Validating the Effectiveness of Switching the Vancomycin TDM Analysis Software Based on the Predictive Accuracy.

PubMed

Imai, Shungo; Yamada, Takehiro; Ishiguro, Nobuhisa; Miyamoto, Takenori; Kagami, Keisuke; Tomiyama, Naoki; Niinuma, Yusuke; Nagasaki, Daisuke; Suzuki, Koji; Yamagami, Akira; Kasashi, Kumiko; Kobayashi, Masaki; Iseki, Ken

2017-01-01

Based on the predictive performance in our previous study, we switched the therapeutic drug monitoring (TDM) analysis software for dose setting of vancomycin (VCM) from "Vancomycin MEEK TDM analysis software Ver2.0" (MEEK) to "SHIONOGI-VCM-TDM ver.2009" (VCM-TDM) in January 2015. In the present study, our aim was to validate the effectiveness of the changing VCM TDM analysis software in initial dose setting of VCM. The enrolled patients were divided into two groups, each having 162 patients in total, who received VCM with the initial dose set using MEEK (MEEK group) or VCM-TDM (VCM-TDM group). We compared the rates of attaining the therapeutic range (trough value; 10-20 μg/mL) of serum VCM concentration between the groups. Multivariate logistic regression analysis was performed to confirm that changing the VCM TDM analysis software was an independent factor related to attaining the therapeutic range. Switching the VCM TDM analysis software from MEEK to VCM-TDM improved the rate of attaining the therapeutic range by 21.6% (MEEK group: 42.6% vs. VCM-TDM group: 64.2%, p<0.01). Patient age ≥65 years, concomitant medication (furosemide) and the TDM analysis software used VCM-TDM were considered to be independent factors for attaining the therapeutic range. These results demonstrated the effectiveness of switching the VCM TDM analysis software from MEEK to VCM-TDM for initial dose setting of VCM.

BrightStat.com: free statistics online.

PubMed

Stricker, Daniel

2008-10-01

Powerful software for statistical analysis is expensive. Here I present BrightStat, a statistical software running on the Internet which is free of charge. BrightStat's goals, its main capabilities and functionalities are outlined. Three different sample runs, a Friedman test, a chi-square test, and a step-wise multiple regression are presented. The results obtained by BrightStat are compared with results computed by SPSS, one of the global leader in providing statistical software, and VassarStats, a collection of scripts for data analysis running on the Internet. Elementary statistics is an inherent part of academic education and BrightStat is an alternative to commercial products.

Chandra X-ray Center Science Data Systems Regression Testing of CIAO

NASA Astrophysics Data System (ADS)

Lee, N. P.; Karovska, M.; Galle, E. C.; Bonaventura, N. R.

2011-07-01

The Chandra Interactive Analysis of Observations (CIAO) is a software system developed for the analysis of Chandra X-ray Observatory observations. An important component of a successful CIAO release is the repeated testing of the tools across various platforms to ensure consistent and scientifically valid results. We describe the procedures of the scientific regression testing of CIAO and the enhancements made to the testing system to increase the efficiency of run time and result validation.

Neural Network and Regression Approximations in High Speed Civil Transport Aircraft Design Optimization

NASA Technical Reports Server (NTRS)

Patniak, Surya N.; Guptill, James D.; Hopkins, Dale A.; Lavelle, Thomas M.

1998-01-01

Nonlinear mathematical-programming-based design optimization can be an elegant method. However, the calculations required to generate the merit function, constraints, and their gradients, which are frequently required, can make the process computational intensive. The computational burden can be greatly reduced by using approximating analyzers derived from an original analyzer utilizing neural networks and linear regression methods. The experience gained from using both of these approximation methods in the design optimization of a high speed civil transport aircraft is the subject of this paper. The Langley Research Center's Flight Optimization System was selected for the aircraft analysis. This software was exercised to generate a set of training data with which a neural network and a regression method were trained, thereby producing the two approximating analyzers. The derived analyzers were coupled to the Lewis Research Center's CometBoards test bed to provide the optimization capability. With the combined software, both approximation methods were examined for use in aircraft design optimization, and both performed satisfactorily. The CPU time for solution of the problem, which had been measured in hours, was reduced to minutes with the neural network approximation and to seconds with the regression method. Instability encountered in the aircraft analysis software at certain design points was also eliminated. On the other hand, there were costs and difficulties associated with training the approximating analyzers. The CPU time required to generate the input-output pairs and to train the approximating analyzers was seven times that required for solution of the problem.

Predictive Utility of Marketed Volumetric Software Tools in Subjects at Risk for Alzheimer Disease: Do Regions Outside the Hippocampus Matter?

PubMed

Tanpitukpongse, T P; Mazurowski, M A; Ikhena, J; Petrella, J R

2017-03-01

Alzheimer disease is a prevalent neurodegenerative disease. Computer assessment of brain atrophy patterns can help predict conversion to Alzheimer disease. Our aim was to assess the prognostic efficacy of individual-versus-combined regional volumetrics in 2 commercially available brain volumetric software packages for predicting conversion of patients with mild cognitive impairment to Alzheimer disease. Data were obtained through the Alzheimer's Disease Neuroimaging Initiative. One hundred ninety-two subjects (mean age, 74.8 years; 39% female) diagnosed with mild cognitive impairment at baseline were studied. All had T1-weighted MR imaging sequences at baseline and 3-year clinical follow-up. Analysis was performed with NeuroQuant and Neuroreader. Receiver operating characteristic curves assessing the prognostic efficacy of each software package were generated by using a univariable approach using individual regional brain volumes and 2 multivariable approaches (multiple regression and random forest), combining multiple volumes. On univariable analysis of 11 NeuroQuant and 11 Neuroreader regional volumes, hippocampal volume had the highest area under the curve for both software packages (0.69, NeuroQuant; 0.68, Neuroreader) and was not significantly different ( P > .05) between packages. Multivariable analysis did not increase the area under the curve for either package (0.63, logistic regression; 0.60, random forest NeuroQuant; 0.65, logistic regression; 0.62, random forest Neuroreader). Of the multiple regional volume measures available in FDA-cleared brain volumetric software packages, hippocampal volume remains the best single predictor of conversion of mild cognitive impairment to Alzheimer disease at 3-year follow-up. Combining volumetrics did not add additional prognostic efficacy. Therefore, future prognostic studies in mild cognitive impairment, combining such tools with demographic and other biomarker measures, are justified in using hippocampal volume as the only volumetric biomarker. © 2017 by American Journal of Neuroradiology.

MetaComp: comprehensive analysis software for comparative meta-omics including comparative metagenomics.

PubMed

Zhai, Peng; Yang, Longshu; Guo, Xiao; Wang, Zhe; Guo, Jiangtao; Wang, Xiaoqi; Zhu, Huaiqiu

2017-10-02

During the past decade, the development of high throughput nucleic sequencing and mass spectrometry analysis techniques have enabled the characterization of microbial communities through metagenomics, metatranscriptomics, metaproteomics and metabolomics data. To reveal the diversity of microbial communities and interactions between living conditions and microbes, it is necessary to introduce comparative analysis based upon integration of all four types of data mentioned above. Comparative meta-omics, especially comparative metageomics, has been established as a routine process to highlight the significant differences in taxon composition and functional gene abundance among microbiota samples. Meanwhile, biologists are increasingly concerning about the correlations between meta-omics features and environmental factors, which may further decipher the adaptation strategy of a microbial community. We developed a graphical comprehensive analysis software named MetaComp comprising a series of statistical analysis approaches with visualized results for metagenomics and other meta-omics data comparison. This software is capable to read files generated by a variety of upstream programs. After data loading, analyses such as multivariate statistics, hypothesis testing of two-sample, multi-sample as well as two-group sample and a novel function-regression analysis of environmental factors are offered. Here, regression analysis regards meta-omic features as independent variable and environmental factors as dependent variables. Moreover, MetaComp is capable to automatically choose an appropriate two-group sample test based upon the traits of input abundance profiles. We further evaluate the performance of its choice, and exhibit applications for metagenomics, metaproteomics and metabolomics samples. MetaComp, an integrative software capable for applying to all meta-omics data, originally distills the influence of living environment on microbial community by regression analysis. Moreover, since the automatically chosen two-group sample test is verified to be outperformed, MetaComp is friendly to users without adequate statistical training. These improvements are aiming to overcome the new challenges under big data era for all meta-omics data. MetaComp is available at: http://cqb.pku.edu.cn/ZhuLab/MetaComp/ and https://github.com/pzhaipku/MetaComp/ .

Automating approximate Bayesian computation by local linear regression.

PubMed

Thornton, Kevin R

2009-07-07

In several biological contexts, parameter inference often relies on computationally-intensive techniques. "Approximate Bayesian Computation", or ABC, methods based on summary statistics have become increasingly popular. A particular flavor of ABC based on using a linear regression to approximate the posterior distribution of the parameters, conditional on the summary statistics, is computationally appealing, yet no standalone tool exists to automate the procedure. Here, I describe a program to implement the method. The software package ABCreg implements the local linear-regression approach to ABC. The advantages are: 1. The code is standalone, and fully-documented. 2. The program will automatically process multiple data sets, and create unique output files for each (which may be processed immediately in R), facilitating the testing of inference procedures on simulated data, or the analysis of multiple data sets. 3. The program implements two different transformation methods for the regression step. 4. Analysis options are controlled on the command line by the user, and the program is designed to output warnings for cases where the regression fails. 5. The program does not depend on any particular simulation machinery (coalescent, forward-time, etc.), and therefore is a general tool for processing the results from any simulation. 6. The code is open-source, and modular.Examples of applying the software to empirical data from Drosophila melanogaster, and testing the procedure on simulated data, are shown. In practice, the ABCreg simplifies implementing ABC based on local-linear regression.

How Statistics "Excel" Online.

ERIC Educational Resources Information Center

Chao, Faith; Davis, James

2000-01-01

Discusses the use of Microsoft Excel software and provides examples of its use in an online statistics course at Golden Gate University in the areas of randomness and probability, sampling distributions, confidence intervals, and regression analysis. (LRW)

Systematic on-site monitoring of compliance dust samples

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

Grayson, R.L.; Gandy, J.R.

1996-12-31

Maintaining compliance with U.S. respirable coal mine dust standards can be difficult on high-productivity longwall panels. Comprehensive and systematic analysis of compliance dust sample data, coupled with access to the U.S. Bureau of Mines (USBM) DUSTPRO, can yield important information for use in maintaining compliance. The objective of this study was to develop and apply a customized software for the collection, storage, modification, and analysis of respirable dust data while providing for flexible export of data and linking with the USBM`s expert advisory system on dust control. An executable, IBM-compatible software was created and customized for use by the personmore » in charge of collecting, submitting, analyzing, and monitoring respirable dust compliance samples. Both descriptive statistics and multiple regression analysis were incorporated. The software allows ASCH files to be exported and directly links with DUSTPRO. After development and validation of the software, longwall compliance data from two different mines was analyzed to evaluate the value of the software. Data included variables on respirable dust concentration, tons produced, the existence of roof/floor rock (dummy variable), and the sampling cycle (dummy variables). Because of confidentiality, specific data will not be presented, only the equations and ANOVA tables. The final regression models explained 83.8% and 61.1% of the variation in the data for the two panels. Important correlations among variables within sampling cycles showed the value of using dummy variables for sampling cycles. The software proved flexible and fast for its intended use. The insights obtained from use improved the systematic monitoring of respirable dust compliance data, especially for pinpointing the most effective dust control methods during specific sampling cycles.« less

Correlative and multivariate analysis of increased radon concentration in underground laboratory.

PubMed

Maletić, Dimitrije M; Udovičić, Vladimir I; Banjanac, Radomir M; Joković, Dejan R; Dragić, Aleksandar L; Veselinović, Nikola B; Filipović, Jelena

2014-11-01

The results of analysis using correlative and multivariate methods, as developed for data analysis in high-energy physics and implemented in the Toolkit for Multivariate Analysis software package, of the relations of the variation of increased radon concentration with climate variables in shallow underground laboratory is presented. Multivariate regression analysis identified a number of multivariate methods which can give a good evaluation of increased radon concentrations based on climate variables. The use of the multivariate regression methods will enable the investigation of the relations of specific climate variable with increased radon concentrations by analysis of regression methods resulting in 'mapped' underlying functional behaviour of radon concentrations depending on a wide spectrum of climate variables. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Advanced statistical methods for improved data analysis of NASA astrophysics missions

NASA Technical Reports Server (NTRS)

Feigelson, Eric D.

1992-01-01

The investigators under this grant studied ways to improve the statistical analysis of astronomical data. They looked at existing techniques, the development of new techniques, and the production and distribution of specialized software to the astronomical community. Abstracts of nine papers that were produced are included, as well as brief descriptions of four software packages. The articles that are abstracted discuss analytical and Monte Carlo comparisons of six different linear least squares fits, a (second) paper on linear regression in astronomy, two reviews of public domain software for the astronomer, subsample and half-sample methods for estimating sampling distributions, a nonparametric estimation of survival functions under dependent competing risks, censoring in astronomical data due to nondetections, an astronomy survival analysis computer package called ASURV, and improving the statistical methodology of astronomical data analysis.

An Alternative Flight Software Trigger Paradigm: Applying Multivariate Logistic Regression to Sense Trigger Conditions Using Inaccurate or Scarce Information

NASA Technical Reports Server (NTRS)

Smith, Kelly M.; Gay, Robert S.; Stachowiak, Susan J.

2013-01-01

In late 2014, NASA will fly the Orion capsule on a Delta IV-Heavy rocket for the Exploration Flight Test-1 (EFT-1) mission. For EFT-1, the Orion capsule will be flying with a new GPS receiver and new navigation software. Given the experimental nature of the flight, the flight software must be robust to the loss of GPS measurements. Once the high-speed entry is complete, the drogue parachutes must be deployed within the proper conditions to stabilize the vehicle prior to deploying the main parachutes. When GPS is available in nominal operations, the vehicle will deploy the drogue parachutes based on an altitude trigger. However, when GPS is unavailable, the navigated altitude errors become excessively large, driving the need for a backup barometric altimeter to improve altitude knowledge. In order to increase overall robustness, the vehicle also has an alternate method of triggering the parachute deployment sequence based on planet-relative velocity if both the GPS and the barometric altimeter fail. However, this backup trigger results in large altitude errors relative to the targeted altitude. Motivated by this challenge, this paper demonstrates how logistic regression may be employed to semi-automatically generate robust triggers based on statistical analysis. Logistic regression is used as a ground processor pre-flight to develop a statistical classifier. The classifier would then be implemented in flight software and executed in real-time. This technique offers improved performance even in the face of highly inaccurate measurements. Although the logistic regression-based trigger approach will not be implemented within EFT-1 flight software, the methodology can be carried forward for future missions and vehicles.

An Alternative Flight Software Paradigm: Applying Multivariate Logistic Regression to Sense Trigger Conditions using Inaccurate or Scarce Information

NASA Technical Reports Server (NTRS)

Smith, Kelly; Gay, Robert; Stachowiak, Susan

2013-01-01

In late 2014, NASA will fly the Orion capsule on a Delta IV-Heavy rocket for the Exploration Flight Test-1 (EFT-1) mission. For EFT-1, the Orion capsule will be flying with a new GPS receiver and new navigation software. Given the experimental nature of the flight, the flight software must be robust to the loss of GPS measurements. Once the high-speed entry is complete, the drogue parachutes must be deployed within the proper conditions to stabilize the vehicle prior to deploying the main parachutes. When GPS is available in nominal operations, the vehicle will deploy the drogue parachutes based on an altitude trigger. However, when GPS is unavailable, the navigated altitude errors become excessively large, driving the need for a backup barometric altimeter to improve altitude knowledge. In order to increase overall robustness, the vehicle also has an alternate method of triggering the parachute deployment sequence based on planet-relative velocity if both the GPS and the barometric altimeter fail. However, this backup trigger results in large altitude errors relative to the targeted altitude. Motivated by this challenge, this paper demonstrates how logistic regression may be employed to semi-automatically generate robust triggers based on statistical analysis. Logistic regression is used as a ground processor pre-flight to develop a statistical classifier. The classifier would then be implemented in flight software and executed in real-time. This technique offers improved performance even in the face of highly inaccurate measurements. Although the logistic regression-based trigger approach will not be implemented within EFT-1 flight software, the methodology can be carried forward for future missions and vehicles

An Alternative Flight Software Trigger Paradigm: Applying Multivariate Logistic Regression to Sense Trigger Conditions using Inaccurate or Scarce Information

NASA Technical Reports Server (NTRS)

Smith, Kelly M.; Gay, Robert S.; Stachowiak, Susan J.

2013-01-01

In late 2014, NASA will fly the Orion capsule on a Delta IV-Heavy rocket for the Exploration Flight Test-1 (EFT-1) mission. For EFT-1, the Orion capsule will be flying with a new GPS receiver and new navigation software. Given the experimental nature of the flight, the flight software must be robust to the loss of GPS measurements. Once the high-speed entry is complete, the drogue parachutes must be deployed within the proper conditions to stabilize the vehicle prior to deploying the main parachutes. When GPS is available in nominal operations, the vehicle will deploy the drogue parachutes based on an altitude trigger. However, when GPS is unavailable, the navigated altitude errors become excessively large, driving the need for a backup barometric altimeter. In order to increase overall robustness, the vehicle also has an alternate method of triggering the drogue parachute deployment based on planet-relative velocity if both the GPS and the barometric altimeter fail. However, this velocity-based trigger results in large altitude errors relative to the targeted altitude. Motivated by this challenge, this paper demonstrates how logistic regression may be employed to automatically generate robust triggers based on statistical analysis. Logistic regression is used as a ground processor pre-flight to develop a classifier. The classifier would then be implemented in flight software and executed in real-time. This technique offers excellent performance even in the face of highly inaccurate measurements. Although the logistic regression-based trigger approach will not be implemented within EFT-1 flight software, the methodology can be carried forward for future missions and vehicles.

Categorical Regression and Benchmark Dose Software 3.0

EPA Science Inventory

The objective of this full-day course is to provide participants with interactive training on the use of the U.S. Environmental Protection Agency’s (EPA) Benchmark Dose software (BMDS, version 3.0, released fall 2018) and Categorical Regression software (CatReg, version 3.1...

U.S. Army Armament Research, Development and Engineering Center Grain Evaluation Software to Numerically Predict Linear Burn Regression for Solid Propellant Grain Geometries

DTIC Science & Technology

2017-10-01

ENGINEERING CENTER GRAIN EVALUATION SOFTWARE TO NUMERICALLY PREDICT LINEAR BURN REGRESSION FOR SOLID PROPELLANT GRAIN GEOMETRIES Brian...author(s) and should not be construed as an official Department of the Army position, policy, or decision, unless so designated by other documentation...U.S. ARMY ARMAMENT RESEARCH, DEVELOPMENT AND ENGINEERING CENTER GRAIN EVALUATION SOFTWARE TO NUMERICALLY PREDICT LINEAR BURN REGRESSION FOR SOLID

Maximum Entropy Discrimination Poisson Regression for Software Reliability Modeling.

PubMed

Chatzis, Sotirios P; Andreou, Andreas S

2015-11-01

Reliably predicting software defects is one of the most significant tasks in software engineering. Two of the major components of modern software reliability modeling approaches are: 1) extraction of salient features for software system representation, based on appropriately designed software metrics and 2) development of intricate regression models for count data, to allow effective software reliability data modeling and prediction. Surprisingly, research in the latter frontier of count data regression modeling has been rather limited. More specifically, a lack of simple and efficient algorithms for posterior computation has made the Bayesian approaches appear unattractive, and thus underdeveloped in the context of software reliability modeling. In this paper, we try to address these issues by introducing a novel Bayesian regression model for count data, based on the concept of max-margin data modeling, effected in the context of a fully Bayesian model treatment with simple and efficient posterior distribution updates. Our novel approach yields a more discriminative learning technique, making more effective use of our training data during model inference. In addition, it allows of better handling uncertainty in the modeled data, which can be a significant problem when the training data are limited. We derive elegant inference algorithms for our model under the mean-field paradigm and exhibit its effectiveness using the publicly available benchmark data sets.

[The analysis of threshold effect using Empower Stats software].

PubMed

Lin, Lin; Chen, Chang-zhong; Yu, Xiao-dan

2013-11-01

In many studies about biomedical research factors influence on the outcome variable, it has no influence or has a positive effect within a certain range. Exceeding a certain threshold value, the size of the effect and/or orientation will change, which called threshold effect. Whether there are threshold effects in the analysis of factors (x) on the outcome variable (y), it can be observed through a smooth curve fitting to see whether there is a piecewise linear relationship. And then using segmented regression model, LRT test and Bootstrap resampling method to analyze the threshold effect. Empower Stats software developed by American X & Y Solutions Inc has a threshold effect analysis module. You can input the threshold value at a given threshold segmentation simulated data. You may not input the threshold, but determined the optimal threshold analog data by the software automatically, and calculated the threshold confidence intervals.

SandiaMRCR

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

2012-01-05

SandiaMCR was developed to identify pure components and their concentrations from spectral data. This software efficiently implements the multivariate calibration regression alternating least squares (MCR-ALS), principal component analysis (PCA), and singular value decomposition (SVD). Version 3.37 also includes the PARAFAC-ALS Tucker-1 (for trilinear analysis) algorithms. The alternating least squares methods can be used to determine the composition without or with incomplete prior information on the constituents and their concentrations. It allows the specification of numerous preprocessing, initialization and data selection and compression options for the efficient processing of large data sets. The software includes numerous options including the definition ofmore » equality and non-negativety constraints to realistically restrict the solution set, various normalization or weighting options based on the statistics of the data, several initialization choices and data compression. The software has been designed to provide a practicing spectroscopist the tools required to routinely analysis data in a reasonable time and without requiring expert intervention.« less

Association between obesity with disease-free survival and overall survival in triple-negative breast cancer: A meta-analysis.

PubMed

Mei, Lin; He, Lin; Song, Yuhua; Lv, Yang; Zhang, Lijiu; Hao, Fengxi; Xu, Mengmeng

2018-05-01

To investigate the relationship between obesity and disease-free survival (DFS) and overall survival (OS) of triple-negative breast cancer. Citations were searched in PubMed, Cochrane Library, and Web of Science. Random effect model meta-analysis was conducted by using Revman software version 5.0, and publication bias was evaluated by creating Egger regression with STATA software version 12. Nine studies (4412 patients) were included for DFS meta-analysis, 8 studies (4392 patients) include for OS meta-analysis. There were no statistical significances between obesity with DFS (P = .60) and OS (P = .71) in triple-negative breast cancer (TNBC) patients. Obesity has no impact on DFS and OS in patients with TNBC.

Introduction to methodology of dose-response meta-analysis for binary outcome: With application on software.

PubMed

Zhang, Chao; Jia, Pengli; Yu, Liu; Xu, Chang

2018-05-01

Dose-response meta-analysis (DRMA) is widely applied to investigate the dose-specific relationship between independent and dependent variables. Such methods have been in use for over 30 years and are increasingly employed in healthcare and clinical decision-making. In this article, we give an overview of the methodology used in DRMA. We summarize the commonly used regression model and the pooled method in DRMA. We also use an example to illustrate how to employ a DRMA by these methods. Five regression models, linear regression, piecewise regression, natural polynomial regression, fractional polynomial regression, and restricted cubic spline regression, were illustrated in this article to fit the dose-response relationship. And two types of pooling approaches, that is, one-stage approach and two-stage approach are illustrated to pool the dose-response relationship across studies. The example showed similar results among these models. Several dose-response meta-analysis methods can be used for investigating the relationship between exposure level and the risk of an outcome. However the methodology of DRMA still needs to be improved. © 2018 Chinese Cochrane Center, West China Hospital of Sichuan University and John Wiley & Sons Australia, Ltd.

Stress Regression Analysis of Asphalt Concrete Deck Pavement Based on Orthogonal Experimental Design and Interlayer Contact

NASA Astrophysics Data System (ADS)

Wang, Xuntao; Feng, Jianhu; Wang, Hu; Hong, Shidi; Zheng, Supei

2018-03-01

A three-dimensional finite element box girder bridge and its asphalt concrete deck pavement were established by ANSYS software, and the interlayer bonding condition of asphalt concrete deck pavement was assumed to be contact bonding condition. Orthogonal experimental design is used to arrange the testing plans of material parameters, and an evaluation of the effect of different material parameters in the mechanical response of asphalt concrete surface layer was conducted by multiple linear regression model and using the results from the finite element analysis. Results indicated that stress regression equations can well predict the stress of the asphalt concrete surface layer, and elastic modulus of waterproof layer has a significant influence on stress values of asphalt concrete surface layer.

Ca analysis: An Excel based program for the analysis of intracellular calcium transients including multiple, simultaneous regression analysis☆

PubMed Central

Greensmith, David J.

2014-01-01

Here I present an Excel based program for the analysis of intracellular Ca transients recorded using fluorescent indicators. The program can perform all the necessary steps which convert recorded raw voltage changes into meaningful physiological information. The program performs two fundamental processes. (1) It can prepare the raw signal by several methods. (2) It can then be used to analyze the prepared data to provide information such as absolute intracellular Ca levels. Also, the rates of change of Ca can be measured using multiple, simultaneous regression analysis. I demonstrate that this program performs equally well as commercially available software, but has numerous advantages, namely creating a simplified, self-contained analysis workflow. PMID:24125908

Early experiences building a software quality prediction model

NASA Technical Reports Server (NTRS)

Agresti, W. W.; Evanco, W. M.; Smith, M. C.

1990-01-01

Early experiences building a software quality prediction model are discussed. The overall research objective is to establish a capability to project a software system's quality from an analysis of its design. The technical approach is to build multivariate models for estimating reliability and maintainability. Data from 21 Ada subsystems were analyzed to test hypotheses about various design structures leading to failure-prone or unmaintainable systems. Current design variables highlight the interconnectivity and visibility of compilation units. Other model variables provide for the effects of reusability and software changes. Reported results are preliminary because additional project data is being obtained and new hypotheses are being developed and tested. Current multivariate regression models are encouraging, explaining 60 to 80 percent of the variation in error density of the subsystems.

Power and sample size for multivariate logistic modeling of unmatched case-control studies.

PubMed

Gail, Mitchell H; Haneuse, Sebastien

2017-01-01

Sample size calculations are needed to design and assess the feasibility of case-control studies. Although such calculations are readily available for simple case-control designs and univariate analyses, there is limited theory and software for multivariate unconditional logistic analysis of case-control data. Here we outline the theory needed to detect scalar exposure effects or scalar interactions while controlling for other covariates in logistic regression. Both analytical and simulation methods are presented, together with links to the corresponding software.

CATREG SOFTWARE FOR CATEGORICAL REGRESSION ANALYSIS

EPA Science Inventory

CatReg is a computer program, written in the R (http://cran.r-project.org) programming language, to support the conduct of exposure-response analyses by toxicologists and health scientists. CatReg can be used to perform categorical regressi...

A Survey of UML Based Regression Testing

NASA Astrophysics Data System (ADS)

Fahad, Muhammad; Nadeem, Aamer

Regression testing is the process of ensuring software quality by analyzing whether changed parts behave as intended, and unchanged parts are not affected by the modifications. Since it is a costly process, a lot of techniques are proposed in the research literature that suggest testers how to build regression test suite from existing test suite with minimum cost. In this paper, we discuss the advantages and drawbacks of using UML diagrams for regression testing and analyze that UML model helps in identifying changes for regression test selection effectively. We survey the existing UML based regression testing techniques and provide an analysis matrix to give a quick insight into prominent features of the literature work. We discuss the open research issues like managing and reducing the size of regression test suite, prioritization of the test cases that would be helpful during strict schedule and resources that remain to be addressed for UML based regression testing.

Functional mixture regression.

PubMed

Yao, Fang; Fu, Yuejiao; Lee, Thomas C M

2011-04-01

In functional linear models (FLMs), the relationship between the scalar response and the functional predictor process is often assumed to be identical for all subjects. Motivated by both practical and methodological considerations, we relax this assumption and propose a new class of functional regression models that allow the regression structure to vary for different groups of subjects. By projecting the predictor process onto its eigenspace, the new functional regression model is simplified to a framework that is similar to classical mixture regression models. This leads to the proposed approach named as functional mixture regression (FMR). The estimation of FMR can be readily carried out using existing software implemented for functional principal component analysis and mixture regression. The practical necessity and performance of FMR are illustrated through applications to a longevity analysis of female medflies and a human growth study. Theoretical investigations concerning the consistent estimation and prediction properties of FMR along with simulation experiments illustrating its empirical properties are presented in the supplementary material available at Biostatistics online. Corresponding results demonstrate that the proposed approach could potentially achieve substantial gains over traditional FLMs.

Ca analysis: an Excel based program for the analysis of intracellular calcium transients including multiple, simultaneous regression analysis.

PubMed

Greensmith, David J

2014-01-01

Here I present an Excel based program for the analysis of intracellular Ca transients recorded using fluorescent indicators. The program can perform all the necessary steps which convert recorded raw voltage changes into meaningful physiological information. The program performs two fundamental processes. (1) It can prepare the raw signal by several methods. (2) It can then be used to analyze the prepared data to provide information such as absolute intracellular Ca levels. Also, the rates of change of Ca can be measured using multiple, simultaneous regression analysis. I demonstrate that this program performs equally well as commercially available software, but has numerous advantages, namely creating a simplified, self-contained analysis workflow. Copyright © 2013 The Author. Published by Elsevier Ireland Ltd.. All rights reserved.

The impact of software quality characteristics on healthcare outcome: a literature review.

PubMed

Aghazadeh, Sakineh; Pirnejad, Habibollah; Moradkhani, Alireza; Aliev, Alvosat

2014-01-01

The aim of this study was to discover the effect of software quality characteristics on healthcare quality and efficiency indicators. Through a systematic literature review, we selected and analyzed 37 original research papers to investigate the impact of the software indicators (coming from the standard ISO 9126 quality characteristics and sub-characteristics) on some of healthcare important outcome indicators and finally ranked these software indicators. The results showed that the software characteristics usability, reliability and efficiency were mostly favored in the studies, indicating their importance. On the other hand, user satisfaction, quality of patient care, clinical workflow efficiency, providers' communication and information exchange, patient satisfaction and care costs were among the healthcare outcome indicators frequently evaluated in relation to the mentioned software characteristics. Regression Logistic Method was the most common assessment methodology, and Confirmatory Factor Analysis and Structural Equation Modeling were performed to test the structural model's fit. The software characteristics were considered to impact the healthcare outcome indicators through other intermediate factors (variables).

Evaluation of methodology for the analysis of 'time-to-event' data in pharmacogenomic genome-wide association studies.

PubMed

Syed, Hamzah; Jorgensen, Andrea L; Morris, Andrew P

2016-06-01

To evaluate the power to detect associations between SNPs and time-to-event outcomes across a range of pharmacogenomic study designs while comparing alternative regression approaches. Simulations were conducted to compare Cox proportional hazards modeling accounting for censoring and logistic regression modeling of a dichotomized outcome at the end of the study. The Cox proportional hazards model was demonstrated to be more powerful than the logistic regression analysis. The difference in power between the approaches was highly dependent on the rate of censoring. Initial evaluation of single-nucleotide polymorphism association signals using computationally efficient software with dichotomized outcomes provides an effective screening tool for some design scenarios, and thus has important implications for the development of analytical protocols in pharmacogenomic studies.

CatReg Software for Categorical Regression Analysis (Jul 2012)

EPA Science Inventory

CatReg is a computer program, written in the R (http://cran.r-project.org) programming language, to support the conduct of exposure-response analyses by toxicologists and health scientists. CatReg can be used to perform categorical regressi...

CatReg Software for Categorical Regression Analysis (Nov 2006)

EPA Science Inventory

CatReg is a computer program, written in the R (http://cran.r-project.org) programming language, to support the conduct of exposure-response analyses by toxicologists and health scientists. CatReg can be used to perform categorical regressi...

CatReg Software for Categorical Regression Analysis (Feb 2011)

EPA Science Inventory

CatReg is a computer program, written in the R (http://cran.r-project.org) programming language, to support the conduct of exposure-response analyses by toxicologists and health scientists. CatReg can be used to perform categorical regressi...

Regression Verification Using Impact Summaries

NASA Technical Reports Server (NTRS)

Backes, John; Person, Suzette J.; Rungta, Neha; Thachuk, Oksana

2013-01-01

Regression verification techniques are used to prove equivalence of syntactically similar programs. Checking equivalence of large programs, however, can be computationally expensive. Existing regression verification techniques rely on abstraction and decomposition techniques to reduce the computational effort of checking equivalence of the entire program. These techniques are sound but not complete. In this work, we propose a novel approach to improve scalability of regression verification by classifying the program behaviors generated during symbolic execution as either impacted or unimpacted. Our technique uses a combination of static analysis and symbolic execution to generate summaries of impacted program behaviors. The impact summaries are then checked for equivalence using an o-the-shelf decision procedure. We prove that our approach is both sound and complete for sequential programs, with respect to the depth bound of symbolic execution. Our evaluation on a set of sequential C artifacts shows that reducing the size of the summaries can help reduce the cost of software equivalence checking. Various reduction, abstraction, and compositional techniques have been developed to help scale software verification techniques to industrial-sized systems. Although such techniques have greatly increased the size and complexity of systems that can be checked, analysis of large software systems remains costly. Regression analysis techniques, e.g., regression testing [16], regression model checking [22], and regression verification [19], restrict the scope of the analysis by leveraging the differences between program versions. These techniques are based on the idea that if code is checked early in development, then subsequent versions can be checked against a prior (checked) version, leveraging the results of the previous analysis to reduce analysis cost of the current version. Regression verification addresses the problem of proving equivalence of closely related program versions [19]. These techniques compare two programs with a large degree of syntactic similarity to prove that portions of one program version are equivalent to the other. Regression verification can be used for guaranteeing backward compatibility, and for showing behavioral equivalence in programs with syntactic differences, e.g., when a program is refactored to improve its performance, maintainability, or readability. Existing regression verification techniques leverage similarities between program versions by using abstraction and decomposition techniques to improve scalability of the analysis [10, 12, 19]. The abstractions and decomposition in the these techniques, e.g., summaries of unchanged code [12] or semantically equivalent methods [19], compute an over-approximation of the program behaviors. The equivalence checking results of these techniques are sound but not complete-they may characterize programs as not functionally equivalent when, in fact, they are equivalent. In this work we describe a novel approach that leverages the impact of the differences between two programs for scaling regression verification. We partition program behaviors of each version into (a) behaviors impacted by the changes and (b) behaviors not impacted (unimpacted) by the changes. Only the impacted program behaviors are used during equivalence checking. We then prove that checking equivalence of the impacted program behaviors is equivalent to checking equivalence of all program behaviors for a given depth bound. In this work we use symbolic execution to generate the program behaviors and leverage control- and data-dependence information to facilitate the partitioning of program behaviors. The impacted program behaviors are termed as impact summaries. The dependence analyses that facilitate the generation of the impact summaries, we believe, could be used in conjunction with other abstraction and decomposition based approaches, [10, 12], as a complementary reduction technique. An evaluation of our regression verification technique shows that our approach is capable of leveraging similarities between program versions to reduce the size of the queries and the time required to check for logical equivalence. The main contributions of this work are: - A regression verification technique to generate impact summaries that can be checked for functional equivalence using an off-the-shelf decision procedure. - A proof that our approach is sound and complete with respect to the depth bound of symbolic execution. - An implementation of our technique using the LLVMcompiler infrastructure, the klee Symbolic Virtual Machine [4], and a variety of Satisfiability Modulo Theory (SMT) solvers, e.g., STP [7] and Z3 [6]. - An empirical evaluation on a set of C artifacts which shows that the use of impact summaries can reduce the cost of regression verification.

Selecting information technology for physicians' practices: a cross-sectional study.

PubMed

Eden, Karen Beekman

2002-04-05

Many physicians are transitioning from paper to electronic formats for billing, scheduling, medical charts, communications, etc. The primary objective of this research was to identify the relationship (if any) between the software selection process and the office staff's perceptions of the software's impact on practice activities. A telephone survey was conducted with office representatives of 407 physician practices in Oregon who had purchased information technology. The respondents, usually office managers, answered scripted questions about their selection process and their perceptions of the software after implementation. Multiple logistic regression revealed that software type, selection steps, and certain factors influencing the purchase were related to whether the respondents felt the software improved the scheduling and financial analysis practice activities. Specifically, practices that selected electronic medical record or practice management software, that made software comparisons, or that considered prior user testimony as important were more likely to have perceived improvements in the scheduling process than were other practices. Practices that considered value important, that did not consider compatibility important, that selected managed care software, that spent less than 10,000 dollars, or that provided learning time (most dramatic increase in odds ratio, 8.2) during implementation were more likely to perceive that the software had improved the financial analysis process than were other practices. Perhaps one of the most important predictors of improvement was providing learning time during implementation, particularly when the software involves several practice activities. Despite this importance, less than half of the practices reported performing this step.

SigrafW: An easy-to-use program for fitting enzyme kinetic data.

PubMed

Leone, Francisco Assis; Baranauskas, José Augusto; Furriel, Rosa Prazeres Melo; Borin, Ivana Aparecida

2005-11-01

SigrafW is Windows-compatible software developed using the Microsoft® Visual Basic Studio program that uses the simplified Hill equation for fitting kinetic data from allosteric and Michaelian enzymes. SigrafW uses a modified Fibonacci search to calculate maximal velocity (V), the Hill coefficient (n), and the enzyme-substrate apparent dissociation constant (K). The estimation of V, K, and the sum of the squares of residuals is performed using a Wilkinson nonlinear regression at any Hill coefficient (n). In contrast to many currently available kinetic analysis programs, SigrafW shows several advantages for the determination of kinetic parameters of both hyperbolic and nonhyperbolic saturation curves. No initial estimates of the kinetic parameters are required, a measure of the goodness-of-the-fit for each calculation performed is provided, the nonlinear regression used for calculations eliminates the statistical bias inherent in linear transformations, and the software can be used for enzyme kinetic simulations either for educational or research purposes. Persons interested in receiving a free copy of the software should contact Dr. F. A. Leone. Copyright © 2005 International Union of Biochemistry and Molecular Biology, Inc.

Statistical analysis of water-quality data containing multiple detection limits: S-language software for regression on order statistics

USGS Publications Warehouse

Lee, L.; Helsel, D.

2005-01-01

Trace contaminants in water, including metals and organics, often are measured at sufficiently low concentrations to be reported only as values below the instrument detection limit. Interpretation of these "less thans" is complicated when multiple detection limits occur. Statistical methods for multiply censored, or multiple-detection limit, datasets have been developed for medical and industrial statistics, and can be employed to estimate summary statistics or model the distributions of trace-level environmental data. We describe S-language-based software tools that perform robust linear regression on order statistics (ROS). The ROS method has been evaluated as one of the most reliable procedures for developing summary statistics of multiply censored data. It is applicable to any dataset that has 0 to 80% of its values censored. These tools are a part of a software library, or add-on package, for the R environment for statistical computing. This library can be used to generate ROS models and associated summary statistics, plot modeled distributions, and predict exceedance probabilities of water-quality standards. ?? 2005 Elsevier Ltd. All rights reserved.

Use of statistical study methods for the analysis of the results of the imitation modeling of radiation transfer

NASA Astrophysics Data System (ADS)

Alekseenko, M. A.; Gendrina, I. Yu.

2017-11-01

Recently, due to the abundance of various types of observational data in the systems of vision through the atmosphere and the need for their processing, the use of various methods of statistical research in the study of such systems as correlation-regression analysis, dynamic series, variance analysis, etc. is actual. We have attempted to apply elements of correlation-regression analysis for the study and subsequent prediction of the patterns of radiation transfer in these systems same as in the construction of radiation models of the atmosphere. In this paper, we present some results of statistical processing of the results of numerical simulation of the characteristics of vision systems through the atmosphere obtained with the help of a special software package.1

Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis

PubMed Central

Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

2015-01-01

Background: Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. Methods: In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. Results: The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Conclusion: Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended. PMID:26793655

Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis.

PubMed

Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

2015-01-01

Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.

Dental age estimation employing CBCT scans enhanced with Mimics software: Comparison of two different approaches using pulp/tooth volumetric analysis.

PubMed

Asif, Muhammad Khan; Nambiar, Phrabhakaran; Mani, Shani Ann; Ibrahim, Norliza Binti; Khan, Iqra Muhammad; Sukumaran, Prema

2018-02-01

The methods of dental age estimation and identification of unknown deceased individuals are evolving with the introduction of advanced innovative imaging technologies in forensic investigations. However, assessing small structures like root canal volumes can be challenging in spite of using highly advanced technology. The aim of the study was to investigate which amongst the two methods of volumetric analysis of maxillary central incisors displayed higher strength of correlation between chronological age and pulp/tooth volume ratio for Malaysian adults. Volumetric analysis of pulp cavity/tooth ratio was employed in Method 1 and pulp chamber/crown ratio (up to cemento-enamel junction) was analysed in Method 2. The images were acquired employing CBCT scans and enhanced by manipulating them with the Mimics software. These scans belonged to 56 males and 54 females and their ages ranged from 16 to 65 years. Pearson correlation and regression analysis indicated that both methods used for volumetric measurements had strong correlation between chronological age and pulp/tooth volume ratio. However, Method 2 gave higher coefficient of determination value (R2 = 0.78) when compared to Method 1 (R2 = 0.64). Moreover, manipulation in Method 2 was less time consuming and revealed higher inter-examiner reliability (0.982) as no manual intervention during 'multiple slice editing phase' of the software was required. In conclusion, this study showed that volumetric analysis of pulp cavity/tooth ratio is a valuable gender independent technique and the Method 2 regression equation should be recommended for dental age estimation. Copyright © 2018 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Robust variance estimation with dependent effect sizes: practical considerations including a software tutorial in Stata and spss.

PubMed

Tanner-Smith, Emily E; Tipton, Elizabeth

2014-03-01

Methodologists have recently proposed robust variance estimation as one way to handle dependent effect sizes in meta-analysis. Software macros for robust variance estimation in meta-analysis are currently available for Stata (StataCorp LP, College Station, TX, USA) and spss (IBM, Armonk, NY, USA), yet there is little guidance for authors regarding the practical application and implementation of those macros. This paper provides a brief tutorial on the implementation of the Stata and spss macros and discusses practical issues meta-analysts should consider when estimating meta-regression models with robust variance estimates. Two example databases are used in the tutorial to illustrate the use of meta-analysis with robust variance estimates. Copyright © 2013 John Wiley & Sons, Ltd.

[Development of the lung cancer diagnostic system].

PubMed

Lv, You-Jiang; Yu, Shou-Yi

2009-07-01

To develop a lung cancer diagnosis system. A retrospective analysis was conducted in 1883 patients with primary lung cancer or benign pulmonary diseases (pneumonia, tuberculosis, or pneumonia pseudotumor). SPSS11.5 software was used for data processing. For the relevant factors, a non-factor Logistic regression analysis was used followed by establishment of the regression model. Microsoft Visual Studio 2005 system development platform and VB.Net corresponding language were used to develop the lung cancer diagnosis system. The non-factor multi-factor regression model showed a goodness-of-fit (R2) of the model of 0.806, with a diagnostic accuracy for benign lung diseases of 92.8%, a diagnostic accuracy for lung cancer of 89.0%, and an overall accuracy of 90.8%. The model system for early clinical diagnosis of lung cancer has been established.

Using Group Explorer in teaching abstract algebra

NASA Astrophysics Data System (ADS)

Schubert, Claus; Gfeller, Mary; Donohue, Christopher

2013-04-01

This study explores the use of Group Explorer in an undergraduate mathematics course in abstract algebra. The visual nature of Group Explorer in representing concepts in group theory is an attractive incentive to use this software in the classroom. However, little is known about students' perceptions on this technology in learning concepts in abstract algebra. A total of 26 participants in an undergraduate course studying group theory were surveyed regarding their experiences using Group Explorer. Findings indicate that all participants believed that the software was beneficial to their learning and described their attitudes regarding the software in terms of using the technology and its helpfulness in learning concepts. A multiple regression analysis reveals that representational fluency of concepts with the software correlated significantly with participants' understanding of group concepts yet, participants' attitudes about Group Explorer and technology in general were not significant factors.

40 CFR 86.1823-01 - Durability demonstration procedures for exhaust emissions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... (including both hardware and software) must be installed and operating for the entire mileage accumulation... decimal places) from the regression analysis; the result shall be rounded to three-decimal places of... less than one shall be changed to one for the purposes of this paragraph. (2) An additive DF will be...

IPMP Global Fit - A one-step direct data analysis tool for predictive microbiology.

PubMed

Huang, Lihan

2017-12-04

The objective of this work is to develop and validate a unified optimization algorithm for performing one-step global regression analysis of isothermal growth and survival curves for determination of kinetic parameters in predictive microbiology. The algorithm is incorporated with user-friendly graphical interfaces (GUIs) to develop a data analysis tool, the USDA IPMP-Global Fit. The GUIs are designed to guide the users to easily navigate through the data analysis process and properly select the initial parameters for different combinations of mathematical models. The software is developed for one-step kinetic analysis to directly construct tertiary models by minimizing the global error between the experimental observations and mathematical models. The current version of the software is specifically designed for constructing tertiary models with time and temperature as the independent model parameters in the package. The software is tested with a total of 9 different combinations of primary and secondary models for growth and survival of various microorganisms. The results of data analysis show that this software provides accurate estimates of kinetic parameters. In addition, it can be used to improve the experimental design and data collection for more accurate estimation of kinetic parameters. IPMP-Global Fit can be used in combination with the regular USDA-IPMP for solving the inverse problems and developing tertiary models in predictive microbiology. Published by Elsevier B.V.

The Digital Shoreline Analysis System (DSAS) Version 4.0 - An ArcGIS extension for calculating shoreline change

USGS Publications Warehouse

Thieler, E. Robert; Himmelstoss, Emily A.; Zichichi, Jessica L.; Ergul, Ayhan

2009-01-01

The Digital Shoreline Analysis System (DSAS) version 4.0 is a software extension to ESRI ArcGIS v.9.2 and above that enables a user to calculate shoreline rate-of-change statistics from multiple historic shoreline positions. A user-friendly interface of simple buttons and menus guides the user through the major steps of shoreline change analysis. Components of the extension and user guide include (1) instruction on the proper way to define a reference baseline for measurements, (2) automated and manual generation of measurement transects and metadata based on user-specified parameters, and (3) output of calculated rates of shoreline change and other statistical information. DSAS computes shoreline rates of change using four different methods: (1) endpoint rate, (2) simple linear regression, (3) weighted linear regression, and (4) least median of squares. The standard error, correlation coefficient, and confidence interval are also computed for the simple and weighted linear-regression methods. The results of all rate calculations are output to a table that can be linked to the transect file by a common attribute field. DSAS is intended to facilitate the shoreline change-calculation process and to provide rate-of-change information and the statistical data necessary to establish the reliability of the calculated results. The software is also suitable for any generic application that calculates positional change over time, such as assessing rates of change of glacier limits in sequential aerial photos, river edge boundaries, land-cover changes, and so on.

Multicriteria analysis of ontologically represented information

NASA Astrophysics Data System (ADS)

Wasielewska, K.; Ganzha, M.; Paprzycki, M.; Bǎdicǎ, C.; Ivanovic, M.; Lirkov, I.

2014-11-01

Our current work concerns the development of a decision support system for the software selection problem. The main idea is to utilize expert knowledge to help the user in selecting the best software / method / computational resource to solve a computational problem. Obviously, this involves multicriterial decision making and the key open question is: which method to choose. The context of the work is provided by the Agents in Grid (AiG) project, where the software selection (and thus multicriterial analysis) is to be realized when all information concerning the problem, the hardware and the software is ontologically represented. Initially, we have considered the Analytical Hierarchy Process (AHP), which is well suited for the hierarchical data structures (e.g., such that have been formulated in terms of ontologies). However, due to its well-known shortcomings, we have decided to extend our search for the multicriterial analysis method best suited for the problem in question. In this paper we report results of our search, which involved: (i) TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), (ii) PROMETHEE, and (iii) GRIP (Generalized Regression with Intensities of Preference). We also briefly argue why other methods have not been considered as valuable candidates.

A method for fitting regression splines with varying polynomial order in the linear mixed model.

PubMed

Edwards, Lloyd J; Stewart, Paul W; MacDougall, James E; Helms, Ronald W

2006-02-15

The linear mixed model has become a widely used tool for longitudinal analysis of continuous variables. The use of regression splines in these models offers the analyst additional flexibility in the formulation of descriptive analyses, exploratory analyses and hypothesis-driven confirmatory analyses. We propose a method for fitting piecewise polynomial regression splines with varying polynomial order in the fixed effects and/or random effects of the linear mixed model. The polynomial segments are explicitly constrained by side conditions for continuity and some smoothness at the points where they join. By using a reparameterization of this explicitly constrained linear mixed model, an implicitly constrained linear mixed model is constructed that simplifies implementation of fixed-knot regression splines. The proposed approach is relatively simple, handles splines in one variable or multiple variables, and can be easily programmed using existing commercial software such as SAS or S-plus. The method is illustrated using two examples: an analysis of longitudinal viral load data from a study of subjects with acute HIV-1 infection and an analysis of 24-hour ambulatory blood pressure profiles.

Methods for scalar-on-function regression.

PubMed

Reiss, Philip T; Goldsmith, Jeff; Shang, Han Lin; Ogden, R Todd

2017-08-01

Recent years have seen an explosion of activity in the field of functional data analysis (FDA), in which curves, spectra, images, etc. are considered as basic functional data units. A central problem in FDA is how to fit regression models with scalar responses and functional data points as predictors. We review some of the main approaches to this problem, categorizing the basic model types as linear, nonlinear and nonparametric. We discuss publicly available software packages, and illustrate some of the procedures by application to a functional magnetic resonance imaging dataset.

Estimating multilevel logistic regression models when the number of clusters is low: a comparison of different statistical software procedures.

PubMed

Austin, Peter C

2010-04-22

Multilevel logistic regression models are increasingly being used to analyze clustered data in medical, public health, epidemiological, and educational research. Procedures for estimating the parameters of such models are available in many statistical software packages. There is currently little evidence on the minimum number of clusters necessary to reliably fit multilevel regression models. We conducted a Monte Carlo study to compare the performance of different statistical software procedures for estimating multilevel logistic regression models when the number of clusters was low. We examined procedures available in BUGS, HLM, R, SAS, and Stata. We found that there were qualitative differences in the performance of different software procedures for estimating multilevel logistic models when the number of clusters was low. Among the likelihood-based procedures, estimation methods based on adaptive Gauss-Hermite approximations to the likelihood (glmer in R and xtlogit in Stata) or adaptive Gaussian quadrature (Proc NLMIXED in SAS) tended to have superior performance for estimating variance components when the number of clusters was small, compared to software procedures based on penalized quasi-likelihood. However, only Bayesian estimation with BUGS allowed for accurate estimation of variance components when there were fewer than 10 clusters. For all statistical software procedures, estimation of variance components tended to be poor when there were only five subjects per cluster, regardless of the number of clusters.

Association between Stereotactic Radiotherapy and Death from Brain Metastases of Epithelial Ovarian Cancer: a Gliwice Data Re-Analysis with Penalization

PubMed

Tukiendorf, Andrzej; Mansournia, Mohammad Ali; Wydmański, Jerzy; Wolny-Rokicka, Edyta

2017-04-01

Background: Clinical datasets for epithelial ovarian cancer brain metastatic patients are usually small in size. When adequate case numbers are lacking, resulting estimates of regression coefficients may demonstrate bias. One of the direct approaches to reduce such sparse-data bias is based on penalized estimation. Methods: A re- analysis of formerly reported hazard ratios in diagnosed patients was performed using penalized Cox regression with a popular SAS package providing additional software codes for a statistical computational procedure. Results: It was found that the penalized approach can readily diminish sparse data artefacts and radically reduce the magnitude of estimated regression coefficients. Conclusions: It was confirmed that classical statistical approaches may exaggerate regression estimates or distort study interpretations and conclusions. The results support the thesis that penalization via weak informative priors and data augmentation are the safest approaches to shrink sparse data artefacts frequently occurring in epidemiological research. Creative Commons Attribution License

Separation in Logistic Regression: Causes, Consequences, and Control.

PubMed

Mansournia, Mohammad Ali; Geroldinger, Angelika; Greenland, Sander; Heinze, Georg

2018-04-01

Separation is encountered in regression models with a discrete outcome (such as logistic regression) where the covariates perfectly predict the outcome. It is most frequent under the same conditions that lead to small-sample and sparse-data bias, such as presence of a rare outcome, rare exposures, highly correlated covariates, or covariates with strong effects. In theory, separation will produce infinite estimates for some coefficients. In practice, however, separation may be unnoticed or mishandled because of software limits in recognizing and handling the problem and in notifying the user. We discuss causes of separation in logistic regression and describe how common software packages deal with it. We then describe methods that remove separation, focusing on the same penalized-likelihood techniques used to address more general sparse-data problems. These methods improve accuracy, avoid software problems, and allow interpretation as Bayesian analyses with weakly informative priors. We discuss likelihood penalties, including some that can be implemented easily with any software package, and their relative advantages and disadvantages. We provide an illustration of ideas and methods using data from a case-control study of contraceptive practices and urinary tract infection.

Comparison of different functional EIT approaches to quantify tidal ventilation distribution.

PubMed

Zhao, Zhanqi; Yun, Po-Jen; Kuo, Yen-Liang; Fu, Feng; Dai, Meng; Frerichs, Inez; Möller, Knut

2018-01-30

The aim of the study was to examine the pros and cons of different types of functional EIT (fEIT) to quantify tidal ventilation distribution in a clinical setting. fEIT images were calculated with (1) standard deviation of pixel time curve, (2) regression coefficients of global and local impedance time curves, or (3) mean tidal variations. To characterize temporal heterogeneity of tidal ventilation distribution, another fEIT image of pixel inspiration times is also proposed. fEIT-regression is very robust to signals with different phase information. When the respiratory signal should be distinguished from the heart-beat related signal, or during high-frequency oscillatory ventilation, fEIT-regression is superior to other types. fEIT-tidal variation is the most stable image type regarding the baseline shift. We recommend using this type of fEIT image for preliminary evaluation of the acquired EIT data. However, all these fEITs would be misleading in their assessment of ventilation distribution in the presence of temporal heterogeneity. The analysis software provided by the currently available commercial EIT equipment only offers either fEIT of standard deviation or tidal variation. Considering the pros and cons of each fEIT type, we recommend embedding more types into the analysis software to allow the physicians dealing with more complex clinical applications with on-line EIT measurements.

Caries risk assessment in schoolchildren - a form based on Cariogram® software

PubMed Central

CABRAL, Renata Nunes; HILGERT, Leandro Augusto; FABER, Jorge; LEAL, Soraya Coelho

2014-01-01

Identifying caries risk factors is an important measure which contributes to best understanding of the cariogenic profile of the patient. The Cariogram® software provides this analysis, and protocols simplifying the method were suggested. Objectives The aim of this study was to determine whether a newly developed Caries Risk Assessment (CRA) form based on the Cariogram® software could classify schoolchildren according to their caries risk and to evaluate relationships between caries risk and the variables in the form. Material and Methods 150 schoolchildren aged 5 to 7 years old were included in this survey. Caries prevalence was obtained according to International Caries Detection and Assessment System (ICDAS) II. Information for filling in the form based on Cariogram® was collected clinically and from questionnaires sent to parents. Linear regression and a forward stepwise multiple regression model were applied to correlate the variables included in the form with the caries risk. Results Caries prevalence, in primary dentition, including enamel and dentine carious lesions was 98.6%, and 77.3% when only dentine lesions were considered. Eighty-six percent of the children were classified as at moderate caries risk. The forward stepwise multiple regression model result was significant (R2=0.904; p<0.00001), showing that the most significant factors influencing caries risk were caries experience, oral hygiene, frequency of food consumption, sugar consumption and fluoride sources. Conclusion The use of the form based on the Cariogram® software enabled classification of the schoolchildren at low, moderate and high caries risk. Caries experience, oral hygiene, frequency of food consumption, sugar consumption and fluoride sources are the variables that were shown to be highly correlated with caries risk. PMID:25466473

A refined method for multivariate meta-analysis and meta-regression.

PubMed

Jackson, Daniel; Riley, Richard D

2014-02-20

Making inferences about the average treatment effect using the random effects model for meta-analysis is problematic in the common situation where there is a small number of studies. This is because estimates of the between-study variance are not precise enough to accurately apply the conventional methods for testing and deriving a confidence interval for the average effect. We have found that a refined method for univariate meta-analysis, which applies a scaling factor to the estimated effects' standard error, provides more accurate inference. We explain how to extend this method to the multivariate scenario and show that our proposal for refined multivariate meta-analysis and meta-regression can provide more accurate inferences than the more conventional approach. We explain how our proposed approach can be implemented using standard output from multivariate meta-analysis software packages and apply our methodology to two real examples. Copyright © 2013 John Wiley & Sons, Ltd.

OPLS statistical model versus linear regression to assess sonographic predictors of stroke prognosis.

PubMed

Vajargah, Kianoush Fathi; Sadeghi-Bazargani, Homayoun; Mehdizadeh-Esfanjani, Robab; Savadi-Oskouei, Daryoush; Farhoudi, Mehdi

2012-01-01

The objective of the present study was to assess the comparable applicability of orthogonal projections to latent structures (OPLS) statistical model vs traditional linear regression in order to investigate the role of trans cranial doppler (TCD) sonography in predicting ischemic stroke prognosis. The study was conducted on 116 ischemic stroke patients admitted to a specialty neurology ward. The Unified Neurological Stroke Scale was used once for clinical evaluation on the first week of admission and again six months later. All data was primarily analyzed using simple linear regression and later considered for multivariate analysis using PLS/OPLS models through the SIMCA P+12 statistical software package. The linear regression analysis results used for the identification of TCD predictors of stroke prognosis were confirmed through the OPLS modeling technique. Moreover, in comparison to linear regression, the OPLS model appeared to have higher sensitivity in detecting the predictors of ischemic stroke prognosis and detected several more predictors. Applying the OPLS model made it possible to use both single TCD measures/indicators and arbitrarily dichotomized measures of TCD single vessel involvement as well as the overall TCD result. In conclusion, the authors recommend PLS/OPLS methods as complementary rather than alternative to the available classical regression models such as linear regression.

Computing and software

USGS Publications Warehouse

White, Gary C.; Hines, J.E.

2004-01-01

The reality is that the statistical methods used for analysis of data depend upon the availability of software. Analysis of marked animal data is no different than the rest of the statistical field. The methods used for analysis are those that are available in reliable software packages. Thus, the critical importance of having reliable, up–to–date software available to biologists is obvious. Statisticians have continued to develop more robust models, ever expanding the suite of potential analysis methodsavailable. But without software to implement these newer methods, they will languish in the abstract, and not be applied to the problems deserving them.In the Computers and Software Session, two new software packages are described, a comparison of implementation of methods for the estimation of nest survival is provided, and a more speculative paper about how the next generation of software might be structured is presented.Rotella et al. (2004) compare nest survival estimation with different software packages: SAS logistic regression, SAS non–linear mixed models, and Program MARK. Nests are assumed to be visited at various, possibly infrequent, intervals. All of the approaches described compute nest survival with the same likelihood, and require that the age of the nest is known to account for nests that eventually hatch. However, each approach offers advantages and disadvantages, explored by Rotella et al. (2004).Efford et al. (2004) present a new software package called DENSITY. The package computes population abundance and density from trapping arrays and other detection methods with a new and unique approach. DENSITY represents the first major addition to the analysis of trapping arrays in 20 years.Barker & White (2004) discuss how existing software such as Program MARK require that each new model’s likelihood must be programmed specifically for that model. They wishfully think that future software might allow the user to combine pieces of likelihood functions together to generate estimates. The idea is interesting, and maybe some bright young statistician can work out the specifics to implement the procedure.Choquet et al. (2004) describe MSURGE, a software package that implements the multistate capture–recapture models. The unique feature of MSURGE is that the design matrix is constructed with an interpreted language called GEMACO. Because MSURGE is limited to just multistate models, the special requirements of these likelihoods can be provided.The software and methods presented in these papers gives biologists and wildlife managers an expanding range of possibilities for data analysis. Although ease–of–use is generally getting better, it does not replace the need for understanding of the requirements and structure of the models being computed. The internet provides access to many free software packages as well as user–discussion groups to share knowledge and ideas. (A starting point for wildlife–related applications is (http://www.phidot.org).

Software-assisted small bowel motility analysis using free-breathing MRI: feasibility study.

PubMed

Bickelhaupt, Sebastian; Froehlich, Johannes M; Cattin, Roger; Raible, Stephan; Bouquet, Hanspeter; Bill, Urs; Patak, Michael A

2014-01-01

To validate a software prototype allowing for small bowel motility analysis in free breathing by comparing it to manual measurements. In all, 25 patients (15 male, 10 female; mean age 39 years) were included in this Institutional Review Board-approved, retrospective study. Magnetic resonance imaging (MRI) was performed on a 1.5T system after standardized preparation acquiring motility sequences in free breathing over 69-84 seconds. Small bowel motility was analyzed manually and with the software. Functional parameters, measurement time, and reproducibility were compared using the coefficient of variance and paired Student's t-test. Correlation was analyzed using Pearson's correlation coefficient and linear regression. The 25 segments were analyzed twice both by hand and using the software with automatic breathing correction. All assessed parameters significantly correlated between the methods (P < 0.01), but the scattering of repeated measurements was significantly (P < 0.01) lower using the software (3.90%, standard deviation [SD] ± 5.69) than manual examinations (9.77%, SD ± 11.08). The time needed was significantly less (P < 0.001) with the software (4.52 minutes, SD ± 1.58) compared to manual measurement, lasting 17.48 minutes for manual (SD ± 1.75 minutes). The use of the software proves reliable and faster small bowel motility measurements in free-breathing MRI compared to manual analyses. The new technique allows for analyses of prolonged sequences acquired in free breathing, improving the informative value of the examinations by amplifying the evaluable data. Copyright © 2013 Wiley Periodicals, Inc.

NCCS Regression Test Harness

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

Tharrington, Arnold N.

2015-09-09

The NCCS Regression Test Harness is a software package that provides a framework to perform regression and acceptance testing on NCCS High Performance Computers. The package is written in Python and has only the dependency of a Subversion repository to store the regression tests.

Temporal Progression of Visual Injury from Blast Exposure

DTIC Science & Technology

2017-09-01

seen throughout the duration of the study. To correlate experimental blast exposures in rodents to human blast exposures, a computational parametric...software (JMP 10.0, Cary,NC). Descriptive and univariate analyses will first be performed to identify the occurrence of delayed visual system...later). The biostatistician evaluating the retrospective data has completed the descriptive analysis and is working on the multiple regression. Table

Background stratified Poisson regression analysis of cohort data.

PubMed

Richardson, David B; Langholz, Bryan

2012-03-01

Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models.

Methods for calculating confidence and credible intervals for the residual between-study variance in random effects meta-regression models

PubMed Central

2014-01-01

Background Meta-regression is becoming increasingly used to model study level covariate effects. However this type of statistical analysis presents many difficulties and challenges. Here two methods for calculating confidence intervals for the magnitude of the residual between-study variance in random effects meta-regression models are developed. A further suggestion for calculating credible intervals using informative prior distributions for the residual between-study variance is presented. Methods Two recently proposed and, under the assumptions of the random effects model, exact methods for constructing confidence intervals for the between-study variance in random effects meta-analyses are extended to the meta-regression setting. The use of Generalised Cochran heterogeneity statistics is extended to the meta-regression setting and a Newton-Raphson procedure is developed to implement the Q profile method for meta-analysis and meta-regression. WinBUGS is used to implement informative priors for the residual between-study variance in the context of Bayesian meta-regressions. Results Results are obtained for two contrasting examples, where the first example involves a binary covariate and the second involves a continuous covariate. Intervals for the residual between-study variance are wide for both examples. Conclusions Statistical methods, and R computer software, are available to compute exact confidence intervals for the residual between-study variance under the random effects model for meta-regression. These frequentist methods are almost as easily implemented as their established counterparts for meta-analysis. Bayesian meta-regressions are also easily performed by analysts who are comfortable using WinBUGS. Estimates of the residual between-study variance in random effects meta-regressions should be routinely reported and accompanied by some measure of their uncertainty. Confidence and/or credible intervals are well-suited to this purpose. PMID:25196829

[Job stressors in software developers--a comparison with other occupations].

PubMed

Kadokura, M

1997-09-01

The aim of this study is to investigate the difference in job stressors among software developers, the sales staff and the clerical staff (n = 2,079) in two companies (A Co. and B Co.) using a self-administered questionnaire that included a job stressor scale and the 30-item General Health Questionnaire (GHQ). We developed the job stressor scale based on the interviews with out-patients who engaged in software development and previous studies about job stressors. Factor analysis with a seven-factor solution showed that seven subscales were abstracted from the job stressor scale, namely, quantitative load of work, dissatisfaction with work, demanding work, uneasiness about work, human relations, ambiguity of work and shortage of private time. Each subscale was significantly (r = .313-.442, p < 0.0001) correlated with the GHQ score and proved to be a reliable instrument, as indicated by a Cronbach's alpha of greater than 0.73. Stepwise multiple regression analysis revealed that quantitative load of work and shortage of private time subscale scores were significantly high in software developers in A Co. Software developers in A Co. tended to score higher (P < .10) than the others in demanding work and ambiguity of work subscale. All subscale scores were significantly low in the clerical staff in B Co. There was no significant difference between the sales staff and software developers in B Co. Results of the interviews with out-patients showed that demanding work, hard deadline, ambiguity of work and precarious work would cause trouble in software developers. The implications of these findings with respect to occupational issues related to software developers are discussed.

Optimizing the Performance of Radionuclide Identification Software in the Hunt for Nuclear Security Threats

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

Fotion, Katherine A.

2016-08-18

The Radionuclide Analysis Kit (RNAK), my team’s most recent nuclide identification software, is entering the testing phase. A question arises: will removing rare nuclides from the software’s library improve its overall performance? An affirmative response indicates fundamental errors in the software’s framework, while a negative response confirms the effectiveness of the software’s key machine learning algorithms. After thorough testing, I found that the performance of RNAK cannot be improved with the library choice effect, thus verifying the effectiveness of RNAK’s algorithms—multiple linear regression, Bayesian network using the Viterbi algorithm, and branch and bound search.

PSHREG: A SAS macro for proportional and nonproportional subdistribution hazards regression

PubMed Central

Kohl, Maria; Plischke, Max; Leffondré, Karen; Heinze, Georg

2015-01-01

We present a new SAS macro %pshreg that can be used to fit a proportional subdistribution hazards model for survival data subject to competing risks. Our macro first modifies the input data set appropriately and then applies SAS's standard Cox regression procedure, PROC PHREG, using weights and counting-process style of specifying survival times to the modified data set. The modified data set can also be used to estimate cumulative incidence curves for the event of interest. The application of PROC PHREG has several advantages, e.g., it directly enables the user to apply the Firth correction, which has been proposed as a solution to the problem of undefined (infinite) maximum likelihood estimates in Cox regression, frequently encountered in small sample analyses. Deviation from proportional subdistribution hazards can be detected by both inspecting Schoenfeld-type residuals and testing correlation of these residuals with time, or by including interactions of covariates with functions of time. We illustrate application of these extended methods for competing risk regression using our macro, which is freely available at: http://cemsiis.meduniwien.ac.at/en/kb/science-research/software/statistical-software/pshreg, by means of analysis of a real chronic kidney disease study. We discuss differences in features and capabilities of %pshreg and the recent (January 2014) SAS PROC PHREG implementation of proportional subdistribution hazards modelling. PMID:25572709

Linear regression analysis for comparing two measurers or methods of measurement: but which regression?

PubMed

Ludbrook, John

2010-07-01

1. There are two reasons for wanting to compare measurers or methods of measurement. One is to calibrate one method or measurer against another; the other is to detect bias. Fixed bias is present when one method gives higher (or lower) values across the whole range of measurement. Proportional bias is present when one method gives values that diverge progressively from those of the other. 2. Linear regression analysis is a popular method for comparing methods of measurement, but the familiar ordinary least squares (OLS) method is rarely acceptable. The OLS method requires that the x values are fixed by the design of the study, whereas it is usual that both y and x values are free to vary and are subject to error. In this case, special regression techniques must be used. 3. Clinical chemists favour techniques such as major axis regression ('Deming's method'), the Passing-Bablok method or the bivariate least median squares method. Other disciplines, such as allometry, astronomy, biology, econometrics, fisheries research, genetics, geology, physics and sports science, have their own preferences. 4. Many Monte Carlo simulations have been performed to try to decide which technique is best, but the results are almost uninterpretable. 5. I suggest that pharmacologists and physiologists should use ordinary least products regression analysis (geometric mean regression, reduced major axis regression): it is versatile, can be used for calibration or to detect bias and can be executed by hand-held calculator or by using the loss function in popular, general-purpose, statistical software.

Novel semi-automated kidney volume measurements in autosomal dominant polycystic kidney disease.

PubMed

Muto, Satoru; Kawano, Haruna; Isotani, Shuji; Ide, Hisamitsu; Horie, Shigeo

2018-06-01

We assessed the effectiveness and convenience of a novel semi-automatic kidney volume (KV) measuring high-speed 3D-image analysis system SYNAPSE VINCENT ® (Fuji Medical Systems, Tokyo, Japan) for autosomal dominant polycystic kidney disease (ADPKD) patients. We developed a novel semi-automated KV measurement software for patients with ADPKD to be included in the imaging analysis software SYNAPSE VINCENT ® . The software extracts renal regions using image recognition software and measures KV (VINCENT KV). The algorithm was designed to work with the manual designation of a long axis of a kidney including cysts. After using the software to assess the predictive accuracy of the VINCENT method, we performed an external validation study and compared accurate KV and ellipsoid KV based on geometric modeling by linear regression analysis and Bland-Altman analysis. Median eGFR was 46.9 ml/min/1.73 m 2 . Median accurate KV, Vincent KV and ellipsoid KV were 627.7, 619.4 ml (IQR 431.5-947.0) and 694.0 ml (IQR 488.1-1107.4), respectively. Compared with ellipsoid KV (r = 0.9504), Vincent KV correlated strongly with accurate KV (r = 0.9968), without systematic underestimation or overestimation (ellipsoid KV; 14.2 ± 22.0%, Vincent KV; - 0.6 ± 6.0%). There were no significant slice thickness-specific differences (p = 0.2980). The VINCENT method is an accurate and convenient semi-automatic method to measure KV in patients with ADPKD compared with the conventional ellipsoid method.

Sampling and sensitivity analyses tools (SaSAT) for computational modelling

PubMed Central

Hoare, Alexander; Regan, David G; Wilson, David P

2008-01-01

SaSAT (Sampling and Sensitivity Analysis Tools) is a user-friendly software package for applying uncertainty and sensitivity analyses to mathematical and computational models of arbitrary complexity and context. The toolbox is built in Matlab®, a numerical mathematical software package, and utilises algorithms contained in the Matlab® Statistics Toolbox. However, Matlab® is not required to use SaSAT as the software package is provided as an executable file with all the necessary supplementary files. The SaSAT package is also designed to work seamlessly with Microsoft Excel but no functionality is forfeited if that software is not available. A comprehensive suite of tools is provided to enable the following tasks to be easily performed: efficient and equitable sampling of parameter space by various methodologies; calculation of correlation coefficients; regression analysis; factor prioritisation; and graphical output of results, including response surfaces, tornado plots, and scatterplots. Use of SaSAT is exemplified by application to a simple epidemic model. To our knowledge, a number of the methods available in SaSAT for performing sensitivity analyses have not previously been used in epidemiological modelling and their usefulness in this context is demonstrated. PMID:18304361

Time-resolved perfusion imaging at the angiography suite: preclinical comparison of a new flat-detector application to computed tomography perfusion.

PubMed

Jürgens, Julian H W; Schulz, Nadine; Wybranski, Christian; Seidensticker, Max; Streit, Sebastian; Brauner, Jan; Wohlgemuth, Walter A; Deuerling-Zheng, Yu; Ricke, Jens; Dudeck, Oliver

2015-02-01

The objective of this study was to compare the parameter maps of a new flat-panel detector application for time-resolved perfusion imaging in the angiography room (FD-CTP) with computed tomography perfusion (CTP) in an experimental tumor model. Twenty-four VX2 tumors were implanted into the hind legs of 12 rabbits. Three weeks later, FD-CTP (Artis zeego; Siemens) and CTP (SOMATOM Definition AS +; Siemens) were performed. The parameter maps for the FD-CTP were calculated using a prototype software, and those for the CTP were calculated with VPCT-body software on a dedicated syngo MultiModality Workplace. The parameters were compared using Pearson product-moment correlation coefficient and linear regression analysis. The Pearson product-moment correlation coefficient showed good correlation values for both the intratumoral blood volume of 0.848 (P < 0.01) and the blood flow of 0.698 (P < 0.01). The linear regression analysis of the perfusion between FD-CTP and CTP showed for the blood volume a regression equation y = 4.44x + 36.72 (P < 0.01) and for the blood flow y = 0.75x + 14.61 (P < 0.01). This preclinical study provides evidence that FD-CTP allows a time-resolved (dynamic) perfusion imaging of tumors similar to CTP, which provides the basis for clinical applications such as the assessment of tumor response to locoregional therapies directly in the angiography suite.

Tutorial on Biostatistics: Linear Regression Analysis of Continuous Correlated Eye Data.

PubMed

Ying, Gui-Shuang; Maguire, Maureen G; Glynn, Robert; Rosner, Bernard

2017-04-01

To describe and demonstrate appropriate linear regression methods for analyzing correlated continuous eye data. We describe several approaches to regression analysis involving both eyes, including mixed effects and marginal models under various covariance structures to account for inter-eye correlation. We demonstrate, with SAS statistical software, applications in a study comparing baseline refractive error between one eye with choroidal neovascularization (CNV) and the unaffected fellow eye, and in a study determining factors associated with visual field in the elderly. When refractive error from both eyes were analyzed with standard linear regression without accounting for inter-eye correlation (adjusting for demographic and ocular covariates), the difference between eyes with CNV and fellow eyes was 0.15 diopters (D; 95% confidence interval, CI -0.03 to 0.32D, p = 0.10). Using a mixed effects model or a marginal model, the estimated difference was the same but with narrower 95% CI (0.01 to 0.28D, p = 0.03). Standard regression for visual field data from both eyes provided biased estimates of standard error (generally underestimated) and smaller p-values, while analysis of the worse eye provided larger p-values than mixed effects models and marginal models. In research involving both eyes, ignoring inter-eye correlation can lead to invalid inferences. Analysis using only right or left eyes is valid, but decreases power. Worse-eye analysis can provide less power and biased estimates of effect. Mixed effects or marginal models using the eye as the unit of analysis should be used to appropriately account for inter-eye correlation and maximize power and precision.

LHCb Build and Deployment Infrastructure for run 2

NASA Astrophysics Data System (ADS)

Clemencic, M.; Couturier, B.

2015-12-01

After the successful run 1 of the LHC, the LHCb Core software team has taken advantage of the long shutdown to consolidate and improve its build and deployment infrastructure. Several of the related projects have already been presented like the build system using Jenkins, as well as the LHCb Performance and Regression testing infrastructure. Some components are completely new, like the Software Configuration Database (using the Graph DB Neo4j), or the new packaging installation using RPM packages. Furthermore all those parts are integrated to allow easier and quicker releases of the LHCb Software stack, therefore reducing the risk of operational errors. Integration and Regression tests are also now easier to implement, allowing to improve further the software checks.

Evaluation of pharyngeal space and its correlation with mandible and hyoid bone in patients with different skeletal classes and facial types.

PubMed

Nejaim, Yuri; Aps, Johan K M; Groppo, Francisco Carlos; Haiter Neto, Francisco

2018-06-01

The purpose of this article was to evaluate the pharyngeal space volume, and the size and shape of the mandible and the hyoid bone, as well as their relationships, in patients with different facial types and skeletal classes. Furthermore, we estimated the volume of the pharyngeal space with a formula using only linear measurements. A total of 161 i-CAT Next Generation (Imaging Sciences International, Hatfield, Pa) cone-beam computed tomography images (80 men, 81 women; ages, 21-58 years; mean age, 27 years) were retrospectively studied. Skeletal class and facial type were determined for each patient from multiplanar reconstructions using the NemoCeph software (Nemotec, Madrid, Spain). Linear and angular measurements were performed using 3D imaging software (version 3.4.3; Carestream Health, Rochester, NY), and volumetric analysis of the pharyngeal space was carried out with ITK-SNAP (version 2.4.0; Cognitica, Philadelphia, Pa) segmentation software. For the statistics, analysis of variance and the Tukey test with a significance level of 0.05, Pearson correlation, and linear regression were used. The pharyngeal space volume, when correlated with mandible and hyoid bone linear and angular measurements, showed significant correlations with skeletal class or facial type. The linear regression performed to estimate the volume of the pharyngeal space showed an R of 0.92 and an adjusted R 2 of 0.8362. There were significant correlations between pharyngeal space volume, and the mandible and hyoid bone measurements, suggesting that the stomatognathic system should be evaluated in an integral and nonindividualized way. Furthermore, it was possible to develop a linear regression model, resulting in a useful formula for estimating the volume of the pharyngeal space. Copyright © 2018 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.

AGSuite: Software to conduct feature analysis of artificial grammar learning performance.

PubMed

Cook, Matthew T; Chubala, Chrissy M; Jamieson, Randall K

2017-10-01

To simplify the problem of studying how people learn natural language, researchers use the artificial grammar learning (AGL) task. In this task, participants study letter strings constructed according to the rules of an artificial grammar and subsequently attempt to discriminate grammatical from ungrammatical test strings. Although the data from these experiments are usually analyzed by comparing the mean discrimination performance between experimental conditions, this practice discards information about the individual items and participants that could otherwise help uncover the particular features of strings associated with grammaticality judgments. However, feature analysis is tedious to compute, often complicated, and ill-defined in the literature. Moreover, the data violate the assumption of independence underlying standard linear regression models, leading to Type I error inflation. To solve these problems, we present AGSuite, a free Shiny application for researchers studying AGL. The suite's intuitive Web-based user interface allows researchers to generate strings from a database of published grammars, compute feature measures (e.g., Levenshtein distance) for each letter string, and conduct a feature analysis on the strings using linear mixed effects (LME) analyses. The LME analysis solves the inflation of Type I errors that afflicts more common methods of repeated measures regression analysis. Finally, the software can generate a number of graphical representations of the data to support an accurate interpretation of results. We hope the ease and availability of these tools will encourage researchers to take full advantage of item-level variance in their datasets in the study of AGL. We moreover discuss the broader applicability of the tools for researchers looking to conduct feature analysis in any field.

A refined method for multivariate meta-analysis and meta-regression

PubMed Central

Jackson, Daniel; Riley, Richard D

2014-01-01

Making inferences about the average treatment effect using the random effects model for meta-analysis is problematic in the common situation where there is a small number of studies. This is because estimates of the between-study variance are not precise enough to accurately apply the conventional methods for testing and deriving a confidence interval for the average effect. We have found that a refined method for univariate meta-analysis, which applies a scaling factor to the estimated effects’ standard error, provides more accurate inference. We explain how to extend this method to the multivariate scenario and show that our proposal for refined multivariate meta-analysis and meta-regression can provide more accurate inferences than the more conventional approach. We explain how our proposed approach can be implemented using standard output from multivariate meta-analysis software packages and apply our methodology to two real examples. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. PMID:23996351

Analysis of an Environmental Exposure Health Questionnaire in a Metropolitan Minority Population Utilizing Logistic Regression and Support Vector Machines

PubMed Central

Chen, Chau-Kuang; Bruce, Michelle; Tyler, Lauren; Brown, Claudine; Garrett, Angelica; Goggins, Susan; Lewis-Polite, Brandy; Weriwoh, Mirabel L; Juarez, Paul D.; Hood, Darryl B.; Skelton, Tyler

2014-01-01

The goal of this study was to analyze a 54-item instrument for assessment of perception of exposure to environmental contaminants within the context of the built environment, or exposome. This exposome was defined in five domains to include 1) home and hobby, 2) school, 3) community, 4) occupation, and 5) exposure history. Interviews were conducted with child-bearing-age minority women at Metro Nashville General Hospital at Meharry Medical College. Data were analyzed utilizing DTReg software for Support Vector Machine (SVM) modeling followed by an SPSS package for a logistic regression model. The target (outcome) variable of interest was respondent's residence by ZIP code. The results demonstrate that the rank order of important variables with respect to SVM modeling versus traditional logistic regression models is almost identical. This is the first study documenting that SVM analysis has discriminate power for determination of higher-ordered spatial relationships on an environmental exposure history questionnaire. PMID:23395953

Analysis of an environmental exposure health questionnaire in a metropolitan minority population utilizing logistic regression and Support Vector Machines.

PubMed

Chen, Chau-Kuang; Bruce, Michelle; Tyler, Lauren; Brown, Claudine; Garrett, Angelica; Goggins, Susan; Lewis-Polite, Brandy; Weriwoh, Mirabel L; Juarez, Paul D; Hood, Darryl B; Skelton, Tyler

2013-02-01

The goal of this study was to analyze a 54-item instrument for assessment of perception of exposure to environmental contaminants within the context of the built environment, or exposome. This exposome was defined in five domains to include 1) home and hobby, 2) school, 3) community, 4) occupation, and 5) exposure history. Interviews were conducted with child-bearing-age minority women at Metro Nashville General Hospital at Meharry Medical College. Data were analyzed utilizing DTReg software for Support Vector Machine (SVM) modeling followed by an SPSS package for a logistic regression model. The target (outcome) variable of interest was respondent's residence by ZIP code. The results demonstrate that the rank order of important variables with respect to SVM modeling versus traditional logistic regression models is almost identical. This is the first study documenting that SVM analysis has discriminate power for determination of higher-ordered spatial relationships on an environmental exposure history questionnaire.

Simulation of parametric model towards the fixed covariate of right censored lung cancer data

NASA Astrophysics Data System (ADS)

Afiqah Muhamad Jamil, Siti; Asrul Affendi Abdullah, M.; Kek, Sie Long; Ridwan Olaniran, Oyebayo; Enera Amran, Syahila

2017-09-01

In this study, simulation procedure was applied to measure the fixed covariate of right censored data by using parametric survival model. The scale and shape parameter were modified to differentiate the analysis of parametric regression survival model. Statistically, the biases, mean biases and the coverage probability were used in this analysis. Consequently, different sample sizes were employed to distinguish the impact of parametric regression model towards right censored data with 50, 100, 150 and 200 number of sample. R-statistical software was utilised to develop the coding simulation with right censored data. Besides, the final model of right censored simulation was compared with the right censored lung cancer data in Malaysia. It was found that different values of shape and scale parameter with different sample size, help to improve the simulation strategy for right censored data and Weibull regression survival model is suitable fit towards the simulation of survival of lung cancer patients data in Malaysia.

TG study of the Li0.4Fe2.4Zn0.2O4 ferrite synthesis

NASA Astrophysics Data System (ADS)

Lysenko, E. N.; Nikolaev, E. V.; Surzhikov, A. P.

2016-02-01

In this paper, the kinetic analysis of Li-Zn ferrite synthesis was studied using thermogravimetry (TG) method through the simultaneous application of non-linear regression to several measurements run at different heating rates (multivariate non-linear regression). Using TG-curves obtained for the four heating rates and Netzsch Thermokinetics software package, the kinetic models with minimal adjustable parameters were selected to quantitatively describe the reaction of Li-Zn ferrite synthesis. It was shown that the experimental TG-curves clearly suggest a two-step process for the ferrite synthesis and therefore a model-fitting kinetic analysis based on multivariate non-linear regressions was conducted. The complex reaction was described by a two-step reaction scheme consisting of sequential reaction steps. It is established that the best results were obtained using the Yander three-dimensional diffusion model at the first stage and Ginstling-Bronstein model at the second step. The kinetic parameters for lithium-zinc ferrite synthesis reaction were found and discussed.

A guide to understanding meta-analysis.

PubMed

Israel, Heidi; Richter, Randy R

2011-07-01

With the focus on evidence-based practice in healthcare, a well-conducted systematic review that includes a meta-analysis where indicated represents a high level of evidence for treatment effectiveness. The purpose of this commentary is to assist clinicians in understanding meta-analysis as a statistical tool using both published articles and explanations of components of the technique. We describe what meta-analysis is, what heterogeneity is, and how it affects meta-analysis, effect size, the modeling techniques of meta-analysis, and strengths and weaknesses of meta-analysis. Common components like forest plot interpretation, software that may be used, special cases for meta-analysis, such as subgroup analysis, individual patient data, and meta-regression, and a discussion of criticisms, are included.

Summary of Documentation for DYNA3D-ParaDyn's Software Quality Assurance Regression Test Problems

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

Zywicz, Edward

The Software Quality Assurance (SQA) regression test suite for DYNA3D (Zywicz and Lin, 2015) and ParaDyn (DeGroot, et al., 2015) currently contains approximately 600 problems divided into 21 suites, and is a required component of ParaDyn’s SQA plan (Ferencz and Oliver, 2013). The regression suite allows developers to ensure that software modifications do not unintentionally alter the code response. The entire regression suite is run prior to permanently incorporating any software modification or addition. When code modifications alter test problem results, the specific cause must be determined and fully understood before the software changes and revised test answers can bemore » incorporated. The regression suite is executed on LLNL platforms using a Python script and an associated data file. The user specifies the DYNA3D or ParaDyn executable, number of processors to use, test problems to run, and other options to the script. The data file details how each problem and its answer extraction scripts are executed. For each problem in the regression suite there exists an input deck, an eight-processor partition file, an answer file, and various extraction scripts. These scripts assemble a temporary answer file in a specific format from the simulation results. The temporary and stored answer files are compared to a specific level of numerical precision, and when differences are detected the test problem is flagged as failed. Presently, numerical results are stored and compared to 16 digits. At this accuracy level different processor types, compilers, number of partitions, etc. impact the results to various degrees. Thus, for consistency purposes the regression suite is run with ParaDyn using 8 processors on machines with a specific processor type (currently the Intel Xeon E5530 processor). For non-parallel regression problems, i.e., the two XFEM problems, DYNA3D is used instead. When environments or platforms change, executables using the current source code and the new resource are created and the regression suite is run. If differences in answers arise, the new answers are retained provided that the differences are inconsequential. This bootstrap approach allows the test suite answers to evolve in a controlled manner with a high level of confidence. Developers also run the entire regression suite with (serial) DYNA3D. While these results normally differ from the stored (parallel) answers, abnormal termination or wildly different values are strong indicators of potential issues.« less

Image analysis software for following progression of peripheral neuropathy

NASA Astrophysics Data System (ADS)

Epplin-Zapf, Thomas; Miller, Clayton; Larkin, Sean; Hermesmeyer, Eduardo; Macy, Jenny; Pellegrini, Marco; Luccarelli, Saverio; Staurenghi, Giovanni; Holmes, Timothy

2009-02-01

A relationship has been reported by several research groups [1 - 4] between the density and shapes of nerve fibers in the cornea and the existence and severity of peripheral neuropathy. Peripheral neuropathy is a complication of several prevalent diseases or conditions, which include diabetes, HIV, prolonged alcohol overconsumption and aging. A common clinical technique for confirming the condition is intramuscular electromyography (EMG), which is invasive, so a noninvasive technique like the one proposed here carries important potential advantages for the physician and patient. A software program that automatically detects the nerve fibers, counts them and measures their shapes is being developed and tested. Tests were carried out with a database of subjects with levels of severity of diabetic neuropathy as determined by EMG testing. Results from this testing, that include a linear regression analysis are shown.

Computer simulation of Cerebral Arteriovenous Malformation-validation analysis of hemodynamics parameters.

PubMed

Kumar, Y Kiran; Mehta, Shashi Bhushan; Ramachandra, Manjunath

2017-01-01

The purpose of this work is to provide some validation methods for evaluating the hemodynamic assessment of Cerebral Arteriovenous Malformation (CAVM). This article emphasizes the importance of validating noninvasive measurements for CAVM patients, which are designed using lumped models for complex vessel structure. The validation of the hemodynamics assessment is based on invasive clinical measurements and cross-validation techniques with the Philips proprietary validated software's Qflow and 2D Perfursion. The modeling results are validated for 30 CAVM patients for 150 vessel locations. Mean flow, diameter, and pressure were compared between modeling results and with clinical/cross validation measurements, using an independent two-tailed Student t test. Exponential regression analysis was used to assess the relationship between blood flow, vessel diameter, and pressure between them. Univariate analysis is used to assess the relationship between vessel diameter, vessel cross-sectional area, AVM volume, AVM pressure, and AVM flow results were performed with linear or exponential regression. Modeling results were compared with clinical measurements from vessel locations of cerebral regions. Also, the model is cross validated with Philips proprietary validated software's Qflow and 2D Perfursion. Our results shows that modeling results and clinical results are nearly matching with a small deviation. In this article, we have validated our modeling results with clinical measurements. The new approach for cross-validation is proposed by demonstrating the accuracy of our results with a validated product in a clinical environment.

Age estimation using exfoliative cytology and radiovisiography: A comparative study

PubMed Central

Nallamala, Shilpa; Guttikonda, Venkateswara Rao; Manchikatla, Praveen Kumar; Taneeru, Sravya

2017-01-01

Introduction: Age estimation is one of the essential factors in establishing the identity of an individual. Among various methods, exfoliative cytology (EC) is a unique, noninvasive technique, involving simple, and pain-free collection of intact cells from the oral cavity for microscopic examination. Objective: The study was undertaken with an aim to estimate the age of an individual from the average cell size of their buccal smears calculated using image analysis morphometric software and the pulp–tooth area ratio in mandibular canine of the same individual using radiovisiography (RVG). Materials and Methods: Buccal smears were collected from 100 apparently healthy individuals. After fixation in 95% alcohol, the smears were stained using Papanicolaou stain. The average cell size was measured using image analysis software (Image-Pro Insight 8.0). The RVG images of mandibular canines were obtained, pulp and tooth areas were traced using AutoCAD 2010 software, and area ratio was calculated. The estimated age was then calculated using regression analysis. Results: The paired t-test between chronological age and estimated age by cell size and pulp–tooth area ratio was statistically nonsignificant (P > 0.05). Conclusion: In the present study, age estimated by pulp–tooth area ratio and EC yielded good results. PMID:29657491

Age estimation using exfoliative cytology and radiovisiography: A comparative study.

PubMed

Nallamala, Shilpa; Guttikonda, Venkateswara Rao; Manchikatla, Praveen Kumar; Taneeru, Sravya

2017-01-01

Age estimation is one of the essential factors in establishing the identity of an individual. Among various methods, exfoliative cytology (EC) is a unique, noninvasive technique, involving simple, and pain-free collection of intact cells from the oral cavity for microscopic examination. The study was undertaken with an aim to estimate the age of an individual from the average cell size of their buccal smears calculated using image analysis morphometric software and the pulp-tooth area ratio in mandibular canine of the same individual using radiovisiography (RVG). Buccal smears were collected from 100 apparently healthy individuals. After fixation in 95% alcohol, the smears were stained using Papanicolaou stain. The average cell size was measured using image analysis software (Image-Pro Insight 8.0). The RVG images of mandibular canines were obtained, pulp and tooth areas were traced using AutoCAD 2010 software, and area ratio was calculated. The estimated age was then calculated using regression analysis. The paired t -test between chronological age and estimated age by cell size and pulp-tooth area ratio was statistically nonsignificant ( P > 0.05). In the present study, age estimated by pulp-tooth area ratio and EC yielded good results.

A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation.

PubMed

Välikangas, Tommi; Suomi, Tomi; Elo, Laura L

2017-05-31

Label-free mass spectrometry (MS) has developed into an important tool applied in various fields of biological and life sciences. Several software exist to process the raw MS data into quantified protein abundances, including open source and commercial solutions. Each software includes a set of unique algorithms for different tasks of the MS data processing workflow. While many of these algorithms have been compared separately, a thorough and systematic evaluation of their overall performance is missing. Moreover, systematic information is lacking about the amount of missing values produced by the different proteomics software and the capabilities of different data imputation methods to account for them.In this study, we evaluated the performance of five popular quantitative label-free proteomics software workflows using four different spike-in data sets. Our extensive testing included the number of proteins quantified and the number of missing values produced by each workflow, the accuracy of detecting differential expression and logarithmic fold change and the effect of different imputation and filtering methods on the differential expression results. We found that the Progenesis software performed consistently well in the differential expression analysis and produced few missing values. The missing values produced by the other software decreased their performance, but this difference could be mitigated using proper data filtering or imputation methods. Among the imputation methods, we found that the local least squares (lls) regression imputation consistently increased the performance of the software in the differential expression analysis, and a combination of both data filtering and local least squares imputation increased performance the most in the tested data sets. © The Author 2017. Published by Oxford University Press.

Software LS-MIDA for efficient mass isotopomer distribution analysis in metabolic modelling.

PubMed

Ahmed, Zeeshan; Zeeshan, Saman; Huber, Claudia; Hensel, Michael; Schomburg, Dietmar; Münch, Richard; Eisenreich, Wolfgang; Dandekar, Thomas

2013-07-09

The knowledge of metabolic pathways and fluxes is important to understand the adaptation of organisms to their biotic and abiotic environment. The specific distribution of stable isotope labelled precursors into metabolic products can be taken as fingerprints of the metabolic events and dynamics through the metabolic networks. An open-source software is required that easily and rapidly calculates from mass spectra of labelled metabolites, derivatives and their fragments global isotope excess and isotopomer distribution. The open-source software "Least Square Mass Isotopomer Analyzer" (LS-MIDA) is presented that processes experimental mass spectrometry (MS) data on the basis of metabolite information such as the number of atoms in the compound, mass to charge ratio (m/e or m/z) values of the compounds and fragments under study, and the experimental relative MS intensities reflecting the enrichments of isotopomers in 13C- or 15 N-labelled compounds, in comparison to the natural abundances in the unlabelled molecules. The software uses Brauman's least square method of linear regression. As a result, global isotope enrichments of the metabolite or fragment under study and the molar abundances of each isotopomer are obtained and displayed. The new software provides an open-source platform that easily and rapidly converts experimental MS patterns of labelled metabolites into isotopomer enrichments that are the basis for subsequent observation-driven analysis of pathways and fluxes, as well as for model-driven metabolic flux calculations.

A Gibbs sampler for Bayesian analysis of site-occupancy data

USGS Publications Warehouse

Dorazio, Robert M.; Rodriguez, Daniel Taylor

2012-01-01

1. A Bayesian analysis of site-occupancy data containing covariates of species occurrence and species detection probabilities is usually completed using Markov chain Monte Carlo methods in conjunction with software programs that can implement those methods for any statistical model, not just site-occupancy models. Although these software programs are quite flexible, considerable experience is often required to specify a model and to initialize the Markov chain so that summaries of the posterior distribution can be estimated efficiently and accurately. 2. As an alternative to these programs, we develop a Gibbs sampler for Bayesian analysis of site-occupancy data that include covariates of species occurrence and species detection probabilities. This Gibbs sampler is based on a class of site-occupancy models in which probabilities of species occurrence and detection are specified as probit-regression functions of site- and survey-specific covariate measurements. 3. To illustrate the Gibbs sampler, we analyse site-occupancy data of the blue hawker, Aeshna cyanea (Odonata, Aeshnidae), a common dragonfly species in Switzerland. Our analysis includes a comparison of results based on Bayesian and classical (non-Bayesian) methods of inference. We also provide code (based on the R software program) for conducting Bayesian and classical analyses of site-occupancy data.

Application of Experimental and Quasi-Experimental Research Designs to Educational Software Evaluation.

ERIC Educational Resources Information Center

Muller, Eugene W.

1985-01-01

Develops generalizations for empirical evaluation of software based upon suitability of several research designs--pretest posttest control group, single-group pretest posttest, nonequivalent control group, time series, and regression discontinuity--to type of software being evaluated, and on circumstances under which evaluation is conducted. (MBR)

Rapid Isolation and Detection for RNA Biomarkers for TBI Diagnostics

DTIC Science & Technology

2015-10-01

V., Grape and wine sensory attributes correlate with pattern- based discrimination of Cabernet Sauvignon wines by a peptidic sensor array, Tetrahedron... wine samples. Partial Least Squares Regression (PLSR) was used for the correlation of wine sensory attributes to the peptide-based receptor...responses. Data analysis was done using the software XLSTAT Addinsoft, NewYork) and R.Absorbance values due to wine without the sensing ensembles were

Tutorial on Biostatistics: Linear Regression Analysis of Continuous Correlated Eye Data

PubMed Central

Ying, Gui-shuang; Maguire, Maureen G; Glynn, Robert; Rosner, Bernard

2017-01-01

Purpose To describe and demonstrate appropriate linear regression methods for analyzing correlated continuous eye data. Methods We describe several approaches to regression analysis involving both eyes, including mixed effects and marginal models under various covariance structures to account for inter-eye correlation. We demonstrate, with SAS statistical software, applications in a study comparing baseline refractive error between one eye with choroidal neovascularization (CNV) and the unaffected fellow eye, and in a study determining factors associated with visual field data in the elderly. Results When refractive error from both eyes were analyzed with standard linear regression without accounting for inter-eye correlation (adjusting for demographic and ocular covariates), the difference between eyes with CNV and fellow eyes was 0.15 diopters (D; 95% confidence interval, CI −0.03 to 0.32D, P=0.10). Using a mixed effects model or a marginal model, the estimated difference was the same but with narrower 95% CI (0.01 to 0.28D, P=0.03). Standard regression for visual field data from both eyes provided biased estimates of standard error (generally underestimated) and smaller P-values, while analysis of the worse eye provided larger P-values than mixed effects models and marginal models. Conclusion In research involving both eyes, ignoring inter-eye correlation can lead to invalid inferences. Analysis using only right or left eyes is valid, but decreases power. Worse-eye analysis can provide less power and biased estimates of effect. Mixed effects or marginal models using the eye as the unit of analysis should be used to appropriately account for inter-eye correlation and maximize power and precision. PMID:28102741

Geometric dimension model of virtual astronaut body for ergonomic analysis of man-machine space system

NASA Astrophysics Data System (ADS)

Qianxiang, Zhou

2012-07-01

It is very important to clarify the geometric characteristic of human body segment and constitute analysis model for ergonomic design and the application of ergonomic virtual human. The typical anthropometric data of 1122 Chinese men aged 20-35 years were collected using three-dimensional laser scanner for human body. According to the correlation between different parameters, curve fitting were made between seven trunk parameters and ten body parameters with the SPSS 16.0 software. It can be concluded that hip circumference and shoulder breadth are the most important parameters in the models and the two parameters have high correlation with the others parameters of human body. By comparison with the conventional regressive curves, the present regression equation with the seven trunk parameters is more accurate to forecast the geometric dimensions of head, neck, height and the four limbs with high precision. Therefore, it is greatly valuable for ergonomic design and analysis of man-machine system.This result will be very useful to astronaut body model analysis and application.

Multinomial Logistic Regression Predicted Probability Map To Visualize The Influence Of Socio-Economic Factors On Breast Cancer Occurrence in Southern Karnataka

NASA Astrophysics Data System (ADS)

Madhu, B.; Ashok, N. C.; Balasubramanian, S.

2014-11-01

Multinomial logistic regression analysis was used to develop statistical model that can predict the probability of breast cancer in Southern Karnataka using the breast cancer occurrence data during 2007-2011. Independent socio-economic variables describing the breast cancer occurrence like age, education, occupation, parity, type of family, health insurance coverage, residential locality and socioeconomic status of each case was obtained. The models were developed as follows: i) Spatial visualization of the Urban- rural distribution of breast cancer cases that were obtained from the Bharat Hospital and Institute of Oncology. ii) Socio-economic risk factors describing the breast cancer occurrences were complied for each case. These data were then analysed using multinomial logistic regression analysis in a SPSS statistical software and relations between the occurrence of breast cancer across the socio-economic status and the influence of other socio-economic variables were evaluated and multinomial logistic regression models were constructed. iii) the model that best predicted the occurrence of breast cancer were identified. This multivariate logistic regression model has been entered into a geographic information system and maps showing the predicted probability of breast cancer occurrence in Southern Karnataka was created. This study demonstrates that Multinomial logistic regression is a valuable tool for developing models that predict the probability of breast cancer Occurrence in Southern Karnataka.

Methods for cost estimation in software project management

NASA Astrophysics Data System (ADS)

Briciu, C. V.; Filip, I.; Indries, I. I.

2016-02-01

The speed in which the processes used in software development field have changed makes it very difficult the task of forecasting the overall costs for a software project. By many researchers, this task has been considered unachievable, but there is a group of scientist for which this task can be solved using the already known mathematical methods (e.g. multiple linear regressions) and the new techniques as genetic programming and neural networks. The paper presents a solution for building a model for the cost estimation models in the software project management using genetic algorithms starting from the PROMISE datasets related COCOMO 81 model. In the first part of the paper, a summary of the major achievements in the research area of finding a model for estimating the overall project costs is presented together with the description of the existing software development process models. In the last part, a basic proposal of a mathematical model of a genetic programming is proposed including here the description of the chosen fitness function and chromosome representation. The perspective of model described it linked with the current reality of the software development considering as basis the software product life cycle and the current challenges and innovations in the software development area. Based on the author's experiences and the analysis of the existing models and product lifecycle it was concluded that estimation models should be adapted with the new technologies and emerging systems and they depend largely by the chosen software development method.

Chicken barn climate and hazardous volatile compounds control using simple linear regression and PID

NASA Astrophysics Data System (ADS)

Abdullah, A. H.; Bakar, M. A. A.; Shukor, S. A. A.; Saad, F. S. A.; Kamis, M. S.; Mustafa, M. H.; Khalid, N. S.

2016-07-01

The hazardous volatile compounds from chicken manure in chicken barn are potentially to be a health threat to the farm animals and workers. Ammonia (NH3) and hydrogen sulphide (H2S) produced in chicken barn are influenced by climate changes. The Electronic Nose (e-nose) is used for the barn's air, temperature and humidity data sampling. Simple Linear Regression is used to identify the correlation between temperature-humidity, humidity-ammonia and ammonia-hydrogen sulphide. MATLAB Simulink software was used for the sample data analysis using PID controller. Results shows that the performance of PID controller using the Ziegler-Nichols technique can improve the system controller to control climate in chicken barn.

Support Vector Machine algorithm for regression and classification

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

Yu, Chenggang; Zavaljevski, Nela

2001-08-01

The software is an implementation of the Support Vector Machine (SVM) algorithm that was invented and developed by Vladimir Vapnik and his co-workers at AT&T Bell Laboratories. The specific implementation reported here is an Active Set method for solving a quadratic optimization problem that forms the major part of any SVM program. The implementation is tuned to specific constraints generated in the SVM learning. Thus, it is more efficient than general-purpose quadratic optimization programs. A decomposition method has been implemented in the software that enables processing large data sets. The size of the learning data is virtually unlimited by themore » capacity of the computer physical memory. The software is flexible and extensible. Two upper bounds are implemented to regulate the SVM learning for classification, which allow users to adjust the false positive and false negative rates. The software can be used either as a standalone, general-purpose SVM regression or classification program, or be embedded into a larger software system.« less

Does Parental Control Work With Smartphone Addiction?: A Cross-Sectional Study of Children in South Korea.

PubMed

Lee, Eun Jee; Ogbolu, Yolanda

The purposes of this study were to (a) examine the relationship between personal characteristics (age, gender), psychological factors (depression), and physical factors (sleep time) on smartphone addiction in children and (b) determine whether parental control is associated with a lower incidence of smartphone addiction. Data were collected from children aged 10-12 years (N = 208) by a self-report questionnaire in two elementary schools and were analyzed using t test, one-way analysis of variance, correlation, and multiple linear regression. Most of the participants (73.3%) owned a smartphone, and the percentage of risky smartphone users was 12%. The multiple linear regression model explained 25.4% (adjusted R = .239) of the variance in the smartphone addiction score (SAS). Three variables were significantly associated with the SAS (age, depression, and parental control), and three variables were excluded (gender, geographic region, and parental control software). Teens, aged 10-12 years, with higher depression scores had higher SASs. The more parental control perceived by the student, the higher the SAS. There was no significant relationship between parental control software and smartphone addiction. This is one of the first studies to examine smartphone addiction in teens. Control-oriented managing by parents of children's smartphone use is not very effective and may exacerbate smartphone addiction. Future research should identify additional strategies, beyond parental control software, that have the potential to prevent, reduce, and eliminate smartphone addiction.

Detection of Cutting Tool Wear using Statistical Analysis and Regression Model

NASA Astrophysics Data System (ADS)

Ghani, Jaharah A.; Rizal, Muhammad; Nuawi, Mohd Zaki; Haron, Che Hassan Che; Ramli, Rizauddin

2010-10-01

This study presents a new method for detecting the cutting tool wear based on the measured cutting force signals. A statistical-based method called Integrated Kurtosis-based Algorithm for Z-Filter technique, called I-kaz was used for developing a regression model and 3D graphic presentation of I-kaz 3D coefficient during machining process. The machining tests were carried out using a CNC turning machine Colchester Master Tornado T4 in dry cutting condition. A Kistler 9255B dynamometer was used to measure the cutting force signals, which were transmitted, analyzed, and displayed in the DasyLab software. Various force signals from machining operation were analyzed, and each has its own I-kaz 3D coefficient. This coefficient was examined and its relationship with flank wear lands (VB) was determined. A regression model was developed due to this relationship, and results of the regression model shows that the I-kaz 3D coefficient value decreases as tool wear increases. The result then is used for real time tool wear monitoring.

Genome-wide regression and prediction with the BGLR statistical package.

PubMed

Pérez, Paulino; de los Campos, Gustavo

2014-10-01

Many modern genomic data analyses require implementing regressions where the number of parameters (p, e.g., the number of marker effects) exceeds sample size (n). Implementing these large-p-with-small-n regressions poses several statistical and computational challenges, some of which can be confronted using Bayesian methods. This approach allows integrating various parametric and nonparametric shrinkage and variable selection procedures in a unified and consistent manner. The BGLR R-package implements a large collection of Bayesian regression models, including parametric variable selection and shrinkage methods and semiparametric procedures (Bayesian reproducing kernel Hilbert spaces regressions, RKHS). The software was originally developed for genomic applications; however, the methods implemented are useful for many nongenomic applications as well. The response can be continuous (censored or not) or categorical (either binary or ordinal). The algorithm is based on a Gibbs sampler with scalar updates and the implementation takes advantage of efficient compiled C and Fortran routines. In this article we describe the methods implemented in BGLR, present examples of the use of the package, and discuss practical issues emerging in real-data analysis. Copyright © 2014 by the Genetics Society of America.

A practical data processing workflow for multi-OMICS projects.

PubMed

Kohl, Michael; Megger, Dominik A; Trippler, Martin; Meckel, Hagen; Ahrens, Maike; Bracht, Thilo; Weber, Frank; Hoffmann, Andreas-Claudius; Baba, Hideo A; Sitek, Barbara; Schlaak, Jörg F; Meyer, Helmut E; Stephan, Christian; Eisenacher, Martin

2014-01-01

Multi-OMICS approaches aim on the integration of quantitative data obtained for different biological molecules in order to understand their interrelation and the functioning of larger systems. This paper deals with several data integration and data processing issues that frequently occur within this context. To this end, the data processing workflow within the PROFILE project is presented, a multi-OMICS project that aims on identification of novel biomarkers and the development of new therapeutic targets for seven important liver diseases. Furthermore, a software called CrossPlatformCommander is sketched, which facilitates several steps of the proposed workflow in a semi-automatic manner. Application of the software is presented for the detection of novel biomarkers, their ranking and annotation with existing knowledge using the example of corresponding Transcriptomics and Proteomics data sets obtained from patients suffering from hepatocellular carcinoma. Additionally, a linear regression analysis of Transcriptomics vs. Proteomics data is presented and its performance assessed. It was shown, that for capturing profound relations between Transcriptomics and Proteomics data, a simple linear regression analysis is not sufficient and implementation and evaluation of alternative statistical approaches are needed. Additionally, the integration of multivariate variable selection and classification approaches is intended for further development of the software. Although this paper focuses only on the combination of data obtained from quantitative Proteomics and Transcriptomics experiments, several approaches and data integration steps are also applicable for other OMICS technologies. Keeping specific restrictions in mind the suggested workflow (or at least parts of it) may be used as a template for similar projects that make use of different high throughput techniques. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan. Copyright © 2013 Elsevier B.V. All rights reserved.

New software for cervical vertebral geometry assessment and its relationship to skeletal maturation—a pilot study

PubMed Central

Cunha, A R; Júnior, G C; Fernandes, N; Campos, M J S; Costa, L F M; Vitral, R W F; Bolognese, A M

2014-01-01

Objectives: In the present study, we developed new software for quantitative analysis of cervical vertebrae maturation, and we evaluated its applicability through a multinomial logistic regression model (MLRM). Methods: Digitized images of the bodies of the second (C2), third (C3) and fourth (C4) cervical vertebrae were analysed in cephalometric radiographs of 236 subjects (116 boys and 120 girls) by using a software developed for digitized vertebrae analysis. The sample was initially distributed into 11 categories according to the Fishman's skeletal maturity indicators and were then grouped into four stages for quantitative cervical maturational changes (QCMC) analysis (QCMC I, II, III and IV). Seven variables of interest were measured and analysed to identify morphologic alterations of the vertebral bodies in each QCMC category. Results: Statistically significant differences (p < 0.05) were observed among all QCMC categories for the variables analysed. The MLRM used to calculate the probability that an individual belonged to each of the four cervical vertebrae maturation categories was constructed by taking into account gender, chronological age and four variables determined by digitized vertebrae analysis (Ang_C3, MP_C3, MP_C4 and SP_C4). The MLRM presented a predictability of 81.4%. The weighted κ test showed almost perfect agreement (κ = 0.832) between the categories defined initially by the method of Fishman and those allocated by the MLRM. Conclusions: Significant alterations in the morphologies of the C2, C3 and C4 vertebral bodies that were analysed through the digitized vertebrae analysis software occur during the different stages of skeletal maturation. The model that combines the four parameters measured on the vertebral bodies, the age and the gender showed an excellent prediction. PMID:24319125

New software for cervical vertebral geometry assessment and its relationship to skeletal maturation--a pilot study.

PubMed

Santiago, R C; Cunha, A R; Júnior, G C; Fernandes, N; Campos, M J S; Costa, L F M; Vitral, R W F; Bolognese, A M

2014-01-01

In the present study, we developed new software for quantitative analysis of cervical vertebrae maturation, and we evaluated its applicability through a multinomial logistic regression model (MLRM). Digitized images of the bodies of the second (C2), third (C3) and fourth (C4) cervical vertebrae were analysed in cephalometric radiographs of 236 subjects (116 boys and 120 girls) by using a software developed for digitized vertebrae analysis. The sample was initially distributed into 11 categories according to the Fishman's skeletal maturity indicators and were then grouped into four stages for quantitative cervical maturational changes (QCMC) analysis (QCMC I, II, III and IV). Seven variables of interest were measured and analysed to identify morphologic alterations of the vertebral bodies in each QCMC category. Statistically significant differences (p < 0.05) were observed among all QCMC categories for the variables analysed. The MLRM used to calculate the probability that an individual belonged to each of the four cervical vertebrae maturation categories was constructed by taking into account gender, chronological age and four variables determined by digitized vertebrae analysis (Ang_C3, MP_C3, MP_C4 and SP_C4). The MLRM presented a predictability of 81.4%. The weighted κ test showed almost perfect agreement (κ = 0.832) between the categories defined initially by the method of Fishman and those allocated by the MLRM. Significant alterations in the morphologies of the C2, C3 and C4 vertebral bodies that were analysed through the digitized vertebrae analysis software occur during the different stages of skeletal maturation. The model that combines the four parameters measured on the vertebral bodies, the age and the gender showed an excellent prediction.

Agile Acceptance Test–Driven Development of Clinical Decision Support Advisories: Feasibility of Using Open Source Software

PubMed Central

Baldwin, Krystal L; Kannan, Vaishnavi; Flahaven, Emily L; Parks, Cassandra J; Ott, Jason M; Willett, Duwayne L

2018-01-01

Background Moving to electronic health records (EHRs) confers substantial benefits but risks unintended consequences. Modern EHRs consist of complex software code with extensive local configurability options, which can introduce defects. Defects in clinical decision support (CDS) tools are surprisingly common. Feasible approaches to prevent and detect defects in EHR configuration, including CDS tools, are needed. In complex software systems, use of test–driven development and automated regression testing promotes reliability. Test–driven development encourages modular, testable design and expanding regression test coverage. Automated regression test suites improve software quality, providing a “safety net” for future software modifications. Each automated acceptance test serves multiple purposes, as requirements (prior to build), acceptance testing (on completion of build), regression testing (once live), and “living” design documentation. Rapid-cycle development or “agile” methods are being successfully applied to CDS development. The agile practice of automated test–driven development is not widely adopted, perhaps because most EHR software code is vendor-developed. However, key CDS advisory configuration design decisions and rules stored in the EHR may prove amenable to automated testing as “executable requirements.” Objective We aimed to establish feasibility of acceptance test–driven development of clinical decision support advisories in a commonly used EHR, using an open source automated acceptance testing framework (FitNesse). Methods Acceptance tests were initially constructed as spreadsheet tables to facilitate clinical review. Each table specified one aspect of the CDS advisory’s expected behavior. Table contents were then imported into a test suite in FitNesse, which queried the EHR database to automate testing. Tests and corresponding CDS configuration were migrated together from the development environment to production, with tests becoming part of the production regression test suite. Results We used test–driven development to construct a new CDS tool advising Emergency Department nurses to perform a swallowing assessment prior to administering oral medication to a patient with suspected stroke. Test tables specified desired behavior for (1) applicable clinical settings, (2) triggering action, (3) rule logic, (4) user interface, and (5) system actions in response to user input. Automated test suite results for the “executable requirements” are shown prior to building the CDS alert, during build, and after successful build. Conclusions Automated acceptance test–driven development and continuous regression testing of CDS configuration in a commercial EHR proves feasible with open source software. Automated test–driven development offers one potential contribution to achieving high-reliability EHR configuration. Vetting acceptance tests with clinicians elicits their input on crucial configuration details early during initial CDS design and iteratively during rapid-cycle optimization. PMID:29653922

Agile Acceptance Test-Driven Development of Clinical Decision Support Advisories: Feasibility of Using Open Source Software.

PubMed

Basit, Mujeeb A; Baldwin, Krystal L; Kannan, Vaishnavi; Flahaven, Emily L; Parks, Cassandra J; Ott, Jason M; Willett, Duwayne L

2018-04-13

Moving to electronic health records (EHRs) confers substantial benefits but risks unintended consequences. Modern EHRs consist of complex software code with extensive local configurability options, which can introduce defects. Defects in clinical decision support (CDS) tools are surprisingly common. Feasible approaches to prevent and detect defects in EHR configuration, including CDS tools, are needed. In complex software systems, use of test-driven development and automated regression testing promotes reliability. Test-driven development encourages modular, testable design and expanding regression test coverage. Automated regression test suites improve software quality, providing a "safety net" for future software modifications. Each automated acceptance test serves multiple purposes, as requirements (prior to build), acceptance testing (on completion of build), regression testing (once live), and "living" design documentation. Rapid-cycle development or "agile" methods are being successfully applied to CDS development. The agile practice of automated test-driven development is not widely adopted, perhaps because most EHR software code is vendor-developed. However, key CDS advisory configuration design decisions and rules stored in the EHR may prove amenable to automated testing as "executable requirements." We aimed to establish feasibility of acceptance test-driven development of clinical decision support advisories in a commonly used EHR, using an open source automated acceptance testing framework (FitNesse). Acceptance tests were initially constructed as spreadsheet tables to facilitate clinical review. Each table specified one aspect of the CDS advisory's expected behavior. Table contents were then imported into a test suite in FitNesse, which queried the EHR database to automate testing. Tests and corresponding CDS configuration were migrated together from the development environment to production, with tests becoming part of the production regression test suite. We used test-driven development to construct a new CDS tool advising Emergency Department nurses to perform a swallowing assessment prior to administering oral medication to a patient with suspected stroke. Test tables specified desired behavior for (1) applicable clinical settings, (2) triggering action, (3) rule logic, (4) user interface, and (5) system actions in response to user input. Automated test suite results for the "executable requirements" are shown prior to building the CDS alert, during build, and after successful build. Automated acceptance test-driven development and continuous regression testing of CDS configuration in a commercial EHR proves feasible with open source software. Automated test-driven development offers one potential contribution to achieving high-reliability EHR configuration. Vetting acceptance tests with clinicians elicits their input on crucial configuration details early during initial CDS design and iteratively during rapid-cycle optimization. ©Mujeeb A Basit, Krystal L Baldwin, Vaishnavi Kannan, Emily L Flahaven, Cassandra J Parks, Jason M Ott, Duwayne L Willett. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 13.04.2018.

Back to the future: An online OSCE Management Information System for nursing OSCEs.

PubMed

Meskell, Pauline; Burke, Eimear; Kropmans, Thomas J B; Byrne, Evelyn; Setyonugroho, Winny; Kennedy, Kieran M

2015-11-01

The Objective Structured Clinical Examination (OSCE) is an established tool in the repertoire of clinical assessment methods in nurse education. The use of OSCEs facilitates the assessment of psychomotor skills as well as knowledge and attitudes. Identified benefits of OSCE assessment include development of students' confidence in their clinical skills and preparation for clinical practice. However, a number of challenges exist with the traditional paper methodology, including documentation errors and inadequate student feedback. To explore electronic OSCE delivery and evaluate the benefits of using an electronic OSCE management system. To explore assessors' perceptions of and attitudes to the computer based package. This study was conducted using electronic software in the management of a four station OSCE assessment with a cohort of first year undergraduate nursing students delivered over two consecutive years (n=203) in one higher education institution in Ireland. A quantitative descriptive survey methodology was used to obtain the views of the assessors on the process and outcome of using the software. OSCE documentation was converted to electronic format. Assessors were trained in the use of the OSCE management software package and laptops were procured to facilitate electronic management of the OSCE assessment. Following the OSCE assessment, assessors were invited to evaluate the experience. Electronic software facilitated the storage and analysis of overall group and individual results thereby offering considerable time savings. Submission of electronic forms was allowed only when fully completed thus removing the potential for missing data. The feedback facility allowed the student to receive timely evaluation on their performance and to benchmark their performance against the class. Assessors' satisfaction with the software was high. Analysis of assessment results can highlight issues around internal consistency being moderate and examiners variability. Regression analysis increases fairness of result calculations. Copyright © 2015. Published by Elsevier Ltd.

SPSS and SAS programs for comparing Pearson correlations and OLS regression coefficients.

PubMed

Weaver, Bruce; Wuensch, Karl L

2013-09-01

Several procedures that use summary data to test hypotheses about Pearson correlations and ordinary least squares regression coefficients have been described in various books and articles. To our knowledge, however, no single resource describes all of the most common tests. Furthermore, many of these tests have not yet been implemented in popular statistical software packages such as SPSS and SAS. In this article, we describe all of the most common tests and provide SPSS and SAS programs to perform them. When they are applicable, our code also computes 100 × (1 - α)% confidence intervals corresponding to the tests. For testing hypotheses about independent regression coefficients, we demonstrate one method that uses summary data and another that uses raw data (i.e., Potthoff analysis). When the raw data are available, the latter method is preferred, because use of summary data entails some loss of precision due to rounding.

Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science.

PubMed

Montes-Torres, Julio; Subirats, José Luis; Ribelles, Nuria; Urda, Daniel; Franco, Leonardo; Alba, Emilio; Jerez, José Manuel

2016-01-01

One of the prevailing applications of machine learning is the use of predictive modelling in clinical survival analysis. In this work, we present our view of the current situation of computer tools for survival analysis, stressing the need of transferring the latest results in the field of machine learning to biomedical researchers. We propose a web based software for survival analysis called OSA (Online Survival Analysis), which has been developed as an open access and user friendly option to obtain discrete time, predictive survival models at individual level using machine learning techniques, and to perform standard survival analysis. OSA employs an Artificial Neural Network (ANN) based method to produce the predictive survival models. Additionally, the software can easily generate survival and hazard curves with multiple options to personalise the plots, obtain contingency tables from the uploaded data to perform different tests, and fit a Cox regression model from a number of predictor variables. In the Materials and Methods section, we depict the general architecture of the application and introduce the mathematical background of each of the implemented methods. The study concludes with examples of use showing the results obtained with public datasets.

Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science

PubMed Central

Montes-Torres, Julio; Subirats, José Luis; Ribelles, Nuria; Urda, Daniel; Franco, Leonardo; Alba, Emilio; Jerez, José Manuel

2016-01-01

One of the prevailing applications of machine learning is the use of predictive modelling in clinical survival analysis. In this work, we present our view of the current situation of computer tools for survival analysis, stressing the need of transferring the latest results in the field of machine learning to biomedical researchers. We propose a web based software for survival analysis called OSA (Online Survival Analysis), which has been developed as an open access and user friendly option to obtain discrete time, predictive survival models at individual level using machine learning techniques, and to perform standard survival analysis. OSA employs an Artificial Neural Network (ANN) based method to produce the predictive survival models. Additionally, the software can easily generate survival and hazard curves with multiple options to personalise the plots, obtain contingency tables from the uploaded data to perform different tests, and fit a Cox regression model from a number of predictor variables. In the Materials and Methods section, we depict the general architecture of the application and introduce the mathematical background of each of the implemented methods. The study concludes with examples of use showing the results obtained with public datasets. PMID:27532883

Regression analysis of clustered failure time data with informative cluster size under the additive transformation models.

PubMed

Chen, Ling; Feng, Yanqin; Sun, Jianguo

2017-10-01

This paper discusses regression analysis of clustered failure time data, which occur when the failure times of interest are collected from clusters. In particular, we consider the situation where the correlated failure times of interest may be related to cluster sizes. For inference, we present two estimation procedures, the weighted estimating equation-based method and the within-cluster resampling-based method, when the correlated failure times of interest arise from a class of additive transformation models. The former makes use of the inverse of cluster sizes as weights in the estimating equations, while the latter can be easily implemented by using the existing software packages for right-censored failure time data. An extensive simulation study is conducted and indicates that the proposed approaches work well in both the situations with and without informative cluster size. They are applied to a dental study that motivated this study.

Viewing the viewers: how adults with attentional deficits watch educational videos.

PubMed

Hassner, Tal; Wolf, Lior; Lerner, Anat; Leitner, Yael

2014-10-01

Knowing how adults with ADHD interact with prerecorded video lessons at home may provide a novel means of early screening and long-term monitoring for ADHD. Viewing patterns of 484 students with known ADHD were compared with 484 age, gender, and academically matched controls chosen from 8,699 non-ADHD students. Transcripts generated by their video playback software were analyzed using t tests and regression analysis. ADHD students displayed significant tendencies (p ≤ .05) to watch videos with more pauses and more reviews of previously watched parts. Other parameters showed similar tendencies. Regression analysis indicated that attentional deficits remained constant for age and gender but varied for learning experience. There were measurable and significant differences between the video-viewing habits of the ADHD and non-ADHD students. This provides a new perspective on how adults cope with attention deficits and suggests a novel means of early screening for ADHD. © 2011 SAGE Publications.

Cognition of and Demand for Education and Teaching in Medical Statistics in China: A Systematic Review and Meta-Analysis

PubMed Central

Li, Gaoming; Yi, Dali; Wu, Xiaojiao; Liu, Xiaoyu; Zhang, Yanqi; Liu, Ling; Yi, Dong

2015-01-01

Background Although a substantial number of studies focus on the teaching and application of medical statistics in China, few studies comprehensively evaluate the recognition of and demand for medical statistics. In addition, the results of these various studies differ and are insufficiently comprehensive and systematic. Objectives This investigation aimed to evaluate the general cognition of and demand for medical statistics by undergraduates, graduates, and medical staff in China. Methods We performed a comprehensive database search related to the cognition of and demand for medical statistics from January 2007 to July 2014 and conducted a meta-analysis of non-controlled studies with sub-group analysis for undergraduates, graduates, and medical staff. Results There are substantial differences with respect to the cognition of theory in medical statistics among undergraduates (73.5%), graduates (60.7%), and medical staff (39.6%). The demand for theory in medical statistics is high among graduates (94.6%), undergraduates (86.1%), and medical staff (88.3%). Regarding specific statistical methods, the cognition of basic statistical methods is higher than of advanced statistical methods. The demand for certain advanced statistical methods, including (but not limited to) multiple analysis of variance (ANOVA), multiple linear regression, and logistic regression, is higher than that for basic statistical methods. The use rates of the Statistical Package for the Social Sciences (SPSS) software and statistical analysis software (SAS) are only 55% and 15%, respectively. Conclusion The overall statistical competence of undergraduates, graduates, and medical staff is insufficient, and their ability to practically apply their statistical knowledge is limited, which constitutes an unsatisfactory state of affairs for medical statistics education. Because the demand for skills in this area is increasing, the need to reform medical statistics education in China has become urgent. PMID:26053876

Cognition of and Demand for Education and Teaching in Medical Statistics in China: A Systematic Review and Meta-Analysis.

PubMed

Wu, Yazhou; Zhou, Liang; Li, Gaoming; Yi, Dali; Wu, Xiaojiao; Liu, Xiaoyu; Zhang, Yanqi; Liu, Ling; Yi, Dong

2015-01-01

Although a substantial number of studies focus on the teaching and application of medical statistics in China, few studies comprehensively evaluate the recognition of and demand for medical statistics. In addition, the results of these various studies differ and are insufficiently comprehensive and systematic. This investigation aimed to evaluate the general cognition of and demand for medical statistics by undergraduates, graduates, and medical staff in China. We performed a comprehensive database search related to the cognition of and demand for medical statistics from January 2007 to July 2014 and conducted a meta-analysis of non-controlled studies with sub-group analysis for undergraduates, graduates, and medical staff. There are substantial differences with respect to the cognition of theory in medical statistics among undergraduates (73.5%), graduates (60.7%), and medical staff (39.6%). The demand for theory in medical statistics is high among graduates (94.6%), undergraduates (86.1%), and medical staff (88.3%). Regarding specific statistical methods, the cognition of basic statistical methods is higher than of advanced statistical methods. The demand for certain advanced statistical methods, including (but not limited to) multiple analysis of variance (ANOVA), multiple linear regression, and logistic regression, is higher than that for basic statistical methods. The use rates of the Statistical Package for the Social Sciences (SPSS) software and statistical analysis software (SAS) are only 55% and 15%, respectively. The overall statistical competence of undergraduates, graduates, and medical staff is insufficient, and their ability to practically apply their statistical knowledge is limited, which constitutes an unsatisfactory state of affairs for medical statistics education. Because the demand for skills in this area is increasing, the need to reform medical statistics education in China has become urgent.

Estimating and Modelling Bias of the Hierarchical Partitioning Public-Domain Software: Implications in Environmental Management and Conservation

PubMed Central

Olea, Pedro P.; Mateo-Tomás, Patricia; de Frutos, Ángel

2010-01-01

Background Hierarchical partitioning (HP) is an analytical method of multiple regression that identifies the most likely causal factors while alleviating multicollinearity problems. Its use is increasing in ecology and conservation by its usefulness for complementing multiple regression analysis. A public-domain software “hier.part package” has been developed for running HP in R software. Its authors highlight a “minor rounding error” for hierarchies constructed from >9 variables, however potential bias by using this module has not yet been examined. Knowing this bias is pivotal because, for example, the ranking obtained in HP is being used as a criterion for establishing priorities of conservation. Methodology/Principal Findings Using numerical simulations and two real examples, we assessed the robustness of this HP module in relation to the order the variables have in the analysis. Results indicated a considerable effect of the variable order on the amount of independent variance explained by predictors for models with >9 explanatory variables. For these models the nominal ranking of importance of the predictors changed with variable order, i.e. predictors declared important by its contribution in explaining the response variable frequently changed to be either most or less important with other variable orders. The probability of changing position of a variable was best explained by the difference in independent explanatory power between that variable and the previous one in the nominal ranking of importance. The lesser is this difference, the more likely is the change of position. Conclusions/Significance HP should be applied with caution when more than 9 explanatory variables are used to know ranking of covariate importance. The explained variance is not a useful parameter to use in models with more than 9 independent variables. The inconsistency in the results obtained by HP should be considered in future studies as well as in those already published. Some recommendations to improve the analysis with this HP module are given. PMID:20657734

Modeling and managing risk early in software development

NASA Technical Reports Server (NTRS)

Briand, Lionel C.; Thomas, William M.; Hetmanski, Christopher J.

1993-01-01

In order to improve the quality of the software development process, we need to be able to build empirical multivariate models based on data collectable early in the software process. These models need to be both useful for prediction and easy to interpret, so that remedial actions may be taken in order to control and optimize the development process. We present an automated modeling technique which can be used as an alternative to regression techniques. We show how it can be used to facilitate the identification and aid the interpretation of the significant trends which characterize 'high risk' components in several Ada systems. Finally, we evaluate the effectiveness of our technique based on a comparison with logistic regression based models.

Prevalence of rapid eye movement sleep behavior disorder (RBD) in Parkinson's disease: a meta and meta-regression analysis.

PubMed

Zhang, Xiaona; Sun, Xiaoxuan; Wang, Junhong; Tang, Liou; Xie, Anmu

2017-01-01

Rapid eye movement sleep behavior disorder (RBD) is thought to be one of the most frequent preceding symptoms of Parkinson's disease (PD). However, the prevalence of RBD in PD stated in the published studies is still inconsistent. We conducted a meta and meta-regression analysis in this paper to estimate the pooled prevalence. We searched the electronic databases of PubMed, ScienceDirect, EMBASE and EBSCO up to June 2016 for related articles. STATA 12.0 statistics software was used to calculate the available data from each research. The prevalence of RBD in PD patients in each study was combined to a pooled prevalence with a 95 % confidence interval (CI). Subgroup analysis and meta-regression analysis were performed to search for the causes of the heterogeneity. A total of 28 studies with 6869 PD cases were deemed eligible and included in our meta-analysis based on the inclusion and exclusion criteria. The pooled prevalence of RBD in PD was 42.3 % (95 % CI 37.4-47.1 %). In subgroup analysis and meta-regression analysis, we found that the important causes of heterogeneity were the diagnosis criteria of RBD and age of PD patients (P = 0.016, P = 0.019, respectively). The results indicate that nearly half of the PD patients are suffering from RBD. Older age and longer duration are risk factors for RBD in PD. We can use the minimal diagnosis criteria for RBD according to the International Classification of Sleep Disorders to diagnose RBD patients in our daily work if polysomnography is not necessary.

BAYESIAN LARGE-SCALE MULTIPLE REGRESSION WITH SUMMARY STATISTICS FROM GENOME-WIDE ASSOCIATION STUDIES1

PubMed Central

Zhu, Xiang; Stephens, Matthew

2017-01-01

Bayesian methods for large-scale multiple regression provide attractive approaches to the analysis of genome-wide association studies (GWAS). For example, they can estimate heritability of complex traits, allowing for both polygenic and sparse models; and by incorporating external genomic data into the priors, they can increase power and yield new biological insights. However, these methods require access to individual genotypes and phenotypes, which are often not easily available. Here we provide a framework for performing these analyses without individual-level data. Specifically, we introduce a “Regression with Summary Statistics” (RSS) likelihood, which relates the multiple regression coefficients to univariate regression results that are often easily available. The RSS likelihood requires estimates of correlations among covariates (SNPs), which also can be obtained from public databases. We perform Bayesian multiple regression analysis by combining the RSS likelihood with previously proposed prior distributions, sampling posteriors by Markov chain Monte Carlo. In a wide range of simulations RSS performs similarly to analyses using the individual data, both for estimating heritability and detecting associations. We apply RSS to a GWAS of human height that contains 253,288 individuals typed at 1.06 million SNPs, for which analyses of individual-level data are practically impossible. Estimates of heritability (52%) are consistent with, but more precise, than previous results using subsets of these data. We also identify many previously unreported loci that show evidence for association with height in our analyses. Software is available at https://github.com/stephenslab/rss. PMID:29399241

NIPTmer: rapid k-mer-based software package for detection of fetal aneuploidies.

PubMed

Sauk, Martin; Žilina, Olga; Kurg, Ants; Ustav, Eva-Liina; Peters, Maire; Paluoja, Priit; Roost, Anne Mari; Teder, Hindrek; Palta, Priit; Brison, Nathalie; Vermeesch, Joris R; Krjutškov, Kaarel; Salumets, Andres; Kaplinski, Lauris

2018-04-04

Non-invasive prenatal testing (NIPT) is a recent and rapidly evolving method for detecting genetic lesions, such as aneuploidies, of a fetus. However, there is a need for faster and cheaper laboratory and analysis methods to make NIPT more widely accessible. We have developed a novel software package for detection of fetal aneuploidies from next-generation low-coverage whole genome sequencing data. Our tool - NIPTmer - is based on counting pre-defined per-chromosome sets of unique k-mers from raw sequencing data, and applying linear regression model on the counts. Additionally, the filtering process used for k-mer list creation allows one to take into account the genetic variance in a specific sample, thus reducing the source of uncertainty. The processing time of one sample is less than 10 CPU-minutes on a high-end workstation. NIPTmer was validated on a cohort of 583 NIPT samples and it correctly predicted 37 non-mosaic fetal aneuploidies. NIPTmer has the potential to reduce significantly the time and complexity of NIPT post-sequencing analysis compared to mapping-based methods. For non-commercial users the software package is freely available at http://bioinfo.ut.ee/NIPTMer/ .

An Investigation of the Effects of Reader Characteristics on Reading Comprehension Of a General Chemistry Text

NASA Astrophysics Data System (ADS)

Neiles, Kelly Y.

There is great concern in the scientific community that students in the United States, when compared with other countries, are falling behind in their scientific achievement. Increasing students' reading comprehension of scientific text may be one of the components involved in students' science achievement. To investigate students' reading comprehension this quantitative study examined the effects of different reader characteristics, namely, students' logical reasoning ability, factual chemistry knowledge, working memory capacity, and schema of the chemistry concepts, on reading comprehension of a chemistry text. Students' reading comprehension was measured through their ability to encode the text, access the meanings of words (lexical access), make bridging and elaborative inferences, and integrate the text with their existing schemas to make a lasting mental representation of the text (situational model). Students completed a series of tasks that measured the reader characteristic and reading comprehension variables. Some of the variables were measured using new technologies and software to investigate different cognitive processes. These technologies and software included eye tracking to investigate students' lexical accessing and a Pathfinder program to investigate students' schema of the chemistry concepts. The results from this study were analyzed using canonical correlation and regression analysis. The canonical correlation analysis allows for the ten variables described previously to be included in one multivariate analysis. Results indicate that the relationship between the reader characteristic variables and the reading comprehension variables is significant. The resulting canonical function accounts for a greater amount of variance in students' responses then any individual variable. Regression analysis was used to further investigate which reader characteristic variables accounted for the differences in students' responses for each reading comprehension variable. The results from this regression analysis indicated that the two schema measures (measured by the Pathfinder program) accounted for the greatest amount of variance in four of the reading comprehension variables (encoding the text, bridging and elaborative inferences, and delayed recall of a general summary). This research suggest that providing students with background information on chemistry concepts prior to having them read the text may result in better understanding and more effective incorporation of the chemistry concepts into their schema.

Software Cost Estimating,

DTIC Science & Technology

1982-05-13

Size Of The Software. A favourite measure for software system size is linos of operational code, or deliverable code (operational code plus...regression models, these conversions are either derived from productivity measures using the "cost per instruction" type of equation or they are...appropriate to different development organisattons, differert project types, different sets of units for measuring e and s, and different items

Implementation of an experimental program to investigate the performance characteristics of OMEGA navigation

NASA Technical Reports Server (NTRS)

Baxa, E. G., Jr.

1974-01-01

A theoretical formulation of differential and composite OMEGA error is presented to establish hypotheses about the functional relationships between various parameters and OMEGA navigational errors. Computer software developed to provide for extensive statistical analysis of the phase data is described. Results from the regression analysis used to conduct parameter sensitivity studies on differential OMEGA error tend to validate the theoretically based hypothesis concerning the relationship between uncorrected differential OMEGA error and receiver separation range and azimuth. Limited results of measurement of receiver repeatability error and line of position measurement error are also presented.

[Design and implementation of online statistical analysis function in information system of air pollution and health impact monitoring].

PubMed

Lü, Yiran; Hao, Shuxin; Zhang, Guoqing; Liu, Jie; Liu, Yue; Xu, Dongqun

2018-01-01

To implement the online statistical analysis function in information system of air pollution and health impact monitoring, and obtain the data analysis information real-time. Using the descriptive statistical method as well as time-series analysis and multivariate regression analysis, SQL language and visual tools to implement online statistical analysis based on database software. Generate basic statistical tables and summary tables of air pollution exposure and health impact data online; Generate tendency charts of each data part online and proceed interaction connecting to database; Generate butting sheets which can lead to R, SAS and SPSS directly online. The information system air pollution and health impact monitoring implements the statistical analysis function online, which can provide real-time analysis result to its users.

Novel non-contact retina camera for the rat and its application to dynamic retinal vessel analysis

PubMed Central

Link, Dietmar; Strohmaier, Clemens; Seifert, Bernd U.; Riemer, Thomas; Reitsamer, Herbert A.; Haueisen, Jens; Vilser, Walthard

2011-01-01

We present a novel non-invasive and non-contact system for reflex-free retinal imaging and dynamic retinal vessel analysis in the rat. Theoretical analysis was performed prior to development of the new optical design, taking into account the optical properties of the rat eye and its specific illumination and imaging requirements. A novel optical model of the rat eye was developed for use with standard optical design software, facilitating both sequential and non-sequential modes. A retinal camera for the rat was constructed using standard optical and mechanical components. The addition of a customized illumination unit and existing standard software enabled dynamic vessel analysis. Seven-minute in-vivo vessel diameter recordings performed on 9 Brown-Norway rats showed stable readings. On average, the coefficient of variation was (1.1 ± 0.19) % for the arteries and (0.6 ± 0.08) % for the veins. The slope of the linear regression analysis was (0.56 ± 0.26) % for the arteries and (0.15 ± 0.27) % for the veins. In conclusion, the device can be used in basic studies of retinal vessel behavior. PMID:22076270

Java Web Start based software for automated quantitative nuclear analysis of prostate cancer and benign prostate hyperplasia.

PubMed

Singh, Swaroop S; Kim, Desok; Mohler, James L

2005-05-11

Androgen acts via androgen receptor (AR) and accurate measurement of the levels of AR protein expression is critical for prostate research. The expression of AR in paired specimens of benign prostate and prostate cancer from 20 African and 20 Caucasian Americans was compared to demonstrate an application of this system. A set of 200 immunopositive and 200 immunonegative nuclei were collected from the images using a macro developed in Image Pro Plus. Linear Discriminant and Logistic Regression analyses were performed on the data to generate classification coefficients. Classification coefficients render the automated image analysis software independent of the type of immunostaining or image acquisition system used. The image analysis software performs local segmentation and uses nuclear shape and size to detect prostatic epithelial nuclei. AR expression is described by (a) percentage of immunopositive nuclei; (b) percentage of immunopositive nuclear area; and (c) intensity of AR expression among immunopositive nuclei or areas. The percent positive nuclei and percent nuclear area were similar by race in both benign prostate hyperplasia and prostate cancer. In prostate cancer epithelial nuclei, African Americans exhibited 38% higher levels of AR immunostaining than Caucasian Americans (two sided Student's t-tests; P < 0.05). Intensity of AR immunostaining was similar between races in benign prostate. The differences measured in the intensity of AR expression in prostate cancer were consistent with previous studies. Classification coefficients are required due to non-standardized immunostaining and image collection methods across medical institutions and research laboratories and helps customize the software for the specimen under study. The availability of a free, automated system creates new opportunities for testing, evaluation and use of this image analysis system by many research groups who study nuclear protein expression.

Case-mix groups for VA hospital-based home care.

PubMed

Smith, M E; Baker, C R; Branch, L G; Walls, R C; Grimes, R M; Karklins, J M; Kashner, M; Burrage, R; Parks, A; Rogers, P

1992-01-01

The purpose of this study is to group hospital-based home care (HBHC) patients homogeneously by their characteristics with respect to cost of care to develop alternative case mix methods for management and reimbursement (allocation) purposes. Six Veterans Affairs (VA) HBHC programs in Fiscal Year (FY) 1986 that maximized patient, program, and regional variation were selected, all of which agreed to participate. All HBHC patients active in each program on October 1, 1987, in addition to all new admissions through September 30, 1988 (FY88), comprised the sample of 874 unique patients. Statistical methods include the use of classification and regression trees (CART software: Statistical Software; Lafayette, CA), analysis of variance, and multiple linear regression techniques. The resulting algorithm is a three-factor model that explains 20% of the cost variance (R2 = 20%, with a cross validation R2 of 12%). Similar classifications such as the RUG-II, which is utilized for VA nursing home and intermediate care, the VA outpatient resource allocation model, and the RUG-HHC, utilized in some states for reimbursing home health care in the private sector, explained less of the cost variance and, therefore, are less adequate for VA home care resource allocation.

Statistical tools for analysis and modeling of cosmic populations and astronomical time series: CUDAHM and TSE

NASA Astrophysics Data System (ADS)

Loredo, Thomas; Budavari, Tamas; Scargle, Jeffrey D.

2018-01-01

This presentation provides an overview of open-source software packages addressing two challenging classes of astrostatistics problems. (1) CUDAHM is a C++ framework for hierarchical Bayesian modeling of cosmic populations, leveraging graphics processing units (GPUs) to enable applying this computationally challenging paradigm to large datasets. CUDAHM is motivated by measurement error problems in astronomy, where density estimation and linear and nonlinear regression must be addressed for populations of thousands to millions of objects whose features are measured with possibly complex uncertainties, potentially including selection effects. An example calculation demonstrates accurate GPU-accelerated luminosity function estimation for simulated populations of $10^6$ objects in about two hours using a single NVIDIA Tesla K40c GPU. (2) Time Series Explorer (TSE) is a collection of software in Python and MATLAB for exploratory analysis and statistical modeling of astronomical time series. It comprises a library of stand-alone functions and classes, as well as an application environment for interactive exploration of times series data. The presentation will summarize key capabilities of this emerging project, including new algorithms for analysis of irregularly-sampled time series.

Bayesian logistic regression in detection of gene-steroid interaction for cancer at PDLIM5 locus.

PubMed

Wang, Ke-Sheng; Owusu, Daniel; Pan, Yue; Xie, Changchun

2016-06-01

The PDZ and LIM domain 5 (PDLIM5) gene may play a role in cancer, bipolar disorder, major depression, alcohol dependence and schizophrenia; however, little is known about the interaction effect of steroid and PDLIM5 gene on cancer. This study examined 47 single-nucleotide polymorphisms (SNPs) within the PDLIM5 gene in the Marshfield sample with 716 cancer patients (any diagnosed cancer, excluding minor skin cancer) and 2848 noncancer controls. Multiple logistic regression model in PLINK software was used to examine the association of each SNP with cancer. Bayesian logistic regression in PROC GENMOD in SAS statistical software, ver. 9.4 was used to detect gene- steroid interactions influencing cancer. Single marker analysis using PLINK identified 12 SNPs associated with cancer (P< 0.05); especially, SNP rs6532496 revealed the strongest association with cancer (P = 6.84 × 10⁻³); while the next best signal was rs951613 (P = 7.46 × 10⁻³). Classic logistic regression in PROC GENMOD showed that both rs6532496 and rs951613 revealed strong gene-steroid interaction effects (OR=2.18, 95% CI=1.31-3.63 with P = 2.9 × 10⁻³ for rs6532496 and OR=2.07, 95% CI=1.24-3.45 with P = 5.43 × 10⁻³ for rs951613, respectively). Results from Bayesian logistic regression showed stronger interaction effects (OR=2.26, 95% CI=1.2-3.38 for rs6532496 and OR=2.14, 95% CI=1.14-3.2 for rs951613, respectively). All the 12 SNPs associated with cancer revealed significant gene-steroid interaction effects (P < 0.05); whereas 13 SNPs showed gene-steroid interaction effects without main effect on cancer. SNP rs4634230 revealed the strongest gene-steroid interaction effect (OR=2.49, 95% CI=1.5-4.13 with P = 4.0 × 10⁻⁴ based on the classic logistic regression and OR=2.59, 95% CI=1.4-3.97 from Bayesian logistic regression; respectively). This study provides evidence of common genetic variants within the PDLIM5 gene and interactions between PLDIM5 gene polymorphisms and steroid use influencing cancer.

Improvement of calculation method for electrical parameters of short network of ore-thermal furnaces

NASA Astrophysics Data System (ADS)

Aliferov, A. I.; Bikeev, R. A.; Goreva, L. P.

2017-10-01

The paper describes a new calculation method for active and inductive resistance of split interleaved current leads packages in ore-thermal electric furnaces. The method is developed on basis of regression analysis of dependencies of active and inductive resistances of the packages on their geometrical parameters, mutual disposition and interleaving pattern. These multi-parametric calculations have been performed with ANSYS software. The proposed method allows solving split current lead electrical parameters minimization and balancing problems for ore-thermal furnaces.

Digitized morphometric analysis of dental pulp of permanent mandibular second molar for age estimation of Davangere population.

PubMed

Kumar, Nerella Narendra; Panchaksharappa, Mamatha Gowda; Annigeri, Rajeshwari G

2016-04-01

The aim of the present study is to estimate the age of Davangere population by evaluating the pulp to tooth area ratio (PTR) by using digitized intraoral periapical radiographs of permanent mandibular second molar. 400 intraoral periapical radiograph (IOPA) of permanent mandibular 2nd molar of both the sexes aged 14-60 years were used. Digital camera was used to image the radiographs. Images were computed and PTR was calculated by AUTOCAD software. Intra and Inter observer variability was also assessed. Regression analysis was used to estimate the age of an individual by taking PTR as dependent variable. The mean PTR of males and females was 0.10 ± 0.02 and 0.09 ± 0.02 respectively. Negative correlation was observed, when age was compared with PTR {r = -0.441, -0.406 & -0.419 among males, females and total subjects (p < 0.001)}. Regression analysis showed a Standard Error of Estimate (SEE) of 12 years. The Kappa coefficient value for the intra and inter examiner variability was 0.85 & 0.83 respectively. Our results showed that permanent mandibular 2(nd) molar can be taken as an index tooth for estimating the age of the adults using digitized periapical radiograph and AUTOCAD software. Also high differences were observed between estimated and chronological age of 12 years which is not in the acceptable range. But it provides a new window for research in the forensic sciences in estimating the adult age. Copyright © 2016 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Age estimation by dentin translucency measurement using digital method: An institutional study

PubMed Central

Gupta, Shalini; Chandra, Akhilesh; Agnihotri, Archana; Gupta, Om Prakash; Maurya, Niharika

2017-01-01

Aims: The aims of the present study were to measure translucency on sectioned teeth using available computer hardware and software, to correlate dimensions of root dentin translucency with age, and to assess whether translucency is reliable for age estimation. Materials and Methods: A pilot study was done on 62 freshly extracted single-rooted permanent teeth from 62 different individuals (35 males and 27 females) and their 250 μm thick sections were prepared by micromotor, carborundum disks, and Arkansas stone. Each tooth section was scanned and the images were opened in the Adobe Photoshop software. Measurement of root dentin translucency (TD length) was done on the scanned image by placing two guides (A and B) along the x-axis of ABFO NO. 2 scale. Unpaired t-test, regression analysis, and Pearson correlation coefficient were used as statistical tools. Results: A linear relationship was observed between TD length and age in the regression analysis. The Pearson correlation analysis showed that there was positive correlation (r = 0.52, P = 0.0001) between TD length and age. However, no significant (P > 0.05) difference was observed in the TD length between male (8.44 ± 2.92 mm) and female (7.80 ± 2.79 mm) samples. Conclusion: Translucency of the root dentin increases with age and it can be used as a reliable parameter for the age estimation. The method used here to digitally select and measure translucent root dentin is more refined, better correlated to age, and produce superior age estimation. PMID:28584476

Water Quality Analysis Tool (WQAT) | Science Inventory | US ...

EPA Pesticide Factsheets

The purpose of the Water Quality Analysis Tool (WQAT) software is to provide a means for analyzing and producing useful remotely sensed data products for an entire estuary, a particular point or area of interest (AOI or POI) in estuaries, or water bodies of interest where pre-processed and geographically gridded remotely sensed images are available. A graphical user interface (GUI), was created to enable the user to select and display imagery from a variety of remote sensing data sources. The user can select a date (or date range) and location to extract pixels from the remotely sensed imagery. The GUI is used to obtain all available pixel values (i.e. pixel from all available bands of all available satellites) for a given location on a given date and time. The resultant data set can be analyzed or saved to a file for future use. The WQAT software provides users with a way to establish algorithms between remote sensing reflectance (Rrs) and any available in situ parameters, as well as statistical and regression analysis. The combined data sets can be used to improve water quality research and studies. Satellites provide spatially synoptic data at high frequency (daily to weekly). These characteristics are desirable for supplementing existing water quality observations and for providing information for large aquatic ecosystems that are historically under-sampled by field programs. Thus, the Water Quality Assessment Tool (WQAT) software tool was developed to suppo

Artificial intelligence and expert systems in-flight software testing

NASA Technical Reports Server (NTRS)

Demasie, M. P.; Muratore, J. F.

1991-01-01

The authors discuss the introduction of advanced information systems technologies such as artificial intelligence, expert systems, and advanced human-computer interfaces directly into Space Shuttle software engineering. The reconfiguration automation project (RAP) was initiated to coordinate this move towards 1990s software technology. The idea behind RAP is to automate several phases of the flight software testing procedure and to introduce AI and ES into space shuttle flight software testing. In the first phase of RAP, conventional tools to automate regression testing have already been developed or acquired. There are currently three tools in use.

A comparison of radiometric correction techniques in the evaluation of the relationship between LST and NDVI in Landsat imagery.

PubMed

Tan, Kok Chooi; Lim, Hwee San; Matjafri, Mohd Zubir; Abdullah, Khiruddin

2012-06-01

Atmospheric corrections for multi-temporal optical satellite images are necessary, especially in change detection analyses, such as normalized difference vegetation index (NDVI) rationing. Abrupt change detection analysis using remote-sensing techniques requires radiometric congruity and atmospheric correction to monitor terrestrial surfaces over time. Two atmospheric correction methods were used for this study: relative radiometric normalization and the simplified method for atmospheric correction (SMAC) in the solar spectrum. A multi-temporal data set consisting of two sets of Landsat images from the period between 1991 and 2002 of Penang Island, Malaysia, was used to compare NDVI maps, which were generated using the proposed atmospheric correction methods. Land surface temperature (LST) was retrieved using ATCOR3_T in PCI Geomatica 10.1 image processing software. Linear regression analysis was utilized to analyze the relationship between NDVI and LST. This study reveals that both of the proposed atmospheric correction methods yielded high accuracy through examination of the linear correlation coefficients. To check for the accuracy of the equation obtained through linear regression analysis for every single satellite image, 20 points were randomly chosen. The results showed that the SMAC method yielded a constant value (in terms of error) to predict the NDVI value from linear regression analysis-derived equation. The errors (average) from both proposed atmospheric correction methods were less than 10%.

Software Testing and Verification in Climate Model Development

NASA Technical Reports Server (NTRS)

Clune, Thomas L.; Rood, RIchard B.

2011-01-01

Over the past 30 years most climate models have grown from relatively simple representations of a few atmospheric processes to a complex multi-disciplinary system. Computer infrastructure over that period has gone from punch card mainframes to modem parallel clusters. Model implementations have become complex, brittle, and increasingly difficult to extend and maintain. Existing verification processes for model implementations rely almost exclusively upon some combination of detailed analysis of output from full climate simulations and system-level regression tests. In additional to being quite costly in terms of developer time and computing resources, these testing methodologies are limited in terms of the types of defects that can be detected, isolated and diagnosed. Mitigating these weaknesses of coarse-grained testing with finer-grained "unit" tests has been perceived as cumbersome and counter-productive. In the commercial software sector, recent advances in tools and methodology have led to a renaissance for systematic fine-grained testing. We discuss the availability of analogous tools for scientific software and examine benefits that similar testing methodologies could bring to climate modeling software. We describe the unique challenges faced when testing complex numerical algorithms and suggest techniques to minimize and/or eliminate the difficulties.

Statistical research using the multiple regression analysis in areas of the cast hipereutectoid steel rolls manufacturing

NASA Astrophysics Data System (ADS)

Kiss, I.; Alexa, V.; Serban, S.; Rackov, M.; Čavić, M.

2018-01-01

The cast hipereutectoid steel (usually named Adamite) is a roll manufacturing destined material, having mechanical, chemical properties and Carbon [C] content of which stands between steelandiron, along-withitsalloyelements such as Nickel [Ni], Chrome [Cr], Molybdenum [Mo] and/or other alloy elements. Adamite Rolls are basically alloy steel rolls (a kind of high carbon steel) having hardness ranging from 40 to 55 degrees Shore C, with Carbon [C] percentage ranging from 1.35% until to 2% (usually between 1.2˜2.3%), the extra Carbon [C] and the special alloying element giving an extra wear resistance and strength. First of all the Adamite roll’s prominent feature is the small variation in hardness of the working surface, and has a good abrasion resistance and bite performance. This paper reviews key aspects of roll material properties and presents an analysis of the influences of chemical composition upon the mechanical properties (hardness) of the cast hipereutectoid steel rolls (Adamite). Using the multiple regression analysis (the double and triple regression equations), some mathematical correlations between the cast hipereutectoid steel rolls’ chemical composition and the obtained hardness are presented. In this work several results and evidence obtained by actual experiments are presented. Thus, several variation boundaries for the chemical composition of cast hipereutectoid steel rolls, in view the obtaining the proper values of the hardness, are revealed. For the multiple regression equations, correlation coefficients and graphical representations the software Matlab was used.

Implementing informative priors for heterogeneity in meta-analysis using meta-regression and pseudo data.

PubMed

Rhodes, Kirsty M; Turner, Rebecca M; White, Ian R; Jackson, Dan; Spiegelhalter, David J; Higgins, Julian P T

2016-12-20

Many meta-analyses combine results from only a small number of studies, a situation in which the between-study variance is imprecisely estimated when standard methods are applied. Bayesian meta-analysis allows incorporation of external evidence on heterogeneity, providing the potential for more robust inference on the effect size of interest. We present a method for performing Bayesian meta-analysis using data augmentation, in which we represent an informative conjugate prior for between-study variance by pseudo data and use meta-regression for estimation. To assist in this, we derive predictive inverse-gamma distributions for the between-study variance expected in future meta-analyses. These may serve as priors for heterogeneity in new meta-analyses. In a simulation study, we compare approximate Bayesian methods using meta-regression and pseudo data against fully Bayesian approaches based on importance sampling techniques and Markov chain Monte Carlo (MCMC). We compare the frequentist properties of these Bayesian methods with those of the commonly used frequentist DerSimonian and Laird procedure. The method is implemented in standard statistical software and provides a less complex alternative to standard MCMC approaches. An importance sampling approach produces almost identical results to standard MCMC approaches, and results obtained through meta-regression and pseudo data are very similar. On average, data augmentation provides closer results to MCMC, if implemented using restricted maximum likelihood estimation rather than DerSimonian and Laird or maximum likelihood estimation. The methods are applied to real datasets, and an extension to network meta-analysis is described. The proposed method facilitates Bayesian meta-analysis in a way that is accessible to applied researchers. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis.

PubMed

Zheng, Jie; Erzurumluoglu, A Mesut; Elsworth, Benjamin L; Kemp, John P; Howe, Laurence; Haycock, Philip C; Hemani, Gibran; Tansey, Katherine; Laurin, Charles; Pourcain, Beate St; Warrington, Nicole M; Finucane, Hilary K; Price, Alkes L; Bulik-Sullivan, Brendan K; Anttila, Verneri; Paternoster, Lavinia; Gaunt, Tom R; Evans, David M; Neale, Benjamin M

2017-01-15

LD score regression is a reliable and efficient method of using genome-wide association study (GWAS) summary-level results data to estimate the SNP heritability of complex traits and diseases, partition this heritability into functional categories, and estimate the genetic correlation between different phenotypes. Because the method relies on summary level results data, LD score regression is computationally tractable even for very large sample sizes. However, publicly available GWAS summary-level data are typically stored in different databases and have different formats, making it difficult to apply LD score regression to estimate genetic correlations across many different traits simultaneously. In this manuscript, we describe LD Hub - a centralized database of summary-level GWAS results for 173 diseases/traits from different publicly available resources/consortia and a web interface that automates the LD score regression analysis pipeline. To demonstrate functionality and validate our software, we replicated previously reported LD score regression analyses of 49 traits/diseases using LD Hub; and estimated SNP heritability and the genetic correlation across the different phenotypes. We also present new results obtained by uploading a recent atopic dermatitis GWAS meta-analysis to examine the genetic correlation between the condition and other potentially related traits. In response to the growing availability of publicly accessible GWAS summary-level results data, our database and the accompanying web interface will ensure maximal uptake of the LD score regression methodology, provide a useful database for the public dissemination of GWAS results, and provide a method for easily screening hundreds of traits for overlapping genetic aetiologies. The web interface and instructions for using LD Hub are available at http://ldsc.broadinstitute.org/ CONTACT: jie.zheng@bristol.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Gis-Based Spatial Statistical Analysis of College Graduates Employment

NASA Astrophysics Data System (ADS)

Tang, R.

2012-07-01

It is urgently necessary to be aware of the distribution and employment status of college graduates for proper allocation of human resources and overall arrangement of strategic industry. This study provides empirical evidence regarding the use of geocoding and spatial analysis in distribution and employment status of college graduates based on the data from 2004-2008 Wuhan Municipal Human Resources and Social Security Bureau, China. Spatio-temporal distribution of employment unit were analyzed with geocoding using ArcGIS software, and the stepwise multiple linear regression method via SPSS software was used to predict the employment and to identify spatially associated enterprise and professionals demand in the future. The results show that the enterprises in Wuhan east lake high and new technology development zone increased dramatically from 2004 to 2008, and tended to distributed southeastward. Furthermore, the models built by statistical analysis suggest that the specialty of graduates major in has an important impact on the number of the employment and the number of graduates engaging in pillar industries. In conclusion, the combination of GIS and statistical analysis which helps to simulate the spatial distribution of the employment status is a potential tool for human resource development research.

Predicting the aquatic toxicity mode of action using logistic regression and linear discriminant analysis.

PubMed

Ren, Y Y; Zhou, L C; Yang, L; Liu, P Y; Zhao, B W; Liu, H X

2016-09-01

The paper highlights the use of the logistic regression (LR) method in the construction of acceptable statistically significant, robust and predictive models for the classification of chemicals according to their aquatic toxic modes of action. Essentials accounting for a reliable model were all considered carefully. The model predictors were selected by stepwise forward discriminant analysis (LDA) from a combined pool of experimental data and chemical structure-based descriptors calculated by the CODESSA and DRAGON software packages. Model predictive ability was validated both internally and externally. The applicability domain was checked by the leverage approach to verify prediction reliability. The obtained models are simple and easy to interpret. In general, LR performs much better than LDA and seems to be more attractive for the prediction of the more toxic compounds, i.e. compounds that exhibit excess toxicity versus non-polar narcotic compounds and more reactive compounds versus less reactive compounds. In addition, model fit and regression diagnostics was done through the influence plot which reflects the hat-values, studentized residuals, and Cook's distance statistics of each sample. Overdispersion was also checked for the LR model. The relationships between the descriptors and the aquatic toxic behaviour of compounds are also discussed.

Laval University and Lakehead University Experiments at TREC 2015 Contextual Suggestion Track

DTIC Science & Technology

2015-11-20

Department of Computer Science and Software Engineering, Laval University 2 Department of Software Engineering, Lakehead University Abstract—In this...Linear Regression and Lambda Mart perform poorly in this case, be- cause the size of the training data per user is small (less than 50 samples). On the

An Excel Solver Exercise to Introduce Nonlinear Regression

ERIC Educational Resources Information Center

Pinder, Jonathan P.

2013-01-01

Business students taking business analytics courses that have significant predictive modeling components, such as marketing research, data mining, forecasting, and advanced financial modeling, are introduced to nonlinear regression using application software that is a "black box" to the students. Thus, although correct models are…

Atmospheric mold spore counts in relation to meteorological parameters

NASA Astrophysics Data System (ADS)

Katial, R. K.; Zhang, Yiming; Jones, Richard H.; Dyer, Philip D.

Fungal spore counts of Cladosporium, Alternaria, and Epicoccum were studied during 8 years in Denver, Colorado. Fungal spore counts were obtained daily during the pollinating season by a Rotorod sampler. Weather data were obtained from the National Climatic Data Center. Daily averages of temperature, relative humidity, daily precipitation, barometric pressure, and wind speed were studied. A time series analysis was performed on the data to mathematically model the spore counts in relation to weather parameters. Using SAS PROC ARIMA software, a regression analysis was performed, regressing the spore counts on the weather variables assuming an autoregressive moving average (ARMA) error structure. Cladosporium was found to be positively correlated (P<0.02) with average daily temperature, relative humidity, and negatively correlated with precipitation. Alternaria and Epicoccum did not show increased predictability with weather variables. A mathematical model was derived for Cladosporium spore counts using the annual seasonal cycle and significant weather variables. The model for Alternaria and Epicoccum incorporated the annual seasonal cycle. Fungal spore counts can be modeled by time series analysis and related to meteorological parameters controlling for seasonallity; this modeling can provide estimates of exposure to fungal aeroallergens.

Additive hazards regression and partial likelihood estimation for ecological monitoring data across space.

PubMed

Lin, Feng-Chang; Zhu, Jun

2012-01-01

We develop continuous-time models for the analysis of environmental or ecological monitoring data such that subjects are observed at multiple monitoring time points across space. Of particular interest are additive hazards regression models where the baseline hazard function can take on flexible forms. We consider time-varying covariates and take into account spatial dependence via autoregression in space and time. We develop statistical inference for the regression coefficients via partial likelihood. Asymptotic properties, including consistency and asymptotic normality, are established for parameter estimates under suitable regularity conditions. Feasible algorithms utilizing existing statistical software packages are developed for computation. We also consider a simpler additive hazards model with homogeneous baseline hazard and develop hypothesis testing for homogeneity. A simulation study demonstrates that the statistical inference using partial likelihood has sound finite-sample properties and offers a viable alternative to maximum likelihood estimation. For illustration, we analyze data from an ecological study that monitors bark beetle colonization of red pines in a plantation of Wisconsin.

[Correlation research on contents of podophyllotoxin and total lignans in Sinopodophyllum hexandrum and ecological factors].

PubMed

Li, Min; Zhong, Guo-yue; Wu, Ao-lin; Zhang, Shou-wen; Jiang, Wei; Liang, Jian

2015-05-01

To explore the correlation between the ecological factors and the contents of podophyllotoxin and total lignans in root and rhizome of Sinopodophyllum hexandrum, podophyllotoxin in 87 samples (from 5 provinces) was determined by HPLC and total lignans by UV. A correlation and regression analysis was made by software SPSS 16.0 in combination with ecological factors (terrain, soil and climate). The content determination results showed a great difference between podophyllotoxin and total lignans, attaining 1.001%-6.230% and 5.350%-16.34%, respective. The correlation and regression analysis by SPSS showed a positive linear correlation between their contents, strong positive correlation between their contents, latitude and annual average rainfall within the sampling area, weak negative correlation with pH value and organic material in soil, weaker and stronger positive correlations with soil potassium, weak negative correlation with slope and annual average temperature and weaker positive correlation between the podophyllotoxin content and soil potassium.

A non-linear regression analysis program for describing electrophysiological data with multiple functions using Microsoft Excel.

PubMed

Brown, Angus M

2006-04-01

The objective of this present study was to demonstrate a method for fitting complex electrophysiological data with multiple functions using the SOLVER add-in of the ubiquitous spreadsheet Microsoft Excel. SOLVER minimizes the difference between the sum of the squares of the data to be fit and the function(s) describing the data using an iterative generalized reduced gradient method. While it is a straightforward procedure to fit data with linear functions, and we have previously demonstrated a method of non-linear regression analysis of experimental data based upon a single function, it is more complex to fit data with multiple functions, usually requiring specialized expensive computer software. In this paper we describe an easily understood program for fitting experimentally acquired data, in this case the stimulus-evoked compound action potential from the mouse optic nerve, with multiple Gaussian functions. The program is flexible and can be applied to describe data with a wide variety of user-input functions.

A study on industrial accident rate forecasting and program development of estimated zero accident time in Korea.

PubMed

Kim, Tae-gu; Kang, Young-sig; Lee, Hyung-won

2011-01-01

To begin a zero accident campaign for industry, the first thing is to estimate the industrial accident rate and the zero accident time systematically. This paper considers the social and technical change of the business environment after beginning the zero accident campaign through quantitative time series analysis methods. These methods include sum of squared errors (SSE), regression analysis method (RAM), exponential smoothing method (ESM), double exponential smoothing method (DESM), auto-regressive integrated moving average (ARIMA) model, and the proposed analytic function method (AFM). The program is developed to estimate the accident rate, zero accident time and achievement probability of an efficient industrial environment. In this paper, MFC (Microsoft Foundation Class) software of Visual Studio 2008 was used to develop a zero accident program. The results of this paper will provide major information for industrial accident prevention and be an important part of stimulating the zero accident campaign within all industrial environments.

Analysis of a database to predict the result of allergy testing in vivo in patients with chronic nasal symptoms.

PubMed

Lacagnina, Valerio; Leto-Barone, Maria S; La Piana, Simona; Seidita, Aurelio; Pingitore, Giuseppe; Di Lorenzo, Gabriele

2014-01-01

This article uses the logistic regression model for diagnostic decision making in patients with chronic nasal symptoms. We studied the ability of the logistic regression model, obtained by the evaluation of a database, to detect patients with positive allergy skin-prick test (SPT) and patients with negative SPT. The model developed was validated using the data set obtained from another medical institution. The analysis was performed using a database obtained from a questionnaire administered to the patients with nasal symptoms containing personal data, clinical data, and results of allergy testing (SPT). All variables found to be significantly different between patients with positive and negative SPT (p < 0.05) were selected for the logistic regression models and were analyzed with backward stepwise logistic regression, evaluated with area under the curve of the receiver operating characteristic curve. A second set of patients from another institution was used to prove the model. The accuracy of the model in identifying, over the second set, both patients whose SPT will be positive and negative was high. The model detected 96% of patients with nasal symptoms and positive SPT and classified 94% of those with negative SPT. This study is preliminary to the creation of a software that could help the primary care doctors in a diagnostic decision making process (need of allergy testing) in patients complaining of chronic nasal symptoms.

Comparison of Survival Models for Analyzing Prognostic Factors in Gastric Cancer Patients

PubMed

Habibi, Danial; Rafiei, Mohammad; Chehrei, Ali; Shayan, Zahra; Tafaqodi, Soheil

2018-03-27

Objective: There are a number of models for determining risk factors for survival of patients with gastric cancer. This study was conducted to select the model showing the best fit with available data. Methods: Cox regression and parametric models (Exponential, Weibull, Gompertz, Log normal, Log logistic and Generalized Gamma) were utilized in unadjusted and adjusted forms to detect factors influencing mortality of patients. Comparisons were made with Akaike Information Criterion (AIC) by using STATA 13 and R 3.1.3 softwares. Results: The results of this study indicated that all parametric models outperform the Cox regression model. The Log normal, Log logistic and Generalized Gamma provided the best performance in terms of AIC values (179.2, 179.4 and 181.1, respectively). On unadjusted analysis, the results of the Cox regression and parametric models indicated stage, grade, largest diameter of metastatic nest, largest diameter of LM, number of involved lymph nodes and the largest ratio of metastatic nests to lymph nodes, to be variables influencing the survival of patients with gastric cancer. On adjusted analysis, according to the best model (log normal), grade was found as the significant variable. Conclusion: The results suggested that all parametric models outperform the Cox model. The log normal model provides the best fit and is a good substitute for Cox regression. Creative Commons Attribution License

Kendall-Theil Robust Line (KTRLine--version 1.0)-A Visual Basic Program for Calculating and Graphing Robust Nonparametric Estimates of Linear-Regression Coefficients Between Two Continuous Variables

USGS Publications Warehouse

Granato, Gregory E.

2006-01-01

The Kendall-Theil Robust Line software (KTRLine-version 1.0) is a Visual Basic program that may be used with the Microsoft Windows operating system to calculate parameters for robust, nonparametric estimates of linear-regression coefficients between two continuous variables. The KTRLine software was developed by the U.S. Geological Survey, in cooperation with the Federal Highway Administration, for use in stochastic data modeling with local, regional, and national hydrologic data sets to develop planning-level estimates of potential effects of highway runoff on the quality of receiving waters. The Kendall-Theil robust line was selected because this robust nonparametric method is resistant to the effects of outliers and nonnormality in residuals that commonly characterize hydrologic data sets. The slope of the line is calculated as the median of all possible pairwise slopes between points. The intercept is calculated so that the line will run through the median of input data. A single-line model or a multisegment model may be specified. The program was developed to provide regression equations with an error component for stochastic data generation because nonparametric multisegment regression tools are not available with the software that is commonly used to develop regression models. The Kendall-Theil robust line is a median line and, therefore, may underestimate total mass, volume, or loads unless the error component or a bias correction factor is incorporated into the estimate. Regression statistics such as the median error, the median absolute deviation, the prediction error sum of squares, the root mean square error, the confidence interval for the slope, and the bias correction factor for median estimates are calculated by use of nonparametric methods. These statistics, however, may be used to formulate estimates of mass, volume, or total loads. The program is used to read a two- or three-column tab-delimited input file with variable names in the first row and data in subsequent rows. The user may choose the columns that contain the independent (X) and dependent (Y) variable. A third column, if present, may contain metadata such as the sample-collection location and date. The program screens the input files and plots the data. The KTRLine software is a graphical tool that facilitates development of regression models by use of graphs of the regression line with data, the regression residuals (with X or Y), and percentile plots of the cumulative frequency of the X variable, Y variable, and the regression residuals. The user may individually transform the independent and dependent variables to reduce heteroscedasticity and to linearize data. The program plots the data and the regression line. The program also prints model specifications and regression statistics to the screen. The user may save and print the regression results. The program can accept data sets that contain up to about 15,000 XY data points, but because the program must sort the array of all pairwise slopes, the program may be perceptibly slow with data sets that contain more than about 1,000 points.

Evaluating Federal Information Technology Program Success Based on Earned Value Management

ERIC Educational Resources Information Center

Moy, Mae N.

2016-01-01

Despite the use of earned value management (EVM) techniques to track development progress, federal information (IT) software programs continue to fail by not meeting identified business requirements. The purpose of this logistic regression study was to examine, using IT software data from federal agencies from 2011 to 2014, whether a relationship…

Poisson Regression Analysis of Illness and Injury Surveillance Data

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

Frome E.L., Watkins J.P., Ellis E.D.

2012-12-12

The Department of Energy (DOE) uses illness and injury surveillance to monitor morbidity and assess the overall health of the work force. Data collected from each participating site include health events and a roster file with demographic information. The source data files are maintained in a relational data base, and are used to obtain stratified tables of health event counts and person time at risk that serve as the starting point for Poisson regression analysis. The explanatory variables that define these tables are age, gender, occupational group, and time. Typical response variables of interest are the number of absences duemore » to illness or injury, i.e., the response variable is a count. Poisson regression methods are used to describe the effect of the explanatory variables on the health event rates using a log-linear main effects model. Results of fitting the main effects model are summarized in a tabular and graphical form and interpretation of model parameters is provided. An analysis of deviance table is used to evaluate the importance of each of the explanatory variables on the event rate of interest and to determine if interaction terms should be considered in the analysis. Although Poisson regression methods are widely used in the analysis of count data, there are situations in which over-dispersion occurs. This could be due to lack-of-fit of the regression model, extra-Poisson variation, or both. A score test statistic and regression diagnostics are used to identify over-dispersion. A quasi-likelihood method of moments procedure is used to evaluate and adjust for extra-Poisson variation when necessary. Two examples are presented using respiratory disease absence rates at two DOE sites to illustrate the methods and interpretation of the results. In the first example the Poisson main effects model is adequate. In the second example the score test indicates considerable over-dispersion and a more detailed analysis attributes the over-dispersion to extra-Poisson variation. The R open source software environment for statistical computing and graphics is used for analysis. Additional details about R and the data that were used in this report are provided in an Appendix. Information on how to obtain R and utility functions that can be used to duplicate results in this report are provided.« less

Approaches to Process Performance Modeling: A Summary from the SEI Series of Workshops on CMMI High Maturity Measurement and Analysis

DTIC Science & Technology

2010-01-01

Soporte de Modelos Sistémicos: Aplicación al Sector de Desarrollo de Software de Argentina,” Tesis de PhD, Universidad Tecnológica Nacional-Facultad...with New Results 31  2.3  Other Simulation Approaches 37  Conceptual Planning , Execution, and Operation of Combat Fire Support Effectiveness: A...Figure 29:  Functional Structure of Multiple Regression Model 80  Figure 30:  TSP Quality Plan One 85  Figure 31:  TSP Quality Plan Two 85  Figure

Robust Bayesian linear regression with application to an analysis of the CODATA values for the Planck constant

NASA Astrophysics Data System (ADS)

Wübbeler, Gerd; Bodnar, Olha; Elster, Clemens

2018-02-01

Weighted least-squares estimation is commonly applied in metrology to fit models to measurements that are accompanied with quoted uncertainties. The weights are chosen in dependence on the quoted uncertainties. However, when data and model are inconsistent in view of the quoted uncertainties, this procedure does not yield adequate results. When it can be assumed that all uncertainties ought to be rescaled by a common factor, weighted least-squares estimation may still be used, provided that a simple correction of the uncertainty obtained for the estimated model is applied. We show that these uncertainties and credible intervals are robust, as they do not rely on the assumption of a Gaussian distribution of the data. Hence, common software for weighted least-squares estimation may still safely be employed in such a case, followed by a simple modification of the uncertainties obtained by that software. We also provide means of checking the assumptions of such an approach. The Bayesian regression procedure is applied to analyze the CODATA values for the Planck constant published over the past decades in terms of three different models: a constant model, a straight line model and a spline model. Our results indicate that the CODATA values may not have yet stabilized.

A comparison of model-based imputation methods for handling missing predictor values in a linear regression model: A simulation study

NASA Astrophysics Data System (ADS)

Hasan, Haliza; Ahmad, Sanizah; Osman, Balkish Mohd; Sapri, Shamsiah; Othman, Nadirah

2017-08-01

In regression analysis, missing covariate data has been a common problem. Many researchers use ad hoc methods to overcome this problem due to the ease of implementation. However, these methods require assumptions about the data that rarely hold in practice. Model-based methods such as Maximum Likelihood (ML) using the expectation maximization (EM) algorithm and Multiple Imputation (MI) are more promising when dealing with difficulties caused by missing data. Then again, inappropriate methods of missing value imputation can lead to serious bias that severely affects the parameter estimates. The main objective of this study is to provide a better understanding regarding missing data concept that can assist the researcher to select the appropriate missing data imputation methods. A simulation study was performed to assess the effects of different missing data techniques on the performance of a regression model. The covariate data were generated using an underlying multivariate normal distribution and the dependent variable was generated as a combination of explanatory variables. Missing values in covariate were simulated using a mechanism called missing at random (MAR). Four levels of missingness (10%, 20%, 30% and 40%) were imposed. ML and MI techniques available within SAS software were investigated. A linear regression analysis was fitted and the model performance measures; MSE, and R-Squared were obtained. Results of the analysis showed that MI is superior in handling missing data with highest R-Squared and lowest MSE when percent of missingness is less than 30%. Both methods are unable to handle larger than 30% level of missingness.

Mental Models of Software Forecasting

NASA Technical Reports Server (NTRS)

Hihn, J.; Griesel, A.; Bruno, K.; Fouser, T.; Tausworthe, R.

1993-01-01

The majority of software engineers resist the use of the currently available cost models. One problem is that the mathematical and statistical models that are currently available do not correspond with the mental models of the software engineers. In an earlier JPL funded study (Hihn and Habib-agahi, 1991) it was found that software engineers prefer to use analogical or analogy-like techniques to derive size and cost estimates, whereas curren CER's hide any analogy in the regression equations. In addition, the currently available models depend upon information which is not available during early planning when the most important forecasts must be made.

Software analysis handbook: Software complexity analysis and software reliability estimation and prediction

NASA Technical Reports Server (NTRS)

Lee, Alice T.; Gunn, Todd; Pham, Tuan; Ricaldi, Ron

1994-01-01

This handbook documents the three software analysis processes the Space Station Software Analysis team uses to assess space station software, including their backgrounds, theories, tools, and analysis procedures. Potential applications of these analysis results are also presented. The first section describes how software complexity analysis provides quantitative information on code, such as code structure and risk areas, throughout the software life cycle. Software complexity analysis allows an analyst to understand the software structure, identify critical software components, assess risk areas within a software system, identify testing deficiencies, and recommend program improvements. Performing this type of analysis during the early design phases of software development can positively affect the process, and may prevent later, much larger, difficulties. The second section describes how software reliability estimation and prediction analysis, or software reliability, provides a quantitative means to measure the probability of failure-free operation of a computer program, and describes the two tools used by JSC to determine failure rates and design tradeoffs between reliability, costs, performance, and schedule.

Age estimation by pulp-to-tooth area ratio using cone-beam computed tomography: A preliminary analysis

PubMed Central

Rai, Arpita; Acharya, Ashith B.; Naikmasur, Venkatesh G.

2016-01-01

Background: Age estimation of living or deceased individuals is an important aspect of forensic sciences. Conventionally, pulp-to-tooth area ratio (PTR) measured from periapical radiographs have been utilized as a nondestructive method of age estimation. Cone-beam computed tomography (CBCT) is a new method to acquire three-dimensional images of the teeth in living individuals. Aims: The present study investigated age estimation based on PTR of the maxillary canines measured in three planes obtained from CBCT image data. Settings and Design: Sixty subjects aged 20–85 years were included in the study. Materials and Methods: For each tooth, mid-sagittal, mid-coronal, and three axial sections—cementoenamel junction (CEJ), one-fourth root level from CEJ, and mid-root—were assessed. PTR was calculated using AutoCAD software after outlining the pulp and tooth. Statistical Analysis Used: All statistical analyses were performed using an SPSS 17.0 software program. Results and Conclusions: Linear regression analysis showed that only PTR in axial plane at CEJ had significant age correlation (r = 0.32; P < 0.05). This is probably because of clearer demarcation of pulp and tooth outline at this level. PMID:28123269

Demand-supply dynamics in tourism systems: A spatio-temporal GIS analysis. The Alberta ski industry case study

NASA Astrophysics Data System (ADS)

Bertazzon, Stefania

The present research focuses on the interaction of supply and demand of down-hill ski tourism in the province of Alberta. The main hypothesis is that the demand for skiing depends on the socio-economic and demographic characteristics of the population living in the province and outside it. A second, consequent hypothesis is that the development of ski resorts (supply) is a response to the demand for skiing. From the latter derives the hypothesis of a dynamic interaction between supply (ski resorts) and demand (skiers). Such interaction occurs in space, within a range determined by physical distance and the means available to overcome it. The above hypotheses implicitly define interactions that take place in space and evolve over time. The hypotheses are tested by temporal, spatial, and spatio-temporal regression models, using the best available data and the latest commercially available software. The main purpose of this research is to explore analytical techniques to model spatial, temporal, and spatio-temporal dynamics in the context of regional science. The completion of the present research has produced more significant contributions than was originally expected. Many of the unexpected contributions resulted from theoretical and applied needs arising from the application of spatial regression models. Spatial regression models are a new and largely under-applied technique. The models are fairly complex and a considerable amount of preparatory work is needed, prior to their specification and estimation. Most of this work is specific to the field of application. The originality of the solutions devised is increased by the lack of applications in the field of tourism. The scarcity of applications in other fields adds to their value for other applications. The estimation of spatio-temporal models has been only partially attained in the present research. This apparent limitation is due to the novelty and complexity of the analytical methods applied. This opens new directions for further work in the field of spatial analysis, in conjunction with the development of specific software.

Geostatistics and GIS: tools for characterizing environmental contamination.

PubMed

Henshaw, Shannon L; Curriero, Frank C; Shields, Timothy M; Glass, Gregory E; Strickland, Paul T; Breysse, Patrick N

2004-08-01

Geostatistics is a set of statistical techniques used in the analysis of georeferenced data that can be applied to environmental contamination and remediation studies. In this study, the 1,1-dichloro-2,2-bis(p-chlorophenyl)ethylene (DDE) contamination at a Superfund site in western Maryland is evaluated. Concern about the site and its future clean up has triggered interest within the community because residential development surrounds the area. Spatial statistical methods, of which geostatistics is a subset, are becoming increasingly popular, in part due to the availability of geographic information system (GIS) software in a variety of application packages. In this article, the joint use of ArcGIS software and the R statistical computing environment are demonstrated as an approach for comprehensive geostatistical analyses. The spatial regression method, kriging, is used to provide predictions of DDE levels at unsampled locations both within the site and the surrounding areas where residential development is ongoing.

Meta-analysis of haplotype-association studies: comparison of methods and empirical evaluation of the literature

PubMed Central

2011-01-01

Background Meta-analysis is a popular methodology in several fields of medical research, including genetic association studies. However, the methods used for meta-analysis of association studies that report haplotypes have not been studied in detail. In this work, methods for performing meta-analysis of haplotype association studies are summarized, compared and presented in a unified framework along with an empirical evaluation of the literature. Results We present multivariate methods that use summary-based data as well as methods that use binary and count data in a generalized linear mixed model framework (logistic regression, multinomial regression and Poisson regression). The methods presented here avoid the inflation of the type I error rate that could be the result of the traditional approach of comparing a haplotype against the remaining ones, whereas, they can be fitted using standard software. Moreover, formal global tests are presented for assessing the statistical significance of the overall association. Although the methods presented here assume that the haplotypes are directly observed, they can be easily extended to allow for such an uncertainty by weighting the haplotypes by their probability. Conclusions An empirical evaluation of the published literature and a comparison against the meta-analyses that use single nucleotide polymorphisms, suggests that the studies reporting meta-analysis of haplotypes contain approximately half of the included studies and produce significant results twice more often. We show that this excess of statistically significant results, stems from the sub-optimal method of analysis used and, in approximately half of the cases, the statistical significance is refuted if the data are properly re-analyzed. Illustrative examples of code are given in Stata and it is anticipated that the methods developed in this work will be widely applied in the meta-analysis of haplotype association studies. PMID:21247440

Age estimation by pulp-to-tooth area ratio using cone-beam computed tomography: A preliminary analysis.

PubMed

Rai, Arpita; Acharya, Ashith B; Naikmasur, Venkatesh G

2016-01-01

Age estimation of living or deceased individuals is an important aspect of forensic sciences. Conventionally, pulp-to-tooth area ratio (PTR) measured from periapical radiographs have been utilized as a nondestructive method of age estimation. Cone-beam computed tomography (CBCT) is a new method to acquire three-dimensional images of the teeth in living individuals. The present study investigated age estimation based on PTR of the maxillary canines measured in three planes obtained from CBCT image data. Sixty subjects aged 20-85 years were included in the study. For each tooth, mid-sagittal, mid-coronal, and three axial sections-cementoenamel junction (CEJ), one-fourth root level from CEJ, and mid-root-were assessed. PTR was calculated using AutoCAD software after outlining the pulp and tooth. All statistical analyses were performed using an SPSS 17.0 software program. Linear regression analysis showed that only PTR in axial plane at CEJ had significant age correlation ( r = 0.32; P < 0.05). This is probably because of clearer demarcation of pulp and tooth outline at this level.

Software for Storage and Management of Microclimatic Data for Preventive Conservation of Cultural Heritage

PubMed Central

Fernández-Navajas, Ángel; Merello, Paloma; Beltrán, Pedro; García-Diego, Fernando-Juan

2013-01-01

Cultural Heritage preventive conservation requires the monitoring of the parameters involved in the process of deterioration of artworks. Thus, both long-term monitoring of the environmental parameters as well as further analysis of the recorded data are necessary. The long-term monitoring at frequencies higher than 1 data point/day generates large volumes of data that are difficult to store, manage and analyze. This paper presents software which uses a free open source database engine that allows managing and interacting with huge amounts of data from environmental monitoring of cultural heritage sites. It is of simple operation and offers multiple capabilities, such as detection of anomalous data, inquiries, graph plotting and mean trajectories. It is also possible to export the data to a spreadsheet for analyses with more advanced statistical methods (principal component analysis, ANOVA, linear regression, etc.). This paper also deals with a practical application developed for the Renaissance frescoes of the Cathedral of Valencia. The results suggest infiltration of rainwater in the vault and weekly relative humidity changes related with the religious service schedules. PMID:23447005

Quantifying female bodily attractiveness by a statistical analysis of body measurements.

PubMed

Gründl, Martin; Eisenmann-Klein, Marita; Prantl, Lukas

2009-03-01

To investigate what makes a female figure attractive, an extensive experiment was conducted using high-quality photographic stimulus material and several systematically varied figure parameters. The objective was to predict female bodily attractiveness by using figure measurements. For generating stimulus material, a frontal-view photograph of a woman with normal body proportions was taken. Using morphing software, 243 variations of this photograph were produced by systematically manipulating the following features: weight, hip width, waist width, bust size, and leg length. More than 34,000 people participated in the web-based experiment and judged the attractiveness of the figures. All of the altered figures were measured (e.g., bust width, underbust width, waist width, hip width, and so on). Based on these measurements, ratios were calculated (e.g., waist-to-hip ratio). A multiple regression analysis was designed to predict the attractiveness rank of a figure by using figure measurements. The results show that the attractiveness of a woman's figure may be predicted by using her body measurements. The regression analysis explains a variance of 80 percent. Important predictors are bust-to-underbust ratio, bust-to-waist ratio, waist-to-hip ratio, and an androgyny index (an indicator of a typical female body). The study shows that the attractiveness of a female figure is the result of complex interactions of numerous factors. It affirms the importance of viewing the appearance of a bodily feature in the context of other bodily features when performing preoperative analysis. Based on the standardized beta-weights of the regression model, the relative importance of figure parameters in context of preoperative analysis is discussed.

Evaluating the effect of a third-party implementation of resolution recovery on the quality of SPECT bone scan imaging using visual grading regression.

PubMed

Hay, Peter D; Smith, Julie; O'Connor, Richard A

2016-02-01

The aim of this study was to evaluate the benefits to SPECT bone scan image quality when applying resolution recovery (RR) during image reconstruction using software provided by a third-party supplier. Bone SPECT data from 90 clinical studies were reconstructed retrospectively using software supplied independent of the gamma camera manufacturer. The current clinical datasets contain 120×10 s projections and are reconstructed using an iterative method with a Butterworth postfilter. Five further reconstructions were created with the following characteristics: 10 s projections with a Butterworth postfilter (to assess intraobserver variation); 10 s projections with a Gaussian postfilter with and without RR; and 5 s projections with a Gaussian postfilter with and without RR. Two expert observers were asked to rate image quality on a five-point scale relative to our current clinical reconstruction. Datasets were anonymized and presented in random order. The benefits of RR on image scores were evaluated using ordinal logistic regression (visual grading regression). The application of RR during reconstruction increased the probability of both observers of scoring image quality as better than the current clinical reconstruction even where the dataset contained half the normal counts. Type of reconstruction and observer were both statistically significant variables in the ordinal logistic regression model. Visual grading regression was found to be a useful method for validating the local introduction of technological developments in nuclear medicine imaging. RR, as implemented by the independent software supplier, improved bone SPECT image quality when applied during image reconstruction. In the majority of clinical cases, acquisition times for bone SPECT intended for the purposes of localization can safely be halved (from 10 s projections to 5 s) when RR is applied.

Fitting program for linear regressions according to Mahon (1996)

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

Trappitsch, Reto G.

2018-01-09

This program takes the users' Input data and fits a linear regression to it using the prescription presented by Mahon (1996). Compared to the commonly used York fit, this method has the correct prescription for measurement error propagation. This software should facilitate the proper fitting of measurements with a simple Interface.

Minimizing bias in biomass allometry: Model selection and log transformation of data

Treesearch

Joseph Mascaro; undefined undefined; Flint Hughes; Amanda Uowolo; Stefan A. Schnitzer

2011-01-01

Nonlinear regression is increasingly used to develop allometric equations for forest biomass estimation (i.e., as opposed to the raditional approach of log-transformation followed by linear regression). Most statistical software packages, however, assume additive errors by default, violating a key assumption of allometric theory and possibly producing spurious models....

Bayesian Asymmetric Regression as a Means to Estimate and Evaluate Oral Reading Fluency Slopes

ERIC Educational Resources Information Center

Solomon, Benjamin G.; Forsberg, Ole J.

2017-01-01

Bayesian techniques have become increasingly present in the social sciences, fueled by advances in computer speed and the development of user-friendly software. In this paper, we forward the use of Bayesian Asymmetric Regression (BAR) to monitor intervention responsiveness when using Curriculum-Based Measurement (CBM) to assess oral reading…

Lumbar subcutaneous edema and degenerative spinal disease in patients with low back pain: a retrospective MRI study.

PubMed

Quattrocchi, C C; Giona, A; Di Martino, A; Gaudino, F; Mallio, C A; Errante, Y; Occhicone, F; Vitali, M A; Zobel, B B; Denaro, V

2015-08-01

This study was designed to determine the association between LSE, spondylolisthesis, facet arthropathy, lumbar canal stenosis, BMI, radiculopathy and bone marrow edema at conventional lumbar spine MR imaging. This is a retrospective radiological study; 441 consecutive patients with low back pain (224 men and 217 women; mean age 57.3 years; mean BMI 26) underwent conventional lumbar MRI using a 1.5-T magnet (Avanto, Siemens). Lumbar MR images were reviewed by consensus for the presence of LSE, spondylolisthesis, facet arthropathy, lumbar canal stenosis, radiculopathy and bone marrow edema. Descriptive statistics and association studies were conducted using STATA software 11.0. Association studies have been performed using linear univariate regression analysis and multivariate regression analysis, considering LSE as response variable. The overall prevalence of LSE was 40%; spondylolisthesis (p = 0.01), facet arthropathy (p < 0.001), BMI (p = 0.008) and lumbar canal stenosis (p < 0.001) were included in the multivariate regression model, whereas bone marrow edema, radiculopathy and age were not. LSE is highly associated with spondylolisthesis, facet arthropathy and BMI, suggesting underestimation of its clinical impact as an integral component in chronic lumbar back pain. Longitudinal simultaneous X-ray/MRI studies should be conducted to test the relationship of LSE with lumbar spinal instability and low back pain.

Application of Scion image software to the simultaneous determination of curcuminoids in turmeric (Curcuma longa).

PubMed

Sotanaphun, Uthai; Phattanawasin, Panadda; Sriphong, Lawan

2009-01-01

Curcumin, desmethoxycurcumin and bisdesmethoxycurcumin are bioactive constituents of turmeric (Curcuma longa). Owing to their different potency, quality control of turmeric based on the content of each curcuminoid is more reliable than that based on total curcuminoids. However, to perform such an assay, high-cost instrument is needed. To develop a simple and low-cost method for the simultaneous quantification of three curcuminoids in turmeric using TLC and the public-domain software Scion Image. The image of a TLC chromatogram of turmeric extract was recorded using a digital scanner. The density of the TLC spot of each curcuminoid was analysed by the Scion Image software. The density value was transformed to concentration by comparison with the calibration curve of standard curcuminoids developed on the same TLC plate. The polynomial regression data for all curcuminoids showed good linear relationship with R(2) > 0.99 in the concentration range of 0.375-6 microg/spot. The limits of detection and quantitation were 43-73 and 143-242 ng/spot, respectively. The method gave adequate precision, accuracy and recovery. The contents of each curcuminoid determined using this method were not significantly different from those determined using the TLC densitometric method. TLC image analysis using Scion Image is shown to be a reliable method for the simultaneous analysis of the content of each curcuminoid in turmeric.

Logistic regression for circular data

NASA Astrophysics Data System (ADS)

Al-Daffaie, Kadhem; Khan, Shahjahan

2017-05-01

This paper considers the relationship between a binary response and a circular predictor. It develops the logistic regression model by employing the linear-circular regression approach. The maximum likelihood method is used to estimate the parameters. The Newton-Raphson numerical method is used to find the estimated values of the parameters. A data set from weather records of Toowoomba city is analysed by the proposed methods. Moreover, a simulation study is considered. The R software is used for all computations and simulations.

Prognostic value of inflammation-based scores in patients with osteosarcoma

PubMed Central

Liu, Bangjian; Huang, Yujing; Sun, Yuanjue; Zhang, Jianjun; Yao, Yang; Shen, Zan; Xiang, Dongxi; He, Aina

2016-01-01

Systemic inflammation responses have been associated with cancer development and progression. C-reactive protein (CRP), Glasgow prognostic score (GPS), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), and neutrophil-platelet score (NPS) have been shown to be independent risk factors in various types of malignant tumors. This retrospective analysis of 162 osteosarcoma cases was performed to estimate their predictive value of survival in osteosarcoma. All statistical analyses were performed by SPSS statistical software. Receiver operating characteristic (ROC) analysis was generated to set optimal thresholds; area under the curve (AUC) was used to show the discriminatory abilities of inflammation-based scores; Kaplan-Meier analysis was performed to plot the survival curve; cox regression models were employed to determine the independent prognostic factors. The optimal cut-off points of NLR, PLR, and LMR were 2.57, 123.5 and 4.73, respectively. GPS and NLR had a markedly larger AUC than CRP, PLR and LMR. High levels of CRP, GPS, NLR, PLR, and low level of LMR were significantly associated with adverse prognosis (P < 0.05). Multivariate Cox regression analyses revealed that GPS, NLR, and occurrence of metastasis were top risk factors associated with death of osteosarcoma patients. PMID:28008988

What Is the Proportion of Studies Reporting Patient and Practitioner Satisfaction with Software Support Tools Used in the Management of Knee Pain and Is This Related to Sample Size, Effect Size, and Journal Impact Factor?

PubMed

Bright, Philip; Hambly, Karen

2017-12-21

E-health software tools have been deployed in managing knee conditions. Reporting of patient and practitioner satisfaction in studies regarding e-health usage is not widely explored. The objective of this review was to identify studies describing patient and practitioner satisfaction with software use concerning knee pain. A computerized search was undertaken: four electronic databases were searched from January 2007 until January 2017. Key words were decision dashboard, clinical decision, Web-based resource, evidence support, and knee. Full texts were scanned for effect of size reporting and satisfaction scales from participants and practitioners. Binary regression was run; impact factor and sample size were predictors with indicators for satisfaction and effect size reporting as dependent variables. Seventy-seven articles were retrieved; 37 studies were included in final analysis. Ten studies reported patient satisfaction ratings (27.8%): a single study reported both patient and practitioner satisfaction (2.8%). Randomized control trials were the most common design (35%) and knee osteoarthritis the most prevalent condition (38%). Electronic patient-reported outcome measures and Web-based training were the most common interventions. No significant dependency was found within the regression models (p > 0.05). The proportion of reporting of patient satisfaction was low; practitioner satisfaction was poorly represented. There may be implications for the suitability of administering e-health, a medium for capturing further meta-evidence needs to be established and used as best practice for implicated studies in future. This is the first review of its kind to address patient and practitioner satisfaction with knee e-health.

New machine-learning algorithms for prediction of Parkinson's disease

NASA Astrophysics Data System (ADS)

Mandal, Indrajit; Sairam, N.

2014-03-01

This article presents an enhanced prediction accuracy of diagnosis of Parkinson's disease (PD) to prevent the delay and misdiagnosis of patients using the proposed robust inference system. New machine-learning methods are proposed and performance comparisons are based on specificity, sensitivity, accuracy and other measurable parameters. The robust methods of treating Parkinson's disease (PD) includes sparse multinomial logistic regression, rotation forest ensemble with support vector machines and principal components analysis, artificial neural networks, boosting methods. A new ensemble method comprising of the Bayesian network optimised by Tabu search algorithm as classifier and Haar wavelets as projection filter is used for relevant feature selection and ranking. The highest accuracy obtained by linear logistic regression and sparse multinomial logistic regression is 100% and sensitivity, specificity of 0.983 and 0.996, respectively. All the experiments are conducted over 95% and 99% confidence levels and establish the results with corrected t-tests. This work shows a high degree of advancement in software reliability and quality of the computer-aided diagnosis system and experimentally shows best results with supportive statistical inference.

[Establishment of cervical vertebral skeletal maturation of female children in Shanghai].

PubMed

Sun, Yan; Chen, Rong-jing; Yu, Quan; Fan, Li; Chen, Wei; Shen, Gang

2009-06-01

To establish a method for quantitatively evaluating skeletal maturation of cervical vertebrae of female children in Shanghai. The samples were selected from lateral cephalometric radiographs of 240 Shanghai girls, aged 8 to 15 years. The parameters were measured to indicate the morphological changes of the third (C3) and fourth (C4) vertebrae in width, height and the depth of the inferior curvature. The independent-sample t test and stepwise multiple regression analysis were used to estimate the growth status and the ratios of C3, C4 cervical vertebrae by SPSS 15.0 software package. The physical and morphological contour of C3, C4 cervical vertebrae increased proportionately with the increment of age. The regression formula for indicating cervical vertebral skeletal age of female children in Shanghai was expressed by the equation Y= -5.696+8.010 AH3/AP3+6.654 AH3/H3+6.045AH4/PH4 (r=0.912). The regression formula resulted from morphological measurements quantitatively indicates the skeletal maturation of cervical vertebrae of female children in Shanghai.

Monthly streamflow forecasting with auto-regressive integrated moving average

NASA Astrophysics Data System (ADS)

Nasir, Najah; Samsudin, Ruhaidah; Shabri, Ani

2017-09-01

Forecasting of streamflow is one of the many ways that can contribute to better decision making for water resource management. The auto-regressive integrated moving average (ARIMA) model was selected in this research for monthly streamflow forecasting with enhancement made by pre-processing the data using singular spectrum analysis (SSA). This study also proposed an extension of the SSA technique to include a step where clustering was performed on the eigenvector pairs before reconstruction of the time series. The monthly streamflow data of Sungai Muda at Jeniang, Sungai Muda at Jambatan Syed Omar and Sungai Ketil at Kuala Pegang was gathered from the Department of Irrigation and Drainage Malaysia. A ratio of 9:1 was used to divide the data into training and testing sets. The ARIMA, SSA-ARIMA and Clustered SSA-ARIMA models were all developed in R software. Results from the proposed model are then compared to a conventional auto-regressive integrated moving average model using the root-mean-square error and mean absolute error values. It was found that the proposed model can outperform the conventional model.

NITPICK: peak identification for mass spectrometry data

PubMed Central

Renard, Bernhard Y; Kirchner, Marc; Steen , Hanno; Steen, Judith AJ; Hamprecht , Fred A

2008-01-01

Background The reliable extraction of features from mass spectra is a fundamental step in the automated analysis of proteomic mass spectrometry (MS) experiments. Results This contribution proposes a sparse template regression approach to peak picking called NITPICK. NITPICK is a Non-greedy, Iterative Template-based peak PICKer that deconvolves complex overlapping isotope distributions in multicomponent mass spectra. NITPICK is based on fractional averagine, a novel extension to Senko's well-known averagine model, and on a modified version of sparse, non-negative least angle regression, for which a suitable, statistically motivated early stopping criterion has been derived. The strength of NITPICK is the deconvolution of overlapping mixture mass spectra. Conclusion Extensive comparative evaluation has been carried out and results are provided for simulated and real-world data sets. NITPICK outperforms pepex, to date the only alternate, publicly available, non-greedy feature extraction routine. NITPICK is available as software package for the R programming language and can be downloaded from . PMID:18755032

Linear and nonlinear models for predicting fish bioconcentration factors for pesticides.

PubMed

Yuan, Jintao; Xie, Chun; Zhang, Ting; Sun, Jinfang; Yuan, Xuejie; Yu, Shuling; Zhang, Yingbiao; Cao, Yunyuan; Yu, Xingchen; Yang, Xuan; Yao, Wu

2016-08-01

This work is devoted to the applications of the multiple linear regression (MLR), multilayer perceptron neural network (MLP NN) and projection pursuit regression (PPR) to quantitative structure-property relationship analysis of bioconcentration factors (BCFs) of pesticides tested on Bluegill (Lepomis macrochirus). Molecular descriptors of a total of 107 pesticides were calculated with the DRAGON Software and selected by inverse enhanced replacement method. Based on the selected DRAGON descriptors, a linear model was built by MLR, nonlinear models were developed using MLP NN and PPR. The robustness of the obtained models was assessed by cross-validation and external validation using test set. Outliers were also examined and deleted to improve predictive power. Comparative results revealed that PPR achieved the most accurate predictions. This study offers useful models and information for BCF prediction, risk assessment, and pesticide formulation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Cactus: An Introduction to Regression

ERIC Educational Resources Information Center

Hyde, Hartley

2008-01-01

When the author first used "VisiCalc," the author thought it a very useful tool when he had the formulas. But how could he design a spreadsheet if there was no known formula for the quantities he was trying to predict? A few months later, the author relates he learned to use multiple linear regression software and suddenly it all clicked into…

Continuous integration for concurrent MOOSE framework and application development on GitHub

DOE PAGES

Slaughter, Andrew E.; Peterson, John W.; Gaston, Derek R.; ...

2015-11-20

For the past several years, Idaho National Laboratory’s MOOSE framework team has employed modern software engineering techniques (continuous integration, joint application/framework source code repos- itories, automated regression testing, etc.) in developing closed-source multiphysics simulation software (Gaston et al., Journal of Open Research Software vol. 2, article e10, 2014). In March 2014, the MOOSE framework was released under an open source license on GitHub, significantly expanding and diversifying the pool of current active and potential future contributors on the project. Despite this recent growth, the same philosophy of concurrent framework and application development continues to guide the project’s development roadmap. Severalmore » specific practices, including techniques for managing multiple repositories, conducting automated regression testing, and implementing a cascading build process are discussed in this short paper. Furthermore, special attention is given to describing the manner in which these practices naturally synergize with the GitHub API and GitHub-specific features such as issue tracking, Pull Requests, and project forks.« less

Continuous integration for concurrent MOOSE framework and application development on GitHub

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

Slaughter, Andrew E.; Peterson, John W.; Gaston, Derek R.

For the past several years, Idaho National Laboratory’s MOOSE framework team has employed modern software engineering techniques (continuous integration, joint application/framework source code repos- itories, automated regression testing, etc.) in developing closed-source multiphysics simulation software (Gaston et al., Journal of Open Research Software vol. 2, article e10, 2014). In March 2014, the MOOSE framework was released under an open source license on GitHub, significantly expanding and diversifying the pool of current active and potential future contributors on the project. Despite this recent growth, the same philosophy of concurrent framework and application development continues to guide the project’s development roadmap. Severalmore » specific practices, including techniques for managing multiple repositories, conducting automated regression testing, and implementing a cascading build process are discussed in this short paper. Furthermore, special attention is given to describing the manner in which these practices naturally synergize with the GitHub API and GitHub-specific features such as issue tracking, Pull Requests, and project forks.« less

Distributed and Collaborative Software Analysis

NASA Astrophysics Data System (ADS)

Ghezzi, Giacomo; Gall, Harald C.

Throughout the years software engineers have come up with a myriad of specialized tools and techniques that focus on a certain type of software analysissoftware analysis such as source code analysis, co-change analysis or bug prediction. However, easy and straight forward synergies between these analyses and tools rarely exist because of their stand-alone nature, their platform dependence, their different input and output formats and the variety of data to analyze. As a consequence, distributed and collaborative software analysiscollaborative software analysis scenarios and in particular interoperability are severely limited. We describe a distributed and collaborative software analysis platform that allows for a seamless interoperability of software analysis tools across platform, geographical and organizational boundaries. We realize software analysis tools as services that can be accessed and composed over the Internet. These distributed analysis services shall be widely accessible in our incrementally augmented Software Analysis Broker software analysis broker where organizations and tool providers can register and share their tools. To allow (semi-) automatic use and composition of these tools, they are classified and mapped into a software analysis taxonomy and adhere to specific meta-models and ontologiesontologies for their category of analysis.

Monitoring of bone regeneration process by means of texture analysis

NASA Astrophysics Data System (ADS)

Kokkinou, E.; Boniatis, I.; Costaridou, L.; Saridis, A.; Panagiotopoulos, E.; Panayiotakis, G.

2009-09-01

An image analysis method is proposed for the monitoring of the regeneration of the tibial bone. For this purpose, 130 digitized radiographs of 13 patients, who had undergone tibial lengthening by the Ilizarov method, were studied. For each patient, 10 radiographs, taken at an equal number of postoperative successive time moments, were available. Employing available software, 3 Regions Of Interest (ROIs), corresponding to the: (a) upper, (b) central, and (c) lower aspect of the gap, where bone regeneration was expected to occur, were determined on each radiograph. Employing custom developed algorithms: (i) a number of textural features were generated from each of the ROIs, and (ii) a texture-feature based regression model was designed for the quantitative monitoring of the bone regeneration process. Statistically significant differences (p < 0.05) were derived for the initial and the final textural features values, generated from the first and the last postoperatively obtained radiographs, respectively. A quadratic polynomial regression equation fitted data adequately (r2 = 0.9, p < 0.001). The suggested method may contribute to the monitoring of the tibial bone regeneration process.

Association between the Type of Workplace and Lung Function in Copper Miners

PubMed Central

Gruszczyński, Leszek; Wojakowska, Anna; Ścieszka, Marek; Turczyn, Barbara; Schmidt, Edward

2016-01-01

The aim of the analysis was to retrospectively assess changes in lung function in copper miners depending on the type of workplace. In the groups of 225 operators, 188 welders, and 475 representatives of other jobs, spirometry was performed at the start of employment and subsequently after 10, 20, and 25 years of work. Spirometry Longitudinal Data Analysis software was used to estimate changes in group means for FEV1 and FVC. Multiple linear regression analysis was used to assess an association between workplace and lung function. Lung function assessed on the basis of calculation of longitudinal FEV1 (FVC) decline was similar in all studied groups. However, multiple linear regression model used in cross-sectional analysis revealed an association between workplace and lung function. In the group of welders, FEF75 was lower in comparison to operators and other miners as early as after 10 years of work. Simultaneously, in smoking welders, the FEV1/FVC ratio was lower than in nonsmokers (p < 0,05). The interactions between type of workplace and smoking (p < 0,05) in their effect on FVC, FEV1, PEF, and FEF50 were shown. Among underground working copper miners, the group of smoking welders is especially threatened by impairment of lung ventilatory function. PMID:27274987

[The trial of business data analysis at the Department of Radiology by constructing the auto-regressive integrated moving-average (ARIMA) model].

PubMed

Tani, Yuji; Ogasawara, Katsuhiko

2012-01-01

This study aimed to contribute to the management of a healthcare organization by providing management information using time-series analysis of business data accumulated in the hospital information system, which has not been utilized thus far. In this study, we examined the performance of the prediction method using the auto-regressive integrated moving-average (ARIMA) model, using the business data obtained at the Radiology Department. We made the model using the data used for analysis, which was the number of radiological examinations in the past 9 years, and we predicted the number of radiological examinations in the last 1 year. Then, we compared the actual value with the forecast value. We were able to establish that the performance prediction method was simple and cost-effective by using free software. In addition, we were able to build the simple model by pre-processing the removal of trend components using the data. The difference between predicted values and actual values was 10%; however, it was more important to understand the chronological change rather than the individual time-series values. Furthermore, our method was highly versatile and adaptable compared to the general time-series data. Therefore, different healthcare organizations can use our method for the analysis and forecasting of their business data.

Periodic limb movements of sleep: empirical and theoretical evidence supporting objective at-home monitoring

PubMed Central

Moro, Marilyn; Goparaju, Balaji; Castillo, Jelina; Alameddine, Yvonne; Bianchi, Matt T

2016-01-01

Introduction Periodic limb movements of sleep (PLMS) may increase cardiovascular and cerebrovascular morbidity. However, most people with PLMS are either asymptomatic or have nonspecific symptoms. Therefore, predicting elevated PLMS in the absence of restless legs syndrome remains an important clinical challenge. Methods We undertook a retrospective analysis of demographic data, subjective symptoms, and objective polysomnography (PSG) findings in a clinical cohort with or without obstructive sleep apnea (OSA) from our laboratory (n=443 with OSA, n=209 without OSA). Correlation analysis and regression modeling were performed to determine predictors of periodic limb movement index (PLMI). Markov decision analysis with TreeAge software compared strategies to detect PLMS: in-laboratory PSG, at-home testing, and a clinical prediction tool based on the regression analysis. Results Elevated PLMI values (>15 per hour) were observed in >25% of patients. PLMI values in No-OSA patients correlated with age, sex, self-reported nocturnal leg jerks, restless legs syndrome symptoms, and hypertension. In OSA patients, PLMI correlated only with age and self-reported psychiatric medications. Regression models indicated only a modest predictive value of demographics, symptoms, and clinical history. Decision modeling suggests that at-home testing is favored as the pretest probability of PLMS increases, given plausible assumptions regarding PLMS morbidity, costs, and assumed benefits of pharmacological therapy. Conclusion Although elevated PLMI values were commonly observed, routinely acquired clinical information had only weak predictive utility. As the clinical importance of elevated PLMI continues to evolve, it is likely that objective measures such as PSG or at-home PLMS monitors will prove increasingly important for clinical and research endeavors. PMID:27540316

[Influencing factors on depression among medical staff in Hunan province under ordinal regression analysis].

PubMed

Liu, Zhi-yu; Zhong, Meng; Hai, Yan; Du, Qi-yun; Wang, Ai-hua; Xie, Dong-hua

2012-11-01

To understand the situation of depression and its related influencing factors among medical staff in Hunan province. Data were collected through random sampling with multi-stage stratified cluster. Wilcoxon rank sum test, Kruskal-Wallis H test and Ordinal regression analysis were used for data analysis by SPSS 17.0 software. This survey was including 16,000 medical personnel with 14, 988 valid questionnaires and the effective rate was 93.68%. from the single factor analysis showed that factors as: level of the hospital grading, gender, education background, age, occupation, title, departments, the number of continue education, income, working overtime every week, the frequency of night work, the number of patients treated in the emergency room etc., had statistical significances (P < 0.05). Data from ordinal regression showed that the probabilities related to depression that clinicians and nurses suffering from were 1.58 times more than the pharmacists (OR = 1.58, 95%CI: 1.30 - 1.92). The probability among those whose income was less than 2000 Yuan/month was 2.19 times of the ones whose earned more than 3000 Yuan/month (OR = 2.19, 95%CI: 2.05 - 2.35). The higher the numbers of days with working overtime every week, the frequencies of night work, and the numbers of patients being treated at the emergency room, with more probabilities of the people with depression seen in our study. Depression seemed to be common among doctors and nurses. We suggested that the government need to increase the monthly income and to reduce the workload and intensity, lessen the overworking time, etc.

A primer on marginal effects-part II: health services research applications.

PubMed

Onukwugha, E; Bergtold, J; Jain, R

2015-02-01

Marginal analysis evaluates changes in a regression function associated with a unit change in a relevant variable. The primary statistic of marginal analysis is the marginal effect (ME). The ME facilitates the examination of outcomes for defined patient profiles or individuals while measuring the change in original units (e.g., costs, probabilities). The ME has a long history in economics; however, it is not widely used in health services research despite its flexibility and ability to provide unique insights. This article, the second in a two-part series, discusses practical issues that arise in the estimation and interpretation of the ME for a variety of regression models often used in health services research. Part one provided an overview of prior studies discussing ME followed by derivation of ME formulas for various regression models relevant for health services research studies examining costs and utilization. The current article illustrates the calculation and interpretation of ME in practice and discusses practical issues that arise during the implementation, including: understanding differences between software packages in terms of functionality available for calculating the ME and its confidence interval, interpretation of average marginal effect versus marginal effect at the mean, and the difference between ME and relative effects (e.g., odds ratio). Programming code to calculate ME using SAS, STATA, LIMDEP, and MATLAB are also provided. The illustration, discussion, and application of ME in this two-part series support the conduct of future studies applying the concept of marginal analysis.

Open-source Software for Demand Forecasting of Clinical Laboratory Test Volumes Using Time-series Analysis.

PubMed

Mohammed, Emad A; Naugler, Christopher

2017-01-01

Demand forecasting is the area of predictive analytics devoted to predicting future volumes of services or consumables. Fair understanding and estimation of how demand will vary facilitates the optimal utilization of resources. In a medical laboratory, accurate forecasting of future demand, that is, test volumes, can increase efficiency and facilitate long-term laboratory planning. Importantly, in an era of utilization management initiatives, accurately predicted volumes compared to the realized test volumes can form a precise way to evaluate utilization management initiatives. Laboratory test volumes are often highly amenable to forecasting by time-series models; however, the statistical software needed to do this is generally either expensive or highly technical. In this paper, we describe an open-source web-based software tool for time-series forecasting and explain how to use it as a demand forecasting tool in clinical laboratories to estimate test volumes. This tool has three different models, that is, Holt-Winters multiplicative, Holt-Winters additive, and simple linear regression. Moreover, these models are ranked and the best one is highlighted. This tool will allow anyone with historic test volume data to model future demand.

Open-source Software for Demand Forecasting of Clinical Laboratory Test Volumes Using Time-series Analysis

PubMed Central

Mohammed, Emad A.; Naugler, Christopher

2017-01-01

Background: Demand forecasting is the area of predictive analytics devoted to predicting future volumes of services or consumables. Fair understanding and estimation of how demand will vary facilitates the optimal utilization of resources. In a medical laboratory, accurate forecasting of future demand, that is, test volumes, can increase efficiency and facilitate long-term laboratory planning. Importantly, in an era of utilization management initiatives, accurately predicted volumes compared to the realized test volumes can form a precise way to evaluate utilization management initiatives. Laboratory test volumes are often highly amenable to forecasting by time-series models; however, the statistical software needed to do this is generally either expensive or highly technical. Method: In this paper, we describe an open-source web-based software tool for time-series forecasting and explain how to use it as a demand forecasting tool in clinical laboratories to estimate test volumes. Results: This tool has three different models, that is, Holt-Winters multiplicative, Holt-Winters additive, and simple linear regression. Moreover, these models are ranked and the best one is highlighted. Conclusion: This tool will allow anyone with historic test volume data to model future demand. PMID:28400996

Variable selection with stepwise and best subset approaches

PubMed Central

2016-01-01

While purposeful selection is performed partly by software and partly by hand, the stepwise and best subset approaches are automatically performed by software. Two R functions stepAIC() and bestglm() are well designed for stepwise and best subset regression, respectively. The stepAIC() function begins with a full or null model, and methods for stepwise regression can be specified in the direction argument with character values “forward”, “backward” and “both”. The bestglm() function begins with a data frame containing explanatory variables and response variables. The response variable should be in the last column. Varieties of goodness-of-fit criteria can be specified in the IC argument. The Bayesian information criterion (BIC) usually results in more parsimonious model than the Akaike information criterion. PMID:27162786

Marginalized zero-inflated Poisson models with missing covariates.

PubMed

Benecha, Habtamu K; Preisser, John S; Divaris, Kimon; Herring, Amy H; Das, Kalyan

2018-05-11

Unlike zero-inflated Poisson regression, marginalized zero-inflated Poisson (MZIP) models for counts with excess zeros provide estimates with direct interpretations for the overall effects of covariates on the marginal mean. In the presence of missing covariates, MZIP and many other count data models are ordinarily fitted using complete case analysis methods due to lack of appropriate statistical methods and software. This article presents an estimation method for MZIP models with missing covariates. The method, which is applicable to other missing data problems, is illustrated and compared with complete case analysis by using simulations and dental data on the caries preventive effects of a school-based fluoride mouthrinse program. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Performance characterization of complex fuel port geometries for hybrid rocket fuel grains

NASA Astrophysics Data System (ADS)

Bath, Andrew

This research investigated the 3D printing and burning of fuel grains with complex geometry and the development of software capable of modeling and predicting the regression of a cross-section of these complex fuel grains. The software developed did predict the geometry to a fair degree of accuracy, especially when enhanced corner rounding was turned on. The model does have some drawbacks, notably being relatively slow, and does not perfectly predict the regression. If corner rounding is turned off, however, the model does become much faster; although less accurate, this method does still predict a relatively accurate resulting burn geometry, and is fast enough to be used for performance-tuning or genetic algorithms. In addition to the modeling method, preliminary investigations into the burning behavior of fuel grains with a helical flow path were performed. The helix fuel grains have a regression rate of nearly 3 times that of any other fuel grain geometry, primarily due to the enhancement of the friction coefficient between the flow and flow path.

Nonlinear Simulation of the Tooth Enamel Spectrum for EPR Dosimetry

NASA Astrophysics Data System (ADS)

Kirillov, V. A.; Dubovsky, S. V.

2016-07-01

Software was developed where initial EPR spectra of tooth enamel were deconvoluted based on nonlinear simulation, line shapes and signal amplitudes in the model initial spectrum were calculated, the regression coefficient was evaluated, and individual spectra were summed. Software validation demonstrated that doses calculated using it agreed excellently with the applied radiation doses and the doses reconstructed by the method of additive doses.

Regression methods for spatially correlated data: an example using beetle attacks in a seed orchard

Treesearch

Preisler Haiganoush; Nancy G. Rappaport; David L. Wood

1997-01-01

We present a statistical procedure for studying the simultaneous effects of observed covariates and unmeasured spatial variables on responses of interest. The procedure uses regression type analyses that can be used with existing statistical software packages. An example using the rate of twig beetle attacks on Douglas-fir trees in a seed orchard illustrates the...

Static and moving solid/gas interface modeling in a hybrid rocket engine

NASA Astrophysics Data System (ADS)

Mangeot, Alexandre; William-Louis, Mame; Gillard, Philippe

2018-07-01

A numerical model was developed with CFD-ACE software to study the working condition of an oxygen-nitrogen/polyethylene hybrid rocket combustor. As a first approach, a simplified numerical model is presented. It includes a compressible transient gas phase in which a two-step combustion mechanism is implemented coupled to a radiative model. The solid phase from the fuel grain is a semi-opaque material with its degradation process modeled by an Arrhenius type law. Two versions of the model were tested. The first considers the solid/gas interface with a static grid while the second uses grid deformation during the computation to follow the asymmetrical regression. The numerical results are obtained with two different regression kinetics originating from ThermoGravimetry Analysis and test bench results. In each case, the fuel surface temperature is retrieved within a range of 5% error. However, good results are only found using kinetics from the test bench. The regression rate is found within 0.03 mm s-1 and average combustor pressure and its variation over time have the same intensity than the measurements conducted on the test bench. The simulation that uses grid deformation to follow the regression shows a good stability over a 10 s simulated time simulation.

Development of parallel line analysis criteria for recombinant adenovirus potency assay and definition of a unit of potency.

PubMed

Ogawa, Yasushi; Fawaz, Farah; Reyes, Candice; Lai, Julie; Pungor, Erno

2007-01-01

Parameter settings of a parallel line analysis procedure were defined by applying statistical analysis procedures to the absorbance data from a cell-based potency bioassay for a recombinant adenovirus, Adenovirus 5 Fibroblast Growth Factor-4 (Ad5FGF-4). The parallel line analysis was performed with a commercially available software, PLA 1.2. The software performs Dixon outlier test on replicates of the absorbance data, performs linear regression analysis to define linear region of the absorbance data, and tests parallelism between the linear regions of standard and sample. Width of Fiducial limit, expressed as a percent of the measured potency, was developed as a criterion for rejection of the assay data and to significantly improve the reliability of the assay results. With the linear range-finding criteria of the software set to a minimum of 5 consecutive dilutions and best statistical outcome, and in combination with the Fiducial limit width acceptance criterion of <135%, 13% of the assay results were rejected. With these criteria applied, the assay was found to be linear over the range of 0.25 to 4 relative potency units, defined as the potency of the sample normalized to the potency of Ad5FGF-4 standard containing 6 x 10(6) adenovirus particles/mL. The overall precision of the assay was estimated to be 52%. Without the application of Fiducial limit width criterion, the assay results were not linear over the range, and an overall precision of 76% was calculated from the data. An absolute unit of potency for the assay was defined by using the parallel line analysis procedure as the amount of Ad5FGF-4 that results in an absorbance value that is 121% of the average absorbance readings of the wells containing cells not infected with the adenovirus.

GIS and statistical analysis for landslide susceptibility mapping in the Daunia area, Italy

NASA Astrophysics Data System (ADS)

Mancini, F.; Ceppi, C.; Ritrovato, G.

2010-09-01

This study focuses on landslide susceptibility mapping in the Daunia area (Apulian Apennines, Italy) and achieves this by using a multivariate statistical method and data processing in a Geographical Information System (GIS). The Logistic Regression (hereafter LR) method was chosen to produce a susceptibility map over an area of 130 000 ha where small settlements are historically threatened by landslide phenomena. By means of LR analysis, the tendency to landslide occurrences was, therefore, assessed by relating a landslide inventory (dependent variable) to a series of causal factors (independent variables) which were managed in the GIS, while the statistical analyses were performed by means of the SPSS (Statistical Package for the Social Sciences) software. The LR analysis produced a reliable susceptibility map of the investigated area and the probability level of landslide occurrence was ranked in four classes. The overall performance achieved by the LR analysis was assessed by local comparison between the expected susceptibility and an independent dataset extrapolated from the landslide inventory. Of the samples classified as susceptible to landslide occurrences, 85% correspond to areas where landslide phenomena have actually occurred. In addition, the consideration of the regression coefficients provided by the analysis demonstrated that a major role is played by the "land cover" and "lithology" causal factors in determining the occurrence and distribution of landslide phenomena in the Apulian Apennines.

MRMPROBS: a data assessment and metabolite identification tool for large-scale multiple reaction monitoring based widely targeted metabolomics.

PubMed

Tsugawa, Hiroshi; Arita, Masanori; Kanazawa, Mitsuhiro; Ogiwara, Atsushi; Bamba, Takeshi; Fukusaki, Eiichiro

2013-05-21

We developed a new software program, MRMPROBS, for widely targeted metabolomics by using the large-scale multiple reaction monitoring (MRM) mode. The strategy became increasingly popular for the simultaneous analysis of up to several hundred metabolites at high sensitivity, selectivity, and quantitative capability. However, the traditional method of assessing measured metabolomics data without probabilistic criteria is not only time-consuming but is often subjective and makeshift work. Our program overcomes these problems by detecting and identifying metabolites automatically, by separating isomeric metabolites, and by removing background noise using a probabilistic score defined as the odds ratio from an optimized multivariate logistic regression model. Our software program also provides a user-friendly graphical interface to curate and organize data matrices and to apply principal component analyses and statistical tests. For a demonstration, we conducted a widely targeted metabolome analysis (152 metabolites) of propagating Saccharomyces cerevisiae measured at 15 time points by gas and liquid chromatography coupled to triple quadrupole mass spectrometry. MRMPROBS is a useful and practical tool for the assessment of large-scale MRM data available to any instrument or any experimental condition.

Predictive Temperature Equations for Three Sites at the Grand Canyon

NASA Astrophysics Data System (ADS)

McLaughlin, Katrina Marie Neitzel

Climate data collected at a number of automated weather stations were used to create a series of predictive equations spanning from December 2009 to May 2010 in order to better predict the temperatures along hiking trails within the Grand Canyon. The central focus of this project is how atmospheric variables interact and can be combined to predict the weather in the Grand Canyon at the Indian Gardens, Phantom Ranch, and Bright Angel sites. Through the use of statistical analysis software and data regression, predictive equations were determined. The predictive equations are simple or multivariable best fits that reflect the curvilinear nature of the data. With data analysis software curves resulting from the predictive equations were plotted along with the observed data. Each equation's reduced chi2 was determined to aid the visual examination of the predictive equations' ability to reproduce the observed data. From this information an equation or pair of equations was determined to be the best of the predictive equations. Although a best predictive equation for each month and season was determined for each site, future work may refine equations to result in a more accurate predictive equation.

SVAw - a web-based application tool for automated surrogate variable analysis of gene expression studies

PubMed Central

2013-01-01

Background Surrogate variable analysis (SVA) is a powerful method to identify, estimate, and utilize the components of gene expression heterogeneity due to unknown and/or unmeasured technical, genetic, environmental, or demographic factors. These sources of heterogeneity are common in gene expression studies, and failing to incorporate them into the analysis can obscure results. Using SVA increases the biological accuracy and reproducibility of gene expression studies by identifying these sources of heterogeneity and correctly accounting for them in the analysis. Results Here we have developed a web application called SVAw (Surrogate variable analysis Web app) that provides a user friendly interface for SVA analyses of genome-wide expression studies. The software has been developed based on open source bioconductor SVA package. In our software, we have extended the SVA program functionality in three aspects: (i) the SVAw performs a fully automated and user friendly analysis workflow; (ii) It calculates probe/gene Statistics for both pre and post SVA analysis and provides a table of results for the regression of gene expression on the primary variable of interest before and after correcting for surrogate variables; and (iii) it generates a comprehensive report file, including graphical comparison of the outcome for the user. Conclusions SVAw is a web server freely accessible solution for the surrogate variant analysis of high-throughput datasets and facilitates removing all unwanted and unknown sources of variation. It is freely available for use at http://psychiatry.igm.jhmi.edu/sva. The executable packages for both web and standalone application and the instruction for installation can be downloaded from our web site. PMID:23497726

Analyses of Field Test Data at the Atucha-1 Spent Fuel Pools

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

Sitaraman, S.

A field test was conducted at the Atucha-1 spent nuclear fuel pools to validate a software package for gross defect detection that is used in conjunction with the inspection tool, Spent Fuel Neutron Counter (SFNC). A set of measurements was taken with the SFNC and the software predictions were compared with these data and analyzed. The data spanned a wide range of cooling times and a set of burnup levels leading to count rates from the several hundreds to around twenty per second. The current calibration in the software using linear fitting required the use of multiple calibration factors tomore » cover the entire range of count rates recorded. The solution to this was to use power regression data fitting to normalize the predicted response and derive one calibration factor that can be applied to the entire set of data. The resulting comparisons between the predicted and measured responses were generally good and provided a quantitative method of detecting missing fuel in virtually all situations. Since the current version of the software uses the linear calibration method, it would need to be updated with the new power regression method to make it more user-friendly for real time verification and fieldable for the range of responses that will be encountered.« less

Developing Signal-Pattern-Recognition Programs

NASA Technical Reports Server (NTRS)

Shelton, Robert O.; Hammen, David

2006-01-01

Pattern Interpretation and Recognition Application Toolkit Environment (PIRATE) is a block-oriented software system that aids the development of application programs that analyze signals in real time in order to recognize signal patterns that are indicative of conditions or events of interest. PIRATE was originally intended for use in writing application programs to recognize patterns in space-shuttle telemetry signals received at Johnson Space Center's Mission Control Center: application programs were sought to (1) monitor electric currents on shuttle ac power busses to recognize activations of specific power-consuming devices, (2) monitor various pressures and infer the states of affected systems by applying a Kalman filter to the pressure signals, (3) determine fuel-leak rates from sensor data, (4) detect faults in gyroscopes through analysis of system measurements in the frequency domain, and (5) determine drift rates in inertial measurement units by regressing measurements against time. PIRATE can also be used to develop signal-pattern-recognition software for different purposes -- for example, to monitor and control manufacturing processes.

Bioinactivation: Software for modelling dynamic microbial inactivation.

PubMed

Garre, Alberto; Fernández, Pablo S; Lindqvist, Roland; Egea, Jose A

2017-03-01

This contribution presents the bioinactivation software, which implements functions for the modelling of isothermal and non-isothermal microbial inactivation. This software offers features such as user-friendliness, modelling of dynamic conditions, possibility to choose the fitting algorithm and generation of prediction intervals. The software is offered in two different formats: Bioinactivation core and Bioinactivation SE. Bioinactivation core is a package for the R programming language, which includes features for the generation of predictions and for the fitting of models to inactivation experiments using non-linear regression or a Markov Chain Monte Carlo algorithm (MCMC). The calculations are based on inactivation models common in academia and industry (Bigelow, Peleg, Mafart and Geeraerd). Bioinactivation SE supplies a user-friendly interface to selected functions of Bioinactivation core, namely the model fitting of non-isothermal experiments and the generation of prediction intervals. The capabilities of bioinactivation are presented in this paper through a case study, modelling the non-isothermal inactivation of Bacillus sporothermodurans. This study has provided a full characterization of the response of the bacteria to dynamic temperature conditions, including confidence intervals for the model parameters and a prediction interval of the survivor curve. We conclude that the MCMC algorithm produces a better characterization of the biological uncertainty and variability than non-linear regression. The bioinactivation software can be relevant to the food and pharmaceutical industry, as well as to regulatory agencies, as part of a (quantitative) microbial risk assessment. Copyright © 2017 Elsevier Ltd. All rights reserved.

[Calculating Pearson residual in logistic regressions: a comparison between SPSS and SAS].

PubMed

Xu, Hao; Zhang, Tao; Li, Xiao-song; Liu, Yuan-yuan

2015-01-01

To compare the results of Pearson residual calculations in logistic regression models using SPSS and SAS. We reviewed Pearson residual calculation methods, and used two sets of data to test logistic models constructed by SPSS and STATA. One model contained a small number of covariates compared to the number of observed. The other contained a similar number of covariates as the number of observed. The two software packages produced similar Pearson residual estimates when the models contained a similar number of covariates as the number of observed, but the results differed when the number of observed was much greater than the number of covariates. The two software packages produce different results of Pearson residuals, especially when the models contain a small number of covariates. Further studies are warranted.

Characterizing the scientific potential of satellite sensors. [San Francisco, California

NASA Technical Reports Server (NTRS)

1984-01-01

Eleven thematic mapper (TM) radiometric calibration programs were tested and evaluated in support of the task to characterize the potential of LANDSAT TM digital imagery for scientific investigations in the Earth sciences and terrestrial physics. Three software errors related to integer overflow, divide by zero, and nonexist file group were found and solved. Raw, calibrated, and corrected image groups that were created and stored on the Barker2 disk are enumerated. Black and white pixel print files were created for various subscenes of a San Francisco scene (ID 40392-18152). The development of linear regression software is discussed. The output of the software and its function are described. Future work in TM radiometric calibration, image processing, and software development is outlined.

Orbiter subsystem hardware/software interaction analysis. Volume 8: AFT reaction control system, part 2

NASA Technical Reports Server (NTRS)

Becker, D. D.

1980-01-01

The orbiter subsystems and interfacing program elements which interact with the orbiter computer flight software are analyzed. The failure modes identified in the subsystem/element failure mode and effects analysis are examined. Potential interaction with the software is examined through an evaluation of the software requirements. The analysis is restricted to flight software requirements and excludes utility/checkout software. The results of the hardware/software interaction analysis for the forward reaction control system are presented.

Online Statistical Modeling (Regression Analysis) for Independent Responses

NASA Astrophysics Data System (ADS)

Made Tirta, I.; Anggraeni, Dian; Pandutama, Martinus

2017-06-01

Regression analysis (statistical analmodelling) are among statistical methods which are frequently needed in analyzing quantitative data, especially to model relationship between response and explanatory variables. Nowadays, statistical models have been developed into various directions to model various type and complex relationship of data. Rich varieties of advanced and recent statistical modelling are mostly available on open source software (one of them is R). However, these advanced statistical modelling, are not very friendly to novice R users, since they are based on programming script or command line interface. Our research aims to developed web interface (based on R and shiny), so that most recent and advanced statistical modelling are readily available, accessible and applicable on web. We have previously made interface in the form of e-tutorial for several modern and advanced statistical modelling on R especially for independent responses (including linear models/LM, generalized linier models/GLM, generalized additive model/GAM and generalized additive model for location scale and shape/GAMLSS). In this research we unified them in the form of data analysis, including model using Computer Intensive Statistics (Bootstrap and Markov Chain Monte Carlo/ MCMC). All are readily accessible on our online Virtual Statistics Laboratory. The web (interface) make the statistical modeling becomes easier to apply and easier to compare them in order to find the most appropriate model for the data.

Secure and Efficient Regression Analysis Using a Hybrid Cryptographic Framework: Development and Evaluation

PubMed Central

Jiang, Xiaoqian; Aziz, Md Momin Al; Wang, Shuang; Mohammed, Noman

2018-01-01

Background Machine learning is an effective data-driven tool that is being widely used to extract valuable patterns and insights from data. Specifically, predictive machine learning models are very important in health care for clinical data analysis. The machine learning algorithms that generate predictive models often require pooling data from different sources to discover statistical patterns or correlations among different attributes of the input data. The primary challenge is to fulfill one major objective: preserving the privacy of individuals while discovering knowledge from data. Objective Our objective was to develop a hybrid cryptographic framework for performing regression analysis over distributed data in a secure and efficient way. Methods Existing secure computation schemes are not suitable for processing the large-scale data that are used in cutting-edge machine learning applications. We designed, developed, and evaluated a hybrid cryptographic framework, which can securely perform regression analysis, a fundamental machine learning algorithm using somewhat homomorphic encryption and a newly introduced secure hardware component of Intel Software Guard Extensions (Intel SGX) to ensure both privacy and efficiency at the same time. Results Experimental results demonstrate that our proposed method provides a better trade-off in terms of security and efficiency than solely secure hardware-based methods. Besides, there is no approximation error. Computed model parameters are exactly similar to plaintext results. Conclusions To the best of our knowledge, this kind of secure computation model using a hybrid cryptographic framework, which leverages both somewhat homomorphic encryption and Intel SGX, is not proposed or evaluated to this date. Our proposed framework ensures data security and computational efficiency at the same time. PMID:29506966

[An investigation on job burnout of medical personnel in a top three hospital].

PubMed

Li, Y Y; Li, L P

2016-05-20

To investigate job burnout status of medical Personnel in a top three hospitals, in order to provide basic data for intervention of the hospital management. A total of 549 doctors and nurses were assessed by Maslach Burnout Inventory-Human Service Survey (MBI-HSS). SPSS 19.0 software package was applied to data description and analysis, including univariate analysis and orderly classification Logistic regression analysis. The rate of high job burnout of doctors and nurses are 36.3% and 42.8% respectively. Female subjects got higher scores (29.4±13.5) on emotional exhaustion than male subjects (26.2±12.8) compared with.Doctors got lower scores (28.2±15.9) on emotional exhaustion and higher scores (31.4±9.3) on personal accomplishment than nurses.Compared with subjects with higher professional title, young subjects with primary professional title got lower scores on personal accomplishment.Subjects with 11-20 years working age got the highest scores on depersonalization.Among all the test departments, medical personnel of emergency department got the highest scores (31.9±12.6) on emotional exhaustion,while the lowest scores (28.1±8.0) on personal accomplishment. According to the results of orderly classification Logistic regression analysis, age, job type,professional qualifications and clinical departments type entered the regression model. Physical resources and emotional resources of medical personnel are overdraft so that they got some high degree of job burnout.Much more attention should be paid to professional mental health of nurses,and personnel who at low age,got low professional titles.Positive measures should be provided, including management mechanism,organizational culture, occupational protection and psychological intervention.

Secure and Efficient Regression Analysis Using a Hybrid Cryptographic Framework: Development and Evaluation.

PubMed

Sadat, Md Nazmus; Jiang, Xiaoqian; Aziz, Md Momin Al; Wang, Shuang; Mohammed, Noman

2018-03-05

Machine learning is an effective data-driven tool that is being widely used to extract valuable patterns and insights from data. Specifically, predictive machine learning models are very important in health care for clinical data analysis. The machine learning algorithms that generate predictive models often require pooling data from different sources to discover statistical patterns or correlations among different attributes of the input data. The primary challenge is to fulfill one major objective: preserving the privacy of individuals while discovering knowledge from data. Our objective was to develop a hybrid cryptographic framework for performing regression analysis over distributed data in a secure and efficient way. Existing secure computation schemes are not suitable for processing the large-scale data that are used in cutting-edge machine learning applications. We designed, developed, and evaluated a hybrid cryptographic framework, which can securely perform regression analysis, a fundamental machine learning algorithm using somewhat homomorphic encryption and a newly introduced secure hardware component of Intel Software Guard Extensions (Intel SGX) to ensure both privacy and efficiency at the same time. Experimental results demonstrate that our proposed method provides a better trade-off in terms of security and efficiency than solely secure hardware-based methods. Besides, there is no approximation error. Computed model parameters are exactly similar to plaintext results. To the best of our knowledge, this kind of secure computation model using a hybrid cryptographic framework, which leverages both somewhat homomorphic encryption and Intel SGX, is not proposed or evaluated to this date. Our proposed framework ensures data security and computational efficiency at the same time. ©Md Nazmus Sadat, Xiaoqian Jiang, Md Momin Al Aziz, Shuang Wang, Noman Mohammed. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 05.03.2018.

Analyzing industrial energy use through ordinary least squares regression models

NASA Astrophysics Data System (ADS)

Golden, Allyson Katherine

Extensive research has been performed using regression analysis and calibrated simulations to create baseline energy consumption models for residential buildings and commercial institutions. However, few attempts have been made to discuss the applicability of these methodologies to establish baseline energy consumption models for industrial manufacturing facilities. In the few studies of industrial facilities, the presented linear change-point and degree-day regression analyses illustrate ideal cases. It follows that there is a need in the established literature to discuss the methodologies and to determine their applicability for establishing baseline energy consumption models of industrial manufacturing facilities. The thesis determines the effectiveness of simple inverse linear statistical regression models when establishing baseline energy consumption models for industrial manufacturing facilities. Ordinary least squares change-point and degree-day regression methods are used to create baseline energy consumption models for nine different case studies of industrial manufacturing facilities located in the southeastern United States. The influence of ambient dry-bulb temperature and production on total facility energy consumption is observed. The energy consumption behavior of industrial manufacturing facilities is only sometimes sufficiently explained by temperature, production, or a combination of the two variables. This thesis also provides methods for generating baseline energy models that are straightforward and accessible to anyone in the industrial manufacturing community. The methods outlined in this thesis may be easily replicated by anyone that possesses basic spreadsheet software and general knowledge of the relationship between energy consumption and weather, production, or other influential variables. With the help of simple inverse linear regression models, industrial manufacturing facilities may better understand their energy consumption and production behavior, and identify opportunities for energy and cost savings. This thesis study also utilizes change-point and degree-day baseline energy models to disaggregate facility annual energy consumption into separate industrial end-user categories. The baseline energy model provides a suitable and economical alternative to sub-metering individual manufacturing equipment. One case study describes the conjoined use of baseline energy models and facility information gathered during a one-day onsite visit to perform an end-point energy analysis of an injection molding facility conducted by the Alabama Industrial Assessment Center. Applying baseline regression model results to the end-point energy analysis allowed the AIAC to better approximate the annual energy consumption of the facility's HVAC system.

Working covariance model selection for generalized estimating equations.

PubMed

Carey, Vincent J; Wang, You-Gan

2011-11-20

We investigate methods for data-based selection of working covariance models in the analysis of correlated data with generalized estimating equations. We study two selection criteria: Gaussian pseudolikelihood and a geodesic distance based on discrepancy between model-sensitive and model-robust regression parameter covariance estimators. The Gaussian pseudolikelihood is found in simulation to be reasonably sensitive for several response distributions and noncanonical mean-variance relations for longitudinal data. Application is also made to a clinical dataset. Assessment of adequacy of both correlation and variance models for longitudinal data should be routine in applications, and we describe open-source software supporting this practice. Copyright © 2011 John Wiley & Sons, Ltd.

Statistical analysis of water-quality data containing multiple detection limits II: S-language software for nonparametric distribution modeling and hypothesis testing

USGS Publications Warehouse

Lee, L.; Helsel, D.

2007-01-01

Analysis of low concentrations of trace contaminants in environmental media often results in left-censored data that are below some limit of analytical precision. Interpretation of values becomes complicated when there are multiple detection limits in the data-perhaps as a result of changing analytical precision over time. Parametric and semi-parametric methods, such as maximum likelihood estimation and robust regression on order statistics, can be employed to model distributions of multiply censored data and provide estimates of summary statistics. However, these methods are based on assumptions about the underlying distribution of data. Nonparametric methods provide an alternative that does not require such assumptions. A standard nonparametric method for estimating summary statistics of multiply-censored data is the Kaplan-Meier (K-M) method. This method has seen widespread usage in the medical sciences within a general framework termed "survival analysis" where it is employed with right-censored time-to-failure data. However, K-M methods are equally valid for the left-censored data common in the geosciences. Our S-language software provides an analytical framework based on K-M methods that is tailored to the needs of the earth and environmental sciences community. This includes routines for the generation of empirical cumulative distribution functions, prediction or exceedance probabilities, and related confidence limits computation. Additionally, our software contains K-M-based routines for nonparametric hypothesis testing among an unlimited number of grouping variables. A primary characteristic of K-M methods is that they do not perform extrapolation and interpolation. Thus, these routines cannot be used to model statistics beyond the observed data range or when linear interpolation is desired. For such applications, the aforementioned parametric and semi-parametric methods must be used.

[Optimization of ultrasonic-assisted extraction of total flavonoids from leaves of the Artocarpus heterophyllus by response surface methodology].

PubMed

Wang, Hong-wu; Liu, Yan-qing; Wang, Yuan-hong

2011-07-01

To investigate the ultrasonic-assisted extract on of total flavonoids from leaves of the Artocarpus heterophyllus. Investigated the effects of ethanol concentration, extraction time, and liquid-solid ratio on flavonoids yield. A 17-run response surface design involving three factors at three levels was generated by the Design-Expert software and experimental data obtained were subjected to quadratic regression analysis to create a mathematical model describing flavonoids extraction. The optimum ultrasonic assisted extraction conditions were: ethanol volume fraction 69.4% and liquid-solid ratio of 22.6:1 for 32 min. Under these optimized conditions, the yield of flavonoids was 7.55 mg/g. The Box-Behnken design and response surface analysis can well optimize the ultrasonic-assisted extraction of total flavonoids from Artocarpus heterophyllus.

Use of partial least squares regression to impute SNP genotypes in Italian cattle breeds.

PubMed

Dimauro, Corrado; Cellesi, Massimo; Gaspa, Giustino; Ajmone-Marsan, Paolo; Steri, Roberto; Marras, Gabriele; Macciotta, Nicolò P P

2013-06-05

The objective of the present study was to test the ability of the partial least squares regression technique to impute genotypes from low density single nucleotide polymorphisms (SNP) panels i.e. 3K or 7K to a high density panel with 50K SNP. No pedigree information was used. Data consisted of 2093 Holstein, 749 Brown Swiss and 479 Simmental bulls genotyped with the Illumina 50K Beadchip. First, a single-breed approach was applied by using only data from Holstein animals. Then, to enlarge the training population, data from the three breeds were combined and a multi-breed analysis was performed. Accuracies of genotypes imputed using the partial least squares regression method were compared with those obtained by using the Beagle software. The impact of genotype imputation on breeding value prediction was evaluated for milk yield, fat content and protein content. In the single-breed approach, the accuracy of imputation using partial least squares regression was around 90 and 94% for the 3K and 7K platforms, respectively; corresponding accuracies obtained with Beagle were around 85% and 90%. Moreover, computing time required by the partial least squares regression method was on average around 10 times lower than computing time required by Beagle. Using the partial least squares regression method in the multi-breed resulted in lower imputation accuracies than using single-breed data. The impact of the SNP-genotype imputation on the accuracy of direct genomic breeding values was small. The correlation between estimates of genetic merit obtained by using imputed versus actual genotypes was around 0.96 for the 7K chip. Results of the present work suggested that the partial least squares regression imputation method could be useful to impute SNP genotypes when pedigree information is not available.

Cost Estimation Techniques for C3I System Software.

DTIC Science & Technology

1984-07-01

opment manmonth have been determined for maxi, midi , and mini .1 type computers. Small to median size timeshared developments used 0.2 to 1.5 hours...development schedule 1.23 1.00 1.10 2.1.3 Detailed Model The final codification of the COCOMO regressions was the development of separate effort...regardless of the software structure level being estimated: D8VC -- the expected development computer (maxi. midi . mini, micro) MODE -- the expected

Quantile Regression for Recurrent Gap Time Data

PubMed Central

Luo, Xianghua; Huang, Chiung-Yu; Wang, Lan

2014-01-01

Summary Evaluating covariate effects on gap times between successive recurrent events is of interest in many medical and public health studies. While most existing methods for recurrent gap time analysis focus on modeling the hazard function of gap times, a direct interpretation of the covariate effects on the gap times is not available through these methods. In this article, we consider quantile regression that can provide direct assessment of covariate effects on the quantiles of the gap time distribution. Following the spirit of the weighted risk-set method by Luo and Huang (2011, Statistics in Medicine 30, 301–311), we extend the martingale-based estimating equation method considered by Peng and Huang (2008, Journal of the American Statistical Association 103, 637–649) for univariate survival data to analyze recurrent gap time data. The proposed estimation procedure can be easily implemented in existing software for univariate censored quantile regression. Uniform consistency and weak convergence of the proposed estimators are established. Monte Carlo studies demonstrate the effectiveness of the proposed method. An application to data from the Danish Psychiatric Central Register is presented to illustrate the methods developed in this article. PMID:23489055

Four applications of a software data collection and analysis methodology

NASA Technical Reports Server (NTRS)

Basili, Victor R.; Selby, Richard W., Jr.

1985-01-01

The evaluation of software technologies suffers because of the lack of quantitative assessment of their effect on software development and modification. A seven-step data collection and analysis methodology couples software technology evaluation with software measurement. Four in-depth applications of the methodology are presented. The four studies represent each of the general categories of analyses on the software product and development process: blocked subject-project studies, replicated project studies, multi-project variation studies, and single project strategies. The four applications are in the areas of, respectively, software testing, cleanroom software development, characteristic software metric sets, and software error analysis.

Leukemia in Iran: Epidemiology and Morphology Trends.

PubMed

Koohi, Fatemeh; Salehiniya, Hamid; Shamlou, Reza; Eslami, Soheyla; Ghojogh, Ziyaeddin Mahery; Kor, Yones; Rafiemanesh, Hosein

2015-01-01

Leukemia accounts for 8% of total cancer cases and involves all age groups with different prevalence and incidence rates in Iran and the entire world and causes a significant death toll and heavy expenses for diagnosis and treatment processes. This study was done to evaluate epidemiology and morphology of blood cancer during 2003-2008. This cross- sectional study was carried out based on re- analysis of the Cancer Registry Center report of the Health Deputy in Iran during a 6-year period (2003 - 2008). Statistical analysis for incidence time trends and morphology change percentage was performed with joinpoint regression analysis using the software Joinpoint Regression Program. During the studied years a total of 18,353 hematopoietic and reticuloendothelial system cancers were recorded. Chi square test showed significant difference between sex and morphological types of blood cancer (P-value<0.001). Joinpoint analysis showed a significant increasing trend for the adjusted standard incidence rate (ASIR) for both sexes (P-value<0.05). Annual percent changes (APC) for women and men were 18.7 and 19.9, respectively. The most common morphological blood cancers were ALL, ALM, MM and CLL which accounted for 60% of total hematopoietic system cancers. Joinpoint analyze showed a significant decreasing trend for ALM in both sexes (P-value<0.05). Hematopoietic system cancers in Iran demonstrate an increasing trend for incidence rate and decreasing trend for ALL, ALM and CLL morphology.

Meta-analysis of association between mobile phone use and glioma risk.

PubMed

Wang, Yabo; Guo, Xiaqing

2016-12-01

The purpose of this study was to evaluate the association between mobile phone use and glioma risk through pooling the published data. By searching Medline, EMBSE, and CNKI databases, we screened the open published case-control or cohort studies about mobile phone use and glioma risk by systematic searching strategy. The pooled odds of mobile use in glioma patients versus healthy controls were calculated by meta-analysis method. The statistical analysis was done by Stata12.0 software (http://www.stata.com). After searching the Medline, EMBSE, and CNKI databases, we ultimately included 11 studies range from 2001 to 2008. For ≥1 year group, the data were pooled by random effects model. The combined data showed that there was no association between mobile phone use and glioma odds ratio (OR) =1.08 (95% confidence interval [CI]: 0.91-1.25,P > 0.05). However, a significant association was found between mobile phone use more than 5 years and glioma risk OR = 1.35 (95% CI: 1.09-1.62, P < 0.05). The publication bias of this study was evaluated by funnel plot and line regression test. The funnel plot and line regression test (t = 0.25,P = 0.81) did not indicate any publication bias. Long-term mobile phone use may increase the risk of developing glioma according to this meta-analysis.

Emergency Department Visit Forecasting and Dynamic Nursing Staff Allocation Using Machine Learning Techniques With Readily Available Open-Source Software.

PubMed

Zlotnik, Alexander; Gallardo-Antolín, Ascensión; Cuchí Alfaro, Miguel; Pérez Pérez, María Carmen; Montero Martínez, Juan Manuel

2015-08-01

Although emergency department visit forecasting can be of use for nurse staff planning, previous research has focused on models that lacked sufficient resolution and realistic error metrics for these predictions to be applied in practice. Using data from a 1100-bed specialized care hospital with 553,000 patients assigned to its healthcare area, forecasts with different prediction horizons, from 2 to 24 weeks ahead, with an 8-hour granularity, using support vector regression, M5P, and stratified average time-series models were generated with an open-source software package. As overstaffing and understaffing errors have different implications, error metrics and potential personnel monetary savings were calculated with a custom validation scheme, which simulated subsequent generation of predictions during a 4-year period. Results were then compared with a generalized estimating equation regression. Support vector regression and M5P models were found to be superior to the stratified average model with a 95% confidence interval. Our findings suggest that medium and severe understaffing situations could be reduced in more than an order of magnitude and average yearly savings of up to €683,500 could be achieved if dynamic nursing staff allocation was performed with support vector regression instead of the static staffing levels currently in use.

Penalized nonparametric scalar-on-function regression via principal coordinates

PubMed Central

Reiss, Philip T.; Miller, David L.; Wu, Pei-Shien; Hua, Wen-Yu

2016-01-01

A number of classical approaches to nonparametric regression have recently been extended to the case of functional predictors. This paper introduces a new method of this type, which extends intermediate-rank penalized smoothing to scalar-on-function regression. In the proposed method, which we call principal coordinate ridge regression, one regresses the response on leading principal coordinates defined by a relevant distance among the functional predictors, while applying a ridge penalty. Our publicly available implementation, based on generalized additive modeling software, allows for fast optimal tuning parameter selection and for extensions to multiple functional predictors, exponential family-valued responses, and mixed-effects models. In an application to signature verification data, principal coordinate ridge regression, with dynamic time warping distance used to define the principal coordinates, is shown to outperform a functional generalized linear model. PMID:29217963

Parameters Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model

NASA Astrophysics Data System (ADS)

Zuhdi, Shaifudin; Retno Sari Saputro, Dewi; Widyaningsih, Purnami

2017-06-01

A regression model is the representation of relationship between independent variable and dependent variable. The dependent variable has categories used in the logistic regression model to calculate odds on. The logistic regression model for dependent variable has levels in the logistics regression model is ordinal. GWOLR model is an ordinal logistic regression model influenced the geographical location of the observation site. Parameters estimation in the model needed to determine the value of a population based on sample. The purpose of this research is to parameters estimation of GWOLR model using R software. Parameter estimation uses the data amount of dengue fever patients in Semarang City. Observation units used are 144 villages in Semarang City. The results of research get GWOLR model locally for each village and to know probability of number dengue fever patient categories.

A New Global Regression Analysis Method for the Prediction of Wind Tunnel Model Weight Corrections

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert Manfred; Bridge, Thomas M.; Amaya, Max A.

2014-01-01

A new global regression analysis method is discussed that predicts wind tunnel model weight corrections for strain-gage balance loads during a wind tunnel test. The method determines corrections by combining "wind-on" model attitude measurements with least squares estimates of the model weight and center of gravity coordinates that are obtained from "wind-off" data points. The method treats the least squares fit of the model weight separate from the fit of the center of gravity coordinates. Therefore, it performs two fits of "wind- off" data points and uses the least squares estimator of the model weight as an input for the fit of the center of gravity coordinates. Explicit equations for the least squares estimators of the weight and center of gravity coordinates are derived that simplify the implementation of the method in the data system software of a wind tunnel. In addition, recommendations for sets of "wind-off" data points are made that take typical model support system constraints into account. Explicit equations of the confidence intervals on the model weight and center of gravity coordinates and two different error analyses of the model weight prediction are also discussed in the appendices of the paper.

Prediction of anthropometric measurements from tooth length--A Dravidian study.

PubMed

Sunitha, J; Ananthalakshmi, R; Sathiya, Jeeva J; Nadeem, Jeddy; Dhanarathnam, Shanmugam

2015-12-01

Anthropometric measurement is essential for identification of both victims and suspects. Often, this data is not readily available in a crime scene situation. The availability of one data set should help in predicting the other. This study was hypothesised on the basis of a correlation and geometry between the tooth length and various body measurements. To correlate face, palm, foot and stature measurements with tooth length. To derive a regression formula to estimate the various measurements from tooth length. The present study was conducted on Dravidian dental students in the age group 18 - 25 with a sample size of 372. All of the dental and physical parameters were measured using standard anthropometric equipments and techniques. The data was analysed using SPSS software and the methods used for statistical analysis were linear regression analysis and Pearson correlation. The parameters (incisor height (IH), face height (FH), palm length (PL), foot length (FL) and stature (S) showed nil to mild correlation (R = 0.2 ≤ 0.4) except for palm length (PL) and foot length (FL). (R>0.6). It is concluded that odontometric data is not a reliable source for estimating the face height (FH), palm length (PL), foot length (FL) and stature (S).

Meta-analysis for genome-wide association studies using case-control design: application and practice

PubMed Central

2016-01-01

This review aimed to arrange the process of a systematic review of genome-wide association studies in order to practice and apply a genome-wide meta-analysis (GWMA). The process has a series of five steps: searching and selection, extraction of related information, evaluation of validity, meta-analysis by type of genetic model, and evaluation of heterogeneity. In contrast to intervention meta-analyses, GWMA has to evaluate the Hardy–Weinberg equilibrium (HWE) in the third step and conduct meta-analyses by five potential genetic models, including dominant, recessive, homozygote contrast, heterozygote contrast, and allelic contrast in the fourth step. The ‘genhwcci’ and ‘metan’ commands of STATA software evaluate the HWE and calculate a summary effect size, respectively. A meta-regression using the ‘metareg’ command of STATA should be conducted to evaluate related factors of heterogeneities. PMID:28092928

Efficacy of a Newly Designed Cephalometric Analysis Software for McNamara Analysis in Comparison with Dolphin Software.

PubMed

Nouri, Mahtab; Hamidiaval, Shadi; Akbarzadeh Baghban, Alireza; Basafa, Mohammad; Fahim, Mohammad

2015-01-01

Cephalometric norms of McNamara analysis have been studied in various populations due to their optimal efficiency. Dolphin cephalometric software greatly enhances the conduction of this analysis for orthodontic measurements. However, Dolphin is very expensive and cannot be afforded by many clinicians in developing countries. A suitable alternative software program in Farsi/English will greatly help Farsi speaking clinicians. The present study aimed to develop an affordable Iranian cephalometric analysis software program and compare it with Dolphin, the standard software available on the market for cephalometric analysis. In this diagnostic, descriptive study, 150 lateral cephalograms of normal occlusion individuals were selected in Mashhad and Qazvin, two major cities of Iran mainly populated with Fars ethnicity, the main Iranian ethnic group. After tracing the cephalograms, the McNamara analysis standards were measured both with Dolphin and the new software. The cephalometric software was designed using Microsoft Visual C++ program in Windows XP. Measurements made with the new software were compared with those of Dolphin software on both series of cephalograms. The validity and reliability were tested using intra-class correlation coefficient. Calculations showed a very high correlation between the results of the Iranian cephalometric analysis software and Dolphin. This confirms the validity and optimal efficacy of the newly designed software (ICC 0.570-1.0). According to our results, the newly designed software has acceptable validity and reliability and can be used for orthodontic diagnosis, treatment planning and assessment of treatment outcome.

The correlation between preoperative volumetry and real graft weight: comparison of two volumetry programs.

PubMed

Mussin, Nadiar; Sumo, Marco; Lee, Kwang-Woong; Choi, YoungRok; Choi, Jin Yong; Ahn, Sung-Woo; Yoon, Kyung Chul; Kim, Hyo-Sin; Hong, Suk Kyun; Yi, Nam-Joon; Suh, Kyung-Suk

2017-04-01

Liver volumetry is a vital component in living donor liver transplantation to determine an adequate graft volume that meets the metabolic demands of the recipient and at the same time ensures donor safety. Most institutions use preoperative contrast-enhanced CT image-based software programs to estimate graft volume. The objective of this study was to evaluate the accuracy of 2 liver volumetry programs (Rapidia vs . Dr. Liver) in preoperative right liver graft estimation compared with real graft weight. Data from 215 consecutive right lobe living donors between October 2013 and August 2015 were retrospectively reviewed. One hundred seven patients were enrolled in Rapidia group and 108 patients were included in the Dr. Liver group. Estimated graft volumes generated by both software programs were compared with real graft weight measured during surgery, and further classified into minimal difference (≤15%) and big difference (>15%). Correlation coefficients and degree of difference were determined. Linear regressions were calculated and results depicted as scatterplots. Minimal difference was observed in 69.4% of cases from Dr. Liver group and big difference was seen in 44.9% of cases from Rapidia group (P = 0.035). Linear regression analysis showed positive correlation in both groups (P < 0.01). However, the correlation coefficient was better for the Dr. Liver group (R 2 = 0.719), than for the Rapidia group (R 2 = 0.688). Dr. Liver can accurately predict right liver graft size better and faster than Rapidia, and can facilitate preoperative planning in living donor liver transplantation.

A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process.

PubMed

Choi, D J; Park, H

2001-11-01

For control and automation of biological treatment processes, lack of reliable on-line sensors to measure water quality parameters is one of the most important problems to overcome. Many parameters cannot be measured directly with on-line sensors. The accuracy of existing hardware sensors is also not sufficient and maintenance problems such as electrode fouling often cause trouble. This paper deals with the development of software sensor techniques that estimate the target water quality parameter from other parameters using the correlation between water quality parameters. We focus our attention on the preprocessing of noisy data and the selection of the best model feasible to the situation. Problems of existing approaches are also discussed. We propose a hybrid neural network as a software sensor inferring wastewater quality parameter. Multivariate regression, artificial neural networks (ANN), and a hybrid technique that combines principal component analysis as a preprocessing stage are applied to data from industrial wastewater processes. The hybrid ANN technique shows an enhancement of prediction capability and reduces the overfitting problem of neural networks. The result shows that the hybrid ANN technique can be used to extract information from noisy data and to describe the nonlinearity of complex wastewater treatment processes.

On Bayesian methods of exploring qualitative interactions for targeted treatment.

PubMed

Chen, Wei; Ghosh, Debashis; Raghunathan, Trivellore E; Norkin, Maxim; Sargent, Daniel J; Bepler, Gerold

2012-12-10

Providing personalized treatments designed to maximize benefits and minimizing harms is of tremendous current medical interest. One problem in this area is the evaluation of the interaction between the treatment and other predictor variables. Treatment effects in subgroups having the same direction but different magnitudes are called quantitative interactions, whereas those having opposite directions in subgroups are called qualitative interactions (QIs). Identifying QIs is challenging because they are rare and usually unknown among many potential biomarkers. Meanwhile, subgroup analysis reduces the power of hypothesis testing and multiple subgroup analyses inflate the type I error rate. We propose a new Bayesian approach to search for QI in a multiple regression setting with adaptive decision rules. We consider various regression models for the outcome. We illustrate this method in two examples of phase III clinical trials. The algorithm is straightforward and easy to implement using existing software packages. We provide a sample code in Appendix A. Copyright © 2012 John Wiley & Sons, Ltd.

NITPICK: peak identification for mass spectrometry data.

PubMed

Renard, Bernhard Y; Kirchner, Marc; Steen, Hanno; Steen, Judith A J; Hamprecht, Fred A

2008-08-28

The reliable extraction of features from mass spectra is a fundamental step in the automated analysis of proteomic mass spectrometry (MS) experiments. This contribution proposes a sparse template regression approach to peak picking called NITPICK. NITPICK is a Non-greedy, Iterative Template-based peak PICKer that deconvolves complex overlapping isotope distributions in multicomponent mass spectra. NITPICK is based on fractional averaging, a novel extension to Senko's well-known averaging model, and on a modified version of sparse, non-negative least angle regression, for which a suitable, statistically motivated early stopping criterion has been derived. The strength of NITPICK is the deconvolution of overlapping mixture mass spectra. Extensive comparative evaluation has been carried out and results are provided for simulated and real-world data sets. NITPICK outperforms pepex, to date the only alternate, publicly available, non-greedy feature extraction routine. NITPICK is available as software package for the R programming language and can be downloaded from (http://hci.iwr.uni-heidelberg.de/mip/proteomics/).

Forensic dental age estimation by measuring root dentin translucency area using a new digital technique.

PubMed

Acharya, Ashith B

2014-05-01

Dentin translucency measurement is an easy yet relatively accurate approach to postmortem age estimation. Translucency area represents a two-dimensional change and may reflect age variations better than length. Manually measuring area is challenging and this paper proposes a new digital method using commercially available computer hardware and software. Area and length were measured on 100 tooth sections (age range, 19-82 years) of 250 μm thickness. Regression analysis revealed lower standard error of estimate and higher correlation with age for length than for area (R = 0.62 vs. 0.60). However, test of regression formulae on a control sample (n = 33, 21-85 years) showed smaller mean absolute difference (8.3 vs. 8.8 years) and greater frequency of smaller errors (73% vs. 67% age estimates ≤ ± 10 years) for area than for length. These suggest that digital area measurements of root translucency may be used as an alternative to length in forensic age estimation. © 2014 American Academy of Forensic Sciences.

Software tool for data mining and its applications

NASA Astrophysics Data System (ADS)

Yang, Jie; Ye, Chenzhou; Chen, Nianyi

2002-03-01

A software tool for data mining is introduced, which integrates pattern recognition (PCA, Fisher, clustering, hyperenvelop, regression), artificial intelligence (knowledge representation, decision trees), statistical learning (rough set, support vector machine), computational intelligence (neural network, genetic algorithm, fuzzy systems). It consists of nine function models: pattern recognition, decision trees, association rule, fuzzy rule, neural network, genetic algorithm, Hyper Envelop, support vector machine, visualization. The principle and knowledge representation of some function models of data mining are described. The software tool of data mining is realized by Visual C++ under Windows 2000. Nonmonotony in data mining is dealt with by concept hierarchy and layered mining. The software tool of data mining has satisfactorily applied in the prediction of regularities of the formation of ternary intermetallic compounds in alloy systems, and diagnosis of brain glioma.

Debugging and Performance Analysis Software Tools for Peregrine System |

Science.gov Websites

High-Performance Computing | NREL Debugging and Performance Analysis Software Tools for Peregrine System Debugging and Performance Analysis Software Tools for Peregrine System Learn about debugging and performance analysis software tools available to use with the Peregrine system. Allinea

Tools to Support Interpreting Multiple Regression in the Face of Multicollinearity

PubMed Central

Kraha, Amanda; Turner, Heather; Nimon, Kim; Zientek, Linda Reichwein; Henson, Robin K.

2012-01-01

While multicollinearity may increase the difficulty of interpreting multiple regression (MR) results, it should not cause undue problems for the knowledgeable researcher. In the current paper, we argue that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression model, but to each other as well. Some of the techniques to interpret MR effects include, but are not limited to, correlation coefficients, beta weights, structure coefficients, all possible subsets regression, commonality coefficients, dominance weights, and relative importance weights. This article will review a set of techniques to interpret MR effects, identify the elements of the data on which the methods focus, and identify statistical software to support such analyses. PMID:22457655

Tools to support interpreting multiple regression in the face of multicollinearity.

PubMed

Kraha, Amanda; Turner, Heather; Nimon, Kim; Zientek, Linda Reichwein; Henson, Robin K

2012-01-01

While multicollinearity may increase the difficulty of interpreting multiple regression (MR) results, it should not cause undue problems for the knowledgeable researcher. In the current paper, we argue that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression model, but to each other as well. Some of the techniques to interpret MR effects include, but are not limited to, correlation coefficients, beta weights, structure coefficients, all possible subsets regression, commonality coefficients, dominance weights, and relative importance weights. This article will review a set of techniques to interpret MR effects, identify the elements of the data on which the methods focus, and identify statistical software to support such analyses.

The multiple imputation method: a case study involving secondary data analysis.

PubMed

Walani, Salimah R; Cleland, Charles M

2015-05-01

To illustrate with the example of a secondary data analysis study the use of the multiple imputation method to replace missing data. Most large public datasets have missing data, which need to be handled by researchers conducting secondary data analysis studies. Multiple imputation is a technique widely used to replace missing values while preserving the sample size and sampling variability of the data. The 2004 National Sample Survey of Registered Nurses. The authors created a model to impute missing values using the chained equation method. They used imputation diagnostics procedures and conducted regression analysis of imputed data to determine the differences between the log hourly wages of internationally educated and US-educated registered nurses. The authors used multiple imputation procedures to replace missing values in a large dataset with 29,059 observations. Five multiple imputed datasets were created. Imputation diagnostics using time series and density plots showed that imputation was successful. The authors also present an example of the use of multiple imputed datasets to conduct regression analysis to answer a substantive research question. Multiple imputation is a powerful technique for imputing missing values in large datasets while preserving the sample size and variance of the data. Even though the chained equation method involves complex statistical computations, recent innovations in software and computation have made it possible for researchers to conduct this technique on large datasets. The authors recommend nurse researchers use multiple imputation methods for handling missing data to improve the statistical power and external validity of their studies.

Ranibizumab alone or in combination with photodynamic therapy vs photodynamic therapy for polypoidal choroidal vasculopathy: a systematic review and Meta-analysis.

PubMed

Tang, Kai; Si, Jun-Kang; Guo, Da-Dong; Cui, Yan; Du, Yu-Xiang; Pan, Xue-Mei; Bi, Hong-Sheng

2015-01-01

To compare the efficacy of intravitreal ranibizumab (IVR) alone or in combination with photodynamic therapy (PDT) vs PDT in patients with symptomatic polypoidal choroidal vasculopathy (PCV). A systematic search of a wide range of databases (including PubMed, EMBASE, Cochrane Library and Web of Science) was searched to identify relevant studies. Both randomized controlled trials (RCTs) and non-RCT studies were included. Methodological quality of included literatures was evaluated according to the Newcastle-Ottawa Scale. RevMan 5.2.7 software was used to do the Meta-analysis. Three RCTs and 6 retrospective studies were included. The results showed that PDT monotherapy had a significantly higher proportion in patients who achieved complete regression of polyps than IVR monotherapy at months 3, 6, and 12 (All P≤0.01), respectively. However, IVR had a tendency to be more effective in improving vision on the basis of RCTs. The proportion of patients who gained complete regression of polyps revealed that there was no significant difference between the combination treatment and PDT monotherapy. The mean change of best-corrected visual acuity (BCVA) from baseline showed that the combination treatment had significant superiority in improving vision vs PDT monotherapy at months 3, 6 and 24 (All P<0.05), respectively. In the mean time, this comparison result was also significant at month 12 (P<0.01) after removal of a heterogeneous study. IVR has non-inferiority compare with PDT either in stabilizing or in improving vision, although it can hardly promote the regression of polyps. The combination treatment of PDT and IVR can exert a synergistic effect on regressing polyps and on maintaining or improving visual acuity. Thus, it can be the first-line therapy for PCV.

Ranibizumab alone or in combination with photodynamic therapy vs photodynamic therapy for polypoidal choroidal vasculopathy: a systematic review and Meta-analysis

PubMed Central

Tang, Kai; Si, Jun-Kang; Guo, Da-Dong; Cui, Yan; Du, Yu-Xiang; Pan, Xue-Mei; Bi, Hong-Sheng

2015-01-01

AIM To compare the efficacy of intravitreal ranibizumab (IVR) alone or in combination with photodynamic therapy (PDT) vs PDT in patients with symptomatic polypoidal choroidal vasculopathy (PCV). METHODS A systematic search of a wide range of databases (including PubMed, EMBASE, Cochrane Library and Web of Science) was searched to identify relevant studies. Both randomized controlled trials (RCTs) and non-RCT studies were included. Methodological quality of included literatures was evaluated according to the Newcastle-Ottawa Scale. RevMan 5.2.7 software was used to do the Meta-analysis. RESULTS Three RCTs and 6 retrospective studies were included. The results showed that PDT monotherapy had a significantly higher proportion in patients who achieved complete regression of polyps than IVR monotherapy at months 3, 6, and 12 (All P≤0.01), respectively. However, IVR had a tendency to be more effective in improving vision on the basis of RCTs. The proportion of patients who gained complete regression of polyps revealed that there was no significant difference between the combination treatment and PDT monotherapy. The mean change of best-corrected visual acuity (BCVA) from baseline showed that the combination treatment had significant superiority in improving vision vs PDT monotherapy at months 3, 6 and 24 (All P<0.05), respectively. In the mean time, this comparison result was also significant at month 12 (P<0.01) after removal of a heterogeneous study. CONCLUSION IVR has non-inferiority compare with PDT either in stabilizing or in improving vision, although it can hardly promote the regression of polyps. The combination treatment of PDT and IVR can exert a synergistic effect on regressing polyps and on maintaining or improving visual acuity. Thus, it can be the first-line therapy for PCV. PMID:26558226

Economic Consequence Analysis of Disasters: The ECAT Software Tool

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

Rose, Adam; Prager, Fynn; Chen, Zhenhua

This study develops a methodology for rapidly obtaining approximate estimates of the economic consequences from numerous natural, man-made and technological threats. This software tool is intended for use by various decision makers and analysts to obtain estimates rapidly. It is programmed in Excel and Visual Basic for Applications (VBA) to facilitate its use. This tool is called E-CAT (Economic Consequence Analysis Tool) and accounts for the cumulative direct and indirect impacts (including resilience and behavioral factors that significantly affect base estimates) on the U.S. economy. E-CAT is intended to be a major step toward advancing the current state of economicmore » consequence analysis (ECA) and also contributing to and developing interest in further research into complex but rapid turnaround approaches. The essence of the methodology involves running numerous simulations in a computable general equilibrium (CGE) model for each threat, yielding synthetic data for the estimation of a single regression equation based on the identification of key explanatory variables (threat characteristics and background conditions). This transforms the results of a complex model, which is beyond the reach of most users, into a "reduced form" model that is readily comprehensible. Functionality has been built into E-CAT so that its users can switch various consequence categories on and off in order to create customized profiles of economic consequences of numerous risk events. E-CAT incorporates uncertainty on both the input and output side in the course of the analysis.« less

Bayesian models for comparative analysis integrating phylogenetic uncertainty.

PubMed

de Villemereuil, Pierre; Wells, Jessie A; Edwards, Robert D; Blomberg, Simon P

2012-06-28

Uncertainty in comparative analyses can come from at least two sources: a) phylogenetic uncertainty in the tree topology or branch lengths, and b) uncertainty due to intraspecific variation in trait values, either due to measurement error or natural individual variation. Most phylogenetic comparative methods do not account for such uncertainties. Not accounting for these sources of uncertainty leads to false perceptions of precision (confidence intervals will be too narrow) and inflated significance in hypothesis testing (e.g. p-values will be too small). Although there is some application-specific software for fitting Bayesian models accounting for phylogenetic error, more general and flexible software is desirable. We developed models to directly incorporate phylogenetic uncertainty into a range of analyses that biologists commonly perform, using a Bayesian framework and Markov Chain Monte Carlo analyses. We demonstrate applications in linear regression, quantification of phylogenetic signal, and measurement error models. Phylogenetic uncertainty was incorporated by applying a prior distribution for the phylogeny, where this distribution consisted of the posterior tree sets from Bayesian phylogenetic tree estimation programs. The models were analysed using simulated data sets, and applied to a real data set on plant traits, from rainforest plant species in Northern Australia. Analyses were performed using the free and open source software OpenBUGS and JAGS. Incorporating phylogenetic uncertainty through an empirical prior distribution of trees leads to more precise estimation of regression model parameters than using a single consensus tree and enables a more realistic estimation of confidence intervals. In addition, models incorporating measurement errors and/or individual variation, in one or both variables, are easily formulated in the Bayesian framework. We show that BUGS is a useful, flexible general purpose tool for phylogenetic comparative analyses, particularly for modelling in the face of phylogenetic uncertainty and accounting for measurement error or individual variation in explanatory variables. Code for all models is provided in the BUGS model description language.

Bayesian models for comparative analysis integrating phylogenetic uncertainty

PubMed Central

2012-01-01

Background Uncertainty in comparative analyses can come from at least two sources: a) phylogenetic uncertainty in the tree topology or branch lengths, and b) uncertainty due to intraspecific variation in trait values, either due to measurement error or natural individual variation. Most phylogenetic comparative methods do not account for such uncertainties. Not accounting for these sources of uncertainty leads to false perceptions of precision (confidence intervals will be too narrow) and inflated significance in hypothesis testing (e.g. p-values will be too small). Although there is some application-specific software for fitting Bayesian models accounting for phylogenetic error, more general and flexible software is desirable. Methods We developed models to directly incorporate phylogenetic uncertainty into a range of analyses that biologists commonly perform, using a Bayesian framework and Markov Chain Monte Carlo analyses. Results We demonstrate applications in linear regression, quantification of phylogenetic signal, and measurement error models. Phylogenetic uncertainty was incorporated by applying a prior distribution for the phylogeny, where this distribution consisted of the posterior tree sets from Bayesian phylogenetic tree estimation programs. The models were analysed using simulated data sets, and applied to a real data set on plant traits, from rainforest plant species in Northern Australia. Analyses were performed using the free and open source software OpenBUGS and JAGS. Conclusions Incorporating phylogenetic uncertainty through an empirical prior distribution of trees leads to more precise estimation of regression model parameters than using a single consensus tree and enables a more realistic estimation of confidence intervals. In addition, models incorporating measurement errors and/or individual variation, in one or both variables, are easily formulated in the Bayesian framework. We show that BUGS is a useful, flexible general purpose tool for phylogenetic comparative analyses, particularly for modelling in the face of phylogenetic uncertainty and accounting for measurement error or individual variation in explanatory variables. Code for all models is provided in the BUGS model description language. PMID:22741602

System dynamic modeling: an alternative method for budgeting.

PubMed

Srijariya, Witsanuchai; Riewpaiboon, Arthorn; Chaikledkaew, Usa

2008-03-01

To construct, validate, and simulate a system dynamic financial model and compare it against the conventional method. The study was a cross-sectional analysis of secondary data retrieved from the National Health Security Office (NHSO) in the fiscal year 2004. The sample consisted of all emergency patients who received emergency services outside their registered hospital-catchments area. The dependent variable used was the amount of reimbursed money. Two types of model were constructed, namely, the system dynamic model using the STELLA software and the multiple linear regression model. The outputs of both methods were compared. The study covered 284,716 patients from various levels of providers. The system dynamic model had the capability of producing various types of outputs, for example, financial and graphical analyses. For the regression analysis, statistically significant predictors were composed of service types (outpatient or inpatient), operating procedures, length of stay, illness types (accident or not), hospital characteristics, age, and hospital location (adjusted R(2) = 0.74). The total budget arrived at from using the system dynamic model and regression model was US$12,159,614.38 and US$7,301,217.18, respectively, whereas the actual NHSO reimbursement cost was US$12,840,805.69. The study illustrated that the system dynamic model is a useful financial management tool, although it is not easy to construct. The model is not only more accurate in prediction but is also more capable of analyzing large and complex real-world situations than the conventional method.

[Visual field progression in glaucoma: cluster analysis].

PubMed

Bresson-Dumont, H; Hatton, J; Foucher, J; Fonteneau, M

2012-11-01

Visual field progression analysis is one of the key points in glaucoma monitoring, but distinction between true progression and random fluctuation is sometimes difficult. There are several different algorithms but no real consensus for detecting visual field progression. The trend analysis of global indices (MD, sLV) may miss localized deficits or be affected by media opacities. Conversely, point-by-point analysis makes progression difficult to differentiate from physiological variability, particularly when the sensitivity of a point is already low. The goal of our study was to analyse visual field progression with the EyeSuite™ Octopus Perimetry Clusters algorithm in patients with no significant changes in global indices or worsening of the analysis of pointwise linear regression. We analyzed the visual fields of 162 eyes (100 patients - 58 women, 42 men, average age 66.8 ± 10.91) with ocular hypertension or glaucoma. For inclusion, at least six reliable visual fields per eye were required, and the trend analysis (EyeSuite™ Perimetry) of visual field global indices (MD and SLV), could show no significant progression. The analysis of changes in cluster mode was then performed. In a second step, eyes with statistically significant worsening of at least one of their clusters were analyzed point-by-point with the Octopus Field Analysis (OFA). Fifty four eyes (33.33%) had a significant worsening in some clusters, while their global indices remained stable over time. In this group of patients, more advanced glaucoma was present than in stable group (MD 6.41 dB vs. 2.87); 64.82% (35/54) of those eyes in which the clusters progressed, however, had no statistically significant change in the trend analysis by pointwise linear regression. Most software algorithms for analyzing visual field progression are essentially trend analyses of global indices, or point-by-point linear regression. This study shows the potential role of analysis by clusters trend. However, for best results, it is preferable to compare the analyses of several tests in combination with morphologic exam. Copyright © 2012 Elsevier Masson SAS. All rights reserved.

Validity and reliability of dental age estimation of teeth root translucency based on digital luminance determination.

PubMed

Ramsthaler, Frank; Kettner, Mattias; Verhoff, Marcel A

2014-01-01

In forensic anthropological casework, estimating age-at-death is key to profiling unknown skeletal remains. The aim of this study was to examine the reliability of a new, simple, fast, and inexpensive digital odontological method for age-at-death estimation. The method is based on the original Lamendin method, which is a widely used technique in the repertoire of odontological aging methods in forensic anthropology. We examined 129 single root teeth employing a digital camera and imaging software for the measurement of the luminance of the teeth's translucent root zone. Variability in luminance detection was evaluated using statistical technical error of measurement analysis. The method revealed stable values largely unrelated to observer experience, whereas requisite formulas proved to be camera-specific and should therefore be generated for an individual recording setting based on samples of known chronological age. Multiple regression analysis showed a highly significant influence of the coefficients of the variables "arithmetic mean" and "standard deviation" of luminance for the regression formula. For the use of this primer multivariate equation for age-at-death estimation in casework, a standard error of the estimate of 6.51 years was calculated. Step-by-step reduction of the number of embedded variables to linear regression analysis employing the best contributor "arithmetic mean" of luminance yielded a regression equation with a standard error of 6.72 years (p < 0.001). The results of this study not only support the premise of root translucency as an age-related phenomenon, but also demonstrate that translucency reflects a number of other influencing factors in addition to age. This new digital measuring technique of the zone of dental root luminance can broaden the array of methods available for estimating chronological age, and furthermore facilitate measurement and age classification due to its low dependence on observer experience.

Comparison of Absolute Apparent Diffusion Coefficient (ADC) Values in ADC Maps Generated Across Different Postprocessing Software: Reproducibility in Endometrial Carcinoma.

PubMed

Ghosh, Adarsh; Singh, Tulika; Singla, Veenu; Bagga, Rashmi; Khandelwal, Niranjan

2017-12-01

Apparent diffusion coefficient (ADC) maps are usually generated by builtin software provided by the MRI scanner vendors; however, various open-source postprocessing software packages are available for image manipulation and parametric map generation. The purpose of this study is to establish the reproducibility of absolute ADC values obtained using different postprocessing software programs. DW images with three b values were obtained with a 1.5-T MRI scanner, and the trace images were obtained. ADC maps were automatically generated by the in-line software provided by the vendor during image generation and were also separately generated on postprocessing software. These ADC maps were compared on the basis of ROIs using paired t test, Bland-Altman plot, mountain plot, and Passing-Bablok regression plot. There was a statistically significant difference in the mean ADC values obtained from the different postprocessing software programs when the same baseline trace DW images were used for the ADC map generation. For using ADC values as a quantitative cutoff for histologic characterization of tissues, standardization of the postprocessing algorithm is essential across processing software packages, especially in view of the implementation of vendor-neutral archiving.

NASA Software Cost Estimation Model: An Analogy Based Estimation Model

NASA Technical Reports Server (NTRS)

Hihn, Jairus; Juster, Leora; Menzies, Tim; Mathew, George; Johnson, James

2015-01-01

The cost estimation of software development activities is increasingly critical for large scale integrated projects such as those at DOD and NASA especially as the software systems become larger and more complex. As an example MSL (Mars Scientific Laboratory) developed at the Jet Propulsion Laboratory launched with over 2 million lines of code making it the largest robotic spacecraft ever flown (Based on the size of the software). Software development activities are also notorious for their cost growth, with NASA flight software averaging over 50% cost growth. All across the agency, estimators and analysts are increasingly being tasked to develop reliable cost estimates in support of program planning and execution. While there has been extensive work on improving parametric methods there is very little focus on the use of models based on analogy and clustering algorithms. In this paper we summarize our findings on effort/cost model estimation and model development based on ten years of software effort estimation research using data mining and machine learning methods to develop estimation models based on analogy and clustering. The NASA Software Cost Model performance is evaluated by comparing it to COCOMO II, linear regression, and K-­ nearest neighbor prediction model performance on the same data set.

Data analysis software for the autoradiographic enhancement process. Volumes 1, 2, and 3, and appendix

NASA Technical Reports Server (NTRS)

Singh, S. P.

1979-01-01

The computer software developed to set up a method for Wiener spectrum analysis of photographic films is presented. This method is used for the quantitative analysis of the autoradiographic enhancement process. The software requirements and design for the autoradiographic enhancement process are given along with the program listings and the users manual. A software description and program listings modification of the data analysis software are included.

Multivariate research in areas of phosphorus cast-iron brake shoes manufacturing using the statistical analysis and the multiple regression equations

NASA Astrophysics Data System (ADS)

Kiss, I.; Cioată, V. G.; Alexa, V.; Raţiu, S. A.

2017-05-01

The braking system is one of the most important and complex subsystems of railway vehicles, especially when it comes for safety. Therefore, installing efficient safe brakes on the modern railway vehicles is essential. Nowadays is devoted attention to solving problems connected with using high performance brake materials and its impact on thermal and mechanical loading of railway wheels. The main factor that influences the selection of a friction material for railway applications is the performance criterion, due to the interaction between the brake block and the wheel produce complex thermos-mechanical phenomena. In this work, the investigated subjects are the cast-iron brake shoes, which are still widely used on freight wagons. Therefore, the cast-iron brake shoes - with lamellar graphite and with a high content of phosphorus (0.8-1.1%) - need a special investigation. In order to establish the optimal condition for the cast-iron brake shoes we proposed a mathematical modelling study by using the statistical analysis and multiple regression equations. Multivariate research is important in areas of cast-iron brake shoes manufacturing, because many variables interact with each other simultaneously. Multivariate visualization comes to the fore when researchers have difficulties in comprehending many dimensions at one time. Technological data (hardness and chemical composition) obtained from cast-iron brake shoes were used for this purpose. In order to settle the multiple correlation between the hardness of the cast-iron brake shoes, and the chemical compositions elements several model of regression equation types has been proposed. Because a three-dimensional surface with variables on three axes is a common way to illustrate multivariate data, in which the maximum and minimum values are easily highlighted, we plotted graphical representation of the regression equations in order to explain interaction of the variables and locate the optimal level of each variable for maximal response. For the calculation of the regression coefficients, dispersion and correlation coefficients, the software Matlab was used.

Experiments with Test Case Generation and Runtime Analysis

NASA Technical Reports Server (NTRS)

Artho, Cyrille; Drusinsky, Doron; Goldberg, Allen; Havelund, Klaus; Lowry, Mike; Pasareanu, Corina; Rosu, Grigore; Visser, Willem; Koga, Dennis (Technical Monitor)

2003-01-01

Software testing is typically an ad hoc process where human testers manually write many test inputs and expected test results, perhaps automating their execution in a regression suite. This process is cumbersome and costly. This paper reports preliminary results on an approach to further automate this process. The approach consists of combining automated test case generation based on systematically exploring the program's input domain, with runtime analysis, where execution traces are monitored and verified against temporal logic specifications, or analyzed using advanced algorithms for detecting concurrency errors such as data races and deadlocks. The approach suggests to generate specifications dynamically per input instance rather than statically once-and-for-all. The paper describes experiments with variants of this approach in the context of two examples, a planetary rover controller and a space craft fault protection system.

Computer assisted analysis of auroral images obtained from high altitude polar satellites

NASA Technical Reports Server (NTRS)

Samadani, Ramin; Flynn, Michael

1993-01-01

Automatic techniques that allow the extraction of physically significant parameters from auroral images were developed. This allows the processing of a much larger number of images than is currently possible with manual techniques. Our techniques were applied to diverse auroral image datasets. These results were made available to geophysicists at NASA and at universities in the form of a software system that performs the analysis. After some feedback from users, an upgraded system was transferred to NASA and to two universities. The feasibility of user-trained search and retrieval of large amounts of data using our automatically derived parameter indices was demonstrated. Techniques based on classification and regression trees (CART) were developed and applied to broaden the types of images to which the automated search and retrieval may be applied. Our techniques were tested with DE-1 auroral images.

Infusing Reliability Techniques into Software Safety Analysis

NASA Technical Reports Server (NTRS)

Shi, Ying

2015-01-01

Software safety analysis for a large software intensive system is always a challenge. Software safety practitioners need to ensure that software related hazards are completely identified, controlled, and tracked. This paper discusses in detail how to incorporate the traditional reliability techniques into the entire software safety analysis process. In addition, this paper addresses how information can be effectively shared between the various practitioners involved in the software safety analyses. The author has successfully applied the approach to several aerospace applications. Examples are provided to illustrate the key steps of the proposed approach.

[A Review on the Use of Effect Size in Nursing Research].

PubMed

Kang, Hyuncheol; Yeon, Kyupil; Han, Sang Tae

2015-10-01

The purpose of this study was to introduce the main concepts of statistical testing and effect size and to provide researchers in nursing science with guidance on how to calculate the effect size for the statistical analysis methods mainly used in nursing. For t-test, analysis of variance, correlation analysis, regression analysis which are used frequently in nursing research, the generally accepted definitions of the effect size were explained. Some formulae for calculating the effect size are described with several examples in nursing research. Furthermore, the authors present the required minimum sample size for each example utilizing G*Power 3 software that is the most widely used program for calculating sample size. It is noted that statistical significance testing and effect size measurement serve different purposes, and the reliance on only one side may be misleading. Some practical guidelines are recommended for combining statistical significance testing and effect size measure in order to make more balanced decisions in quantitative analyses.

Multivariate meta-analysis using individual participant data

PubMed Central

Riley, R. D.; Price, M. J.; Jackson, D.; Wardle, M.; Gueyffier, F.; Wang, J.; Staessen, J. A.; White, I. R.

2016-01-01

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment–covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models. PMID:26099484

Intelligible machine learning with malibu.

PubMed

Langlois, Robert E; Lu, Hui

2008-01-01

malibu is an open-source machine learning work-bench developed in C/C++ for high-performance real-world applications, namely bioinformatics and medical informatics. It leverages third-party machine learning implementations for more robust bug-free software. This workbench handles several well-studied supervised machine learning problems including classification, regression, importance-weighted classification and multiple-instance learning. The malibu interface was designed to create reproducible experiments ideally run in a remote and/or command line environment. The software can be found at: http://proteomics.bioengr. uic.edu/malibu/index.html.

Relationship between Urbanization and Cancer Incidence in Iran Using Quantile Regression.

PubMed

Momenyan, Somayeh; Sadeghifar, Majid; Sarvi, Fatemeh; Khodadost, Mahmoud; Mosavi-Jarrahi, Alireza; Ghaffari, Mohammad Ebrahim; Sekhavati, Eghbal

2016-01-01

Quantile regression is an efficient method for predicting and estimating the relationship between explanatory variables and percentile points of the response distribution, particularly for extreme percentiles of the distribution. To study the relationship between urbanization and cancer morbidity, we here applied quantile regression. This cross-sectional study was conducted for 9 cancers in 345 cities in 2007 in Iran. Data were obtained from the Ministry of Health and Medical Education and the relationship between urbanization and cancer morbidity was investigated using quantile regression and least square regression. Fitting models were compared using AIC criteria. R (3.0.1) software and the Quantreg package were used for statistical analysis. With the quantile regression model all percentiles for breast, colorectal, prostate, lung and pancreas cancers demonstrated increasing incidence rate with urbanization. The maximum increase for breast cancer was in the 90th percentile (β=0.13, p-value<0.001), for colorectal cancer was in the 75th percentile (β=0.048, p-value<0.001), for prostate cancer the 95th percentile (β=0.55, p-value<0.001), for lung cancer was in 95th percentile (β=0.52, p-value=0.006), for pancreas cancer was in 10th percentile (β=0.011, p-value<0.001). For gastric, esophageal and skin cancers, with increasing urbanization, the incidence rate was decreased. The maximum decrease for gastric cancer was in the 90th percentile(β=0.003, p-value<0.001), for esophageal cancer the 95th (β=0.04, p-value=0.4) and for skin cancer also the 95th (β=0.145, p-value=0.071). The AIC showed that for upper percentiles, the fitting of quantile regression was better than least square regression. According to the results of this study, the significant impact of urbanization on cancer morbidity requirs more effort and planning by policymakers and administrators in order to reduce risk factors such as pollution in urban areas and ensure proper nutrition recommendations are made.

Many-level multilevel structural equation modeling: An efficient evaluation strategy.

PubMed

Pritikin, Joshua N; Hunter, Michael D; von Oertzen, Timo; Brick, Timothy R; Boker, Steven M

2017-01-01

Structural equation models are increasingly used for clustered or multilevel data in cases where mixed regression is too inflexible. However, when there are many levels of nesting, these models can become difficult to estimate. We introduce a novel evaluation strategy, Rampart, that applies an orthogonal rotation to the parts of a model that conform to commonly met requirements. This rotation dramatically simplifies fit evaluation in a way that becomes more potent as the size of the data set increases. We validate and evaluate the implementation using a 3-level latent regression simulation study. Then we analyze data from a state-wide child behavioral health measure administered by the Oklahoma Department of Human Services. We demonstrate the efficiency of Rampart compared to other similar software using a latent factor model with a 5-level decomposition of latent variance. Rampart is implemented in OpenMx, a free and open source software.

Outcome modelling strategies in epidemiology: traditional methods and basic alternatives

PubMed Central

Greenland, Sander; Daniel, Rhian; Pearce, Neil

2016-01-01

Abstract Controlling for too many potential confounders can lead to or aggravate problems of data sparsity or multicollinearity, particularly when the number of covariates is large in relation to the study size. As a result, methods to reduce the number of modelled covariates are often deployed. We review several traditional modelling strategies, including stepwise regression and the ‘change-in-estimate’ (CIE) approach to deciding which potential confounders to include in an outcome-regression model for estimating effects of a targeted exposure. We discuss their shortcomings, and then provide some basic alternatives and refinements that do not require special macros or programming. Throughout, we assume the main goal is to derive the most accurate effect estimates obtainable from the data and commercial software. Allowing that most users must stay within standard software packages, this goal can be roughly approximated using basic methods to assess, and thereby minimize, mean squared error (MSE). PMID:27097747

Modeling Relationships Between Flight Crew Demographics and Perceptions of Interval Management

NASA Technical Reports Server (NTRS)

Remy, Benjamin; Wilson, Sara R.

2016-01-01

The Interval Management Alternative Clearances (IMAC) human-in-the-loop simulation experiment was conducted to assess interval management system performance and participants' acceptability and workload while performing three interval management clearance types. Twenty-four subject pilots and eight subject controllers flew ten high-density arrival scenarios into Denver International Airport during two weeks of data collection. This analysis examined the possible relationships between subject pilot demographics on reported perceptions of interval management in IMAC. Multiple linear regression models were created with a new software tool to predict subject pilot questionnaire item responses from demographic information. General patterns were noted across models that may indicate flight crew demographics influence perceptions of interval management.

Off-pump versus on-pump coronary artery bypass surgery: meta-analysis and meta-regression of 13,524 patients from randomized trials.

PubMed

Sá, Michel Pompeu Barros de Oliveira; Ferraz, Paulo Ernando; Escobar, Rodrigo Renda; Martins, Wendell Nunes; Lustosa, Pablo César; Nunes, Eliobas de Oliveira; Vasconcelos, Frederico Pires; Lima, Ricardo Carvalho

2012-12-01

Most recent published meta-analysis of randomized controlled trials (RCTs) showed that off-pump coronary artery bypass graft surgery (CABG) reduces incidence of stroke by 30% compared with on-pump CABG, but showed no difference in other outcomes. New RCTs were published, indicating need of new meta-analysis to investigate pooled results adding these further studies. MEDLINE, EMBASE, CENTRAL/CCTR, SciELO, LILACS, Google Scholar and reference lists of relevant articles were searched for RCTs that compared outcomes (30-day mortality for all-cause, myocardial infarction or stroke) between off-pump versus on-pump CABG until May 2012. The principal summary measures were relative risk (RR) with 95% Confidence Interval (CI) and P values (considered statistically significant when <0.05). The RR's were combined across studies using DerSimonian-Laird random effects weighted model. Meta-analysis and meta-regression were completed using the software Comprehensive Meta-Analysis version 2 (Biostat Inc., Englewood, New Jersey, USA). Forty-seven RCTs were identified and included 13,524 patients (6,758 for off-pump and 6,766 for on-pump CABG). There was no significant difference between off-pump and on-pump CABG groups in RR for 30-day mortality or myocardial infarction, but there was difference about stroke in favor to off-pump CABG (RR 0.793, 95% CI 0.660-0.920, P=0.049). It was observed no important heterogeneity of effects about any outcome, but it was observed publication bias about outcome "stroke". Meta-regression did not demonstrate influence of female gender, number of grafts or age in outcomes. Off-pump CABG reduces the incidence of post-operative stroke by 20.7% and has no substantial effect on mortality or myocardial infarction in comparison to on-pump CABG. Patient gender, number of grafts performed and age do not seem to explain the effect of off-pump CABG on mortality, myocardial infarction or stroke, respectively.

Towards an Early Software Effort Estimation Based on Functional and Non-Functional Requirements

NASA Astrophysics Data System (ADS)

Kassab, Mohamed; Daneva, Maya; Ormandjieva, Olga

The increased awareness of the non-functional requirements as a key to software project and product success makes explicit the need to include them in any software project effort estimation activity. However, the existing approaches to defining size-based effort relationships still pay insufficient attention to this need. This paper presents a flexible, yet systematic approach to the early requirements-based effort estimation, based on Non-Functional Requirements ontology. It complementarily uses one standard functional size measurement model and a linear regression technique. We report on a case study which illustrates the application of our solution approach in context and also helps evaluate our experiences in using it.

Spatial regression analysis of traffic crashes in Seoul.

PubMed

Rhee, Kyoung-Ah; Kim, Joon-Ki; Lee, Young-ihn; Ulfarsson, Gudmundur F

2016-06-01

Traffic crashes can be spatially correlated events and the analysis of the distribution of traffic crash frequency requires evaluation of parameters that reflect spatial properties and correlation. Typically this spatial aspect of crash data is not used in everyday practice by planning agencies and this contributes to a gap between research and practice. A database of traffic crashes in Seoul, Korea, in 2010 was developed at the traffic analysis zone (TAZ) level with a number of GIS developed spatial variables. Practical spatial models using available software were estimated. The spatial error model was determined to be better than the spatial lag model and an ordinary least squares baseline regression. A geographically weighted regression model provided useful insights about localization of effects. The results found that an increased length of roads with speed limit below 30 km/h and a higher ratio of residents below age of 15 were correlated with lower traffic crash frequency, while a higher ratio of residents who moved to the TAZ, more vehicle-kilometers traveled, and a greater number of access points with speed limit difference between side roads and mainline above 30 km/h all increased the number of traffic crashes. This suggests, for example, that better control or design for merging lower speed roads with higher speed roads is important. A key result is that the length of bus-only center lanes had the largest effect on increasing traffic crashes. This is important as bus-only center lanes with bus stop islands have been increasingly used to improve transit times. Hence the potential negative safety impacts of such systems need to be studied further and mitigated through improved design of pedestrian access to center bus stop islands. Copyright © 2016 Elsevier Ltd. All rights reserved.

Non-destructive evaluation of chlorophyll content in quinoa and amaranth leaves by simple and multiple regression analysis of RGB image components.

PubMed

Riccardi, M; Mele, G; Pulvento, C; Lavini, A; d'Andria, R; Jacobsen, S-E

2014-06-01

Leaf chlorophyll content provides valuable information about physiological status of plants; it is directly linked to photosynthetic potential and primary production. In vitro assessment by wet chemical extraction is the standard method for leaf chlorophyll determination. This measurement is expensive, laborious, and time consuming. Over the years alternative methods, rapid and non-destructive, have been explored. The aim of this work was to evaluate the applicability of a fast and non-invasive field method for estimation of chlorophyll content in quinoa and amaranth leaves based on RGB components analysis of digital images acquired with a standard SLR camera. Digital images of leaves from different genotypes of quinoa and amaranth were acquired directly in the field. Mean values of each RGB component were evaluated via image analysis software and correlated to leaf chlorophyll provided by standard laboratory procedure. Single and multiple regression models using RGB color components as independent variables have been tested and validated. The performance of the proposed method was compared to that of the widely used non-destructive SPAD method. Sensitivity of the best regression models for different genotypes of quinoa and amaranth was also checked. Color data acquisition of the leaves in the field with a digital camera was quick, more effective, and lower cost than SPAD. The proposed RGB models provided better correlation (highest R (2)) and prediction (lowest RMSEP) of the true value of foliar chlorophyll content and had a lower amount of noise in the whole range of chlorophyll studied compared with SPAD and other leaf image processing based models when applied to quinoa and amaranth.

Inertial Upper Stage (IUS) software analysis

NASA Technical Reports Server (NTRS)

Grayson, W. L.; Nickel, C. E.; Rose, P. L.; Singh, R. P.

1979-01-01

The Inertial Upper Stage (IUS) System, an extension of the Space Transportation System (STS) operating regime to include higher orbits, orbital plane changes, geosynchronous orbits, and interplanetary trajectories is presented. The IUS software design, the IUS software interfaces with other systems, and the cost effectiveness in software verification are described. Tasks of the IUS discussed include: (1) design analysis; (2) validation requirements analysis; (3) interface analysis; and (4) requirements analysis.

Bayesian inference for psychology. Part II: Example applications with JASP.

PubMed

Wagenmakers, Eric-Jan; Love, Jonathon; Marsman, Maarten; Jamil, Tahira; Ly, Alexander; Verhagen, Josine; Selker, Ravi; Gronau, Quentin F; Dropmann, Damian; Boutin, Bruno; Meerhoff, Frans; Knight, Patrick; Raj, Akash; van Kesteren, Erik-Jan; van Doorn, Johnny; Šmíra, Martin; Epskamp, Sacha; Etz, Alexander; Matzke, Dora; de Jong, Tim; van den Bergh, Don; Sarafoglou, Alexandra; Steingroever, Helen; Derks, Koen; Rouder, Jeffrey N; Morey, Richard D

2018-02-01

Bayesian hypothesis testing presents an attractive alternative to p value hypothesis testing. Part I of this series outlined several advantages of Bayesian hypothesis testing, including the ability to quantify evidence and the ability to monitor and update this evidence as data come in, without the need to know the intention with which the data were collected. Despite these and other practical advantages, Bayesian hypothesis tests are still reported relatively rarely. An important impediment to the widespread adoption of Bayesian tests is arguably the lack of user-friendly software for the run-of-the-mill statistical problems that confront psychologists for the analysis of almost every experiment: the t-test, ANOVA, correlation, regression, and contingency tables. In Part II of this series we introduce JASP ( http://www.jasp-stats.org ), an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems. JASP is based in part on the Bayesian analyses implemented in Morey and Rouder's BayesFactor package for R. Armed with JASP, the practical advantages of Bayesian hypothesis testing are only a mouse click away.

Calibration sets selection strategy for the construction of robust PLS models for prediction of biodiesel/diesel blends physico-chemical properties using NIR spectroscopy

NASA Astrophysics Data System (ADS)

Palou, Anna; Miró, Aira; Blanco, Marcelo; Larraz, Rafael; Gómez, José Francisco; Martínez, Teresa; González, Josep Maria; Alcalà, Manel

2017-06-01

Even when the feasibility of using near infrared (NIR) spectroscopy combined with partial least squares (PLS) regression for prediction of physico-chemical properties of biodiesel/diesel blends has been widely demonstrated, inclusion in the calibration sets of the whole variability of diesel samples from diverse production origins still remains as an important challenge when constructing the models. This work presents a useful strategy for the systematic selection of calibration sets of samples of biodiesel/diesel blends from diverse origins, based on a binary code, principal components analysis (PCA) and the Kennard-Stones algorithm. Results show that using this methodology the models can keep their robustness over time. PLS calculations have been done using a specialized chemometric software as well as the software of the NIR instrument installed in plant, and both produced RMSEP under reproducibility values of the reference methods. The models have been proved for on-line simultaneous determination of seven properties: density, cetane index, fatty acid methyl esters (FAME) content, cloud point, boiling point at 95% of recovery, flash point and sulphur.

Estimating the Dead Space Volume Between a Headform and N95 Filtering Facepiece Respirator Using Microsoft Kinect.

PubMed

Xu, Ming; Lei, Zhipeng; Yang, James

2015-01-01

N95 filtering facepiece respirator (FFR) dead space is an important factor for respirator design. The dead space refers to the cavity between the internal surface of the FFR and the wearer's facial surface. This article presents a novel method to estimate the dead space volume of FFRs and experimental validation. In this study, six FFRs and five headforms (small, medium, large, long/narrow, and short/wide) are used for various FFR and headform combinations. Microsoft Kinect Sensors (Microsoft Corporation, Redmond, WA) are used to scan the headforms without respirators and then scan the headforms with the FFRs donned. The FFR dead space is formed through geometric modeling software, and finally the volume is obtained through LS-DYNA (Livermore Software Technology Corporation, Livermore, CA). In the experimental validation, water is used to measure the dead space. The simulation and experimental dead space volumes are 107.5-167.5 mL and 98.4-165.7 mL, respectively. Linear regression analysis is conducted to correlate the results from Kinect and water, and R(2) = 0.85.

Software Safety Progress in NASA

NASA Technical Reports Server (NTRS)

Radley, Charles F.

1995-01-01

NASA has developed guidelines for development and analysis of safety-critical software. These guidelines have been documented in a Guidebook for Safety Critical Software Development and Analysis. The guidelines represent a practical 'how to' approach, to assist software developers and safety analysts in cost effective methods for software safety. They provide guidance in the implementation of the recent NASA Software Safety Standard NSS-1740.13 which was released as 'Interim' version in June 1994, scheduled for formal adoption late 1995. This paper is a survey of the methods in general use, resulting in the NASA guidelines for safety critical software development and analysis.

Dynamic prediction in functional concurrent regression with an application to child growth.

PubMed

Leroux, Andrew; Xiao, Luo; Crainiceanu, Ciprian; Checkley, William

2018-04-15

In many studies, it is of interest to predict the future trajectory of subjects based on their historical data, referred to as dynamic prediction. Mixed effects models have traditionally been used for dynamic prediction. However, the commonly used random intercept and slope model is often not sufficiently flexible for modeling subject-specific trajectories. In addition, there may be useful exposures/predictors of interest that are measured concurrently with the outcome, complicating dynamic prediction. To address these problems, we propose a dynamic functional concurrent regression model to handle the case where both the functional response and the functional predictors are irregularly measured. Currently, such a model cannot be fit by existing software. We apply the model to dynamically predict children's length conditional on prior length, weight, and baseline covariates. Inference on model parameters and subject-specific trajectories is conducted using the mixed effects representation of the proposed model. An extensive simulation study shows that the dynamic functional regression model provides more accurate estimation and inference than existing methods. Methods are supported by fast, flexible, open source software that uses heavily tested smoothing techniques. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

CEval: All-in-one software for data processing and statistical evaluations in affinity capillary electrophoresis.

PubMed

Dubský, Pavel; Ördögová, Magda; Malý, Michal; Riesová, Martina

2016-05-06

We introduce CEval software (downloadable for free at echmet.natur.cuni.cz) that was developed for quicker and easier electrophoregram evaluation and further data processing in (affinity) capillary electrophoresis. This software allows for automatic peak detection and evaluation of common peak parameters, such as its migration time, area, width etc. Additionally, the software includes a nonlinear regression engine that performs peak fitting with the Haarhoff-van der Linde (HVL) function, including automated initial guess of the HVL function parameters. HVL is a fundamental peak-shape function in electrophoresis, based on which the correct effective mobility of the analyte represented by the peak is evaluated. Effective mobilities of an analyte at various concentrations of a selector can be further stored and plotted in an affinity CE mode. Consequently, the mobility of the free analyte, μA, mobility of the analyte-selector complex, μAS, and the apparent complexation constant, K('), are first guessed automatically from the linearized data plots and subsequently estimated by the means of nonlinear regression. An option that allows two complexation dependencies to be fitted at once is especially convenient for enantioseparations. Statistical processing of these data is also included, which allowed us to: i) express the 95% confidence intervals for the μA, μAS and K(') least-squares estimates, ii) do hypothesis testing on the estimated parameters for the first time. We demonstrate the benefits of the CEval software by inspecting complexation of tryptophan methyl ester with two cyclodextrins, neutral heptakis(2,6-di-O-methyl)-β-CD and charged heptakis(6-O-sulfo)-β-CD. Copyright © 2016 Elsevier B.V. All rights reserved.

CATS, continuous automated testing of seismological, hydroacoustic, and infrasound (SHI) processing software.

NASA Astrophysics Data System (ADS)

Brouwer, Albert; Brown, David; Tomuta, Elena

2017-04-01

To detect nuclear explosions, waveform data from over 240 SHI stations world-wide flows into the International Data Centre (IDC) of the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO), located in Vienna, Austria. A complex pipeline of software applications processes this data in numerous ways to form event hypotheses. The software codebase comprises over 2 million lines of code, reflects decades of development, and is subject to frequent enhancement and revision. Since processing must run continuously and reliably, software changes are subjected to thorough testing before being put into production. To overcome the limitations and cost of manual testing, the Continuous Automated Testing System (CATS) has been created. CATS provides an isolated replica of the IDC processing environment, and is able to build and test different versions of the pipeline software directly from code repositories that are placed under strict configuration control. Test jobs are scheduled automatically when code repository commits are made. Regressions are reported. We present the CATS design choices and test methods. Particular attention is paid to how the system accommodates the individual testing of strongly interacting software components that lack test instrumentation.

Forecasting daily meteorological time series using ARIMA and regression models

NASA Astrophysics Data System (ADS)

Murat, Małgorzata; Malinowska, Iwona; Gos, Magdalena; Krzyszczak, Jaromir

2018-04-01

The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.

In vitro Cell Viability by CellProfiler® Software as Equivalent to MTT Assay.

PubMed

Gasparini, Luciana S; Macedo, Nayana D; Pimentel, Elisângela F; Fronza, Marcio; Junior, Valdemar L; Borges, Warley S; Cole, Eduardo R; Andrade, Tadeu U; Endringer, Denise C; Lenz, Dominik

2017-07-01

This study evaluated in vitro cell viability by the colorimetric MTT stands for 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide) (3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide) assay compared to image analysis by CellProfiler ® software. Hepatoma (Hepa-1c1c7) and fibroblast (L929) cells were exposed to isolated substances, camptothecin, lycorine, tazettine, albomaculine, 3-epimacronine, trispheridine, galanthine and Padina gymnospora , Sargassum sp. methanolic extract, and Habranthus itaobinus Ravenna ethyl acetate in different concentrations. After MTT assay, cells were stained with Panotic dye kit. Cell images were obtained with an inverted microscope equipped with a digital camera. The images were analyzed by CellProfiler ® . No cytotoxicity at the highest concentration analyzed for 3-epimacronine, albomaculine, galanthine, trispheridine, P. gymnospora extract and Sargassum sp. extract where detected. Tazettine offered cytotoxicity only against the Hepa1c1c7 cell line. Lycorine, camptothecin, and H. itaobinus extract exhibited cytotoxic effects in both cell lines. The viability methods tested were correlated demonstrated by Bland-Atman test with normal distribution with mean difference between the two methods close to zero, bias value 3.0263. The error was within the limits of the confidence intervals and these values had a narrow difference. The correlation between the two methods was demonstrated by the linear regression plotted as R 2 . CellProfiler ® image analysis presented similar results to the MTT assay in the identification of viable cells, and image analysis may assist part of biological analysis procedures. The presented methodology is inexpensive and reproducible. In vitro cell viability assessment with MTT (3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide) assay may be replaced by image analysis by CellProfiler ® . The viability methods tested were correlated demonstrated by Bland-Atman test with normal distribution with mean difference between the two methods close to zero, bias value 3.0263. The correlation between the two methods was demonstrated by the linear regression plotted as R2. Abbreviations: HPLC: High pressure liquid chromatography MTT: (3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide) (3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide).

Table-driven configuration and formatting of telemetry data in the Deep Space Network

NASA Technical Reports Server (NTRS)

Manning, Evan

1994-01-01

With a restructured software architecture for telemetry system control and data processing, the NASA/Deep Space Network (DSN) has substantially improved its ability to accommodate a wide variety of spacecraft in an era of 'better, faster, cheaper'. In the new architecture, the permanent software implements all capabilities needed by any system user, and text tables specify how these capabilities are to be used for each spacecraft. Most changes can now be made rapidly, outside of the traditional software development cycle. The system can be updated to support a new spacecraft through table changes rather than software changes, reducing the implementation, test, and delivery cycle for such a change from three months to three weeks. The mechanical separation of the text table files from the program software, with tables only loaded into memory when that mission is being supported, dramatically reduces the level of regression testing required. The format of each table is a different compromise between ease of human interpretation, efficiency of computer interpretation, and flexibility.

Preoperative Biometric Parameters Predict the Vault after ICL Implantation: A Retrospective Clinical Study.

PubMed

Zheng, Qian-Yin; Xu, Wen; Liang, Guan-Lu; Wu, Jing; Shi, Jun-Ting

2016-01-01

To investigate the correlation between the preoperative biometric parameters of the anterior segment and the vault after implantable Collamer lens (ICL) implantation via this retrospective study. Retrospective clinical study. A total of 78 eyes from 41 patients who underwent ICL implantation surgery were included in this study. Preoperative biometric parameters, including white-to-white (WTW) diameter, central corneal thickness, keratometer, pupil diameter, anterior chamber depth, sulcus-to-sulcus diameter, anterior chamber area (ACA) and central curvature radius of the anterior surface of the lens (Lenscur), were measured. Lenscur and ACA were measured with Rhinoceros 5.0 software on the image scanned with ultrasound biomicroscopy (UBM). The vault was assessed by UBM 3 months after surgery. Multiple stepwise regression analysis was employed to identify the variables that were correlated with the vault. The results showed that the vault was correlated with 3 variables: ACA (22.4 ± 4.25 mm2), WTW (11.36 ± 0.29 mm) and Lenscur (9.15 ± 1.21 mm). The regressive equation was: vault (mm) = 1.785 + 0.017 × ACA + 0.051 × Lenscur - 0.203 × WTW. Biometric parameters of the anterior segment (ACA, WTW and Lenscur) can predict the vault after ICL implantation using a new regression equation. © 2016 The Author(s) Published by S. Karger AG, Basel.

Using software security analysis to verify the secure socket layer (SSL) protocol

NASA Technical Reports Server (NTRS)

Powell, John D.

2004-01-01

nal Aeronautics and Space Administration (NASA) have tens of thousands of networked computer systems and applications. Software Security vulnerabilities present risks such as lost or corrupted data, information the3, and unavailability of critical systems. These risks represent potentially enormous costs to NASA. The NASA Code Q research initiative 'Reducing Software Security Risk (RSSR) Trough an Integrated Approach '' offers, among its capabilities, formal verification of software security properties, through the use of model based verification (MBV) to address software security risks. [1,2,3,4,5,6] MBV is a formal approach to software assurance that combines analysis of software, via abstract models, with technology, such as model checkers, that provide automation of the mechanical portions of the analysis process. This paper will discuss: The need for formal analysis to assure software systems with respect to software and why testing alone cannot provide it. The means by which MBV with a Flexible Modeling Framework (FMF) accomplishes the necessary analysis task. An example of FMF style MBV in the verification of properties over the Secure Socket Layer (SSL) communication protocol as a demonstration.

Development of Automated Image Analysis Software for Suspended Marine Particle Classification

DTIC Science & Technology

2003-09-30

Development of Automated Image Analysis Software for Suspended Marine Particle Classification Scott Samson Center for Ocean Technology...REPORT TYPE 3. DATES COVERED 00-00-2003 to 00-00-2003 4. TITLE AND SUBTITLE Development of Automated Image Analysis Software for Suspended...objective is to develop automated image analysis software to reduce the effort and time required for manual identification of plankton images. Automated

A tool to include gamma analysis software into a quality assurance program.

PubMed

Agnew, Christina E; McGarry, Conor K

2016-03-01

To provide a tool to enable gamma analysis software algorithms to be included in a quality assurance (QA) program. Four image sets were created comprising two geometric images to independently test the distance to agreement (DTA) and dose difference (DD) elements of the gamma algorithm, a clinical step and shoot IMRT field and a clinical VMAT arc. The images were analysed using global and local gamma analysis with 2 in-house and 8 commercially available software encompassing 15 software versions. The effect of image resolution on gamma pass rates was also investigated. All but one software accurately calculated the gamma passing rate for the geometric images. Variation in global gamma passing rates of 1% at 3%/3mm and over 2% at 1%/1mm was measured between software and software versions with analysis of appropriately sampled images. This study provides a suite of test images and the gamma pass rates achieved for a selection of commercially available software. This image suite will enable validation of gamma analysis software within a QA program and provide a frame of reference by which to compare results reported in the literature from various manufacturers and software versions. Copyright © 2015. Published by Elsevier Ireland Ltd.

A tool for selective inline quantification of co-eluting proteins in chromatography using spectral analysis and partial least squares regression.

PubMed

Brestrich, Nina; Briskot, Till; Osberghaus, Anna; Hubbuch, Jürgen

2014-07-01

Selective quantification of co-eluting proteins in chromatography is usually performed by offline analytics. This is time-consuming and can lead to late detection of irregularities in chromatography processes. To overcome this analytical bottleneck, a methodology for selective protein quantification in multicomponent mixtures by means of spectral data and partial least squares regression was presented in two previous studies. In this paper, a powerful integration of software and chromatography hardware will be introduced that enables the applicability of this methodology for a selective inline quantification of co-eluting proteins in chromatography. A specific setup consisting of a conventional liquid chromatography system, a diode array detector, and a software interface to Matlab® was developed. The established tool for selective inline quantification was successfully applied for a peak deconvolution of a co-eluting ternary protein mixture consisting of lysozyme, ribonuclease A, and cytochrome c on SP Sepharose FF. Compared to common offline analytics based on collected fractions, no loss of information regarding the retention volumes and peak flanks was observed. A comparison between the mass balances of both analytical methods showed, that the inline quantification tool can be applied for a rapid determination of pool yields. Finally, the achieved inline peak deconvolution was successfully applied to make product purity-based real-time pooling decisions. This makes the established tool for selective inline quantification a valuable approach for inline monitoring and control of chromatographic purification steps and just in time reaction on process irregularities. © 2014 Wiley Periodicals, Inc.

The correlation between preoperative volumetry and real graft weight: comparison of two volumetry programs

PubMed Central

Mussin, Nadiar; Sumo, Marco; Choi, YoungRok; Choi, Jin Yong; Ahn, Sung-Woo; Yoon, Kyung Chul; Kim, Hyo-Sin; Hong, Suk Kyun; Yi, Nam-Joon; Suh, Kyung-Suk

2017-01-01

Purpose Liver volumetry is a vital component in living donor liver transplantation to determine an adequate graft volume that meets the metabolic demands of the recipient and at the same time ensures donor safety. Most institutions use preoperative contrast-enhanced CT image-based software programs to estimate graft volume. The objective of this study was to evaluate the accuracy of 2 liver volumetry programs (Rapidia vs. Dr. Liver) in preoperative right liver graft estimation compared with real graft weight. Methods Data from 215 consecutive right lobe living donors between October 2013 and August 2015 were retrospectively reviewed. One hundred seven patients were enrolled in Rapidia group and 108 patients were included in the Dr. Liver group. Estimated graft volumes generated by both software programs were compared with real graft weight measured during surgery, and further classified into minimal difference (≤15%) and big difference (>15%). Correlation coefficients and degree of difference were determined. Linear regressions were calculated and results depicted as scatterplots. Results Minimal difference was observed in 69.4% of cases from Dr. Liver group and big difference was seen in 44.9% of cases from Rapidia group (P = 0.035). Linear regression analysis showed positive correlation in both groups (P < 0.01). However, the correlation coefficient was better for the Dr. Liver group (R2 = 0.719), than for the Rapidia group (R2 = 0.688). Conclusion Dr. Liver can accurately predict right liver graft size better and faster than Rapidia, and can facilitate preoperative planning in living donor liver transplantation. PMID:28382294

The Role of Data Analysis Software in Graduate Programs in Education and Post-Graduate Research

ERIC Educational Resources Information Center

Harwell, Michael

2018-01-01

The importance of data analysis software in graduate programs in education and post-graduate educational research is self-evident. However the role of this software in facilitating supererogated statistical practice versus "cookbookery" is unclear. The need to rigorously document the role of data analysis software in students' graduate…

Instrument control software development process for the multi-star AO system ARGOS

NASA Astrophysics Data System (ADS)

Kulas, M.; Barl, L.; Borelli, J. L.; Gässler, W.; Rabien, S.

2012-09-01

The ARGOS project (Advanced Rayleigh guided Ground layer adaptive Optics System) will upgrade the Large Binocular Telescope (LBT) with an AO System consisting of six Rayleigh laser guide stars. This adaptive optics system integrates several control loops and many different components like lasers, calibration swing arms and slope computers that are dispersed throughout the telescope. The purpose of the instrument control software (ICS) is running this AO system and providing convenient client interfaces to the instruments and the control loops. The challenges for the ARGOS ICS are the development of a distributed and safety-critical software system with no defects in a short time, the creation of huge and complex software programs with a maintainable code base, the delivery of software components with the desired functionality and the support of geographically distributed project partners. To tackle these difficult tasks, the ARGOS software engineers reuse existing software like the novel middleware from LINC-NIRVANA, an instrument for the LBT, provide many tests at different functional levels like unit tests and regression tests, agree about code and architecture style and deliver software incrementally while closely collaborating with the project partners. Many ARGOS ICS components are already successfully in use in the laboratories for testing ARGOS control loops.

Usability study of clinical exome analysis software: top lessons learned and recommendations.

PubMed

Shyr, Casper; Kushniruk, Andre; Wasserman, Wyeth W

2014-10-01

New DNA sequencing technologies have revolutionized the search for genetic disruptions. Targeted sequencing of all protein coding regions of the genome, called exome analysis, is actively used in research-oriented genetics clinics, with the transition to exomes as a standard procedure underway. This transition is challenging; identification of potentially causal mutation(s) amongst ∼10(6) variants requires specialized computation in combination with expert assessment. This study analyzes the usability of user interfaces for clinical exome analysis software. There are two study objectives: (1) To ascertain the key features of successful user interfaces for clinical exome analysis software based on the perspective of expert clinical geneticists, (2) To assess user-system interactions in order to reveal strengths and weaknesses of existing software, inform future design, and accelerate the clinical uptake of exome analysis. Surveys, interviews, and cognitive task analysis were performed for the assessment of two next-generation exome sequence analysis software packages. The subjects included ten clinical geneticists who interacted with the software packages using the "think aloud" method. Subjects' interactions with the software were recorded in their clinical office within an urban research and teaching hospital. All major user interface events (from the user interactions with the packages) were time-stamped and annotated with coding categories to identify usability issues in order to characterize desired features and deficiencies in the user experience. We detected 193 usability issues, the majority of which concern interface layout and navigation, and the resolution of reports. Our study highlights gaps in specific software features typical within exome analysis. The clinicians perform best when the flow of the system is structured into well-defined yet customizable layers for incorporation within the clinical workflow. The results highlight opportunities to dramatically accelerate clinician analysis and interpretation of patient genomic data. We present the first application of usability methods to evaluate software interfaces in the context of exome analysis. Our results highlight how the study of user responses can lead to identification of usability issues and challenges and reveal software reengineering opportunities for improving clinical next-generation sequencing analysis. While the evaluation focused on two distinctive software tools, the results are general and should inform active and future software development for genome analysis software. As large-scale genome analysis becomes increasingly common in healthcare, it is critical that efficient and effective software interfaces are provided to accelerate clinical adoption of the technology. Implications for improved design of such applications are discussed. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Completely automated open-path FT-IR spectrometry.

PubMed

Griffiths, Peter R; Shao, Limin; Leytem, April B

2009-01-01

Atmospheric analysis by open-path Fourier-transform infrared (OP/FT-IR) spectrometry has been possible for over two decades but has not been widely used because of the limitations of the software of commercial instruments. In this paper, we describe the current state-of-the-art of the hardware and software that constitutes a contemporary OP/FT-IR spectrometer. We then describe advances that have been made in our laboratory that have enabled many of the limitations of this type of instrument to be overcome. These include not having to acquire a single-beam background spectrum that compensates for absorption features in the spectra of atmospheric water vapor and carbon dioxide. Instead, an easily measured "short path-length" background spectrum is used for calculation of each absorbance spectrum that is measured over a long path-length. To accomplish this goal, the algorithm used to calculate the concentrations of trace atmospheric molecules was changed from classical least-squares regression (CLS) to partial least-squares regression (PLS). For calibration, OP/FT-IR spectra are measured in pristine air over a wide variety of path-lengths, temperatures, and humidities, ratioed against a short-path background, and converted to absorbance; the reference spectrum of each analyte is then multiplied by randomly selected coefficients and added to these background spectra. Automatic baseline correction for small molecules with resolved rotational fine structure, such as ammonia and methane, is effected using wavelet transforms. A novel method of correcting for the effect of the nonlinear response of mercury cadmium telluride detectors is also incorporated. Finally, target factor analysis may be used to detect the onset of a given pollutant when its concentration exceeds a certain threshold. In this way, the concentration of atmospheric species has been obtained from OP/FT-IR spectra measured at intervals of 1 min over a period of many hours with no operator intervention.

Advances in Highly Constrained Multi-Phase Trajectory Generation using the General Pseudospectral Optimization Software (GPOPS)

DTIC Science & Technology

2013-08-01

release; distribution unlimited. PA Number 412-TW-PA-13395 f generic function g acceleration due to gravity h altitude L aerodynamic lift force L Lagrange...cost m vehicle mass M Mach number n number of coefficients in polynomial regression p highest order of polynomial regression Q dynamic pressure R...Method (RPM); the collocation points are defined by the roots of Legendre -Gauss- Radau (LGR) functions.9 GPOPS also automatically refines the “mesh” by

Estimation of octanol/water partition coefficients using LSER parameters

USGS Publications Warehouse

Luehrs, Dean C.; Hickey, James P.; Godbole, Kalpana A.; Rogers, Tony N.

1998-01-01

The logarithms of octanol/water partition coefficients, logKow, were regressed against the linear solvation energy relationship (LSER) parameters for a training set of 981 diverse organic chemicals. The standard deviation for logKow was 0.49. The regression equation was then used to estimate logKow for a test of 146 chemicals which included pesticides and other diverse polyfunctional compounds. Thus the octanol/water partition coefficient may be estimated by LSER parameters without elaborate software but only moderate accuracy should be expected.

Fault Tree Analysis Application for Safety and Reliability

NASA Technical Reports Server (NTRS)

Wallace, Dolores R.

2003-01-01

Many commercial software tools exist for fault tree analysis (FTA), an accepted method for mitigating risk in systems. The method embedded in the tools identifies a root as use in system components, but when software is identified as a root cause, it does not build trees into the software component. No commercial software tools have been built specifically for development and analysis of software fault trees. Research indicates that the methods of FTA could be applied to software, but the method is not practical without automated tool support. With appropriate automated tool support, software fault tree analysis (SFTA) may be a practical technique for identifying the underlying cause of software faults that may lead to critical system failures. We strive to demonstrate that existing commercial tools for FTA can be adapted for use with SFTA, and that applied to a safety-critical system, SFTA can be used to identify serious potential problems long before integrator and system testing.

RELAP-7 Software Verification and Validation Plan

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

Smith, Curtis L.; Choi, Yong-Joon; Zou, Ling

This INL plan comprehensively describes the software for RELAP-7 and documents the software, interface, and software design requirements for the application. The plan also describes the testing-based software verification and validation (SV&V) process—a set of specially designed software models used to test RELAP-7. The RELAP-7 (Reactor Excursion and Leak Analysis Program) code is a nuclear reactor system safety analysis code being developed at Idaho National Laboratory (INL). The code is based on the INL’s modern scientific software development framework – MOOSE (Multi-Physics Object-Oriented Simulation Environment). The overall design goal of RELAP-7 is to take advantage of the previous thirty yearsmore » of advancements in computer architecture, software design, numerical integration methods, and physical models. The end result will be a reactor systems analysis capability that retains and improves upon RELAP5’s capability and extends the analysis capability for all reactor system simulation scenarios.« less

Inverse odds ratio-weighted estimation for causal mediation analysis.

PubMed

Tchetgen Tchetgen, Eric J

2013-11-20

An important scientific goal of studies in the health and social sciences is increasingly to determine to what extent the total effect of a point exposure is mediated by an intermediate variable on the causal pathway between the exposure and the outcome. A causal framework has recently been proposed for mediation analysis, which gives rise to new definitions, formal identification results and novel estimators of direct and indirect effects. In the present paper, the author describes a new inverse odds ratio-weighted approach to estimate so-called natural direct and indirect effects. The approach, which uses as a weight the inverse of an estimate of the odds ratio function relating the exposure and the mediator, is universal in that it can be used to decompose total effects in a number of regression models commonly used in practice. Specifically, the approach may be used for effect decomposition in generalized linear models with a nonlinear link function, and in a number of other commonly used models such as the Cox proportional hazards regression for a survival outcome. The approach is simple and can be implemented in standard software provided a weight can be specified for each observation. An additional advantage of the method is that it easily incorporates multiple mediators of a categorical, discrete or continuous nature. Copyright © 2013 John Wiley & Sons, Ltd.

Genetic Programming as Alternative for Predicting Development Effort of Individual Software Projects

PubMed Central

Chavoya, Arturo; Lopez-Martin, Cuauhtemoc; Andalon-Garcia, Irma R.; Meda-Campaña, M. E.

2012-01-01

Statistical and genetic programming techniques have been used to predict the software development effort of large software projects. In this paper, a genetic programming model was used for predicting the effort required in individually developed projects. Accuracy obtained from a genetic programming model was compared against one generated from the application of a statistical regression model. A sample of 219 projects developed by 71 practitioners was used for generating the two models, whereas another sample of 130 projects developed by 38 practitioners was used for validating them. The models used two kinds of lines of code as well as programming language experience as independent variables. Accuracy results from the model obtained with genetic programming suggest that it could be used to predict the software development effort of individual projects when these projects have been developed in a disciplined manner within a development-controlled environment. PMID:23226305

Proceedings of the 14th Annual Software Engineering Workshop

NASA Technical Reports Server (NTRS)

1989-01-01

Several software related topics are presented. Topics covered include studies and experiment at the Software Engineering Laboratory at the Goddard Space Flight Center, predicting project success from the Software Project Management Process, software environments, testing in a reuse environment, domain directed reuse, and classification tree analysis using the Amadeus measurement and empirical analysis.

HYPOTrace: image analysis software for measuring hypocotyl growth and shape demonstrated on Arabidopsis seedlings undergoing photomorphogenesis.

PubMed

Wang, Liya; Uilecan, Ioan Vlad; Assadi, Amir H; Kozmik, Christine A; Spalding, Edgar P

2009-04-01

Analysis of time series of images can quantify plant growth and development, including the effects of genetic mutations (phenotypes) that give information about gene function. Here is demonstrated a software application named HYPOTrace that automatically extracts growth and shape information from electronic gray-scale images of Arabidopsis (Arabidopsis thaliana) seedlings. Key to the method is the iterative application of adaptive local principal components analysis to extract a set of ordered midline points (medial axis) from images of the seedling hypocotyl. Pixel intensity is weighted to avoid the medial axis being diverted by the cotyledons in areas where the two come in contact. An intensity feature useful for terminating the midline at the hypocotyl apex was isolated in each image by subtracting the baseline with a robust local regression algorithm. Applying the algorithm to time series of images of Arabidopsis seedlings responding to light resulted in automatic quantification of hypocotyl growth rate, apical hook opening, and phototropic bending with high spatiotemporal resolution. These functions are demonstrated here on wild-type, cryptochrome1, and phototropin1 seedlings for the purpose of showing that HYPOTrace generated expected results and to show how much richer the machine-vision description is compared to methods more typical in plant biology. HYPOTrace is expected to benefit seedling development research, particularly in the photomorphogenesis field, by replacing many tedious, error-prone manual measurements with a precise, largely automated computational tool.

Hierarchical Adaptive Regression Kernels for Regression with Functional Predictors.

PubMed

Woodard, Dawn B; Crainiceanu, Ciprian; Ruppert, David

2013-01-01

We propose a new method for regression using a parsimonious and scientifically interpretable representation of functional predictors. Our approach is designed for data that exhibit features such as spikes, dips, and plateaus whose frequency, location, size, and shape varies stochastically across subjects. We propose Bayesian inference of the joint functional and exposure models, and give a method for efficient computation. We contrast our approach with existing state-of-the-art methods for regression with functional predictors, and show that our method is more effective and efficient for data that include features occurring at varying locations. We apply our methodology to a large and complex dataset from the Sleep Heart Health Study, to quantify the association between sleep characteristics and health outcomes. Software and technical appendices are provided in online supplemental materials.

Design and validation of Segment--freely available software for cardiovascular image analysis.

PubMed

Heiberg, Einar; Sjögren, Jane; Ugander, Martin; Carlsson, Marcus; Engblom, Henrik; Arheden, Håkan

2010-01-11

Commercially available software for cardiovascular image analysis often has limited functionality and frequently lacks the careful validation that is required for clinical studies. We have already implemented a cardiovascular image analysis software package and released it as freeware for the research community. However, it was distributed as a stand-alone application and other researchers could not extend it by writing their own custom image analysis algorithms. We believe that the work required to make a clinically applicable prototype can be reduced by making the software extensible, so that researchers can develop their own modules or improvements. Such an initiative might then serve as a bridge between image analysis research and cardiovascular research. The aim of this article is therefore to present the design and validation of a cardiovascular image analysis software package (Segment) and to announce its release in a source code format. Segment can be used for image analysis in magnetic resonance imaging (MRI), computed tomography (CT), single photon emission computed tomography (SPECT) and positron emission tomography (PET). Some of its main features include loading of DICOM images from all major scanner vendors, simultaneous display of multiple image stacks and plane intersections, automated segmentation of the left ventricle, quantification of MRI flow, tools for manual and general object segmentation, quantitative regional wall motion analysis, myocardial viability analysis and image fusion tools. Here we present an overview of the validation results and validation procedures for the functionality of the software. We describe a technique to ensure continued accuracy and validity of the software by implementing and using a test script that tests the functionality of the software and validates the output. The software has been made freely available for research purposes in a source code format on the project home page http://segment.heiberg.se. Segment is a well-validated comprehensive software package for cardiovascular image analysis. It is freely available for research purposes provided that relevant original research publications related to the software are cited.

Novel Approach to Three-Dimensional Echocardiographic Quantification of Right Ventricular Volumes and Function from Focused Views.

PubMed

Medvedofsky, Diego; Addetia, Karima; Patel, Amit R; Sedlmeier, Anke; Baumann, Rolf; Mor-Avi, Victor; Lang, Roberto M

2015-10-01

Echocardiographic assessment of the right ventricle is difficult because of its complex shape. Three-dimensional echocardiographic (3DE) imaging allows more accurate and reproducible analysis of the right ventricle than two-dimensional methodology. However, three-dimensional volumetric analysis has been hampered by difficulties obtaining consistently high-quality coronal views, required by the existing software packages. The aim of this study was to test a new approach for volumetric analysis without coronal views by using instead right ventricle-focused three-dimensional acquisition with multiple short-axis views extracted from the same data set. Transthoracic 3DE and cardiovascular magnetic resonance (CMR) images were prospectively obtained on the same day in 147 patients with wide ranges of right ventricular (RV) size and function. RV volumes and ejection fraction were measured from 3DE images using the new software and compared with CMR reference values. Comparisons included linear regression and Bland-Altman analyses. Repeated measurements were performed to assess measurement variability. Sixteen patients were excluded because of suboptimal image quality (89% feasibility). RV volumes and ejection fraction obtained with the new 3DE technique were in good agreement with CMR (end-diastolic volume, r = 0.95; end-systolic volume, r = 0.96; ejection fraction, r = 0.83). Biases were, respectively, -6 ± 11%, 0 ± 15%, and -7 ± 17% of the mean measured values. In a subset of patients with suboptimal 3DE images, the new analysis resulted in significantly improved accuracy against CMR and reproducibility, compared with previously used coronal view-based techniques. The time required for the 3DE analysis was approximately 4 min. The new software is fast, reproducible, and accurate compared with CMR over a wide range of RV size and function. Because right ventricle-focused 3DE acquisition is feasible in most patients, this approach may be applicable to a broader population of patients who can benefit from RV volumetric assessment. Copyright © 2015 American Society of Echocardiography. Published by Elsevier Inc. All rights reserved.

User-driven integrated software lives: ``Paleomag'' paleomagnetics analysis on the Macintosh

NASA Astrophysics Data System (ADS)

Jones, Craig H.

2002-12-01

"PaleoMag," a paleomagnetics analysis package originally developed for the Macintosh operating system in 1988, allows examination of demagnetization of individual samples and analysis of directional data from collections of samples. Prior to recent reinvigorated development of the software for both Macintosh and Windows, it was widely used despite not running properly on machines and operating systems sold after 1995. This somewhat surprising situation demonstrates that there is a continued need for integrated analysis software within the earth sciences, in addition to well-developed scripting and batch-mode software. One distinct advantage of software like PaleoMag is in the ability to combine quality control with analysis within a unique graphical environment. Because such demands are frequent within the earth sciences, means of nurturing the development of similar software should be found.

Reliability of a Single Light Source Purkinjemeter in Pseudophakic Eyes.

PubMed

Janunts, Edgar; Chashchina, Ekaterina; Seitz, Berthold; Schaeffel, Frank; Langenbucher, Achim

2015-08-01

To study the reliability of Purkinje image analysis for assessment of intraocular lens tilt and decentration in pseudophakic eyes. The study comprised 64 eyes of 39 patients. All eyes underwent phacoemulsification with intraocular lens implanted in the capsular bag. Lens decentration and tilt were measured multiple times by an infrared Purkinjemeter. A total of 396 measurements were performed 1 week and 1 month postoperatively. Lens tilt (Tx, Ty) and decentration (Dx, Dy) in horizontal and vertical directions, respectively, were calculated by dedicated software based on regression analysis for each measurement using only four images, and afterward, the data were averaged (mean values, MV) for repeated sequence of measurements. New software was designed by us for recalculating lens misalignment parameters offline, using a complete set of Purkinje images obtained through the repeated measurements (9 to 15 Purkinje images) (recalculated values, MV'). MV and MV' were compared using SPSS statistical software package. MV and MV' were found to be highly correlated for the Tx and Ty parameters (R2 > 0.9; p < 0.001), moderately correlated for the Dx parameter (R2 > 0.7; p < 0.001), and weakly correlated for the Dy parameter (R2 = 0.23; p < 0.05). Reliability was high (Cronbach α > 0.9) for all measured parameters. Standard deviation values were 0.86 ± 0.69 degrees, 0.72 ± 0.65 degrees, 0.04 ± 0.05 mm, and 0.23 ± 0.34 mm for Tx, Ty, Dx, and Dy, respectively. The Purkinjemeter demonstrated high reliability and reproducibility for lens misalignment parameters. To further improve reliability, we recommend capturing at least six Purkinje images instead of three.

Prediction of accommodative optical response in prepresbyopic patients using ultrasound biomicroscopy

PubMed Central

Ramasubramanian, Viswanathan; Glasser, Adrian

2015-01-01

PURPOSE To determine whether relatively low-resolution ultrasound biomicroscopy (UBM) can predict the accommodative optical response in prepresbyopic eyes as well as in a previous study of young phakic subjects, despite lower accommodative amplitudes. SETTING College of Optometry, University of Houston, Houston, USA. DESIGN Observational cross-sectional study. METHODS Static accommodative optical response was measured with infrared photorefraction and an autorefractor (WR-5100K) in subjects aged 36 to 46 years. A 35 MHz UBM device (Vumax, Sonomed Escalon) was used to image the left eye, while the right eye viewed accommodative stimuli. Custom-developed Matlab image-analysis software was used to perform automated analysis of UBM images to measure the ocular biometry parameters. The accommodative optical response was predicted from biometry parameters using linear regression, 95% confidence intervals (CIs), and 95% prediction intervals. RESULTS The study evaluated 25 subjects. Per-diopter (D) accommodative changes in anterior chamber depth (ACD), lens thickness, anterior and posterior lens radii of curvature, and anterior segment length were similar to previous values from young subjects. The standard deviations (SDs) of accommodative optical response predicted from linear regressions for UBM-measured biometry parameters were ACD, 0.15 D; lens thickness, 0.25 D; anterior lens radii of curvature, 0.09 D; posterior lens radii of curvature, 0.37 D; and anterior segment length, 0.42 D. CONCLUSIONS Ultrasound biomicroscopy parameters can, on average, predict accommodative optical response with SDs of less than 0.55 D using linear regressions and 95% CIs. Ultrasound biomicroscopy can be used to visualize and quantify accommodative biometric changes and predict accommodative optical response in prepresbyopic eyes. PMID:26049831

Analysis of relativistic nucleus-nucleus interactions in emulsion chambers

NASA Technical Reports Server (NTRS)

Mcguire, Stephen C.

1987-01-01

The development of a computer-assisted method is reported for the determination of the angular distribution data for secondary particles produced in relativistic nucleus-nucleus collisions in emulsions. The method is applied to emulsion detectors that were placed in a constant, uniform magnetic field and exposed to beams of 60 and 200 GeV/nucleon O-16 ions at the Super Proton Synchrotron (SPS) of the European Center for Nuclear Research (CERN). Linear regression analysis is used to determine the azimuthal and polar emission angles from measured track coordinate data. The software, written in BASIC, is designed to be machine independent, and adaptable to an automated system for acquiring the track coordinates. The fitting algorithm is deterministic, and takes into account the experimental uncertainty in the measured points. Further, a procedure for using the track data to estimate the linear momenta of the charged particles observed in the detectors is included.

How Does Physical Activity Intervention Improve Self-Esteem and Self-Concept in Children and Adolescents? Evidence from a Meta-Analysis.

PubMed

Liu, Mingli; Wu, Lang; Ming, Qingsen

2015-01-01

To perform a systematic review and meta-analysis for the effects of physical activity intervention on self-esteem and self-concept in children and adolescents, and to identify moderator variables by meta-regression. A meta-analysis and meta-regression. Relevant studies were identified through a comprehensive search of electronic databases. Study inclusion criteria were: (1) intervention should be supervised physical activity, (2) reported sufficient data to estimate pooled effect sizes of physical activity intervention on self-esteem or self-concept, (3) participants' ages ranged from 3 to 20 years, and (4) a control or comparison group was included. For each study, study design, intervention design and participant characteristics were extracted. R software (version 3.1.3) and Stata (version 12.0) were used to synthesize effect sizes and perform moderation analyses for determining moderators. Twenty-five randomized controlled trial (RCT) studies and 13 non-randomized controlled trial (non-RCT) studies including a total of 2991 cases were identified. Significant positive effects were found in RCTs for intervention of physical activity alone on general self outcomes (Hedges' g = 0.29, 95% confidence interval [CI]: 0.14 to 0.45; p = 0.001), self-concept (Hedges' g = 0.49, 95%CI: 0.10 to 0.88, p = 0.014) and self-worth (Hedges' g = 0.31, 95%CI: 0.13 to 0.49, p = 0.005). There was no significant effect of intervention of physical activity alone on any outcomes in non-RCTs, as well as in studies with intervention of physical activity combined with other strategies. Meta-regression analysis revealed that higher treatment effects were associated with setting of intervention in RCTs (β = 0.31, 95%CI: 0.07 to 0.55, p = 0.013). Intervention of physical activity alone is associated with increased self-concept and self-worth in children and adolescents. And there is a stronger association with school-based and gymnasium-based intervention compared with other settings.

Semantic Metrics for Analysis of Software

NASA Technical Reports Server (NTRS)

Etzkorn, Letha H.; Cox, Glenn W.; Farrington, Phil; Utley, Dawn R.; Ghalston, Sampson; Stein, Cara

2005-01-01

A recently conceived suite of object-oriented software metrics focus is on semantic aspects of software, in contradistinction to traditional software metrics, which focus on syntactic aspects of software. Semantic metrics represent a more human-oriented view of software than do syntactic metrics. The semantic metrics of a given computer program are calculated by use of the output of a knowledge-based analysis of the program, and are substantially more representative of software quality and more readily comprehensible from a human perspective than are the syntactic metrics.

Data on the application of Functional Data Analysis in food fermentations.

PubMed

Ruiz-Bellido, M A; Romero-Gil, V; García-García, P; Rodríguez-Gómez, F; Arroyo-López, F N; Garrido-Fernández, A

2016-12-01

This article refers to the paper "Assessment of table olive fermentation by functional data analysis" (Ruiz-Bellido et al., 2016) [1]. The dataset include pH, titratable acidity, yeast count and area values obtained during fermentation process (380 days) of Aloreña de Málaga olives subjected to five different fermentation systems: i) control of acidified cured olives, ii) highly acidified cured olives, iii) intermediate acidified cured olives, iv) control of traditional cracked olives, and v) traditional olives cracked after 72 h of exposure to air. Many of the Tables and Figures shown in this paper were deduced after application of Functional Data Analysis to raw data using a routine executed under R software for comparison among treatments by the transformation of raw data into smooth curves and the application of a new battery of statistical tools (functional pointwise estimation of the averages and standard deviations, maximum, minimum, first and second derivatives, functional regression, and functional F and t-tests).

Using Logistic Regression To Predict the Probability of Debris Flows Occurring in Areas Recently Burned By Wildland Fires

USGS Publications Warehouse

Rupert, Michael G.; Cannon, Susan H.; Gartner, Joseph E.

2003-01-01

Logistic regression was used to predict the probability of debris flows occurring in areas recently burned by wildland fires. Multiple logistic regression is conceptually similar to multiple linear regression because statistical relations between one dependent variable and several independent variables are evaluated. In logistic regression, however, the dependent variable is transformed to a binary variable (debris flow did or did not occur), and the actual probability of the debris flow occurring is statistically modeled. Data from 399 basins located within 15 wildland fires that burned during 2000-2002 in Colorado, Idaho, Montana, and New Mexico were evaluated. More than 35 independent variables describing the burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated. The models were developed as follows: (1) Basins that did and did not produce debris flows were delineated from National Elevation Data using a Geographic Information System (GIS). (2) Data describing the burn severity, geology, land surface gradient, rainfall, and soil properties were determined for each basin. These data were then downloaded to a statistics software package for analysis using logistic regression. (3) Relations between the occurrence/non-occurrence of debris flows and burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated and several preliminary multivariate logistic regression models were constructed. All possible combinations of independent variables were evaluated to determine which combination produced the most effective model. The multivariate model that best predicted the occurrence of debris flows was selected. (4) The multivariate logistic regression model was entered into a GIS, and a map showing the probability of debris flows was constructed. The most effective model incorporates the percentage of each basin with slope greater than 30 percent, percentage of land burned at medium and high burn severity in each basin, particle size sorting, average storm intensity (millimeters per hour), soil organic matter content, soil permeability, and soil drainage. The results of this study demonstrate that logistic regression is a valuable tool for predicting the probability of debris flows occurring in recently-burned landscapes.

A Change Impact Analysis to Characterize Evolving Program Behaviors

NASA Technical Reports Server (NTRS)

Rungta, Neha Shyam; Person, Suzette; Branchaud, Joshua

2012-01-01

Change impact analysis techniques estimate the potential effects of changes made to software. Directed Incremental Symbolic Execution (DiSE) is an intraprocedural technique for characterizing the impact of software changes on program behaviors. DiSE first estimates the impact of the changes on the source code using program slicing techniques, and then uses the impact sets to guide symbolic execution to generate path conditions that characterize impacted program behaviors. DiSE, however, cannot reason about the flow of impact between methods and will fail to generate path conditions for certain impacted program behaviors. In this work, we present iDiSE, an extension to DiSE that performs an interprocedural analysis. iDiSE combines static and dynamic calling context information to efficiently generate impacted program behaviors across calling contexts. Information about impacted program behaviors is useful for testing, verification, and debugging of evolving programs. We present a case-study of our implementation of the iDiSE algorithm to demonstrate its efficiency at computing impacted program behaviors. Traditional notions of coverage are insufficient for characterizing the testing efforts used to validate evolving program behaviors because they do not take into account the impact of changes to the code. In this work we present novel definitions of impacted coverage metrics that are useful for evaluating the testing effort required to test evolving programs. We then describe how the notions of impacted coverage can be used to configure techniques such as DiSE and iDiSE in order to support regression testing related tasks. We also discuss how DiSE and iDiSE can be configured for debugging finding the root cause of errors introduced by changes made to the code. In our empirical evaluation we demonstrate that the configurations of DiSE and iDiSE can be used to support various software maintenance tasks

Cognitive versus Software-Assisted Registration: Development of a New Nomogram Predicting Prostate Cancer at MRI-Targeted Biopsies.

PubMed

Kaufmann, Sascha; Russo, Giorgio I; Thaiss, Wolfgang; Notohamiprodjo, Mike; Bamberg, Fabian; Bedke, Jens; Morgia, Giuseppe; Nikolaou, Konstantin; Stenzl, Arnulf; Kruck, Stephan

2018-04-03

Multiparametric magnetic resonance imaging (mpMRI) is gaining acceptance to guide targeted biopsy (TB) in prostate cancer (PC) diagnosis. We aimed to compare the detection rate of software-assisted fusion TB (SA-TB) versus cognitive fusion TB (COG-TB) for PC and to evaluate potential clinical features in detecting PC and clinically significant PC (csPC) at TB. This was a retrospective cohort study of patients with rising and/or persistently elevated prostate-specific antigen (PSA) undergoing mpMRI followed by either transperineal SA-TB or transrectal COG-TB. The analysis showed a matched-paired analysis between SA-TB versus COG-TB without differences in clinical or radiological characteristics. Differences among detection of PC/csPC among groups were analyzed. A multivariable logistic regression model predicting PC at TB was fitted. The model was evaluated using the receiver operating characteristic-derived area under the curve, goodness of fit test, and decision-curve analyses. One hundred ninety-one and 87 patients underwent SA-TB or COG-TB, respectively. The multivariate logistic analysis showed that SA-TB was associated with overall PC (odds ratio [OR], 5.70; P < .01) and PC at TB (OR, 3.00; P < .01) but not with overall csPC (P = .40) and csPC at TB (P = .40). A nomogram predicting PC at TB was constructed using the Prostate Imaging Reporting and Data System version 2.0, age, PSA density and biopsy technique, showing improved clinical risk prediction against a threshold probability of 10% with a c-index of 0.83. In patients with suspected PC, software-assisted biopsy detects most cancers and outperforms the cognitive approach in targeting magnetic resonance imaging-visible lesions. Furthermore, we introduced a prebiopsy nomogram for the probability of PC in TB. Copyright © 2018 Elsevier Inc. All rights reserved.

Large-area landslide susceptibility with optimized slope-units

NASA Astrophysics Data System (ADS)

Alvioli, Massimiliano; Marchesini, Ivan; Reichenbach, Paola; Rossi, Mauro; Ardizzone, Francesca; Fiorucci, Federica; Guzzetti, Fausto

2017-04-01

A Slope-Unit (SU) is a type of morphological terrain unit bounded by drainage and divide lines that maximize the within-unit homogeneity and the between-unit heterogeneity across distinct physical and geographical boundaries [1]. Compared to other terrain subdivisions, SU are morphological terrain unit well related to the natural (i.e., geological, geomorphological, hydrological) processes that shape and characterize natural slopes. This makes SU easily recognizable in the field or in topographic base maps, and well suited for environmental and geomorphological analysis, in particular for landslide susceptibility (LS) modelling. An optimal subdivision of an area into a set of SU depends on multiple factors: size and complexity of the study area, quality and resolution of the available terrain elevation data, purpose of the terrain subdivision, scale and resolution of the phenomena for which SU are delineated. We use the recently developed r.slopeunits software [2,3] for the automatic, parametric delineation of SU within the open source GRASS GIS based on terrain elevation data and a small number of user-defined parameters. The software provides subdivisions consisting of SU with different shapes and sizes, as a function of the input parameters. In this work, we describe a procedure for the optimal selection of the user parameters through the production of a large number of realizations of the LS model. We tested the software and the optimization procedure in a 2,000 km2 area in Umbria, Central Italy. For LS zonation we adopt a logistic regression model implemented in an well-known software [4,5], using about 50 independent variables. To select the optimal SU partition for LS zonation, we want to define a metric which is able to quantify simultaneously: (i) slope-unit internal homogeneity (ii) slope-unit external heterogeneity (iii) landslide susceptibility model performance. To this end, we define a comprehensive objective function S, as the product of three normalized objective functions dealing with the points (i)-(ii)-(iii) independently. We use an intra-segment variance function V, the Moran's autocorrelation index I and the AUCROC function R arising from the application of the logistic regression model. Maximization of the objective function S = f(I,V,R) as a function of the r.slopeunits input parameters provides an objective and reproducible way to select the optimal parameter combination for a proper SU subdivision for LS modelling. We further perform an analysis of the statistical significance of the LS models as a function of the r.slopeunits input parameters, focusing on the degree of coarseness of each subdivision. We find that the LRM, when applied to subdivisions with large average SU size, has a very poor statistical significance, resulting in only few (5%, typically lithological) variables being used in the regression due to the large heterogeneity of all variables within each unit, while up to 35% of the variables are used when SU are very small. This behavior was largely expected and provides further evidence that an objective method to select SU size is highly desirable. [1] Guzzetti, F. et al., Geomorphology 31, (1999) 181-216 [2] Alvioli, M. et al., Geoscientific Model Development 9 (2016), 3975-3991 [3] http://geomorphology.irpi.cnr.it/tools/slope-units [4] Rossi, M. et al., Geomorphology 114, (2010) 129-142 [5] Rossi, M. and Reichenbach, P., Geoscientific Model Development 9 (2016), 3533-3543

Parametric regression model for survival data: Weibull regression model as an example

PubMed Central

2016-01-01

Weibull regression model is one of the most popular forms of parametric regression model that it provides estimate of baseline hazard function, as well as coefficients for covariates. Because of technical difficulties, Weibull regression model is seldom used in medical literature as compared to the semi-parametric proportional hazard model. To make clinical investigators familiar with Weibull regression model, this article introduces some basic knowledge on Weibull regression model and then illustrates how to fit the model with R software. The SurvRegCensCov package is useful in converting estimated coefficients to clinical relevant statistics such as hazard ratio (HR) and event time ratio (ETR). Model adequacy can be assessed by inspecting Kaplan-Meier curves stratified by categorical variable. The eha package provides an alternative method to model Weibull regression model. The check.dist() function helps to assess goodness-of-fit of the model. Variable selection is based on the importance of a covariate, which can be tested using anova() function. Alternatively, backward elimination starting from a full model is an efficient way for model development. Visualization of Weibull regression model after model development is interesting that it provides another way to report your findings. PMID:28149846

Software Reliability Analysis of NASA Space Flight Software: A Practical Experience

PubMed Central

Sukhwani, Harish; Alonso, Javier; Trivedi, Kishor S.; Mcginnis, Issac

2017-01-01

In this paper, we present the software reliability analysis of the flight software of a recently launched space mission. For our analysis, we use the defect reports collected during the flight software development. We find that this software was developed in multiple releases, each release spanning across all software life-cycle phases. We also find that the software releases were developed and tested for four different hardware platforms, spanning from off-the-shelf or emulation hardware to actual flight hardware. For releases that exhibit reliability growth or decay, we fit Software Reliability Growth Models (SRGM); otherwise we fit a distribution function. We find that most releases exhibit reliability growth, with Log-Logistic (NHPP) and S-Shaped (NHPP) as the best-fit SRGMs. For the releases that experience reliability decay, we investigate the causes for the same. We find that such releases were the first software releases to be tested on a new hardware platform, and hence they encountered major hardware integration issues. Also such releases seem to have been developed under time pressure in order to start testing on the new hardware platform sooner. Such releases exhibit poor reliability growth, and hence exhibit high predicted failure rate. Other problems include hardware specification changes and delivery delays from vendors. Thus, our analysis provides critical insights and inputs to the management to improve the software development process. As NASA has moved towards a product line engineering for its flight software development, software for future space missions will be developed in a similar manner and hence the analysis results for this mission can be considered as a baseline for future flight software missions. PMID:29278255

Software Reliability Analysis of NASA Space Flight Software: A Practical Experience.

PubMed

Sukhwani, Harish; Alonso, Javier; Trivedi, Kishor S; Mcginnis, Issac

2016-01-01

In this paper, we present the software reliability analysis of the flight software of a recently launched space mission. For our analysis, we use the defect reports collected during the flight software development. We find that this software was developed in multiple releases, each release spanning across all software life-cycle phases. We also find that the software releases were developed and tested for four different hardware platforms, spanning from off-the-shelf or emulation hardware to actual flight hardware. For releases that exhibit reliability growth or decay, we fit Software Reliability Growth Models (SRGM); otherwise we fit a distribution function. We find that most releases exhibit reliability growth, with Log-Logistic (NHPP) and S-Shaped (NHPP) as the best-fit SRGMs. For the releases that experience reliability decay, we investigate the causes for the same. We find that such releases were the first software releases to be tested on a new hardware platform, and hence they encountered major hardware integration issues. Also such releases seem to have been developed under time pressure in order to start testing on the new hardware platform sooner. Such releases exhibit poor reliability growth, and hence exhibit high predicted failure rate. Other problems include hardware specification changes and delivery delays from vendors. Thus, our analysis provides critical insights and inputs to the management to improve the software development process. As NASA has moved towards a product line engineering for its flight software development, software for future space missions will be developed in a similar manner and hence the analysis results for this mission can be considered as a baseline for future flight software missions.

Learn by Yourself: The Self-Learning Tools for Qualitative Analysis Software Packages

ERIC Educational Resources Information Center

Freitas, Fábio; Ribeiro, Jaime; Brandão, Catarina; Reis, Luís Paulo; de Souza, Francislê Neri; Costa, António Pedro

2017-01-01

Computer Assisted Qualitative Data Analysis Software (CAQDAS) are tools that help researchers to develop qualitative research projects. These software packages help the users with tasks such as transcription analysis, coding and text interpretation, writing and annotation, content search and analysis, recursive abstraction, grounded theory…

Kubios HRV--heart rate variability analysis software.

PubMed

Tarvainen, Mika P; Niskanen, Juha-Pekka; Lipponen, Jukka A; Ranta-Aho, Perttu O; Karjalainen, Pasi A

2014-01-01

Kubios HRV is an advanced and easy to use software for heart rate variability (HRV) analysis. The software supports several input data formats for electrocardiogram (ECG) data and beat-to-beat RR interval data. It includes an adaptive QRS detection algorithm and tools for artifact correction, trend removal and analysis sample selection. The software computes all the commonly used time-domain and frequency-domain HRV parameters and several nonlinear parameters. There are several adjustable analysis settings through which the analysis methods can be optimized for different data. The ECG derived respiratory frequency is also computed, which is important for reliable interpretation of the analysis results. The analysis results can be saved as an ASCII text file (easy to import into MS Excel or SPSS), Matlab MAT-file, or as a PDF report. The software is easy to use through its compact graphical user interface. The software is available free of charge for Windows and Linux operating systems at http://kubios.uef.fi. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

[The relationship of halitosis and Helicobacter pylori].

PubMed

Chen, Xi; Tao, Dan-ying; Li, Qing; Feng, Xi-ping

2007-06-01

The aim of the study was to investigate the relationship between halitosis and Helicobacter pylori infection in stomach. Fifty subjects without periodontal diseases and systematic disease (exclude gastrointestinal diseases) were included. Infection of H.pylori was diagnosed by biopsy and (14)C-urea breath test. SPSS11.5 software package was used to analyze the data. All the subjects were periodontal healthy according to the periodontal index. The prevalence of H.pylori infection in halitosis subjects was significantly higher than that in the normal subjects (57.1% VS 18.2%, P<0.01). Logistic regression analysis showed that H.pylori was the only significant variable in the equation(P<0.05). H.pylori in stomach may be involved in the presence of halitosis in periodontal healthy subjects.

An online database for plant image analysis software tools.

PubMed

Lobet, Guillaume; Draye, Xavier; Périlleux, Claire

2013-10-09

Recent years have seen an increase in methods for plant phenotyping using image analyses. These methods require new software solutions for data extraction and treatment. These solutions are instrumental in supporting various research pipelines, ranging from the localisation of cellular compounds to the quantification of tree canopies. However, due to the variety of existing tools and the lack of central repository, it is challenging for researchers to identify the software that is best suited for their research. We present an online, manually curated, database referencing more than 90 plant image analysis software solutions. The website, plant-image-analysis.org, presents each software in a uniform and concise manner enabling users to identify the available solutions for their experimental needs. The website also enables user feedback, evaluations and new software submissions. The plant-image-analysis.org database provides an overview of existing plant image analysis software. The aim of such a toolbox is to help users to find solutions, and to provide developers a way to exchange and communicate about their work.

GWAMA: software for genome-wide association meta-analysis.

PubMed

Mägi, Reedik; Morris, Andrew P

2010-05-28

Despite the recent success of genome-wide association studies in identifying novel loci contributing effects to complex human traits, such as type 2 diabetes and obesity, much of the genetic component of variation in these phenotypes remains unexplained. One way to improving power to detect further novel loci is through meta-analysis of studies from the same population, increasing the sample size over any individual study. Although statistical software analysis packages incorporate routines for meta-analysis, they are ill equipped to meet the challenges of the scale and complexity of data generated in genome-wide association studies. We have developed flexible, open-source software for the meta-analysis of genome-wide association studies. The software incorporates a variety of error trapping facilities, and provides a range of meta-analysis summary statistics. The software is distributed with scripts that allow simple formatting of files containing the results of each association study and generate graphical summaries of genome-wide meta-analysis results. The GWAMA (Genome-Wide Association Meta-Analysis) software has been developed to perform meta-analysis of summary statistics generated from genome-wide association studies of dichotomous phenotypes or quantitative traits. Software with source files, documentation and example data files are freely available online at http://www.well.ox.ac.uk/GWAMA.

A meta-analysis of association between glutathione S-transferase M1 gene polymorphism and Parkinson's disease susceptibility.

PubMed

Weikang, Chen; Jie, Li; Likang, Lan; Weiwen, Qiu; Liping, Lu

2016-01-01

The aim of this meta-analysis was to evaluate whether there was an association between glutathione S-transferase M1(GSTM1)gene polymorphism and Parkinson's disease (PD) susceptibility by pooling published data. We performed comprehensive electronic database search for articles published between February12,2015 and April30 2016. The published case-control or cohort studies related to GSTM1 gene polymorphism and Parkinson's disease susceptibility were screened, reviewed, and included in this meta-analysis. The correlation between GSTM1 gene polymorphism and PD susceptibility was expressed by odds ratio (OR) and its corresponding 95% confidence interval (95%CI). Publication bias was evaluated by Begg's funnel plot and Egger's line regression test. All analysis was done by stata11.0 software. After searching the PubMed, EMBASE, and CNKI databases, seventeen case-control studies with 3,538 PD and 5,180 controls were included in the final meta-analysis. The data was pooled by a fixed-effect model for lack of statistical heterogeneity across the studies; the results showed GSTM1 null expression can significant increase the susceptibility of PD (OR=1.11, 95% CI:1.01-1.21, P<0.05). Subgroup analysis indicated GSTM1 gene polymorphism was associated with PD susceptibility in the Caucasian ethnic group (OR=1.15, 95% CI:1.05-1.27, P<0.05) but not in the Asian ethnic group (OR=0.89, 95% CI:0.70-1.12, P>0.05). Begg's funnel plot and Egger's line regression test showed no significant publication bias. Based on the present evidence, GSTM1 null expression can significant increase the susceptibility of PD in persons of Caucasian ethnicity.

The National Flood Frequency Program, version 3 : a computer program for estimating magnitude and frequency of floods for ungaged sites

USGS Publications Warehouse

Ries, Kernell G.; Crouse, Michele Y.

2002-01-01

For many years, the U.S. Geological Survey (USGS) has been developing regional regression equations for estimating flood magnitude and frequency at ungaged sites. These regression equations are used to transfer flood characteristics from gaged to ungaged sites through the use of watershed and climatic characteristics as explanatory or predictor variables. Generally, these equations have been developed on a Statewide or metropolitan-area basis as part of cooperative study programs with specific State Departments of Transportation. In 1994, the USGS released a computer program titled the National Flood Frequency Program (NFF), which compiled all the USGS available regression equations for estimating the magnitude and frequency of floods in the United States and Puerto Rico. NFF was developed in cooperation with the Federal Highway Administration and the Federal Emergency Management Agency. Since the initial release of NFF, the USGS has produced new equations for many areas of the Nation. A new version of NFF has been developed that incorporates these new equations and provides additional functionality and ease of use. NFF version 3 provides regression-equation estimates of flood-peak discharges for unregulated rural and urban watersheds, flood-frequency plots, and plots of typical flood hydrographs for selected recurrence intervals. The Program also provides weighting techniques to improve estimates of flood-peak discharges for gaging stations and ungaged sites. The information provided by NFF should be useful to engineers and hydrologists for planning and design applications. This report describes the flood-regionalization techniques used in NFF and provides guidance on the applicability and limitations of the techniques. The NFF software and the documentation for the regression equations included in NFF are available at http://water.usgs.gov/software/nff.html.

Software Engineering Improvement Activities/Plan

NASA Technical Reports Server (NTRS)

2003-01-01

bd Systems personnel accomplished the technical responsibilities for this reporting period, as planned. A close working relationship was maintained with personnel of the MSFC Avionics Department Software Group (ED14). Work accomplishments included development, evaluation, and enhancement of a software cost model, performing literature search and evaluation of software tools available for code analysis and requirements analysis, and participating in other relevant software engineering activities. Monthly reports were submitted. This support was provided to the Flight Software Group/ED 1 4 in accomplishing the software engineering improvement engineering activities of the Marshall Space Flight Center (MSFC) Software Engineering Improvement Plan.

Visualization of multiple influences on ocellar flight control in giant honeybees with the data-mining tool Viscovery SOMine.

PubMed

Kastberger, G; Kranner, G

2000-02-01

Viscovery SOMine is a software tool for advanced analysis and monitoring of numerical data sets. It was developed for professional use in business, industry, and science and to support dependency analysis, deviation detection, unsupervised clustering, nonlinear regression, data association, pattern recognition, and animated monitoring. Based on the concept of self-organizing maps (SOMs), it employs a robust variant of unsupervised neural networks--namely, Kohonen's Batch-SOM, which is further enhanced with a new scaling technique for speeding up the learning process. This tool provides a powerful means by which to analyze complex data sets without prior statistical knowledge. The data representation contained in the trained SOM is systematically converted to be used in a spectrum of visualization techniques, such as evaluating dependencies between components, investigating geometric properties of the data distribution, searching for clusters, or monitoring new data. We have used this software tool to analyze and visualize multiple influences of the ocellar system on free-flight behavior in giant honeybees. Occlusion of ocelli will affect orienting reactivities in relation to flight target, level of disturbance, and position of the bee in the flight chamber; it will induce phototaxis and make orienting imprecise and dependent on motivational settings. Ocelli permit the adjustment of orienting strategies to environmental demands by enforcing abilities such as centering or flight kinetics and by providing independent control of posture and flight course.

Heuristic Identification of Biological Architectures for Simulating Complex Hierarchical Genetic Interactions

PubMed Central

Moore, Jason H; Amos, Ryan; Kiralis, Jeff; Andrews, Peter C

2015-01-01

Simulation plays an essential role in the development of new computational and statistical methods for the genetic analysis of complex traits. Most simulations start with a statistical model using methods such as linear or logistic regression that specify the relationship between genotype and phenotype. This is appealing due to its simplicity and because these statistical methods are commonly used in genetic analysis. It is our working hypothesis that simulations need to move beyond simple statistical models to more realistically represent the biological complexity of genetic architecture. The goal of the present study was to develop a prototype genotype–phenotype simulation method and software that are capable of simulating complex genetic effects within the context of a hierarchical biology-based framework. Specifically, our goal is to simulate multilocus epistasis or gene–gene interaction where the genetic variants are organized within the framework of one or more genes, their regulatory regions and other regulatory loci. We introduce here the Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (HIBACHI) method and prototype software for simulating data in this manner. This approach combines a biological hierarchy, a flexible mathematical framework, a liability threshold model for defining disease endpoints, and a heuristic search strategy for identifying high-order epistatic models of disease susceptibility. We provide several simulation examples using genetic models exhibiting independent main effects and three-way epistatic effects. PMID:25395175

Development of a New VLBI Data Analysis Software

NASA Technical Reports Server (NTRS)

Bolotin, Sergei; Gipson, John M.; MacMillan, Daniel S.

2010-01-01

We present an overview of a new VLBI analysis software under development at NASA GSFC. The new software will replace CALC/SOLVE and many related utility programs. It will have the capabilities of the current system as well as incorporate new models and data analysis techniques. In this paper we give a conceptual overview of the new software. We formulate the main goals of the software. The software should be flexible and modular to implement models and estimation techniques that currently exist or will appear in future. On the other hand it should be reliable and possess production quality for processing standard VLBI sessions. Also, it needs to be capable of processing observations from a fully deployed network of VLBI2010 stations in a reasonable time. We describe the software development process and outline the software architecture.

Development of Automated Image Analysis Software for Suspended Marine Particle Classification

DTIC Science & Technology

2002-09-30

Development of Automated Image Analysis Software for Suspended Marine Particle Classification Scott Samson Center for Ocean Technology...and global water column. 1 OBJECTIVES The project’s objective is to develop automated image analysis software to reduce the effort and time

Perl-speaks-NONMEM (PsN)--a Perl module for NONMEM related programming.

PubMed

Lindbom, Lars; Ribbing, Jakob; Jonsson, E Niclas

2004-08-01

The NONMEM program is the most widely used nonlinear regression software in population pharmacokinetic/pharmacodynamic (PK/PD) analyses. In this article we describe a programming library, Perl-speaks-NONMEM (PsN), intended for programmers that aim at using the computational capability of NONMEM in external applications. The library is object oriented and written in the programming language Perl. The classes of the library are built around NONMEM's data, model and output files. The specification of the NONMEM model is easily set or changed through the model and data file classes while the output from a model fit is accessed through the output file class. The classes have methods that help the programmer perform common repetitive tasks, e.g. summarising the output from a NONMEM run, setting the initial estimates of a model based on a previous run or truncating values over a certain threshold in the data file. PsN creates a basis for the development of high-level software using NONMEM as the regression tool.

Application-level regression testing framework using Jenkins

DOE PAGES

Budiardja, Reuben; Bouvet, Timothy; Arnold, Galen

2017-09-26

Monitoring and testing for regression of large-scale systems such as the NCSA's Blue Waters supercomputer are challenging tasks. In this paper, we describe the solution we came up with to perform those tasks. The goal was to find an automated solution for running user-level regression tests to evaluate system usability and performance. Jenkins, an automation server software, was chosen for its versatility, large user base, and multitude of plugins including collecting data and plotting test results over time. We also describe our Jenkins deployment to launch and monitor jobs on remote HPC system, perform authentication with one-time password, and integratemore » with our LDAP server for its authorization. We show some use cases and describe our best practices for successfully using Jenkins as a user-level system-wide regression testing and monitoring framework for large supercomputer systems.« less

Application-level regression testing framework using Jenkins

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

Budiardja, Reuben; Bouvet, Timothy; Arnold, Galen

Monitoring and testing for regression of large-scale systems such as the NCSA's Blue Waters supercomputer are challenging tasks. In this paper, we describe the solution we came up with to perform those tasks. The goal was to find an automated solution for running user-level regression tests to evaluate system usability and performance. Jenkins, an automation server software, was chosen for its versatility, large user base, and multitude of plugins including collecting data and plotting test results over time. We also describe our Jenkins deployment to launch and monitor jobs on remote HPC system, perform authentication with one-time password, and integratemore » with our LDAP server for its authorization. We show some use cases and describe our best practices for successfully using Jenkins as a user-level system-wide regression testing and monitoring framework for large supercomputer systems.« less

Computer-assisted qualitative data analysis software.

PubMed

Cope, Diane G

2014-05-01

Advances in technology have provided new approaches for data collection methods and analysis for researchers. Data collection is no longer limited to paper-and-pencil format, and numerous methods are now available through Internet and electronic resources. With these techniques, researchers are not burdened with entering data manually and data analysis is facilitated by software programs. Quantitative research is supported by the use of computer software and provides ease in the management of large data sets and rapid analysis of numeric statistical methods. New technologies are emerging to support qualitative research with the availability of computer-assisted qualitative data analysis software (CAQDAS).CAQDAS will be presented with a discussion of advantages, limitations, controversial issues, and recommendations for this type of software use.

Orbiter subsystem hardware/software interaction analysis. Volume 8: Forward reaction control system

NASA Technical Reports Server (NTRS)

Becker, D. D.

1980-01-01

The results of the orbiter hardware/software interaction analysis for the AFT reaction control system are presented. The interaction between hardware failure modes and software are examined in order to identify associated issues and risks. All orbiter subsystems and interfacing program elements which interact with the orbiter computer flight software are analyzed. The failure modes identified in the subsystem/element failure mode and effects analysis are discussed.

Sneak Analysis Application Guidelines

DTIC Science & Technology

1982-06-01

Hardware Program Change Cost Trend, Airborne Environment ....... ....................... 111 3-11 Relative Software Program Change Costs...113 3-50 Derived Software Program Change Cost by Phase,* Airborne Environment ..... ............... 114 3-51 Derived Software Program Change...Cost by Phase, Ground/Water Environment ... ............. .... 114 3-52 Total Software Program Change Costs ................ 115 3-53 Sneak Analysis

Software Users Manual (SUM): Extended Testability Analysis (ETA) Tool

NASA Technical Reports Server (NTRS)

Maul, William A.; Fulton, Christopher E.

2011-01-01

This software user manual describes the implementation and use the Extended Testability Analysis (ETA) Tool. The ETA Tool is a software program that augments the analysis and reporting capabilities of a commercial-off-the-shelf (COTS) testability analysis software package called the Testability Engineering And Maintenance System (TEAMS) Designer. An initial diagnostic assessment is performed by the TEAMS Designer software using a qualitative, directed-graph model of the system being analyzed. The ETA Tool utilizes system design information captured within the diagnostic model and testability analysis output from the TEAMS Designer software to create a series of six reports for various system engineering needs. The ETA Tool allows the user to perform additional studies on the testability analysis results by determining the detection sensitivity to the loss of certain sensors or tests. The ETA Tool was developed to support design and development of the NASA Ares I Crew Launch Vehicle. The diagnostic analysis provided by the ETA Tool was proven to be valuable system engineering output that provided consistency in the verification of system engineering requirements. This software user manual provides a description of each output report generated by the ETA Tool. The manual also describes the example diagnostic model and supporting documentation - also provided with the ETA Tool software release package - that were used to generate the reports presented in the manual

Software for quantitative analysis of radiotherapy: overview, requirement analysis and design solutions.

PubMed

Zhang, Lanlan; Hub, Martina; Mang, Sarah; Thieke, Christian; Nix, Oliver; Karger, Christian P; Floca, Ralf O

2013-06-01

Radiotherapy is a fast-developing discipline which plays a major role in cancer care. Quantitative analysis of radiotherapy data can improve the success of the treatment and support the prediction of outcome. In this paper, we first identify functional, conceptional and general requirements on a software system for quantitative analysis of radiotherapy. Further we present an overview of existing radiotherapy analysis software tools and check them against the stated requirements. As none of them could meet all of the demands presented herein, we analyzed possible conceptional problems and present software design solutions and recommendations to meet the stated requirements (e.g. algorithmic decoupling via dose iterator pattern; analysis database design). As a proof of concept we developed a software library "RTToolbox" following the presented design principles. The RTToolbox is available as open source library and has already been tested in a larger-scale software system for different use cases. These examples demonstrate the benefit of the presented design principles. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Impact of Dental Disorders and its Influence on Self Esteem Levels among Adolescents.

PubMed

Kaur, Puneet; Singh, Simarpreet; Mathur, Anmol; Makkar, Diljot Kaur; Aggarwal, Vikram Pal; Batra, Manu; Sharma, Anshika; Goyal, Nikita

2017-04-01

Self esteem is more of a psychological concept therefore, even the common dental disorders like dental trauma, tooth loss and untreated carious lesions may affect the self esteem thus influencing the quality of life. This study aims to assess the impact of dental disorders among the adolescents on their self esteem level. The present cross-sectional study was conducted among 10 to 17 years adolescents. In order to obtain a representative sample, multistage sampling technique was used and sample was selected based on Probability Proportional to Enrolment size (PPE). Oral health assessment was carried out using WHO type III examination and self esteem was estimated using the Rosenberg Self Esteem Scale score (RSES). The descriptive and inferential analysis of the data was done by using IBM SPSS software. Logistic and linear regression analysis was executed to test the individual association of different independent clinical variables with self esteem. Total sample of 1140 adolescents with mean age of 14.95 ±2.08 and RSES of 27.09 ±3.12 were considered. Stepwise multiple linear regression analysis was applied and best predictors in relation to RSES in the descending order were Dental Health Component (DHC), Aesthetic Component (AC), dental decay {(aesthetic zone), (masticatory zone)}, tooth loss {(aesthetic zone), (masticatory zone)} and anterior fracture of tooth. It was found that various dental disorders like malocclusion, anterior traumatic tooth, tooth loss and untreated decay causes a profound impact on aesthetics and psychosocial behaviour of adolescents, thus affecting their self esteem.

Prediction equations for maximal respiratory pressures of Brazilian adolescents.

PubMed

Mendes, Raquel E F; Campos, Tania F; Macêdo, Thalita M F; Borja, Raíssa O; Parreira, Verônica F; Mendonça, Karla M P P

2013-01-01

The literature emphasizes the need for studies to provide reference values and equations able to predict respiratory muscle strength of Brazilian subjects at different ages and from different regions of Brazil. To develop prediction equations for maximal respiratory pressures (MRP) of Brazilian adolescents. In total, 182 healthy adolescents (98 boys and 84 girls) aged between 12 and 18 years, enrolled in public and private schools in the city of Natal-RN, were evaluated using an MVD300 digital manometer (Globalmed®) according to a standardized protocol. Statistical analysis was performed using SPSS Statistics 17.0 software, with a significance level of 5%. Data normality was verified using the Kolmogorov-Smirnov test, and descriptive analysis results were expressed as the mean and standard deviation. To verify the correlation between the MRP and the independent variables (age, weight, height and sex), the Pearson correlation test was used. To obtain the prediction equations, stepwise multiple linear regression was used. The variables height, weight and sex were correlated to MRP. However, weight and sex explained part of the variability of MRP, and the regression analysis in this study indicated that these variables contributed significantly in predicting maximal inspiratory pressure, and only sex contributed significantly to maximal expiratory pressure. This study provides reference values and two models of prediction equations for maximal inspiratory and expiratory pressures and sets the necessary normal lower limits for the assessment of the respiratory muscle strength of Brazilian adolescents.

Multivariate meta-analysis using individual participant data.

PubMed

Riley, R D; Price, M J; Jackson, D; Wardle, M; Gueyffier, F; Wang, J; Staessen, J A; White, I R

2015-06-01

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment-covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models. © 2014 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.

Automation of Flight Software Regression Testing

NASA Technical Reports Server (NTRS)

Tashakkor, Scott B.

2016-01-01

NASA is developing the Space Launch System (SLS) to be a heavy lift launch vehicle supporting human and scientific exploration beyond earth orbit. SLS will have a common core stage, an upper stage, and different permutations of boosters and fairings to perform various crewed or cargo missions. Marshall Space Flight Center (MSFC) is writing the Flight Software (FSW) that will operate the SLS launch vehicle. The FSW is developed in an incremental manner based on "Agile" software techniques. As the FSW is incrementally developed, testing the functionality of the code needs to be performed continually to ensure that the integrity of the software is maintained. Manually testing the functionality on an ever-growing set of requirements and features is not an efficient solution and therefore needs to be done automatically to ensure testing is comprehensive. To support test automation, a framework for a regression test harness has been developed and used on SLS FSW. The test harness provides a modular design approach that can compile or read in the required information specified by the developer of the test. The modularity provides independence between groups of tests and the ability to add and remove tests without disturbing others. This provides the SLS FSW team a time saving feature that is essential to meeting SLS Program technical and programmatic requirements. During development of SLS FSW, this technique has proved to be a useful tool to ensure all requirements have been tested, and that desired functionality is maintained, as changes occur. It also provides a mechanism for developers to check functionality of the code that they have developed. With this system, automation of regression testing is accomplished through a scheduling tool and/or commit hooks. Key advantages of this test harness capability includes execution support for multiple independent test cases, the ability for developers to specify precisely what they are testing and how, the ability to add automation, and the ability of the harness and cases to be executed continually. This test concept is an approach that can be adapted to support other projects.

Theoretical and software considerations for nonlinear dynamic analysis

NASA Technical Reports Server (NTRS)

Schmidt, R. J.; Dodds, R. H., Jr.

1983-01-01

In the finite element method for structural analysis, it is generally necessary to discretize the structural model into a very large number of elements to accurately evaluate displacements, strains, and stresses. As the complexity of the model increases, the number of degrees of freedom can easily exceed the capacity of present-day software system. Improvements of structural analysis software including more efficient use of existing hardware and improved structural modeling techniques are discussed. One modeling technique that is used successfully in static linear and nonlinear analysis is multilevel substructuring. This research extends the use of multilevel substructure modeling to include dynamic analysis and defines the requirements for a general purpose software system capable of efficient nonlinear dynamic analysis. The multilevel substructuring technique is presented, the analytical formulations and computational procedures for dynamic analysis and nonlinear mechanics are reviewed, and an approach to the design and implementation of a general purpose structural software system is presented.

Kepler AutoRegressive Planet Search: Motivation & Methodology

NASA Astrophysics Data System (ADS)

Caceres, Gabriel; Feigelson, Eric; Jogesh Babu, G.; Bahamonde, Natalia; Bertin, Karine; Christen, Alejandra; Curé, Michel; Meza, Cristian

2015-08-01

The Kepler AutoRegressive Planet Search (KARPS) project uses statistical methodology associated with autoregressive (AR) processes to model Kepler lightcurves in order to improve exoplanet transit detection in systems with high stellar variability. We also introduce a planet-search algorithm to detect transits in time-series residuals after application of the AR models. One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The variability displayed by many stars may have autoregressive properties, wherein later flux values are correlated with previous ones in some manner. Auto-Regressive Moving-Average (ARMA) models, Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH), and related models are flexible, phenomenological methods used with great success to model stochastic temporal behaviors in many fields of study, particularly econometrics. Powerful statistical methods are implemented in the public statistical software environment R and its many packages. Modeling involves maximum likelihood fitting, model selection, and residual analysis. These techniques provide a useful framework to model stellar variability and are used in KARPS with the objective of reducing stellar noise to enhance opportunities to find as-yet-undiscovered planets. Our analysis procedure consisting of three steps: pre-processing of the data to remove discontinuities, gaps and outliers; ARMA-type model selection and fitting; and transit signal search of the residuals using a new Transit Comb Filter (TCF) that replaces traditional box-finding algorithms. We apply the procedures to simulated Kepler-like time series with known stellar and planetary signals to evaluate the effectiveness of the KARPS procedures. The ARMA-type modeling is effective at reducing stellar noise, but also reduces and transforms the transit signal into ingress/egress spikes. A periodogram based on the TCF is constructed to concentrate the signal of these periodic spikes. When a periodic transit is found, the model is displayed on a standard period-folded averaged light curve. We also illustrate the efficient coding in R.

Using recurrence plot analysis for software execution interpretation and fault detection

NASA Astrophysics Data System (ADS)

Mosdorf, M.

2015-09-01

This paper shows a method targeted at software execution interpretation and fault detection using recurrence plot analysis. In in the proposed approach recurrence plot analysis is applied to software execution trace that contains executed assembly instructions. Results of this analysis are subject to further processing with PCA (Principal Component Analysis) method that simplifies number coefficients used for software execution classification. This method was used for the analysis of five algorithms: Bubble Sort, Quick Sort, Median Filter, FIR, SHA-1. Results show that some of the collected traces could be easily assigned to particular algorithms (logs from Bubble Sort and FIR algorithms) while others are more difficult to distinguish.

Performing Quantitative Imaging Acquisition, Analysis and Visualization Using the Best of Open Source and Commercial Software Solutions.

PubMed

Shenoy, Shailesh M

2016-07-01

A challenge in any imaging laboratory, especially one that uses modern techniques, is to achieve a sustainable and productive balance between using open source and commercial software to perform quantitative image acquisition, analysis and visualization. In addition to considering the expense of software licensing, one must consider factors such as the quality and usefulness of the software's support, training and documentation. Also, one must consider the reproducibility with which multiple people generate results using the same software to perform the same analysis, how one may distribute their methods to the community using the software and the potential for achieving automation to improve productivity.

Analyzing qualitative data with computer software.

PubMed Central

Weitzman, E A

1999-01-01

OBJECTIVE: To provide health services researchers with an overview of the qualitative data analysis process and the role of software within it; to provide a principled approach to choosing among software packages to support qualitative data analysis; to alert researchers to the potential benefits and limitations of such software; and to provide an overview of the developments to be expected in the field in the near future. DATA SOURCES, STUDY DESIGN, METHODS: This article does not include reports of empirical research. CONCLUSIONS: Software for qualitative data analysis can benefit the researcher in terms of speed, consistency, rigor, and access to analytic methods not available by hand. Software, however, is not a replacement for methodological training. PMID:10591282

In vitro porcine blood-brain barrier model for permeability studies: pCEL-X software pKa(FLUX) method for aqueous boundary layer correction and detailed data analysis.

PubMed

Yusof, Siti R; Avdeef, Alex; Abbott, N Joan

2014-12-18

In vitro blood-brain barrier (BBB) models from primary brain endothelial cells can closely resemble the in vivo BBB, offering valuable models to assay BBB functions and to screen potential central nervous system drugs. We have recently developed an in vitro BBB model using primary porcine brain endothelial cells. The model shows expression of tight junction proteins and high transendothelial electrical resistance, evidence for a restrictive paracellular pathway. Validation studies using small drug-like compounds demonstrated functional uptake and efflux transporters, showing the suitability of the model to assay drug permeability. However, one limitation of in vitro model permeability measurement is the presence of the aqueous boundary layer (ABL) resulting from inefficient stirring during the permeability assay. The ABL can be a rate-limiting step in permeation, particularly for lipophilic compounds, causing underestimation of the permeability. If the ABL effect is ignored, the permeability measured in vitro will not reflect the permeability in vivo. To address the issue, we explored the combination of in vitro permeability measurement using our porcine model with the pKa(FLUX) method in pCEL-X software to correct for the ABL effect and allow a detailed analysis of in vitro (transendothelial) permeability data, Papp. Published Papp using porcine models generated by our group and other groups are also analyzed. From the Papp, intrinsic transcellular permeability (P0) is derived by simultaneous refinement using a weighted nonlinear regression, taking into account permeability through the ABL, paracellular permeability and filter restrictions on permeation. The in vitro P0 derived for 22 compounds (35 measurements) showed good correlation with P0 derived from in situ brain perfusion data (r(2)=0.61). The analysis also gave evidence for carrier-mediated uptake of naloxone, propranolol and vinblastine. The combination of the in vitro porcine model and the software analysis provides a useful tool to better predict BBB permeability in vivo and gain better mechanistic information about BBB permeation. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Psoas muscle area is not representative of total skeletal muscle area in the assessment of sarcopenia in ovarian cancer.

PubMed

Rutten, Iris J G; Ubachs, Jorne; Kruitwagen, Roy F P M; Beets-Tan, Regina G H; Olde Damink, Steven W M; Van Gorp, Toon

2017-08-01

Computed tomography measurements of total skeletal muscle area can detect changes and predict overall survival (OS) in patients with advanced ovarian cancer. This study investigates whether assessment of psoas muscle area reflects total muscle area and can be used to assess sarcopenia in ovarian cancer patients. Ovarian cancer patients (n = 150) treated with induction chemotherapy and interval debulking were enrolled retrospectively in this longitudinal study. Muscle was measured cross sectionally with computed tomography in three ways: (i) software quantification of total skeletal muscle area (SMA); (ii) software quantification of psoas muscle area (PA); and (iii) manual measurement of length and width of the psoas muscle to derive the psoas surface area (PLW). Pearson correlation between the different methods was studied. Patients were divided into two groups based on the extent of change in muscle area, and agreement was measured with kappa coefficients. Cox-regression was used to test predictors for OS. Correlation between SMA and both psoas muscle area measurements was poor (r = 0.52 and 0.39 for PA and PLW, respectively). After categorizing patients into muscle loss or gain, kappa agreement was also poor for all comparisons (all κ < 0.40). In regression analysis, SMA loss was predictive of poor OS (hazard ratio 1.698 (95%CI 1.038-2.778), P = 0.035). No relationship with OS was seen for PA or PLW loss. Change in psoas muscle area is not representative of total muscle area change and should not be used to substitute total skeletal muscle to predict survival in patients with ovarian cancer. © 2017 The Authors. Journal of Cachexia, Sarcopenia and Muscle published by John Wiley & Sons Ltd on behalf of the Society on Sarcopenia, Cachexia and Wasting Disorders.

An Analysis of Mission Critical Computer Software in Naval Aviation

DTIC Science & Technology

1991-03-01

No. Task No. Work Unit Accesion Number 11. TITLE (Include Security Classification) AN ANALYSIS OF MISSION CRITICAL COMPUTER SOFTWARE IN NAVAL AVIATION...software development schedules were sustained without a milestone change being made. Also, software that was released to the fleet had no major...fleet contain any major defects? This research has revealed that only about half of the original software development schedules were sustained without a

LV software support for supersonic flow analysis

NASA Technical Reports Server (NTRS)

Bell, W. A.; Lepicovsky, J.

1992-01-01

The software for configuring an LV counter processor system has been developed using structured design. The LV system includes up to three counter processors and a rotary encoder. The software for configuring and testing the LV system has been developed, tested, and included in an overall software package for data acquisition, analysis, and reduction. Error handling routines respond to both operator and instrument errors which often arise in the course of measuring complex, high-speed flows. The use of networking capabilities greatly facilitates the software development process by allowing software development and testing from a remote site. In addition, high-speed transfers allow graphics files or commands to provide viewing of the data from a remote site. Further advances in data analysis require corresponding advances in procedures for statistical and time series analysis of nonuniformly sampled data.

LV software support for supersonic flow analysis

NASA Technical Reports Server (NTRS)

Bell, William A.

1992-01-01

The software for configuring a Laser Velocimeter (LV) counter processor system was developed using structured design. The LV system includes up to three counter processors and a rotary encoder. The software for configuring and testing the LV system was developed, tested, and included in an overall software package for data acquisition, analysis, and reduction. Error handling routines respond to both operator and instrument errors which often arise in the course of measuring complex, high-speed flows. The use of networking capabilities greatly facilitates the software development process by allowing software development and testing from a remote site. In addition, high-speed transfers allow graphics files or commands to provide viewing of the data from a remote site. Further advances in data analysis require corresponding advances in procedures for statistical and time series analysis of nonuniformly sampled data.

Spacelab user implementation assessment study. (Software requirements analysis). Volume 2: Technical report

NASA Technical Reports Server (NTRS)

1976-01-01

The engineering analyses and evaluation studies conducted for the Software Requirements Analysis are discussed. Included are the development of the study data base, synthesis of implementation approaches for software required by both mandatory onboard computer services and command/control functions, and identification and implementation of software for ground processing activities.

Biostatistics Series Module 10: Brief Overview of Multivariate Methods.

PubMed

Hazra, Avijit; Gogtay, Nithya

2017-01-01

Multivariate analysis refers to statistical techniques that simultaneously look at three or more variables in relation to the subjects under investigation with the aim of identifying or clarifying the relationships between them. These techniques have been broadly classified as dependence techniques, which explore the relationship between one or more dependent variables and their independent predictors, and interdependence techniques, that make no such distinction but treat all variables equally in a search for underlying relationships. Multiple linear regression models a situation where a single numerical dependent variable is to be predicted from multiple numerical independent variables. Logistic regression is used when the outcome variable is dichotomous in nature. The log-linear technique models count type of data and can be used to analyze cross-tabulations where more than two variables are included. Analysis of covariance is an extension of analysis of variance (ANOVA), in which an additional independent variable of interest, the covariate, is brought into the analysis. It tries to examine whether a difference persists after "controlling" for the effect of the covariate that can impact the numerical dependent variable of interest. Multivariate analysis of variance (MANOVA) is a multivariate extension of ANOVA used when multiple numerical dependent variables have to be incorporated in the analysis. Interdependence techniques are more commonly applied to psychometrics, social sciences and market research. Exploratory factor analysis and principal component analysis are related techniques that seek to extract from a larger number of metric variables, a smaller number of composite factors or components, which are linearly related to the original variables. Cluster analysis aims to identify, in a large number of cases, relatively homogeneous groups called clusters, without prior information about the groups. The calculation intensive nature of multivariate analysis has so far precluded most researchers from using these techniques routinely. The situation is now changing with wider availability, and increasing sophistication of statistical software and researchers should no longer shy away from exploring the applications of multivariate methods to real-life data sets.

Estimating Time to Event From Longitudinal Categorical Data: An Analysis of Multiple Sclerosis Progression.

PubMed

Mandel, Micha; Gauthier, Susan A; Guttmann, Charles R G; Weiner, Howard L; Betensky, Rebecca A

2007-12-01

The expanded disability status scale (EDSS) is an ordinal score that measures progression in multiple sclerosis (MS). Progression is defined as reaching EDSS of a certain level (absolute progression) or increasing of one point of EDSS (relative progression). Survival methods for time to progression are not adequate for such data since they do not exploit the EDSS level at the end of follow-up. Instead, we suggest a Markov transitional model applicable for repeated categorical or ordinal data. This approach enables derivation of covariate-specific survival curves, obtained after estimation of the regression coefficients and manipulations of the resulting transition matrix. Large sample theory and resampling methods are employed to derive pointwise confidence intervals, which perform well in simulation. Methods for generating survival curves for time to EDSS of a certain level, time to increase of EDSS of at least one point, and time to two consecutive visits with EDSS greater than three are described explicitly. The regression models described are easily implemented using standard software packages. Survival curves are obtained from the regression results using packages that support simple matrix calculation. We present and demonstrate our method on data collected at the Partners MS center in Boston, MA. We apply our approach to progression defined by time to two consecutive visits with EDSS greater than three, and calculate crude (without covariates) and covariate-specific curves.

Gamification as a tool for enhancing graduate medical education

PubMed Central

Nevin, Christa R; Westfall, Andrew O; Rodriguez, J Martin; Dempsey, Donald M; Cherrington, Andrea; Roy, Brita; Patel, Mukesh; Willig, James H

2014-01-01

Introduction The last decade has seen many changes in graduate medical education training in the USA, most notably the implementation of duty hour standards for residents by the Accreditation Council of Graduate Medical Education. As educators are left to balance more limited time available between patient care and resident education, new methods to augment traditional graduate medical education are needed. Objectives To assess acceptance and use of a novel gamification-based medical knowledge software among internal medicine residents and to determine retention of information presented to participants by this medical knowledge software. Methods We designed and developed software using principles of gamification to deliver a web-based medical knowledge competition among internal medicine residents at the University of Alabama (UA) at Birmingham and UA at Huntsville in 2012–2013. Residents participated individually and in teams. Participants accessed daily questions and tracked their online leaderboard competition scores through any internet-enabled device. We completed focus groups to assess participant acceptance and analysed software use, retention of knowledge and factors associated with loss of participants (attrition). Results Acceptance: In focus groups, residents (n=17) reported leaderboards were the most important motivator of participation. Use: 16 427 questions were completed: 28.8% on Saturdays/Sundays, 53.1% between 17:00 and 08:00. Retention of knowledge: 1046 paired responses (for repeated questions) were collected. Correct responses increased by 11.9% (p<0.0001) on retest. Differences per time since question introduction, trainee level and style of play were observed. Attrition: In ordinal regression analyses, completing more questions (0.80 per 10% increase; 0.70 to 0.93) decreased, while postgraduate year 3 class (4.25; 1.44 to 12.55) and non-daily play (4.51; 1.50 to 13.58) increased odds of attrition. Conclusions Our software-enabled, gamification-based educational intervention was well accepted among our millennial learners. Coupling software with gamification and analysis of trainee use and engagement data can be used to develop strategies to augment learning in time-constrained educational settings. PMID:25352673

Gamification as a tool for enhancing graduate medical education.

PubMed

Nevin, Christa R; Westfall, Andrew O; Rodriguez, J Martin; Dempsey, Donald M; Cherrington, Andrea; Roy, Brita; Patel, Mukesh; Willig, James H

2014-12-01

The last decade has seen many changes in graduate medical education training in the USA, most notably the implementation of duty hour standards for residents by the Accreditation Council of Graduate Medical Education. As educators are left to balance more limited time available between patient care and resident education, new methods to augment traditional graduate medical education are needed. To assess acceptance and use of a novel gamification-based medical knowledge software among internal medicine residents and to determine retention of information presented to participants by this medical knowledge software. We designed and developed software using principles of gamification to deliver a web-based medical knowledge competition among internal medicine residents at the University of Alabama (UA) at Birmingham and UA at Huntsville in 2012-2013. Residents participated individually and in teams. Participants accessed daily questions and tracked their online leaderboard competition scores through any internet-enabled device. We completed focus groups to assess participant acceptance and analysed software use, retention of knowledge and factors associated with loss of participants (attrition). Acceptance: In focus groups, residents (n=17) reported leaderboards were the most important motivator of participation. Use: 16 427 questions were completed: 28.8% on Saturdays/Sundays, 53.1% between 17:00 and 08:00. Retention of knowledge: 1046 paired responses (for repeated questions) were collected. Correct responses increased by 11.9% (p<0.0001) on retest. Differences per time since question introduction, trainee level and style of play were observed. Attrition: In ordinal regression analyses, completing more questions (0.80 per 10% increase; 0.70 to 0.93) decreased, while postgraduate year 3 class (4.25; 1.44 to 12.55) and non-daily play (4.51; 1.50 to 13.58) increased odds of attrition. Our software-enabled, gamification-based educational intervention was well accepted among our millennial learners. Coupling software with gamification and analysis of trainee use and engagement data can be used to develop strategies to augment learning in time-constrained educational settings. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Biological Parametric Mapping: A Statistical Toolbox for Multi-Modality Brain Image Analysis

PubMed Central

Casanova, Ramon; Ryali, Srikanth; Baer, Aaron; Laurienti, Paul J.; Burdette, Jonathan H.; Hayasaka, Satoru; Flowers, Lynn; Wood, Frank; Maldjian, Joseph A.

2006-01-01

In recent years multiple brain MR imaging modalities have emerged; however, analysis methodologies have mainly remained modality specific. In addition, when comparing across imaging modalities, most researchers have been forced to rely on simple region-of-interest type analyses, which do not allow the voxel-by-voxel comparisons necessary to answer more sophisticated neuroscience questions. To overcome these limitations, we developed a toolbox for multimodal image analysis called biological parametric mapping (BPM), based on a voxel-wise use of the general linear model. The BPM toolbox incorporates information obtained from other modalities as regressors in a voxel-wise analysis, thereby permitting investigation of more sophisticated hypotheses. The BPM toolbox has been developed in MATLAB with a user friendly interface for performing analyses, including voxel-wise multimodal correlation, ANCOVA, and multiple regression. It has a high degree of integration with the SPM (statistical parametric mapping) software relying on it for visualization and statistical inference. Furthermore, statistical inference for a correlation field, rather than a widely-used T-field, has been implemented in the correlation analysis for more accurate results. An example with in-vivo data is presented demonstrating the potential of the BPM methodology as a tool for multimodal image analysis. PMID:17070709

A Practical Guide to Conducting a Systematic Review and Meta-analysis of Health State Utility Values.

PubMed

Petrou, Stavros; Kwon, Joseph; Madan, Jason

2018-05-10

Economic analysts are increasingly likely to rely on systematic reviews and meta-analyses of health state utility values to inform the parameter inputs of decision-analytic modelling-based economic evaluations. Beyond the context of economic evaluation, evidence from systematic reviews and meta-analyses of health state utility values can be used to inform broader health policy decisions. This paper provides practical guidance on how to conduct a systematic review and meta-analysis of health state utility values. The paper outlines a number of stages in conducting a systematic review, including identifying the appropriate evidence, study selection, data extraction and presentation, and quality and relevance assessment. The paper outlines three broad approaches that can be used to synthesise multiple estimates of health utilities for a given health state or condition, namely fixed-effect meta-analysis, random-effects meta-analysis and mixed-effects meta-regression. Each approach is illustrated by a synthesis of utility values for a hypothetical decision problem, and software code is provided. The paper highlights a number of methodological issues pertinent to the conduct of meta-analysis or meta-regression. These include the importance of limiting synthesis to 'comparable' utility estimates, for example those derived using common utility measurement approaches and sources of valuation; the effects of reliance on limited or poorly reported published data from primary utility assessment studies; the use of aggregate outcomes within analyses; approaches to generating measures of uncertainty; handling of median utility values; challenges surrounding the disentanglement of utility estimates collected serially within the context of prospective observational studies or prospective randomised trials; challenges surrounding the disentanglement of intervention effects; and approaches to measuring model validity. Areas of methodological debate and avenues for future research are highlighted.

Ultrasound texture analysis: Association with lymph node metastasis of papillary thyroid microcarcinoma.

PubMed

Kim, Soo-Yeon; Lee, Eunjung; Nam, Se Jin; Kim, Eun-Kyung; Moon, Hee Jung; Yoon, Jung Hyun; Han, Kyung Hwa; Kwak, Jin Young

2017-01-01

This retrospective study aimed to evaluate whether ultrasound texture analysis is useful to predict lymph node metastasis in patients with papillary thyroid microcarcinoma (PTMC). This study was approved by the Institutional Review Board, and the need to obtain informed consent was waived. Between May and July 2013, 361 patients (mean age, 43.8 ± 11.3 years; range, 16-72 years) who underwent staging ultrasound (US) and subsequent thyroidectomy for conventional PTMC ≤ 10 mm between May and July 2013 were included. Each PTMC was manually segmented and its histogram parameters (Mean, Standard deviation, Skewness, Kurtosis, and Entropy) were extracted with Matlab software. The mean values of histogram parameters and clinical and US features were compared according to lymph node metastasis using the independent t-test and Chi-square test. Multivariate logistic regression analysis was performed to identify the independent factors associated with lymph node metastasis. Tumors with lymph node metastasis (n = 117) had significantly higher entropy compared to those without lymph node metastasis (n = 244) (mean±standard deviation, 6.268±0.407 vs. 6.171±.0.405; P = .035). No additional histogram parameters showed differences in mean values according to lymph node metastasis. Entropy was not independently associated with lymph node metastasis on multivariate logistic regression analysis (Odds ratio, 0.977 [95% confidence interval (CI), 0.482-1.980]; P = .949). Younger age (Odds ratio, 0.962 [95% CI, 0.940-0.984]; P = .001) and lymph node metastasis on US (Odds ratio, 7.325 [95% CI, 3.573-15.020]; P < .001) were independently associated with lymph node metastasis. Texture analysis was not useful in predicting lymph node metastasis in patients with PTMC.

Toward fully automated processing of dynamic susceptibility contrast perfusion MRI for acute ischemic cerebral stroke.

PubMed

Kim, Jinsuh; Leira, Enrique C; Callison, Richard C; Ludwig, Bryan; Moritani, Toshio; Magnotta, Vincent A; Madsen, Mark T

2010-05-01

We developed fully automated software for dynamic susceptibility contrast (DSC) MR perfusion-weighted imaging (PWI) to efficiently and reliably derive critical hemodynamic information for acute stroke treatment decisions. Brain MR PWI was performed in 80 consecutive patients with acute nonlacunar ischemic stroke within 24h after onset of symptom from January 2008 to August 2009. These studies were automatically processed to generate hemodynamic parameters that included cerebral blood flow and cerebral blood volume, and the mean transit time (MTT). To develop reliable software for PWI analysis, we used computationally robust algorithms including the piecewise continuous regression method to determine bolus arrival time (BAT), log-linear curve fitting, arrival time independent deconvolution method and sophisticated motion correction methods. An optimal arterial input function (AIF) search algorithm using a new artery-likelihood metric was also developed. Anatomical locations of the automatically determined AIF were reviewed and validated. The automatically computed BAT values were statistically compared with estimated BAT by a single observer. In addition, gamma-variate curve-fitting errors of AIF and inter-subject variability of AIFs were analyzed. Lastly, two observes independently assessed the quality and area of hypoperfusion mismatched with restricted diffusion area from motion corrected MTT maps and compared that with time-to-peak (TTP) maps using the standard approach. The AIF was identified within an arterial branch and enhanced areas of perfusion deficit were visualized in all evaluated cases. Total processing time was 10.9+/-2.5s (mean+/-s.d.) without motion correction and 267+/-80s (mean+/-s.d.) with motion correction on a standard personal computer. The MTT map produced with our software adequately estimated brain areas with perfusion deficit and was significantly less affected by random noise of the PWI when compared with the TTP map. Results of image quality assessment by two observers revealed that the MTT maps exhibited superior quality over the TTP maps (88% good rating of MTT as compared to 68% of TTP). Our software allowed fully automated deconvolution analysis of DSC PWI using proven efficient algorithms that can be applied to acute stroke treatment decisions. Our streamlined method also offers promise for further development of automated quantitative analysis of the ischemic penumbra. Copyright (c) 2009 Elsevier Ireland Ltd. All rights reserved.

Heavy Metal Level in Human Semen with Different Fertility: a Meta-Analysis.

PubMed

Sun, Jiantao; Yu, Guangxia; Zhang, Yucheng; Liu, Xi; Du, Chuang; Wang, Lu; Li, Zhen; Wang, Chunhong

2017-03-01

There are conflicting reports on the heavy metal levels in human semen with different fertilities. The purpose of this analysis is to merge and analyze the differences of heavy metal lead (Pb), cadmium (Cd), zinc (Zn), and copper (Cu) levels in male semen with normal and low fertilities. All documents in both Chinese and English were collected from the PubMed, Web of Science, and Chinese National Knowledge Infrastructure (CNKI) database from inception date to February 19, 2016. We have used RevMan software (version 5.2) for the meta-analysis and Stata software (version 12.0) for the meta-regression and sensitivity analyses. A total of 20 literatures were included in the study. The results of the meta-analysis indicate a significant difference between fertility with three metal ions (Pb, Cd, Zn) while no significant difference with copper, detailed as follows: (i) 10 studies on the lead concentrations with a standardized mean difference (SMD) = 2.07, 95 %CI (0.97, 3.17), P < 0.01; (ii) 13 studies on the cadmium concentrations with an SMD = 0.75, 95 %CI (0.44, 1.07), P < 0.01; (iii) 8 studies on the concentrations of zinc with an SMD = -0.61, 95 %CI (-1.08, -0.14), P < 0.01; and (iv) 9 studies on the copper concentrations with an SMD = 0.42, 95 %CI (-0.29, 1.13), P = 0.247. The results indicate that the men with low fertility have higher semen Pb and Cd levels and lower semen Zn levels; more studies are needed to indicate the association of the semen copper level with fertility.

Quantitative coronary plaque analysis predicts high-risk plaque morphology on coronary computed tomography angiography: results from the ROMICAT II trial.

PubMed

Liu, Ting; Maurovich-Horvat, Pál; Mayrhofer, Thomas; Puchner, Stefan B; Lu, Michael T; Ghemigian, Khristine; Kitslaar, Pieter H; Broersen, Alexander; Pursnani, Amit; Hoffmann, Udo; Ferencik, Maros

2018-02-01

Semi-automated software can provide quantitative assessment of atherosclerotic plaques on coronary CT angiography (CTA). The relationship between established qualitative high-risk plaque features and quantitative plaque measurements has not been studied. We analyzed the association between quantitative plaque measurements and qualitative high-risk plaque features on coronary CTA. We included 260 patients with plaque who underwent coronary CTA in the Rule Out Myocardial Infarction/Ischemia Using Computer Assisted Tomography (ROMICAT) II trial. Quantitative plaque assessment and qualitative plaque characterization were performed on a per coronary segment basis. Quantitative coronary plaque measurements included plaque volume, plaque burden, remodeling index, and diameter stenosis. In qualitative analysis, high-risk plaque was present if positive remodeling, low CT attenuation plaque, napkin-ring sign or spotty calcium were detected. Univariable and multivariable logistic regression analyses were performed to assess the association between quantitative and qualitative high-risk plaque assessment. Among 888 segments with coronary plaque, high-risk plaque was present in 391 (44.0%) segments by qualitative analysis. In quantitative analysis, segments with high-risk plaque had higher total plaque volume, low CT attenuation plaque volume, plaque burden and remodeling index. Quantitatively assessed low CT attenuation plaque volume (odds ratio 1.12 per 1 mm 3 , 95% CI 1.04-1.21), positive remodeling (odds ratio 1.25 per 0.1, 95% CI 1.10-1.41) and plaque burden (odds ratio 1.53 per 0.1, 95% CI 1.08-2.16) were associated with high-risk plaque. Quantitative coronary plaque characteristics (low CT attenuation plaque volume, positive remodeling and plaque burden) measured by semi-automated software correlated with qualitative assessment of high-risk plaque features.

A multianalytical approach to evaluate the association of 55 SNPs in 28 genes with obesity risk in North Indian adults.

PubMed

Srivastava, Apurva; Mittal, Balraj; Prakash, Jai; Srivastava, Pranjal; Srivastava, Nimisha; Srivastava, Neena

2017-03-01

The aim of the study was to investigate the association of 55 SNPs in 28 genes with obesity risk in a North Indian population using a multianalytical approach. Overall, 480 subjects from the North Indian population were studied using strict inclusion/exclusion criteria. SNP Genotyping was carried out by Sequenom Mass ARRAY platform (Sequenom, San Diego, CA) and validated Taqman ® allelic discrimination (Applied Biosystems ® ). Statistical analyses were performed using SPSS software version 19.0, SNPStats, GMDR software (version 6) and GENEMANIA. Logistic regression analysis of 55 SNPs revealed significant associations (P < .05) of 49 SNPs with BMI linked obesity risk whereas the remaining 6 SNPs revealed no association (P > .05). The pathway-wise G-score revealed the significant role (P = .0001) of food intake-energy expenditure pathway genes. In CART analysis, the combined genotypes of FTO rs9939609 and TCF7L2 rs7903146 revealed the highest risk for BMI linked obesity. The analysis of the FTO-IRX3 locus revealed high LD and high order gene-gene interactions for BMI linked obesity. The interaction network of all of the associated genes in the present study generated by GENEMANIA revealed direct and indirect connections. In addition, the analysis with centralized obesity revealed that none of the SNPs except for FTO rs17818902 were significantly associated (P < .05). In this multi-analytical approach, FTO rs9939609 and IRX3 rs3751723, along with TCF7L2 rs7903146 and TMEM18 rs6548238, emerged as the major SNPs contributing to BMI linked obesity risk in the North Indian population. © 2016 Wiley Periodicals, Inc.

GammaLib and ctools. A software framework for the analysis of astronomical gamma-ray data

NASA Astrophysics Data System (ADS)

Knödlseder, J.; Mayer, M.; Deil, C.; Cayrou, J.-B.; Owen, E.; Kelley-Hoskins, N.; Lu, C.-C.; Buehler, R.; Forest, F.; Louge, T.; Siejkowski, H.; Kosack, K.; Gerard, L.; Schulz, A.; Martin, P.; Sanchez, D.; Ohm, S.; Hassan, T.; Brau-Nogué, S.

2016-08-01

The field of gamma-ray astronomy has seen important progress during the last decade, yet to date no common software framework has been developed for the scientific analysis of gamma-ray telescope data. We propose to fill this gap by means of the GammaLib software, a generic library that we have developed to support the analysis of gamma-ray event data. GammaLib was written in C++ and all functionality is available in Python through an extension module. Based on this framework we have developed the ctools software package, a suite of software tools that enables flexible workflows to be built for the analysis of Imaging Air Cherenkov Telescope event data. The ctools are inspired by science analysis software available for existing high-energy astronomy instruments, and they follow the modular ftools model developed by the High Energy Astrophysics Science Archive Research Center. The ctools were written in Python and C++, and can be either used from the command line via shell scripts or directly from Python. In this paper we present the GammaLib and ctools software versions 1.0 that were released at the end of 2015. GammaLib and ctools are ready for the science analysis of Imaging Air Cherenkov Telescope event data, and also support the analysis of Fermi-LAT data and the exploitation of the COMPTEL legacy data archive. We propose using ctools as the science tools software for the Cherenkov Telescope Array Observatory.

Trends and spatial distribution of deaths of children aged 12-60 months in São Paulo, Brazil, 1980-98.

PubMed Central

Antunes, José Leopoldo Ferreira; Waldman, Eliseu Alves

2002-01-01

OBJECTIVE: To describe trends in the mortality of children aged 12-60 months and to perform spatial data analysis of its distribution at the inner city district level in São Paulo from 1980 to 1998. METHODS: Official mortality data were analysed in relation to the underlying causes of death. The population of children aged 12-60 months, disaggregated by sex and age, was estimated for each year. Educational levels, income, employment status, and other socioeconomic indices were also assessed. Statistical Package for Social Sciences software was used for the statistical processing of time series. The Cochrane-Orcutt procedure of generalized least squares regression analysis was used to estimate the regression parameters with control of first-order autocorrelation. Spatial data analysis employed the discrimination of death rates and socioeconomic indices at the inner city district level. For classifying area-level death rates the method of K-means cluster analysis was used. Spatial correlation between variables was analysed by the simultaneous autoregressive regression method. FINDINGS: There was a steady decline in death rates during the 1980s at an average rate of 3.08% per year, followed by a levelling off. Infectious diseases remained the major cause of mortality, accounting for 43.1% of deaths during the last three years of the study. Injuries accounted for 16.5% of deaths. Mortality rates at the area level clearly demonstrated inequity in the city's health profile: there was an increasing difference between the rich and the underprivileged social strata in this respect. CONCLUSION: The overall mortality rate among children aged 12-60 months dropped by almost 30% during the study period. Most of the decline happened during the 1980s. Many people still live in a state of deprivation in underserved areas. Time-series and spatial data analysis provided indications of potential value in the planning of social policies promoting well-being, through the identification of factors affecting child survival and the regions with the worst health profiles, to which programmes and resources should be preferentially directed. PMID:12077615

Comparison of classical statistical methods and artificial neural network in traffic noise prediction

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

Nedic, Vladimir, E-mail: vnedic@kg.ac.rs; Despotovic, Danijela, E-mail: ddespotovic@kg.ac.rs; Cvetanovic, Slobodan, E-mail: slobodan.cvetanovic@eknfak.ni.ac.rs

2014-11-15

Traffic is the main source of noise in urban environments and significantly affects human mental and physical health and labor productivity. Therefore it is very important to model the noise produced by various vehicles. Techniques for traffic noise prediction are mainly based on regression analysis, which generally is not good enough to describe the trends of noise. In this paper the application of artificial neural networks (ANNs) for the prediction of traffic noise is presented. As input variables of the neural network, the proposed structure of the traffic flow and the average speed of the traffic flow are chosen. Themore » output variable of the network is the equivalent noise level in the given time period L{sub eq}. Based on these parameters, the network is modeled, trained and tested through a comparative analysis of the calculated values and measured levels of traffic noise using the originally developed user friendly software package. It is shown that the artificial neural networks can be a useful tool for the prediction of noise with sufficient accuracy. In addition, the measured values were also used to calculate equivalent noise level by means of classical methods, and comparative analysis is given. The results clearly show that ANN approach is superior in traffic noise level prediction to any other statistical method. - Highlights: • We proposed an ANN model for prediction of traffic noise. • We developed originally designed user friendly software package. • The results are compared with classical statistical methods. • The results are much better predictive capabilities of ANN model.« less

Elements of strategic capability for software outsourcing enterprises based on the resource

NASA Astrophysics Data System (ADS)

Shi, Wengeng

2011-10-01

Software outsourcing enterprises as an emerging high-tech enterprises, the rise of the speed and the number was very amazing. In addition to Chinese software outsourcing for giving preferential policies, the software outsourcing business has its ability to upgrade, and in general the software companies have not had the related characteristics. View from the resource base of the theory, the analysis software outsourcing companies have the ability and resources of rare and valuable and non-mimic, we try to give an initial framework for theoretical analysis based on this.

Statistical performance of image cytometry for DNA, lipids, cytokeratin, & CD45 in a model system for circulation tumor cell detection.

PubMed

Futia, Gregory L; Schlaepfer, Isabel R; Qamar, Lubna; Behbakht, Kian; Gibson, Emily A

2017-07-01

Detection of circulating tumor cells (CTCs) in a blood sample is limited by the sensitivity and specificity of the biomarker panel used to identify CTCs over other blood cells. In this work, we present Bayesian theory that shows how test sensitivity and specificity set the rarity of cell that a test can detect. We perform our calculation of sensitivity and specificity on our image cytometry biomarker panel by testing on pure disease positive (D + ) populations (MCF7 cells) and pure disease negative populations (D - ) (leukocytes). In this system, we performed multi-channel confocal fluorescence microscopy to image biomarkers of DNA, lipids, CD45, and Cytokeratin. Using custom software, we segmented our confocal images into regions of interest consisting of individual cells and computed the image metrics of total signal, second spatial moment, spatial frequency second moment, and the product of the spatial-spatial frequency moments. We present our analysis of these 16 features. The best performing of the 16 features produced an average separation of three standard deviations between D + and D - and an average detectable rarity of ∼1 in 200. We performed multivariable regression and feature selection to combine multiple features for increased performance and showed an average separation of seven standard deviations between the D + and D - populations making our average detectable rarity of ∼1 in 480. Histograms and receiver operating characteristics (ROC) curves for these features and regressions are presented. We conclude that simple regression analysis holds promise to further improve the separation of rare cells in cytometry applications. © 2017 International Society for Advancement of Cytometry. © 2017 International Society for Advancement of Cytometry.

[Bibliometrics and visualization analysis of land use regression models in ambient air pollution research].

PubMed

Zhang, Y J; Zhou, D H; Bai, Z P; Xue, F X

2018-02-10

Objective: To quantitatively analyze the current status and development trends regarding the land use regression (LUR) models on ambient air pollution studies. Methods: Relevant literature from the PubMed database before June 30, 2017 was analyzed, using the Bibliographic Items Co-occurrence Matrix Builder (BICOMB 2.0). Keywords co-occurrence networks, cluster mapping and timeline mapping were generated, using the CiteSpace 5.1.R5 software. Relevant literature identified in three Chinese databases was also reviewed. Results: Four hundred sixty four relevant papers were retrieved from the PubMed database. The number of papers published showed an annual increase, in line with the growing trend of the index. Most papers were published in the journal of Environmental Health Perspectives . Results from the Co-word cluster analysis identified five clusters: cluster#0 consisted of birth cohort studies related to the health effects of prenatal exposure to air pollution; cluster#1 referred to land use regression modeling and exposure assessment; cluster#2 was related to the epidemiology on traffic exposure; cluster#3 dealt with the exposure to ultrafine particles and related health effects; cluster#4 described the exposure to black carbon and related health effects. Data from Timeline mapping indicated that cluster#0 and#1 were the main research areas while cluster#3 and#4 were the up-coming hot areas of research. Ninety four relevant papers were retrieved from the Chinese databases with most of them related to studies on modeling. Conclusion: In order to better assess the health-related risks of ambient air pollution, and to best inform preventative public health intervention policies, application of LUR models to environmental epidemiology studies in China should be encouraged.

Merchantable sawlog and bole-length equations for the Northeastern United States

Treesearch

Daniel A. Yaussy; Martin E. Dale; Martin E. Dale

1991-01-01

A modified Richards growth model is used to develop species-specific coefficients for equations estimating the merchantable sawlog and bole lengths of trees from 25 species groups common to the Northeastern United States. These regression coefficients have been incorporated into the growth-and-yield simulation software, NE-TWIGS.

Obtaining Predictions from Models Fit to Multiply Imputed Data

ERIC Educational Resources Information Center

Miles, Andrew

2016-01-01

Obtaining predictions from regression models fit to multiply imputed data can be challenging because treatments of multiple imputation seldom give clear guidance on how predictions can be calculated, and because available software often does not have built-in routines for performing the necessary calculations. This research note reviews how…

The Comparison of VLBI Data Analysis Using Software Globl and Globk

NASA Astrophysics Data System (ADS)

Guangli, W.; Xiaoya, W.; Jinling, L.; Wenyao, Z.

The comparison of different geodetic data analysis software is one of the quite of- ten mentioned topics. In this paper we try to find out the difference between software GLOBL and GLOBK when use them to process the same set of VLBI data. GLOBL is a software developed by VLBI team, geodesy branch, GSFC/NASA to process geode- tic VLBI data using algorithm of arc-parameter-elimination, while GLOBK using al- gorithm of kalman filtering is mainly used in GPS data analysis, and it is also used in VLBI data analysis. Our work focus on whether there are significant difference when use the two softwares to analyze the same VLBI data set and investigate the reasons caused the difference.

Using video-annotation software to identify interactions in group therapies for schizophrenia: assessing reliability and associations with outcomes.

PubMed

Orfanos, Stavros; Akther, Syeda Ferhana; Abdul-Basit, Muhammad; McCabe, Rosemarie; Priebe, Stefan

2017-02-10

Research has shown that interactions in group therapies for people with schizophrenia are associated with a reduction in negative symptoms. However, it is unclear which specific interactions in groups are linked with these improvements. The aims of this exploratory study were to i) develop and test the reliability of using video-annotation software to measure interactions in group therapies in schizophrenia and ii) explore the relationship between interactions in group therapies for schizophrenia with clinically relevant changes in negative symptoms. Video-annotation software was used to annotate interactions from participants selected across nine video-recorded out-patient therapy groups (N = 81). Using the Individual Group Member Interpersonal Process Scale, interactions were coded from participants who demonstrated either a clinically significant improvement (N = 9) or no change (N = 8) in negative symptoms at the end of therapy. Interactions were measured from the first and last sessions of attendance (>25 h of therapy). Inter-rater reliability between two independent raters was measured. Binary logistic regression analysis was used to explore the association between the frequency of interactive behaviors and changes in negative symptoms, assessed using the Positive and Negative Syndrome Scale. Of the 1275 statements that were annotated using ELAN, 1191 (93%) had sufficient audio and visual quality to be coded using the Individual Group Member Interpersonal Process Scale. Rater-agreement was high across all interaction categories (>95% average agreement). A higher frequency of self-initiated statements measured in the first session was associated with improvements in negative symptoms. The frequency of questions and giving advice measured in the first session of attendance was associated with improvements in negative symptoms; although this was only a trend. Video-annotation software can be used to reliably identify interactive behaviors in groups for schizophrenia. The results suggest that proactive communicative gestures, as assessed by the video-analysis, predict outcomes. Future research should use this novel method in larger and clinically different samples to explore which aspects of therapy facilitate such proactive communication early on in therapy.

SlicerRT: radiation therapy research toolkit for 3D Slicer.

PubMed

Pinter, Csaba; Lasso, Andras; Wang, An; Jaffray, David; Fichtinger, Gabor

2012-10-01

Interest in adaptive radiation therapy research is constantly growing, but software tools available for researchers are mostly either expensive, closed proprietary applications, or free open-source packages with limited scope, extensibility, reliability, or user support. To address these limitations, we propose SlicerRT, a customizable, free, and open-source radiation therapy research toolkit. SlicerRT aspires to be an open-source toolkit for RT research, providing fast computations, convenient workflows for researchers, and a general image-guided therapy infrastructure to assist clinical translation of experimental therapeutic approaches. It is a medium into which RT researchers can integrate their methods and algorithms, and conduct comparative testing. SlicerRT was implemented as an extension for the widely used 3D Slicer medical image visualization and analysis application platform. SlicerRT provides functionality specifically designed for radiation therapy research, in addition to the powerful tools that 3D Slicer offers for visualization, registration, segmentation, and data management. The feature set of SlicerRT was defined through consensus discussions with a large pool of RT researchers, including both radiation oncologists and medical physicists. The development processes used were similar to those of 3D Slicer to ensure software quality. Standardized mechanisms of 3D Slicer were applied for documentation, distribution, and user support. The testing and validation environment was configured to automatically launch a regression test upon each software change and to perform comparison with ground truth results provided by other RT applications. Modules have been created for importing and loading DICOM-RT data, computing and displaying dose volume histograms, creating accumulated dose volumes, comparing dose volumes, and visualizing isodose lines and surfaces. The effectiveness of using 3D Slicer with the proposed SlicerRT extension for radiation therapy research was demonstrated on multiple use cases. A new open-source software toolkit has been developed for radiation therapy research. SlicerRT can import treatment plans from various sources into 3D Slicer for visualization, analysis, comparison, and processing. The provided algorithms are extensively tested and they are accessible through a convenient graphical user interface as well as a flexible application programming interface.

ACES: Space shuttle flight software analysis expert system

NASA Technical Reports Server (NTRS)

Satterwhite, R. Scott

1990-01-01

The Analysis Criteria Evaluation System (ACES) is a knowledge based expert system that automates the final certification of the Space Shuttle onboard flight software. Guidance, navigation and control of the Space Shuttle through all its flight phases are accomplished by a complex onboard flight software system. This software is reconfigured for each flight to allow thousands of mission-specific parameters to be introduced and must therefore be thoroughly certified prior to each flight. This certification is performed in ground simulations by executing the software in the flight computers. Flight trajectories from liftoff to landing, including abort scenarios, are simulated and the results are stored for analysis. The current methodology of performing this analysis is repetitive and requires many man-hours. The ultimate goals of ACES are to capture the knowledge of the current experts and improve the quality and reduce the manpower required to certify the Space Shuttle onboard flight software.

Geographic Distribution of Urologists in Korea, 2007 to 2012

PubMed Central

Song, Yun Seob; Shim, Sung Ryul; Jung, Insoo; Sun, Hwa Yeon; Song, Soo Hyun; Kwon, Soon-Sun; Ko, Young Myoung

2015-01-01

The adequacy of the urologist work force in Korea has never been investigated. This study investigated the geographic distribution of urologists in Korea. County level data from the National Health Insurance Service and National Statistical Office was analyzed in this ecological study. Urologist density was defined by the number of urologists per 100,000 individuals. National patterns of urologist density were mapped graphically at the county level using GIS software. To control the time sequence, regression analysis with fitted line plot was conducted. The difference of distribution of urologist density was analyzed by ANCOVA. Urologists density showed an uneven distribution according to county characteristics (metropolitan cities vs. nonmetropolitan cities vs. rural areas; mean square=102.329, P<0.001) and also according to year (mean square=9.747, P=0.048). Regression analysis between metropolitan and non-metropolitan cities showed significant difference in the change of urologists per year (P=0.019). Metropolitan cities vs. rural areas and non-metropolitan cities vs. rural areas showed no differences. Among the factors, the presence of training hospitals was the affecting factor for the uneven distribution of urologist density (P<0.001).Uneven distribution of urologists in Korea likely originated from the relatively low urologist density in rural areas. However, considering the time sequencing data from 2007 to 2012, there was a difference between the increase of urologist density in metropolitan and non-metropolitan cities. PMID:26539009

Geographic Distribution of Urologists in Korea, 2007 to 2012.

PubMed

Song, Yun Seob; Shim, Sung Ryul; Jung, Insoo; Sun, Hwa Yeon; Song, Soo Hyun; Kwon, Soon-Sun; Ko, Young Myoung; Kim, Jae Heon

2015-11-01

The adequacy of the urologist work force in Korea has never been investigated. This study investigated the geographic distribution of urologists in Korea. County level data from the National Health Insurance Service and National Statistical Office was analyzed in this ecological study. Urologist density was defined by the number of urologists per 100,000 individuals. National patterns of urologist density were mapped graphically at the county level using GIS software. To control the time sequence, regression analysis with fitted line plot was conducted. The difference of distribution of urologist density was analyzed by ANCOVA. Urologists density showed an uneven distribution according to county characteristics (metropolitan cities vs. nonmetropolitan cities vs. rural areas; mean square=102.329, P<0.001) and also according to year (mean square=9.747, P=0.048). Regression analysis between metropolitan and non-metropolitan cities showed significant difference in the change of urologists per year (P=0.019). Metropolitan cities vs. rural areas and non-metropolitan cities vs. rural areas showed no differences. Among the factors, the presence of training hospitals was the affecting factor for the uneven distribution of urologist density (P<0.001). Uneven distribution of urologists in Korea likely originated from the relatively low urologist density in rural areas. However, considering the time sequencing data from 2007 to 2012, there was a difference between the increase of urologist density in metropolitan and non-metropolitan cities.

[Suicidal Behavior and Attention Decifit Hyperactivity Disorder in Adolescents of Medellin (Colombia), 2011-2012].

PubMed

Restrepo-Bernal, Diana; Bonfante-Olivares, Laura; Torres de Galvis, Yolanda; Berbesi-Fernández, Dedsy; Sierra-Hincapié, Gloria

2014-01-01

Suicide is a public health problem. In Colombia, teenagers are considered a group at high risk for suicidal behavior. To explore the possible association between suicidal behavior and attention deficit hyperactivity disorder in adolescents of Medellin. Observational, cross-sectional, analytical study. The Composite International Diagnostic Interview was applied to a total of 447 adolescents and the sociodemographic, clinical, familiar, and life event variables of interest were analyzed. The descriptive analysis of qualitative variables are presented as absolute values and frequencies, and the age was described with median [interquartile range]. A logistic regression model was constructed with explanatory variables that showed statistical association. Data were analyzed with SPSS® software version 21.0. Of the total, 59.1% were female, and the median age was 16 [14-18] years. Suicidal behavior was presented in 31% of females and 23% of males. Attention deficit was present in 6.3% of adolescents. The logistic regression analysis showed that the variables that best explained the suicidal behavior of adolescents were: female sex, post-traumatic stress disorder, panic disorder, and cocaine use. The diagnosis and early intervention of attention deficit hyperactivity disorder in children may be a useful strategy in the prevention of suicidal behavior in adolescents. Copyright © 2014 Asociación Colombiana de Psiquiatría. Publicado por Elsevier España. All rights reserved.

Incidence Trend and Epidemiology of Common Cancers in the Center of Iran.

PubMed

Rafiemanesh, Hosein; Rajaei-Behbahani, Narjes; Khani, Yousef; Hosseini, Sayedehafagh; Pournamdar, Zahra; Mohammadian-Hafshejani, Abdollah; Soltani, Shahin; Hosseini, Seyedeh Akram; Khazaei, Salman; Salehiniya, Hamid

2015-07-13

Cancer is a major public health problem in Iran and many other parts of the world. The cancer incidence is different in various countries and in country provinces. Geographical differences in the cancer incidence lead to be important to conduct an epidemiological study of the disease. This study aimed to investigate cancer epidemiology and trend in the province of Qom, located in center of Iran. This is an analytical cross-sectional study carried out based on re-analysis cancer registry report and the disease management center of health ministry from 2004 to 2008 in the province of Qom. To describe incidence time trends, we carried out join point regression analysis using the software Join point Regression Program, Version 4.1.1.1. There were 3,029 registered cases of cancer during 5 years studied. Sex ratio was 1.32 (male to female). Considering the frequency and mean standardized incidence, the most common cancer in women were breast, skin, colorectal, stomach, and esophagus, respectively while in men the most common cancers included skin, stomach, colorectal, bladder, and prostate, respectively. There was an increasing and significant trend, according to the annual percentage change (APC) equal to 8.08% (CI: 5.1-11.1) for all site cancer in women. The incidence trend of all cancers was increasing in this area. Hence, planning for identifying risk factors and performing programs for dealing with the disease are essential.

What automated age estimation of hand and wrist MRI data tells us about skeletal maturation in male adolescents.

PubMed

Urschler, Martin; Grassegger, Sabine; Štern, Darko

2015-01-01

Age estimation of individuals is important in human biology and has various medical and forensic applications. Recent interest in MR-based methods aims to investigate alternatives for established methods involving ionising radiation. Automatic, software-based methods additionally promise improved estimation objectivity. To investigate how informative automatically selected image features are regarding their ability to discriminate age, by exploring a recently proposed software-based age estimation method for MR images of the left hand and wrist. One hundred and two MR datasets of left hand images are used to evaluate age estimation performance, consisting of bone and epiphyseal gap volume localisation, computation of one age regression model per bone mapping image features to age and fusion of individual bone age predictions to a final age estimate. Quantitative results of the software-based method show an age estimation performance with a mean absolute difference of 0.85 years (SD = 0.58 years) to chronological age, as determined by a cross-validation experiment. Qualitatively, it is demonstrated how feature selection works and which image features of skeletal maturation are automatically chosen to model the non-linear regression function. Feasibility of automatic age estimation based on MRI data is shown and selected image features are found to be informative for describing anatomical changes during physical maturation in male adolescents.

The Effects of Development Team Skill on Software Product Quality

NASA Technical Reports Server (NTRS)

Beaver, Justin M.; Schiavone, Guy A.

2006-01-01

This paper provides an analysis of the effect of the skill/experience of the software development team on the quality of the final software product. A method for the assessment of software development team skill and experience is proposed, and was derived from a workforce management tool currently in use by the National Aeronautics and Space Administration. Using data from 26 smallscale software development projects, the team skill measures are correlated to 5 software product quality metrics from the ISO/IEC 9126 Software Engineering Product Quality standard. in the analysis of the results, development team skill is found to be a significant factor in the adequacy of the design and implementation. In addition, the results imply that inexperienced software developers are tasked with responsibilities ill-suited to their skill level, and thus have a significant adverse effect on the quality of the software product. Keywords: software quality, development skill, software metrics

SAO mission support software and data standards, version 1.0

NASA Technical Reports Server (NTRS)

Hsieh, P.

1993-01-01

This document defines the software developed by the SAO AXAF Mission Support (MS) Program and defines standards for the software development process and control of data products generated by the software. The SAO MS is tasked to develop and use software to perform a variety of functions in support of the AXAF mission. Software is developed by software engineers and scientists, and commercial off-the-shelf (COTS) software is used either directly or customized through the use of scripts to implement analysis procedures. Software controls real-time laboratory instruments, performs data archiving, displays data, and generates model predictions. Much software is used in the analysis of data to generate data products that are required by the AXAF project, for example, on-orbit mirror performance predictions or detailed characterization of the mirror reflection performance with energy.

Long-term Preservation of Data Analysis Capabilities

NASA Astrophysics Data System (ADS)

Gabriel, C.; Arviset, C.; Ibarra, A.; Pollock, A.

2015-09-01

While the long-term preservation of scientific data obtained by large astrophysics missions is ensured through science archives, the issue of data analysis software preservation has hardly been addressed. Efforts by large data centres have contributed so far to maintain some instrument or mission-specific data reduction packages on top of high-level general purpose data analysis software. However, it is always difficult to keep software alive without support and maintenance once the active phase of a mission is over. This is especially difficult in the budgetary model followed by space agencies. We discuss the importance of extending the lifetime of dedicated data analysis packages and review diverse strategies under development at ESA using new paradigms such as Virtual Machines, Cloud Computing, and Software as a Service for making possible full availability of data analysis and calibration software for decades at minimal cost.

Teaching meta-analysis using MetaLight.

PubMed

Thomas, James; Graziosi, Sergio; Higgins, Steve; Coe, Robert; Torgerson, Carole; Newman, Mark

2012-10-18

Meta-analysis is a statistical method for combining the results of primary studies. It is often used in systematic reviews and is increasingly a method and topic that appears in student dissertations. MetaLight is a freely available software application that runs simple meta-analyses and contains specific functionality to facilitate the teaching and learning of meta-analysis. While there are many courses and resources for meta-analysis available and numerous software applications to run meta-analyses, there are few pieces of software which are aimed specifically at helping those teaching and learning meta-analysis. Valuable teaching time can be spent learning the mechanics of a new software application, rather than on the principles and practices of meta-analysis. We discuss ways in which the MetaLight tool can be used to present some of the main issues involved in undertaking and interpreting a meta-analysis. While there are many software tools available for conducting meta-analysis, in the context of a teaching programme such software can require expenditure both in terms of money and in terms of the time it takes to learn how to use it. MetaLight was developed specifically as a tool to facilitate the teaching and learning of meta-analysis and we have presented here some of the ways it might be used in a training situation.

Spacecraft Trajectory Analysis and Mission Planning Simulation (STAMPS) Software

NASA Technical Reports Server (NTRS)

Puckett, Nancy; Pettinger, Kris; Hallstrom,John; Brownfield, Dana; Blinn, Eric; Williams, Frank; Wiuff, Kelli; McCarty, Steve; Ramirez, Daniel; Lamotte, Nicole;

On-Orbit Software Analysis

NASA Technical Reports Server (NTRS)

Moran, Susanne I.

2004-01-01

The On-Orbit Software Analysis Research Infusion Project was done by Intrinsyx Technologies Corporation (Intrinsyx) at the National Aeronautics and Space Administration (NASA) Ames Research Center (ARC). The Project was a joint collaborative effort between NASA Codes IC and SL, Kestrel Technology (Kestrel), and Intrinsyx. The primary objectives of the Project were: Discovery and verification of software program properties and dependencies, Detection and isolation of software defects across different versions of software, and Compilation of historical data and technical expertise for future applications

An overview of the mathematical and statistical analysis component of RICIS

NASA Technical Reports Server (NTRS)

Hallum, Cecil R.

1987-01-01

Mathematical and statistical analysis components of RICIS (Research Institute for Computing and Information Systems) can be used in the following problem areas: (1) quantification and measurement of software reliability; (2) assessment of changes in software reliability over time (reliability growth); (3) analysis of software-failure data; and (4) decision logic for whether to continue or stop testing software. Other areas of interest to NASA/JSC where mathematical and statistical analysis can be successfully employed include: math modeling of physical systems, simulation, statistical data reduction, evaluation methods, optimization, algorithm development, and mathematical methods in signal processing.

Integrating remote sensing with species distribution models; Mapping tamarisk invasions using the Software for Assisted Habitat Modeling (SAHM)

USGS Publications Warehouse

West, Amanda M.; Evangelista, Paul H.; Jarnevich, Catherine S.; Young, Nicholas E.; Stohlgren, Thomas J.; Talbert, Colin; Talbert, Marian; Morisette, Jeffrey; Anderson, Ryan

2016-01-01

Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM).

PubMed

West, Amanda M; Evangelista, Paul H; Jarnevich, Catherine S; Young, Nicholas E; Stohlgren, Thomas J; Talbert, Colin; Talbert, Marian; Morisette, Jeffrey; Anderson, Ryan

2016-10-11

Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.

How Safe Is Control Software

NASA Technical Reports Server (NTRS)

Dunn, William R.; Corliss, Lloyd D.

1991-01-01

Paper examines issue of software safety. Presents four case histories of software-safety analysis. Concludes that, to be safe, software, for all practical purposes, must be free of errors. Backup systems still needed to prevent catastrophic software failures.

Design and implementation of software for automated quality control and data analysis for a complex LC/MS/MS assay for urine opiates and metabolites.

PubMed

Dickerson, Jane A; Schmeling, Michael; Hoofnagle, Andrew N; Hoffman, Noah G

2013-01-16

Mass spectrometry provides a powerful platform for performing quantitative, multiplexed assays in the clinical laboratory, but at the cost of increased complexity of analysis and quality assurance calculations compared to other methodologies. Here we describe the design and implementation of a software application that performs quality control calculations for a complex, multiplexed, mass spectrometric analysis of opioids and opioid metabolites. The development and implementation of this application improved our data analysis and quality assurance processes in several ways. First, use of the software significantly improved the procedural consistency for performing quality control calculations. Second, it reduced the amount of time technologists spent preparing and reviewing the data, saving on average over four hours per run, and in some cases improving turnaround time by a day. Third, it provides a mechanism for coupling procedural and software changes with the results of each analysis. We describe several key details of the implementation including the use of version control software and automated unit tests. These generally useful software engineering principles should be considered for any software development project in the clinical lab. Copyright © 2012 Elsevier B.V. All rights reserved.

Software system safety

NASA Technical Reports Server (NTRS)

Uber, James G.

1988-01-01

Software itself is not hazardous, but since software and hardware share common interfaces there is an opportunity for software to create hazards. Further, these software systems are complex, and proven methods for the design, analysis, and measurement of software safety are not yet available. Some past software failures, future NASA software trends, software engineering methods, and tools and techniques for various software safety analyses are reviewed. Recommendations to NASA are made based on this review.

Space station software reliability analysis based on failures observed during testing at the multisystem integration facility

NASA Technical Reports Server (NTRS)

Tamayo, Tak Chai

1987-01-01

Quality of software not only is vital to the successful operation of the space station, it is also an important factor in establishing testing requirements, time needed for software verification and integration as well as launching schedules for the space station. Defense of management decisions can be greatly strengthened by combining engineering judgments with statistical analysis. Unlike hardware, software has the characteristics of no wearout and costly redundancies, thus making traditional statistical analysis not suitable in evaluating reliability of software. A statistical model was developed to provide a representation of the number as well as types of failures occur during software testing and verification. From this model, quantitative measure of software reliability based on failure history during testing are derived. Criteria to terminate testing based on reliability objectives and methods to estimate the expected number of fixings required are also presented.

Analysis of a hardware and software fault tolerant processor for critical applications

NASA Technical Reports Server (NTRS)

Dugan, Joanne B.

1993-01-01

Computer systems for critical applications must be designed to tolerate software faults as well as hardware faults. A unified approach to tolerating hardware and software faults is characterized by classifying faults in terms of duration (transient or permanent) rather than source (hardware or software). Errors arising from transient faults can be handled through masking or voting, but errors arising from permanent faults require system reconfiguration to bypass the failed component. Most errors which are caused by software faults can be considered transient, in that they are input-dependent. Software faults are triggered by a particular set of inputs. Quantitative dependability analysis of systems which exhibit a unified approach to fault tolerance can be performed by a hierarchical combination of fault tree and Markov models. A methodology for analyzing hardware and software fault tolerant systems is applied to the analysis of a hypothetical system, loosely based on the Fault Tolerant Parallel Processor. The models consider both transient and permanent faults, hardware and software faults, independent and related software faults, automatic recovery, and reconfiguration.

Event time analysis of longitudinal neuroimage data.

PubMed

Sabuncu, Mert R; Bernal-Rusiel, Jorge L; Reuter, Martin; Greve, Douglas N; Fischl, Bruce

2014-08-15

This paper presents a method for the statistical analysis of the associations between longitudinal neuroimaging measurements, e.g., of cortical thickness, and the timing of a clinical event of interest, e.g., disease onset. The proposed approach consists of two steps, the first of which employs a linear mixed effects (LME) model to capture temporal variation in serial imaging data. The second step utilizes the extended Cox regression model to examine the relationship between time-dependent imaging measurements and the timing of the event of interest. We demonstrate the proposed method both for the univariate analysis of image-derived biomarkers, e.g., the volume of a structure of interest, and the exploratory mass-univariate analysis of measurements contained in maps, such as cortical thickness and gray matter density. The mass-univariate method employs a recently developed spatial extension of the LME model. We applied our method to analyze structural measurements computed using FreeSurfer, a widely used brain Magnetic Resonance Image (MRI) analysis software package. We provide a quantitative and objective empirical evaluation of the statistical performance of the proposed method on longitudinal data from subjects suffering from Mild Cognitive Impairment (MCI) at baseline. Copyright © 2014 Elsevier Inc. All rights reserved.

State-of-the-Art Resources (SOAR) for Software Vulnerability Detection, Test, and Evaluation

DTIC Science & Technology

2014-07-01

preclude in-depth analysis, and widespread use of a Software -as-a- Service ( SaaS ) model that limits data availability and application to DoD systems...provide mobile application analysis using a Software - as-a- Service ( SaaS ) model. In this case, any software to be analyzed must be sent to the...tools are only available through a SaaS model. The widespread use of a Software -as-a- Service ( SaaS ) model as a sole evaluation model limits data

A Method for Populating the Knowledge Base of AFIT’s Domain-Oriented Application Composition System

DTIC Science & Technology

1993-12-01

Analysis ( FODA ). The approach identifies prominent features (similarities) and distinctive features (differences) of software systems within an... analysis approaches we have summarized, the re- searchers described FODA in sufficient detail to use on large domain analysis projects (ones with...Software Technology Center, July 1991. 18. Kang, Kyo C. and others. Feature-Oriented Domain Analysis ( FODA ) Feasibility Study. Technical Report, Software

Selection of software for mechanical engineering undergraduates

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

Cheah, C. T.; Yin, C. S.; Halim, T.

A major problem with the undergraduate mechanical course is the limited exposure of students to software packages coupled with the long learning curve on the existing software packages. This work proposes the use of appropriate software packages for the entire mechanical engineering curriculum to ensure students get sufficient exposure real life design problems. A variety of software packages are highlighted as being suitable for undergraduate work in mechanical engineering, e.g. simultaneous non-linear equations; uncertainty analysis; 3-D modeling software with the FEA; analysis tools for the solution of problems in thermodynamics, fluid mechanics, mechanical system design, and solid mechanics.

A Case Study of Measuring Process Risk for Early Insights into Software Safety

NASA Technical Reports Server (NTRS)

Layman, Lucas; Basili, Victor; Zelkowitz, Marvin V.; Fisher, Karen L.

2011-01-01

In this case study, we examine software safety risk in three flight hardware systems in NASA's Constellation spaceflight program. We applied our Technical and Process Risk Measurement (TPRM) methodology to the Constellation hazard analysis process to quantify the technical and process risks involving software safety in the early design phase of these projects. We analyzed 154 hazard reports and collected metrics to measure the prevalence of software in hazards and the specificity of descriptions of software causes of hazardous conditions. We found that 49-70% of 154 hazardous conditions could be caused by software or software was involved in the prevention of the hazardous condition. We also found that 12-17% of the 2013 hazard causes involved software, and that 23-29% of all causes had a software control. The application of the TPRM methodology identified process risks in the application of the hazard analysis process itself that may lead to software safety risk.

Knowledge and utilization of computer-software for statistics among Nigerian dentists.

PubMed

Chukwuneke, F N; Anyanechi, C E; Obiakor, A O; Amobi, O; Onyejiaka, N; Alamba, I

2013-01-01

The use of computer soft ware for generation of statistic analysis has transformed health information and data to simplest form in the areas of access, storage, retrieval and analysis in the field of research. This survey therefore was carried out to assess the level of knowledge and utilization of computer software for statistical analysis among dental researchers in eastern Nigeria. Questionnaires on the use of computer software for statistical analysis were randomly distributed to 65 practicing dental surgeons of above 5 years experience in the tertiary academic hospitals in eastern Nigeria. The focus was on: years of clinical experience; research work experience; knowledge and application of computer generated software for data processing and stastistical analysis. Sixty-two (62/65; 95.4%) of these questionnaires were returned anonymously, which were used in our data analysis. Twenty-nine (29/62; 46.8%) respondents fall within those with 5-10 years of clinical experience out of which none has completed the specialist training programme. Practitioners with above 10 years clinical experiences were 33 (33/62; 53.2%) out of which 15 (15/33; 45.5%) are specialists representing 24.2% (15/62) of the total number of respondents. All the 15 specialists are actively involved in research activities and only five (5/15; 33.3%) can utilize software statistical analysis unaided. This study has i dentified poor utilization of computer software for statistic analysis among dental researchers in eastern Nigeria. This is strongly associated with lack of exposure on the use of these software early enough especially during the undergraduate training. This call for introduction of computer training programme in dental curriculum to enable practitioners develops the attitude of using computer software for their research.

Melanie II--a third-generation software package for analysis of two-dimensional electrophoresis images: I. Features and user interface.

PubMed

Appel, R D; Palagi, P M; Walther, D; Vargas, J R; Sanchez, J C; Ravier, F; Pasquali, C; Hochstrasser, D F

1997-12-01

Although two-dimensional electrophoresis (2-DE) computer analysis software packages have existed ever since 2-DE technology was developed, it is only now that the hardware and software technology allows large-scale studies to be performed on low-cost personal computers or workstations, and that setting up a 2-DE computer analysis system in a small laboratory is no longer considered a luxury. After a first attempt in the seventies and early eighties to develop 2-DE analysis software systems on hardware that had poor or even no graphical capabilities, followed in the late eighties by a wave of innovative software developments that were possible thanks to new graphical interface standards such as XWindows, a third generation of 2-DE analysis software packages has now come to maturity. It can be run on a variety of low-cost, general-purpose personal computers, thus making the purchase of a 2-DE analysis system easily attainable for even the smallest laboratory that is involved in proteome research. Melanie II 2-D PAGE, developed at the University Hospital of Geneva, is such a third-generation software system for 2-DE analysis. Based on unique image processing algorithms, this user-friendly object-oriented software package runs on multiple platforms, including Unix, MS-Windows 95 and NT, and Power Macintosh. It provides efficient spot detection and quantitation, state-of-the-art image comparison, statistical data analysis facilities, and is Internet-ready. Linked to proteome databases such as those available on the World Wide Web, it represents a valuable tool for the "Virtual Lab" of the post-genome area.

FunRich proteomics software analysis, let the fun begin!

PubMed

Benito-Martin, Alberto; Peinado, Héctor

2015-08-01

Protein MS analysis is the preferred method for unbiased protein identification. It is normally applied to a large number of both small-scale and high-throughput studies. However, user-friendly computational tools for protein analysis are still needed. In this issue, Mathivanan and colleagues (Proteomics 2015, 15, 2597-2601) report the development of FunRich software, an open-access software that facilitates the analysis of proteomics data, providing tools for functional enrichment and interaction network analysis of genes and proteins. FunRich is a reinterpretation of proteomic software, a standalone tool combining ease of use with customizable databases, free access, and graphical representations. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Common mental disorder and its association with academic performance among Debre Berhan University students, Ethiopia.

PubMed

Haile, Yohannes Gebreegziabhere; Alemu, Sisay Mulugeta; Habtewold, Tesfa Dejenie

2017-01-01

Common mental disorder (CMD) is prevalent in industrialized and non-industrialized countries. The prevalence of CMD among university students was 28.8-44.7% and attributed to several risk factors, such as schooling. The aim of this study was to assess the prevalence and risk factors of CMD. In addition, the association between CMD and academic performance was tested. Institution based cross-sectional study was conducted with 422 students at Debre Berhan university from March to April 2015. CMD was the primary outcome variable whereas academic performance was the secondary outcome variable. Kessler psychological distress (K10) scale was used to assess CMD. Bivariate and multiple logistic regression analysis were performed for modeling the primary outcome variable; independent samples T test and linear regression analysis were carried out for modeling the secondary outcome variable. The strength of association was interpreted using odds ratio and regression coefficient (β) and decision on statistical significance was made at a p value of 0.05. Data were entered using EPI-data version 3.1 software and analyzed using the Statistical Package for the Social Sciences (SPSS) version 20.01 software. The prevalence of CMD was 63.1%. Field of study (p = 0.008, OR = 0.2, 95% CI 0.04-0.61), worshiping (p = 0.04, OR = 1.8, 95% CI 1.02-3.35), insomnia (p < 0.001, OR = 3.8, 95% CI 2.21-6.57), alcohol drinking (p = 0.006, OR = 2.7, 95% CI 1.33-5.66), and headache (p = 0.02, OR = 2.1, 95% CI 1.10-3.86) were identified risk factors for CMD. The mean cumulative grade point average of students with CMD was lower by 0.02 compared to those without CMD, but not statistically significant (p = 0.70, β = -0.02, 95% CI -0.15 to 0.10). CMD explained only 0.8% (r 2  = 0.008) of the difference in academic performance between students. At least three out of five students fulfilled CMD diagnostic criteria. The statistically significant risk factors were field of study, worshiping, insomnia, alcohol drinking, and headache. Moreover, there was no statistically significant association between CMD and academic performance. Undertaking integrated evidence-based intervention focusing on students with poor sleep quality, poor physical health, and who drink alcohol is essential if the present finding confirmed by a longitudinal study.

A Reference Model for Software and System Inspections. White Paper

NASA Technical Reports Server (NTRS)

He, Lulu; Shull, Forrest

2009-01-01

Software Quality Assurance (SQA) is an important component of the software development process. SQA processes provide assurance that the software products and processes in the project life cycle conform to their specified requirements by planning, enacting, and performing a set of activities to provide adequate confidence that quality is being built into the software. Typical techniques include: (1) Testing (2) Simulation (3) Model checking (4) Symbolic execution (5) Management reviews (6) Technical reviews (7) Inspections (8) Walk-throughs (9) Audits (10) Analysis (complexity analysis, control flow analysis, algorithmic analysis) (11) Formal method Our work over the last few years has resulted in substantial knowledge about SQA techniques, especially the areas of technical reviews and inspections. But can we apply the same QA techniques to the system development process? If yes, what kind of tailoring do we need before applying them in the system engineering context? If not, what types of QA techniques are actually used at system level? And, is there any room for improvement.) After a brief examination of the system engineering literature (especially focused on NASA and DoD guidance) we found that: (1) System and software development process interact with each other at different phases through development life cycle (2) Reviews are emphasized in both system and software development. (Figl.3). For some reviews (e.g. SRR, PDR, CDR), there are both system versions and software versions. (3) Analysis techniques are emphasized (e.g. Fault Tree Analysis, Preliminary Hazard Analysis) and some details are given about how to apply them. (4) Reviews are expected to use the outputs of the analysis techniques. In other words, these particular analyses are usually conducted in preparation for (before) reviews. The goal of our work is to explore the interaction between the Quality Assurance (QA) techniques at the system level and the software level.

Standardizing Activation Analysis: New Software for Photon Activation Analysis

NASA Astrophysics Data System (ADS)

Sun, Z. J.; Wells, D.; Segebade, C.; Green, J.

2011-06-01

Photon Activation Analysis (PAA) of environmental, archaeological and industrial samples requires extensive data analysis that is susceptible to error. For the purpose of saving time, manpower and minimizing error, a computer program was designed, built and implemented using SQL, Access 2007 and asp.net technology to automate this process. Based on the peak information of the spectrum and assisted by its PAA library, the program automatically identifies elements in the samples and calculates their concentrations and respective uncertainties. The software also could be operated in browser/server mode, which gives the possibility to use it anywhere the internet is accessible. By switching the nuclide library and the related formula behind, the new software can be easily expanded to neutron activation analysis (NAA), charged particle activation analysis (CPAA) or proton-induced X-ray emission (PIXE). Implementation of this would standardize the analysis of nuclear activation data. Results from this software were compared to standard PAA analysis with excellent agreement. With minimum input from the user, the software has proven to be fast, user-friendly and reliable.

The diagnostic value of narrow-band imaging for early and invasive lung cancer: a meta-analysis.

PubMed

Zhu, Juanjuan; Li, Wei; Zhou, Jihong; Chen, Yuqing; Zhao, Chenling; Zhang, Ting; Peng, Wenjia; Wang, Xiaojing

2017-07-01

This study aimed to compare the ability of narrow-band imaging to detect early and invasive lung cancer with that of conventional pathological analysis and white-light bronchoscopy. We searched the PubMed, EMBASE, Sinomed, and China National Knowledge Infrastructure databases for relevant studies. Meta-disc software was used to perform data analysis, meta-regression analysis, sensitivity analysis, and heterogeneity testing, and STATA software was used to determine if publication bias was present, as well as to calculate the relative risks for the sensitivity and specificity of narrow-band imaging vs those of white-light bronchoscopy for the detection of early and invasive lung cancer. A random-effects model was used to assess the diagnostic efficacy of the above modalities in cases in which a high degree of between-study heterogeneity was noted with respect to their diagnostic efficacies. The database search identified six studies including 578 patients. The pooled sensitivity and specificity of narrow-band imaging were 86% (95% confidence interval: 83-88%) and 81% (95% confidence interval: 77-84%), respectively, and the pooled sensitivity and specificity of white-light bronchoscopy were 70% (95% confidence interval: 66-74%) and 66% (95% confidence interval: 62-70%), respectively. The pooled relative risks for the sensitivity and specificity of narrow-band imaging vs the sensitivity and specificity of white-light bronchoscopy for the detection of early and invasive lung cancer were 1.33 (95% confidence interval: 1.07-1.67) and 1.09 (95% confidence interval: 0.84-1.42), respectively, and sensitivity analysis showed that narrow-band imaging exhibited good diagnostic efficacy with respect to detecting early and invasive lung cancer and that the results of the study were stable. Narrow-band imaging was superior to white light bronchoscopy with respect to detecting early and invasive lung cancer; however, the specificities of the two modalities did not differ significantly.

Qualitative and quantitative analysis of pyrolysis oil by gas chromatography with flame ionization detection and comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry.

PubMed

Sfetsas, Themistoklis; Michailof, Chrysa; Lappas, Angelos; Li, Qiangyi; Kneale, Brian

2011-05-27

Pyrolysis oils have attracted a lot of interest, as they are liquid energy carriers and general sources of chemicals. In this work, gas chromatography with flame ionization detector (GC-FID) and two-dimensional gas chromatography with time-of-flight mass spectrometry (GC×GC-TOFMS) techniques were used to provide both qualitative and quantitative results of the analysis of three different pyrolysis oils. The chromatographic methods and parameters were optimized and solvent choice and separation restrictions are discussed. Pyrolysis oil samples were diluted in suitable organic solvent and were analyzed by GC×GC-TOFMS. An average of 300 compounds were detected and identified in all three samples using the ChromaToF (Leco) software. The deconvoluted spectra were compared with the NIST software library for correct matching. Group type classification was performed by use of the ChromaToF software. The quantification of 11 selected compounds was performed by means of a multiple-point external calibration curve. Afterwards, the pyrolysis oils were extracted with water, and the aqueous phase was analyzed both by GC-FID and, after proper change of solvent, by GC×GC-TOFMS. As previously, the selected compounds were quantified by both techniques, by means of multiple point external calibration curves. The parameters of the calibration curves were calculated by weighted linear regression analysis. The limit of detection, limit of quantitation and linearity range for each standard compound with each method are presented. The potency of GC×GC-TOFMS for an efficient mapping of the pyrolysis oil is undisputable, and the possibility of using it for quantification as well has been demonstrated. On the other hand, the GC-FID analysis provides reliable results that allow for a rapid screening of the pyrolysis oil. To the best of our knowledge, very few papers have been reported with quantification attempts on pyrolysis oil samples using GC×GC-TOFMS most of which make use of the internal standard method. This work provides the ground for further analysis of pyrolysis oils of diverse sources for a rational design of both their production and utilization process. Copyright © 2010 Elsevier B.V. All rights reserved.

Second Generation Product Line Engineering Takes Hold in the DoD

DTIC Science & Technology

2014-01-01

Feature- Oriented Domain Analysis ( FODA ) Feasibility Study” (CMU/SEI-90- TR-021, ADA235785). Pittsburgh, PA: Software Engineering Institute...software product line engineering and software architecture documentation and analysis . Clements is co-author of three practitioner-oriented books about

The Holistic Targeting (HOT) Methodology as the Means to Improve Information Operations (IO) Target Development and Prioritization

DTIC Science & Technology

2008-09-01

software facilitate targeting problem understanding and the network analysis tool, Palantir , as an efficient and tailored semi-automated means to...the use of compendium software facilitate targeting problem understanding and the network analysis tool, Palantir , as an efficient and tailored semi...OBJECTIVES USING COMPENDIUM SOFTWARE .....63 E. HOT TARGET PRIORITIZATION AND DEVELOPMENT USING PALANTIR SOFTWARE .................................69 1

Software Defined Network Monitoring Scheme Using Spectral Graph Theory and Phantom Nodes

DTIC Science & Technology

2014-09-01

networks is the emergence of software - defined networking ( SDN ) [1]. SDN has existed for the...Chapter III for network monitoring. A. SOFTWARE DEFINED NETWORKS SDNs provide a new and innovative method to simplify network hardware by logically...and R. Giladi, “Performance analysis of software - defined networking ( SDN ),” in Proc. of IEEE 21st International Symposium on Modeling, Analysis

Age estimation by canines' pulp/tooth ratio in an Iranian population using digital panoramic radiography.

PubMed

Dehghani, Mahdieh; Shadkam, Elaheh; Ahrari, Farzaneh; Dehghani, Mahboobe

2018-04-01

Age estimation in adults is an important issue in forensic science. This study aimed to estimate the chronological age of Iranians by means of pulp/tooth area ratio (AR) of canines in digital panoramic radiographs. The sample consisted of panoramic radiographs of 271 male and female subjects aged 16-64 years. The pulp/tooth area ratio (AR) of upper and lower canines was calculated by AutoCAD software. Data were subjected to correlation and regression analysis. There was a significant and inverse correlation between age and pulp/tooth area ratio of upper and lower canines (r=-0.794 for upper canine and r=-0.282 for lower canine; p-value<0.001). Linear regression equations were derived separately for upper, lower and both canines. The mean difference between actual and estimated age using upper canine was 6.07±1.7. The results showed that the pulp/tooth area ratios of canines are a reliable method for age estimation in Iranians. The pulp/tooth area ratio of upper canine was better correlated with chronological age than that of lower canine. Copyright © 2018 Elsevier B.V. All rights reserved.

An Optimization of Inventory Demand Forecasting in University Healthcare Centre

NASA Astrophysics Data System (ADS)

Bon, A. T.; Ng, T. K.

2017-01-01

Healthcare industry becomes an important field for human beings nowadays as it concerns about one’s health. With that, forecasting demand for health services is an important step in managerial decision making for all healthcare organizations. Hence, a case study was conducted in University Health Centre to collect historical demand data of Panadol 650mg for 68 months from January 2009 until August 2014. The aim of the research is to optimize the overall inventory demand through forecasting techniques. Quantitative forecasting or time series forecasting model was used in the case study to forecast future data as a function of past data. Furthermore, the data pattern needs to be identified first before applying the forecasting techniques. Trend is the data pattern and then ten forecasting techniques are applied using Risk Simulator Software. Lastly, the best forecasting techniques will be find out with the least forecasting error. Among the ten forecasting techniques include single moving average, single exponential smoothing, double moving average, double exponential smoothing, regression, Holt-Winter’s additive, Seasonal additive, Holt-Winter’s multiplicative, seasonal multiplicative and Autoregressive Integrated Moving Average (ARIMA). According to the forecasting accuracy measurement, the best forecasting technique is regression analysis.

The interaction of flavonoid-lysozyme and the relationship between molecular structure of flavonoids and their binding activity to lysozyme.

PubMed

Yang, Ran; Yu, Lanlan; Zeng, Huajin; Liang, Ruiling; Chen, Xiaolan; Qu, Lingbo

2012-11-01

In this work, the interactions of twelve structurally different flavonoids with Lysozyme (Lys) were studied by fluorescence quenching method. The interaction mechanism and binding properties were investigated. It was found that the binding capacities of flavonoids to Lys were highly depend on the number and position of hydrogen, the kind and position of glycosyl. To explore the selectivity of the bindings of flavonoids with Lys, the structure descriptors of the flavonoids were calculated under QSAR software package of Cerius2, the quantitative relationship between the structures of flavonoids and their binding activities to Lys (QSAR) was performed through genetic function approximation (GFA) regression analysis. The QSAR regression equation was K(A) = 37850.460 + 1630.01Dipole +3038.330HD-171.795MR. (r = 0.858, r(CV)(2) = 0.444, F((11,3)) = 7.48), where K(A) is binding constants, Dipole, HD and MR was dipole moment, number of hydrogen-bond donor and molecular refractivity, respectively. The obtained results make us understand better how the molecular structures influencing their binding to protein which may open up new avenues for the design of the most suitable flavonoids derivatives with structure variants.

Development of new vibration energy flow analysis software and its applications to vehicle systems

NASA Astrophysics Data System (ADS)

Kim, D.-J.; Hong, S.-Y.; Park, Y.-H.

2005-09-01

The Energy flow analysis (EFA) offers very promising results in predicting the noise and vibration responses of system structures in medium-to-high frequency ranges. We have developed the Energy flow finite element method (EFFEM) based software, EFADSC++ R4, for the vibration analysis. The software can analyze the system structures composed of beam, plate, spring-damper, rigid body elements and many other components developed, and has many useful functions in analysis. For convenient use of the software, the main functions of the whole software are modularized into translator, model-converter, and solver. The translator module makes it possible to use finite element (FE) model for the vibration analysis. The model-converter module changes FE model into energy flow finite element (EFFE) model, and generates joint elements to cover the vibrational attenuation in the complex structures composed of various elements and can solve the joint element equations by using the wave tra! nsmission approach very quickly. The solver module supports the various direct and iterative solvers for multi-DOF structures. The predictions of vibration for real vehicles by using the developed software were performed successfully.

The Implication of Using NVivo Software in Qualitative Data Analysis: Evidence-Based Reflections.

PubMed

Zamawe, F C

2015-03-01

For a long time, electronic data analysis has been associated with quantitative methods. However, Computer Assisted Qualitative Data Analysis Software (CAQDAS) are increasingly being developed. Although the CAQDAS has been there for decades, very few qualitative health researchers report using it. This may be due to the difficulties that one has to go through to master the software and the misconceptions that are associated with using CAQDAS. While the issue of mastering CAQDAS has received ample attention, little has been done to address the misconceptions associated with CAQDAS. In this paper, the author reflects on his experience of interacting with one of the popular CAQDAS (NVivo) in order to provide evidence-based implications of using the software. The key message is that unlike statistical software, the main function of CAQDAS is not to analyse data but rather to aid the analysis process, which the researcher must always remain in control of. In other words, researchers must equally know that no software can analyse qualitative data. CAQDAS are basically data management packages, which support the researcher during analysis.

Mathcad in the Chemistry Curriculum Symbolic Software in the Chemistry Curriculum

NASA Astrophysics Data System (ADS)

Zielinski, Theresa Julia

2000-05-01

Physical chemistry is such a broad discipline that the topics we expect average students to complete in two semesters usually exceed their ability for meaningful learning. Consequently, the number and kind of topics and the efficiency with which students can learn them are important concerns. What topics are essential and what can we do to provide efficient and effective access to those topics? How do we accommodate the fact that students come to upper-division chemistry courses with a variety of nonuniformly distributed skills, a bit of calculus, and some physics studied one or more years before physical chemistry? The critical balance between depth and breadth of learning in courses and curricula may be achieved through appropriate use of technology and especially through the use of symbolic mathematics software. Software programs such as Mathcad, Mathematica, and Maple, however, have learning curves that diminish their effectiveness for novices. There are several ways to address the learning curve conundrum. First, basic instruction in the software provided during laboratory sessions should be followed by requiring laboratory reports that use the software. Second, one should assign weekly homework that requires the software and builds student skills within the discipline and with the software. Third, a complementary method, supported by this column, is to provide students with Mathcad worksheets or templates that focus on one set of related concepts and incorporate a variety of features of the software that they are to use to learn chemistry. In this column we focus on two significant topics for young chemists. The first is curve-fitting and the statistical analysis of the fitting parameters. The second is the analysis of the rotation/vibration spectrum of a diatomic molecule, HCl. A broad spectrum of Mathcad documents exists for teaching chemistry. One collection of 50 documents can be found at http://www.monmouth.edu/~tzielins/mathcad/Lists/index.htm. Another collection of peer-reviewed documents is developing through this column at the JCE Internet Web site, http://jchemed.chem.wisc.edu/JCEWWW/Features/ McadInChem/index.html. With this column we add three peer-reviewed and tested Mathcad documents to the JCE site. In Linear Least-Squares Regression, Sidney H. Young and Andrzej Wierzbicki demonstrate various implicit and explicit methods for determining the slope and intercept of the regression line for experimental data. The document shows how to determine the standard deviation for the slope, the intercept, and the standard deviation of the overall fit. Students are next given the opportunity to examine the confidence level for the fit through the Student's t-test. Examination of the residuals of the fit leads students to explore the possibility of rejecting points in a set of data. The document concludes with a discussion of and practice with adding a quadratic term to create a polynomial fit to a set of data and how to determine if the quadratic term is statistically significant. There is full documentation of the various steps used throughout the exposition of the statistical concepts. Although the statistical methods presented in this worksheet are generally accessible to average physical chemistry students, an instructor would be needed to explain the finer points of the matrix methods used in some sections of the worksheet. The worksheet is accompanied by a set of data for students to use to practice the techniques presented. It would be worthwhile for students to spend one or two laboratory periods learning to use the concepts presented and then to apply them to experimental data they have collected for themselves. Any linear or linearizable data set would be appropriate for use with this Mathcad worksheet. Alternatively, instructors may select sections of the document suited to the skill level of their students and the laboratory tasks at hand. In a second Mathcad document, Non-Linear Least-Squares Regression, Young and Wierzbicki introduce the basic concepts of nonlinear curve-fitting and develop the techniques needed to fit a variety of mathematical functions to experimental data. This approach is especially important when mathematical models for chemical processes cannot be linearized. In Mathcad the Levenberg-Marquardt algorithm is used to determine the best fitting parameters for a particular mathematical model. As in linear least-squares, the goal of the fitting process is to find the values for the fitting parameters that minimize the sum of the squares of the deviations between the data and the mathematical model. Students are asked to determine the fitting parameters, use the Hessian matrix to compute the standard deviation of the fitting parameters, test for the significance of the parameters using Student's t-test, use residual analysis to test for data points to remove, and repeat the calculations for another set of data. The nonlinear least-squares procedure follows closely on the pattern set up for linear least-squares by the same authors (see above). If students master the linear least-squares worksheet content they will be able to master the nonlinear least-squares technique (see also refs 1, 2). In the third document, The Analysis of the Vibrational Spectrum of a Linear Molecule by Richard Schwenz, William Polik, and Sidney Young, the authors build on the concepts presented in the curve fitting worksheets described above. This vibrational analysis document, which supports a classic experiment performed in the physical chemistry laboratory, shows how a Mathcad worksheet can increase the efficiency by which a set of complicated manipulations for data reduction can be made more accessible for students. The increase in efficiency frees up time for students to develop a fuller understanding of the physical chemistry concepts important to the interpretation of spectra and understanding of bond vibrations in general. The analysis of the vibration/rotation spectrum for a linear molecule worksheet builds on the rich literature for this topic (3). Before analyzing their own spectral data, students practice and learn the concepts and methods of the HCl spectral analysis by using the fundamental and first harmonic vibrational frequencies provided by the authors. This approach has a fundamental pedagogical advantage. Most explanations in laboratory texts are very concise and lack mathematical details required by average students. This Mathcad worksheet acts as a tutor; it guides students through the essential concepts for data reduction and lets them focus on learning important spectroscopic concepts. The Mathcad worksheet is amply annotated. Students who have moderate skill with the software and have learned about regression analysis from the curve-fitting worksheets described in this column will be able to complete and understand their analysis of the IR spectrum of HCl. The three Mathcad worksheets described here stretch the physical chemistry curriculum by presenting important topics in forms that students can use with only moderate Mathcad skills. The documents facilitate learning by giving students opportunities to interact with the material in meaningful ways in addition to using the documents as sources of techniques for building their own data-reduction worksheets. However, working through these Mathcad worksheets is not a trivial task for the average student. Support needs to be provided by the instructor to ease students through more advanced mathematical and Mathcad processes. These worksheets raise the question of how much we can ask diligent students to do in one course and how much time they need to spend to master the essential concepts of that course. The Mathcad documents and associated PDF versions are available at the JCE Internet WWW site. The Mathcad documents require Mathcad version 6.0 or higher and the PDF files require Adobe Acrobat. Every effort has been made to make the documents fully compatible across the various Mathcad versions. Users may need to refer to Mathcad manuals for functions that vary with the Mathcad version number. Literature Cited 1. Bevington, P. R. Data Reduction and Error Analysis for the Physical Sciences; McGraw-Hill: New York, 1969. 2. Zielinski, T. J.; Allendoerfer, R. D. J. Chem. Educ. 1997, 74, 1001. 3. Schwenz, R. W.; Polik, W. F. J. Chem. Educ. 1999, 76, 1302.

Description of the GMAO OSSE for Weather Analysis Software Package: Version 3

NASA Technical Reports Server (NTRS)

Koster, Randal D. (Editor); Errico, Ronald M.; Prive, Nikki C.; Carvalho, David; Sienkiewicz, Meta; El Akkraoui, Amal; Guo, Jing; Todling, Ricardo; McCarty, Will; Putman, William M.;

Analytical and Methodological Issues in the Use of Qualitative Data Analysis Software: A Description of Three Studies.

ERIC Educational Resources Information Center

Margerum-Leys, Jon; Kupperman, Jeff; Boyle-Heimann, Kristen

This paper presents perspectives on the use of data analysis software in the process of qualitative research. These perspectives were gained in the conduct of three qualitative research studies that differed in theoretical frames, areas of interests, and scope. Their common use of a particular data analysis software package allows the exploration…

ElectroMagnetoEncephalography Software: Overview and Integration with Other EEG/MEG Toolboxes

PubMed Central

Peyk, Peter; De Cesarei, Andrea; Junghöfer, Markus

2011-01-01

EMEGS (electromagnetic encephalography software) is a MATLAB toolbox designed to provide novice as well as expert users in the field of neuroscience with a variety of functions to perform analysis of EEG and MEG data. The software consists of a set of graphical interfaces devoted to preprocessing, analysis, and visualization of electromagnetic data. Moreover, it can be extended using a plug-in interface. Here, an overview of the capabilities of the toolbox is provided, together with a simple tutorial for both a standard ERP analysis and a time-frequency analysis. Latest features and future directions of the software development are presented in the final section. PMID:21577273

ElectroMagnetoEncephalography software: overview and integration with other EEG/MEG toolboxes.

PubMed

Peyk, Peter; De Cesarei, Andrea; Junghöfer, Markus

2011-01-01

EMEGS (electromagnetic encephalography software) is a MATLAB toolbox designed to provide novice as well as expert users in the field of neuroscience with a variety of functions to perform analysis of EEG and MEG data. The software consists of a set of graphical interfaces devoted to preprocessing, analysis, and visualization of electromagnetic data. Moreover, it can be extended using a plug-in interface. Here, an overview of the capabilities of the toolbox is provided, together with a simple tutorial for both a standard ERP analysis and a time-frequency analysis. Latest features and future directions of the software development are presented in the final section.

Report: Scientific Software.

ERIC Educational Resources Information Center

Borman, Stuart A.

1985-01-01

Discusses various aspects of scientific software, including evaluation and selection of commercial software products; program exchanges, catalogs, and other information sources; major data analysis packages; statistics and chemometrics software; and artificial intelligence. (JN)

Noninvasive detection of hepatic lipidosis in dairy cows with calibrated ultrasonographic image analysis.

PubMed

Starke, A; Haudum, A; Weijers, G; Herzog, K; Wohlsein, P; Beyerbach, M; de Korte, C L; Thijssen, J M; Rehage, J

2010-07-01

The aim was to test the accuracy of calibrated digital analysis of ultrasonographic hepatic images for diagnosing fatty liver in dairy cows. Digital analysis was performed by means of a novel method, computer-aided ultrasound diagnosis (CAUS), previously published by the authors. This method implies a set of pre- and postprocessing steps to normalize and correct the transcutaneous ultrasonographic images. Transcutaneous hepatic ultrasonography was performed before surgical correction on 151 German Holstein dairy cows (mean +/- standard error of the means; body weight: 571+/-7 kg; age: 4.9+/-0.2 yr; DIM: 35+/-5) with left-sided abomasal displacement. Concentration of triacylglycerol (TAG) was biochemically determined in liver samples collected via biopsy and values were considered the gold standard to which ultrasound estimates were compared. According to histopathologic examination of biopsies, none of the cows suffered from hepatic disorders other than hepatic lipidosis. Hepatic TAG concentrations ranged from 4.6 to 292.4 mg/g of liver fresh weight (FW). High correlations were found between the hepatic TAG and mean echo level (r=0.59) and residual attenuation (ResAtt; r=0.80) obtained in ultrasonographic imaging. High correlation existed between ResAtt and mean echo level (r=0.76). The 151 studied cows were split randomly into a training set of 76 cows and a test set of 75 cows. Based on the data from the training set, ResAtt was statistically selected by means of stepwise multiple regression analysis for hepatic TAG prediction (R(2)=0.69). Then, using the predicted TAG data of the test set, receiver operating characteristic analysis was performed to summarize the accuracy and predictive potential of the differentiation between various measured hepatic TAG values, based on TAG predicted from the regression formula. The area under the curve values of the receiver operating characteristic based on the regression equation were 0.94 (<50 vs. >or=50mg of TAG/g of FW), 0.83 (<100 vs. >or=100mg of TAG/g of FW), and 0.97 (<50 vs. >or=100mg of TAG/g of FW). The CAUS methodology and software for digitally analyzing liver ultrasonographic images is considered feasible for noninvasive screening of fatty liver in dairy herd health programs. Using the single parameter linear regression equation might be ideal for practical applications. Copyright (c) 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Using Commercial-Off-The-Shelf Speech Recognition Software for Conning U.S. Warships

DTIC Science & Technology

2003-06-01

Linear Regression , 2nd Edition, (John Wiley & Sons, St. Paul, Minnesota, 1985), pp. 267-269. 44 Current Projects About the Sigmoid Curve, Sigmoid Curve...Disabilities Conference, Conference Proceedings, [www.csun.edu/cod/conf/1998/proceedings/csun98_052.htm], as of June 2, 2003. 43 Weisberg, S., Applied

Content and Method in the Teaching of Marketing Research Revisited

ERIC Educational Resources Information Center

Wilson, Holt; Neeley, Concha; Niedzwiecki, Kelly

2009-01-01

This paper presents the findings from a survey of marketing research faculty. The study finds SPSS is the most used statistical software, that cross tabulation, single, independent, and dependent t-tests, and ANOVA are among the most important statistical tools according to respondents. Bivariate and multiple regression are also considered…

The microcomputer scientific software series 5: the BIOMASS user's guide.

Treesearch

George E. Host; Stephen C. Westin; William G. Cole; Kurt S. Pregitzer

1989-01-01

BIOMASS is an interactive microcomputer program that uses allometric regression equations to calculate aboveground biomass of common tree species of the Lake States. The equations are species-specific and most use both diameter and height as independent variables. The program accommodates fixed area and variable radius sample designs and produces both individual tree...

Interaction Models for Functional Regression.

PubMed

Usset, Joseph; Staicu, Ana-Maria; Maity, Arnab

2016-02-01

A functional regression model with a scalar response and multiple functional predictors is proposed that accommodates two-way interactions in addition to their main effects. The proposed estimation procedure models the main effects using penalized regression splines, and the interaction effect by a tensor product basis. Extensions to generalized linear models and data observed on sparse grids or with measurement error are presented. A hypothesis testing procedure for the functional interaction effect is described. The proposed method can be easily implemented through existing software. Numerical studies show that fitting an additive model in the presence of interaction leads to both poor estimation performance and lost prediction power, while fitting an interaction model where there is in fact no interaction leads to negligible losses. The methodology is illustrated on the AneuRisk65 study data.

The Python Spectral Analysis Tool (PySAT): A Powerful, Flexible, Preprocessing and Machine Learning Library and Interface

NASA Astrophysics Data System (ADS)

Anderson, R. B.; Finch, N.; Clegg, S. M.; Graff, T. G.; Morris, R. V.; Laura, J.; Gaddis, L. R.

2017-12-01

Machine learning is a powerful but underutilized approach that can enable planetary scientists to derive meaningful results from the rapidly-growing quantity of available spectral data. For example, regression methods such as Partial Least Squares (PLS) and Least Absolute Shrinkage and Selection Operator (LASSO), can be used to determine chemical concentrations from ChemCam and SuperCam Laser-Induced Breakdown Spectroscopy (LIBS) data [1]. Many scientists are interested in testing different spectral data processing and machine learning methods, but few have the time or expertise to write their own software to do so. We are therefore developing a free open-source library of software called the Python Spectral Analysis Tool (PySAT) along with a flexible, user-friendly graphical interface to enable scientists to process and analyze point spectral data without requiring significant programming or machine-learning expertise. A related but separately-funded effort is working to develop a graphical interface for orbital data [2]. The PySAT point-spectra tool includes common preprocessing steps (e.g. interpolation, normalization, masking, continuum removal, dimensionality reduction), plotting capabilities, and capabilities to prepare data for machine learning such as creating stratified folds for cross validation, defining training and test sets, and applying calibration transfer so that data collected on different instruments or under different conditions can be used together. The tool leverages the scikit-learn library [3] to enable users to train and compare the results from a variety of multivariate regression methods. It also includes the ability to combine multiple "sub-models" into an overall model, a method that has been shown to improve results and is currently used for ChemCam data [4]. Although development of the PySAT point-spectra tool has focused primarily on the analysis of LIBS spectra, the relevant steps and methods are applicable to any spectral data. The tool is available at https://github.com/USGS-Astrogeology/PySAT_Point_Spectra_GUI. [1] Clegg, S.M., et al. (2017) Spectrochim Acta B. 129, 64-85. [2] Gaddis, L. et al. (2017) 3rd Planetary Data Workshop, #1986. [3] http://scikit-learn.org/ [4] Anderson, R.B., et al. (2017) Spectrochim. Acta B. 129, 49-57.

Perception of Construction Participants in Construction delays: A case study in Tamilnadu, India.

NASA Astrophysics Data System (ADS)

Rathinakumar, V.; Vignesh, T.; Dhivagar, K.

2017-07-01

Delays in the construction industry are universal fact, which affects the construction participants. The research work spotlights on determining the prevailing delays in the cities of Tamil Nadu, as perceived by the participants. After a few field level interactions, a questionnaire was framed and administered to the participants i.e., Consultants (50 Nos.), contractors (50 Nos.) and clients (150 Nos.) to understand their perception on the causes of delays. The factors for delay was categorized into 4 groups say Improper project planning, Design related issues, Finance related issues and Resource related issues. The responses were analysed using the SPSS software by applying ANOVA and Regression analysis. From the analysis, it was found that the personal financial problems of the client dominantly affect the entire construction progress and the subsequent design changes by the clients, Inadequate early project planning, labour related issues. Also, the delay groups were found to be the improper project planning and the Resource related issues.

A flexible, interpretable framework for assessing sensitivity to unmeasured confounding.

PubMed

Dorie, Vincent; Harada, Masataka; Carnegie, Nicole Bohme; Hill, Jennifer

2016-09-10

When estimating causal effects, unmeasured confounding and model misspecification are both potential sources of bias. We propose a method to simultaneously address both issues in the form of a semi-parametric sensitivity analysis. In particular, our approach incorporates Bayesian Additive Regression Trees into a two-parameter sensitivity analysis strategy that assesses sensitivity of posterior distributions of treatment effects to choices of sensitivity parameters. This results in an easily interpretable framework for testing for the impact of an unmeasured confounder that also limits the number of modeling assumptions. We evaluate our approach in a large-scale simulation setting and with high blood pressure data taken from the Third National Health and Nutrition Examination Survey. The model is implemented as open-source software, integrated into the treatSens package for the R statistical programming language. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Risk Factors for premature birth in a hospital 1

PubMed Central

Ahumada-Barrios, Margarita E.; Alvarado, German F.

2016-01-01

Abstract Objective: to determine the risk factors for premature birth. Methods: retrospective case-control study of 600 pregnant women assisted in a hospital, with 298 pregnant women in the case group (who gave birth prematurely <37 weeks) and 302 pregnant women who gave birth to a full-term newborn in the control group. Stata software version 12.2 was used. The Chi-square test was used in bivariate analysis and logistic regression was used in multivariate analysis, from which Odds Ratios (OR) and Confidence Intervals (CI) of 95% were derived. Results: risk factors associated with premature birth were current twin pregnancy (adjusted OR= 2.4; p= 0.02), inadequate prenatal care (< 6 controls) (adjusted OR= 3.2; p <0.001), absent prenatal care (adjusted OR= 3.0; p <0.001), history of premature birth (adjusted OR= 3.7; p <0.001) and preeclampsia (adjusted OR= 1.9; p= 0.005). Conclusion: history of premature birth, preeclampsia, not receiving prenatal care and receiving inadequate prenatal care were risk factors for premature birth. PMID:27463110

Estimating Interaction Effects With Incomplete Predictor Variables

PubMed Central

Enders, Craig K.; Baraldi, Amanda N.; Cham, Heining

2014-01-01

The existing missing data literature does not provide a clear prescription for estimating interaction effects with missing data, particularly when the interaction involves a pair of continuous variables. In this article, we describe maximum likelihood and multiple imputation procedures for this common analysis problem. We outline 3 latent variable model specifications for interaction analyses with missing data. These models apply procedures from the latent variable interaction literature to analyses with a single indicator per construct (e.g., a regression analysis with scale scores). We also discuss multiple imputation for interaction effects, emphasizing an approach that applies standard imputation procedures to the product of 2 raw score predictors. We thoroughly describe the process of probing interaction effects with maximum likelihood and multiple imputation. For both missing data handling techniques, we outline centering and transformation strategies that researchers can implement in popular software packages, and we use a series of real data analyses to illustrate these methods. Finally, we use computer simulations to evaluate the performance of the proposed techniques. PMID:24707955

Meta-analysis of diagnostic tests accounting for disease prevalence: a new model using trivariate copulas.

PubMed

Hoyer, A; Kuss, O

2015-05-20

In real life and somewhat contrary to biostatistical textbook knowledge, sensitivity and specificity (and not only predictive values) of diagnostic tests can vary with the underlying prevalence of disease. In meta-analysis of diagnostic studies, accounting for this fact naturally leads to a trivariate expansion of the traditional bivariate logistic regression model with random study effects. In this paper, a new model is proposed using trivariate copulas and beta-binomial marginal distributions for sensitivity, specificity, and prevalence as an expansion of the bivariate model. Two different copulas are used, the trivariate Gaussian copula and a trivariate vine copula based on the bivariate Plackett copula. This model has a closed-form likelihood, so standard software (e.g., SAS PROC NLMIXED) can be used. The results of a simulation study have shown that the copula models perform at least as good but frequently better than the standard model. The methods are illustrated by two examples. Copyright © 2015 John Wiley & Sons, Ltd.

Trans-ethnic meta-regression of genome-wide association studies accounting for ancestry increases power for discovery and improves fine-mapping resolution

PubMed Central

Mägi, Reedik; Horikoshi, Momoko; Sofer, Tamar; Mahajan, Anubha; Kitajima, Hidetoshi; Franceschini, Nora; McCarthy, Mark I.; Morris, Andrew P.

2017-01-01

Abstract Trans-ethnic meta-analysis of genome-wide association studies (GWAS) across diverse populations can increase power to detect complex trait loci when the underlying causal variants are shared between ancestry groups. However, heterogeneity in allelic effects between GWAS at these loci can occur that is correlated with ancestry. Here, a novel approach is presented to detect SNP association and quantify the extent of heterogeneity in allelic effects that is correlated with ancestry. We employ trans-ethnic meta-regression to model allelic effects as a function of axes of genetic variation, derived from a matrix of mean pairwise allele frequency differences between GWAS, and implemented in the MR-MEGA software. Through detailed simulations, we demonstrate increased power to detect association for MR-MEGA over fixed- and random-effects meta-analysis across a range of scenarios of heterogeneity in allelic effects between ethnic groups. We also demonstrate improved fine-mapping resolution, in loci containing a single causal variant, compared to these meta-analysis approaches and PAINTOR, and equivalent performance to MANTRA at reduced computational cost. Application of MR-MEGA to trans-ethnic GWAS of kidney function in 71,461 individuals indicates stronger signals of association than fixed-effects meta-analysis when heterogeneity in allelic effects is correlated with ancestry. Application of MR-MEGA to fine-mapping four type 2 diabetes susceptibility loci in 22,086 cases and 42,539 controls highlights: (i) strong evidence for heterogeneity in allelic effects that is correlated with ancestry only at the index SNP for the association signal at the CDKAL1 locus; and (ii) 99% credible sets with six or fewer variants for five distinct association signals. PMID:28911207

Texture analysis of B-mode ultrasound images to stage hepatic lipidosis in the dairy cow: A methodological study.

PubMed

Banzato, Tommaso; Fiore, Enrico; Morgante, Massimo; Manuali, Elisabetta; Zotti, Alessandro

2016-10-01

Hepatic lipidosis is the most diffused hepatic disease in the lactating cow. A new methodology to estimate the degree of fatty infiltration of the liver in lactating cows by means of texture analysis of B-mode ultrasound images is proposed. B-mode ultrasonography of the liver was performed in 48 Holstein Friesian cows using standardized ultrasound parameters. Liver biopsies to determine the triacylglycerol content of the liver (TAGqa) were obtained from each animal. A large number of texture parameters were calculated on the ultrasound images by means of a free software. Based on the TAGqa content of the liver, 29 samples were classified as mild (TAGqa<50mg/g), 6 as moderate (50mg/g100mg/g) and 13 as severe (TAG>100mg/g) in steatosis. Stepwise linear regression analysis was performed to predict the TAGqa content of the liver (TAGpred) from the texture parameters calculated on the ultrasound images. A five-variable model was used to predict the TAG content from the ultrasound images. The regression model explained 83.4% of the variance. An area under the curve (AUC) of 0.949 was calculated for <50mg/g vs >50mg/g of TAGqa; using an optimal cut-off value of 72mg/g TAGpred had a sensitivity of 86.2% and a specificity of 84.2%. An AUC of 0.978 for <100mg/g vs >100mg/g of TAGqa was calculated; using an optimal cut-off value of 89mg/g, TAGpred sensitivity was 92.3% and specificity was 88.6%. Texture analysis of B-mode ultrasound images may therefore be used to accurately predict the TAG content of the liver in lactating cows. Copyright © 2016 Elsevier Ltd. All rights reserved.

Lipidomics study of plasma phospholipid metabolism in early type 2 diabetes rats with ancient prescription Huang-Qi-San intervention by UPLC/Q-TOF-MS and correlation coefficient.

PubMed

Wu, Xia; Zhu, Jian-Cheng; Zhang, Yu; Li, Wei-Min; Rong, Xiang-Lu; Feng, Yi-Fan

2016-08-25

Potential impact of lipid research has been increasingly realized both in disease treatment and prevention. An effective metabolomics approach based on ultra-performance liquid chromatography/quadrupole-time-of-flight mass spectrometry (UPLC/Q-TOF-MS) along with multivariate statistic analysis has been applied for investigating the dynamic change of plasma phospholipids compositions in early type 2 diabetic rats after the treatment of an ancient prescription of Chinese Medicine Huang-Qi-San. The exported UPLC/Q-TOF-MS data of plasma samples were subjected to SIMCA-P and processed by bioMark, mixOmics, Rcomdr packages with R software. A clear score plots of plasma sample groups, including normal control group (NC), model group (MC), positive medicine control group (Flu) and Huang-Qi-San group (HQS), were achieved by principal-components analysis (PCA), partial least-squares discriminant analysis (PLS-DA) and orthogonal partial least-squares discriminant analysis (OPLS-DA). Biomarkers were screened out using student T test, principal component regression (PCR), partial least-squares regression (PLS) and important variable method (variable influence on projection, VIP). Structures of metabolites were identified and metabolic pathways were deduced by correlation coefficient. The relationship between compounds was explained by the correlation coefficient diagram, and the metabolic differences between similar compounds were illustrated. Based on KEGG database, the biological significances of identified biomarkers were described. The correlation coefficient was firstly applied to identify the structure and deduce the metabolic pathways of phospholipids metabolites, and the study provided a new methodological cue for further understanding the molecular mechanisms of metabolites in the process of regulating Huang-Qi-San for treating early type 2 diabetes. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

An unjustified benefit: immortal time bias in the analysis of time-dependent events.

PubMed

Gleiss, Andreas; Oberbauer, Rainer; Heinze, Georg

2018-02-01

Immortal time bias is a problem arising from methodologically wrong analyses of time-dependent events in survival analyses. We illustrate the problem by analysis of a kidney transplantation study. Following patients from transplantation to death, groups defined by the occurrence or nonoccurrence of graft failure during follow-up seemingly had equal overall mortality. Such naive analysis assumes that patients were assigned to the two groups at time of transplantation, which actually are a consequence of occurrence of a time-dependent event later during follow-up. We introduce landmark analysis as the method of choice to avoid immortal time bias. Landmark analysis splits the follow-up time at a common, prespecified time point, the so-called landmark. Groups are then defined by time-dependent events having occurred before the landmark, and outcome events are only considered if occurring after the landmark. Landmark analysis can be easily implemented with common statistical software. In our kidney transplantation example, landmark analyses with landmarks set at 30 and 60 months clearly identified graft failure as a risk factor for overall mortality. We give further typical examples from transplantation research and discuss strengths and limitations of landmark analysis and other methods to address immortal time bias such as Cox regression with time-dependent covariables. © 2017 Steunstichting ESOT.

Pharmacokinetics of isochlorgenic acid C in rats by HPLC-MS: Absolute bioavailability and dose proportionality.

PubMed

Huang, Li Hua; Xiong, Xiao Hong; Zhong, Yun Ming; Cen, Mei Feng; Cheng, Xuan Ge; Wang, Gui Xiang; Zang, Lin Quan; Wang, Su Jun

2016-06-05

Isochlorgenic acid C (IAC), one of the bioactive compounds of Lonicera japonica, exhibited diverse pharmacological effects. However, its pharmacokinetic properties and bioavailability remained unresolved. To determine the absolute bioavailability in rats and the dose proportionality on the pharmacokinetics of single oral dose of IAC. A validated HPLC-MS method was developed for the determination of IAC in rat plasma. Plasma concentration versus time data were generated following oral and intravenous dosing. The pharmacokinetic analysis was performed using DAS 3.0 software analysis. Absolute bioavailability in rats was determined by comparing pharmacokinetic data after administration of single oral (5, 10 and 25mgkg(-1)) and intravenous (5mgkg(-1)) doses of IAC. The dose proportionality of AUC(0-∞) and Cmax were analyzed by linear regression. Experimental data showed that absolute oral bioavailability of IAC in rats across the doses ranged between 14.4% and 16.9%. The regression analysis of AUC(0-∞) and Cmax at the three doses (5, 10 and 25mgkg(-1)) indicated that the equations were y=35.23x+117.20 (r=0.998) and y=121.03x+255.74 (r=0.995), respectively. A new HPLC-MS method was developed to determine the bioavailability and the dose proportionality of IAC. Bioavailability of IAC in rats was poor and both Cmax and AUC(0-∞) of IAC had a positive correlation with dose. Evaluation of the pharmacokinetics of IAC will be useful in assessing concentration-effect relationships for the potential therapeutic applications of IAC. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Reliability and validity of tongue color analysis in the prediction of symptom patterns in terms of East Asian Medicine.

PubMed

Park, Young-Jae; Lee, Jin-Moo; Yoo, Seung-Yeon; Park, Young-Bae

2016-04-01

To examine whether color parameters of tongue inspection (TI) using a digital camera was reliable and valid, and to examine which color parameters serve as predictors of symptom patterns in terms of East Asian medicine (EAM). Two hundred female subjects' tongue substances were photographed by a mega-pixel digital camera. Together with the photographs, the subjects were asked to complete Yin deficiency, Phlegm pattern, and Cold-Heat pattern questionnaires. Using three sets of digital imaging software, each digital image was exposure- and white balance-corrected, and finally L* (luminance), a* (red-green balance), and b* (yellow-blue balance) values of the tongues were calculated. To examine intra- and inter-rater reliabilities and criterion validity of the color analysis method, three raters were asked to calculate color parameters for 20 digital image samples. Finally, four hierarchical regression models were formed. Color parameters showed good or excellent reliability (0.627-0.887 for intra-class correlation coefficients) and significant criterion validity (0.523-0.718 for Spearman's correlation). In the hierarchical regression models, age was a significant predictor of Yin deficiency (β = 0.192), and b* value of the tip of the tongue was a determinant predictor of Yin deficiency, Phlegm, and Heat patterns (β = - 0.212, - 0.172, and - 0.163). Luminance (L*) was predictive of Yin deficiency (β = -0.172) and Cold (β = 0.173) pattern. Our results suggest that color analysis of the tongue using the L*a*b* system is reliable and valid, and that color parameters partially serve as symptom pattern predictors in EAM practice.

African Ancestry Analysis and Admixture Genetic Mapping for Proliferative Diabetic Retinopathy in African Americans.

PubMed

Tandon, Arti; Chen, Ching J; Penman, Alan; Hancock, Heather; James, Maurice; Husain, Deeba; Andreoli, Christopher; Li, Xiaohui; Kuo, Jane Z; Idowu, Omolola; Riche, Daniel; Papavasilieou, Evangelia; Brauner, Stacey; Smith, Sataria O; Hoadley, Suzanne; Richardson, Cole; Kieser, Troy; Vazquez, Vanessa; Chi, Cheryl; Fernandez, Marlene; Harden, Maegan; Cotch, Mary Frances; Siscovick, David; Taylor, Herman A; Wilson, James G; Reich, David; Wong, Tien Y; Klein, Ronald; Klein, Barbara E K; Rotter, Jerome I; Patterson, Nick; Sobrin, Lucia

2015-06-01

To examine the relationship between proportion of African ancestry (PAA) and proliferative diabetic retinopathy (PDR) and to identify genetic loci associated with PDR using admixture mapping in African Americans with type 2 diabetes (T2D). Between 1993 and 2013, 1440 participants enrolled in four different studies had fundus photographs graded using the Early Treatment Diabetic Retinopathy Study scale. Cases (n = 305) had PDR while controls (n = 1135) had nonproliferative diabetic retinopathy (DR) or no DR. Covariates included diabetes duration, hemoglobin A1C, systolic blood pressure, income, and education. Genotyping was performed on the Affymetrix platform. The association between PAA and PDR was evaluated using logistic regression. Genome-wide admixture scanning was performed using ANCESTRYMAP software. In the univariate analysis, PDR was associated with increased PAA (odds ratio [OR] = 1.36, 95% confidence interval [CI] = 1.16-1.59, P = 0.0002). In multivariate regression adjusting for traditional DR risk factors, income and education, the association between PAA and PDR was attenuated and no longer significant (OR = 1.21, 95% CI = 0.59-2.47, P = 0.61). For the admixture analyses, the maximum genome-wide score was 1.44 on chromosome 1. In this largest study of PDR in African Americans with T2D to date, an association between PAA and PDR is not present after adjustment for clinical, demographic, and socioeconomic factors. No genome-wide significant locus (defined as having a locus-genome statistic > 5) was identified with admixture analysis. Further analyses with even larger sample sizes are needed to definitively assess if any admixture signal for DR is present.

Software selection based on analysis and forecasting methods, practised in 1C

NASA Astrophysics Data System (ADS)

Vazhdaev, A. N.; Chernysheva, T. Y.; Lisacheva, E. I.

2015-09-01

The research focuses on the problem of a “1C: Enterprise 8” platform inboard mechanisms for data analysis and forecasting. It is important to evaluate and select proper software to develop effective strategies for customer relationship management in terms of sales, as well as implementation and further maintenance of software. Research data allows creating new forecast models to schedule further software distribution.

Software for Real-Time Analysis of Subsonic Test Shot Accuracy

DTIC Science & Technology

2014-03-01

used the C++ programming language, the Open Source Computer Vision ( OpenCV ®) software library, and Microsoft Windows® Application Programming...video for comparison through OpenCV image analysis tools. Based on the comparison, the software then computed the coordinates of each shot relative to...DWB researchers wanted to use the Open Source Computer Vision ( OpenCV ) software library for capturing and analyzing frames of video. OpenCV contains

Software ion scan functions in analysis of glycomic and lipidomic MS/MS datasets.

PubMed

Haramija, Marko

2018-03-01

Hardware ion scan functions unique to tandem mass spectrometry (MS/MS) mode of data acquisition, such as precursor ion scan (PIS) and neutral loss scan (NLS), are important for selective extraction of key structural data from complex MS/MS spectra. However, their software counterparts, software ion scan (SIS) functions, are still not regularly available. Software ion scan functions can be easily coded for additional functionalities, such as software multiple precursor ion scan, software no ion scan, and software variable ion scan functions. These are often necessary, since they allow more efficient analysis of complex MS/MS datasets, often encountered in glycomics and lipidomics. Software ion scan functions can be easily coded by using modern script languages and can be independent of instrument manufacturer. Here we demonstrate the utility of SIS functions on a medium-size glycomic MS/MS dataset. Knowledge of sample properties, as well as of diagnostic and conditional diagnostic ions crucial for data analysis, was needed. Based on the tables constructed with the output data from the SIS functions performed, a detailed analysis of a complex MS/MS glycomic dataset could be carried out in a quick, accurate, and efficient manner. Glycomic research is progressing slowly, and with respect to the MS experiments, one of the key obstacles for moving forward is the lack of appropriate bioinformatic tools necessary for fast analysis of glycomic MS/MS datasets. Adding novel SIS functionalities to the glycomic MS/MS toolbox has a potential to significantly speed up the glycomic data analysis process. Similar tools are useful for analysis of lipidomic MS/MS datasets as well, as will be discussed briefly. Copyright © 2017 John Wiley & Sons, Ltd.

New software for statistical analysis of Cambridge Structural Database data

PubMed Central

Sykes, Richard A.; McCabe, Patrick; Allen, Frank H.; Battle, Gary M.; Bruno, Ian J.; Wood, Peter A.

2011-01-01

A collection of new software tools is presented for the analysis of geometrical, chemical and crystallographic data from the Cambridge Structural Database (CSD). This software supersedes the program Vista. The new functionality is integrated into the program Mercury in order to provide statistical, charting and plotting options alongside three-dimensional structural visualization and analysis. The integration also permits immediate access to other information about specific CSD entries through the Mercury framework, a common requirement in CSD data analyses. In addition, the new software includes a range of more advanced features focused towards structural analysis such as principal components analysis, cone-angle correction in hydrogen-bond analyses and the ability to deal with topological symmetry that may be exhibited in molecular search fragments. PMID:22477784

Development of Cell Analysis Software for Cultivated Corneal Endothelial Cells.

PubMed

Okumura, Naoki; Ishida, Naoya; Kakutani, Kazuya; Hongo, Akane; Hiwa, Satoru; Hiroyasu, Tomoyuki; Koizumi, Noriko

2017-11-01

To develop analysis software for cultured human corneal endothelial cells (HCECs). Software was designed to recognize cell borders and to provide parameters such as cell density, coefficient of variation, and polygonality of cultured HCECs based on phase contrast images. Cultured HCECs with high or low cell density were incubated with Ca-free and Mg-free phosphate-buffered saline for 10 minutes to reveal the cell borders and were then analyzed with software (n = 50). Phase contrast images showed that cell borders were not distinctly outlined, but these borders became more distinctly outlined after phosphate-buffered saline treatment and were recognized by cell analysis software. The cell density value provided by software was similar to that obtained using manual cell counting by an experienced researcher. Morphometric parameters, such as the coefficient of variation and polygonality, were also produced by software, and these values were significantly correlated with cell density (Pearson correlation coefficients -0.62 and 0.63, respectively). The software described here provides morphometric information from phase contrast images, and it enables subjective and noninvasive quality assessment for tissue engineering therapy of the corneal endothelium.

Team Software Development for Aerothermodynamic and Aerodynamic Analysis and Design

NASA Technical Reports Server (NTRS)

Alexandrov, N.; Atkins, H. L.; Bibb, K. L.; Biedron, R. T.; Carpenter, M. H.; Gnoffo, P. A.; Hammond, D. P.; Jones, W. T.; Kleb, W. L.; Lee-Rausch, E. M.

2003-01-01

A collaborative approach to software development is described. The approach employs the agile development techniques: project retrospectives, Scrum status meetings, and elements of Extreme Programming to efficiently develop a cohesive and extensible software suite. The software product under development is a fluid dynamics simulator for performing aerodynamic and aerothermodynamic analysis and design. The functionality of the software product is achieved both through the merging, with substantial rewrite, of separate legacy codes and the authorship of new routines. Examples of rapid implementation of new functionality demonstrate the benefits obtained with this agile software development process. The appendix contains a discussion of coding issues encountered while porting legacy Fortran 77 code to Fortran 95, software design principles, and a Fortran 95 coding standard.

Using Combined SFTA and SFMECA Techniques for Space Critical Software

NASA Astrophysics Data System (ADS)

Nicodemos, F. G.; Lahoz, C. H. N.; Abdala, M. A. D.; Saotome, O.

2012-01-01

This work addresses the combined Software Fault Tree Analysis (SFTA) and Software Failure Modes, Effects and Criticality Analysis (SFMECA) techniques applied to space critical software of satellite launch vehicles. The combined approach is under research as part of the Verification and Validation (V&V) efforts to increase software dependability and as future application in other projects under development at Instituto de Aeronáutica e Espaço (IAE). The applicability of such approach was conducted on system software specification and applied to a case study based on the Brazilian Satellite Launcher (VLS). The main goal is to identify possible failure causes and obtain compensating provisions that lead to inclusion of new functional and non-functional system software requirements.

Software development predictors, error analysis, reliability models and software metric analysis

NASA Technical Reports Server (NTRS)

Basili, Victor

1983-01-01

The use of dynamic characteristics as predictors for software development was studied. It was found that there are some significant factors that could be useful as predictors. From a study on software errors and complexity, it was shown that meaningful results can be obtained which allow insight into software traits and the environment in which it is developed. Reliability models were studied. The research included the field of program testing because the validity of some reliability models depends on the answers to some unanswered questions about testing. In studying software metrics, data collected from seven software engineering laboratory (FORTRAN) projects were examined and three effort reporting accuracy checks were applied to demonstrate the need to validate a data base. Results are discussed.

Principal component regression analysis with SPSS.

PubMed

Liu, R X; Kuang, J; Gong, Q; Hou, X L

2003-06-01

The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.

How Does Physical Activity Intervention Improve Self-Esteem and Self-Concept in Children and Adolescents? Evidence from a Meta-Analysis

PubMed Central

Liu, Mingli; Wu, Lang; Ming, Qingsen

2015-01-01

Objective To perform a systematic review and meta-analysis for the effects of physical activity intervention on self-esteem and self-concept in children and adolescents, and to identify moderator variables by meta-regression. Design A meta-analysis and meta-regression. Method Relevant studies were identified through a comprehensive search of electronic databases. Study inclusion criteria were: (1) intervention should be supervised physical activity, (2) reported sufficient data to estimate pooled effect sizes of physical activity intervention on self-esteem or self-concept, (3) participants’ ages ranged from 3 to 20 years, and (4) a control or comparison group was included. For each study, study design, intervention design and participant characteristics were extracted. R software (version 3.1.3) and Stata (version 12.0) were used to synthesize effect sizes and perform moderation analyses for determining moderators. Results Twenty-five randomized controlled trial (RCT) studies and 13 non-randomized controlled trial (non-RCT) studies including a total of 2991 cases were identified. Significant positive effects were found in RCTs for intervention of physical activity alone on general self outcomes (Hedges’ g = 0.29, 95% confidence interval [CI]: 0.14 to 0.45; p = 0.001), self-concept (Hedges’ g = 0.49, 95%CI: 0.10 to 0.88, p = 0.014) and self-worth (Hedges’ g = 0.31, 95%CI: 0.13 to 0.49, p = 0.005). There was no significant effect of intervention of physical activity alone on any outcomes in non-RCTs, as well as in studies with intervention of physical activity combined with other strategies. Meta-regression analysis revealed that higher treatment effects were associated with setting of intervention in RCTs (β = 0.31, 95%CI: 0.07 to 0.55, p = 0.013). Conclusion Intervention of physical activity alone is associated with increased self-concept and self-worth in children and adolescents. And there is a stronger association with school-based and gymnasium-based intervention compared with other settings. PMID:26241879

Trends in Alabama teen driving death and injury.

PubMed

Monroe, Kathy; Irons, Elizabeth; Crew, Marie; Norris, Jesse; Nichols, Michele; King, William D

2014-09-01

Motor vehicle crashes (MVCs) are a leading cause of morbidity and mortality in teens. Alabama has been in the Top 5 states for MVC fatality rate among teens in the United States for several years. Twelve years of teen MVC deaths and injuries were evaluated. Our hypothesis is that the teen driving motor vehicle-related deaths and injuries have decreased related to legislative and community awareness activities. A retrospective analysis of Alabama teen MVC deaths and injury for the years 2000 to 2011 was conducted. MVC data were obtained from a Fatality Analysis Reporting System data set managed by the Center for Advanced Public Safety at the University of Alabama. A Lowess regression-scattergram analysis was used to identify period specific changes in deaths and injury over time. Statistical analysis was conducted using True Epistat 5.0 software. When the Lowess regression was applied, there was an obvious change in the trend line in 2007. To test that observation, we then compared medians in the pre-2007 and post-2007 periods, which validated our observation. Moreover, it provided a near-even number of observations for comparison. The Spearman rank correlation was used to test for correlation of deaths and injury over time. The Mann-Whitney U-test was used to evaluate median differences in deaths and injury comparing pre-2007 and post-2007 data. Alabama teen MVC deaths and injury demonstrated a significant negative correlation over the 12-year period (Rs for deaths and injury, -0.87 [p < 0.001] and -0.92 [p < 0.001], respectively). Lowess regression identified a notable decline in deaths and injury after the year 2006. Median deaths and injury for the pre-2007 period were significantly higher than the post-2007 period, (U = 35.0, p = 0.003). Alabama teen driver deaths and injury have decreased during the 12-year study period, most notably after 2006. Factors that may have contributed to this trend may include stricter laws for teen drivers (enacted in 2002 and updated in 2010), less teen driving because of a nationwide economic downturn, delayed licensing in teens, steady improvements in overall seat belt use, and heightened public awareness of risky behaviors in teen driving.

The Short-term Prognostic Value of the Triglyceride-to-high-density Lipoprotein Cholesterol Ratio in Acute Ischemic Stroke

PubMed Central

Wang, Huan; Lei, Leix; Zhang, Han-Qing; Gu, Zheng-Tian; Xing, Fang-Lan; Yan, Fu-Ling

2018-01-01

The triglyceride (TG)-to-high-density lipoprotein cholesterol (HDL-C) ratio (TG/HDL-C) is a simple approach to predicting unfavorable outcomes in cardiovascular disease. The influence of TG/HDL-C on acute ischemic stroke remains elusive. The purpose of this study was to investigate the precise effect of TG/HDL-C on 3-month mortality after acute ischemic stroke (AIS). Patients with AIS were enrolled in the present study from 2011 to 2017. A total of 1459 participants from a single city in China were divided into retrospective training and prospective test cohorts. Medical records were collected periodically to determine the incidence of fatal events. All participants were followed for 3 months. Optimal cutoff values were determined using X-tile software to separate the training cohort patients into higher and lower survival groups based on their lipid levels. A survival analysis was conducted using Kaplan-Meier curves and a Cox proportional hazards regression model. A total of 1459 patients with AIS (median age 68.5 years, 58.5% male) were analyzed. Univariate Cox regression analysis confirmed that TG/HDL-C was a significant prognostic factor for 3-month survival. X-tile identified 0.9 as an optimal cutoff for TG/HDL-C. In the univariate analysis, the prognosis of the TG/HDL-C >0.9 group was markedly superior to that of TG/HDL-C ≤0.9 group (P<0.001). A multivariate Cox regression analysis showed that TG/HDL-C was independently correlated with a reduced risk of mortality (hazard ratio [HR], 0.39; 95% confidence interval [CI], 0.24-0.62; P<0.001). These results were confirmed in the 453 patients in the test cohort. A nomogram was constructed to predict 3-month case-fatality, and the c-indexes of predictive accuracy were 0.684 and 0.670 in the training and test cohorts, respectively (P<0.01). The serum TG/HDL-C ratio may be useful for predicting short-term mortality after AIS. PMID:29896437

The Short-term Prognostic Value of the Triglyceride-to-high-density Lipoprotein Cholesterol Ratio in Acute Ischemic Stroke.

PubMed

Deng, Qi-Wen; Li, Shuo; Wang, Huan; Lei, Leix; Zhang, Han-Qing; Gu, Zheng-Tian; Xing, Fang-Lan; Yan, Fu-Ling

2018-06-01

The triglyceride (TG)-to-high-density lipoprotein cholesterol (HDL-C) ratio (TG/HDL-C) is a simple approach to predicting unfavorable outcomes in cardiovascular disease. The influence of TG/HDL-C on acute ischemic stroke remains elusive. The purpose of this study was to investigate the precise effect of TG/HDL-C on 3-month mortality after acute ischemic stroke (AIS). Patients with AIS were enrolled in the present study from 2011 to 2017. A total of 1459 participants from a single city in China were divided into retrospective training and prospective test cohorts. Medical records were collected periodically to determine the incidence of fatal events. All participants were followed for 3 months. Optimal cutoff values were determined using X-tile software to separate the training cohort patients into higher and lower survival groups based on their lipid levels. A survival analysis was conducted using Kaplan-Meier curves and a Cox proportional hazards regression model. A total of 1459 patients with AIS (median age 68.5 years, 58.5% male) were analyzed. Univariate Cox regression analysis confirmed that TG/HDL-C was a significant prognostic factor for 3-month survival. X-tile identified 0.9 as an optimal cutoff for TG/HDL-C. In the univariate analysis, the prognosis of the TG/HDL-C >0.9 group was markedly superior to that of TG/HDL-C ≤0.9 group (P<0.001). A multivariate Cox regression analysis showed that TG/HDL-C was independently correlated with a reduced risk of mortality (hazard ratio [HR], 0.39; 95% confidence interval [CI], 0.24-0.62; P<0.001). These results were confirmed in the 453 patients in the test cohort. A nomogram was constructed to predict 3-month case-fatality, and the c-indexes of predictive accuracy were 0.684 and 0.670 in the training and test cohorts, respectively (P<0.01). The serum TG/HDL-C ratio may be useful for predicting short-term mortality after AIS.

Automated daily quality control analysis for mammography in a multi-unit imaging center.

PubMed

Sundell, Veli-Matti; Mäkelä, Teemu; Meaney, Alexander; Kaasalainen, Touko; Savolainen, Sauli

2018-01-01

Background The high requirements for mammography image quality necessitate a systematic quality assurance process. Digital imaging allows automation of the image quality analysis, which can potentially improve repeatability and objectivity compared to a visual evaluation made by the users. Purpose To develop an automatic image quality analysis software for daily mammography quality control in a multi-unit imaging center. Material and Methods An automated image quality analysis software using the discrete wavelet transform and multiresolution analysis was developed for the American College of Radiology accreditation phantom. The software was validated by analyzing 60 randomly selected phantom images from six mammography systems and 20 phantom images with different dose levels from one mammography system. The results were compared to a visual analysis made by four reviewers. Additionally, long-term image quality trends of a full-field digital mammography system and a computed radiography mammography system were investigated. Results The automated software produced feature detection levels comparable to visual analysis. The agreement was good in the case of fibers, while the software detected somewhat more microcalcifications and characteristic masses. Long-term follow-up via a quality assurance web portal demonstrated the feasibility of using the software for monitoring the performance of mammography systems in a multi-unit imaging center. Conclusion Automated image quality analysis enables monitoring the performance of digital mammography systems in an efficient, centralized manner.

The effects of social interactions with in-vehicle agents on a driver's anger level, driving performance, situation awareness, and perceived workload.

PubMed

Jeon, Myounghoon; Walker, Bruce N; Gable, Thomas M

2015-09-01

Research has suggested that interaction with an in-vehicle software agent can improve a driver's psychological state and increase road safety. The present study explored the possibility of using an in-vehicle software agent to mitigate effects of driver anger on driving behavior. After either anger or neutral mood induction, 60 undergraduates drove in a simulator with two types of agent intervention. Results showed that both speech-based agents not only enhance driver situation awareness and driving performance, but also reduce their anger level and perceived workload. Regression models show that a driver's anger influences driving performance measures, mediated by situation awareness. The practical implications include design guidelines for the design of social interaction with in-vehicle software agents. Copyright © 2015 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Studies in Software Cost Model Behavior: Do We Really Understand Cost Model Performance?

NASA Technical Reports Server (NTRS)

Lum, Karen; Hihn, Jairus; Menzies, Tim

2006-01-01

While there exists extensive literature on software cost estimation techniques, industry practice continues to rely upon standard regression-based algorithms. These software effort models are typically calibrated or tuned to local conditions using local data. This paper cautions that current approaches to model calibration often produce sub-optimal models because of the large variance problem inherent in cost data and by including far more effort multipliers than the data supports. Building optimal models requires that a wider range of models be considered while correctly calibrating these models requires rejection rules that prune variables and records and use multiple criteria for evaluating model performance. The main contribution of this paper is to document a standard method that integrates formal model identification, estimation, and validation. It also documents what we call the large variance problem that is a leading cause of cost model brittleness or instability.

Reservoir characterization using core, well log, and seismic data and intelligent software

NASA Astrophysics Data System (ADS)

Soto Becerra, Rodolfo

We have developed intelligent software, Oilfield Intelligence (OI), as an engineering tool to improve the characterization of oil and gas reservoirs. OI integrates neural networks and multivariate statistical analysis. It is composed of five main subsystems: data input, preprocessing, architecture design, graphics design, and inference engine modules. More than 1,200 lines of programming code as M-files using the language MATLAB been written. The degree of success of many oil and gas drilling, completion, and production activities depends upon the accuracy of the models used in a reservoir description. Neural networks have been applied for identification of nonlinear systems in almost all scientific fields of humankind. Solving reservoir characterization problems is no exception. Neural networks have a number of attractive features that can help to extract and recognize underlying patterns, structures, and relationships among data. However, before developing a neural network model, we must solve the problem of dimensionality such as determining dominant and irrelevant variables. We can apply principal components and factor analysis to reduce the dimensionality and help the neural networks formulate more realistic models. We validated OI by obtaining confident models in three different oil field problems: (1) A neural network in-situ stress model using lithology and gamma ray logs for the Travis Peak formation of east Texas, (2) A neural network permeability model using porosity and gamma ray and a neural network pseudo-gamma ray log model using 3D seismic attributes for the reservoir VLE 196 Lamar field located in Block V of south-central Lake Maracaibo (Venezuela), and (3) Neural network primary ultimate oil recovery (PRUR), initial waterflooding ultimate oil recovery (IWUR), and infill drilling ultimate oil recovery (IDUR) models using reservoir parameters for San Andres and Clearfork carbonate formations in west Texas. In all cases, we compared the results from the neural network models with the results from regression statistical and non-parametric approach models. The results show that it is possible to obtain the highest cross-correlation coefficient between predicted and actual target variables, and the lowest average absolute errors using the integrated techniques of multivariate statistical analysis and neural networks in our intelligent software.

Analysis of Software Systems for Specialized Computers,

DTIC Science & Technology

computer) with given computer hardware and software . The object of study is the software system of a computer, designed for solving a fixed complex of...purpose of the analysis is to find parameters that characterize the system and its elements during operation, i.e., when servicing the given requirement flow. (Author)

Flexible Software Architecture for Visualization and Seismic Data Analysis

NASA Astrophysics Data System (ADS)

Petunin, S.; Pavlov, I.; Mogilenskikh, D.; Podzyuban, D.; Arkhipov, A.; Baturuin, N.; Lisin, A.; Smith, A.; Rivers, W.; Harben, P.

2007-12-01

Research in the field of seismology requires software and signal processing utilities for seismogram manipulation and analysis. Seismologists and data analysts often encounter a major problem in the use of any particular software application specific to seismic data analysis: the tuning of commands and windows to the specific waveforms and hot key combinations so as to fit their familiar informational environment. The ability to modify the user's interface independently from the developer requires an adaptive code structure. An adaptive code structure also allows for expansion of software capabilities such as new signal processing modules and implementation of more efficient algorithms. Our approach is to use a flexible "open" architecture for development of geophysical software. This report presents an integrated solution for organizing a logical software architecture based on the Unix version of the Geotool software implemented on the Microsoft NET 2.0 platform. Selection of this platform greatly expands the variety and number of computers that can implement the software, including laptops that can be utilized in field conditions. It also facilitates implementation of communication functions for seismic data requests from remote databases through the Internet. The main principle of the new architecture for Geotool is that scientists should be able to add new routines for digital waveform analysis via software plug-ins that utilize the basic Geotool display for GUI interaction. The use of plug-ins allows the efficient integration of diverse signal-processing software, including software still in preliminary development, into an organized platform without changing the fundamental structure of that platform itself. An analyst's use of Geotool is tracked via a metadata file so that future studies can reconstruct, and alter, the original signal processing operations. The work has been completed in the framework of a joint Russian- American project.

The association of longitudinal trend of fasting plasma glucose with retinal microvasculature in people without established diabetes.

PubMed

Hu, Yin; Niu, Yong; Wang, Dandan; Wang, Ying; Holden, Brien A; He, Mingguang

2015-01-22

Structural changes of retinal vasculature, such as altered retinal vascular calibers, are considered as early signs of systemic vascular damage. We examined the associations of 5-year mean level, longitudinal trend, and fluctuation in fasting plasma glucose (FPG) with retinal vascular caliber in people without established diabetes. A prospective study was conducted in a cohort of Chinese people age ≥40 years in Guangzhou, southern China. The FPG was measured at baseline in 2008 and annually until 2012. In 2012, retinal vascular caliber was assessed using standard fundus photographs and validated software. A total of 3645 baseline nondiabetic participants with baseline and follow-up data on FPG for 3 or more visits was included for statistical analysis. The associations of retinal vascular caliber with 5-year mean FPG level, longitudinal FPG trend (slope of linear regression-FPG), and fluctuation (standard deviation and root mean square error of FPG) were analyzed using multivariable linear regression analyses. Multivariate regression models adjusted for baseline FPG and other potential confounders showed that a 10% annual increase in FPG was associated independently with a 2.65-μm narrowing in retinal arterioles (P = 0.008) and a 3.47-μm widening in venules (P = 0. 0.004). Associations with mean FPG level and fluctuation were not statistically significant. Annual rising trend in FPG, but not its mean level or fluctuation, is associated with altered retinal vasculature in nondiabetic people. Copyright 2015 The Association for Research in Vision and Ophthalmology, Inc.

Health Monitors for Chronic Disease by Gait Analysis with Mobile Phones

PubMed Central

Juen, Joshua; Cheng, Qian; Prieto-Centurion, Valentin; Krishnan, Jerry A.

2014-01-01

Abstract We have developed GaitTrack, a phone application to detect health status while the smartphone is carried normally. GaitTrack software monitors walking patterns, using only accelerometers embedded in phones to record spatiotemporal motion, without the need for sensors external to the phone. Our software transforms smartphones into health monitors, using eight parameters of phone motion transformed into body motion by the gait model. GaitTrack is designed to detect health status while the smartphone is carried during normal activities, namely, free-living walking. The current method for assessing free-living walking is medical accelerometers, so we present evidence that mobile phones running our software are more accurate. We then show our gait model is more accurate than medical pedometers for counting steps of patients with chronic disease. Our gait model was evaluated in a pilot study involving 30 patients with chronic lung disease. The six-minute walk test (6MWT) is a major assessment for chronic heart and lung disease, including congestive heart failure and especially chronic obstructive pulmonary disease (COPD), affecting millions of persons. The 6MWT consists of walking back and forth along a measured distance for 6 minutes. The gait model using linear regression performed with 94.13% accuracy in measuring walk distance, compared with the established standard of direct observation. We also evaluated a different statistical model using the same gait parameters to predict health status through lung function. This gait model has high accuracy when applied to demographic cohorts, for example, 89.22% accuracy testing the cohort of 12 female patients with ages 50–64 years. PMID:24694291

SPSS macros to compare any two fitted values from a regression model.

PubMed

Weaver, Bruce; Dubois, Sacha

2012-12-01

In regression models with first-order terms only, the coefficient for a given variable is typically interpreted as the change in the fitted value of Y for a one-unit increase in that variable, with all other variables held constant. Therefore, each regression coefficient represents the difference between two fitted values of Y. But the coefficients represent only a fraction of the possible fitted value comparisons that might be of interest to researchers. For many fitted value comparisons that are not captured by any of the regression coefficients, common statistical software packages do not provide the standard errors needed to compute confidence intervals or carry out statistical tests-particularly in more complex models that include interactions, polynomial terms, or regression splines. We describe two SPSS macros that implement a matrix algebra method for comparing any two fitted values from a regression model. The !OLScomp and !MLEcomp macros are for use with models fitted via ordinary least squares and maximum likelihood estimation, respectively. The output from the macros includes the standard error of the difference between the two fitted values, a 95% confidence interval for the difference, and a corresponding statistical test with its p-value.

Influence analysis of Github repositories.

PubMed

Hu, Yan; Zhang, Jun; Bai, Xiaomei; Yu, Shuo; Yang, Zhuo

2016-01-01

With the support of cloud computing techniques, social coding platforms have changed the style of software development. Github is now the most popular social coding platform and project hosting service. Software developers of various levels keep entering Github, and use Github to save their public and private software projects. The large amounts of software developers and software repositories on Github are posing new challenges to the world of software engineering. This paper tries to tackle one of the important problems: analyzing the importance and influence of Github repositories. We proposed a HITS based influence analysis on graphs that represent the star relationship between Github users and repositories. A weighted version of HITS is applied to the overall star graph, and generates a different set of top influential repositories other than the results from standard version of HITS algorithm. We also conduct the influential analysis on per-month star graph, and study the monthly influence ranking of top repositories.

A Visual Basic simulation software tool for performance analysis of a membrane-based advanced water treatment plant.

PubMed

Pal, P; Kumar, R; Srivastava, N; Chaudhuri, J

2014-02-01

A Visual Basic simulation software (WATTPPA) has been developed to analyse the performance of an advanced wastewater treatment plant. This user-friendly and menu-driven software is based on the dynamic mathematical model for an industrial wastewater treatment scheme that integrates chemical, biological and membrane-based unit operations. The software-predicted results corroborate very well with the experimental findings as indicated in the overall correlation coefficient of the order of 0.99. The software permits pre-analysis and manipulation of input data, helps in optimization and exhibits performance of an integrated plant visually on a graphical platform. It allows quick performance analysis of the whole system as well as the individual units. The software first of its kind in its domain and in the well-known Microsoft Excel environment is likely to be very useful in successful design, optimization and operation of an advanced hybrid treatment plant for hazardous wastewater.

Real Time Intraoperative Monitoring of Blood Loss with a Novel Tablet Application.

PubMed

Sharareh, Behnam; Woolwine, Spencer; Satish, Siddarth; Abraham, Peter; Schwarzkopf, Ran

2015-01-01

Real-time monitoring of blood loss is critical in fluid management. Visual estimation remains the standard of care in estimating blood loss, yet is demonstrably inaccurate. Photometric analysis, which is the referenced "gold-standard" for measuring blood loss, is both time-consuming and costly. The purpose of this study was to evaluate the efficacy of a novel tablet-monitoring device for measurement of Hb loss during orthopaedic procedures. This is a prospective study of 50 patients in a consecutive series of joint arthroplasty cases. The novel System with Feature Extraction Technology was used to measure the amount of Hb contained within surgical sponges intra-operatively. The system's measures were then compared with those obtained via gravimetric method and photometric analysis. Accuracy was evaluated using linear regression and Bland-Altman analysis. Our results showed a significant positive correlation between Triton tablet system and photometric analysis with respect to intra-operative hemoglobin and blood loss at 0.92 and 0.91, respectively. This novel system can accurately determine Hb loss contained within surgical sponges. We believe that this user-friendly software can be used for measurement of total intraoperative blood loss and thus aid in a more accurate fluid management protocols during orthopaedic surgical procedures.

Optimization of the trienzyme extraction for the microbiological assay of folate in vegetables.

PubMed

Chen, Liwen; Eitenmiller, Ronald R

2007-05-16

Response surface methodology was applied to optimize the trienzyme digestion for the extraction of folate from vegetables. Trienzyme extraction is a combined enzymatic digestion by protease, alpha-amylase, and conjugase (gamma-glutamyl hydrolase) to liberate the carbohydrate and protein-bound folates from food matrices for total folate analysis. It is the extraction method used in AOAC Official Method 2004.05 for assay of total folate in cereal grain products. Certified reference material (CRM) 485 mixed vegetables was used to represent the matrix of vegetables. Regression and ridge analysis were performed by statistical analysis software. The predicted second-order polynomial model was adequate (R2 = 0.947), without significant lack of fit (p > 0.1). Both protease and alpha-amylase have significant effects on the extraction. Ridge analysis gave an optimum trienzyme digestion time: Pronase, 1.5 h; alpha-amylase, 1.5 h; and conjugase, 3 h. The experimental value for CRM 485 under this optimum digestion was close to the predicted value from the model, confirming the validity and adequacy of the model. The optimized trienzyme digestion condition was applied to five vegetables and yielded higher folate levels than the trienzyme digestion parameters employed in AOAC Official Method 2004.05.

Monte Carlo simulation of parameter confidence intervals for non-linear regression analysis of biological data using Microsoft Excel.

PubMed

Lambert, Ronald J W; Mytilinaios, Ioannis; Maitland, Luke; Brown, Angus M

2012-08-01

This study describes a method to obtain parameter confidence intervals from the fitting of non-linear functions to experimental data, using the SOLVER and Analysis ToolPaK Add-In of the Microsoft Excel spreadsheet. Previously we have shown that Excel can fit complex multiple functions to biological data, obtaining values equivalent to those returned by more specialized statistical or mathematical software. However, a disadvantage of using the Excel method was the inability to return confidence intervals for the computed parameters or the correlations between them. Using a simple Monte-Carlo procedure within the Excel spreadsheet (without recourse to programming), SOLVER can provide parameter estimates (up to 200 at a time) for multiple 'virtual' data sets, from which the required confidence intervals and correlation coefficients can be obtained. The general utility of the method is exemplified by applying it to the analysis of the growth of Listeria monocytogenes, the growth inhibition of Pseudomonas aeruginosa by chlorhexidine and the further analysis of the electrophysiological data from the compound action potential of the rodent optic nerve. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers

PubMed Central

García-Gonzalo, Esperanza; Fernández-Muñiz, Zulima; García Nieto, Paulino José; Bernardo Sánchez, Antonio; Menéndez Fernández, Marta

2016-01-01

The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine). The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine. PMID:28773653

Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers.

PubMed

García-Gonzalo, Esperanza; Fernández-Muñiz, Zulima; García Nieto, Paulino José; Bernardo Sánchez, Antonio; Menéndez Fernández, Marta

2016-06-29

The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine). The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine.

Experience report: Using formal methods for requirements analysis of critical spacecraft software

NASA Technical Reports Server (NTRS)

Lutz, Robyn R.; Ampo, Yoko

1994-01-01

Formal specification and analysis of requirements continues to gain support as a method for producing more reliable software. However, the introduction of formal methods to a large software project is difficult, due in part to the unfamiliarity of the specification languages and the lack of graphics. This paper reports results of an investigation into the effectiveness of formal methods as an aid to the requirements analysis of critical, system-level fault-protection software on a spacecraft currently under development. Our experience indicates that formal specification and analysis can enhance the accuracy of the requirements and add assurance prior to design development in this domain. The work described here is part of a larger, NASA-funded research project whose purpose is to use formal-methods techniques to improve the quality of software in space applications. The demonstration project described here is part of the effort to evaluate experimentally the effectiveness of supplementing traditional engineering approaches to requirements specification with the more rigorous specification and analysis available with formal methods.

Software design for analysis of multichannel intracardial and body surface electrocardiograms.

PubMed

Potse, Mark; Linnenbank, André C; Grimbergen, Cornelis A

2002-11-01

Analysis of multichannel ECG recordings (body surface maps (BSMs) and intracardial maps) requires special software. We created a software package and a user interface on top of a commercial data analysis package (MATLAB) by a combination of high-level and low-level programming. Our software was created to satisfy the needs of a diverse group of researchers. It can handle a large variety of recording configurations. It allows for interactive usage through a fast and robust user interface, and batch processing for the analysis of large amounts of data. The package is user-extensible, includes routines for both common and experimental data processing tasks, and works on several computer platforms. The source code is made intelligible using software for structured documentation and is available to the users. The package is currently used by more than ten research groups analysing ECG data worldwide.

Learning from examples - Generation and evaluation of decision trees for software resource analysis

NASA Technical Reports Server (NTRS)

Selby, Richard W.; Porter, Adam A.

1988-01-01

A general solution method for the automatic generation of decision (or classification) trees is investigated. The approach is to provide insights through in-depth empirical characterization and evaluation of decision trees for software resource data analysis. The trees identify classes of objects (software modules) that had high development effort. Sixteen software systems ranging from 3,000 to 112,000 source lines were selected for analysis from a NASA production environment. The collection and analysis of 74 attributes (or metrics), for over 4,700 objects, captured information about the development effort, faults, changes, design style, and implementation style. A total of 9,600 decision trees were automatically generated and evaluated. The trees correctly identified 79.3 percent of the software modules that had high development effort or faults, and the trees generated from the best parameter combinations correctly identified 88.4 percent of the modules on the average.

78 FR 1162 - Cardiovascular Devices; Reclassification of External Cardiac Compressor

Federal Register 2010, 2011, 2012, 2013, 2014

2013-01-08

... safety and electromagnetic compatibility; For devices containing software, software verification... electromagnetic compatibility; For devices containing software, software verification, validation, and hazard... electrical components, appropriate analysis and testing must validate electrical safety and electromagnetic...

FASEA: A FPGA Acquisition System and Software Event Analysis for liquid scintillation counting

NASA Astrophysics Data System (ADS)

Steele, T.; Mo, L.; Bignell, L.; Smith, M.; Alexiev, D.

2009-10-01

The FASEA (FPGA based Acquisition and Software Event Analysis) system has been developed to replace the MAC3 for coincidence pulse processing. The system uses a National Instruments Virtex 5 FPGA card (PXI-7842R) for data acquisition and a purpose developed data analysis software for data analysis. Initial comparisons to the MAC3 unit are included based on measurements of 89Sr and 3H, confirming that the system is able to accurately emulate the behaviour of the MAC3 unit.

A User's Guide to the Meta-Analysis of Research Studies. Meta-Stat: Software To Aid in the Meta-Analysis of Research Findings.

ERIC Educational Resources Information Center

Rudner, Lawrence M.; Glass Gene V.; Evartt, David L.; Emery, Patrick J.

This manual and the accompanying software are intended to provide a step-by-step guide to conducting a meta-analytic study along with references for further reading and free high-quality software, "Meta-Stat.""Meta-Stat" is a comprehensive package designed to help in the meta-analysis of research studies in the social and behavioral sciences.…

Regression Analysis by Example. 5th Edition

ERIC Educational Resources Information Center

Chatterjee, Samprit; Hadi, Ali S.

2012-01-01

Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. "Regression Analysis by Example, Fifth Edition" has been expanded and thoroughly…

Chemical Fingerprint Analysis and Quantitative Analysis of Rosa rugosa by UPLC-DAD.

PubMed

Mansur, Sanawar; Abdulla, Rahima; Ayupbec, Amatjan; Aisa, Haji Akbar

2016-12-21

A method based on ultra performance liquid chromatography with a diode array detector (UPLC-DAD) was developed for quantitative analysis of five active compounds and chemical fingerprint analysis of Rosa rugosa . Ten batches of R. rugosa collected from different plantations in the Xinjiang region of China were used to establish the fingerprint. The feasibility and advantages of the used UPLC fingerprint were verified for its similarity evaluation by systematically comparing chromatograms with professional analytical software recommended by State Food and Drug Administration (SFDA) of China. In quantitative analysis, the five compounds showed good regression (R² = 0.9995) within the test ranges, and the recovery of the method was in the range of 94.2%-103.8%. The similarities of liquid chromatography fingerprints of 10 batches of R. rugosa were more than 0.981. The developed UPLC fingerprint method is simple, reliable, and validated for the quality control and identification of R. rugosa . Additionally, simultaneous quantification of five major bioactive ingredients in the R. rugosa samples was conducted to interpret the consistency of the quality test. The results indicated that the UPLC fingerprint, as a characteristic distinguishing method combining similarity evaluation and quantification analysis, can be successfully used to assess the quality and to identify the authenticity of R. rugosa .

Development of an automated asbestos counting software based on fluorescence microscopy.

PubMed

Alexandrov, Maxym; Ichida, Etsuko; Nishimura, Tomoki; Aoki, Kousuke; Ishida, Takenori; Hirota, Ryuichi; Ikeda, Takeshi; Kawasaki, Tetsuo; Kuroda, Akio

2015-01-01

An emerging alternative to the commonly used analytical methods for asbestos analysis is fluorescence microscopy (FM), which relies on highly specific asbestos-binding probes to distinguish asbestos from interfering non-asbestos fibers. However, all types of microscopic asbestos analysis require laborious examination of large number of fields of view and are prone to subjective errors and large variability between asbestos counts by different analysts and laboratories. A possible solution to these problems is automated counting of asbestos fibers by image analysis software, which would lower the cost and increase the reliability of asbestos testing. This study seeks to develop a fiber recognition and counting software for FM-based asbestos analysis. We discuss the main features of the developed software and the results of its testing. Software testing showed good correlation between automated and manual counts for the samples with medium and high fiber concentrations. At low fiber concentrations, the automated counts were less accurate, leading us to implement correction mode for automated counts. While the full automation of asbestos analysis would require further improvements in accuracy of fiber identification, the developed software could already assist professional asbestos analysts and record detailed fiber dimensions for the use in epidemiological research.

IFDOTMETER: A New Software Application for Automated Immunofluorescence Analysis.

PubMed

Rodríguez-Arribas, Mario; Pizarro-Estrella, Elisa; Gómez-Sánchez, Rubén; Yakhine-Diop, S M S; Gragera-Hidalgo, Antonio; Cristo, Alejandro; Bravo-San Pedro, Jose M; González-Polo, Rosa A; Fuentes, José M

2016-04-01

Most laboratories interested in autophagy use different imaging software for managing and analyzing heterogeneous parameters in immunofluorescence experiments (e.g., LC3-puncta quantification and determination of the number and size of lysosomes). One solution would be software that works on a user's laptop or workstation that can access all image settings and provide quick and easy-to-use analysis of data. Thus, we have designed and implemented an application called IFDOTMETER, which can run on all major operating systems because it has been programmed using JAVA (Sun Microsystems). Briefly, IFDOTMETER software has been created to quantify a variety of biological hallmarks, including mitochondrial morphology and nuclear condensation. The program interface is intuitive and user-friendly, making it useful for users not familiar with computer handling. By setting previously defined parameters, the software can automatically analyze a large number of images without the supervision of the researcher. Once analysis is complete, the results are stored in a spreadsheet. Using software for high-throughput cell image analysis offers researchers the possibility of performing comprehensive and precise analysis of a high number of images in an automated manner, making this routine task easier. © 2015 Society for Laboratory Automation and Screening.

FluxPyt: a Python-based free and open-source software for 13C-metabolic flux analyses.

PubMed

Desai, Trunil S; Srivastava, Shireesh

2018-01-01

13 C-Metabolic flux analysis (MFA) is a powerful approach to estimate intracellular reaction rates which could be used in strain analysis and design. Processing and analysis of labeling data for calculation of fluxes and associated statistics is an essential part of MFA. However, various software currently available for data analysis employ proprietary platforms and thus limit accessibility. We developed FluxPyt, a Python-based truly open-source software package for conducting stationary 13 C-MFA data analysis. The software is based on the efficient elementary metabolite unit framework. The standard deviations in the calculated fluxes are estimated using the Monte-Carlo analysis. FluxPyt also automatically creates flux maps based on a template for visualization of the MFA results. The flux distributions calculated by FluxPyt for two separate models: a small tricarboxylic acid cycle model and a larger Corynebacterium glutamicum model, were found to be in good agreement with those calculated by a previously published software. FluxPyt was tested in Microsoft™ Windows 7 and 10, as well as in Linux Mint 18.2. The availability of a free and open 13 C-MFA software that works in various operating systems will enable more researchers to perform 13 C-MFA and to further modify and develop the package.

FluxPyt: a Python-based free and open-source software for 13C-metabolic flux analyses

PubMed Central

Desai, Trunil S.

2018-01-01

13C-Metabolic flux analysis (MFA) is a powerful approach to estimate intracellular reaction rates which could be used in strain analysis and design. Processing and analysis of labeling data for calculation of fluxes and associated statistics is an essential part of MFA. However, various software currently available for data analysis employ proprietary platforms and thus limit accessibility. We developed FluxPyt, a Python-based truly open-source software package for conducting stationary 13C-MFA data analysis. The software is based on the efficient elementary metabolite unit framework. The standard deviations in the calculated fluxes are estimated using the Monte-Carlo analysis. FluxPyt also automatically creates flux maps based on a template for visualization of the MFA results. The flux distributions calculated by FluxPyt for two separate models: a small tricarboxylic acid cycle model and a larger Corynebacterium glutamicum model, were found to be in good agreement with those calculated by a previously published software. FluxPyt was tested in Microsoft™ Windows 7 and 10, as well as in Linux Mint 18.2. The availability of a free and open 13C-MFA software that works in various operating systems will enable more researchers to perform 13C-MFA and to further modify and develop the package. PMID:29736347

Reliability of skeletal maturity analysis using the cervical vertebrae maturation method on dedicated software.

PubMed

Padalino, Saverio; Sfondrini, Maria Francesca; Chenuil, Laura; Scudeller, Luigia; Gandini, Paola

2014-12-01

The aim of this study was to assess the feasibility of skeletal maturation analysis using the Cervical Vertebrae Maturation (CVM) method by means of dedicated software, developed in collaboration with Outside Format (Paullo-Milan), as compared with manual analysis. From a sample of patients aged 7-21 years, we gathered 100 lateral cephalograms, 20 for each of the five CVM stages. For each cephalogram, we traced cervical vertebrae C2, C3 and C4 by hand using a lead pencil and an acetate sheet and dedicated software. All the tracings were made by an experienced operator (a dentofacial orthopedics resident) and by an inexperienced operator (a student in dental surgery). Each operator recorded the time needed to make each tracing in order to demonstrate differences in the times taken. Concordance between the manual analysis and the analysis performed using the dedicated software was 94% for the resident and 93% for the student. Interobserver concordance was 99%. The hand-tracing was quicker than that performed by means of the software (28 seconds more on average). The cervical vertebrae analysis software offers excellent clinical performance, even if the method takes longer than the manual technique. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

New software for 3D fracture network analysis and visualization

NASA Astrophysics Data System (ADS)

Song, J.; Noh, Y.; Choi, Y.; Um, J.; Hwang, S.

2013-12-01

This study presents new software to perform analysis and visualization of the fracture network system in 3D. The developed software modules for the analysis and visualization, such as BOUNDARY, DISK3D, FNTWK3D, CSECT and BDM, have been developed using Microsoft Visual Basic.NET and Visualization TookKit (VTK) open-source library. Two case studies revealed that each module plays a role in construction of analysis domain, visualization of fracture geometry in 3D, calculation of equivalent pipes, production of cross-section map and management of borehole data, respectively. The developed software for analysis and visualization of the 3D fractured rock mass can be used to tackle the geomechanical problems related to strength, deformability and hydraulic behaviors of the fractured rock masses.

Novel features and enhancements in BioBin, a tool for the biologically inspired binning and association analysis of rare variants

PubMed Central

Byrska-Bishop, Marta; Wallace, John; Frase, Alexander T; Ritchie, Marylyn D

2018-01-01

Abstract Motivation BioBin is an automated bioinformatics tool for the multi-level biological binning of sequence variants. Herein, we present a significant update to BioBin which expands the software to facilitate a comprehensive rare variant analysis and incorporates novel features and analysis enhancements. Results In BioBin 2.3, we extend our software tool by implementing statistical association testing, updating the binning algorithm, as well as incorporating novel analysis features providing for a robust, highly customizable, and unified rare variant analysis tool. Availability and implementation The BioBin software package is open source and freely available to users at http://www.ritchielab.com/software/biobin-download Contact mdritchie@geisinger.edu Supplementary information Supplementary data are available at Bioinformatics online. PMID:28968757

Predicting the survival of diabetes using neural network

NASA Astrophysics Data System (ADS)

Mamuda, Mamman; Sathasivam, Saratha

2017-08-01

Data mining techniques at the present time are used in predicting diseases of health care industries. Neural Network is one among the prevailing method in data mining techniques of an intelligent field for predicting diseases in health care industries. This paper presents a study on the prediction of the survival of diabetes diseases using different learning algorithms from the supervised learning algorithms of neural network. Three learning algorithms are considered in this study: (i) The levenberg-marquardt learning algorithm (ii) The Bayesian regulation learning algorithm and (iii) The scaled conjugate gradient learning algorithm. The network is trained using the Pima Indian Diabetes Dataset with the help of MATLAB R2014(a) software. The performance of each algorithm is further discussed through regression analysis. The prediction accuracy of the best algorithm is further computed to validate the accurate prediction

Development problem analysis of correlation leak detector’s software

NASA Astrophysics Data System (ADS)

Faerman, V. A.; Avramchuk, V. S.; Marukyan, V. M.

2018-05-01

In the article, the practical application and the structure of the correlation leak detectors’ software is studied and the task of its designing is analyzed. In the first part of the research paper, the expediency of the facilities development of correlation leak detectors for the following operating efficiency of public utilities exploitation is shown. The analysis of the functional structure of correlation leak detectors is conducted and its program software tasks are defined. In the second part of the research paper some development steps of the software package – requirement forming, program structure definition and software concept creation – are examined in the context of the usage experience of the hardware-software prototype of correlation leak detector.

relaxGUI: a new software for fast and simple NMR relaxation data analysis and calculation of ps-ns and μs motion of proteins.

PubMed

Bieri, Michael; d'Auvergne, Edward J; Gooley, Paul R

2011-06-01

Investigation of protein dynamics on the ps-ns and μs-ms timeframes provides detailed insight into the mechanisms of enzymes and the binding properties of proteins. Nuclear magnetic resonance (NMR) is an excellent tool for studying protein dynamics at atomic resolution. Analysis of relaxation data using model-free analysis can be a tedious and time consuming process, which requires good knowledge of scripting procedures. The software relaxGUI was developed for fast and simple model-free analysis and is fully integrated into the software package relax. It is written in Python and uses wxPython to build the graphical user interface (GUI) for maximum performance and multi-platform use. This software allows the analysis of NMR relaxation data with ease and the generation of publication quality graphs as well as color coded images of molecular structures. The interface is designed for simple data analysis and management. The software was tested and validated against the command line version of relax.

Performance of Different Analytical Software Packages in Quantification of DNA Methylation by Pyrosequencing.

PubMed

Grasso, Chiara; Trevisan, Morena; Fiano, Valentina; Tarallo, Valentina; De Marco, Laura; Sacerdote, Carlotta; Richiardi, Lorenzo; Merletti, Franco; Gillio-Tos, Anna

2016-01-01

Pyrosequencing has emerged as an alternative method of nucleic acid sequencing, well suited for many applications which aim to characterize single nucleotide polymorphisms, mutations, microbial types and CpG methylation in the target DNA. The commercially available pyrosequencing systems can harbor two different types of software which allow analysis in AQ or CpG mode, respectively, both widely employed for DNA methylation analysis. Aim of the study was to assess the performance for DNA methylation analysis at CpG sites of the two pyrosequencing software which allow analysis in AQ or CpG mode, respectively. Despite CpG mode having been specifically generated for CpG methylation quantification, many investigations on this topic have been carried out with AQ mode. As proof of equivalent performance of the two software for this type of analysis is not available, the focus of this paper was to evaluate if the two modes currently used for CpG methylation assessment by pyrosequencing may give overlapping results. We compared the performance of the two software in quantifying DNA methylation in the promoter of selected genes (GSTP1, MGMT, LINE-1) by testing two case series which include DNA from paraffin embedded prostate cancer tissues (PC study, N = 36) and DNA from blood fractions of healthy people (DD study, N = 28), respectively. We found discrepancy in the two pyrosequencing software-based quality assignment of DNA methylation assays. Compared to the software for analysis in the AQ mode, less permissive criteria are supported by the Pyro Q-CpG software, which enables analysis in CpG mode. CpG mode warns the operators about potential unsatisfactory performance of the assay and ensures a more accurate quantitative evaluation of DNA methylation at CpG sites. The implementation of CpG mode is strongly advisable in order to improve the reliability of the methylation analysis results achievable by pyrosequencing.

Exploring the Perceptions of College Instructors towards Computer Simulation Software Programs: A Quantitative Study

ERIC Educational Resources Information Center

Punch, Raymond J.

2012-01-01

The purpose of the quantitative regression study was to explore and to identify relationships between attitudes toward use and perceptions of value of computer-based simulation programs, of college instructors, toward computer based simulation programs. A relationship has been reported between attitudes toward use and perceptions of the value of…