reveal the latent rules of accident causation to reduce the occurrence of railway accidents.

Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposure.

PubMed

Zhu, Hao; Martin, Todd M; Ye, Lin; Sedykh, Alexander; Young, Douglas M; Tropsha, Alexander

2009-12-01

Few quantitative structure-activity relationship (QSAR) studies have successfully modeled large, diverse rodent toxicity end points. In this study, a comprehensive data set of 7385 compounds with their most conservative lethal dose (LD(50)) values has been compiled. A combinatorial QSAR approach has been employed to develop robust and predictive models of acute toxicity in rats caused by oral exposure to chemicals. To enable fair comparison between the predictive power of models generated in this study versus a commercial toxicity predictor, TOPKAT (Toxicity Prediction by Komputer Assisted Technology), a modeling subset of the entire data set was selected that included all 3472 compounds used in TOPKAT's training set. The remaining 3913 compounds, which were not present in the TOPKAT training set, were used as the external validation set. QSAR models of five different types were developed for the modeling set. The prediction accuracy for the external validation set was estimated by determination coefficient R(2) of linear regression between actual and predicted LD(50) values. The use of the applicability domain threshold implemented in most models generally improved the external prediction accuracy but expectedly led to the decrease in chemical space coverage; depending on the applicability domain threshold, R(2) ranged from 0.24 to 0.70. Ultimately, several consensus models were developed by averaging the predicted LD(50) for every compound using all five models. The consensus models afforded higher prediction accuracy for the external validation data set with the higher coverage as compared to individual constituent models. The validated consensus LD(50) models developed in this study can be used as reliable computational predictors of in vivo acute toxicity.

Quantitative image quality evaluation of MR images using perceptual difference models

PubMed Central

Miao, Jun; Huo, Donglai; Wilson, David L.

2008-01-01

The authors are using a perceptual difference model (Case-PDM) to quantitatively evaluate image quality of the thousands of test images which can be created when optimizing fast magnetic resonance (MR) imaging strategies and reconstruction techniques. In this validation study, they compared human evaluation of MR images from multiple organs and from multiple image reconstruction algorithms to Case-PDM and similar models. The authors found that Case-PDM compared very favorably to human observers in double-stimulus continuous-quality scale and functional measurement theory studies over a large range of image quality. The Case-PDM threshold for nonperceptible differences in a 2-alternative forced choice study varied with the type of image under study, but was ≈1.1 for diffuse image effects, providing a rule of thumb. Ordering the image quality evaluation models, we found in overall Case-PDM ≈ IDM (Sarnoff Corporation) ≈ SSIM [Wang et al. IEEE Trans. Image Process. 13, 600–612 (2004)] > mean squared error ≈ NR [Wang et al. (2004) (unpublished)] > DCTune (NASA) > IQM (MITRE Corporation). The authors conclude that Case-PDM is very useful in MR image evaluation but that one should probably restrict studies to similar images and similar processing, normally not a limitation in image reconstruction studies. PMID:18649487

On normality, ethnicity, and missing values in quantitative trait locus mapping

PubMed Central

Labbe, Aurélie; Wormald, Hanna

2005-01-01

Background This paper deals with the detection of significant linkage for quantitative traits using a variance components approach. Microsatellite markers were obtained for the Genetic Analysis Workshop 14 Collaborative Study on the Genetics of Alcoholism data. Ethnic heterogeneity, highly skewed quantitative measures, and a high rate of missing values are all present in this dataset and well known to impact upon linkage analysis. This makes it a good candidate for investigation. Results As expected, we observed a number of changes in LOD scores, especially for chromosomes 1, 7, and 18, along with the three factors studied. A dramatic example of such changes can be found in chromosome 7. Highly significant linkage to one of the quantitative traits became insignificant when a proper normalizing transformation of the trait was used and when analysis was carried out on an ethnically homogeneous subset of the original pedigrees. Conclusion In agreement with existing literature, transforming a trait to ensure normality using a Box-Cox transformation is highly recommended in order to avoid false-positive linkages. Furthermore, pedigrees should be sorted by ethnic groups and analyses should be carried out separately. Finally, one should be aware that the inclusion of covariates with a high rate of missing values reduces considerably the number of subjects included in the model. In such a case, the loss in power may be large. Imputation methods are then recommended. PMID:16451664

Report on Integration of Existing Grid Models for N-R HES Interaction Focused on Balancing Authorities for Sub-hour Penalties and Opportunities

DOE Office of Scientific and Technical Information (OSTI.GOV)

McJunkin, Timothy; Epiney, Aaron; Rabiti, Cristian

2017-06-01

This report provides a summary of the effort in the Nuclear-Renewable Hybrid Energy System (N-R HES) project on the level 4 milestone to consider integration of existing grid models into the factors for optimization on shorter time intervals than the existing electric grid models with the Risk Analysis Virtual Environment (RAVEN) and Modelica [1] optimizations and economic analysis that are the focus of the project to date.

How to simulate pedestrian behaviors in seismic evacuation for vulnerability reduction of existing buildings

NASA Astrophysics Data System (ADS)

Quagliarini, Enrico; Bernardini, Gabriele; D'Orazio, Marco

2017-07-01

Understanding and representing how individuals behave in earthquake emergencies would be essentially to assess the impact of vulnerability reduction strategies on existing buildings in seismic areas. In fact, interactions between individuals and the scenario (modified by the earthquake occurrence) are really important in order to understand the possible additional risks for people, especially during the evacuation phase. The current approach is based on "qualitative" aspects, in order to define best practice guidelines for Civil Protection and populations. On the contrary, a "quantitative" description of human response and evacuation motion in similar conditions is urgently needed. Hence, this work defines the rules for pedestrians' earthquake evacuation in urban scenarios, by taking advantages of previous results of real-world evacuation analyses. In particular, motion laws for pedestrians is defined by modifying the Social Force model equation. The proposed model could be used for evaluating individuals' evacuation process and so for defining operative strategies for interferences reduction in critical urban fabric parts (e.g.: interventions on particular buildings, evacuation strategies definition, city parts projects).

Preclinical MR fingerprinting (MRF) at 7 T: effective quantitative imaging for rodent disease models.

PubMed

Gao, Ying; Chen, Yong; Ma, Dan; Jiang, Yun; Herrmann, Kelsey A; Vincent, Jason A; Dell, Katherine M; Drumm, Mitchell L; Brady-Kalnay, Susann M; Griswold, Mark A; Flask, Chris A; Lu, Lan

2015-03-01

High-field preclinical MRI scanners are now commonly used to quantitatively assess disease status and the efficacy of novel therapies in a wide variety of rodent models. Unfortunately, conventional MRI methods are highly susceptible to respiratory and cardiac motion artifacts resulting in potentially inaccurate and misleading data. We have developed an initial preclinical 7.0-T MRI implementation of the highly novel MR fingerprinting (MRF) methodology which has been described previously for clinical imaging applications. The MRF technology combines a priori variation in the MRI acquisition parameters with dictionary-based matching of acquired signal evolution profiles to simultaneously generate quantitative maps of T1 and T2 relaxation times and proton density. This preclinical MRF acquisition was constructed from a fast imaging with steady-state free precession (FISP) MRI pulse sequence to acquire 600 MRF images with both evolving T1 and T2 weighting in approximately 30 min. This initial high-field preclinical MRF investigation demonstrated reproducible and differentiated estimates of in vitro phantoms with different relaxation times. In vivo preclinical MRF results in mouse kidneys and brain tumor models demonstrated an inherent resistance to respiratory motion artifacts as well as sensitivity to known pathology. These results suggest that MRF methodology may offer the opportunity for the quantification of numerous MRI parameters for a wide variety of preclinical imaging applications. Copyright © 2015 John Wiley & Sons, Ltd.

Endoscopic skull base training using 3D printed models with pre-existing pathology.

PubMed

Narayanan, Vairavan; Narayanan, Prepageran; Rajagopalan, Raman; Karuppiah, Ravindran; Rahman, Zainal Ariff Abdul; Wormald, Peter-John; Van Hasselt, Charles Andrew; Waran, Vicknes

2015-03-01

Endoscopic base of skull surgery has been growing in acceptance in the recent past due to improvements in visualisation and micro instrumentation as well as the surgical maturing of early endoscopic skull base practitioners. Unfortunately, these demanding procedures have a steep learning curve. A physical simulation that is able to reproduce the complex anatomy of the anterior skull base provides very useful means of learning the necessary skills in a safe and effective environment. This paper aims to assess the ease of learning endoscopic skull base exposure and drilling techniques using an anatomically accurate physical model with a pre-existing pathology (i.e., basilar invagination) created from actual patient data. Five models of a patient with platy-basia and basilar invagination were created from the original MRI and CT imaging data of a patient. The models were used as part of a training workshop for ENT surgeons with varying degrees of experience in endoscopic base of skull surgery, from trainees to experienced consultants. The surgeons were given a list of key steps to achieve in exposing and drilling the skull base using the simulation model. They were then asked to list the level of difficulty of learning these steps using the model. The participants found the models suitable for learning registration, navigation and skull base drilling techniques. All participants also found the deep structures to be accurately represented spatially as confirmed by the navigation system. These models allow structured simulation to be conducted in a workshop environment where surgeons and trainees can practice to perform complex procedures in a controlled fashion under the supervision of experts.

Application of an unsteady-state model for predicting vertical temperature distribution to an existing atrium

DOE Office of Scientific and Technical Information (OSTI.GOV)

Takemasa, Yuichi; Togari, Satoshi; Arai, Yoshinobu

1996-11-01

Vertical temperature differences tend to be great in a large indoor space such as an atrium, and it is important to predict variations of vertical temperature distribution in the early stage of the design. The authors previously developed and reported on a new simplified unsteady-state calculation model for predicting vertical temperature distribution in a large space. In this paper, this model is applied to predicting the vertical temperature distribution in an existing low-rise atrium that has a skylight and is affected by transmitted solar radiation. Detailed calculation procedures that use the model are presented with all the boundary conditions, andmore » analytical simulations are carried out for the cooling condition. Calculated values are compared with measured results. The results of the comparison demonstrate that the calculation model can be applied to the design of a large space. The effects of occupied-zone cooling are also discussed and compared with those of all-zone cooling.« less

Integrating Quantitative Thinking into an Introductory Biology Course Improves Students' Mathematical Reasoning in Biological Contexts

ERIC Educational Resources Information Center

Hester, Susan; Buxner, Sanlyn; Elfring, Lisa; Nagy, Lisa

2014-01-01

Recent calls for improving undergraduate biology education have emphasized the importance of students learning to apply quantitative skills to biological problems. Motivated by students' apparent inability to transfer their existing quantitative skills to biological contexts, we designed and taught an introductory molecular and cell biology course…

Quantitative evaluation of the risk induced by dominant geomorphological processes on different land uses, based on GIS spatial analysis models

NASA Astrophysics Data System (ADS)

Ştefan, Bilaşco; Sanda, Roşca; Ioan, Fodorean; Iuliu, Vescan; Sorin, Filip; Dănuţ, Petrea

2017-12-01

Maramureş Land is mostly characterized by agricultural and forestry land use due to its specific configuration of topography and its specific pedoclimatic conditions. Taking into consideration the trend of the last century from the perspective of land management, a decrease in the surface of agricultural lands to the advantage of built-up and grass lands, as well as an accelerated decrease in the forest cover due to uncontrolled and irrational forest exploitation, has become obvious. The field analysis performed on the territory of Maramureş Land has highlighted a high frequency of two geomorphologic processes — landslides and soil erosion — which have a major negative impact on land use due to their rate of occurrence. The main aim of the present study is the GIS modeling of the two geomorphologic processes, determining a state of vulnerability (the USLE model for soil erosion and a quantitative model based on the morphometric characteristics of the territory, derived from the HG. 447/2003) and their integration in a complex model of cumulated vulnerability identification. The modeling of the risk exposure was performed using a quantitative approach based on models and equations of spatial analysis, which were developed with modeled raster data structures and primary vector data, through a matrix highlighting the correspondence between vulnerability and land use classes. The quantitative analysis of the risk was performed by taking into consideration the exposure classes as modeled databases and the land price as a primary alphanumeric database using spatial analysis techniques for each class by means of the attribute table. The spatial results highlight the territories with a high risk to present geomorphologic processes that have a high degree of occurrence and represent a useful tool in the process of spatial planning.

Quantitative evaluation of the risk induced by dominant geomorphological processes on different land uses, based on GIS spatial analysis models

NASA Astrophysics Data System (ADS)

Ştefan, Bilaşco; Sanda, Roşca; Ioan, Fodorean; Iuliu, Vescan; Sorin, Filip; Dănuţ, Petrea

2018-06-01

Maramureş Land is mostly characterized by agricultural and forestry land use due to its specific configuration of topography and its specific pedoclimatic conditions. Taking into consideration the trend of the last century from the perspective of land management, a decrease in the surface of agricultural lands to the advantage of built-up and grass lands, as well as an accelerated decrease in the forest cover due to uncontrolled and irrational forest exploitation, has become obvious. The field analysis performed on the territory of Maramureş Land has highlighted a high frequency of two geomorphologic processes — landslides and soil erosion — which have a major negative impact on land use due to their rate of occurrence. The main aim of the present study is the GIS modeling of the two geomorphologic processes, determining a state of vulnerability (the USLE model for soil erosion and a quantitative model based on the morphometric characteristics of the territory, derived from the HG. 447/2003) and their integration in a complex model of cumulated vulnerability identification. The modeling of the risk exposure was performed using a quantitative approach based on models and equations of spatial analysis, which were developed with modeled raster data structures and primary vector data, through a matrix highlighting the correspondence between vulnerability and land use classes. The quantitative analysis of the risk was performed by taking into consideration the exposure classes as modeled databases and the land price as a primary alphanumeric database using spatial analysis techniques for each class by means of the attribute table. The spatial results highlight the territories with a high risk to present geomorphologic processes that have a high degree of occurrence and represent a useful tool in the process of spatial planning.

Quantitative Analysis of Intracellular Motility Based on Optical Flow Model

PubMed Central

Li, Heng

2017-01-01

Analysis of cell mobility is a key issue for abnormality identification and classification in cell biology research. However, since cell deformation induced by various biological processes is random and cell protrusion is irregular, it is difficult to measure cell morphology and motility in microscopic images. To address this dilemma, we propose an improved variation optical flow model for quantitative analysis of intracellular motility, which not only extracts intracellular motion fields effectively but also deals with optical flow computation problem at the border by taking advantages of the formulation based on L1 and L2 norm, respectively. In the energy functional of our proposed optical flow model, the data term is in the form of L2 norm; the smoothness of the data changes with regional features through an adaptive parameter, using L1 norm near the edge of the cell and L2 norm away from the edge. We further extract histograms of oriented optical flow (HOOF) after optical flow field of intracellular motion is computed. Then distances of different HOOFs are calculated as the intracellular motion features to grade the intracellular motion. Experimental results show that the features extracted from HOOFs provide new insights into the relationship between the cell motility and the special pathological conditions. PMID:29065574

Prognostic Value of Quantitative Stress Perfusion Cardiac Magnetic Resonance.

PubMed

Sammut, Eva C; Villa, Adriana D M; Di Giovine, Gabriella; Dancy, Luke; Bosio, Filippo; Gibbs, Thomas; Jeyabraba, Swarna; Schwenke, Susanne; Williams, Steven E; Marber, Michael; Alfakih, Khaled; Ismail, Tevfik F; Razavi, Reza; Chiribiri, Amedeo

2018-05-01

This study sought to evaluate the prognostic usefulness of visual and quantitative perfusion cardiac magnetic resonance (CMR) ischemic burden in an unselected group of patients and to assess the validity of consensus-based ischemic burden thresholds extrapolated from nuclear studies. There are limited data on the prognostic value of assessing myocardial ischemic burden by CMR, and there are none using quantitative perfusion analysis. Patients with suspected coronary artery disease referred for adenosine-stress perfusion CMR were included (n = 395; 70% male; age 58 ± 13 years). The primary endpoint was a composite of cardiovascular death, nonfatal myocardial infarction, aborted sudden death, and revascularization after 90 days. Perfusion scans were assessed visually and with quantitative analysis. Cross-validated Cox regression analysis and net reclassification improvement were used to assess the incremental prognostic value of visual or quantitative perfusion analysis over a baseline clinical model, initially as continuous covariates, then using accepted thresholds of ≥2 segments or ≥10% myocardium. After a median 460 days (interquartile range: 190 to 869 days) follow-up, 52 patients reached the primary endpoint. At 2 years, the addition of ischemic burden was found to increase prognostic value over a baseline model of age, sex, and late gadolinium enhancement (baseline model area under the curve [AUC]: 0.75; visual AUC: 0.84; quantitative AUC: 0.85). Dichotomized quantitative ischemic burden performed better than visual assessment (net reclassification improvement 0.043 vs. 0.003 against baseline model). This study was the first to address the prognostic benefit of quantitative analysis of perfusion CMR and to support the use of consensus-based ischemic burden thresholds by perfusion CMR for prognostic evaluation of patients with suspected coronary artery disease. Quantitative analysis provided incremental prognostic value to visual assessment and

Quantitative multiphase model for hydrothermal liquefaction of algal biomass

DOE PAGES

Li, Yalin; Leow, Shijie; Fedders, Anna C.; ...

2017-01-17

Here, optimized incorporation of hydrothermal liquefaction (HTL, reaction in water at elevated temperature and pressure) within an integrated biorefinery requires accurate models to predict the quantity and quality of all HTL products. Existing models primarily focus on biocrude product yields with limited consideration for biocrude quality and aqueous, gas, and biochar co-products, and have not been validated with an extensive collection of feedstocks. In this study, HTL experiments (300 °C, 30 min) were conducted using 24 different batches of microalgae feedstocks with distinctive feedstock properties, which resulted in a wide range of biocrude (21.3–54.3 dry weight basis, dw%), aqueous (4.6–31.2more » dw%), gas (7.1–35.6 dw%), and biochar (1.3–35.0 dw%) yields. Based on these results, a multiphase component additivity (MCA) model was introduced to predict yields and characteristics of the HTL biocrude product and aqueous, gas, and biochar co-products, with only feedstock biochemical (lipid, protein, carbohydrate, and ash) and elemental (C/H/N) composition as model inputs. Biochemical components were determined to distribute across biocrude product/HTL co-products as follows: lipids to biocrude; proteins to biocrude > aqueous > gas; carbohydrates to gas ≈ biochar > biocrude; and ash to aqueous > biochar. Modeled quality indicators included biocrude C/H/N contents, higher heating value (HHV), and energy recovery (ER); aqueous total organic carbon (TOC) and total nitrogen (TN) contents; and biochar carbon content. The model was validated with HTL data from the literature, the potential to expand the application of this modeling framework to include waste biosolids (e.g., wastewater sludge, manure) was explored, and future research needs for industrial application were identified. Ultimately, the MCA model represents a critical step towards the integration of cultivation models with downstream HTL and biorefinery operations to enable system

2D Hydrodynamic Based Logic Modeling Tool for River Restoration Decision Analysis: A Quantitative Approach to Project Prioritization

NASA Astrophysics Data System (ADS)

Bandrowski, D.; Lai, Y.; Bradley, N.; Gaeuman, D. A.; Murauskas, J.; Som, N. A.; Martin, A.; Goodman, D.; Alvarez, J.

2014-12-01

In the field of river restoration sciences there is a growing need for analytical modeling tools and quantitative processes to help identify and prioritize project sites. 2D hydraulic models have become more common in recent years and with the availability of robust data sets and computing technology, it is now possible to evaluate large river systems at the reach scale. The Trinity River Restoration Program is now analyzing a 40 mile segment of the Trinity River to determine priority and implementation sequencing for its Phase II rehabilitation projects. A comprehensive approach and quantitative tool has recently been developed to analyze this complex river system referred to as: 2D-Hydrodynamic Based Logic Modeling (2D-HBLM). This tool utilizes various hydraulic output parameters combined with biological, ecological, and physical metrics at user-defined spatial scales. These metrics and their associated algorithms are the underpinnings of the 2D-HBLM habitat module used to evaluate geomorphic characteristics, riverine processes, and habitat complexity. The habitat metrics are further integrated into a comprehensive Logic Model framework to perform statistical analyses to assess project prioritization. The Logic Model will analyze various potential project sites by evaluating connectivity using principal component methods. The 2D-HBLM tool will help inform management and decision makers by using a quantitative process to optimize desired response variables with balancing important limiting factors in determining the highest priority locations within the river corridor to implement restoration projects. Effective river restoration prioritization starts with well-crafted goals that identify the biological objectives, address underlying causes of habitat change, and recognizes that social, economic, and land use limiting factors may constrain restoration options (Bechie et. al. 2008). Applying natural resources management actions, like restoration prioritization, is

Quantitative multimodality imaging in cancer research and therapy.

PubMed

Yankeelov, Thomas E; Abramson, Richard G; Quarles, C Chad

2014-11-01

Advances in hardware and software have enabled the realization of clinically feasible, quantitative multimodality imaging of tissue pathophysiology. Earlier efforts relating to multimodality imaging of cancer have focused on the integration of anatomical and functional characteristics, such as PET-CT and single-photon emission CT (SPECT-CT), whereas more-recent advances and applications have involved the integration of multiple quantitative, functional measurements (for example, multiple PET tracers, varied MRI contrast mechanisms, and PET-MRI), thereby providing a more-comprehensive characterization of the tumour phenotype. The enormous amount of complementary quantitative data generated by such studies is beginning to offer unique insights into opportunities to optimize care for individual patients. Although important technical optimization and improved biological interpretation of multimodality imaging findings are needed, this approach can already be applied informatively in clinical trials of cancer therapeutics using existing tools. These concepts are discussed herein.

Building Coalitions To Provide HIV Legal Advocacy Services: Utilizing Existing Disability Models. AIDS Technical Report, No. 5.

ERIC Educational Resources Information Center

Harvey, David C.; Ardinger, Robert S.

This technical report is part of a series on AIDS/HIV (Acquired Immune Deficiency Syndrome/Human Immunodeficiency Virus) and is intended to help link various legal advocacy organizations providing services to persons with mental illness or developmental disabilities. This report discusses strategies to utilize existing disability models for…

Quantitative traits and diversification.

PubMed

FitzJohn, Richard G

2010-12-01

Quantitative traits have long been hypothesized to affect speciation and extinction rates. For example, smaller body size or increased specialization may be associated with increased rates of diversification. Here, I present a phylogenetic likelihood-based method (quantitative state speciation and extinction [QuaSSE]) that can be used to test such hypotheses using extant character distributions. This approach assumes that diversification follows a birth-death process where speciation and extinction rates may vary with one or more traits that evolve under a diffusion model. Speciation and extinction rates may be arbitrary functions of the character state, allowing much flexibility in testing models of trait-dependent diversification. I test the approach using simulated phylogenies and show that a known relationship between speciation and a quantitative character could be recovered in up to 80% of the cases on large trees (500 species). Consistent with other approaches, detecting shifts in diversification due to differences in extinction rates was harder than when due to differences in speciation rates. Finally, I demonstrate the application of QuaSSE to investigate the correlation between body size and diversification in primates, concluding that clade-specific differences in diversification may be more important than size-dependent diversification in shaping the patterns of diversity within this group.

Quantitative self-assembly prediction yields targeted nanomedicines

NASA Astrophysics Data System (ADS)

Shamay, Yosi; Shah, Janki; Işık, Mehtap; Mizrachi, Aviram; Leibold, Josef; Tschaharganeh, Darjus F.; Roxbury, Daniel; Budhathoki-Uprety, Januka; Nawaly, Karla; Sugarman, James L.; Baut, Emily; Neiman, Michelle R.; Dacek, Megan; Ganesh, Kripa S.; Johnson, Darren C.; Sridharan, Ramya; Chu, Karen L.; Rajasekhar, Vinagolu K.; Lowe, Scott W.; Chodera, John D.; Heller, Daniel A.

2018-02-01

Development of targeted nanoparticle drug carriers often requires complex synthetic schemes involving both supramolecular self-assembly and chemical modification. These processes are generally difficult to predict, execute, and control. We describe herein a targeted drug delivery system that is accurately and quantitatively predicted to self-assemble into nanoparticles based on the molecular structures of precursor molecules, which are the drugs themselves. The drugs assemble with the aid of sulfated indocyanines into particles with ultrahigh drug loadings of up to 90%. We devised quantitative structure-nanoparticle assembly prediction (QSNAP) models to identify and validate electrotopological molecular descriptors as highly predictive indicators of nano-assembly and nanoparticle size. The resulting nanoparticles selectively targeted kinase inhibitors to caveolin-1-expressing human colon cancer and autochthonous liver cancer models to yield striking therapeutic effects while avoiding pERK inhibition in healthy skin. This finding enables the computational design of nanomedicines based on quantitative models for drug payload selection.

Quantitative Diagnosis of Continuous-Valued, Stead-State Systems

NASA Technical Reports Server (NTRS)

Rouquette, N.

1995-01-01

Quantitative diagnosis involves numerically estimating the values of unobservable parameters that best explain the observed parameter values. We consider quantitative diagnosis for continuous, lumped- parameter, steady-state physical systems because such models are easy to construct and the diagnosis problem is considerably simpler than that for corresponding dynamic models. To further tackle the difficulties of numerically inverting a simulation model to compute a diagnosis, we propose to decompose a physical system model in terms of feedback loops. This decomposition reduces the dimension of the problem and consequently decreases the diagnosis search space. We illustrate this approach on a model of thermal control system studied in earlier research.

QUANTITATIVE PROCEDURES FOR NEUROTOXICOLOGY RISK ASSESSMENT

EPA Science Inventory

In this project, previously published information on biologically based dose-response model for brain development was used to quantitatively evaluate critical neurodevelopmental processes, and to assess potential chemical impacts on early brain development. This model has been ex...

A quantitative, comprehensive analytical model for ``fast'' magnetic reconnection in Hall MHD

NASA Astrophysics Data System (ADS)

Simakov, Andrei N.

2008-11-01

Magnetic reconnection in nature usually happens on fast (e.g. dissipation independent) time scales. While such scales have been observed computationally [1], a fundamental analytical model capable of explaining them has been lacking. Here, we propose such a quantitative model for 2D Hall MHD reconnection without a guide field. The model recovers the Sweet-Parker and the electron MHD [2] results in the appropriate limits of the ion inertial length, di, and is valid everywhere in between [3]. The model predicts the dissipation region aspect ratio and the reconnection rate Ez in terms of dissipation and inertial parameters, and has been found to be in excellent agreement with non-linear simulations. It confirms a number of long-standing empirical results and resolves several controversies. In particular, we find that both open X-point and elongated dissipation regions allow ``fast'' reconnection and that Ez depends on di. Moreover, when applied to electron-positron plasmas, the model demonstrates that fast dispersive waves are not instrumental for ``fast'' reconnection [4]. [1] J. Birn et al., J. Geophys. Res. 106, 3715 (2001). [2] L. Chac'on, A. N. Simakov, and A. Zocco, Phys. Rev. Lett. 99, 235001 (2007). [3] A. N. Simakov and L. Chac'on, submitted to Phys. Rev. Lett. [4] L. Chac'on, A. N. Simakov, V. Lukin, and A. Zocco, Phys. Rev. Lett. 101, 025003 (2008).

A Quantitative Study of Faculty Perceptions and Attitudes on Asynchronous Virtual Teamwork Using the Technology Acceptance Model

ERIC Educational Resources Information Center

Wolusky, G. Anthony

2016-01-01

This quantitative study used a web-based questionnaire to assess the attitudes and perceptions of online and hybrid faculty towards student-centered asynchronous virtual teamwork (AVT) using the technology acceptance model (TAM) of Davis (1989). AVT is online student participation in a team approach to problem-solving culminating in a written…

Quantitative descriptions of generalized arousal, an elementary function of the vertebrate brain

PubMed Central

Quinkert, Amy Wells; Vimal, Vivek; Weil, Zachary M.; Reeke, George N.; Schiff, Nicholas D.; Banavar, Jayanth R.; Pfaff, Donald W.

2011-01-01

We review a concept of the most primitive, fundamental function of the vertebrate CNS, generalized arousal (GA). Three independent lines of evidence indicate the existence of GA: statistical, genetic, and mechanistic. Here we ask, is this concept amenable to quantitative analysis? Answering in the affirmative, four quantitative approaches have proven useful: (i) factor analysis, (ii) information theory, (iii) deterministic chaos, and (iv) application of a Gaussian equation. It strikes us that, to date, not just one but at least four different quantitative approaches seem necessary for describing different aspects of scientific work on GA. PMID:21555568

Twelve recommendations for integrating existing systematic reviews into new reviews: EPC guidance.

PubMed

Robinson, Karen A; Chou, Roger; Berkman, Nancy D; Newberry, Sydne J; Fu, Rongwei; Hartling, Lisa; Dryden, Donna; Butler, Mary; Foisy, Michelle; Anderson, Johanna; Motu'apuaka, Makalapua; Relevo, Rose; Guise, Jeanne-Marie; Chang, Stephanie

2016-02-01

As time and cost constraints in the conduct of systematic reviews increase, the need to consider the use of existing systematic reviews also increases. We developed guidance on the integration of systematic reviews into new reviews. A workgroup of methodologists from Evidence-based Practice Centers developed consensus-based recommendations. Discussions were informed by a literature scan and by interviews with organizations that conduct systematic reviews. Twelve recommendations were developed addressing selecting reviews, assessing risk of bias, qualitative and quantitative synthesis, and summarizing and assessing body of evidence. We provide preliminary guidance for an efficient and unbiased approach to integrating existing systematic reviews with primary studies in a new review. Copyright © 2016 Elsevier Inc. All rights reserved.

Challenges in Developing Models Describing Complex Soil Systems

NASA Astrophysics Data System (ADS)

Simunek, J.; Jacques, D.

2014-12-01

Quantitative mechanistic models that consider basic physical, mechanical, chemical, and biological processes have the potential to be powerful tools to integrate our understanding of complex soil systems, and the soil science community has often called for models that would include a large number of these diverse processes. However, once attempts have been made to develop such models, the response from the community has not always been overwhelming, especially after it discovered that these models are consequently highly complex, requiring not only a large number of parameters, not all of which can be easily (or at all) measured and/or identified, and which are often associated with large uncertainties, but also requiring from their users deep knowledge of all/most of these implemented physical, mechanical, chemical and biological processes. Real, or perceived, complexity of these models then discourages users from using them even for relatively simple applications, for which they would be perfectly adequate. Due to the nonlinear nature and chemical/biological complexity of the soil systems, it is also virtually impossible to verify these types of models analytically, raising doubts about their applicability. Code inter-comparisons, which is then likely the most suitable method to assess code capabilities and model performance, requires existence of multiple models of similar/overlapping capabilities, which may not always exist. It is thus a challenge not only to developed models describing complex soil systems, but also to persuade the soil science community in using them. As a result, complex quantitative mechanistic models are still an underutilized tool in soil science research. We will demonstrate some of the challenges discussed above on our own efforts in developing quantitative mechanistic models (such as HP1/2) for complex soil systems.

Quantitative property-structural relation modeling on polymeric dielectric materials

NASA Astrophysics Data System (ADS)

Wu, Ke

Nowadays, polymeric materials have attracted more and more attention in dielectric applications. But searching for a material with desired properties is still largely based on trial and error. To facilitate the development of new polymeric materials, heuristic models built using the Quantitative Structure Property Relationships (QSPR) techniques can provide reliable "working solutions". In this thesis, the application of QSPR on polymeric materials is studied from two angles: descriptors and algorithms. A novel set of descriptors, called infinite chain descriptors (ICD), are developed to encode the chemical features of pure polymers. ICD is designed to eliminate the uncertainty of polymer conformations and inconsistency of molecular representation of polymers. Models for the dielectric constant, band gap, dielectric loss tangent and glass transition temperatures of organic polymers are built with high prediction accuracy. Two new algorithms, the physics-enlightened learning method (PELM) and multi-mechanism detection, are designed to deal with two typical challenges in material QSPR. PELM is a meta-algorithm that utilizes the classic physical theory as guidance to construct the candidate learning function. It shows better out-of-domain prediction accuracy compared to the classic machine learning algorithm (support vector machine). Multi-mechanism detection is built based on a cluster-weighted mixing model similar to a Gaussian mixture model. The idea is to separate the data into subsets where each subset can be modeled by a much simpler model. The case study on glass transition temperature shows that this method can provide better overall prediction accuracy even though less data is available for each subset model. In addition, the techniques developed in this work are also applied to polymer nanocomposites (PNC). PNC are new materials with outstanding dielectric properties. As a key factor in determining the dispersion state of nanoparticles in the polymer matrix

Quantitating Organoleptic Volatile Phenols in Smoke-Exposed Vitis vinifera Berries.

PubMed

Noestheden, Matthew; Thiessen, Katelyn; Dennis, Eric G; Tiet, Ben; Zandberg, Wesley F

2017-09-27

Accurate methods for quantitating volatile phenols (i.e., guaiacol, syringol, 4-ethylphenol, etc.) in smoke-exposed Vitis vinifera berries prior to fermentation are needed to predict the likelihood of perceptible smoke taint following vinification. Reported here is a complete, cross-validated analytical workflow to accurately quantitate free and glycosidically bound volatile phenols in smoke-exposed berries using liquid-liquid extraction, acid-mediated hydrolysis, and gas chromatography-tandem mass spectrometry. The reported workflow addresses critical gaps in existing methods for volatile phenols that impact quantitative accuracy, most notably the effect of injection port temperature and the variability in acid-mediated hydrolytic procedures currently used. Addressing these deficiencies will help the wine industry make accurate, informed decisions when producing wines from smoke-exposed berries.

Quantitative impedance measurements for eddy current model validation

NASA Astrophysics Data System (ADS)

Khan, T. A.; Nakagawa, N.

2000-05-01

This paper reports on a series of laboratory-based impedance measurement data, collected by the use of a quantitatively accurate, mechanically controlled measurement station. The purpose of the measurement is to validate a BEM-based eddy current model against experiment. We have therefore selected two "validation probes," which are both split-D differential probes. Their internal structures and dimensions are extracted from x-ray CT scan data, and thus known within the measurement tolerance. A series of measurements was carried out, using the validation probes and two Ti-6Al-4V block specimens, one containing two 1-mm long fatigue cracks, and the other containing six EDM notches of a range of sizes. Motor-controlled XY scanner performed raster scans over the cracks, with the probe riding on the surface with a spring-loaded mechanism to maintain the lift off. Both an impedance analyzer and a commercial EC instrument were used in the measurement. The probes were driven in both differential and single-coil modes for the specific purpose of model validation. The differential measurements were done exclusively by the eddyscope, while the single-coil data were taken with both the impedance analyzer and the eddyscope. From the single-coil measurements, we obtained the transfer function to translate the voltage output of the eddyscope into impedance values, and then used it to translate the differential measurement data into impedance results. The presentation will highlight the schematics of the measurement procedure, a representative of raw data, explanation of the post data-processing procedure, and then a series of resulting 2D flaw impedance results. A noise estimation will be given also, in order to quantify the accuracy of these measurements, and to be used in probability-of-detection estimation.—This work was supported by the NSF Industry/University Cooperative Research Program.

Linking Antisocial Behavior, Substance Use, and Personality: An Integrative Quantitative Model of the Adult Externalizing Spectrum

PubMed Central

Krueger, Robert F.; Markon, Kristian E.; Patrick, Christopher J.; Benning, Stephen D.; Kramer, Mark D.

2008-01-01

Antisocial behavior, substance use, and impulsive and aggressive personality traits often co-occur, forming a coherent spectrum of personality and psychopathology. In the current research, the authors developed a novel quantitative model of this spectrum. Over 3 waves of iterative data collection, 1,787 adult participants selected to represent a range across the externalizing spectrum provided extensive data about specific externalizing behaviors. Statistical methods such as item response theory and semiparametric factor analysis were used to model these data. The model and assessment instrument that emerged from the research shows how externalizing phenomena are organized hierarchically and cover a wide range of individual differences. The authors discuss the utility of this model for framing research on the correlates and the etiology of externalizing phenomena. PMID:18020714

Quantitative X-ray Differential Interference Contrast Microscopy

NASA Astrophysics Data System (ADS)

Nakamura, Takashi

Full-field soft x-ray microscopes are widely used in many fields of sciences. Advances in nanofabrication technology enabled short wavelength focusing elements with significantly improved spatial resolution. In the soft x-ray spectral region, samples as small as 12 nm can be resolved using micro zone-plates as the objective lens. In addition to conventional x-ray microscopy in which x-ray absorption difference provides the image contrast, phase contrast mechanisms such as differential phase contrast (DIC) and Zernike phase contrast have also been demonstrated These phase contrast imaging mechanisms are especially attractive at the x-ray wavelengths where phase contrast of most materials is typically 10 times stronger than the absorption contrast. With recent progresses in plasma-based x- ray sources and increasing accessibility to synchrotron user facilities, x-ray microscopes are quickly becoming standard measurement equipment in the laboratory. To further the usefulness of x-ray DIC microscopy this thesis explicitly addresses three known issues with this imaging modality by introducing new techniques and devices First, as opposed to its visible-light counterpart, no quantitative phase imaging technique exists for x-ray DIC microscopy. To address this issue, two nanoscale x-ray quantitative phase imaging techniques, using exclusive OR (XOR) patterns and zone-plate doublets, respectively, are proposed. Unlike existing x-ray quantitative phase imaging techniques such as Talbot interferometry and ptychography, no dedicated experimental setups or stringent illumination coherence are needed for quantitative phase retrieval. Second, to the best of our knowledge, no quantitative performance characterization of DIC microscopy exists to date. Therefore the imaging system's response to sample's spatial frequency is not known In order to gain in-depth understanding of this imaging modality, performance of x-ray DIC microscopy is quantified using modulation transfer function

A Review of the Statistical and Quantitative Methods Used to Study Alcohol-Attributable Crime.

PubMed

Fitterer, Jessica L; Nelson, Trisalyn A

2015-01-01

Modelling the relationship between alcohol consumption and crime generates new knowledge for crime prevention strategies. Advances in data, particularly data with spatial and temporal attributes, have led to a growing suite of applied methods for modelling. In support of alcohol and crime researchers we synthesized and critiqued existing methods of spatially and quantitatively modelling the effects of alcohol exposure on crime to aid method selection, and identify new opportunities for analysis strategies. We searched the alcohol-crime literature from 1950 to January 2014. Analyses that statistically evaluated or mapped the association between alcohol and crime were included. For modelling purposes, crime data were most often derived from generalized police reports, aggregated to large spatial units such as census tracts or postal codes, and standardized by residential population data. Sixty-eight of the 90 selected studies included geospatial data of which 48 used cross-sectional datasets. Regression was the prominent modelling choice (n = 78) though dependent on data many variations existed. There are opportunities to improve information for alcohol-attributable crime prevention by using alternative population data to standardize crime rates, sourcing crime information from non-traditional platforms (social media), increasing the number of panel studies, and conducting analysis at the local level (neighbourhood, block, or point). Due to the spatio-temporal advances in crime data, we expect a continued uptake of flexible Bayesian hierarchical modelling, a greater inclusion of spatial-temporal point pattern analysis, and shift toward prospective (forecast) modelling over small areas (e.g., blocks).

Comparative Analysis of Predictive Models for Liver Toxicity Using ToxCast Assays and Quantitative Structure-Activity Relationships (MCBIOS)

EPA Science Inventory

Comparative Analysis of Predictive Models for Liver Toxicity Using ToxCast Assays and Quantitative Structure-Activity Relationships Jie Liu1,2, Richard Judson1, Matthew T. Martin1, Huixiao Hong3, Imran Shah1 1National Center for Computational Toxicology (NCCT), US EPA, RTP, NC...

77 FR 41985 - Use of Influenza Disease Models To Quantitatively Evaluate the Benefits and Risks of Vaccines: A...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-17

... Evaluation and Research (CBER) and suggestions for further development. The public workshop will include... Evaluation and Research (HFM-210), Food and Drug Administration, 1401 Rockville Pike, suite 200N, Rockville... models to generate quantitative estimates of the benefits and risks of influenza vaccination. The public...

Transcriptome discovery in non-model wild fish species for the development of quantitative transcript abundance assays.

PubMed

Hahn, Cassidy M; Iwanowicz, Luke R; Cornman, Robert S; Mazik, Patricia M; Blazer, Vicki S

2016-12-01

Environmental studies increasingly identify the presence of both contaminants of emerging concern (CECs) and legacy contaminants in aquatic environments; however, the biological effects of these compounds on resident fishes remain largely unknown. High throughput methodologies were employed to establish partial transcriptomes for three wild-caught, non-model fish species; smallmouth bass (Micropterus dolomieu), white sucker (Catostomus commersonii) and brown bullhead (Ameiurus nebulosus). Sequences from these transcriptome databases were utilized in the development of a custom nCounter CodeSet that allowed for direct multiplexed measurement of 50 transcript abundance endpoints in liver tissue. Sequence information was also utilized in the development of quantitative real-time PCR (qPCR) primers. Cross-species hybridization allowed the smallmouth bass nCounter CodeSet to be used for quantitative transcript abundance analysis of an additional non-model species, largemouth bass (Micropterus salmoides). We validated the nCounter analysis data system with qPCR for a subset of genes and confirmed concordant results. Changes in transcript abundance biomarkers between sexes and seasons were evaluated to provide baseline data on transcript modulation for each species of interest. Published by Elsevier Inc.

Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images.

PubMed

Echegaray, Sebastian; Bakr, Shaimaa; Rubin, Daniel L; Napel, Sandy

2017-10-06

The aim of this study was to develop an open-source, modular, locally run or server-based system for 3D radiomics feature computation that can be used on any computer system and included in existing workflows for understanding associations and building predictive models between image features and clinical data, such as survival. The QIFE exploits various levels of parallelization for use on multiprocessor systems. It consists of a managing framework and four stages: input, pre-processing, feature computation, and output. Each stage contains one or more swappable components, allowing run-time customization. We benchmarked the engine using various levels of parallelization on a cohort of CT scans presenting 108 lung tumors. Two versions of the QIFE have been released: (1) the open-source MATLAB code posted to Github, (2) a compiled version loaded in a Docker container, posted to DockerHub, which can be easily deployed on any computer. The QIFE processed 108 objects (tumors) in 2:12 (h/mm) using 1 core, and 1:04 (h/mm) hours using four cores with object-level parallelization. We developed the Quantitative Image Feature Engine (QIFE), an open-source feature-extraction framework that focuses on modularity, standards, parallelism, provenance, and integration. Researchers can easily integrate it with their existing segmentation and imaging workflows by creating input and output components that implement their existing interfaces. Computational efficiency can be improved by parallelizing execution at the cost of memory usage. Different parallelization levels provide different trade-offs, and the optimal setting will depend on the size and composition of the dataset to be processed.

Understanding responder neurobiology in schizophrenia using a quantitative systems pharmacology model: application to iloperidone.

PubMed

Geerts, Hugo; Roberts, Patrick; Spiros, Athan; Potkin, Steven

2015-04-01

The concept of targeted therapies remains a holy grail for the pharmaceutical drug industry for identifying responder populations or new drug targets. Here we provide quantitative systems pharmacology as an alternative to the more traditional approach of retrospective responder pharmacogenomics analysis and applied this to the case of iloperidone in schizophrenia. This approach implements the actual neurophysiological effect of genotypes in a computer-based biophysically realistic model of human neuronal circuits, is parameterized with human imaging and pathology, and is calibrated by clinical data. We keep the drug pharmacology constant, but allowed the biological model coupling values to fluctuate in a restricted range around their calibrated values, thereby simulating random genetic mutations and representing variability in patient response. Using hypothesis-free Design of Experiments methods the dopamine D4 R-AMPA (receptor-alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) receptor coupling in cortical neurons was found to drive the beneficial effect of iloperidone, likely corresponding to the rs2513265 upstream of the GRIA4 gene identified in a traditional pharmacogenomics analysis. The serotonin 5-HT3 receptor-mediated effect on interneuron gamma-aminobutyric acid conductance was identified as the process that moderately drove the differentiation of iloperidone versus ziprasidone. This paper suggests that reverse-engineered quantitative systems pharmacology is a powerful alternative tool to characterize the underlying neurobiology of a responder population and possibly identifying new targets. © The Author(s) 2015.

An adaptive model approach for quantitative wrist rigidity evaluation during deep brain stimulation surgery.

PubMed

Assis, Sofia; Costa, Pedro; Rosas, Maria Jose; Vaz, Rui; Silva Cunha, Joao Paulo

2016-08-01

Intraoperative evaluation of the efficacy of Deep Brain Stimulation includes evaluation of the effect on rigidity. A subjective semi-quantitative scale is used, dependent on the examiner perception and experience. A system was proposed previously, aiming to tackle this subjectivity, using quantitative data and providing real-time feedback of the computed rigidity reduction, hence supporting the physician decision. This system comprised of a gyroscope-based motion sensor in a textile band, placed in the patients hand, which communicated its measurements to a laptop. The latter computed a signal descriptor from the angular velocity of the hand during wrist flexion in DBS surgery. The first approach relied on using a general rigidity reduction model, regardless of the initial severity of the symptom. Thus, to enhance the performance of the previously presented system, we aimed to develop models for high and low baseline rigidity, according to the examiner assessment before any stimulation. This would allow a more patient-oriented approach. Additionally, usability was improved by having in situ processing in a smartphone, instead of a computer. Such system has shown to be reliable, presenting an accuracy of 82.0% and a mean error of 3.4%. Relatively to previous results, the performance was similar, further supporting the importance of considering the cogwheel rigidity to better infer about the reduction in rigidity. Overall, we present a simple, wearable, mobile system, suitable for intra-operatory conditions during DBS, supporting a physician in decision-making when setting stimulation parameters.

Quantitative Outline-based Shape Analysis and Classification of Planetary Craterforms using Supervised Learning Models

NASA Astrophysics Data System (ADS)

Slezak, Thomas Joseph; Radebaugh, Jani; Christiansen, Eric

2017-10-01

The shapes of craterform morphology on planetary surfaces provides rich information about their origins and evolution. While morphologic information provides rich visual clues to geologic processes and properties, the ability to quantitatively communicate this information is less easily accomplished. This study examines the morphology of craterforms using the quantitative outline-based shape methods of geometric morphometrics, commonly used in biology and paleontology. We examine and compare landforms on planetary surfaces using shape, a property of morphology that is invariant to translation, rotation, and size. We quantify the shapes of paterae on Io, martian calderas, terrestrial basaltic shield calderas, terrestrial ash-flow calderas, and lunar impact craters using elliptic Fourier analysis (EFA) and the Zahn and Roskies (Z-R) shape function, or tangent angle approach to produce multivariate shape descriptors. These shape descriptors are subjected to multivariate statistical analysis including canonical variate analysis (CVA), a multiple-comparison variant of discriminant analysis, to investigate the link between craterform shape and classification. Paterae on Io are most similar in shape to terrestrial ash-flow calderas and the shapes of terrestrial basaltic shield volcanoes are most similar to martian calderas. The shapes of lunar impact craters, including simple, transitional, and complex morphology, are classified with a 100% rate of success in all models. Multiple CVA models effectively predict and classify different craterforms using shape-based identification and demonstrate significant potential for use in the analysis of planetary surfaces.

Reproducibility of Quantitative Structural and Physiological MRI Measurements

DTIC Science & Technology

2017-08-09

per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and...Journal Article 3. DATES COVERED (From – To) January 2015 – July 2017 4. TITLE AND SUBTITLE Reproducibility of Quantitative Structural and...NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) USAF School of Aerospace Medicine Aeromedical Research Dept/FHOH 2510 Fifth St., Bldg

Non Linear Programming (NLP) Formulation for Quantitative Modeling of Protein Signal Transduction Pathways

PubMed Central

Morris, Melody K.; Saez-Rodriguez, Julio; Lauffenburger, Douglas A.; Alexopoulos, Leonidas G.

2012-01-01

Modeling of signal transduction pathways plays a major role in understanding cells' function and predicting cellular response. Mathematical formalisms based on a logic formalism are relatively simple but can describe how signals propagate from one protein to the next and have led to the construction of models that simulate the cells response to environmental or other perturbations. Constrained fuzzy logic was recently introduced to train models to cell specific data to result in quantitative pathway models of the specific cellular behavior. There are two major issues in this pathway optimization: i) excessive CPU time requirements and ii) loosely constrained optimization problem due to lack of data with respect to large signaling pathways. Herein, we address both issues: the former by reformulating the pathway optimization as a regular nonlinear optimization problem; and the latter by enhanced algorithms to pre/post-process the signaling network to remove parts that cannot be identified given the experimental conditions. As a case study, we tackle the construction of cell type specific pathways in normal and transformed hepatocytes using medium and large-scale functional phosphoproteomic datasets. The proposed Non Linear Programming (NLP) formulation allows for fast optimization of signaling topologies by combining the versatile nature of logic modeling with state of the art optimization algorithms. PMID:23226239

Non Linear Programming (NLP) formulation for quantitative modeling of protein signal transduction pathways.

PubMed

Mitsos, Alexander; Melas, Ioannis N; Morris, Melody K; Saez-Rodriguez, Julio; Lauffenburger, Douglas A; Alexopoulos, Leonidas G

2012-01-01

Modeling of signal transduction pathways plays a major role in understanding cells' function and predicting cellular response. Mathematical formalisms based on a logic formalism are relatively simple but can describe how signals propagate from one protein to the next and have led to the construction of models that simulate the cells response to environmental or other perturbations. Constrained fuzzy logic was recently introduced to train models to cell specific data to result in quantitative pathway models of the specific cellular behavior. There are two major issues in this pathway optimization: i) excessive CPU time requirements and ii) loosely constrained optimization problem due to lack of data with respect to large signaling pathways. Herein, we address both issues: the former by reformulating the pathway optimization as a regular nonlinear optimization problem; and the latter by enhanced algorithms to pre/post-process the signaling network to remove parts that cannot be identified given the experimental conditions. As a case study, we tackle the construction of cell type specific pathways in normal and transformed hepatocytes using medium and large-scale functional phosphoproteomic datasets. The proposed Non Linear Programming (NLP) formulation allows for fast optimization of signaling topologies by combining the versatile nature of logic modeling with state of the art optimization algorithms.

A test for selection employing quantitative trait locus and mutation accumulation data.

PubMed

Rice, Daniel P; Townsend, Jeffrey P

2012-04-01

Evolutionary biologists attribute much of the phenotypic diversity observed in nature to the action of natural selection. However, for many phenotypic traits, especially quantitative phenotypic traits, it has been challenging to test for the historical action of selection. An important challenge for biologists studying quantitative traits, therefore, is to distinguish between traits that have evolved under the influence of strong selection and those that have evolved neutrally. Most existing tests for selection employ molecular data, but selection also leaves a mark on the genetic architecture underlying a trait. In particular, the distribution of quantitative trait locus (QTL) effect sizes and the distribution of mutational effects together provide information regarding the history of selection. Despite the increasing availability of QTL and mutation accumulation data, such data have not yet been effectively exploited for this purpose. We present a model of the evolution of QTL and employ it to formulate a test for historical selection. To provide a baseline for neutral evolution of the trait, we estimate the distribution of mutational effects from mutation accumulation experiments. We then apply a maximum-likelihood-based method of inference to estimate the range of selection strengths under which such a distribution of mutations could generate the observed QTL. Our test thus represents the first integration of population genetic theory and QTL data to measure the historical influence of selection.

Mechanical Model Analysis for Quantitative Evaluation of Liver Fibrosis Based on Ultrasound Tissue Elasticity Imaging

NASA Astrophysics Data System (ADS)

Shiina, Tsuyoshi; Maki, Tomonori; Yamakawa, Makoto; Mitake, Tsuyoshi; Kudo, Masatoshi; Fujimoto, Kenji

2012-07-01

Precise evaluation of the stage of chronic hepatitis C with respect to fibrosis has become an important issue to prevent the occurrence of cirrhosis and to initiate appropriate therapeutic intervention such as viral eradication using interferon. Ultrasound tissue elasticity imaging, i.e., elastography can visualize tissue hardness/softness, and its clinical usefulness has been studied to detect and evaluate tumors. We have recently reported that the texture of elasticity image changes as fibrosis progresses. To evaluate fibrosis progression quantitatively on the basis of ultrasound tissue elasticity imaging, we introduced a mechanical model of fibrosis progression and simulated the process by which hepatic fibrosis affects elasticity images and compared the results with those clinical data analysis. As a result, it was confirmed that even in diffuse diseases like chronic hepatitis, the patterns of elasticity images are related to fibrous structural changes caused by hepatic disease and can be used to derive features for quantitative evaluation of fibrosis stage.

[Variable selection methods combined with local linear embedding theory used for optimization of near infrared spectral quantitative models].

PubMed

Hao, Yong; Sun, Xu-Dong; Yang, Qiang

2012-12-01

Variables selection strategy combined with local linear embedding (LLE) was introduced for the analysis of complex samples by using near infrared spectroscopy (NIRS). Three methods include Monte Carlo uninformation variable elimination (MCUVE), successive projections algorithm (SPA) and MCUVE connected with SPA were used for eliminating redundancy spectral variables. Partial least squares regression (PLSR) and LLE-PLSR were used for modeling complex samples. The results shown that MCUVE can both extract effective informative variables and improve the precision of models. Compared with PLSR models, LLE-PLSR models can achieve more accurate analysis results. MCUVE combined with LLE-PLSR is an effective modeling method for NIRS quantitative analysis.

Congruent climate-related genecological responses from molecular markers and quantitative traits for western white pine (Pinus monticola)

Treesearch

Bryce A. Richardson; Gerald E. Rehfeldt; Mee-Sook Kim

2009-01-01

Analyses of molecular and quantitative genetic data demonstrate the existence of congruent climate-related patterns in western white pine (Pinus monticola). Two independent studies allowed comparisons of amplified fragment length polymorphism (AFLP) markers with quantitative variation in adaptive traits. Principal component analyses...

Mapping of quantitative trait loci using the skew-normal distribution.

PubMed

Fernandes, Elisabete; Pacheco, António; Penha-Gonçalves, Carlos

2007-11-01

In standard interval mapping (IM) of quantitative trait loci (QTL), the QTL effect is described by a normal mixture model. When this assumption of normality is violated, the most commonly adopted strategy is to use the previous model after data transformation. However, an appropriate transformation may not exist or may be difficult to find. Also this approach can raise interpretation issues. An interesting alternative is to consider a skew-normal mixture model in standard IM, and the resulting method is here denoted as skew-normal IM. This flexible model that includes the usual symmetric normal distribution as a special case is important, allowing continuous variation from normality to non-normality. In this paper we briefly introduce the main peculiarities of the skew-normal distribution. The maximum likelihood estimates of parameters of the skew-normal distribution are obtained by the expectation-maximization (EM) algorithm. The proposed model is illustrated with real data from an intercross experiment that shows a significant departure from the normality assumption. The performance of the skew-normal IM is assessed via stochastic simulation. The results indicate that the skew-normal IM has higher power for QTL detection and better precision of QTL location as compared to standard IM and nonparametric IM.

An augmented classical least squares method for quantitative Raman spectral analysis against component information loss.

PubMed

Zhou, Yan; Cao, Hui

2013-01-01

We propose an augmented classical least squares (ACLS) calibration method for quantitative Raman spectral analysis against component information loss. The Raman spectral signals with low analyte concentration correlations were selected and used as the substitutes for unknown quantitative component information during the CLS calibration procedure. The number of selected signals was determined by using the leave-one-out root-mean-square error of cross-validation (RMSECV) curve. An ACLS model was built based on the augmented concentration matrix and the reference spectral signal matrix. The proposed method was compared with partial least squares (PLS) and principal component regression (PCR) using one example: a data set recorded from an experiment of analyte concentration determination using Raman spectroscopy. A 2-fold cross-validation with Venetian blinds strategy was exploited to evaluate the predictive power of the proposed method. The one-way variance analysis (ANOVA) was used to access the predictive power difference between the proposed method and existing methods. Results indicated that the proposed method is effective at increasing the robust predictive power of traditional CLS model against component information loss and its predictive power is comparable to that of PLS or PCR.

Clines in quantitative traits: The role of migration patterns and selection scenarios

PubMed Central

Geroldinger, Ludwig; Bürger, Reinhard

2015-01-01

The existence, uniqueness, and shape of clines in a quantitative trait under selection toward a spatially varying optimum is studied. The focus is on deterministic diploid two-locus n-deme models subject to various migration patterns and selection scenarios. Migration patterns may exhibit isolation by distance, as in the stepping-stone model, or random dispersal, as in the island model. The phenotypic optimum may change abruptly in a single environmental step, more gradually, or not at all. Symmetry assumptions are imposed on phenotypic optima and migration rates. We study clines in the mean, variance, and linkage disequilibrium (LD). Clines result from polymorphic equilibria. The possible equilibrium configurations are determined as functions of the migration rate. Whereas for weak migration, many polymorphic equilibria may be simultaneously stable, their number decreases with increasing migration rate. Also for intermediate migration rates polymorphic equilibria are in general not unique, however, for loci of equal effects the corresponding clines in the mean, variance, and LD are unique. For sufficiently strong migration, no polymorphism is maintained. Both migration pattern and selection scenario exert strong influence on the existence and shape of clines. The results for discrete demes are compared with those from models in which space varies continuously and dispersal is modeled by diffusion. Comparisons with previous studies, which investigated clines under neutrality or under linkage equilibrium, are performed. If there is no long-distance migration, the environment does not change abruptly, and linkage is not very tight, populations are almost everywhere close to linkage equilibrium. PMID:25446959

Catching the right wave: evaluating wave energy resources and potential compatibility with existing marine and coastal uses.

PubMed

Kim, Choong-Ki; Toft, Jodie E; Papenfus, Michael; Verutes, Gregory; Guerry, Anne D; Ruckelshaus, Marry H; Arkema, Katie K; Guannel, Gregory; Wood, Spencer A; Bernhardt, Joanna R; Tallis, Heather; Plummer, Mark L; Halpern, Benjamin S; Pinsky, Malin L; Beck, Michael W; Chan, Francis; Chan, Kai M A; Levin, Phil S; Polasky, Stephen

2012-01-01

Many hope that ocean waves will be a source for clean, safe, reliable and affordable energy, yet wave energy conversion facilities may affect marine ecosystems through a variety of mechanisms, including competition with other human uses. We developed a decision-support tool to assist siting wave energy facilities, which allows the user to balance the need for profitability of the facilities with the need to minimize conflicts with other ocean uses. Our wave energy model quantifies harvestable wave energy and evaluates the net present value (NPV) of a wave energy facility based on a capital investment analysis. The model has a flexible framework and can be easily applied to wave energy projects at local, regional, and global scales. We applied the model and compatibility analysis on the west coast of Vancouver Island, British Columbia, Canada to provide information for ongoing marine spatial planning, including potential wave energy projects. In particular, we conducted a spatial overlap analysis with a variety of existing uses and ecological characteristics, and a quantitative compatibility analysis with commercial fisheries data. We found that wave power and harvestable wave energy gradually increase offshore as wave conditions intensify. However, areas with high economic potential for wave energy facilities were closer to cable landing points because of the cost of bringing energy ashore and thus in nearshore areas that support a number of different human uses. We show that the maximum combined economic benefit from wave energy and other uses is likely to be realized if wave energy facilities are sited in areas that maximize wave energy NPV and minimize conflict with existing ocean uses. Our tools will help decision-makers explore alternative locations for wave energy facilities by mapping expected wave energy NPV and helping to identify sites that provide maximal returns yet avoid spatial competition with existing ocean uses.

Catching the Right Wave: Evaluating Wave Energy Resources and Potential Compatibility with Existing Marine and Coastal Uses

PubMed Central

Kim, Choong-Ki; Toft, Jodie E.; Papenfus, Michael; Verutes, Gregory; Guerry, Anne D.; Ruckelshaus, Marry H.; Arkema, Katie K.; Guannel, Gregory; Wood, Spencer A.; Bernhardt, Joanna R.; Tallis, Heather; Plummer, Mark L.; Halpern, Benjamin S.; Pinsky, Malin L.; Beck, Michael W.; Chan, Francis; Chan, Kai M. A.; Levin, Phil S.; Polasky, Stephen

2012-01-01

Many hope that ocean waves will be a source for clean, safe, reliable and affordable energy, yet wave energy conversion facilities may affect marine ecosystems through a variety of mechanisms, including competition with other human uses. We developed a decision-support tool to assist siting wave energy facilities, which allows the user to balance the need for profitability of the facilities with the need to minimize conflicts with other ocean uses. Our wave energy model quantifies harvestable wave energy and evaluates the net present value (NPV) of a wave energy facility based on a capital investment analysis. The model has a flexible framework and can be easily applied to wave energy projects at local, regional, and global scales. We applied the model and compatibility analysis on the west coast of Vancouver Island, British Columbia, Canada to provide information for ongoing marine spatial planning, including potential wave energy projects. In particular, we conducted a spatial overlap analysis with a variety of existing uses and ecological characteristics, and a quantitative compatibility analysis with commercial fisheries data. We found that wave power and harvestable wave energy gradually increase offshore as wave conditions intensify. However, areas with high economic potential for wave energy facilities were closer to cable landing points because of the cost of bringing energy ashore and thus in nearshore areas that support a number of different human uses. We show that the maximum combined economic benefit from wave energy and other uses is likely to be realized if wave energy facilities are sited in areas that maximize wave energy NPV and minimize conflict with existing ocean uses. Our tools will help decision-makers explore alternative locations for wave energy facilities by mapping expected wave energy NPV and helping to identify sites that provide maximal returns yet avoid spatial competition with existing ocean uses. PMID:23144824

Utilization of data estimation via existing models, within a tiered data quality system, for populating species sensitivity distributions

EPA Science Inventory

The acquisition toxicity test data of sufficient quality from open literature to fulfill taxonomic diversity requirements can be a limiting factor in the creation of new 304(a) Aquatic Life Criteria. The use of existing models (WebICE and ACE) that estimate acute and chronic eff...

Potential usefulness of a topic model-based categorization of lung cancers as quantitative CT biomarkers for predicting the recurrence risk after curative resection

NASA Astrophysics Data System (ADS)

Kawata, Y.; Niki, N.; Ohmatsu, H.; Satake, M.; Kusumoto, M.; Tsuchida, T.; Aokage, K.; Eguchi, K.; Kaneko, M.; Moriyama, N.

2014-03-01

In this work, we investigate a potential usefulness of a topic model-based categorization of lung cancers as quantitative CT biomarkers for predicting the recurrence risk after curative resection. The elucidation of the subcategorization of a pulmonary nodule type in CT images is an important preliminary step towards developing the nodule managements that are specific to each patient. We categorize lung cancers by analyzing volumetric distributions of CT values within lung cancers via a topic model such as latent Dirichlet allocation. Through applying our scheme to 3D CT images of nonsmall- cell lung cancer (maximum lesion size of 3 cm) , we demonstrate the potential usefulness of the topic model-based categorization of lung cancers as quantitative CT biomarkers.

40 CFR Table 4 to Subpart Bbbb of... - Model Rule-Class II Emission Limits for Existing Small Municipal Waste Combustion Unit a

Code of Federal Regulations, 2011 CFR

2011-07-01

... Existing Small Municipal Waste Combustion Unit a 4 Table 4 to Subpart BBBB of Part 60 Protection of... NEW STATIONARY SOURCES Emission Guidelines and Compliance Times for Small Municipal Waste Combustion... Part 60—Model Rule—Class II Emission Limits for Existing Small Municipal Waste Combustion Unit a For...

40 CFR Table 2 to Subpart Bbbb of... - Model Rule-Class I Emission Limits for Existing Small Municipal Waste Combustion Units a

Code of Federal Regulations, 2014 CFR

2014-07-01

... Existing Small Municipal Waste Combustion Units a 2 Table 2 to Subpart BBBB of Part 60 Protection of... NEW STATIONARY SOURCES Emission Guidelines and Compliance Times for Small Municipal Waste Combustion... Part 60—Model Rule—Class I Emission Limits for Existing Small Municipal Waste Combustion Units a For...

40 CFR Table 4 to Subpart Bbbb of... - Model Rule-Class II Emission Limits for Existing Small Municipal Waste Combustion Unit a

Code of Federal Regulations, 2014 CFR

2014-07-01

... Existing Small Municipal Waste Combustion Unit a 4 Table 4 to Subpart BBBB of Part 60 Protection of... NEW STATIONARY SOURCES Emission Guidelines and Compliance Times for Small Municipal Waste Combustion... Part 60—Model Rule—Class II Emission Limits for Existing Small Municipal Waste Combustion Unit a For...

40 CFR Table 4 to Subpart Bbbb of... - Model Rule-Class II Emission Limits for Existing Small Municipal Waste Combustion Unit a

Code of Federal Regulations, 2010 CFR

2010-07-01

... Existing Small Municipal Waste Combustion Unit a 4 Table 4 to Subpart BBBB of Part 60 Protection of... NEW STATIONARY SOURCES Emission Guidelines and Compliance Times for Small Municipal Waste Combustion... Part 60—Model Rule—Class II Emission Limits for Existing Small Municipal Waste Combustion Unit a For...

40 CFR Table 2 to Subpart Bbbb of... - Model Rule-Class I Emission Limits for Existing Small Municipal Waste Combustion Units a

Code of Federal Regulations, 2012 CFR

2012-07-01

... Existing Small Municipal Waste Combustion Units a 2 Table 2 to Subpart BBBB of Part 60 Protection of... NEW STATIONARY SOURCES Emission Guidelines and Compliance Times for Small Municipal Waste Combustion... Part 60—Model Rule—Class I Emission Limits for Existing Small Municipal Waste Combustion Units a For...

40 CFR Table 2 to Subpart Bbbb of... - Model Rule-Class I Emission Limits for Existing Small Municipal Waste Combustion Units a

Code of Federal Regulations, 2013 CFR

2013-07-01

... Existing Small Municipal Waste Combustion Units a 2 Table 2 to Subpart BBBB of Part 60 Protection of... NEW STATIONARY SOURCES Emission Guidelines and Compliance Times for Small Municipal Waste Combustion... Part 60—Model Rule—Class I Emission Limits for Existing Small Municipal Waste Combustion Units a For...

40 CFR Table 2 to Subpart Bbbb of... - Model Rule-Class I Emission Limits for Existing Small Municipal Waste Combustion Units a

Code of Federal Regulations, 2010 CFR

2010-07-01

... Existing Small Municipal Waste Combustion Units a 2 Table 2 to Subpart BBBB of Part 60 Protection of... NEW STATIONARY SOURCES Emission Guidelines and Compliance Times for Small Municipal Waste Combustion... Part 60—Model Rule—Class I Emission Limits for Existing Small Municipal Waste Combustion Units a For...

40 CFR Table 2 to Subpart Bbbb of... - Model Rule-Class I Emission Limits for Existing Small Municipal Waste Combustion Units a

Code of Federal Regulations, 2011 CFR

2011-07-01

... Existing Small Municipal Waste Combustion Units a 2 Table 2 to Subpart BBBB of Part 60 Protection of... NEW STATIONARY SOURCES Emission Guidelines and Compliance Times for Small Municipal Waste Combustion... Part 60—Model Rule—Class I Emission Limits for Existing Small Municipal Waste Combustion Units a For...

40 CFR Table 4 to Subpart Bbbb of... - Model Rule-Class II Emission Limits for Existing Small Municipal Waste Combustion Unit a

Code of Federal Regulations, 2012 CFR

2012-07-01

... Existing Small Municipal Waste Combustion Unit a 4 Table 4 to Subpart BBBB of Part 60 Protection of... NEW STATIONARY SOURCES Emission Guidelines and Compliance Times for Small Municipal Waste Combustion... Part 60—Model Rule—Class II Emission Limits for Existing Small Municipal Waste Combustion Unit a For...

40 CFR Table 4 to Subpart Bbbb of... - Model Rule-Class II Emission Limits for Existing Small Municipal Waste Combustion Unit a

Code of Federal Regulations, 2013 CFR

2013-07-01

... Existing Small Municipal Waste Combustion Unit a 4 Table 4 to Subpart BBBB of Part 60 Protection of... NEW STATIONARY SOURCES Emission Guidelines and Compliance Times for Small Municipal Waste Combustion... Part 60—Model Rule—Class II Emission Limits for Existing Small Municipal Waste Combustion Unit a For...

Why equal treatment is not always equitable: the impact of existing ethnic health inequalities in cost-effectiveness modeling.

PubMed

McLeod, Melissa; Blakely, Tony; Kvizhinadze, Giorgi; Harris, Ricci

2014-01-01

A critical first step toward incorporating equity into cost-effectiveness analyses is to appropriately model interventions by population subgroups. In this paper we use a standardized treatment intervention to examine the impact of using ethnic-specific (Māori and non-Māori) data in cost-utility analyses for three cancers. We estimate gains in health-adjusted life years (HALYs) for a simple intervention (20% reduction in excess cancer mortality) for lung, female breast, and colon cancers, using Markov modeling. Base models include ethnic-specific cancer incidence with other parameters either turned off or set to non-Māori levels for both groups. Subsequent models add ethnic-specific cancer survival, morbidity, and life expectancy. Costs include intervention and downstream health system costs. For the three cancers, including existing inequalities in background parameters (population mortality and comorbidities) for Māori attributes less value to a year of life saved compared to non-Māori and lowers the relative health gains for Māori. In contrast, ethnic inequalities in cancer parameters have less predictable effects. Despite Māori having higher excess mortality from all three cancers, modeled health gains for Māori were less from the lung cancer intervention than for non-Māori but higher for the breast and colon interventions. Cost-effectiveness modeling is a useful tool in the prioritization of health services. But there are important (and sometimes counterintuitive) implications of including ethnic-specific background and disease parameters. In order to avoid perpetuating existing ethnic inequalities in health, such analyses should be undertaken with care.

Why equal treatment is not always equitable: the impact of existing ethnic health inequalities in cost-effectiveness modeling

PubMed Central

2014-01-01

Background A critical first step toward incorporating equity into cost-effectiveness analyses is to appropriately model interventions by population subgroups. In this paper we use a standardized treatment intervention to examine the impact of using ethnic-specific (Māori and non-Māori) data in cost-utility analyses for three cancers. Methods We estimate gains in health-adjusted life years (HALYs) for a simple intervention (20% reduction in excess cancer mortality) for lung, female breast, and colon cancers, using Markov modeling. Base models include ethnic-specific cancer incidence with other parameters either turned off or set to non-Māori levels for both groups. Subsequent models add ethnic-specific cancer survival, morbidity, and life expectancy. Costs include intervention and downstream health system costs. Results For the three cancers, including existing inequalities in background parameters (population mortality and comorbidities) for Māori attributes less value to a year of life saved compared to non-Māori and lowers the relative health gains for Māori. In contrast, ethnic inequalities in cancer parameters have less predictable effects. Despite Māori having higher excess mortality from all three cancers, modeled health gains for Māori were less from the lung cancer intervention than for non-Māori but higher for the breast and colon interventions. Conclusions Cost-effectiveness modeling is a useful tool in the prioritization of health services. But there are important (and sometimes counterintuitive) implications of including ethnic-specific background and disease parameters. In order to avoid perpetuating existing ethnic inequalities in health, such analyses should be undertaken with care. PMID:24910540

How predictive quantitative modelling of tissue organisation can inform liver disease pathogenesis.

PubMed

Drasdo, Dirk; Hoehme, Stefan; Hengstler, Jan G

2014-10-01

From the more than 100 liver diseases described, many of those with high incidence rates manifest themselves by histopathological changes, such as hepatitis, alcoholic liver disease, fatty liver disease, fibrosis, and, in its later stages, cirrhosis, hepatocellular carcinoma, primary biliary cirrhosis and other disorders. Studies of disease pathogeneses are largely based on integrating -omics data pooled from cells at different locations with spatial information from stained liver structures in animal models. Even though this has led to significant insights, the complexity of interactions as well as the involvement of processes at many different time and length scales constrains the possibility to condense disease processes in illustrations, schemes and tables. The combination of modern imaging modalities with image processing and analysis, and mathematical models opens up a promising new approach towards a quantitative understanding of pathologies and of disease processes. This strategy is discussed for two examples, ammonia metabolism after drug-induced acute liver damage, and the recovery of liver mass as well as architecture during the subsequent regeneration process. This interdisciplinary approach permits integration of biological mechanisms and models of processes contributing to disease progression at various scales into mathematical models. These can be used to perform in silico simulations to promote unravelling the relation between architecture and function as below illustrated for liver regeneration, and bridging from the in vitro situation and animal models to humans. In the near future novel mechanisms will usually not be directly elucidated by modelling. However, models will falsify hypotheses and guide towards the most informative experimental design. Copyright © 2014 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.

A Review of Energy Models with Particular Reference to Employment and Manpower Analysis.

ERIC Educational Resources Information Center

Eckstein, Albert J.; Heien, Dale M.

To analyze the application of quantitative models to energy-employment issues, the energy problem was viewed in three distinct, but related, phases: the post-embargo shock effects, the intermediate-term process of adjustment, and the long-run equilibrium. Against this background eighteen existing energy models (government supported as well as…

Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models

PubMed Central

Chiu, Chi-yang; Jung, Jeesun; Chen, Wei; Weeks, Daniel E; Ren, Haobo; Boehnke, Michael; Amos, Christopher I; Liu, Aiyi; Mills, James L; Ting Lee, Mei-ling; Xiong, Momiao; Fan, Ruzong

2017-01-01

To analyze next-generation sequencing data, multivariate functional linear models are developed for a meta-analysis of multiple studies to connect genetic variant data to multiple quantitative traits adjusting for covariates. The goal is to take the advantage of both meta-analysis and pleiotropic analysis in order to improve power and to carry out a unified association analysis of multiple studies and multiple traits of complex disorders. Three types of approximate F -distributions based on Pillai–Bartlett trace, Hotelling–Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants. Simulation analysis is performed to evaluate false-positive rates and power of the proposed tests. The proposed methods are applied to analyze lipid traits in eight European cohorts. It is shown that it is more advantageous to perform multivariate analysis than univariate analysis in general, and it is more advantageous to perform meta-analysis of multiple studies instead of analyzing the individual studies separately. The proposed models require individual observations. The value of the current paper can be seen at least for two reasons: (a) the proposed methods can be applied to studies that have individual genotype data; (b) the proposed methods can be used as a criterion for future work that uses summary statistics to build test statistics to meta-analyze the data. PMID:28000696

Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models.

PubMed

Chiu, Chi-Yang; Jung, Jeesun; Chen, Wei; Weeks, Daniel E; Ren, Haobo; Boehnke, Michael; Amos, Christopher I; Liu, Aiyi; Mills, James L; Ting Lee, Mei-Ling; Xiong, Momiao; Fan, Ruzong

2017-02-01

To analyze next-generation sequencing data, multivariate functional linear models are developed for a meta-analysis of multiple studies to connect genetic variant data to multiple quantitative traits adjusting for covariates. The goal is to take the advantage of both meta-analysis and pleiotropic analysis in order to improve power and to carry out a unified association analysis of multiple studies and multiple traits of complex disorders. Three types of approximate F -distributions based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants. Simulation analysis is performed to evaluate false-positive rates and power of the proposed tests. The proposed methods are applied to analyze lipid traits in eight European cohorts. It is shown that it is more advantageous to perform multivariate analysis than univariate analysis in general, and it is more advantageous to perform meta-analysis of multiple studies instead of analyzing the individual studies separately. The proposed models require individual observations. The value of the current paper can be seen at least for two reasons: (a) the proposed methods can be applied to studies that have individual genotype data; (b) the proposed methods can be used as a criterion for future work that uses summary statistics to build test statistics to meta-analyze the data.

Preclinical Magnetic Resonance Fingerprinting (MRF) at 7 T: Effective Quantitative Imaging for Rodent Disease Models

PubMed Central

Gao, Ying; Chen, Yong; Ma, Dan; Jiang, Yun; Herrmann, Kelsey A.; Vincent, Jason A.; Dell, Katherine M.; Drumm, Mitchell L.; Brady-Kalnay, Susann M.; Griswold, Mark A.; Flask, Chris A.; Lu, Lan

2015-01-01

High field, preclinical magnetic resonance imaging (MRI) scanners are now commonly used to quantitatively assess disease status and efficacy of novel therapies in a wide variety of rodent models. Unfortunately, conventional MRI methods are highly susceptible to respiratory and cardiac motion artifacts resulting in potentially inaccurate and misleading data. We have developed an initial preclinical, 7.0 T MRI implementation of the highly novel Magnetic Resonance Fingerprinting (MRF) methodology that has been previously described for clinical imaging applications. The MRF technology combines a priori variation in the MRI acquisition parameters with dictionary-based matching of acquired signal evolution profiles to simultaneously generate quantitative maps of T1 and T2 relaxation times and proton density. This preclinical MRF acquisition was constructed from a Fast Imaging with Steady-state Free Precession (FISP) MRI pulse sequence to acquire 600 MRF images with both evolving T1 and T2 weighting in approximately 30 minutes. This initial high field preclinical MRF investigation demonstrated reproducible and differentiated estimates of in vitro phantoms with different relaxation times. In vivo preclinical MRF results in mouse kidneys and brain tumor models demonstrated an inherent resistance to respiratory motion artifacts as well as sensitivity to known pathology. These results suggest that MRF methodology may offer the opportunity for quantification of numerous MRI parameters for a wide variety of preclinical imaging applications. PMID:25639694

Implementation of partnership management model of SMK (Vocational High School) with existing industries in mechanical engineering expertise in Central Java

NASA Astrophysics Data System (ADS)

Sumbodo, Wirawan; Pardjono, Samsudi, Rahadjo, Winarno Dwi

2018-03-01

This study aims to determine the existing conditions of implementation of partnership management model of SMK with the industry on the mechanical engineering expertise in Central Java. The method used is descriptive analysis. The research result shows that the implementation of partnership management model of SMK based on new existing industry produces ready graduates of 62.5% which belongs to low category, although the partnership program of SMK with the industry is done well with the average score of 3.17. As many as 37.5% of SMK graduates of Mechanical Engineering Expertise Program choose to continue their studies or to be an entrepreneur. It is expected that the partnership model of SMK with the industry can be developed into a reference for government policy in developing SMK that is able to produce graduates who are ready to work according to the needs of partner industry.

Modelling Framework and the Quantitative Analysis of Distributed Energy Resources in Future Distribution Networks

NASA Astrophysics Data System (ADS)

Han, Xue; Sandels, Claes; Zhu, Kun; Nordström, Lars

2013-08-01

There has been a large body of statements claiming that the large-scale deployment of Distributed Energy Resources (DERs) could eventually reshape the future distribution grid operation in numerous ways. Thus, it is necessary to introduce a framework to measure to what extent the power system operation will be changed by various parameters of DERs. This article proposed a modelling framework for an overview analysis on the correlation between DERs. Furthermore, to validate the framework, the authors described the reference models of different categories of DERs with their unique characteristics, comprising distributed generation, active demand and electric vehicles. Subsequently, quantitative analysis was made on the basis of the current and envisioned DER deployment scenarios proposed for Sweden. Simulations are performed in two typical distribution network models for four seasons. The simulation results show that in general the DER deployment brings in the possibilities to reduce the power losses and voltage drops by compensating power from the local generation and optimizing the local load profiles.

[A new method of processing quantitative PCR data].

PubMed

Ke, Bing-Shen; Li, Guang-Yun; Chen, Shi-Min; Huang, Xiang-Yan; Chen, Ying-Jian; Xu, Jun

2003-05-01

Today standard PCR can't satisfy the need of biotechnique development and clinical research any more. After numerous dynamic research, PE company found there is a linear relation between initial template number and cycling time when the accumulating fluorescent product is detectable.Therefore,they developed a quantitative PCR technique to be used in PE7700 and PE5700. But the error of this technique is too great to satisfy the need of biotechnique development and clinical research. A better quantitative PCR technique is needed. The mathematical model submitted here is combined with the achievement of relative science,and based on the PCR principle and careful analysis of molecular relationship of main members in PCR reaction system. This model describes the function relation between product quantity or fluorescence intensity and initial template number and other reaction conditions, and can reflect the accumulating rule of PCR product molecule accurately. Accurate quantitative PCR analysis can be made use this function relation. Accumulated PCR product quantity can be obtained from initial template number. Using this model to do quantitative PCR analysis,result error is only related to the accuracy of fluorescence intensity or the instrument used. For an example, when the fluorescence intensity is accurate to 6 digits and the template size is between 100 to 1,000,000, the quantitative result accuracy will be more than 99%. The difference of result error is distinct using same condition,same instrument but different analysis method. Moreover,if the PCR quantitative analysis system is used to process data, it will get result 80 times of accuracy than using CT method.

Nonstandard Work Schedules and Partnership Quality: Quantitative and Qualitative Findings

ERIC Educational Resources Information Center

Mills, Melinda; Taht, Kadri

2010-01-01

This article questions existing findings and provides new evidence about the consequences of nonstandard work schedules on partnership quality. Using quantitative couple data from The Netherlands Kinship Panel Study (NKPS) (N = 3,016) and semistructured qualitative interviews (N = 34), we found that, for women, schedules with varying hours…

Evaluation of an ensemble of genetic models for prediction of a quantitative trait.

PubMed

Milton, Jacqueline N; Steinberg, Martin H; Sebastiani, Paola

2014-01-01

Many genetic markers have been shown to be associated with common quantitative traits in genome-wide association studies. Typically these associated genetic markers have small to modest effect sizes and individually they explain only a small amount of the variability of the phenotype. In order to build a genetic prediction model without fitting a multiple linear regression model with possibly hundreds of genetic markers as predictors, researchers often summarize the joint effect of risk alleles into a genetic score that is used as a covariate in the genetic prediction model. However, the prediction accuracy can be highly variable and selecting the optimal number of markers to be included in the genetic score is challenging. In this manuscript we present a strategy to build an ensemble of genetic prediction models from data and we show that the ensemble-based method makes the challenge of choosing the number of genetic markers more amenable. Using simulated data with varying heritability and number of genetic markers, we compare the predictive accuracy and inclusion of true positive and false positive markers of a single genetic prediction model and our proposed ensemble method. The results show that the ensemble of genetic models tends to include a larger number of genetic variants than a single genetic model and it is more likely to include all of the true genetic markers. This increased sensitivity is obtained at the price of a lower specificity that appears to minimally affect the predictive accuracy of the ensemble.

Mapping quantitative trait loci for traits defined as ratios.

PubMed

Yang, Runqing; Li, Jiahan; Xu, Shizhong

2008-03-01

Many traits are defined as ratios of two quantitative traits. Methods of QTL mapping for regular quantitative traits are not optimal when applied to ratios due to lack of normality for traits defined as ratios. We develop a new method of QTL mapping for traits defined as ratios. The new method uses a special linear combination of the two component traits, and thus takes advantage of the normal property of the new variable. Simulation study shows that the new method can substantially increase the statistical power of QTL detection relative to the method which treats ratios as regular quantitative traits. The new method also outperforms the method that uses Box-Cox transformed ratio as the phenotype. A real example of QTL mapping for relative growth rate in soybean demonstrates that the new method can detect more QTL than existing methods of QTL mapping for traits defined as ratios.

Implementing online quantitative support modules in an intermediate-level course

NASA Astrophysics Data System (ADS)

Daly, J.

2011-12-01

While instructors typically anticipate that students in introductory geology courses enter a class with a wide range of quantitative ability, we often overlook the fact that this may also be true in upper-level courses. Some students are drawn to the subject and experience success in early courses with an emphasis on descriptive geology, then experience frustration and disappointment in mid- and upper-level courses that are more quantitative. To bolster student confidence in quantitative skills and enhance their performance in an upper-level course, I implemented several modules from The Math You Need (TMYN) online resource with a 200-level geomorphology class. Student facility with basic quantitative skills (rearranging equations, manipulating units, and graphing) was assessed with an online pre- and post-test. During the semester, modules were assigned to complement existing course activities (for example, the module on manipulating units was assigned prior to a lab on measurement of channel area and water velocity, then calculation of discharge). The implementation was designed to be a concise review of relevant skills for students with higher confidence in their quantitative abilities, and to provide a self-paced opportunity for students with less quantitative facility to build skills. This course already includes a strong emphasis on quantitative data collection, analysis, and presentation; in the past, student performance in the course has been strongly influenced by their individual quantitative ability. I anticipate that giving students the opportunity to improve mastery of fundamental quantitative skills will improve their performance on higher-stakes assignments and exams, and will enhance their sense of accomplishment in the course.

Socioeconomic influences on biodiversity, ecosystem services and human well-being: a quantitative application of the DPSIR model in Jiangsu, China.

PubMed

Hou, Ying; Zhou, Shudong; Burkhard, Benjamin; Müller, Felix

2014-08-15

One focus of ecosystem service research is the connection between biodiversity, ecosystem services and human well-being as well as the socioeconomic influences on them. Despite existing investigations, exact impacts from the human system on the dynamics of biodiversity, ecosystem services and human well-being are still uncertain because of the insufficiency of the respective quantitative analyses. Our research aims are discerning the socioeconomic influences on biodiversity, ecosystem services and human well-being and demonstrating mutual impacts between these items. We propose a DPSIR framework coupling ecological integrity, ecosystem services as well as human well-being and suggest DPSIR indicators for the case study area Jiangsu, China. Based on available statistical and surveying data, we revealed the factors significantly impacting biodiversity, ecosystem services and human well-being in the research area through factor analysis and correlation analysis, using the 13 prefecture-level cities of Jiangsu as samples. The results show that urbanization and industrialization in the urban areas have predominant positive influences on regional biodiversity, agricultural productivity and tourism services as well as rural residents' living standards. Additionally, the knowledge, technology and finance inputs for agriculture also have generally positive impacts on these system components. Concerning regional carbon storage, non-cropland vegetation cover obviously plays a significant positive role. Contrarily, the expansion of farming land and the increase of total food production are two important negative influential factors of biodiversity, ecosystem's food provisioning service capacity, regional tourism income and the well-being of the rural population. Our study provides a promising approach based on the DPSIR model to quantitatively capture the socioeconomic influential factors of biodiversity, ecosystem services and human well-being for human-environmental systems

Propagating Qualitative Values Through Quantitative Equations

NASA Technical Reports Server (NTRS)