The importance of regional models in assessing canine cancer incidences in Switzerland

PubMed Central

Leyk, Stefan; Brunsdon, Christopher; Graf, Ramona; Pospischil, Andreas; Fabrikant, Sara Irina

2018-01-01

Fitting canine cancer incidences through a conventional regression model assumes constant statistical relationships across the study area in estimating the model coefficients. However, it is often more realistic to consider that these relationships may vary over space. Such a condition, known as spatial non-stationarity, implies that the model coefficients need to be estimated locally. In these kinds of local models, the geographic scale, or spatial extent, employed for coefficient estimation may also have a pervasive influence. This is because important variations in the local model coefficients across geographic scales may impact the understanding of local relationships. In this study, we fitted canine cancer incidences across Swiss municipal units through multiple regional models. We computed diagnostic summaries across the different regional models, and contrasted them with the diagnostics of the conventional regression model, using value-by-alpha maps and scalograms. The results of this comparative assessment enabled us to identify variations in the goodness-of-fit and coefficient estimates. We detected spatially non-stationary relationships, in particular, for the variables related to biological risk factors. These variations in the model coefficients were more important at small geographic scales, making a case for the need to model canine cancer incidences locally in contrast to more conventional global approaches. However, we contend that prior to undertaking local modeling efforts, a deeper understanding of the effects of geographic scale is needed to better characterize and identify local model relationships. PMID:29652921

The importance of regional models in assessing canine cancer incidences in Switzerland.

PubMed

Boo, Gianluca; Leyk, Stefan; Brunsdon, Christopher; Graf, Ramona; Pospischil, Andreas; Fabrikant, Sara Irina

2018-01-01

Fitting canine cancer incidences through a conventional regression model assumes constant statistical relationships across the study area in estimating the model coefficients. However, it is often more realistic to consider that these relationships may vary over space. Such a condition, known as spatial non-stationarity, implies that the model coefficients need to be estimated locally. In these kinds of local models, the geographic scale, or spatial extent, employed for coefficient estimation may also have a pervasive influence. This is because important variations in the local model coefficients across geographic scales may impact the understanding of local relationships. In this study, we fitted canine cancer incidences across Swiss municipal units through multiple regional models. We computed diagnostic summaries across the different regional models, and contrasted them with the diagnostics of the conventional regression model, using value-by-alpha maps and scalograms. The results of this comparative assessment enabled us to identify variations in the goodness-of-fit and coefficient estimates. We detected spatially non-stationary relationships, in particular, for the variables related to biological risk factors. These variations in the model coefficients were more important at small geographic scales, making a case for the need to model canine cancer incidences locally in contrast to more conventional global approaches. However, we contend that prior to undertaking local modeling efforts, a deeper understanding of the effects of geographic scale is needed to better characterize and identify local model relationships.

A spatially filtered multilevel model to account for spatial dependency: application to self-rated health status in South Korea

PubMed Central

2014-01-01

Background This study aims to suggest an approach that integrates multilevel models and eigenvector spatial filtering methods and apply it to a case study of self-rated health status in South Korea. In many previous health-related studies, multilevel models and single-level spatial regression are used separately. However, the two methods should be used in conjunction because the objectives of both approaches are important in health-related analyses. The multilevel model enables the simultaneous analysis of both individual and neighborhood factors influencing health outcomes. However, the results of conventional multilevel models are potentially misleading when spatial dependency across neighborhoods exists. Spatial dependency in health-related data indicates that health outcomes in nearby neighborhoods are more similar to each other than those in distant neighborhoods. Spatial regression models can address this problem by modeling spatial dependency. This study explores the possibility of integrating a multilevel model and eigenvector spatial filtering, an advanced spatial regression for addressing spatial dependency in datasets. Methods In this spatially filtered multilevel model, eigenvectors function as additional explanatory variables accounting for unexplained spatial dependency within the neighborhood-level error. The specification addresses the inability of conventional multilevel models to account for spatial dependency, and thereby, generates more robust outputs. Results The findings show that sex, employment status, monthly household income, and perceived levels of stress are significantly associated with self-rated health status. Residents living in neighborhoods with low deprivation and a high doctor-to-resident ratio tend to report higher health status. The spatially filtered multilevel model provides unbiased estimations and improves the explanatory power of the model compared to conventional multilevel models although there are no changes in the signs of parameters and the significance levels between the two models in this case study. Conclusions The integrated approach proposed in this paper is a useful tool for understanding the geographical distribution of self-rated health status within a multilevel framework. In future research, it would be useful to apply the spatially filtered multilevel model to other datasets in order to clarify the differences between the two models. It is anticipated that this integrated method will also out-perform conventional models when it is used in other contexts. PMID:24571639

Comparing near-infrared conventional diffuse reflectance spectroscopy and hyperspectral imaging for determination of the bulk properties of solid samples by multivariate regression: determination of Mooney viscosity and plasticity indices of natural rubber.

PubMed

Juliano da Silva, Carlos; Pasquini, Celio

2015-01-21

Conventional reflectance spectroscopy (NIRS) and hyperspectral imaging (HI) in the near-infrared region (1000-2500 nm) are evaluated and compared, using, as the case study, the determination of relevant properties related to the quality of natural rubber. Mooney viscosity (MV) and plasticity indices (PI) (PI0 - original plasticity, PI30 - plasticity after accelerated aging, and PRI - the plasticity retention index after accelerated aging) of rubber were determined using multivariate regression models. Two hundred and eighty six samples of rubber were measured using conventional and hyperspectral near-infrared imaging reflectance instruments in the range of 1000-2500 nm. The sample set was split into regression (n = 191) and external validation (n = 95) sub-sets. Three instruments were employed for data acquisition: a line scanning hyperspectral camera and two conventional FT-NIR spectrometers. Sample heterogeneity was evaluated using hyperspectral images obtained with a resolution of 150 × 150 μm and principal component analysis. The probed sample area (5 cm(2); 24,000 pixels) to achieve representativeness was found to be equivalent to the average of 6 spectra for a 1 cm diameter probing circular window of one FT-NIR instrument. The other spectrophotometer can probe the whole sample in only one measurement. The results show that the rubber properties can be determined with very similar accuracy and precision by Partial Least Square (PLS) regression models regardless of whether HI-NIR or conventional FT-NIR produce the spectral datasets. The best Root Mean Square Errors of Prediction (RMSEPs) of external validation for MV, PI0, PI30, and PRI were 4.3, 1.8, 3.4, and 5.3%, respectively. Though the quantitative results provided by the three instruments can be considered equivalent, the hyperspectral imaging instrument presents a number of advantages, being about 6 times faster than conventional bulk spectrometers, producing robust spectral data by ensuring sample representativeness, and minimizing the effect of the presence of contaminants.

Prediction of dynamical systems by symbolic regression

NASA Astrophysics Data System (ADS)

Quade, Markus; Abel, Markus; Shafi, Kamran; Niven, Robert K.; Noack, Bernd R.

2016-07-01

We study the modeling and prediction of dynamical systems based on conventional models derived from measurements. Such algorithms are highly desirable in situations where the underlying dynamics are hard to model from physical principles or simplified models need to be found. We focus on symbolic regression methods as a part of machine learning. These algorithms are capable of learning an analytically tractable model from data, a highly valuable property. Symbolic regression methods can be considered as generalized regression methods. We investigate two particular algorithms, the so-called fast function extraction which is a generalized linear regression algorithm, and genetic programming which is a very general method. Both are able to combine functions in a certain way such that a good model for the prediction of the temporal evolution of a dynamical system can be identified. We illustrate the algorithms by finding a prediction for the evolution of a harmonic oscillator based on measurements, by detecting an arriving front in an excitable system, and as a real-world application, the prediction of solar power production based on energy production observations at a given site together with the weather forecast.

Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards.

PubMed

Churpek, Matthew M; Yuen, Trevor C; Winslow, Christopher; Meltzer, David O; Kattan, Michael W; Edelson, Dana P

2016-02-01

Machine learning methods are flexible prediction algorithms that may be more accurate than conventional regression. We compared the accuracy of different techniques for detecting clinical deterioration on the wards in a large, multicenter database. Observational cohort study. Five hospitals, from November 2008 until January 2013. Hospitalized ward patients None Demographic variables, laboratory values, and vital signs were utilized in a discrete-time survival analysis framework to predict the combined outcome of cardiac arrest, intensive care unit transfer, or death. Two logistic regression models (one using linear predictor terms and a second utilizing restricted cubic splines) were compared to several different machine learning methods. The models were derived in the first 60% of the data by date and then validated in the next 40%. For model derivation, each event time window was matched to a non-event window. All models were compared to each other and to the Modified Early Warning score, a commonly cited early warning score, using the area under the receiver operating characteristic curve (AUC). A total of 269,999 patients were admitted, and 424 cardiac arrests, 13,188 intensive care unit transfers, and 2,840 deaths occurred in the study. In the validation dataset, the random forest model was the most accurate model (AUC, 0.80 [95% CI, 0.80-0.80]). The logistic regression model with spline predictors was more accurate than the model utilizing linear predictors (AUC, 0.77 vs 0.74; p < 0.01), and all models were more accurate than the MEWS (AUC, 0.70 [95% CI, 0.70-0.70]). In this multicenter study, we found that several machine learning methods more accurately predicted clinical deterioration than logistic regression. Use of detection algorithms derived from these techniques may result in improved identification of critically ill patients on the wards.

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.

Detecting isotopic ratio outliers

NASA Astrophysics Data System (ADS)

Bayne, C. K.; Smith, D. H.

An alternative method is proposed for improving isotopic ratio estimates. This method mathematically models pulse-count data and uses iterative reweighted Poisson regression to estimate model parameters to calculate the isotopic ratios. This computer-oriented approach provides theoretically better methods than conventional techniques to establish error limits and to identify outliers.

Genomic Bayesian functional regression models with interactions for predicting wheat grain yield using hyper-spectral image data.

PubMed

Montesinos-López, Abelardo; Montesinos-López, Osval A; Cuevas, Jaime; Mata-López, Walter A; Burgueño, Juan; Mondal, Sushismita; Huerta, Julio; Singh, Ravi; Autrique, Enrique; González-Pérez, Lorena; Crossa, José

2017-01-01

Modern agriculture uses hyperspectral cameras that provide hundreds of reflectance data at discrete narrow bands in many environments. These bands often cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra. With the bands, vegetation indices are constructed for predicting agronomically important traits such as grain yield and biomass. However, since vegetation indices only use some wavelengths (referred to as bands), we propose using all bands simultaneously as predictor variables for the primary trait grain yield; results of several multi-environment maize (Aguate et al. in Crop Sci 57(5):1-8, 2017) and wheat (Montesinos-López et al. in Plant Methods 13(4):1-23, 2017) breeding trials indicated that using all bands produced better prediction accuracy than vegetation indices. However, until now, these prediction models have not accounted for the effects of genotype × environment (G × E) and band × environment (B × E) interactions incorporating genomic or pedigree information. In this study, we propose Bayesian functional regression models that take into account all available bands, genomic or pedigree information, the main effects of lines and environments, as well as G × E and B × E interaction effects. The data set used is comprised of 976 wheat lines evaluated for grain yield in three environments (Drought, Irrigated and Reduced Irrigation). The reflectance data were measured in 250 discrete narrow bands ranging from 392 to 851 nm (nm). The proposed Bayesian functional regression models were implemented using two types of basis: B-splines and Fourier. Results of the proposed Bayesian functional regression models, including all the wavelengths for predicting grain yield, were compared with results from conventional models with and without bands. We observed that the models with B × E interaction terms were the most accurate models, whereas the functional regression models (with B-splines and Fourier basis) and the conventional models performed similarly in terms of prediction accuracy. However, the functional regression models are more parsimonious and computationally more efficient because the number of beta coefficients to be estimated is 21 (number of basis), rather than estimating the 250 regression coefficients for all bands. In this study adding pedigree or genomic information did not increase prediction accuracy.

Detection of melamine in milk powders using Near-Infrared Hyperspectral imaging combined with regression coefficient of partial least square regression model

USDA-ARS?s Scientific Manuscript database

Illegal use of nitrogen-rich melamine (C3H6N6) to boost perceived protein content of food products such as milk, infant formula, frozen yogurt, pet food, biscuits, and coffee drinks has caused serious food safety problems. Conventional methods to detect melamine in foods, such as Enzyme-linked immun...

Clinicopathologic features and survival in Spitzoid malignant melanoma and conventional malignant melanoma.

PubMed

Semkova, Kristina; Lott, Jason P; Lazova, Rossitza

2014-09-01

Although recent advances in genetics have revealed distinct mutational profiles and molecular signaling pathways associated with Spitzoid malignant melanoma (SMM), less is known about the clinicopathologic characteristics and behavior of SMM compared with conventional melanoma. We sought to determine the clinicopathologic characteristics and mortality risk associated with SMM and conventional malignant melanoma. We conducted a retrospective study of 30 patients with SMM and 30 patients with conventional melanoma. The two groups were matched by age, gender, and depth of tumor invasion. Additional patient- and tumor-level characteristics were compared between groups and regression modeling was used to assess relative mortality risk. Unadjusted analyses of SMM and conventional malignant melanoma revealed no significant differences in clinical impression, anatomic location, mitotic rate, and presence of ulceration. Sentinel lymph node biopsy, completion lymphadenectomy, and visceral metastases did not differ between groups. Cox proportional hazards regression showed no differences in mortality between Spitzoid and conventional melanoma. Small sample size, short follow-up duration, and residual confounding may limit the accuracy and generalizability of our results. SMM and conventional malignant melanoma differ in some clinicopathologic features. We did not find a statistically significant difference in mortality between the two. Copyright © 2014 American Academy of Dermatology, Inc. Published by Mosby, Inc. All rights reserved.

Testing homogeneity in Weibull-regression models.

PubMed

Bolfarine, Heleno; Valença, Dione M

2005-10-01

In survival studies with families or geographical units it may be of interest testing whether such groups are homogeneous for given explanatory variables. In this paper we consider score type tests for group homogeneity based on a mixing model in which the group effect is modelled as a random variable. As opposed to hazard-based frailty models, this model presents survival times that conditioned on the random effect, has an accelerated failure time representation. The test statistics requires only estimation of the conventional regression model without the random effect and does not require specifying the distribution of the random effect. The tests are derived for a Weibull regression model and in the uncensored situation, a closed form is obtained for the test statistic. A simulation study is used for comparing the power of the tests. The proposed tests are applied to real data sets with censored data.

Bayesian dose-response analysis for epidemiological studies with complex uncertainty in dose estimation.

PubMed

Kwon, Deukwoo; Hoffman, F Owen; Moroz, Brian E; Simon, Steven L

2016-02-10

Most conventional risk analysis methods rely on a single best estimate of exposure per person, which does not allow for adjustment for exposure-related uncertainty. Here, we propose a Bayesian model averaging method to properly quantify the relationship between radiation dose and disease outcomes by accounting for shared and unshared uncertainty in estimated dose. Our Bayesian risk analysis method utilizes multiple realizations of sets (vectors) of doses generated by a two-dimensional Monte Carlo simulation method that properly separates shared and unshared errors in dose estimation. The exposure model used in this work is taken from a study of the risk of thyroid nodules among a cohort of 2376 subjects who were exposed to fallout from nuclear testing in Kazakhstan. We assessed the performance of our method through an extensive series of simulations and comparisons against conventional regression risk analysis methods. When the estimated doses contain relatively small amounts of uncertainty, the Bayesian method using multiple a priori plausible draws of dose vectors gave similar results to the conventional regression-based methods of dose-response analysis. However, when large and complex mixtures of shared and unshared uncertainties are present, the Bayesian method using multiple dose vectors had significantly lower relative bias than conventional regression-based risk analysis methods and better coverage, that is, a markedly increased capability to include the true risk coefficient within the 95% credible interval of the Bayesian-based risk estimate. An evaluation of the dose-response using our method is presented for an epidemiological study of thyroid disease following radiation exposure. Copyright © 2015 John Wiley & Sons, Ltd.

Local Intrinsic Dimension Estimation by Generalized Linear Modeling.

PubMed

Hino, Hideitsu; Fujiki, Jun; Akaho, Shotaro; Murata, Noboru

2017-07-01

We propose a method for intrinsic dimension estimation. By fitting the power of distance from an inspection point and the number of samples included inside a ball with a radius equal to the distance, to a regression model, we estimate the goodness of fit. Then, by using the maximum likelihood method, we estimate the local intrinsic dimension around the inspection point. The proposed method is shown to be comparable to conventional methods in global intrinsic dimension estimation experiments. Furthermore, we experimentally show that the proposed method outperforms a conventional local dimension estimation method.

Methods for estimating population density in data-limited areas: evaluating regression and tree-based models in Peru.

PubMed

Anderson, Weston; Guikema, Seth; Zaitchik, Ben; Pan, William

2014-01-01

Obtaining accurate small area estimates of population is essential for policy and health planning but is often difficult in countries with limited data. In lieu of available population data, small area estimate models draw information from previous time periods or from similar areas. This study focuses on model-based methods for estimating population when no direct samples are available in the area of interest. To explore the efficacy of tree-based models for estimating population density, we compare six different model structures including Random Forest and Bayesian Additive Regression Trees. Results demonstrate that without information from prior time periods, non-parametric tree-based models produced more accurate predictions than did conventional regression methods. Improving estimates of population density in non-sampled areas is important for regions with incomplete census data and has implications for economic, health and development policies.

Methods for Estimating Population Density in Data-Limited Areas: Evaluating Regression and Tree-Based Models in Peru

PubMed Central

Anderson, Weston; Guikema, Seth; Zaitchik, Ben; Pan, William

2014-01-01

Obtaining accurate small area estimates of population is essential for policy and health planning but is often difficult in countries with limited data. In lieu of available population data, small area estimate models draw information from previous time periods or from similar areas. This study focuses on model-based methods for estimating population when no direct samples are available in the area of interest. To explore the efficacy of tree-based models for estimating population density, we compare six different model structures including Random Forest and Bayesian Additive Regression Trees. Results demonstrate that without information from prior time periods, non-parametric tree-based models produced more accurate predictions than did conventional regression methods. Improving estimates of population density in non-sampled areas is important for regions with incomplete census data and has implications for economic, health and development policies. PMID:24992657

Interquantile Shrinkage in Regression Models

PubMed Central

Jiang, Liewen; Wang, Huixia Judy; Bondell, Howard D.

2012-01-01

Conventional analysis using quantile regression typically focuses on fitting the regression model at different quantiles separately. However, in situations where the quantile coefficients share some common feature, joint modeling of multiple quantiles to accommodate the commonality often leads to more efficient estimation. One example of common features is that a predictor may have a constant effect over one region of quantile levels but varying effects in other regions. To automatically perform estimation and detection of the interquantile commonality, we develop two penalization methods. When the quantile slope coefficients indeed do not change across quantile levels, the proposed methods will shrink the slopes towards constant and thus improve the estimation efficiency. We establish the oracle properties of the two proposed penalization methods. Through numerical investigations, we demonstrate that the proposed methods lead to estimations with competitive or higher efficiency than the standard quantile regression estimation in finite samples. Supplemental materials for the article are available online. PMID:24363546

Factor complexity of crash occurrence: An empirical demonstration using boosted regression trees.

PubMed

Chung, Yi-Shih

2013-12-01

Factor complexity is a characteristic of traffic crashes. This paper proposes a novel method, namely boosted regression trees (BRT), to investigate the complex and nonlinear relationships in high-variance traffic crash data. The Taiwanese 2004-2005 single-vehicle motorcycle crash data are used to demonstrate the utility of BRT. Traditional logistic regression and classification and regression tree (CART) models are also used to compare their estimation results and external validities. Both the in-sample cross-validation and out-of-sample validation results show that an increase in tree complexity provides improved, although declining, classification performance, indicating a limited factor complexity of single-vehicle motorcycle crashes. The effects of crucial variables including geographical, time, and sociodemographic factors explain some fatal crashes. Relatively unique fatal crashes are better approximated by interactive terms, especially combinations of behavioral factors. BRT models generally provide improved transferability than conventional logistic regression and CART models. This study also discusses the implications of the results for devising safety policies. Copyright © 2012 Elsevier Ltd. All rights reserved.

Logistic quantile regression provides improved estimates for bounded avian counts: a case study of California Spotted Owl fledgling production

Treesearch

Brian S. Cade; Barry R. Noon; Rick D. Scherer; John J. Keane

2017-01-01

Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical...

Confounder summary scores when comparing the effects of multiple drug exposures.

PubMed

Cadarette, Suzanne M; Gagne, Joshua J; Solomon, Daniel H; Katz, Jeffrey N; Stürmer, Til

2010-01-01

Little information is available comparing methods to adjust for confounding when considering multiple drug exposures. We compared three analytic strategies to control for confounding based on measured variables: conventional multivariable, exposure propensity score (EPS), and disease risk score (DRS). Each method was applied to a dataset (2000-2006) recently used to examine the comparative effectiveness of four drugs. The relative effectiveness of risedronate, nasal calcitonin, and raloxifene in preventing non-vertebral fracture, were each compared to alendronate. EPSs were derived both by using multinomial logistic regression (single model EPS) and by three separate logistic regression models (separate model EPS). DRSs were derived and event rates compared using Cox proportional hazard models. DRSs derived among the entire cohort (full cohort DRS) was compared to DRSs derived only among the referent alendronate (unexposed cohort DRS). Less than 8% deviation from the base estimate (conventional multivariable) was observed applying single model EPS, separate model EPS or full cohort DRS. Applying the unexposed cohort DRS when background risk for fracture differed between comparison drug exposure cohorts resulted in -7 to + 13% deviation from our base estimate. With sufficient numbers of exposed and outcomes, either conventional multivariable, EPS or full cohort DRS may be used to adjust for confounding to compare the effects of multiple drug exposures. However, our data also suggest that unexposed cohort DRS may be problematic when background risks differ between referent and exposed groups. Further empirical and simulation studies will help to clarify the generalizability of our findings.

Forecasting Enrollments with Fuzzy Time Series.

ERIC Educational Resources Information Center

Song, Qiang; Chissom, Brad S.

The concept of fuzzy time series is introduced and used to forecast the enrollment of a university. Fuzzy time series, an aspect of fuzzy set theory, forecasts enrollment using a first-order time-invariant model. To evaluate the model, the conventional linear regression technique is applied and the predicted values obtained are compared to the…

Using Bayesian regression to test hypotheses about relationships between parameters and covariates in cognitive models.

PubMed

Boehm, Udo; Steingroever, Helen; Wagenmakers, Eric-Jan

2018-06-01

An important tool in the advancement of cognitive science are quantitative models that represent different cognitive variables in terms of model parameters. To evaluate such models, their parameters are typically tested for relationships with behavioral and physiological variables that are thought to reflect specific cognitive processes. However, many models do not come equipped with the statistical framework needed to relate model parameters to covariates. Instead, researchers often revert to classifying participants into groups depending on their values on the covariates, and subsequently comparing the estimated model parameters between these groups. Here we develop a comprehensive solution to the covariate problem in the form of a Bayesian regression framework. Our framework can be easily added to existing cognitive models and allows researchers to quantify the evidential support for relationships between covariates and model parameters using Bayes factors. Moreover, we present a simulation study that demonstrates the superiority of the Bayesian regression framework to the conventional classification-based approach.

The Use of Linear Instrumental Variables Methods in Health Services Research and Health Economics: A Cautionary Note

PubMed Central

Terza, Joseph V; Bradford, W David; Dismuke, Clara E

2008-01-01

Objective To investigate potential bias in the use of the conventional linear instrumental variables (IV) method for the estimation of causal effects in inherently nonlinear regression settings. Data Sources Smoking Supplement to the 1979 National Health Interview Survey, National Longitudinal Alcohol Epidemiologic Survey, and simulated data. Study Design Potential bias from the use of the linear IV method in nonlinear models is assessed via simulation studies and real world data analyses in two commonly encountered regression setting: (1) models with a nonnegative outcome (e.g., a count) and a continuous endogenous regressor; and (2) models with a binary outcome and a binary endogenous regressor. Principle Findings The simulation analyses show that substantial bias in the estimation of causal effects can result from applying the conventional IV method in inherently nonlinear regression settings. Moreover, the bias is not attenuated as the sample size increases. This point is further illustrated in the survey data analyses in which IV-based estimates of the relevant causal effects diverge substantially from those obtained with appropriate nonlinear estimation methods. Conclusions We offer this research as a cautionary note to those who would opt for the use of linear specifications in inherently nonlinear settings involving endogeneity. PMID:18546544

Longitudinal analysis of the strengths and difficulties questionnaire scores of the Millennium Cohort Study children in England using M-quantile random-effects regression.

PubMed

Tzavidis, Nikos; Salvati, Nicola; Schmid, Timo; Flouri, Eirini; Midouhas, Emily

2016-02-01

Multilevel modelling is a popular approach for longitudinal data analysis. Statistical models conventionally target a parameter at the centre of a distribution. However, when the distribution of the data is asymmetric, modelling other location parameters, e.g. percentiles, may be more informative. We present a new approach, M -quantile random-effects regression, for modelling multilevel data. The proposed method is used for modelling location parameters of the distribution of the strengths and difficulties questionnaire scores of children in England who participate in the Millennium Cohort Study. Quantile mixed models are also considered. The analyses offer insights to child psychologists about the differential effects of risk factors on children's outcomes.

Determination of suitable drying curve model for bread moisture loss during baking

NASA Astrophysics Data System (ADS)

Soleimani Pour-Damanab, A. R.; Jafary, A.; Rafiee, S.

2013-03-01

This study presents mathematical modelling of bread moisture loss or drying during baking in a conventional bread baking process. In order to estimate and select the appropriate moisture loss curve equation, 11 different models, semi-theoretical and empirical, were applied to the experimental data and compared according to their correlation coefficients, chi-squared test and root mean square error which were predicted by nonlinear regression analysis. Consequently, of all the drying models, a Page model was selected as the best one, according to the correlation coefficients, chi-squared test, and root mean square error values and its simplicity. Mean absolute estimation error of the proposed model by linear regression analysis for natural and forced convection modes was 2.43, 4.74%, respectively.

A general framework for the use of logistic regression models in meta-analysis.

PubMed

Simmonds, Mark C; Higgins, Julian Pt

2016-12-01

Where individual participant data are available for every randomised trial in a meta-analysis of dichotomous event outcomes, "one-stage" random-effects logistic regression models have been proposed as a way to analyse these data. Such models can also be used even when individual participant data are not available and we have only summary contingency table data. One benefit of this one-stage regression model over conventional meta-analysis methods is that it maximises the correct binomial likelihood for the data and so does not require the common assumption that effect estimates are normally distributed. A second benefit of using this model is that it may be applied, with only minor modification, in a range of meta-analytic scenarios, including meta-regression, network meta-analyses and meta-analyses of diagnostic test accuracy. This single model can potentially replace the variety of often complex methods used in these areas. This paper considers, with a range of meta-analysis examples, how random-effects logistic regression models may be used in a number of different types of meta-analyses. This one-stage approach is compared with widely used meta-analysis methods including Bayesian network meta-analysis and the bivariate and hierarchical summary receiver operating characteristic (ROC) models for meta-analyses of diagnostic test accuracy. © The Author(s) 2014.

Differentiation of orbital lymphoma and idiopathic orbital inflammatory pseudotumor: combined diagnostic value of conventional MRI and histogram analysis of ADC maps.

PubMed

Ren, Jiliang; Yuan, Ying; Wu, Yingwei; Tao, Xiaofeng

2018-05-02

The overlap of morphological feature and mean ADC value restricted clinical application of MRI in the differential diagnosis of orbital lymphoma and idiopathic orbital inflammatory pseudotumor (IOIP). In this paper, we aimed to retrospectively evaluate the combined diagnostic value of conventional magnetic resonance imaging (MRI) and whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps in the differentiation of the two lesions. In total, 18 patients with orbital lymphoma and 22 patients with IOIP were included, who underwent both conventional MRI and diffusion weighted imaging before treatment. Conventional MRI features and histogram parameters derived from ADC maps, including mean ADC (ADC mean ), median ADC (ADC median ), skewness, kurtosis, 10th, 25th, 75th and 90th percentiles of ADC (ADC 10 , ADC 25 , ADC 75 , ADC 90 ) were evaluated and compared between orbital lymphoma and IOIP. Multivariate logistic regression analysis was used to identify the most valuable variables for discriminating. Differential model was built upon the selected variables and receiver operating characteristic (ROC) analysis was also performed to determine the differential ability of the model. Multivariate logistic regression showed ADC 10 (P = 0.023) and involvement of orbit preseptal space (P = 0.029) were the most promising indexes in the discrimination of orbital lymphoma and IOIP. The logistic model defined by ADC 10 and involvement of orbit preseptal space was built, which achieved an AUC of 0.939, with sensitivity of 77.30% and specificity of 94.40%. Conventional MRI feature of involvement of orbit preseptal space and ADC histogram parameter of ADC 10 are valuable in differential diagnosis of orbital lymphoma and IOIP.

Efficient least angle regression for identification of linear-in-the-parameters models

PubMed Central

Beach, Thomas H.; Rezgui, Yacine

2017-01-01

Least angle regression, as a promising model selection method, differentiates itself from conventional stepwise and stagewise methods, in that it is neither too greedy nor too slow. It is closely related to L1 norm optimization, which has the advantage of low prediction variance through sacrificing part of model bias property in order to enhance model generalization capability. In this paper, we propose an efficient least angle regression algorithm for model selection for a large class of linear-in-the-parameters models with the purpose of accelerating the model selection process. The entire algorithm works completely in a recursive manner, where the correlations between model terms and residuals, the evolving directions and other pertinent variables are derived explicitly and updated successively at every subset selection step. The model coefficients are only computed when the algorithm finishes. The direct involvement of matrix inversions is thereby relieved. A detailed computational complexity analysis indicates that the proposed algorithm possesses significant computational efficiency, compared with the original approach where the well-known efficient Cholesky decomposition is involved in solving least angle regression. Three artificial and real-world examples are employed to demonstrate the effectiveness, efficiency and numerical stability of the proposed algorithm. PMID:28293140

Evaluation and application of regional turbidity-sediment regression models in Virginia

USGS Publications Warehouse

Hyer, Kenneth; Jastram, John D.; Moyer, Douglas; Webber, James S.; Chanat, Jeffrey G.

2015-01-01

Conventional thinking has long held that turbidity-sediment surrogate-regression equations are site specific and that regression equations developed at a single monitoring station should not be applied to another station; however, few studies have evaluated this issue in a rigorous manner. If robust regional turbidity-sediment models can be developed successfully, their applications could greatly expand the usage of these methods. Suspended sediment load estimation could occur as soon as flow and turbidity monitoring commence at a site, suspended sediment sampling frequencies for various projects potentially could be reduced, and special-project applications (sediment monitoring following dam removal, for example) could be significantly enhanced. The objective of this effort was to investigate the turbidity-suspended sediment concentration (SSC) relations at all available USGS monitoring sites within Virginia to determine whether meaningful turbidity-sediment regression models can be developed by combining the data from multiple monitoring stations into a single model, known as a “regional” model. Following the development of the regional model, additional objectives included a comparison of predicted SSCs between the regional model and commonly used site-specific models, as well as an evaluation of why specific monitoring stations did not fit the regional model.

Machine learning to predict the occurrence of bisphosphonate-related osteonecrosis of the jaw associated with dental extraction: A preliminary report.

PubMed

Kim, Dong Wook; Kim, Hwiyoung; Nam, Woong; Kim, Hyung Jun; Cha, In-Ho

2018-04-23

The aim of this study was to build and validate five types of machine learning models that can predict the occurrence of BRONJ associated with dental extraction in patients taking bisphosphonates for the management of osteoporosis. A retrospective review of the medical records was conducted to obtain cases and controls for the study. Total 125 patients consisting of 41 cases and 84 controls were selected for the study. Five machine learning prediction algorithms including multivariable logistic regression model, decision tree, support vector machine, artificial neural network, and random forest were implemented. The outputs of these models were compared with each other and also with conventional methods, such as serum CTX level. Area under the receiver operating characteristic (ROC) curve (AUC) was used to compare the results. The performance of machine learning models was significantly superior to conventional statistical methods and single predictors. The random forest model yielded the best performance (AUC = 0.973), followed by artificial neural network (AUC = 0.915), support vector machine (AUC = 0.882), logistic regression (AUC = 0.844), decision tree (AUC = 0.821), drug holiday alone (AUC = 0.810), and CTX level alone (AUC = 0.630). Machine learning methods showed superior performance in predicting BRONJ associated with dental extraction compared to conventional statistical methods using drug holiday and serum CTX level. Machine learning can thus be applied in a wide range of clinical studies. Copyright © 2017. Published by Elsevier Inc.

Machine Learning Algorithms Outperform Conventional Regression Models in Predicting Development of Hepatocellular Carcinoma

PubMed Central

Singal, Amit G.; Mukherjee, Ashin; Elmunzer, B. Joseph; Higgins, Peter DR; Lok, Anna S.; Zhu, Ji; Marrero, Jorge A; Waljee, Akbar K

2015-01-01

Background Predictive models for hepatocellular carcinoma (HCC) have been limited by modest accuracy and lack of validation. Machine learning algorithms offer a novel methodology, which may improve HCC risk prognostication among patients with cirrhosis. Our study's aim was to develop and compare predictive models for HCC development among cirrhotic patients, using conventional regression analysis and machine learning algorithms. Methods We enrolled 442 patients with Child A or B cirrhosis at the University of Michigan between January 2004 and September 2006 (UM cohort) and prospectively followed them until HCC development, liver transplantation, death, or study termination. Regression analysis and machine learning algorithms were used to construct predictive models for HCC development, which were tested on an independent validation cohort from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial. Both models were also compared to the previously published HALT-C model. Discrimination was assessed using receiver operating characteristic curve analysis and diagnostic accuracy was assessed with net reclassification improvement and integrated discrimination improvement statistics. Results After a median follow-up of 3.5 years, 41 patients developed HCC. The UM regression model had a c-statistic of 0.61 (95%CI 0.56-0.67), whereas the machine learning algorithm had a c-statistic of 0.64 (95%CI 0.60–0.69) in the validation cohort. The machine learning algorithm had significantly better diagnostic accuracy as assessed by net reclassification improvement (p<0.001) and integrated discrimination improvement (p=0.04). The HALT-C model had a c-statistic of 0.60 (95%CI 0.50-0.70) in the validation cohort and was outperformed by the machine learning algorithm (p=0.047). Conclusion Machine learning algorithms improve the accuracy of risk stratifying patients with cirrhosis and can be used to accurately identify patients at high-risk for developing HCC. PMID:24169273

Boosting structured additive quantile regression for longitudinal childhood obesity data.

PubMed

Fenske, Nora; Fahrmeir, Ludwig; Hothorn, Torsten; Rzehak, Peter; Höhle, Michael

2013-07-25

Childhood obesity and the investigation of its risk factors has become an important public health issue. Our work is based on and motivated by a German longitudinal study including 2,226 children with up to ten measurements on their body mass index (BMI) and risk factors from birth to the age of 10 years. We introduce boosting of structured additive quantile regression as a novel distribution-free approach for longitudinal quantile regression. The quantile-specific predictors of our model include conventional linear population effects, smooth nonlinear functional effects, varying-coefficient terms, and individual-specific effects, such as intercepts and slopes. Estimation is based on boosting, a computer intensive inference method for highly complex models. We propose a component-wise functional gradient descent boosting algorithm that allows for penalized estimation of the large variety of different effects, particularly leading to individual-specific effects shrunken toward zero. This concept allows us to flexibly estimate the nonlinear age curves of upper quantiles of the BMI distribution, both on population and on individual-specific level, adjusted for further risk factors and to detect age-varying effects of categorical risk factors. Our model approach can be regarded as the quantile regression analog of Gaussian additive mixed models (or structured additive mean regression models), and we compare both model classes with respect to our obesity data.

Evaluation of Cox's model and logistic regression for matched case-control data with time-dependent covariates: a simulation study.

PubMed

Leffondré, Karen; Abrahamowicz, Michal; Siemiatycki, Jack

2003-12-30

Case-control studies are typically analysed using the conventional logistic model, which does not directly account for changes in the covariate values over time. Yet, many exposures may vary over time. The most natural alternative to handle such exposures would be to use the Cox model with time-dependent covariates. However, its application to case-control data opens the question of how to manipulate the risk sets. Through a simulation study, we investigate how the accuracy of the estimates of Cox's model depends on the operational definition of risk sets and/or on some aspects of the time-varying exposure. We also assess the estimates obtained from conventional logistic regression. The lifetime experience of a hypothetical population is first generated, and a matched case-control study is then simulated from this population. We control the frequency, the age at initiation, and the total duration of exposure, as well as the strengths of their effects. All models considered include a fixed-in-time covariate and one or two time-dependent covariate(s): the indicator of current exposure and/or the exposure duration. Simulation results show that none of the models always performs well. The discrepancies between the odds ratios yielded by logistic regression and the 'true' hazard ratio depend on both the type of the covariate and the strength of its effect. In addition, it seems that logistic regression has difficulty separating the effects of inter-correlated time-dependent covariates. By contrast, each of the two versions of Cox's model systematically induces either a serious under-estimation or a moderate over-estimation bias. The magnitude of the latter bias is proportional to the true effect, suggesting that an improved manipulation of the risk sets may eliminate, or at least reduce, the bias. Copyright 2003 JohnWiley & Sons, Ltd.

Assessment of Response Surface Models using Independent Confirmation Point Analysis

NASA Technical Reports Server (NTRS)

DeLoach, Richard

2010-01-01

This paper highlights various advantages that confirmation-point residuals have over conventional model design-point residuals in assessing the adequacy of a response surface model fitted by regression techniques to a sample of experimental data. Particular advantages are highlighted for the case of design matrices that may be ill-conditioned for a given sample of data. The impact of both aleatory and epistemological uncertainty in response model adequacy assessments is considered.

Pointwise influence matrices for functional-response regression.

PubMed

Reiss, Philip T; Huang, Lei; Wu, Pei-Shien; Chen, Huaihou; Colcombe, Stan

2017-12-01

We extend the notion of an influence or hat matrix to regression with functional responses and scalar predictors. For responses depending linearly on a set of predictors, our definition is shown to reduce to the conventional influence matrix for linear models. The pointwise degrees of freedom, the trace of the pointwise influence matrix, are shown to have an adaptivity property that motivates a two-step bivariate smoother for modeling nonlinear dependence on a single predictor. This procedure adapts to varying complexity of the nonlinear model at different locations along the function, and thereby achieves better performance than competing tensor product smoothers in an analysis of the development of white matter microstructure in the brain. © 2017, The International Biometric Society.

Artificial Intelligence Systems as Prognostic and Predictive Tools in Ovarian Cancer.

PubMed

Enshaei, A; Robson, C N; Edmondson, R J

2015-11-01

The ability to provide accurate prognostic and predictive information to patients is becoming increasingly important as clinicians enter an era of personalized medicine. For a disease as heterogeneous as epithelial ovarian cancer, conventional algorithms become too complex for routine clinical use. This study therefore investigated the potential for an artificial intelligence model to provide this information and compared it with conventional statistical approaches. The authors created a database comprising 668 cases of epithelial ovarian cancer during a 10-year period and collected data routinely available in a clinical environment. They also collected survival data for all the patients, then constructed an artificial intelligence model capable of comparing a variety of algorithms and classifiers alongside conventional statistical approaches such as logistic regression. The model was used to predict overall survival and demonstrated that an artificial neural network (ANN) algorithm was capable of predicting survival with high accuracy (93 %) and an area under the curve (AUC) of 0.74 and that this outperformed logistic regression. The model also was used to predict the outcome of surgery and again showed that ANN could predict outcome (complete/optimal cytoreduction vs. suboptimal cytoreduction) with 77 % accuracy and an AUC of 0.73. These data are encouraging and demonstrate that artificial intelligence systems may have a role in providing prognostic and predictive data for patients. The performance of these systems likely will improve with increasing data set size, and this needs further investigation.

[Local Regression Algorithm Based on Net Analyte Signal and Its Application in Near Infrared Spectral Analysis].

PubMed

Zhang, Hong-guang; Lu, Jian-gang

2016-02-01

Abstract To overcome the problems of significant difference among samples and nonlinearity between the property and spectra of samples in spectral quantitative analysis, a local regression algorithm is proposed in this paper. In this algorithm, net signal analysis method(NAS) was firstly used to obtain the net analyte signal of the calibration samples and unknown samples, then the Euclidean distance between net analyte signal of the sample and net analyte signal of calibration samples was calculated and utilized as similarity index. According to the defined similarity index, the local calibration sets were individually selected for each unknown sample. Finally, a local PLS regression model was built on each local calibration sets for each unknown sample. The proposed method was applied to a set of near infrared spectra of meat samples. The results demonstrate that the prediction precision and model complexity of the proposed method are superior to global PLS regression method and conventional local regression algorithm based on spectral Euclidean distance.

A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples.

PubMed

Li, Yankun; Shao, Xueguang; Cai, Wensheng

2007-04-15

Consensus modeling of combining the results of multiple independent models to produce a single prediction avoids the instability of single model. Based on the principle of consensus modeling, a consensus least squares support vector regression (LS-SVR) method for calibrating the near-infrared (NIR) spectra was proposed. In the proposed approach, NIR spectra of plant samples were firstly preprocessed using discrete wavelet transform (DWT) for filtering the spectral background and noise, then, consensus LS-SVR technique was used for building the calibration model. With an optimization of the parameters involved in the modeling, a satisfied model was achieved for predicting the content of reducing sugar in plant samples. The predicted results show that consensus LS-SVR model is more robust and reliable than the conventional partial least squares (PLS) and LS-SVR methods.

Computing mammographic density from a multiple regression model constructed with image-acquisition parameters from a full-field digital mammographic unit

PubMed Central

Lu, Lee-Jane W.; Nishino, Thomas K.; Khamapirad, Tuenchit; Grady, James J; Leonard, Morton H.; Brunder, Donald G.

2009-01-01

Breast density (the percentage of fibroglandular tissue in the breast) has been suggested to be a useful surrogate marker for breast cancer risk. It is conventionally measured using screen-film mammographic images by a labor intensive histogram segmentation method (HSM). We have adapted and modified the HSM for measuring breast density from raw digital mammograms acquired by full-field digital mammography. Multiple regression model analyses showed that many of the instrument parameters for acquiring the screening mammograms (e.g. breast compression thickness, radiological thickness, radiation dose, compression force, etc) and image pixel intensity statistics of the imaged breasts were strong predictors of the observed threshold values (model R2=0.93) and %density (R2=0.84). The intra-class correlation coefficient of the %-density for duplicate images was estimated to be 0.80, using the regression model-derived threshold values, and 0.94 if estimated directly from the parameter estimates of the %-density prediction regression model. Therefore, with additional research, these mathematical models could be used to compute breast density objectively, automatically bypassing the HSM step, and could greatly facilitate breast cancer research studies. PMID:17671343

Modeling the North American vertical datum of 1988 errors in the conterminous United States

NASA Astrophysics Data System (ADS)

Li, X.

2018-02-01

A large systematic difference (ranging from -20 cm to +130 cm) was found between NAVD 88 (North AmericanVertical Datum of 1988) and the pure gravimetric geoid models. This difference not only makes it very difficult to augment the local geoid model by directly using the vast NAVD 88 network with state-of-the-art technologies recently developed in geodesy, but also limits the ability of researchers to effectively demonstrate the geoid model improvements on the NAVD 88 network. Here, both conventional regression analyses based on various predefined basis functions such as polynomials, B-splines, and Legendre functions and the Latent Variable Analysis (LVA) such as the Factor Analysis (FA) are used to analyze the systematic difference. Besides giving a mathematical model, the regression results do not reveal a great deal about the physical reasons that caused the large differences in NAVD 88, which may be of interest to various researchers. Furthermore, there is still a significant amount of no-Gaussian signals left in the residuals of the conventional regression models. On the other side, the FA method not only provides a better not of the data, but also offers possible explanations of the error sources. Without requiring extra hypothesis tests on the model coefficients, the results from FA are more efficient in terms of capturing the systematic difference. Furthermore, without using a covariance model, a novel interpolating method based on the relationship between the loading matrix and the factor scores is developed for predictive purposes. The prediction error analysis shows that about 3-7 cm precision is expected in NAVD 88 after removing the systematic difference.

Meta-analytical synthesis of regression coefficients under different categorization scheme of continuous covariates.

PubMed

Yoneoka, Daisuke; Henmi, Masayuki

2017-11-30

Recently, the number of clinical prediction models sharing the same regression task has increased in the medical literature. However, evidence synthesis methodologies that use the results of these regression models have not been sufficiently studied, particularly in meta-analysis settings where only regression coefficients are available. One of the difficulties lies in the differences between the categorization schemes of continuous covariates across different studies. In general, categorization methods using cutoff values are study specific across available models, even if they focus on the same covariates of interest. Differences in the categorization of covariates could lead to serious bias in the estimated regression coefficients and thus in subsequent syntheses. To tackle this issue, we developed synthesis methods for linear regression models with different categorization schemes of covariates. A 2-step approach to aggregate the regression coefficient estimates is proposed. The first step is to estimate the joint distribution of covariates by introducing a latent sampling distribution, which uses one set of individual participant data to estimate the marginal distribution of covariates with categorization. The second step is to use a nonlinear mixed-effects model with correction terms for the bias due to categorization to estimate the overall regression coefficients. Especially in terms of precision, numerical simulations show that our approach outperforms conventional methods, which only use studies with common covariates or ignore the differences between categorization schemes. The method developed in this study is also applied to a series of WHO epidemiologic studies on white blood cell counts. Copyright © 2017 John Wiley & Sons, Ltd.

Acquisition Challenge: The Importance of Incompressibility in Comparing Learning Curve Models

DTIC Science & Technology

2015-10-01

parameters for all four learning mod- els used in the study . The learning rate factor, b, is the slope of the linear regression line, which in this case is...incorporated within the DoD acquisition environment. This study tested three alternative learning models (the Stanford-B model, DeJong’s learning formula...appropriate tools to calculate accurate and reliable predictions. However, conventional learning curve methodology has been in practice since the pre

Fuzzy regression modeling for tool performance prediction and degradation detection.

PubMed

Li, X; Er, M J; Lim, B S; Zhou, J H; Gan, O P; Rutkowski, L

2010-10-01

In this paper, the viability of using Fuzzy-Rule-Based Regression Modeling (FRM) algorithm for tool performance and degradation detection is investigated. The FRM is developed based on a multi-layered fuzzy-rule-based hybrid system with Multiple Regression Models (MRM) embedded into a fuzzy logic inference engine that employs Self Organizing Maps (SOM) for clustering. The FRM converts a complex nonlinear problem to a simplified linear format in order to further increase the accuracy in prediction and rate of convergence. The efficacy of the proposed FRM is tested through a case study - namely to predict the remaining useful life of a ball nose milling cutter during a dry machining process of hardened tool steel with a hardness of 52-54 HRc. A comparative study is further made between four predictive models using the same set of experimental data. It is shown that the FRM is superior as compared with conventional MRM, Back Propagation Neural Networks (BPNN) and Radial Basis Function Networks (RBFN) in terms of prediction accuracy and learning speed.

Sensors vs. experts - a performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients.

PubMed

Marschollek, Michael; Rehwald, Anja; Wolf, Klaus-Hendrik; Gietzelt, Matthias; Nemitz, Gerhard; zu Schwabedissen, Hubertus Meyer; Schulze, Mareike

2011-06-28

Fall events contribute significantly to mortality, morbidity and costs in our ageing population. In order to identify persons at risk and to target preventive measures, many scores and assessment tools have been developed. These often require expertise and are costly to implement. Recent research investigates the use of wearable inertial sensors to provide objective data on motion features which can be used to assess individual fall risk automatically. So far it is unknown how well this new method performs in comparison with conventional fall risk assessment tools. The aim of our research is to compare the predictive performance of our new sensor-based method with conventional and established methods, based on prospective data. In a first study phase, 119 inpatients of a geriatric clinic took part in motion measurements using a wireless triaxial accelerometer during a Timed Up&Go (TUG) test and a 20 m walk. Furthermore, the St. Thomas Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) was performed, and the multidisciplinary geriatric care team estimated the patients' fall risk. In a second follow-up phase of the study, 46 of the participants were interviewed after one year, including a fall and activity assessment. The predictive performances of the TUG, the STRATIFY and team scores are compared. Furthermore, two automatically induced logistic regression models based on conventional clinical and assessment data (CONV) as well as sensor data (SENSOR) are matched. Among the risk assessment scores, the geriatric team score (sensitivity 56%, specificity 80%) outperforms STRATIFY and TUG. The induced logistic regression models CONV and SENSOR achieve similar performance values (sensitivity 68%/58%, specificity 74%/78%, AUC 0.74/0.72, +LR 2.64/2.61). Both models are able to identify more persons at risk than the simple scores. Sensor-based objective measurements of motion parameters in geriatric patients can be used to assess individual fall risk, and our prediction model's performance matches that of a model based on conventional clinical and assessment data. Sensor-based measurements using a small wearable device may contribute significant information to conventional methods and are feasible in an unsupervised setting. More prospective research is needed to assess the cost-benefit relation of our approach.

Sensors vs. experts - A performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients

PubMed Central

2011-01-01

Background Fall events contribute significantly to mortality, morbidity and costs in our ageing population. In order to identify persons at risk and to target preventive measures, many scores and assessment tools have been developed. These often require expertise and are costly to implement. Recent research investigates the use of wearable inertial sensors to provide objective data on motion features which can be used to assess individual fall risk automatically. So far it is unknown how well this new method performs in comparison with conventional fall risk assessment tools. The aim of our research is to compare the predictive performance of our new sensor-based method with conventional and established methods, based on prospective data. Methods In a first study phase, 119 inpatients of a geriatric clinic took part in motion measurements using a wireless triaxial accelerometer during a Timed Up&Go (TUG) test and a 20 m walk. Furthermore, the St. Thomas Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) was performed, and the multidisciplinary geriatric care team estimated the patients' fall risk. In a second follow-up phase of the study, 46 of the participants were interviewed after one year, including a fall and activity assessment. The predictive performances of the TUG, the STRATIFY and team scores are compared. Furthermore, two automatically induced logistic regression models based on conventional clinical and assessment data (CONV) as well as sensor data (SENSOR) are matched. Results Among the risk assessment scores, the geriatric team score (sensitivity 56%, specificity 80%) outperforms STRATIFY and TUG. The induced logistic regression models CONV and SENSOR achieve similar performance values (sensitivity 68%/58%, specificity 74%/78%, AUC 0.74/0.72, +LR 2.64/2.61). Both models are able to identify more persons at risk than the simple scores. Conclusions Sensor-based objective measurements of motion parameters in geriatric patients can be used to assess individual fall risk, and our prediction model's performance matches that of a model based on conventional clinical and assessment data. Sensor-based measurements using a small wearable device may contribute significant information to conventional methods and are feasible in an unsupervised setting. More prospective research is needed to assess the cost-benefit relation of our approach. PMID:21711504

Carbon dioxide stripping in aquaculture -- part III: model verification

USGS Publications Warehouse

Colt, John; Watten, Barnaby; Pfeiffer, Tim

2012-01-01

Based on conventional mass transfer models developed for oxygen, the use of the non-linear ASCE method, 2-point method, and one parameter linear-regression method were evaluated for carbon dioxide stripping data. For values of KLaCO2 < approximately 1.5/h, the 2-point or ASCE method are a good fit to experimental data, but the fit breaks down at higher values of KLaCO2. How to correct KLaCO2 for gas phase enrichment remains to be determined. The one-parameter linear regression model was used to vary the C*CO2 over the test, but it did not result in a better fit to the experimental data when compared to the ASCE or fixed C*CO2 assumptions.

Support vector regression to predict porosity and permeability: Effect of sample size

NASA Astrophysics Data System (ADS)

Al-Anazi, A. F.; Gates, I. D.

2012-02-01

Porosity and permeability are key petrophysical parameters obtained from laboratory core analysis. Cores, obtained from drilled wells, are often few in number for most oil and gas fields. Porosity and permeability correlations based on conventional techniques such as linear regression or neural networks trained with core and geophysical logs suffer poor generalization to wells with only geophysical logs. The generalization problem of correlation models often becomes pronounced when the training sample size is small. This is attributed to the underlying assumption that conventional techniques employing the empirical risk minimization (ERM) inductive principle converge asymptotically to the true risk values as the number of samples increases. In small sample size estimation problems, the available training samples must span the complexity of the parameter space so that the model is able both to match the available training samples reasonably well and to generalize to new data. This is achieved using the structural risk minimization (SRM) inductive principle by matching the capability of the model to the available training data. One method that uses SRM is support vector regression (SVR) network. In this research, the capability of SVR to predict porosity and permeability in a heterogeneous sandstone reservoir under the effect of small sample size is evaluated. Particularly, the impact of Vapnik's ɛ-insensitivity loss function and least-modulus loss function on generalization performance was empirically investigated. The results are compared to the multilayer perception (MLP) neural network, a widely used regression method, which operates under the ERM principle. The mean square error and correlation coefficients were used to measure the quality of predictions. The results demonstrate that SVR yields consistently better predictions of the porosity and permeability with small sample size than the MLP method. Also, the performance of SVR depends on both kernel function type and loss functions used.

Kinetic microplate bioassays for relative potency of antibiotics improved by partial Least Square (PLS) regression.

PubMed

Francisco, Fabiane Lacerda; Saviano, Alessandro Morais; Almeida, Túlia de Souza Botelho; Lourenço, Felipe Rebello

2016-05-01

Microbiological assays are widely used to estimate the relative potencies of antibiotics in order to guarantee the efficacy, safety, and quality of drug products. Despite of the advantages of turbidimetric bioassays when compared to other methods, it has limitations concerning the linearity and range of the dose-response curve determination. Here, we proposed to use partial least squares (PLS) regression to solve these limitations and to improve the prediction of relative potencies of antibiotics. Kinetic-reading microplate turbidimetric bioassays for apramacyin and vancomycin were performed using Escherichia coli (ATCC 8739) and Bacillus subtilis (ATCC 6633), respectively. Microbial growths were measured as absorbance up to 180 and 300min for apramycin and vancomycin turbidimetric bioassays, respectively. Conventional dose-response curves (absorbances or area under the microbial growth curve vs. log of antibiotic concentration) showed significant regression, however there were significant deviation of linearity. Thus, they could not be used for relative potency estimations. PLS regression allowed us to construct a predictive model for estimating the relative potencies of apramycin and vancomycin without over-fitting and it improved the linear range of turbidimetric bioassay. In addition, PLS regression provided predictions of relative potencies equivalent to those obtained from agar diffusion official methods. Therefore, we conclude that PLS regression may be used to estimate the relative potencies of antibiotics with significant advantages when compared to conventional dose-response curve determination. Copyright © 2016 Elsevier B.V. All rights reserved.

An improved method for permeability estimation of the bioclastic limestone reservoir based on NMR data

NASA Astrophysics Data System (ADS)

Ge, Xinmin; Fan, Yiren; Liu, Jianyu; Zhang, Li; Han, Yujiao; Xing, Donghui

2017-10-01

Permeability is an important parameter in formation evaluation since it controls the fluid transportation of porous rocks. However, it is challengeable to compute the permeability of bioclastic limestone reservoirs by conventional methods linking petrophysical and geophysical data, due to the complex pore distributions. A new method is presented to estimate the permeability based on laboratory and downhole nuclear magnetic resonance (NMR) measurements. We divide the pore space into four intervals by the inflection points between the pore radius and the transversal relaxation time. Relationships between permeability and percentages of different pore intervals are investigated to investigate influential factors on the fluid transportation. Furthermore, an empirical model, which takes into account of the pore size distributions, is presented to compute the permeability. 212 core samples in our case show that the accuracy of permeability calculation is improved from 0.542 (SDR model), 0.507 (TIM model), 0.455 (conventional porosity-permeability regressions) to 0.803. To enhance the precision of downhole application of the new model, we developed a fluid correction algorithm to construct the water spectrum of in-situ NMR data, aiming to eliminate the influence of oil on the magnetization. The result reveals that permeability is positively correlated with percentages of mega-pores and macro-pores, but negatively correlated with the percentage of micro-pores. Poor correlation is observed between permeability and the percentage of meso-pores. NMR magnetizations and T2 spectrums after the fluid correction agree well with laboratory results for samples saturated with water. Field application indicates that the improved method provides better performance than conventional models such as Schlumberger-Doll Research equation, Timur-Coates equation, and porosity-permeability regressions.

Multivariate modelling of density, strength, and stiffness from near infared for mature, juvenile, and pith wood of longleaf pine (Pinus Palustris)

Treesearch

Brian K. Via; Todd F. Shupe; Leslie H. Groom; Michael Stine; Chi-Leung So

2003-01-01

In manufacturing, monitoring the mechanical properties of wood with near infrared spectroscopy (NIR) is an attractive alternative to more conventional methods. However, no attention has been given to see if models differ between juvenile and mature wood. Additionally, it would be convenient if multiple linear regression (MLR) could perform well in the place of more...

Height and Weight Estimation From Anthropometric Measurements Using Machine Learning Regressions

PubMed Central

Fernandes, Bruno J. T.; Roque, Alexandre

2018-01-01

Height and weight are measurements explored to tracking nutritional diseases, energy expenditure, clinical conditions, drug dosages, and infusion rates. Many patients are not ambulant or may be unable to communicate, and a sequence of these factors may not allow accurate estimation or measurements; in those cases, it can be estimated approximately by anthropometric means. Different groups have proposed different linear or non-linear equations which coefficients are obtained by using single or multiple linear regressions. In this paper, we present a complete study of the application of different learning models to estimate height and weight from anthropometric measurements: support vector regression, Gaussian process, and artificial neural networks. The predicted values are significantly more accurate than that obtained with conventional linear regressions. In all the cases, the predictions are non-sensitive to ethnicity, and to gender, if more than two anthropometric parameters are analyzed. The learning model analysis creates new opportunities for anthropometric applications in industry, textile technology, security, and health care. PMID:29651366

Social Influence on Information Technology Adoption and Sustained Use in Healthcare: A Hierarchical Bayesian Learning Method Analysis

ERIC Educational Resources Information Center

Hao, Haijing

2013-01-01

Information technology adoption and diffusion is currently a significant challenge in the healthcare delivery setting. This thesis includes three papers that explore social influence on information technology adoption and sustained use in the healthcare delivery environment using conventional regression models and novel hierarchical Bayesian…

Automated Screening of Children With Obstructive Sleep Apnea Using Nocturnal Oximetry: An Alternative to Respiratory Polygraphy in Unattended Settings

PubMed Central

Álvarez, Daniel; Alonso-Álvarez, María L.; Gutiérrez-Tobal, Gonzalo C.; Crespo, Andrea; Kheirandish-Gozal, Leila; Hornero, Roberto; Gozal, David; Terán-Santos, Joaquín; Del Campo, Félix

2017-01-01

Study Objectives: Nocturnal oximetry has become known as a simple, readily available, and potentially useful diagnostic tool of childhood obstructive sleep apnea (OSA). However, at-home respiratory polygraphy (HRP) remains the preferred alternative to polysomnography (PSG) in unattended settings. The aim of this study was twofold: (1) to design and assess a novel methodology for pediatric OSA screening based on automated analysis of at-home oxyhemoglobin saturation (SpO2), and (2) to compare its diagnostic performance with HRP. Methods: SpO2 recordings were parameterized by means of time, frequency, and conventional oximetric measures. Logistic regression models were optimized using genetic algorithms (GAs) for three cutoffs for OSA: 1, 3, and 5 events/h. The diagnostic performance of logistic regression models, manual obstructive apnea-hypopnea index (OAHI) from HRP, and the conventional oxygen desaturation index ≥ 3% (ODI3) were assessed. Results: For a cutoff of 1 event/h, the optimal logistic regression model significantly outperformed both conventional HRP-derived ODI3 and OAHI: 85.5% accuracy (HRP 74.6%; ODI3 65.9%) and 0.97 area under the receiver operating characteristics curve (AUC) (HRP 0.78; ODI3 0.75) were reached. For a cutoff of 3 events/h, the logistic regression model achieved 83.4% accuracy (HRP 85.0%; ODI3 74.5%) and 0.96 AUC (HRP 0.93; ODI3 0.85) whereas using a cutoff of 5 events/h, oximetry reached 82.8% accuracy (HRP 85.1%; ODI3 76.7) and 0.97 AUC (HRP 0.95; ODI3 0.84). Conclusions: Automated analysis of at-home SpO2 recordings provide accurate detection of children with high pretest probability of OSA. Thus, unsupervised nocturnal oximetry may enable a simple and effective alternative to HRP and PSG in unattended settings. Citation: Álvarez D, Alonso-Álvarez ML, Gutiérrez-Tobal GC, Crespo A, Kheirandish-Gozal L, Hornero R, Gozal D, Terán-Santos J, Del Campo F. Automated screening of children with obstructive sleep apnea using nocturnal oximetry: an alternative to respiratory polygraphy in unattended settings. J Clin Sleep Med. 2017;13(5):693–702. PMID:28356177

Radiographic cup anteversion measurement corrected from pelvic tilt.

PubMed

Wang, Liao; Thoreson, Andrew R; Trousdale, Robert T; Morrey, Bernard F; Dai, Kerong; An, Kai-Nan

2017-11-01

The purpose of this study was to develop a novel technique to improve the accuracy of radiographic cup anteversion measurement by correcting the influence of pelvic tilt. Ninety virtual total hip arthroplasties were simulated from computed tomography data of 6 patients with 15 predetermined cup orientations. For each simulated implantation, anteroposterior (AP) virtual pelvic radiographs were generated for 11 predetermined pelvic tilts. A linear regression model was created to capture the relationship between radiographic cup anteversion angle error measured on AP pelvic radiographs and pelvic tilt. Overall, nine hundred and ninety virtual AP pelvic radiographs were measured, and 90 linear regression models were created. Pearson's correlation analyses confirmed a strong correlation between the errors of conventional radiographic cup anteversion angle measured on AP pelvic radiographs and the magnitude of pelvic tilt (P < 0.001). The mean of 90 slopes and y-intercepts of the regression lines were -0.8 and -2.5°, which were applied as the general correction parameters for the proposed tool to correct conventional cup anteversion angle from the influence of pelvic tilt. The current method proposes to measure the pelvic tilt on a lateral radiograph, and to use it as a correction for the radiographic cup anteversion measurement on an AP pelvic radiograph. Thus, both AP and lateral pelvic radiographs are required for the measurement of pelvic posture-integrated cup anteversion. Compared with conventional radiographic cup anteversion, the errors of pelvic posture-integrated radiographic cup anteversion were reduced from 10.03 (SD = 5.13) degrees to 2.53 (SD = 1.33) degrees. Pelvic posture-integrated cup anteversion measurement improves the accuracy of radiographic cup anteversion measurement, which shows the potential of further clarifying the etiology of postoperative instability based on planar radiographs. Copyright © 2017 IPEM. Published by Elsevier Ltd. All rights reserved.

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.

Automatic segmentation and classification of mycobacterium tuberculosis with conventional light microscopy

NASA Astrophysics Data System (ADS)

Xu, Chao; Zhou, Dongxiang; Zhai, Yongping; Liu, Yunhui

2015-12-01

This paper realizes the automatic segmentation and classification of Mycobacterium tuberculosis with conventional light microscopy. First, the candidate bacillus objects are segmented by the marker-based watershed transform. The markers are obtained by an adaptive threshold segmentation based on the adaptive scale Gaussian filter. The scale of the Gaussian filter is determined according to the color model of the bacillus objects. Then the candidate objects are extracted integrally after region merging and contaminations elimination. Second, the shape features of the bacillus objects are characterized by the Hu moments, compactness, eccentricity, and roughness, which are used to classify the single, touching and non-bacillus objects. We evaluated the logistic regression, random forest, and intersection kernel support vector machines classifiers in classifying the bacillus objects respectively. Experimental results demonstrate that the proposed method yields to high robustness and accuracy. The logistic regression classifier performs best with an accuracy of 91.68%.

Neurological and mental health outcomes among conventional and organic farmers in Indiana, USA.

PubMed

Khan, Khalid M; Baidya, Retushi; Aryal, Ashamsa; Farmer, James R; Valliant, Julia

2018-06-20

Every farming method, whether conventional or organic, has been associated with some sort of risky behaviors leading to health issues among farmers. Substantial evidence is not available in the literature to determine whether the magnitudes of health outcomes vary between conventional and organic farmers. The study investigated whether self-reported neurological and mental health symptoms differ between conventional and organic farmers living in Indiana, USA. A self-reported questionnaire survey collected information from 200 conventional and 157 organic farmers of Indiana on demographic characteristics, depression and neurological symptoms. Statistical analyses were conducted to observe the differences in self-reported symptoms by groups of farmers. It was observed that the conventional farmers had significantly higher age-adjusted mean neurological symptom score (p<0.01) than the organic farmers. Regression models revealed positive and significant associations of conventional farming with total (β =1.34; p=0.02), sensory (β =0.83; p=0.001) and behavioural (β =0.09; p=0.03) symptoms after accounting for age, income, education and years in farming. Positive but non-significant associations were also observed in conventional farmers with cognitive and motor symptoms, and with all subscales of depression symptoms in the adjusted models. The findings obtained suggest the importance of a larger study to further explain the difference in mental and neurological health effects in these two categories of farmers.

Fuzzy classifier based support vector regression framework for Poisson ratio determination

NASA Astrophysics Data System (ADS)

Asoodeh, Mojtaba; Bagheripour, Parisa

2013-09-01

Poisson ratio is considered as one of the most important rock mechanical properties of hydrocarbon reservoirs. Determination of this parameter through laboratory measurement is time, cost, and labor intensive. Furthermore, laboratory measurements do not provide continuous data along the reservoir intervals. Hence, a fast, accurate, and inexpensive way of determining Poisson ratio which produces continuous data over the whole reservoir interval is desirable. For this purpose, support vector regression (SVR) method based on statistical learning theory (SLT) was employed as a supervised learning algorithm to estimate Poisson ratio from conventional well log data. SVR is capable of accurately extracting the implicit knowledge contained in conventional well logs and converting the gained knowledge into Poisson ratio data. Structural risk minimization (SRM) principle which is embedded in the SVR structure in addition to empirical risk minimization (EMR) principle provides a robust model for finding quantitative formulation between conventional well log data and Poisson ratio. Although satisfying results were obtained from an individual SVR model, it had flaws of overestimation in low Poisson ratios and underestimation in high Poisson ratios. These errors were eliminated through implementation of fuzzy classifier based SVR (FCBSVR). The FCBSVR significantly improved accuracy of the final prediction. This strategy was successfully applied to data from carbonate reservoir rocks of an Iranian Oil Field. Results indicated that SVR predicted Poisson ratio values are in good agreement with measured values.

Prediction model for the return to work of workers with injuries in Hong Kong.

PubMed

Xu, Yanwen; Chan, Chetwyn C H; Lo, Karen Hui Yu-Ling; Tang, Dan

2008-01-01

This study attempts to formulate a prediction model of return to work for a group of workers who have been suffering from chronic pain and physical injury while also being out of work in Hong Kong. The study used Case-based Reasoning (CBR) method, and compared the result with the statistical method of logistic regression model. The database of the algorithm of CBR was composed of 67 cases who were also used in the logistic regression model. The testing cases were 32 participants who had a similar background and characteristics to those in the database. The methods of setting constraints and Euclidean distance metric were used in CBR to search the closest cases to the trial case based on the matrix. The usefulness of the algorithm was tested on 32 new participants, and the accuracy of predicting return to work outcomes was 62.5%, which was no better than the 71.2% accuracy derived from the logistic regression model. The results of the study would enable us to have a better understanding of the CBR applied in the field of occupational rehabilitation by comparing with the conventional regression analysis. The findings would also shed light on the development of relevant interventions for the return-to-work process of these workers.

Logistic quantile regression provides improved estimates for bounded avian counts: A case study of California Spotted Owl fledgling production

USGS Publications Warehouse

Cade, Brian S.; Noon, Barry R.; Scherer, Rick D.; Keane, John J.

2017-01-01

Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical conditional distribution of a bounded discrete random variable. The logistic quantile regression model requires that counts are randomly jittered to a continuous random variable, logit transformed to bound them between specified lower and upper values, then estimated in conventional linear quantile regression, repeating the 3 steps and averaging estimates. Back-transformation to the original discrete scale relies on the fact that quantiles are equivariant to monotonic transformations. We demonstrate this statistical procedure by modeling 20 years of California Spotted Owl fledgling production (0−3 per territory) on the Lassen National Forest, California, USA, as related to climate, demographic, and landscape habitat characteristics at territories. Spotted Owl fledgling counts increased nonlinearly with decreasing precipitation in the early nesting period, in the winter prior to nesting, and in the prior growing season; with increasing minimum temperatures in the early nesting period; with adult compared to subadult parents; when there was no fledgling production in the prior year; and when percentage of the landscape surrounding nesting sites (202 ha) with trees ≥25 m height increased. Changes in production were primarily driven by changes in the proportion of territories with 2 or 3 fledglings. Average variances of the discrete cumulative distributions of the estimated fledgling counts indicated that temporal changes in climate and parent age class explained 18% of the annual variance in owl fledgling production, which was 34% of the total variance. Prior fledgling production explained as much of the variance in the fledgling counts as climate, parent age class, and landscape habitat predictors. Our logistic quantile regression model can be used for any discrete response variables with fixed upper and lower bounds.

Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis.

PubMed

Armstrong, Ben G; Gasparrini, Antonio; Tobias, Aurelio

2014-11-24

The time stratified case cross-over approach is a popular alternative to conventional time series regression for analysing associations between time series of environmental exposures (air pollution, weather) and counts of health outcomes. These are almost always analyzed using conditional logistic regression on data expanded to case-control (case crossover) format, but this has some limitations. In particular adjusting for overdispersion and auto-correlation in the counts is not possible. It has been established that a Poisson model for counts with stratum indicators gives identical estimates to those from conditional logistic regression and does not have these limitations, but it is little used, probably because of the overheads in estimating many stratum parameters. The conditional Poisson model avoids estimating stratum parameters by conditioning on the total event count in each stratum, thus simplifying the computing and increasing the number of strata for which fitting is feasible compared with the standard unconditional Poisson model. Unlike the conditional logistic model, the conditional Poisson model does not require expanding the data, and can adjust for overdispersion and auto-correlation. It is available in Stata, R, and other packages. By applying to some real data and using simulations, we demonstrate that conditional Poisson models were simpler to code and shorter to run than are conditional logistic analyses and can be fitted to larger data sets than possible with standard Poisson models. Allowing for overdispersion or autocorrelation was possible with the conditional Poisson model but when not required this model gave identical estimates to those from conditional logistic regression. Conditional Poisson regression models provide an alternative to case crossover analysis of stratified time series data with some advantages. The conditional Poisson model can also be used in other contexts in which primary control for confounding is by fine stratification.

The quest for conditional independence in prospectivity modeling: weights-of-evidence, boost weights-of-evidence, and logistic regression

NASA Astrophysics Data System (ADS)

Schaeben, Helmut; Semmler, Georg

2016-09-01

The objective of prospectivity modeling is prediction of the conditional probability of the presence T = 1 or absence T = 0 of a target T given favorable or prohibitive predictors B, or construction of a two classes 0,1 classification of T. A special case of logistic regression called weights-of-evidence (WofE) is geologists' favorite method of prospectivity modeling due to its apparent simplicity. However, the numerical simplicity is deceiving as it is implied by the severe mathematical modeling assumption of joint conditional independence of all predictors given the target. General weights of evidence are explicitly introduced which are as simple to estimate as conventional weights, i.e., by counting, but do not require conditional independence. Complementary to the regression view is the classification view on prospectivity modeling. Boosting is the construction of a strong classifier from a set of weak classifiers. From the regression point of view it is closely related to logistic regression. Boost weights-of-evidence (BoostWofE) was introduced into prospectivity modeling to counterbalance violations of the assumption of conditional independence even though relaxation of modeling assumptions with respect to weak classifiers was not the (initial) purpose of boosting. In the original publication of BoostWofE a fabricated dataset was used to "validate" this approach. Using the same fabricated dataset it is shown that BoostWofE cannot generally compensate lacking conditional independence whatever the consecutively processing order of predictors. Thus the alleged features of BoostWofE are disproved by way of counterexamples, while theoretical findings are confirmed that logistic regression including interaction terms can exactly compensate violations of joint conditional independence if the predictors are indicators.

Tourist visitation impacts of the accident at Three Mile Island

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

Himmelberger, J.J.; Ogneva-Himmelberger, Y.A.; Baughman, M.L.

This paper analyzes tourist visitation impacts of the March 27, 1979 accident at Three Mile Island. A review of the literature, supplemented with recollections from Pennsylvanian public officials, are used to specify a conventional tourism impact model which holds that depressed 1979 summer tourism season was more influenced by gasoline shortages and possibly other confounding variables (such as rainy local weather conditions and a polio outbreak) than by the nuclear accident. Regression analysis using monthly visitation data for Hershey Chocolate World, Gettysburg National Park, The Pennsylvania Dutch Convention and Visitor Bureau, and several state parks as dependent variables provide supportmore » for this model. Potential tourism implications of an accident at Yucca Mountain are briefly discussed in light of our findings.« less

Locally-constrained Boundary Regression for Segmentation of Prostate and Rectum in the Planning CT Images

PubMed Central

Shao, Yeqin; Gao, Yaozong; Wang, Qian; Yang, Xin; Shen, Dinggang

2015-01-01

Automatic and accurate segmentation of the prostate and rectum in planning CT images is a challenging task due to low image contrast, unpredictable organ (relative) position, and uncertain existence of bowel gas across different patients. Recently, regression forest was adopted for organ deformable segmentation on 2D medical images by training one landmark detector for each point on the shape model. However, it seems impractical for regression forest to guide 3D deformable segmentation as a landmark detector, due to large number of vertices in the 3D shape model as well as the difficulty in building accurate 3D vertex correspondence for each landmark detector. In this paper, we propose a novel boundary detection method by exploiting the power of regression forest for prostate and rectum segmentation. The contributions of this paper are as follows: 1) we introduce regression forest as a local boundary regressor to vote the entire boundary of a target organ, which avoids training a large number of landmark detectors and building an accurate 3D vertex correspondence for each landmark detector; 2) an auto-context model is integrated with regression forest to improve the accuracy of the boundary regression; 3) we further combine a deformable segmentation method with the proposed local boundary regressor for the final organ segmentation by integrating organ shape priors. Our method is evaluated on a planning CT image dataset with 70 images from 70 different patients. The experimental results show that our proposed boundary regression method outperforms the conventional boundary classification method in guiding the deformable model for prostate and rectum segmentations. Compared with other state-of-the-art methods, our method also shows a competitive performance. PMID:26439938

The Development of Statistical Models for Predicting Surgical Site Infections in Japan: Toward a Statistical Model-Based Standardized Infection Ratio.

PubMed

Fukuda, Haruhisa; Kuroki, Manabu

2016-03-01

To develop and internally validate a surgical site infection (SSI) prediction model for Japan. Retrospective observational cohort study. We analyzed surveillance data submitted to the Japan Nosocomial Infections Surveillance system for patients who had undergone target surgical procedures from January 1, 2010, through December 31, 2012. Logistic regression analyses were used to develop statistical models for predicting SSIs. An SSI prediction model was constructed for each of the procedure categories by statistically selecting the appropriate risk factors from among the collected surveillance data and determining their optimal categorization. Standard bootstrapping techniques were applied to assess potential overfitting. The C-index was used to compare the predictive performances of the new statistical models with those of models based on conventional risk index variables. The study sample comprised 349,987 cases from 428 participant hospitals throughout Japan, and the overall SSI incidence was 7.0%. The C-indices of the new statistical models were significantly higher than those of the conventional risk index models in 21 (67.7%) of the 31 procedure categories (P<.05). No significant overfitting was detected. Japan-specific SSI prediction models were shown to generally have higher accuracy than conventional risk index models. These new models may have applications in assessing hospital performance and identifying high-risk patients in specific procedure categories.

The wound healing trajectory and predictors with combined electric stimulation and conventional care: one outpatient wound care clinic's experience.

PubMed

Zhou, Kehua; Schenk, Ronald; Brogan, Michael S

2016-12-01

Electric stimulation (E-stim) has been found to be an effective treatment in improving wound healing rates. However, the wound healing trajectory and its related predictors for complete wound closure (CWC) have not been reported with E-stim treatment. This was a retrospective study. Data on 159 patients treated at an outpatient wound clinic utilizing combined intervention of E-stim and conventional care were included. The Kaplan-Meier healing curve together with linear regression models depicted the percentage of patients with CWC against time. With 100, 112 and 140 days of treatment, the percentages of patients with CWC were 59·12%, 61·01% and 65·41%, respectively. Linear regression models predicted that all patients would achieve CWC by 21·55, 22·26 and 24·80 weeks, respectively. The speed for the increase in the number and percentage of patients with CWC peaked between 50-75 days of treatment. To optimize timely healing, referral to other treatment facilities or change of treatment protocol is warranted around the peak time. With the combined intervention of E-stim and conventional care, positive predictors for CWC included a shorter wound duration at initial evaluation (P = 0·005, OR = 3·10), better compliance with appointments (P = 0·007, OR = 3·38) and the diagnosis of venous leg ulcer (P = 0·001, OR = 3·88). This study provided preliminary data on wound healing trajectory and predictors with combined E-stim and conventional care. E-stim seemed to expedite wound healing; however, further research studies are needed. © 2016 Stichting European Society for Clinical Investigation Journal Foundation.

Coronary artery calcium distributions in older persons in the AGES-Reykjavik study

PubMed Central

Gudmundsson, Elias Freyr; Gudnason, Vilmundur; Sigurdsson, Sigurdur; Launer, Lenore J.; Harris, Tamara B.; Aspelund, Thor

2013-01-01

Coronary Artery Calcium (CAC) is a sign of advanced atherosclerosis and an independent risk factor for cardiac events. Here, we describe CAC-distributions in an unselected aged population and compare modelling methods to characterize CAC-distribution. CAC is difficult to model because it has a skewed and zero inflated distribution with over-dispersion. Data are from the AGES-Reykjavik sample, a large population based study [2002-2006] in Iceland of 5,764 persons aged 66-96 years. Linear regressions using logarithmic- and Box-Cox transformations on CAC+1, quantile regression and a Zero-Inflated Negative Binomial model (ZINB) were applied. Methods were compared visually and with the PRESS-statistic, R2 and number of detected associations with concurrently measured variables. There were pronounced differences in CAC according to sex, age, history of coronary events and presence of plaque in the carotid artery. Associations with conventional coronary artery disease (CAD) risk factors varied between the sexes. The ZINB model provided the best results with respect to the PRESS-statistic, R2, and predicted proportion of zero scores. The ZINB model detected similar numbers of associations as the linear regression on ln(CAC+1) and usually with the same risk factors. PMID:22990371

Study of Rapid-Regression Liquefying Hybrid Rocket Fuels

NASA Technical Reports Server (NTRS)

Zilliac, Greg; DeZilwa, Shane; Karabeyoglu, M. Arif; Cantwell, Brian J.; Castellucci, Paul

2004-01-01

A report describes experiments directed toward the development of paraffin-based hybrid rocket fuels that burn at regression rates greater than those of conventional hybrid rocket fuels like hydroxyl-terminated butadiene. The basic approach followed in this development is to use materials such that a hydrodynamically unstable liquid layer forms on the melting surface of a burning fuel body. Entrainment of droplets from the liquid/gas interface can substantially increase the rate of fuel mass transfer, leading to surface regression faster than can be achieved using conventional fuels. The higher regression rate eliminates the need for the complex multi-port grain structures of conventional solid rocket fuels, making it possible to obtain acceptable performance from single-port structures. The high-regression-rate fuels contain no toxic or otherwise hazardous components and can be shipped commercially as non-hazardous commodities. Among the experiments performed on these fuels were scale-up tests using gaseous oxygen. The data from these tests were found to agree with data from small-scale, low-pressure and low-mass-flux laboratory tests and to confirm the expectation that these fuels would burn at high regression rates, chamber pressures, and mass fluxes representative of full-scale rocket motors.

Examining the influence of link function misspecification in conventional regression models for developing crash modification factors.

PubMed

Wu, Lingtao; Lord, Dominique

2017-05-01

This study further examined the use of regression models for developing crash modification factors (CMFs), specifically focusing on the misspecification in the link function. The primary objectives were to validate the accuracy of CMFs derived from the commonly used regression models (i.e., generalized linear models or GLMs with additive linear link functions) when some of the variables have nonlinear relationships and quantify the amount of bias as a function of the nonlinearity. Using the concept of artificial realistic data, various linear and nonlinear crash modification functions (CM-Functions) were assumed for three variables. Crash counts were randomly generated based on these CM-Functions. CMFs were then derived from regression models for three different scenarios. The results were compared with the assumed true values. The main findings are summarized as follows: (1) when some variables have nonlinear relationships with crash risk, the CMFs for these variables derived from the commonly used GLMs are all biased, especially around areas away from the baseline conditions (e.g., boundary areas); (2) with the increase in nonlinearity (i.e., nonlinear relationship becomes stronger), the bias becomes more significant; (3) the quality of CMFs for other variables having linear relationships can be influenced when mixed with those having nonlinear relationships, but the accuracy may still be acceptable; and (4) the misuse of the link function for one or more variables can also lead to biased estimates for other parameters. This study raised the importance of the link function when using regression models for developing CMFs. Copyright © 2017 Elsevier Ltd. All rights reserved.

A novel model incorporating two variability sources for describing motor evoked potentials

PubMed Central

Goetz, Stefan M.; Luber, Bruce; Lisanby, Sarah H.; Peterchev, Angel V.

2014-01-01

Objective Motor evoked potentials (MEPs) play a pivotal role in transcranial magnetic stimulation (TMS), e.g., for determining the motor threshold and probing cortical excitability. Sampled across the range of stimulation strengths, MEPs outline an input–output (IO) curve, which is often used to characterize the corticospinal tract. More detailed understanding of the signal generation and variability of MEPs would provide insight into the underlying physiology and aid correct statistical treatment of MEP data. Methods A novel regression model is tested using measured IO data of twelve subjects. The model splits MEP variability into two independent contributions, acting on both sides of a strong sigmoidal nonlinearity that represents neural recruitment. Traditional sigmoidal regression with a single variability source after the nonlinearity is used for comparison. Results The distribution of MEP amplitudes varied across different stimulation strengths, violating statistical assumptions in traditional regression models. In contrast to the conventional regression model, the dual variability source model better described the IO characteristics including phenomena such as changing distribution spread and skewness along the IO curve. Conclusions MEP variability is best described by two sources that most likely separate variability in the initial excitation process from effects occurring later on. The new model enables more accurate and sensitive estimation of the IO curve characteristics, enhancing its power as a detection tool, and may apply to other brain stimulation modalities. Furthermore, it extracts new information from the IO data concerning the neural variability—information that has previously been treated as noise. PMID:24794287

Multivariate decoding of brain images using ordinal regression.

PubMed

Doyle, O M; Ashburner, J; Zelaya, F O; Williams, S C R; Mehta, M A; Marquand, A F

2013-11-01

Neuroimaging data are increasingly being used to predict potential outcomes or groupings, such as clinical severity, drug dose response, and transitional illness states. In these examples, the variable (target) we want to predict is ordinal in nature. Conventional classification schemes assume that the targets are nominal and hence ignore their ranked nature, whereas parametric and/or non-parametric regression models enforce a metric notion of distance between classes. Here, we propose a novel, alternative multivariate approach that overcomes these limitations - whole brain probabilistic ordinal regression using a Gaussian process framework. We applied this technique to two data sets of pharmacological neuroimaging data from healthy volunteers. The first study was designed to investigate the effect of ketamine on brain activity and its subsequent modulation with two compounds - lamotrigine and risperidone. The second study investigates the effect of scopolamine on cerebral blood flow and its modulation using donepezil. We compared ordinal regression to multi-class classification schemes and metric regression. Considering the modulation of ketamine with lamotrigine, we found that ordinal regression significantly outperformed multi-class classification and metric regression in terms of accuracy and mean absolute error. However, for risperidone ordinal regression significantly outperformed metric regression but performed similarly to multi-class classification both in terms of accuracy and mean absolute error. For the scopolamine data set, ordinal regression was found to outperform both multi-class and metric regression techniques considering the regional cerebral blood flow in the anterior cingulate cortex. Ordinal regression was thus the only method that performed well in all cases. Our results indicate the potential of an ordinal regression approach for neuroimaging data while providing a fully probabilistic framework with elegant approaches for model selection. Copyright © 2013. Published by Elsevier Inc.

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

Assessing Hospital Performance After Percutaneous Coronary Intervention Using Big Data.

PubMed

Spertus, Jacob V; T Normand, Sharon-Lise; Wolf, Robert; Cioffi, Matt; Lovett, Ann; Rose, Sherri

2016-11-01

Although risk adjustment remains a cornerstone for comparing outcomes across hospitals, optimal strategies continue to evolve in the presence of many confounders. We compared conventional regression-based model to approaches particularly suited to leveraging big data. We assessed hospital all-cause 30-day excess mortality risk among 8952 adults undergoing percutaneous coronary intervention between October 1, 2011, and September 30, 2012, in 24 Massachusetts hospitals using clinical registry data linked with billing data. We compared conventional logistic regression models with augmented inverse probability weighted estimators and targeted maximum likelihood estimators to generate more efficient and unbiased estimates of hospital effects. We also compared a clinically informed and a machine-learning approach to confounder selection, using elastic net penalized regression in the latter case. Hospital excess risk estimates range from -1.4% to 2.0% across methods and confounder sets. Some hospitals were consistently classified as low or as high excess mortality outliers; others changed classification depending on the method and confounder set used. Switching from the clinically selected list of 11 confounders to a full set of 225 confounders increased the estimation uncertainty by an average of 62% across methods as measured by confidence interval length. Agreement among methods ranged from fair, with a κ statistic of 0.39 (SE: 0.16), to perfect, with a κ of 1 (SE: 0.0). Modern causal inference techniques should be more frequently adopted to leverage big data while minimizing bias in hospital performance assessments. © 2016 American Heart Association, Inc.

QSRR modeling for the chromatographic retention behavior of some β-lactam antibiotics using forward and firefly variable selection algorithms coupled with multiple linear regression.

PubMed

Fouad, Marwa A; Tolba, Enas H; El-Shal, Manal A; El Kerdawy, Ahmed M

2018-05-11

The justified continuous emerging of new β-lactam antibiotics provokes the need for developing suitable analytical methods that accelerate and facilitate their analysis. A face central composite experimental design was adopted using different levels of phosphate buffer pH, acetonitrile percentage at zero time and after 15 min in a gradient program to obtain the optimum chromatographic conditions for the elution of 31 β-lactam antibiotics. Retention factors were used as the target property to build two QSRR models utilizing the conventional forward selection and the advanced nature-inspired firefly algorithm for descriptor selection, coupled with multiple linear regression. The obtained models showed high performance in both internal and external validation indicating their robustness and predictive ability. Williams-Hotelling test and student's t-test showed that there is no statistical significant difference between the models' results. Y-randomization validation showed that the obtained models are due to significant correlation between the selected molecular descriptors and the analytes' chromatographic retention. These results indicate that the generated FS-MLR and FFA-MLR models are showing comparable quality on both the training and validation levels. They also gave comparable information about the molecular features that influence the retention behavior of β-lactams under the current chromatographic conditions. We can conclude that in some cases simple conventional feature selection algorithm can be used to generate robust and predictive models comparable to that are generated using advanced ones. Copyright © 2018 Elsevier B.V. All rights reserved.

Simplified large African carnivore density estimators from track indices.

PubMed

Winterbach, Christiaan W; Ferreira, Sam M; Funston, Paul J; Somers, Michael J

2016-01-01

The range, population size and trend of large carnivores are important parameters to assess their status globally and to plan conservation strategies. One can use linear models to assess population size and trends of large carnivores from track-based surveys on suitable substrates. The conventional approach of a linear model with intercept may not intercept at zero, but may fit the data better than linear model through the origin. We assess whether a linear regression through the origin is more appropriate than a linear regression with intercept to model large African carnivore densities and track indices. We did simple linear regression with intercept analysis and simple linear regression through the origin and used the confidence interval for ß in the linear model y  =  αx  + ß, Standard Error of Estimate, Mean Squares Residual and Akaike Information Criteria to evaluate the models. The Lion on Clay and Low Density on Sand models with intercept were not significant ( P  > 0.05). The other four models with intercept and the six models thorough origin were all significant ( P  < 0.05). The models using linear regression with intercept all included zero in the confidence interval for ß and the null hypothesis that ß = 0 could not be rejected. All models showed that the linear model through the origin provided a better fit than the linear model with intercept, as indicated by the Standard Error of Estimate and Mean Square Residuals. Akaike Information Criteria showed that linear models through the origin were better and that none of the linear models with intercept had substantial support. Our results showed that linear regression through the origin is justified over the more typical linear regression with intercept for all models we tested. A general model can be used to estimate large carnivore densities from track densities across species and study areas. The formula observed track density = 3.26 × carnivore density can be used to estimate densities of large African carnivores using track counts on sandy substrates in areas where carnivore densities are 0.27 carnivores/100 km 2 or higher. To improve the current models, we need independent data to validate the models and data to test for non-linear relationship between track indices and true density at low densities.

Robust colour calibration of an imaging system using a colour space transform and advanced regression modelling.

PubMed

Jackman, Patrick; Sun, Da-Wen; Elmasry, Gamal

2012-08-01

A new algorithm for the conversion of device dependent RGB colour data into device independent L*a*b* colour data without introducing noticeable error has been developed. By combining a linear colour space transform and advanced multiple regression methodologies it was possible to predict L*a*b* colour data with less than 2.2 colour units of error (CIE 1976). By transforming the red, green and blue colour components into new variables that better reflect the structure of the L*a*b* colour space, a low colour calibration error was immediately achieved (ΔE(CAL) = 14.1). Application of a range of regression models on the data further reduced the colour calibration error substantially (multilinear regression ΔE(CAL) = 5.4; response surface ΔE(CAL) = 2.9; PLSR ΔE(CAL) = 2.6; LASSO regression ΔE(CAL) = 2.1). Only the PLSR models deteriorated substantially under cross validation. The algorithm is adaptable and can be easily recalibrated to any working computer vision system. The algorithm was tested on a typical working laboratory computer vision system and delivered only a very marginal loss of colour information ΔE(CAL) = 2.35. Colour features derived on this system were able to safely discriminate between three classes of ham with 100% correct classification whereas colour features measured on a conventional colourimeter were not. Copyright © 2012 Elsevier Ltd. All rights reserved.

Modeling of an integrated fermentation/membrane extraction process for the production of 2-phenylethanol and 2-phenylethylacetate.

PubMed

Adler, Philipp; Hugen, Thorsten; Wiewiora, Marzena; Kunz, Benno

2011-03-07

An unstructured model for an integrated fermentation/membrane extraction process for the production of the aroma compounds 2-phenylethanol and 2-phenylethylacetate by Kluyveromyces marxianus CBS 600 was developed. The extent to which this model, based only on data from the conventional fermentation and separation processes, provided an estimation of the integrated process was evaluated. The effect of product inhibition on specific growth rate and on biomass yield by both aroma compounds was approximated by multivariate regression. Simulations of the respective submodels for fermentation and the separation process matched well with experimental results. With respect to the in situ product removal (ISPR) process, the effect of reduced product inhibition due to product removal on specific growth rate and biomass yield was predicted adequately by the model simulations. Overall product yields were increased considerably in this process (4.0 g/L 2-PE+2-PEA vs. 1.4 g/L in conventional fermentation) and were even higher than predicted by the model. To describe the effect of product concentration on product formation itself, the model was extended using results from the conventional and the ISPR process, thus agreement between model and experimental data improved notably. Therefore, this model can be a useful tool for the development and optimization of an efficient integrated bioprocess. Copyright © 2010 Elsevier Inc. All rights reserved.

Regression models to predict hip joint centers in pathological hip population.

PubMed

Mantovani, Giulia; Ng, K C Geoffrey; Lamontagne, Mario

2016-02-01

The purpose was to investigate the validity of Harrington's and Davis's hip joint center (HJC) regression equations on a population affected by a hip deformity, (i.e., femoroacetabular impingement). Sixty-seven participants (21 healthy controls, 46 with a cam-type deformity) underwent pelvic CT imaging. Relevant bony landmarks and geometric HJCs were digitized from the images, and skin thickness was measured for the anterior and posterior superior iliac spines. Non-parametric statistical and Bland-Altman tests analyzed differences between the predicted HJC (from regression equations) and the actual HJC (from CT images). The error from Davis's model (25.0 ± 6.7 mm) was larger than Harrington's (12.3 ± 5.9 mm, p<0.001). There were no differences between groups, thus, studies on femoroacetabular impingement can implement conventional regression models. Measured skin thickness was 9.7 ± 7.0mm and 19.6 ± 10.9 mm for the anterior and posterior bony landmarks, respectively, and correlated with body mass index. Skin thickness estimates can be considered to reduce the systematic error introduced by surface markers. New adult-specific regression equations were developed from the CT dataset, with the hypothesis that they could provide better estimates when tuned to a larger adult-specific dataset. The linear models were validated on external datasets and using leave-one-out cross-validation techniques; Prediction errors were comparable to those of Harrington's model, despite the adult-specific population and the larger sample size, thus, prediction accuracy obtained from these parameters could not be improved. Copyright © 2015 Elsevier B.V. All rights reserved.

Modeling energy expenditure in children and adolescents using quantile regression

PubMed Central

Yang, Yunwen; Adolph, Anne L.; Puyau, Maurice R.; Vohra, Firoz A.; Zakeri, Issa F.

2013-01-01

Advanced mathematical models have the potential to capture the complex metabolic and physiological processes that result in energy expenditure (EE). Study objective is to apply quantile regression (QR) to predict EE and determine quantile-dependent variation in covariate effects in nonobese and obese children. First, QR models will be developed to predict minute-by-minute awake EE at different quantile levels based on heart rate (HR) and physical activity (PA) accelerometry counts, and child characteristics of age, sex, weight, and height. Second, the QR models will be used to evaluate the covariate effects of weight, PA, and HR across the conditional EE distribution. QR and ordinary least squares (OLS) regressions are estimated in 109 children, aged 5–18 yr. QR modeling of EE outperformed OLS regression for both nonobese and obese populations. Average prediction errors for QR compared with OLS were not only smaller at the median τ = 0.5 (18.6 vs. 21.4%), but also substantially smaller at the tails of the distribution (10.2 vs. 39.2% at τ = 0.1 and 8.7 vs. 19.8% at τ = 0.9). Covariate effects of weight, PA, and HR on EE for the nonobese and obese children differed across quantiles (P < 0.05). The associations (linear and quadratic) between PA and HR with EE were stronger for the obese than nonobese population (P < 0.05). In conclusion, QR provided more accurate predictions of EE compared with conventional OLS regression, especially at the tails of the distribution, and revealed substantially different covariate effects of weight, PA, and HR on EE in nonobese and obese children. PMID:23640591

Commensurate Priors for Incorporating Historical Information in Clinical Trials Using General and Generalized Linear Models

PubMed Central

Hobbs, Brian P.; Sargent, Daniel J.; Carlin, Bradley P.

2014-01-01

Assessing between-study variability in the context of conventional random-effects meta-analysis is notoriously difficult when incorporating data from only a small number of historical studies. In order to borrow strength, historical and current data are often assumed to be fully homogeneous, but this can have drastic consequences for power and Type I error if the historical information is biased. In this paper, we propose empirical and fully Bayesian modifications of the commensurate prior model (Hobbs et al., 2011) extending Pocock (1976), and evaluate their frequentist and Bayesian properties for incorporating patient-level historical data using general and generalized linear mixed regression models. Our proposed commensurate prior models lead to preposterior admissible estimators that facilitate alternative bias-variance trade-offs than those offered by pre-existing methodologies for incorporating historical data from a small number of historical studies. We also provide a sample analysis of a colon cancer trial comparing time-to-disease progression using a Weibull regression model. PMID:24795786

Potential application of machine learning in health outcomes research and some statistical cautions.

PubMed

Crown, William H

2015-03-01

Traditional analytic methods are often ill-suited to the evolving world of health care big data characterized by massive volume, complexity, and velocity. In particular, methods are needed that can estimate models efficiently using very large datasets containing healthcare utilization data, clinical data, data from personal devices, and many other sources. Although very large, such datasets can also be quite sparse (e.g., device data may only be available for a small subset of individuals), which creates problems for traditional regression models. Many machine learning methods address such limitations effectively but are still subject to the usual sources of bias that commonly arise in observational studies. Researchers using machine learning methods such as lasso or ridge regression should assess these models using conventional specification tests. Copyright © 2015 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Applications of Support Vector Machines In Chemo And Bioinformatics

NASA Astrophysics Data System (ADS)

Jayaraman, V. K.; Sundararajan, V.

2010-10-01

Conventional linear & nonlinear tools for classification, regression & data driven modeling are being replaced on a rapid scale by newer techniques & tools based on artificial intelligence and machine learning. While the linear techniques are not applicable for inherently nonlinear problems, newer methods serve as attractive alternatives for solving real life problems. Support Vector Machine (SVM) classifiers are a set of universal feed-forward network based classification algorithms that have been formulated from statistical learning theory and structural risk minimization principle. SVM regression closely follows the classification methodology. In this work recent applications of SVM in Chemo & Bioinformatics will be described with suitable illustrative examples.

Application of Neural Networks to Wind tunnel Data Response Surface Methods

NASA Technical Reports Server (NTRS)

Lo, Ching F.; Zhao, J. L.; DeLoach, Richard

2000-01-01

The integration of nonlinear neural network methods with conventional linear regression techniques is demonstrated for representative wind tunnel force balance data modeling. This work was motivated by a desire to formulate precision intervals for response surfaces produced by neural networks. Applications are demonstrated for representative wind tunnel data acquired at NASA Langley Research Center and the Arnold Engineering Development Center in Tullahoma, TN.

Intravoxel Incoherent Motion MR Imaging in the Differentiation of Benign and Malignant Sinonasal Lesions: Comparison with Conventional Diffusion-Weighted MR Imaging.

PubMed

Xiao, Z; Tang, Z; Qiang, J; Wang, S; Qian, W; Zhong, Y; Wang, R; Wang, J; Wu, L; Tang, W; Zhang, Z

2018-01-25

Intravoxel incoherent motion is a promising method for the differentiation of sinonasal lesions. This study aimed to evaluate the value of intravoxel incoherent motion in the differentiation of benign and malignant sinonasal lesions and to compare the diagnostic performance of intravoxel incoherent motion with that of conventional DWI. One hundred thirty-one patients with histologically proved solid sinonasal lesions (56 benign and 75 malignant) who underwent conventional DWI and intravoxel incoherent motion were recruited in this study. The diffusion coefficient ( D ), pseudodiffusion coefficient ( D *), and perfusion fraction ( f ) values derived from intravoxel incoherent motion and ADC values derived from conventional DWI were measured and compared between the 2 groups using the Student t test. Receiver operating characteristic curve analysis, logistic regression analysis, and 10-fold cross-validation were performed to evaluate the diagnostic performance of single-parametric and multiparametric models. The mean ADC and D values were significantly lower in malignant sinonasal lesions than in benign sinonasal lesions (both P < .001). The mean f value was higher in malignant lesions than in benign lesions ( P = .003). Multiparametric models can significantly improve the cross-validated areas under the curve for the differentiation of sinonasal lesions compared with single-parametric models (all corrected P < .05 except the D value). The model of D + f provided a better diagnostic performance than the ADC value (corrected P < .001). Intravoxel incoherent motion appears to be a more effective MR imaging technique than conventional DWI in the differentiation of benign and malignant sinonasal lesions. © 2018 by American Journal of Neuroradiology.

Using methods from the data mining and machine learning literature for disease classification and prediction: A case study examining classification of heart failure sub-types

PubMed Central

Austin, Peter C.; Tu, Jack V.; Ho, Jennifer E.; Levy, Daniel; Lee, Douglas S.

2014-01-01

Objective Physicians classify patients into those with or without a specific disease. Furthermore, there is often interest in classifying patients according to disease etiology or subtype. Classification trees are frequently used to classify patients according to the presence or absence of a disease. However, classification trees can suffer from limited accuracy. In the data-mining and machine learning literature, alternate classification schemes have been developed. These include bootstrap aggregation (bagging), boosting, random forests, and support vector machines. Study design and Setting We compared the performance of these classification methods with those of conventional classification trees to classify patients with heart failure according to the following sub-types: heart failure with preserved ejection fraction (HFPEF) vs. heart failure with reduced ejection fraction (HFREF). We also compared the ability of these methods to predict the probability of the presence of HFPEF with that of conventional logistic regression. Results We found that modern, flexible tree-based methods from the data mining literature offer substantial improvement in prediction and classification of heart failure sub-type compared to conventional classification and regression trees. However, conventional logistic regression had superior performance for predicting the probability of the presence of HFPEF compared to the methods proposed in the data mining literature. Conclusion The use of tree-based methods offers superior performance over conventional classification and regression trees for predicting and classifying heart failure subtypes in a population-based sample of patients from Ontario. However, these methods do not offer substantial improvements over logistic regression for predicting the presence of HFPEF. PMID:23384592

Identification of subgroups by risk of graft failure after paediatric renal transplantation: application of survival tree models on the ESPN/ERA-EDTA Registry.

PubMed

Lofaro, Danilo; Jager, Kitty J; Abu-Hanna, Ameen; Groothoff, Jaap W; Arikoski, Pekka; Hoecker, Britta; Roussey-Kesler, Gwenaelle; Spasojević, Brankica; Verrina, Enrico; Schaefer, Franz; van Stralen, Karlijn J

2016-02-01

Identification of patient groups by risk of renal graft loss might be helpful for accurate patient counselling and clinical decision-making. Survival tree models are an alternative statistical approach to identify subgroups, offering cut-off points for covariates and an easy-to-interpret representation. Within the European Society of Pediatric Nephrology/European Renal Association-European Dialysis and Transplant Association (ESPN/ERA-EDTA) Registry data we identified paediatric patient groups with specific profiles for 5-year renal graft survival. Two analyses were performed, including (i) parameters known at time of transplantation and (ii) additional clinical measurements obtained early after transplantation. The identified subgroups were added as covariates in two survival models. The prognostic performance of the models was tested and compared with conventional Cox regression analyses. The first analysis included 5275 paediatric renal transplants. The best 5-year graft survival (90.4%) was found among patients who received a renal graft as a pre-emptive transplantation or after short-term dialysis (<45 days), whereas graft survival was poorest (51.7%) in adolescents transplanted after long-term dialysis (>2.2 years). The Cox model including both pre-transplant factors and tree subgroups had a significantly better predictive performance than conventional Cox regression (P < 0.001). In the analysis including clinical factors, graft survival ranged from 97.3% [younger patients with estimated glomerular filtration rate (eGFR) >30 mL/min/1.73 m(2) and dialysis <20 months] to 34.7% (adolescents with eGFR <60 mL/min/1.73 m(2) and dialysis >20 months). Also in this case combining tree findings and clinical factors improved the predictive performance as compared with conventional Cox model models (P < 0.0001). In conclusion, we demonstrated the tree model to be an accurate and attractive tool to predict graft failure for patients with specific characteristics. This may aid the evaluation of individual graft prognosis and thereby the design of measures to improve graft survival in the poor prognosis groups. © The Author 2015. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

Neural network modeling for surgical decisions on traumatic brain injury patients.

PubMed

Li, Y C; Liu, L; Chiu, W T; Jian, W S

2000-01-01

Computerized medical decision support systems have been a major research topic in recent years. Intelligent computer programs were implemented to aid physicians and other medical professionals in making difficult medical decisions. This report compares three different mathematical models for building a traumatic brain injury (TBI) medical decision support system (MDSS). These models were developed based on a large TBI patient database. This MDSS accepts a set of patient data such as the types of skull fracture, Glasgow Coma Scale (GCS), episode of convulsion and return the chance that a neurosurgeon would recommend an open-skull surgery for this patient. The three mathematical models described in this report including a logistic regression model, a multi-layer perceptron (MLP) neural network and a radial-basis-function (RBF) neural network. From the 12,640 patients selected from the database. A randomly drawn 9480 cases were used as the training group to develop/train our models. The other 3160 cases were in the validation group which we used to evaluate the performance of these models. We used sensitivity, specificity, areas under receiver-operating characteristics (ROC) curve and calibration curves as the indicator of how accurate these models are in predicting a neurosurgeon's decision on open-skull surgery. The results showed that, assuming equal importance of sensitivity and specificity, the logistic regression model had a (sensitivity, specificity) of (73%, 68%), compared to (80%, 80%) from the RBF model and (88%, 80%) from the MLP model. The resultant areas under ROC curve for logistic regression, RBF and MLP neural networks are 0.761, 0.880 and 0.897, respectively (P < 0.05). Among these models, the logistic regression has noticeably poorer calibration. This study demonstrated the feasibility of applying neural networks as the mechanism for TBI decision support systems based on clinical databases. The results also suggest that neural networks may be a better solution for complex, non-linear medical decision support systems than conventional statistical techniques such as logistic regression.

A spatial regression procedure for evaluating the relationship between AVHRR-NDVI and climate in the northern Great Plains

USGS Publications Warehouse

Ji, Lei; Peters, Albert J.

2004-01-01

The relationship between vegetation and climate in the grassland and cropland of the northern US Great Plains was investigated with Normalized Difference Vegetation Index (NDVI) (1989–1993) images derived from the Advanced Very High Resolution Radiometer (AVHRR), and climate data from automated weather stations. The relationship was quantified using a spatial regression technique that adjusts for spatial autocorrelation inherent in these data. Conventional regression techniques used frequently in previous studies are not adequate, because they are based on the assumption of independent observations. Six climate variables during the growing season; precipitation, potential evapotranspiration, daily maximum and minimum air temperature, soil temperature, solar irradiation were regressed on NDVI derived from a 10-km weather station buffer. The regression model identified precipitation and potential evapotranspiration as the most significant climatic variables, indicating that the water balance is the most important factor controlling vegetation condition at an annual timescale. The model indicates that 46% and 24% of variation in NDVI is accounted for by climate in grassland and cropland, respectively, indicating that grassland vegetation has a more pronounced response to climate variation than cropland. Other factors contributing to NDVI variation include environmental factors (soil, groundwater and terrain), human manipulation of crops, and sensor variation.

Influence of Curing Mode on the Surface Energy and Sorption/Solubility of Dental Self-Adhesive Resin Cements

PubMed Central

Kim, Hyun-Jin; Bagheri, Rafat; Kim, Young Kyung; Son, Jun Sik; Kwon, Tae-Yub

2017-01-01

This study investigated the influence of curing mode (dual- or self-cure) on the surface energy and sorption/solubility of four self-adhesive resin cements (SARCs) and one conventional resin cement. The degree of conversion (DC) and surface energy parameters including degree of hydrophilicity (DH) were determined using Fourier transform infrared spectroscopy and contact angle measurements, respectively (n = 5). Sorption and solubility were assessed by mass gain or loss after storage in distilled water or lactic acid for 60 days (n = 5). A linear regression model was used to correlate between the results (%DC vs. DH and %DC/DH vs. sorption/solubility). For all materials, the dual-curing consistently produced significantly higher %DC values than the self-curing (p < 0.05). Significant negative linear regressions were established between the %DC and DH in both curing modes (p < 0.05). Overall, the SARCs showed higher sorption/solubility values, in particular when immersed in lactic acid, than the conventional resin cement. Linear regression revealed that %DC and DH were negatively and positively correlated with the sorption/solubility values, respectively. Dual-curing of SARCs seems to lower the sorption and/or solubility in comparison with self-curing by increased %DC and occasionally decreased hydrophilicity. PMID:28772489

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.

Estimating effects of limiting factors with regression quantiles

USGS Publications Warehouse

Cade, B.S.; Terrell, J.W.; Schroeder, R.L.

1999-01-01

In a recent Concepts paper in Ecology, Thomson et al. emphasized that assumptions of conventional correlation and regression analyses fundamentally conflict with the ecological concept of limiting factors, and they called for new statistical procedures to address this problem. The analytical issue is that unmeasured factors may be the active limiting constraint and may induce a pattern of unequal variation in the biological response variable through an interaction with the measured factors. Consequently, changes near the maxima, rather than at the center of response distributions, are better estimates of the effects expected when the observed factor is the active limiting constraint. Regression quantiles provide estimates for linear models fit to any part of a response distribution, including near the upper bounds, and require minimal assumptions about the form of the error distribution. Regression quantiles extend the concept of one-sample quantiles to the linear model by solving an optimization problem of minimizing an asymmetric function of absolute errors. Rank-score tests for regression quantiles provide tests of hypotheses and confidence intervals for parameters in linear models with heteroscedastic errors, conditions likely to occur in models of limiting ecological relations. We used selected regression quantiles (e.g., 5th, 10th, ..., 95th) and confidence intervals to test hypotheses that parameters equal zero for estimated changes in average annual acorn biomass due to forest canopy cover of oak (Quercus spp.) and oak species diversity. Regression quantiles also were used to estimate changes in glacier lily (Erythronium grandiflorum) seedling numbers as a function of lily flower numbers, rockiness, and pocket gopher (Thomomys talpoides fossor) activity, data that motivated the query by Thomson et al. for new statistical procedures. Both example applications showed that effects of limiting factors estimated by changes in some upper regression quantile (e.g., 90-95th) were greater than if effects were estimated by changes in the means from standard linear model procedures. Estimating a range of regression quantiles (e.g., 5-95th) provides a comprehensive description of biological response patterns for exploratory and inferential analyses in observational studies of limiting factors, especially when sampling large spatial and temporal scales.

Verifying the performance of artificial neural network and multiple linear regression in predicting the mean seasonal municipal solid waste generation rate: A case study of Fars province, Iran.

PubMed

Azadi, Sama; Karimi-Jashni, Ayoub

2016-02-01

Predicting the mass of solid waste generation plays an important role in integrated solid waste management plans. In this study, the performance of two predictive models, Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) was verified to predict mean Seasonal Municipal Solid Waste Generation (SMSWG) rate. The accuracy of the proposed models is illustrated through a case study of 20 cities located in Fars Province, Iran. Four performance measures, MAE, MAPE, RMSE and R were used to evaluate the performance of these models. The MLR, as a conventional model, showed poor prediction performance. On the other hand, the results indicated that the ANN model, as a non-linear model, has a higher predictive accuracy when it comes to prediction of the mean SMSWG rate. As a result, in order to develop a more cost-effective strategy for waste management in the future, the ANN model could be used to predict the mean SMSWG rate. Copyright © 2015 Elsevier Ltd. All rights reserved.

Large scale air pollution estimation method combining land use regression and chemical transport modeling in a geostatistical framework.

PubMed

Akita, Yasuyuki; Baldasano, Jose M; Beelen, Rob; Cirach, Marta; de Hoogh, Kees; Hoek, Gerard; Nieuwenhuijsen, Mark; Serre, Marc L; de Nazelle, Audrey

2014-04-15

In recognition that intraurban exposure gradients may be as large as between-city variations, recent air pollution epidemiologic studies have become increasingly interested in capturing within-city exposure gradients. In addition, because of the rapidly accumulating health data, recent studies also need to handle large study populations distributed over large geographic domains. Even though several modeling approaches have been introduced, a consistent modeling framework capturing within-city exposure variability and applicable to large geographic domains is still missing. To address these needs, we proposed a modeling framework based on the Bayesian Maximum Entropy method that integrates monitoring data and outputs from existing air quality models based on Land Use Regression (LUR) and Chemical Transport Models (CTM). The framework was applied to estimate the yearly average NO2 concentrations over the region of Catalunya in Spain. By jointly accounting for the global scale variability in the concentration from the output of CTM and the intraurban scale variability through LUR model output, the proposed framework outperformed more conventional approaches.

Explaining Spatial Variability in Wellbore Impairment Risk for Pennsylvania Oil and Gas Wells, 2000-2014

NASA Astrophysics Data System (ADS)

Santoro, R.; Ingraffea, A. R.

2015-12-01

Previous modeling (ingraffea et al. PNAS, 2014) indicated roughly two-times higher cumulative risk for wellbore impairment in unconventional wells, relative to conventional wells, and large spatial variation in risk for oil and gas wells drilled in the state of Pennsylvania. Impairment risk for wells in the northeast portion of the state were found to be 8.5-times greater than that of wells drilled in the rest of the state. Here, we set out to explain this apparent regional variability through Boosted Regression Tree (BRT) analysis of geographic, developmental, and general well attributes. We find that regional variability is largely driven by the nature of the development, i.e. whether conventional or unconventional development is dominant. Oil and natural gas market prices and total well depths present as major influences in wellbore impairment, with moderate influences from well densities and geologic factors. The figure depicts influence paths for predictors of impairments for the state (top left), SW region (top right), unconventional/NE region (bottom left) and conventional/NW region (bottom right) models. Influences are scaled to reflect percent contributions in explaining variability in the model.

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.

A dose-response model for the conventional phototherapy of the newborn.

PubMed

Osaku, Nelson Ossamu; Lopes, Heitor Silvério

2006-06-01

Jaundice of the newborn is a common problem as a consequence of the rapid increment of blood bilirubin in the first days of live. In most cases, it is considered a physiological transient situation, but unmanaged hyperbilirubinemia can lead to death or serious injuries for the survivors. For decades, phototherapy has been used as the main method for prevention and treatment of hyperbilirubinaemia of the newborn. This work aims at finding a predictive model for the decrement of blood bilirubin for patients submitted to conventional phototherapy. Data from the phototherapy of 90 term newborns were collected and used in a multiple regression method. A rigorous statistical analysis was done in order to guarantee a correct and valid model. The obtained model was able to explain 78% of the variation of the dependent variable. We show that it is possible to predict the total serum bilirubin of the patient under conventional phototherapy by knowing its birth weight, bilirubin level at the beginning of treatment and the radiant energy density (dose). Besides, it is possible to infer the time necessary for a given decrement of bilirubin, under approximately constant irradiance. Statistical analysis of the obtained model shows that it is valid for several ranges of birth weight, initial bilirubin level, and radiant energy density. It is expected that the proposed model can be useful in the clinical management of hyperbilirubinemia of the newborn.

Prognostic Estimation of Advanced Heart Failure With Low Left Ventricular Ejection Fraction and Wide QRS Interval.

PubMed

Oh, Changmyung; Chang, Hyuk-Jae; Sung, Ji Min; Kim, Ji Ye; Yang, Wooin; Shim, Jiyoung; Kang, Seok-Min; Ha, Jongwon; Rim, Se-Joong; Chung, Namsik

2012-10-01

Cardiac resynchronization therapy (CRT) has been known to improve the outcome of advanced heart failure (HF) but is still underutilized in clinical practice. We investigated the prognosis of patients with advanced HF who were suitable for CRT but were treated with conventional strategies. We also developed a risk model to predict mortality to improve the facilitation of CRT. Patients with symptomatic HF with left ventricular ejection fraction ≤35% and QRS interval >120 ms were consecutively enrolled at cardiovascular hospital. After excluding those patients who had received device therapy, 239 patients (160 males, mean 67±11 years) were eventually recruited. During a follow-up of 308±236 days, 56 (23%) patients died. Prior stroke, heart rate >90 bpm, serum Na ≤135 mEq/L, and serum creatinine ≥1.5 mg/dL were identified as independent factors using Cox proportional hazards regression. Based on the risk model, points were assigned to each of the risk factors proportional to the regression coefficient, and patients were stratified into three risk groups: low- (0), intermediate-(1-5), and high-risk (>5 points). The 2-year mortality rates of each risk group were 5, 31, and 64 percent, respectively. The C statistic of the risk model was 0.78, and the model was validated in a cohort from a different institution where the C statistic was 0.80. The mortality of patients with advanced HF who were managed conventionally was effectively stratified using a risk model. It may be useful for clinicians to be more proactive about adopting CRT to improve patient prognosis.

PM(10) emission forecasting using artificial neural networks and genetic algorithm input variable optimization.

PubMed

Antanasijević, Davor Z; Pocajt, Viktor V; Povrenović, Dragan S; Ristić, Mirjana Đ; Perić-Grujić, Aleksandra A

2013-01-15

This paper describes the development of an artificial neural network (ANN) model for the forecasting of annual PM(10) emissions at the national level, using widely available sustainability and economical/industrial parameters as inputs. The inputs for the model were selected and optimized using a genetic algorithm and the ANN was trained using the following variables: gross domestic product, gross inland energy consumption, incineration of wood, motorization rate, production of paper and paperboard, sawn wood production, production of refined copper, production of aluminum, production of pig iron and production of crude steel. The wide availability of the input parameters used in this model can overcome a lack of data and basic environmental indicators in many countries, which can prevent or seriously impede PM emission forecasting. The model was trained and validated with the data for 26 EU countries for the period from 1999 to 2006. PM(10) emission data, collected through the Convention on Long-range Transboundary Air Pollution - CLRTAP and the EMEP Programme or as emission estimations by the Regional Air Pollution Information and Simulation (RAINS) model, were obtained from Eurostat. The ANN model has shown very good performance and demonstrated that the forecast of PM(10) emission up to two years can be made successfully and accurately. The mean absolute error for two-year PM(10) emission prediction was only 10%, which is more than three times better than the predictions obtained from the conventional multi-linear regression and principal component regression models that were trained and tested using the same datasets and input variables. Copyright © 2012 Elsevier B.V. All rights reserved.

The natural outcome of melamine-induced bladder stones with bladder epithelial hyperplasia after the withdrawal of melamine in mice.

PubMed

Ren, Shu-Ting; Xu, Chang-Fu; Du, Yun-Xia; Gao, Xiao-Li; Sun, Ying; Jiang, Yi-Na

2012-07-01

The natural outcome of melamine-induced bladder stones (cystoliths) with bladder epithelial hyperplasia (BEH) after melamine withdrawn is unclear. Using an ideal dual-model system, three experiments were conducted in BALB/c mice. Each experiment included a control, model 1 and model 2 groups. The mice were fed a regular diet in controls or a 9373 ppm melamine diet in models, and the first day was designated as dosing day 1. The melamine diet was then replaced by the regular diet in the model 2 groups, and the first day was designated as post-dosing day 1. On dosing days 12, 35 and 49, the incidence of cystoliths and diffusely active BEH was 8/8 in the mice of three model 1 groups. On post-dosing days 1, 4 and 8, in the mice of three model 2 groups, the incidence of cystoliths was 2/8, 0/8 and 1/8, respectively, and the progressive regression of BEH was observed. In conclusion, both the stones and BEH have the natural property of rapid development and rapid regression, and melamine withdrawn plays a key role in the stone dissolution-discharge necessary for BEH regression. BEH may be reversible after the discharge of the stones. The conventionally conservative therapy is thus reasonable. Copyright © 2012 Elsevier Ltd. All rights reserved.

Quantitative monitoring of sucrose, reducing sugar and total sugar dynamics for phenotyping of water-deficit stress tolerance in rice through spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Das, Bappa; Sahoo, Rabi N.; Pargal, Sourabh; Krishna, Gopal; Verma, Rakesh; Chinnusamy, Viswanathan; Sehgal, Vinay K.; Gupta, Vinod K.; Dash, Sushanta K.; Swain, Padmini

2018-03-01

In the present investigation, the changes in sucrose, reducing and total sugar content due to water-deficit stress in rice leaves were modeled using visible, near infrared (VNIR) and shortwave infrared (SWIR) spectroscopy. The objectives of the study were to identify the best vegetation indices and suitable multivariate technique based on precise analysis of hyperspectral data (350 to 2500 nm) and sucrose, reducing sugar and total sugar content measured at different stress levels from 16 different rice genotypes. Spectral data analysis was done to identify suitable spectral indices and models for sucrose estimation. Novel spectral indices in near infrared (NIR) range viz. ratio spectral index (RSI) and normalised difference spectral indices (NDSI) sensitive to sucrose, reducing sugar and total sugar content were identified which were subsequently calibrated and validated. The RSI and NDSI models had R2 values of 0.65, 0.71 and 0.67; RPD values of 1.68, 1.95 and 1.66 for sucrose, reducing sugar and total sugar, respectively for validation dataset. Different multivariate spectral models such as artificial neural network (ANN), multivariate adaptive regression splines (MARS), multiple linear regression (MLR), partial least square regression (PLSR), random forest regression (RFR) and support vector machine regression (SVMR) were also evaluated. The best performing multivariate models for sucrose, reducing sugars and total sugars were found to be, MARS, ANN and MARS, respectively with respect to RPD values of 2.08, 2.44, and 1.93. Results indicated that VNIR and SWIR spectroscopy combined with multivariate calibration can be used as a reliable alternative to conventional methods for measurement of sucrose, reducing sugars and total sugars of rice under water-deficit stress as this technique is fast, economic, and noninvasive.

Rapid Measurement of Soil Carbon in Rice Paddy Field of Lombok Island Indonesia Using Near Infrared Technology

NASA Astrophysics Data System (ADS)

Kusumo, B. H.; Sukartono, S.; Bustan, B.

2018-02-01

Measuring soil organic carbon (C) using conventional analysis is tedious procedure, time consuming and expensive. It is needed simple procedure which is cheap and saves time. Near infrared technology offers rapid procedure as it works based on the soil spectral reflectance and without any chemicals. The aim of this research is to test whether this technology able to rapidly measure soil organic C in rice paddy field. Soil samples were collected from rice paddy field of Lombok Island Indonesia, and the coordinates of the samples were recorded. Parts of the samples were analysed using conventional analysis (Walkley and Black) and some other parts were scanned using near infrared spectroscopy (NIRS) for soil spectral collection. Partial Least Square Regression (PLSR) Models were developed using data of soil C analysed using conventional analysis and data from soil spectral reflectance. The models were moderately successful to measure soil C in rice paddy field of Lombok Island. This shows that the NIR technology can be further used to monitor the C change in rice paddy soil.

Analytics of Radioactive Materials Released in the Fukushima Daiichi Nuclear Accident

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

Egarievwe, Stephen U.; Nuclear Engineering Department, University of Tennessee, Knoxville, TN; Coble, Jamie B.

The 2011 Fukushima Daiichi nuclear accident in Japan resulted in the release of radioactive materials into the atmosphere, the nearby sea, and the surrounding land. Following the accident, several meteorological models were used to predict the transport of the radioactive materials to other continents such as North America and Europe. Also of high importance is the dispersion of radioactive materials locally and within Japan. Based on the International Atomic Energy Agency (IAEA) Convention on Early Notification of a nuclear accident, several radiological data sets were collected on the accident by the Japanese authorities. Among the radioactive materials monitored, are I-131more » and Cs-137 which form the major contributions to the contamination of drinking water. The radiation dose in the atmosphere was also measured. It is impractical to measure contamination and radiation dose in every place of interest. Therefore, modeling helps to predict contamination and radiation dose. Some modeling studies that have been reported in the literature include the simulation of transport and deposition of I-131 and Cs-137 from the accident, Cs-137 deposition and contamination of Japanese soils, and preliminary estimates of I-131 and Cs-137 discharged from the plant into the atmosphere. In this paper, we present statistical analytics of I-131 and Cs-137 with the goal of predicting gamma dose from the Fukushima Daiichi nuclear accident. The data sets used in our study were collected from the IAEA Fukushima Monitoring Database. As part of this study, we investigated several regression models to find the best algorithm for modeling the gamma dose. The modeling techniques used in our study include linear regression, principal component regression (PCR), partial least square (PLS) regression, and ridge regression. Our preliminary results on the first set of data showed that the linear regression model with one variable was the best with a root mean square error of 0.0133 μSv/h, compared to 0.0210 μSv/h for PCR, 0.231 μSv/h for ridge regression L-curve, 0.0856 μSv/h for PLS, and 0.0860 μSv/h for ridge regression cross validation. Complete results using the full datasets for these models will also be presented. (authors)« less

Genetic correlations among body condition score, yield, and fertility in first-parity cows estimated by random regression models.

PubMed

Veerkamp, R F; Koenen, E P; De Jong, G

2001-10-01

Twenty type classifiers scored body condition (BCS) of 91,738 first-parity cows from 601 sires and 5518 maternal grandsires. Fertility data during first lactation were extracted for 177,220 cows, of which 67,278 also had a BCS observation, and first-lactation 305-d milk, fat, and protein yields were added for 180,631 cows. Heritabilities and genetic correlations were estimated using a sire-maternal grandsire model. Heritability of BCS was 0.38. Heritabilities for fertility traits were low (0.01 to 0.07), but genetic standard deviations were substantial, 9 d for days to first service and calving interval, 0.25 for number of services, and 5% for first-service conception. Phenotypic correlations between fertility and yield or BCS were small (-0.15 to 0.20). Genetic correlations between yield and all fertility traits were unfavorable (0.37 to 0.74). Genetic correlations with BCS were between -0.4 and -0.6 for calving interval and days to first service. Random regression analysis (RR) showed that correlations changed with days in milk for BCS. Little agreement was found between variances and correlations from RR, and analysis including a single month (mo 1 to 10) of data for BCS, especially during early and late lactation. However, this was due to excluding data from the conventional analysis, rather than due to the polynomials used. RR and a conventional five-traits model where BCS in mo 1, 4, 7, and 10 was treated as a separate traits (plus yield or fertility) gave similar results. Thus a parsimonious random regression model gave more realistic estimates for the (co)variances than a series of bivariate analysis on subsets of the data for BCS. A higher genetic merit for yield has unfavorable effects on fertility, but the genetic correlation suggests that BCS (at some stages of lactation) might help to alleviate the unfavorable effect of selection for higher yield on fertility.

A stepwise model to predict monthly streamflow

NASA Astrophysics Data System (ADS)

Mahmood Al-Juboori, Anas; Guven, Aytac

2016-12-01

In this study, a stepwise model empowered with genetic programming is developed to predict the monthly flows of Hurman River in Turkey and Diyalah and Lesser Zab Rivers in Iraq. The model divides the monthly flow data to twelve intervals representing the number of months in a year. The flow of a month, t is considered as a function of the antecedent month's flow (t - 1) and it is predicted by multiplying the antecedent monthly flow by a constant value called K. The optimum value of K is obtained by a stepwise procedure which employs Gene Expression Programming (GEP) and Nonlinear Generalized Reduced Gradient Optimization (NGRGO) as alternative to traditional nonlinear regression technique. The degree of determination and root mean squared error are used to evaluate the performance of the proposed models. The results of the proposed model are compared with the conventional Markovian and Auto Regressive Integrated Moving Average (ARIMA) models based on observed monthly flow data. The comparison results based on five different statistic measures show that the proposed stepwise model performed better than Markovian model and ARIMA model. The R2 values of the proposed model range between 0.81 and 0.92 for the three rivers in this study.

Comparison of anatomical, functional and regression methods for estimating the rotation axes of the forearm.

PubMed

Fraysse, François; Thewlis, Dominic

2014-11-07

Numerous methods exist to estimate the pose of the axes of rotation of the forearm. These include anatomical definitions, such as the conventions proposed by the ISB, and functional methods based on instantaneous helical axes, which are commonly accepted as the modelling gold standard for non-invasive, in-vivo studies. We investigated the validity of a third method, based on regression equations, to estimate the rotation axes of the forearm. We also assessed the accuracy of both ISB methods. Axes obtained from a functional method were considered as the reference. Results indicate a large inter-subject variability in the axes positions, in accordance with previous studies. Both ISB methods gave the same level of accuracy in axes position estimations. Regression equations seem to improve estimation of the flexion-extension axis but not the pronation-supination axis. Overall, given the large inter-subject variability, the use of regression equations cannot be recommended. Copyright © 2014 Elsevier Ltd. All rights reserved.

Applying spatial regression to evaluate risk factors for microbiological contamination of urban groundwater sources in Juba, South Sudan

NASA Astrophysics Data System (ADS)

Engström, Emma; Mörtberg, Ulla; Karlström, Anders; Mangold, Mikael

2017-06-01

This study developed methodology for statistically assessing groundwater contamination mechanisms. It focused on microbial water pollution in low-income regions. Risk factors for faecal contamination of groundwater-fed drinking-water sources were evaluated in a case study in Juba, South Sudan. The study was based on counts of thermotolerant coliforms in water samples from 129 sources, collected by the humanitarian aid organisation Médecins Sans Frontières in 2010. The factors included hydrogeological settings, land use and socio-economic characteristics. The results showed that the residuals of a conventional probit regression model had a significant positive spatial autocorrelation (Moran's I = 3.05, I-stat = 9.28); therefore, a spatial model was developed that had better goodness-of-fit to the observations. The most significant factor in this model ( p-value 0.005) was the distance from a water source to the nearest Tukul area, an area with informal settlements that lack sanitation services. It is thus recommended that future remediation and monitoring efforts in the city be concentrated in such low-income regions. The spatial model differed from the conventional approach: in contrast with the latter case, lowland topography was not significant at the 5% level, as the p-value was 0.074 in the spatial model and 0.040 in the traditional model. This study showed that statistical risk-factor assessments of groundwater contamination need to consider spatial interactions when the water sources are located close to each other. Future studies might further investigate the cut-off distance that reflects spatial autocorrelation. Particularly, these results advise research on urban groundwater quality.

Estimation of true phosphorus digestibility and endogenous phosphorus loss in growing chicks fed conventional and low-phytate soybean meals.

PubMed

Dilger, R N; Adeola, O

2006-04-01

This study evaluated regression of total P output against dietary P intake to simultaneously estimate endogenous P loss and true P utilization in broiler chicks. Soybean meal (SBM) served as the model ingredient, and a comparison was made between conventional and low-phytate SBM varieties. These feedstuffs were chosen to minimize nutritive differences to dietary phytate content. Low-phytate SBM contained 57% less phytate than conventional SBM. Four isocaloric diets were formulated to contain graded levels of each soybean meal (8 diets total); therefore, the diets also contained graded levels of dietary P. Chromium sesquioxide was included in diets as an indigestible marker, and free access to experimental diets was provided to 288 male broiler chicks from 15 to 22 d posthatch. The experiment was arranged as a randomized complete block design with 6 blocks of 8 cages (6 birds per cage) and similar initial BW across dietary treatments. As P intake ranged from 0.9 to 3.9 g/ kg of DM, apparent prececal P digestibilities increased (linear and quadratic, P < 0.01) for conventional SBM and low-phytate SBM. Increasing linear relationships (P < 0.01) were observed for total P output (mg/kg of DM intake) with graded P intake, regardless of SBM variety. True P retention was greater (P < 0.01) for low-phytate SBM (76.9%) than for conventional SBM (59.8%). Endogenous P estimates were not different between soybean meals (P > 0.10), and an overall estimate of 235 mg of P/ kg of DM intake was observed. This study concluded 1) the regression approach may be applicable in the estimation of endogenous P loss in broiler chicks and 2) the difference in P utilization between conventional and low-phytate soybean meals is influenced by dietary phytate content when broiler chicks are fed P-deficient diets.

Adherence to oral anticoagulants in patients with atrial fibrillation-a population-based retrospective cohort study linking health information systems in the Valencia region, Spain: a study protocol.

PubMed

Sanfélix-Gimeno, G; Rodríguez-Bernal, C L; Hurtado, I; Baixáuli-Pérez, C; Librero, J; Peiró, S

2015-10-19

Adherence to oral anticoagulation (OAC) treatment, vitamin K antagonists or new oral anticoagulants, is an essential element for effectiveness. Information on adherence to OAC in atrial fibrillation (AF) and the impact of adherence on clinical outcomes using real-world data barely exists. We aim to describe the patterns of adherence to OAC over time in patients with AF, estimate the associated factors and their impact on clinical events, and assess the same issues with conventional measures of primary and secondary adherence-proportion of days covered (PDC) and persistence-in routine clinical practice. This is a population-based retrospective cohort study including all patients with AF treated with OAC from 2010 to date in Valencia, Spain; data will be obtained from diverse electronic records of the Valencia Health Agency. adherence trajectories. (1) primary non-adherence; (2) secondary adherence: (a) PDC, (b) persistence. Clinical outcomes: hospitalisation for haemorrhagic or thromboembolic events and death during follow-up. (1) description of baseline characteristics, adherence patterns (trajectory models or latent class growth analysis models) and conventional adherence measures; (2) logistic or Cox multivariate regression models, to assess the associations between adherence measures and the covariates, and logistic multinomial regression models, to identify characteristics associated with each trajectory; (3) Cox proportional hazard models, to assess the relationship between adherence and clinical outcomes, with propensity score adjustment applied to further control for potential confounders; (4) to estimate the importance of different healthcare levels in the variations of adherence, logistic or Cox multilevel regression models. This study has been approved by the corresponding Clinical Research Ethics Committee. We plan to disseminate the project's findings through peer-reviewed publications and presentations at relevant health conferences. Policy reports will also be prepared in order to promote the translation of our findings into policy and clinical practice. 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.

Non-stationary hydrologic frequency analysis using B-spline quantile regression

NASA Astrophysics Data System (ADS)

Nasri, B.; Bouezmarni, T.; St-Hilaire, A.; Ouarda, T. B. M. J.

2017-11-01

Hydrologic frequency analysis is commonly used by engineers and hydrologists to provide the basic information on planning, design and management of hydraulic and water resources systems under the assumption of stationarity. However, with increasing evidence of climate change, it is possible that the assumption of stationarity, which is prerequisite for traditional frequency analysis and hence, the results of conventional analysis would become questionable. In this study, we consider a framework for frequency analysis of extremes based on B-Spline quantile regression which allows to model data in the presence of non-stationarity and/or dependence on covariates with linear and non-linear dependence. A Markov Chain Monte Carlo (MCMC) algorithm was used to estimate quantiles and their posterior distributions. A coefficient of determination and Bayesian information criterion (BIC) for quantile regression are used in order to select the best model, i.e. for each quantile, we choose the degree and number of knots of the adequate B-spline quantile regression model. The method is applied to annual maximum and minimum streamflow records in Ontario, Canada. Climate indices are considered to describe the non-stationarity in the variable of interest and to estimate the quantiles in this case. The results show large differences between the non-stationary quantiles and their stationary equivalents for an annual maximum and minimum discharge with high annual non-exceedance probabilities.

Neither fixed nor random: weighted least squares meta-regression.

PubMed

Stanley, T D; Doucouliagos, Hristos

2017-03-01

Our study revisits and challenges two core conventional meta-regression estimators: the prevalent use of 'mixed-effects' or random-effects meta-regression analysis and the correction of standard errors that defines fixed-effects meta-regression analysis (FE-MRA). We show how and explain why an unrestricted weighted least squares MRA (WLS-MRA) estimator is superior to conventional random-effects (or mixed-effects) meta-regression when there is publication (or small-sample) bias that is as good as FE-MRA in all cases and better than fixed effects in most practical applications. Simulations and statistical theory show that WLS-MRA provides satisfactory estimates of meta-regression coefficients that are practically equivalent to mixed effects or random effects when there is no publication bias. When there is publication selection bias, WLS-MRA always has smaller bias than mixed effects or random effects. In practical applications, an unrestricted WLS meta-regression is likely to give practically equivalent or superior estimates to fixed-effects, random-effects, and mixed-effects meta-regression approaches. However, random-effects meta-regression remains viable and perhaps somewhat preferable if selection for statistical significance (publication bias) can be ruled out and when random, additive normal heterogeneity is known to directly affect the 'true' regression coefficient. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Data-driven methods towards learning the highly nonlinear inverse kinematics of tendon-driven surgical manipulators.

PubMed

Xu, Wenjun; Chen, Jie; Lau, Henry Y K; Ren, Hongliang

2017-09-01

Accurate motion control of flexible surgical manipulators is crucial in tissue manipulation tasks. The tendon-driven serpentine manipulator (TSM) is one of the most widely adopted flexible mechanisms in minimally invasive surgery because of its enhanced maneuverability in torturous environments. TSM, however, exhibits high nonlinearities and conventional analytical kinematics model is insufficient to achieve high accuracy. To account for the system nonlinearities, we applied a data driven approach to encode the system inverse kinematics. Three regression methods: extreme learning machine (ELM), Gaussian mixture regression (GMR) and K-nearest neighbors regression (KNNR) were implemented to learn a nonlinear mapping from the robot 3D position states to the control inputs. The performance of the three algorithms was evaluated both in simulation and physical trajectory tracking experiments. KNNR performed the best in the tracking experiments, with the lowest RMSE of 2.1275 mm. The proposed inverse kinematics learning methods provide an alternative and efficient way to accurately model the tendon driven flexible manipulator. Copyright © 2016 John Wiley & Sons, Ltd.

Imaging genetics approach to predict progression of Parkinson's diseases.

PubMed

Mansu Kim; Seong-Jin Son; Hyunjin Park

2017-07-01

Imaging genetics is a tool to extract genetic variants associated with both clinical phenotypes and imaging information. The approach can extract additional genetic variants compared to conventional approaches to better investigate various diseased conditions. Here, we applied imaging genetics to study Parkinson's disease (PD). We aimed to extract significant features derived from imaging genetics and neuroimaging. We built a regression model based on extracted significant features combining genetics and neuroimaging to better predict clinical scores of PD progression (i.e. MDS-UPDRS). Our model yielded high correlation (r = 0.697, p <; 0.001) and low root mean squared error (8.36) between predicted and actual MDS-UPDRS scores. Neuroimaging (from 123 I-Ioflupane SPECT) predictors of regression model were computed from independent component analysis approach. Genetic features were computed using image genetics approach based on identified neuroimaging features as intermediate phenotypes. Joint modeling of neuroimaging and genetics could provide complementary information and thus have the potential to provide further insight into the pathophysiology of PD. Our model included newly found neuroimaging features and genetic variants which need further investigation.

Comparing the efficiency of digital and conventional soil mapping to predict soil types in a semi-arid region in Iran

NASA Astrophysics Data System (ADS)

Zeraatpisheh, Mojtaba; Ayoubi, Shamsollah; Jafari, Azam; Finke, Peter

2017-05-01

The efficiency of different digital and conventional soil mapping approaches to produce categorical maps of soil types is determined by cost, sample size, accuracy and the selected taxonomic level. The efficiency of digital and conventional soil mapping approaches was examined in the semi-arid region of Borujen, central Iran. This research aimed to (i) compare two digital soil mapping approaches including Multinomial logistic regression and random forest, with the conventional soil mapping approach at four soil taxonomic levels (order, suborder, great group and subgroup levels), (ii) validate the predicted soil maps by the same validation data set to determine the best method for producing the soil maps, and (iii) select the best soil taxonomic level by different approaches at three sample sizes (100, 80, and 60 point observations), in two scenarios with and without a geomorphology map as a spatial covariate. In most predicted maps, using both digital soil mapping approaches, the best results were obtained using the combination of terrain attributes and the geomorphology map, although differences between the scenarios with and without the geomorphology map were not significant. Employing the geomorphology map increased map purity and the Kappa index, and led to a decrease in the 'noisiness' of soil maps. Multinomial logistic regression had better performance at higher taxonomic levels (order and suborder levels); however, random forest showed better performance at lower taxonomic levels (great group and subgroup levels). Multinomial logistic regression was less sensitive than random forest to a decrease in the number of training observations. The conventional soil mapping method produced a map with larger minimum polygon size because of traditional cartographic criteria used to make the geological map 1:100,000 (on which the conventional soil mapping map was largely based). Likewise, conventional soil mapping map had also a larger average polygon size that resulted in a lower level of detail. Multinomial logistic regression at the order level (map purity of 0.80), random forest at the suborder (map purity of 0.72) and great group level (map purity of 0.60), and conventional soil mapping at the subgroup level (map purity of 0.48) produced the most accurate maps in the study area. The multinomial logistic regression method was identified as the most effective approach based on a combined index of map purity, map information content, and map production cost. The combined index also showed that smaller sample size led to a preference for the order level, while a larger sample size led to a preference for the great group level.

Time-dependent oral absorption models

NASA Technical Reports Server (NTRS)

Higaki, K.; Yamashita, S.; Amidon, G. L.

2001-01-01

The plasma concentration-time profiles following oral administration of drugs are often irregular and cannot be interpreted easily with conventional models based on first- or zero-order absorption kinetics and lag time. Six new models were developed using a time-dependent absorption rate coefficient, ka(t), wherein the time dependency was varied to account for the dynamic processes such as changes in fluid absorption or secretion, in absorption surface area, and in motility with time, in the gastrointestinal tract. In the present study, the plasma concentration profiles of propranolol obtained in human subjects following oral dosing were analyzed using the newly derived models based on mass balance and compared with the conventional models. Nonlinear regression analysis indicated that the conventional compartment model including lag time (CLAG model) could not predict the rapid initial increase in plasma concentration after dosing and the predicted Cmax values were much lower than that observed. On the other hand, all models with the time-dependent absorption rate coefficient, ka(t), were superior to the CLAG model in predicting plasma concentration profiles. Based on Akaike's Information Criterion (AIC), the fluid absorption model without lag time (FA model) exhibited the best overall fit to the data. The two-phase model including lag time, TPLAG model was also found to be a good model judging from the values of sum of squares. This model also described the irregular profiles of plasma concentration with time and frequently predicted Cmax values satisfactorily. A comparison of the absorption rate profiles also suggested that the TPLAG model is better at prediction of irregular absorption kinetics than the FA model. In conclusion, the incorporation of a time-dependent absorption rate coefficient ka(t) allows the prediction of nonlinear absorption characteristics in a more reliable manner.

A simple linear model for estimating ozone AOT40 at forest sites from raw passive sampling data.

PubMed

Ferretti, Marco; Cristofolini, Fabiana; Cristofori, Antonella; Gerosa, Giacomo; Gottardini, Elena

2012-08-01

A rapid, empirical method is described for estimating weekly AOT40 from ozone concentrations measured with passive samplers at forest sites. The method is based on linear regression and was developed after three years of measurements in Trentino (northern Italy). It was tested against an independent set of data from passive sampler sites across Italy. It provides good weekly estimates compared with those measured by conventional monitors (0.85 ≤R(2)≤ 0.970; 97 ≤ RMSE ≤ 302). Estimates obtained using passive sampling at forest sites are comparable to those obtained by another estimation method based on modelling hourly concentrations (R(2) = 0.94; 131 ≤ RMSE ≤ 351). Regression coefficients of passive sampling are similar to those obtained with conventional monitors at forest sites. Testing against an independent dataset generated by passive sampling provided similar results (0.86 ≤R(2)≤ 0.99; 65 ≤ RMSE ≤ 478). Errors tend to accumulate when weekly AOT40 estimates are summed to obtain the total AOT40 over the May-July period, and the median deviation between the two estimation methods based on passive sampling is 11%. The method proposed does not require any assumptions, complex calculation or modelling technique, and can be useful when other estimation methods are not feasible, either in principle or in practice. However, the method is not useful when estimates of hourly concentrations are of interest.

Comparison between a Weibull proportional hazards model and a linear model for predicting the genetic merit of US Jersey sires for daughter longevity.

PubMed

Caraviello, D Z; Weigel, K A; Gianola, D

2004-05-01

Predicted transmitting abilities (PTA) of US Jersey sires for daughter longevity were calculated using a Weibull proportional hazards sire model and compared with predictions from a conventional linear animal model. Culling data from 268,008 Jersey cows with first calving from 1981 to 2000 were used. The proportional hazards model included time-dependent effects of herd-year-season contemporary group and parity by stage of lactation interaction, as well as time-independent effects of sire and age at first calving. Sire variances and parameters of the Weibull distribution were estimated, providing heritability estimates of 4.7% on the log scale and 18.0% on the original scale. The PTA of each sire was expressed as the expected risk of culling relative to daughters of an average sire. Risk ratios (RR) ranged from 0.7 to 1.3, indicating that the risk of culling for daughters of the best sires was 30% lower than for daughters of average sires and nearly 50% lower than than for daughters of the poorest sires. Sire PTA from the proportional hazards model were compared with PTA from a linear model similar to that used for routine national genetic evaluation of length of productive life (PL) using cross-validation in independent samples of herds. Models were compared using logistic regression of daughters' stayability to second, third, fourth, or fifth lactation on their sires' PTA values, with alternative approaches for weighting the contribution of each sire. Models were also compared using logistic regression of daughters' stayability to 36, 48, 60, 72, and 84 mo of life. The proportional hazards model generally yielded more accurate predictions according to these criteria, but differences in predictive ability between methods were smaller when using a Kullback-Leibler distance than with other approaches. Results of this study suggest that survival analysis methodology may provide more accurate predictions of genetic merit for longevity than conventional linear models.

Mortality Predicted Accuracy for Hepatocellular Carcinoma Patients with Hepatic Resection Using Artificial Neural Network

PubMed Central

Chiu, Herng-Chia; Ho, Te-Wei; Lee, King-Teh; Chen, Hong-Yaw; Ho, Wen-Hsien

2013-01-01

The aim of this present study is firstly to compare significant predictors of mortality for hepatocellular carcinoma (HCC) patients undergoing resection between artificial neural network (ANN) and logistic regression (LR) models and secondly to evaluate the predictive accuracy of ANN and LR in different survival year estimation models. We constructed a prognostic model for 434 patients with 21 potential input variables by Cox regression model. Model performance was measured by numbers of significant predictors and predictive accuracy. The results indicated that ANN had double to triple numbers of significant predictors at 1-, 3-, and 5-year survival models as compared with LR models. Scores of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) of 1-, 3-, and 5-year survival estimation models using ANN were superior to those of LR in all the training sets and most of the validation sets. The study demonstrated that ANN not only had a great number of predictors of mortality variables but also provided accurate prediction, as compared with conventional methods. It is suggested that physicians consider using data mining methods as supplemental tools for clinical decision-making and prognostic evaluation. PMID:23737707

Detection of melamine in milk powders using near-infrared hyperspectral imaging combined with regression coefficient of partial least square regression model.

PubMed

Lim, Jongguk; Kim, Giyoung; Mo, Changyeun; Kim, Moon S; Chao, Kuanglin; Qin, Jianwei; Fu, Xiaping; Baek, Insuck; Cho, Byoung-Kwan

2016-05-01

Illegal use of nitrogen-rich melamine (C3H6N6) to boost perceived protein content of food products such as milk, infant formula, frozen yogurt, pet food, biscuits, and coffee drinks has caused serious food safety problems. Conventional methods to detect melamine in foods, such as Enzyme-linked immunosorbent assay (ELISA), High-performance liquid chromatography (HPLC), and Gas chromatography-mass spectrometry (GC-MS), are sensitive but they are time-consuming, expensive, and labor-intensive. In this research, near-infrared (NIR) hyperspectral imaging technique combined with regression coefficient of partial least squares regression (PLSR) model was used to detect melamine particles in milk powders easily and quickly. NIR hyperspectral reflectance imaging data in the spectral range of 990-1700nm were acquired from melamine-milk powder mixture samples prepared at various concentrations ranging from 0.02% to 1%. PLSR models were developed to correlate the spectral data (independent variables) with melamine concentration (dependent variables) in melamine-milk powder mixture samples. PLSR models applying various pretreatment methods were used to reconstruct the two-dimensional PLS images. PLS images were converted to the binary images to detect the suspected melamine pixels in milk powder. As the melamine concentration was increased, the numbers of suspected melamine pixels of binary images were also increased. These results suggested that NIR hyperspectral imaging technique and the PLSR model can be regarded as an effective tool to detect melamine particles in milk powders. Copyright © 2016 Elsevier B.V. All rights reserved.

Applications of Monte Carlo method to nonlinear regression of rheological data

NASA Astrophysics Data System (ADS)

Kim, Sangmo; Lee, Junghaeng; Kim, Sihyun; Cho, Kwang Soo

2018-02-01

In rheological study, it is often to determine the parameters of rheological models from experimental data. Since both rheological data and values of the parameters vary in logarithmic scale and the number of the parameters is quite large, conventional method of nonlinear regression such as Levenberg-Marquardt (LM) method is usually ineffective. The gradient-based method such as LM is apt to be caught in local minima which give unphysical values of the parameters whenever the initial guess of the parameters is far from the global optimum. Although this problem could be solved by simulated annealing (SA), the Monte Carlo (MC) method needs adjustable parameter which could be determined in ad hoc manner. We suggest a simplified version of SA, a kind of MC methods which results in effective values of the parameters of most complicated rheological models such as the Carreau-Yasuda model of steady shear viscosity, discrete relaxation spectrum and zero-shear viscosity as a function of concentration and molecular weight.

Estimating procedure times for surgeries by determining location parameters for the lognormal model.

PubMed

Spangler, William E; Strum, David P; Vargas, Luis G; May, Jerrold H

2004-05-01

We present an empirical study of methods for estimating the location parameter of the lognormal distribution. Our results identify the best order statistic to use, and indicate that using the best order statistic instead of the median may lead to less frequent incorrect rejection of the lognormal model, more accurate critical value estimates, and higher goodness-of-fit. Using simulation data, we constructed and compared two models for identifying the best order statistic, one based on conventional nonlinear regression and the other using a data mining/machine learning technique. Better surgical procedure time estimates may lead to improved surgical operations.

Construction cost estimation of spherical storage tanks: artificial neural networks and hybrid regression—GA algorithms

NASA Astrophysics Data System (ADS)

Arabzadeh, Vida; Niaki, S. T. A.; Arabzadeh, Vahid

2017-10-01

One of the most important processes in the early stages of construction projects is to estimate the cost involved. This process involves a wide range of uncertainties, which make it a challenging task. Because of unknown issues, using the experience of the experts or looking for similar cases are the conventional methods to deal with cost estimation. The current study presents data-driven methods for cost estimation based on the application of artificial neural network (ANN) and regression models. The learning algorithms of the ANN are the Levenberg-Marquardt and the Bayesian regulated. Moreover, regression models are hybridized with a genetic algorithm to obtain better estimates of the coefficients. The methods are applied in a real case, where the input parameters of the models are assigned based on the key issues involved in a spherical tank construction. The results reveal that while a high correlation between the estimated cost and the real cost exists; both ANNs could perform better than the hybridized regression models. In addition, the ANN with the Levenberg-Marquardt learning algorithm (LMNN) obtains a better estimation than the ANN with the Bayesian-regulated learning algorithm (BRNN). The correlation between real data and estimated values is over 90%, while the mean square error is achieved around 0.4. The proposed LMNN model can be effective to reduce uncertainty and complexity in the early stages of the construction project.

Learning curve of single port laparoscopic cholecystectomy determined using the non-linear ordinary least squares method based on a non-linear regression model: An analysis of 150 consecutive patients.

PubMed

Han, Hyung Joon; Choi, Sae Byeol; Park, Man Sik; Lee, Jin Suk; Kim, Wan Bae; Song, Tae Jin; Choi, Sang Yong

2011-07-01

Single port laparoscopic surgery has come to the forefront of minimally invasive surgery. For those familiar with conventional techniques, however, this type of operation demands a different type of eye/hand coordination and involves unfamiliar working instruments. Herein, the authors describe the learning curve and the clinical outcomes of single port laparoscopic cholecystectomy for 150 consecutive patients with benign gallbladder disease. All patients underwent single port laparoscopic cholecystectomy using a homemade glove port by one of five operators with different levels of experiences of laparoscopic surgery. The learning curve for each operator was fitted using the non-linear ordinary least squares method based on a non-linear regression model. Mean operating time was 77.6 ± 28.5 min. Fourteen patients (6.0%) were converted to conventional laparoscopic cholecystectomy. Complications occurred in 15 patients (10.0%), as follows: bile duct injury (n = 2), surgical site infection (n = 8), seroma (n = 2), and wound pain (n = 3). One operator achieved a learning curve plateau at 61.4 min per procedure after 8.5 cases and his time improved by 95.3 min as compared with initial operation time. Younger surgeons showed significant decreases in mean operation time and achieved stable mean operation times. In particular, younger surgeons showed significant decreases in operation times after 20 cases. Experienced laparoscopic surgeons can safely perform single port laparoscopic cholecystectomy using conventional or angled laparoscopic instruments. The present study shows that an operator can overcome the single port laparoscopic cholecystectomy learning curve in about eight cases.

Temperature-viscosity models reassessed.

PubMed

Peleg, Micha

2017-05-04

The temperature effect on viscosity of liquid and semi-liquid foods has been traditionally described by the Arrhenius equation, a few other mathematical models, and more recently by the WLF and VTF (or VFT) equations. The essence of the Arrhenius equation is that the viscosity is proportional to the absolute temperature's reciprocal and governed by a single parameter, namely, the energy of activation. However, if the absolute temperature in K in the Arrhenius equation is replaced by T + b where both T and the adjustable b are in °C, the result is a two-parameter model, which has superior fit to experimental viscosity-temperature data. This modified version of the Arrhenius equation is also mathematically equal to the WLF and VTF equations, which are known to be equal to each other. Thus, despite their dissimilar appearances all three equations are essentially the same model, and when used to fit experimental temperature-viscosity data render exactly the same very high regression coefficient. It is shown that three new hybrid two-parameter mathematical models, whose formulation bears little resemblance to any of the conventional models, can also have excellent fit with r 2 ∼ 1. This is demonstrated by comparing the various models' regression coefficients to published viscosity-temperature relationships of 40% sucrose solution, soybean oil, and 70°Bx pear juice concentrate at different temperature ranges. Also compared are reconstructed temperature-viscosity curves using parameters calculated directly from 2 or 3 data points and fitted curves obtained by nonlinear regression using a larger number of experimental viscosity measurements.

Fatty acid content, vitamins and selenium in bulk tank milk from organic and conventional Swedish dairy herds during the indoor season.

PubMed

Fall, Nils; Emanuelson, Ulf

2011-08-01

Fatty acids, vitamins and minerals in milk are important for the human consumer, the calf and the cow. Studies indicate that milk from organic and conventional dairy herds may differ in these aspects. The aim of this study was therefore to investigate whether there are differences in the fatty acid composition and concentration of vitamins and selenium in milk between organic and conventional herds in Sweden. Bulk tank milk was sampled in 18 organic and 19 conventional dairy herds on three occasions during the indoor season 2005-2006. Herd characteristics were collected by questionnaires and from the official milk recording scheme. Multivariable linear mixed models were used to evaluate the associations between milk composition and type of herd, while adjusting for potential confounders and the repeated observations within herd. In addition to management type, variables included in the initial models were housing type, milk fat content, herd size, average milk yield and time on pasture during summer. The median concentration of conjugated linoleic fatty acids (CLA) was 0·63% in organic compared with 0·48% in conventional herds, the content of total n-3 fatty acids was 1·44% and 1·04% in organic and conventional milk, respectively, and the content of total n-6 fatty acids was 2·72% and 2·20% in organic and conventional milk, respectively. The multivariable regression models indicated significantly higher concentrations of CLA, total n-3 and n-6 fatty acids in organic milk and a more desirable ratio of n-6 to n-3 fatty acids, for the human consumer, in organic milk. The multivariable models did not demonstrate any differences in retinol, α-tocopherol, β-carotene or selenium concentrations between systems. Median concentrations of α-tocopherol were 0·80 μg/ml in organic and 0·88 μg/ml in conventional milk, while for β-carotene the median concentrations were 0·19 and 0·18 μg/ml, respectively; for retinol, the median concentration was 0·32 μg/ml in both groups; the median concentrations of selenium were 13·0 and 13·5 μg/kg, respectively, for organic and conventional systems.

Osteoporosis risk prediction using machine learning and conventional methods.

PubMed

Kim, Sung Kean; Yoo, Tae Keun; Oh, Ein; Kim, Deok Won

2013-01-01

A number of clinical decision tools for osteoporosis risk assessment have been developed to select postmenopausal women for the measurement of bone mineral density. We developed and validated machine learning models with the aim of more accurately identifying the risk of osteoporosis in postmenopausal women, and compared with the ability of a conventional clinical decision tool, osteoporosis self-assessment tool (OST). We collected medical records from Korean postmenopausal women based on the Korea National Health and Nutrition Surveys (KNHANES V-1). The training data set was used to construct models based on popular machine learning algorithms such as support vector machines (SVM), random forests (RF), artificial neural networks (ANN), and logistic regression (LR) based on various predictors associated with low bone density. The learning models were compared with OST. SVM had significantly better area under the curve (AUC) of the receiver operating characteristic (ROC) than ANN, LR, and OST. Validation on the test set showed that SVM predicted osteoporosis risk with an AUC of 0.827, accuracy of 76.7%, sensitivity of 77.8%, and specificity of 76.0%. We were the first to perform comparisons of the performance of osteoporosis prediction between the machine learning and conventional methods using population-based epidemiological data. The machine learning methods may be effective tools for identifying postmenopausal women at high risk for osteoporosis.

Immune response in melanoma: an in-depth analysis of the primary tumor and corresponding sentinel lymph node

PubMed Central

Ma, Michelle W.; Medicherla, Ratna C.; Qian, Meng; de Miera, Eleazar Vega-Saenz; Friedman, Erica B.; Berman, Russell S.; Shapiro, Richard L.; Pavlick, Anna C.; Ott, Patrick A.; Bhardwaj, Nina; Shao, Yongzhao; Osman, Iman; Darvishian, Farbod

2013-01-01

The sentinel lymph node is the initial site of metastasis. Down-regulation of anti-tumor immunity plays a role in nodal progression. Our objective was to investigate the relationship between immune modulation and sentinel lymph node positivity, correlating it with outcome in melanoma patients. Lymph node/primary tissues from melanoma patients prospectively accrued and followed at New York University Medical Center were evaluated for the presence of regulatory T-cells (Foxp3+) and dendritic cells (conventional: CD11c+, mature: CD86+) using immunohistochemistry. Primary melanoma immune cell profiles from sentinel lymph node-positive/-negative patients were compared. Logistic regression models inclusive of standard-of-care/immunologic primary tumor characteristics were constructed to predict the risk of sentinel lymph node positivity. Immunological responses in the positive sentinel lymph node were also compared to those in the negative non-sentinel node from the same nodal basin and matched negative sentinel lymph node. Decreased immune response was defined as increased regulatory T-cells or decreased dendritic cells. Associations between the expression of these immune modulators, clinicopathologic variables, and clinical outcome were evaluated using univariate/multivariate analyses. Primary tumor conventional dendritic cells and regression were protective against sentinel lymph node metastasis (odds ratio=0.714, 0.067; P=0.0099, 0.0816, respectively). Anti-tumor immunity was down-regulated in the positive sentinel lymph node with an increase in regulatory T-cells compared to the negative non-sentinel node from the same nodal basin (P=0.0005) and matched negative sentinel lymph node (P=0.0002). The positive sentinel lymph node also had decreased numbers of conventional dendritic cells compared to the negative sentinel lymph node (P<0.0001). Adding sentinel lymph node regulatory T-cell expression improved the discriminative power of a recurrence risk assessment model using clinical stage. Primary tumor regression was associated with prolonged disease-free (P=0.025) and melanoma-specific (P=0.014) survival. Our results support an assessment of local immune profiles in both the primary tumor and sentinel lymph node to help guide therapeutic decisions. PMID:22425909

Parametric study and performance analysis of hybrid rocket motors with double-tube configuration

NASA Astrophysics Data System (ADS)

Yu, Nanjia; Zhao, Bo; Lorente, Arnau Pons; Wang, Jue

2017-03-01

The practical implementation of hybrid rocket motors has historically been hampered by the slow regression rate of the solid fuel. In recent years, the research on advanced injector designs has achieved notable results in the enhancement of the regression rate and combustion efficiency of hybrid rockets. Following this path, this work studies a new configuration called double-tube characterized by injecting the gaseous oxidizer through a head end injector and an inner tube with injector holes distributed along the motor longitudinal axis. This design has demonstrated a significant potential for improving the performance of hybrid rockets by means of a better mixing of the species achieved through a customized injection of the oxidizer. Indeed, the CFD analysis of the double-tube configuration has revealed that this design may increase the regression rate over 50% with respect to the same motor with a conventional axial showerhead injector. However, in order to fully exploit the advantages of the double-tube concept, it is necessary to acquire a deeper understanding of the influence of the different design parameters in the overall performance. In this way, a parametric study is carried out taking into account the variation of the oxidizer mass flux rate, the ratio of oxidizer mass flow rate injected through the inner tube to the total oxidizer mass flow rate, and injection angle. The data for the analysis have been gathered from a large series of three-dimensional numerical simulations that considered the changes in the design parameters. The propellant combination adopted consists of gaseous oxygen as oxidizer and high-density polyethylene as solid fuel. Furthermore, the numerical model comprises Navier-Stokes equations, k-ε turbulence model, eddy-dissipation combustion model and solid-fuel pyrolysis, which is computed through user-defined functions. This numerical model was previously validated by analyzing the computational and experimental results obtained for conventional hybrid rocket designs. In addition, a performance analysis is conducted in order to evaluate the influence in the performance provoked by the possible growth of the diameter of the inner fuel grain holes during the motor operation. The latter phenomenon is known as burn through holes. Finally, after a statistical analysis of the data, a regression rate expression as a function of the design parameters is obtained.

Auto Regressive Moving Average (ARMA) Modeling Method for Gyro Random Noise Using a Robust Kalman Filter

PubMed Central

Huang, Lei

2015-01-01

To solve the problem in which the conventional ARMA modeling methods for gyro random noise require a large number of samples and converge slowly, an ARMA modeling method using a robust Kalman filtering is developed. The ARMA model parameters are employed as state arguments. Unknown time-varying estimators of observation noise are used to achieve the estimated mean and variance of the observation noise. Using the robust Kalman filtering, the ARMA model parameters are estimated accurately. The developed ARMA modeling method has the advantages of a rapid convergence and high accuracy. Thus, the required sample size is reduced. It can be applied to modeling applications for gyro random noise in which a fast and accurate ARMA modeling method is required. PMID:26437409

A spectral-spatial-dynamic hierarchical Bayesian (SSD-HB) model for estimating soybean yield

NASA Astrophysics Data System (ADS)

Kazama, Yoriko; Kujirai, Toshihiro

2014-10-01

A method called a "spectral-spatial-dynamic hierarchical-Bayesian (SSD-HB) model," which can deal with many parameters (such as spectral and weather information all together) by reducing the occurrence of multicollinearity, is proposed. Experiments conducted on soybean yields in Brazil fields with a RapidEye satellite image indicate that the proposed SSD-HB model can predict soybean yield with a higher degree of accuracy than other estimation methods commonly used in remote-sensing applications. In the case of the SSD-HB model, the mean absolute error between estimated yield of the target area and actual yield is 0.28 t/ha, compared to 0.34 t/ha when conventional PLS regression was applied, showing the potential effectiveness of the proposed model.

Perceptions of the Harm and Addictiveness of Conventional Cigarette Smoking Among Adolescent E-Cigarette Users.

PubMed

Owotomo, Olusegun; Maslowsky, Julie; Loukas, Alexandra

2018-01-01

Although existing evidence indicates that e-cigarette use is a risk factor for cigarette smoking initiation, mechanisms of this association are not yet known. E-cigarette users perceive e-cigarette use to be less harmful relative to conventional cigarettes, but their absolute perceptions of addictiveness of conventional cigarette smoking are unknown. This study examines how e-cigarette users compare with nonusers (non-e-cigarette users/nonconventional cigarette smokers), conventional cigarette smokers, and dual users on perceptions of harm and the addictiveness of conventional cigarette smoking and on other known predictors of cigarette smoking such as peer smoking, influence of antismoking ads, and risk-taking propensity. National samples of 8th- and 10th-grade students from 2014 and 2015 (N = 14,151) were obtained from the Monitoring the Future Study. Multivariate logistic regression models were used to examine relationships between adolescent smoking status and perceptions of harm and the addictiveness of conventional cigarette smoking while controlling for potential confounders. E-cigarette users had lower perceptions of the addictiveness of conventional cigarette smoking compared with nonusers but higher than cigarette smokers and dual users. E-cigarette users reported lower influence by antismoking ads, more conventional cigarette-smoking peers, and greater risk-taking propensity than nonusers. E-cigarette users and cigarette smokers did not differ in their perceived harm of conventional cigarette smoking or in their risk-taking propensity. E-cigarette users' attitudes and perceptions regarding conventional cigarette smoking may leave them vulnerable to becoming conventional cigarette smokers. Future studies should explore the prospective relationship between smoking-related perceptions of conventional cigarette smoking among e-cigarette users and the onset of cigarette smoking. Copyright © 2017 The Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

The Effect of Sitagliptin on the Regression of Carotid Intima-Media Thickening in Patients with Type 2 Diabetes Mellitus: A Post Hoc Analysis of the Sitagliptin Preventive Study of Intima-Media Thickness Evaluation.

PubMed

Mita, Tomoya; Katakami, Naoto; Shiraiwa, Toshihiko; Yoshii, Hidenori; Gosho, Masahiko; Shimomura, Iichiro; Watada, Hirotaka

2017-01-01

Background. The effect of dipeptidyl peptidase-4 (DPP-4) inhibitors on the regression of carotid IMT remains largely unknown. The present study aimed to clarify whether sitagliptin, DPP-4 inhibitor, could regress carotid intima-media thickness (IMT) in insulin-treated patients with type 2 diabetes mellitus (T2DM). Methods . This is an exploratory analysis of a randomized trial in which we investigated the effect of sitagliptin on the progression of carotid IMT in insulin-treated patients with T2DM. Here, we compared the efficacy of sitagliptin treatment on the number of patients who showed regression of carotid IMT of ≥0.10 mm in a post hoc analysis. Results . The percentages of the number of the patients who showed regression of mean-IMT-CCA (28.9% in the sitagliptin group versus 16.4% in the conventional group, P  = 0.022) and left max-IMT-CCA (43.0% in the sitagliptin group versus 26.2% in the conventional group, P  = 0.007), but not right max-IMT-CCA, were higher in the sitagliptin treatment group compared with those in the non-DPP-4 inhibitor treatment group. In multiple logistic regression analysis, sitagliptin treatment significantly achieved higher target attainment of mean-IMT-CCA ≥0.10 mm and right and left max-IMT-CCA ≥0.10 mm compared to conventional treatment. Conclusions . Our data suggested that DPP-4 inhibitors were associated with the regression of carotid atherosclerosis in insulin-treated T2DM patients. This study has been registered with the University Hospital Medical Information Network Clinical Trials Registry (UMIN000007396).

A binary genetic programing model for teleconnection identification between global sea surface temperature and local maximum monthly rainfall events

NASA Astrophysics Data System (ADS)

Danandeh Mehr, Ali; Nourani, Vahid; Hrnjica, Bahrudin; Molajou, Amir

2017-12-01

The effectiveness of genetic programming (GP) for solving regression problems in hydrology has been recognized in recent studies. However, its capability to solve classification problems has not been sufficiently explored so far. This study develops and applies a novel classification-forecasting model, namely Binary GP (BGP), for teleconnection studies between sea surface temperature (SST) variations and maximum monthly rainfall (MMR) events. The BGP integrates certain types of data pre-processing and post-processing methods with conventional GP engine to enhance its ability to solve both regression and classification problems simultaneously. The model was trained and tested using SST series of Black Sea, Mediterranean Sea, and Red Sea as potential predictors as well as classified MMR events at two locations in Iran as predictand. Skill of the model was measured in regard to different rainfall thresholds and SST lags and compared to that of the hybrid decision tree-association rule (DTAR) model available in the literature. The results indicated that the proposed model can identify potential teleconnection signals of surrounding seas beneficial to long-term forecasting of the occurrence of the classified MMR events.

Estimating the global incidence of traumatic spinal cord injury.

PubMed

Fitzharris, M; Cripps, R A; Lee, B B

2014-02-01

Population modelling--forecasting. To estimate the global incidence of traumatic spinal cord injury (TSCI). An initiative of the International Spinal Cord Society (ISCoS) Prevention Committee. Regression techniques were used to derive regional and global estimates of TSCI incidence. Using the findings of 31 published studies, a regression model was fitted using a known number of TSCI cases as the dependent variable and the population at risk as the single independent variable. In the process of deriving TSCI incidence, an alternative TSCI model was specified in an attempt to arrive at an optimal way of estimating the global incidence of TSCI. The global incidence of TSCI was estimated to be 23 cases per 1,000,000 persons in 2007 (179,312 cases per annum). World Health Organization's regional results are provided. Understanding the incidence of TSCI is important for health service planning and for the determination of injury prevention priorities. In the absence of high-quality epidemiological studies of TSCI in each country, the estimation of TSCI obtained through population modelling can be used to overcome known deficits in global spinal cord injury (SCI) data. The incidence of TSCI is context specific, and an alternative regression model demonstrated how TSCI incidence estimates could be improved with additional data. The results highlight the need for data standardisation and comprehensive reporting of national level TSCI data. A step-wise approach from the collation of conventional epidemiological data through to population modelling is suggested.

Creating a Methodology for Coordinating High-resolution Air Quality Improvement Map and Greenhouse Gas Mitigation Strategies in Pittsburgh City

NASA Astrophysics Data System (ADS)

Shi, J.; Donahue, N. M.; Klima, K.; Blackhurst, M.

2016-12-01

In order to tradeoff global impacts of greenhouse gases with highly local impacts of conventional air pollution, researchers require a method to compare global and regional impacts. Unfortunately, we are not aware of a method that allows these to be compared, "apples-to-apples". In this research we propose a three-step model to compare possible city-wide actions to reduce greenhouse gases and conventional air pollutants. We focus on Pittsburgh, PA, a city with consistently poor air quality that is interested in reducing both greenhouse gases and conventional air pollutants. First, we use the 2013 Pittsburgh Greenhouse Gas Inventory to update the Blackhurst et al. model and conduct a greenhouse gas abatement potentials and implementation costs of proposed greenhouse gas reduction efforts. Second, we use field tests for PM2.5, NOx, SOx, organic carbon (OC) and elemental carbon (EC) data to inform a Land-use Regression Model for local air pollution at a 100m x 100m spatial level, which combined with a social cost of air pollution model (EASIUR) allows us to calculate economic social damages. Third, we combine these two models into a three-dimensional greenhouse gas cost abatement curve to understand the implementation costs and social benefits in terms of air quality improvement and greenhouse gas abatement for each potential intervention. We anticipated such results could provide policy-maker insights in green city development.

Near-infrared hyperspectral imaging and partial least squares regression for rapid and reagentless determination of Enterobacteriaceae on chicken fillets.

PubMed

Feng, Yao-Ze; Elmasry, Gamal; Sun, Da-Wen; Scannell, Amalia G M; Walsh, Des; Morcy, Noha

2013-06-01

Bacterial pathogens are the main culprits for outbreaks of food-borne illnesses. This study aimed to use the hyperspectral imaging technique as a non-destructive tool for quantitative and direct determination of Enterobacteriaceae loads on chicken fillets. Partial least squares regression (PLSR) models were established and the best model using full wavelengths was obtained in the spectral range 930-1450 nm with coefficients of determination R(2)≥ 0.82 and root mean squared errors (RMSEs) ≤ 0.47 log(10)CFUg(-1). In further development of simplified models, second derivative spectra and weighted PLS regression coefficients (BW) were utilised to select important wavelengths. However, the three wavelengths (930, 1121 and 1345 nm) selected from BW were competent and more preferred for predicting Enterobacteriaceae loads with R(2) of 0.89, 0.86 and 0.87 and RMSEs of 0.33, 0.40 and 0.45 log(10)CFUg(-1) for calibration, cross-validation and prediction, respectively. Besides, the constructed prediction map provided the distribution of Enterobacteriaceae bacteria on chicken fillets, which cannot be achieved by conventional methods. It was demonstrated that hyperspectral imaging is a potential tool for determining food sanitation and detecting bacterial pathogens on food matrix without using complicated laboratory regimes. Copyright © 2012 Elsevier Ltd. All rights reserved.

Deciphering factors controlling groundwater arsenic spatial variability in Bangladesh

NASA Astrophysics Data System (ADS)

Tan, Z.; Yang, Q.; Zheng, C.; Zheng, Y.

2017-12-01

Elevated concentrations of geogenic arsenic in groundwater have been found in many countries to exceed 10 μg/L, the WHO's guideline value for drinking water. A common yet unexplained characteristic of groundwater arsenic spatial distribution is the extensive variability at various spatial scales. This study investigates factors influencing the spatial variability of groundwater arsenic in Bangladesh to improve the accuracy of models predicting arsenic exceedance rate spatially. A novel boosted regression tree method is used to establish a weak-learning ensemble model, which is compared to a linear model using a conventional stepwise logistic regression method. The boosted regression tree models offer the advantage of parametric interaction when big datasets are analyzed in comparison to the logistic regression. The point data set (n=3,538) of groundwater hydrochemistry with 19 parameters was obtained by the British Geological Survey in 2001. The spatial data sets of geological parameters (n=13) were from the Consortium for Spatial Information, Technical University of Denmark, University of East Anglia and the FAO, while the soil parameters (n=42) were from the Harmonized World Soil Database. The aforementioned parameters were regressed to categorical groundwater arsenic concentrations below or above three thresholds: 5 μg/L, 10 μg/L and 50 μg/L to identify respective controlling factors. Boosted regression tree method outperformed logistic regression methods in all three threshold levels in terms of accuracy, specificity and sensitivity, resulting in an improvement of spatial distribution map of probability of groundwater arsenic exceeding all three thresholds when compared to disjunctive-kriging interpolated spatial arsenic map using the same groundwater arsenic dataset. Boosted regression tree models also show that the most important controlling factors of groundwater arsenic distribution include groundwater iron content and well depth for all three thresholds. The probability of a well with iron content higher than 5mg/L to contain greater than 5 μg/L, 10 μg/L and 50 μg/L As is estimated to be more than 91%, 85% and 51%, respectively, while the probability of a well from depth more than 160m to contain more than 5 μg/L, 10 μg/L and 50 μg/L As is estimated to be less than 38%, 25% and 14%, respectively.

A combined evaluation of the characteristics and acute toxicity of antibiotic wastewater.

PubMed

Yu, Xin; Zuo, Jiane; Li, Ruixia; Gan, Lili; Li, Zaixing; Zhang, Fei

2014-08-01

The conventional parameters and acute toxicities of antibiotic wastewater collected from each treatment unit of an antibiotic wastewater treatment plant have been investigated. The investigation of the conventional parameters indicated that the antibiotic wastewater treatment plant performed well under the significant fluctuation in influent water quality. The results of acute toxicity indicated that the toxicity of antibiotic wastewater could be reduced by 94.3 percent on average after treatment. However, treated antibiotic effluents were still toxic to Vibrio fischeri. The toxicity of antibiotic production wastewater could be attributed to the joint effects of toxic compound mixtures in wastewater. Moreover, aerobic biological treatment processes, including sequencing batch reactor (SBR) and aerobic biofilm reactor, played the most important role in reducing toxicity by 92.4 percent. Pearson׳s correlation coefficients revealed that toxicity had a strong and positive linear correlation with organic substances, nitrogenous compounds, S(2-), volatile phenol, cyanide, As, Zn, Cd, Ni and Fe. Ammonia nitrogen (NH4(+)) was the greatest contributor to toxicity according to the stepwise regression method. The multiple regression model was a good fit for [TU50-15 min] as a function of [NH₄(+)] with the determination coefficient of 0.981. Copyright © 2014 Elsevier Inc. All rights reserved.

Aeromagnetic gradient compensation method for helicopter based on ɛ-support vector regression algorithm

NASA Astrophysics Data System (ADS)

Wu, Peilin; Zhang, Qunying; Fei, Chunjiao; Fang, Guangyou

2017-04-01

Aeromagnetic gradients are typically measured by optically pumped magnetometers mounted on an aircraft. Any aircraft, particularly helicopters, produces significant levels of magnetic interference. Therefore, aeromagnetic compensation is essential, and least square (LS) is the conventional method used for reducing interference levels. However, the LSs approach to solving the aeromagnetic interference model has a few difficulties, one of which is in handling multicollinearity. Therefore, we propose an aeromagnetic gradient compensation method, specifically targeted for helicopter use but applicable on any airborne platform, which is based on the ɛ-support vector regression algorithm. The structural risk minimization criterion intrinsic to the method avoids multicollinearity altogether. Local aeromagnetic anomalies can be retained, and platform-generated fields are suppressed simultaneously by constructing an appropriate loss function and kernel function. The method was tested using an unmanned helicopter and obtained improvement ratios of 12.7 and 3.5 in the vertical and horizontal gradient data, respectively. Both of these values are probably better than those that would have been obtained from the conventional method applied to the same data, had it been possible to do so in a suitable comparative context. The validity of the proposed method is demonstrated by the experimental result.

Frequency of unsatisfactory cervical cytology smears in cancer screening of Japanese women: A systematic review and meta-analysis.

PubMed

Hosono, Satoyo; Terasawa, Teruhiko; Katayama, Takafumi; Sasaki, Seiju; Hoshi, Keika; Hamashima, Chisato

2018-04-01

The Bethesda system (TBS) has been used for cervical cytological diagnosis in Japan since 2008. Evaluation of specimen adequacy is the most important aspect of quality assurance and for precise diagnosis in TBS. A systematic review and meta-analysis were carried out to assess the unsatisfactory specimen rate in the primary cervical cancer screening setting in Japan. Ovid Medline and Ichushi-Web databases were searched from inception through to May 2017. Prospective and retrospective studies that reported the proportion of unsatisfactory specimens in healthy asymptomatic Japanese women in a cervical cancer screening program were eligible for inclusion; 17 studies were included in the meta-analysis. The random-effects model meta-analysis calculated summary estimates of the unsatisfactory rate of 0.60% (95% confidence interval [CI], 0.18-1.96%; I 2 = 99%) for conventional cytology and 0.04% (95% CI, 0.00-0.35%; I 2 = 99%) for liquid-based cytology (LBC). However, comparative results between conventional and liquid-based cytology, based on four direct and nine comparative studies, showed no significant difference (summary odds ratio = 3.5 × 10 -2 favoring LBC [95% CI, 6.9 × 10 -4 -1.7]; I 2 = 98%). In the subgroup analyses and meta-regressions, use of non-cotton devices for conventional cytology and use of a particular platform for LBC were associated with lower unsatisfactory rates. Meta-regression also suggested chronological improvement in unsatisfactory rates for both tests. In Japanese cervical cancer screening programs, conventional cytology remains prevalent. Future research needs to focus on evaluating the impact of screening programs using LBC by comparing the accuracy, performance, and cost-effectiveness with conventional cytology in the Japanese population. © 2018 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.

Nonparametric and Semiparametric Regression Estimation for Length-biased Survival Data

PubMed Central

Shen, Yu; Ning, Jing; Qin, Jing

2016-01-01

For the past several decades, nonparametric and semiparametric modeling for conventional right-censored survival data has been investigated intensively under a noninformative censoring mechanism. However, these methods may not be applicable for analyzing right-censored survival data that arise from prevalent cohorts when the failure times are subject to length-biased sampling. This review article is intended to provide a summary of some newly developed methods as well as established methods for analyzing length-biased data. PMID:27086362

Soil sail content estimation in the yellow river delta with satellite hyperspectral data

USGS Publications Warehouse

Weng, Yongling; Gong, Peng; Zhu, Zhi-Liang

2008-01-01

Soil salinization is one of the most common land degradation processes and is a severe environmental hazard. The primary objective of this study is to investigate the potential of predicting salt content in soils with hyperspectral data acquired with EO-1 Hyperion. Both partial least-squares regression (PLSR) and conventional multiple linear regression (MLR), such as stepwise regression (SWR), were tested as the prediction model. PLSR is commonly used to overcome the problem caused by high-dimensional and correlated predictors. Chemical analysis of 95 samples collected from the top layer of soils in the Yellow River delta area shows that salt content was high on average, and the dominant chemicals in the saline soil were NaCl and MgCl2. Multivariate models were established between soil contents and hyperspectral data. Our results indicate that the PLSR technique with laboratory spectral data has a strong prediction capacity. Spectral bands at 1487-1527, 1971-1991, 2032-2092, and 2163-2355 nm possessed large absolute values of regression coefficients, with the largest coefficient at 2203 nm. We obtained a root mean squared error (RMSE) for calibration (with 61 samples) of RMSEC = 0.753 (R2 = 0.893) and a root mean squared error for validation (with 30 samples) of RMSEV = 0.574. The prediction model was applied on a pixel-by-pixel basis to a Hyperion reflectance image to yield a quantitative surface distribution map of soil salt content. The result was validated successfully from 38 sampling points. We obtained an RMSE estimate of 1.037 (R2 = 0.784) for the soil salt content map derived by the PLSR model. The salinity map derived from the SWR model shows that the predicted value is higher than the true value. These results demonstrate that the PLSR method is a more suitable technique than stepwise regression for quantitative estimation of soil salt content in a large area. ?? 2008 CASI.

Implementing Generalized Additive Models to Estimate the Expected Value of Sample Information in a Microsimulation Model: Results of Three Case Studies.

PubMed

Rabideau, Dustin J; Pei, Pamela P; Walensky, Rochelle P; Zheng, Amy; Parker, Robert A

2018-02-01

The expected value of sample information (EVSI) can help prioritize research but its application is hampered by computational infeasibility, especially for complex models. We investigated an approach by Strong and colleagues to estimate EVSI by applying generalized additive models (GAM) to results generated from a probabilistic sensitivity analysis (PSA). For 3 potential HIV prevention and treatment strategies, we estimated life expectancy and lifetime costs using the Cost-effectiveness of Preventing AIDS Complications (CEPAC) model, a complex patient-level microsimulation model of HIV progression. We fitted a GAM-a flexible regression model that estimates the functional form as part of the model fitting process-to the incremental net monetary benefits obtained from the CEPAC PSA. For each case study, we calculated the expected value of partial perfect information (EVPPI) using both the conventional nested Monte Carlo approach and the GAM approach. EVSI was calculated using the GAM approach. For all 3 case studies, the GAM approach consistently gave similar estimates of EVPPI compared with the conventional approach. The EVSI behaved as expected: it increased and converged to EVPPI for larger sample sizes. For each case study, generating the PSA results for the GAM approach required 3 to 4 days on a shared cluster, after which EVPPI and EVSI across a range of sample sizes were evaluated in minutes. The conventional approach required approximately 5 weeks for the EVPPI calculation alone. Estimating EVSI using the GAM approach with results from a PSA dramatically reduced the time required to conduct a computationally intense project, which would otherwise have been impractical. Using the GAM approach, we can efficiently provide policy makers with EVSI estimates, even for complex patient-level microsimulation models.

A comparative analysis of conventional and pretargeted radioimmunotherapy of B-cell lymphomas by targeting CD20, CD22, and HLA-DR singly and in combinations

PubMed Central

Orgun, Nural; Hamlin, Donald K.; Wilbur, D. Scott; Gooley, Theodore A.; Gopal, Ajay K.; Park, Steven I.; Green, Damian J.; Lin, Yukang; Press, Oliver W.

2009-01-01

Relapsed B-cell lymphomas are currently incurable with conventional chemotherapy and radiation treatments. Radiolabeled antibodies directed against B-cell surface antigens have emerged as effective and safe therapies for relapsed lymphomas. We therefore investigated the potential utility of both directly radiolabeled 1F5 (anti-CD20), HD39 (anti-CD22), and Lym-1 (anti-DR) antibodies (Abs) and of pretargeted radioimmunotherapy (RIT) using Ab-streptavidin (SA) conjugates, followed by an N-acetylgalactosamine dendrimeric clearing agent and radiometal-labeled DOTA-biotin, for treatment of lymphomas in mouse models using Ramos, Raji, and FL-18 human lymphoma xenografts. This study demonstrates the marked superiority of pretargeted RIT for each of the antigenic targets with more complete tumor regressions and longer mouse survival compared with conventional one-step RIT. The Ab-SA conjugate yielding the best tumor regression and progression-free survival after pretargeted RIT varied depending upon the lymphoma cell line used, with 1F5 Ab-SA and Lym-1 Ab-SA conjugates yielding the most promising results overall. Contrary to expectations, the best rates of mouse survival were obtained using optimal single Ab-SA conjugates rather than combinations of conjugates targeting different antigens. We hypothesize that clinical implementation of pretargeted RIT methods will provide a meaningful prolongation of survival for patients with relapsed lymphomas compared with currently available treatment strategies. PMID:19124831

Colon Cancer Survival With Herbal Medicine and Vitamins Combined With Standard Therapy in a Whole-Systems Approach: Ten-Year Follow-up Data Analyzed With Marginal Structural Models and Propensity Score Methods

PubMed Central

McCulloch, Michael; Broffman, Michael; van der Laan, Mark; Hubbard, Alan; Kushi, Lawrence; Abrams, Donald I.; Gao, Jin; Colford, John M.

2014-01-01

Although localized colon cancer is often successfully treated with surgery, advanced disease requires aggressive systemic therapy that has lower effectiveness. Approximately 30% to 75% of patients with colon cancer use complementary and alternative medicine (CAM), but there is limited formal evidence of survival efficacy. In a consecutive case series with 10-year follow-up of all colon cancer patients (n = 193) presenting at a San Francisco Bay-Area center for Chinese medicine (Pine Street Clinic, San Anselmo, CA), the authors compared survival in patients choosing short-term treatment lasting the duration of chemotherapy/radiotherapy with those continuing long-term. To put these data into the context of treatment responses seen in conventional medical practice, they also compared survival with Pan-Asian medicine + vitamins (PAM+V) with that of concurrent external controls from Kaiser Permanente Northern California and California Cancer Registries. Kaplan-Meier, traditional Cox regression, and more modern methods were used for causal inference—namely, propensity score and marginal structural models (MSMs), which have not been used before in studies of cancer survival and Chinese herbal medicine. PAM+V combined with conventional therapy, compared with conventional therapy alone, reduced the risk of death in stage I by 95%, stage II by 64%, stage III by 29%, and stage IV by 75%. There was no significant difference between short-term and long-term PAM+V. Combining PAM+V with conventional therapy improved survival, compared with conventional therapy alone, suggesting that prospective trials combining PAM+V with conventional therapy are justified. PMID:21964510

A Linear Dynamical Systems Approach to Streamflow Reconstruction Reveals History of Regime Shifts in Northern Thailand

NASA Astrophysics Data System (ADS)

Nguyen, Hung T. T.; Galelli, Stefano

2018-03-01

Catchment dynamics is not often modeled in streamflow reconstruction studies; yet, the streamflow generation process depends on both catchment state and climatic inputs. To explicitly account for this interaction, we contribute a linear dynamic model, in which streamflow is a function of both catchment state (i.e., wet/dry) and paleoclimatic proxies. The model is learned using a novel variant of the Expectation-Maximization algorithm, and it is used with a paleo drought record—the Monsoon Asia Drought Atlas—to reconstruct 406 years of streamflow for the Ping River (northern Thailand). Results for the instrumental period show that the dynamic model has higher accuracy than conventional linear regression; all performance scores improve by 45-497%. Furthermore, the reconstructed trajectory of the state variable provides valuable insights about the catchment history—e.g., regime-like behavior—thereby complementing the information contained in the reconstructed streamflow time series. The proposed technique can replace linear regression, since it only requires information on streamflow and climatic proxies (e.g., tree-rings, drought indices); furthermore, it is capable of readily generating stochastic streamflow replicates. With a marginal increase in computational requirements, the dynamic model brings more desirable features and value to streamflow reconstructions.

Regression rate study of porous axial-injection, endburning hybrid fuel grains

NASA Astrophysics Data System (ADS)

Hitt, Matthew A.

This experimental and theoretical work examines the effects of gaseous oxidizer flow rates and pressure on the regression rates of porous fuels for hybrid rocket applications. Testing was conducted using polyethylene as the porous fuel and both gaseous oxygen and nitrous oxide as the oxidizer. Nominal test articles were tested using 200, 100, 50, and 15 micron fuel pore sizes. Pressures tested ranged from atmospheric to 1160 kPa for the gaseous oxygen tests and from 207 kPa to 1054 kPa for the nitrous oxide tests, and oxidizer injection velocities ranged from 35 m/s to 80 m/s for the gaseous oxygen tests and from 7.5 m/s to 16.8 m/s for the nitrous oxide tests. Regression rates were determined using pretest and posttest length measurements of the solid fuel. Experimental results demonstrated that the regression rate of the porous axial-injection, end-burning hybrid was a function of the chamber pressure, as opposed to the oxidizer mass flux typical in conventional hybrids. Regression rates ranged from approximately 0.75 mm/s at atmospheric pressure to 8.89 mm/s at 1160 kPa for the gaseous oxygen tests and 0.21 mm/s at 207 kPa to 1.44 mm/s at 1054 kPa for the nitrous oxide tests. The analytical model was developed based on a standard ablative model modified to include oxidizer flow through the grain. The heat transfer from the flame was primarily modeled using an empirically determined flame coefficient that included all heat transfer mechanisms in one term. An exploratory flame model based on the Granular Diffusion Flame model used for solid rocket motors was also adapted for comparison with the empirical flame coefficient. This model was then evaluated quantitatively using the experimental results of the gaseous oxygen tests as well as qualitatively using the experimental results of the nitrous oxide tests. The model showed agreement with the experimental results indicating it has potential for giving insight into the flame structure in this motor configuration. Results from the model suggested that both kinetic and diffusion processes could be relevant to the combustion depending on the chamber pressure.

Supporting Regularized Logistic Regression Privately and Efficiently.

PubMed

Li, Wenfa; Liu, Hongzhe; Yang, Peng; Xie, Wei

2016-01-01

As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc.

Supporting Regularized Logistic Regression Privately and Efficiently

PubMed Central

Li, Wenfa; Liu, Hongzhe; Yang, Peng; Xie, Wei

2016-01-01

As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Concerns over data privacy make it increasingly difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used statistical model while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluations on several studies validate the privacy guarantee, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc. PMID:27271738

Bootstrap-based methods for estimating standard errors in Cox's regression analyses of clustered event times.

PubMed

Xiao, Yongling; Abrahamowicz, Michal

2010-03-30

We propose two bootstrap-based methods to correct the standard errors (SEs) from Cox's model for within-cluster correlation of right-censored event times. The cluster-bootstrap method resamples, with replacement, only the clusters, whereas the two-step bootstrap method resamples (i) the clusters, and (ii) individuals within each selected cluster, with replacement. In simulations, we evaluate both methods and compare them with the existing robust variance estimator and the shared gamma frailty model, which are available in statistical software packages. We simulate clustered event time data, with latent cluster-level random effects, which are ignored in the conventional Cox's model. For cluster-level covariates, both proposed bootstrap methods yield accurate SEs, and type I error rates, and acceptable coverage rates, regardless of the true random effects distribution, and avoid serious variance under-estimation by conventional Cox-based standard errors. However, the two-step bootstrap method over-estimates the variance for individual-level covariates. We also apply the proposed bootstrap methods to obtain confidence bands around flexible estimates of time-dependent effects in a real-life analysis of cluster event times.

Determination of fat and total protein content in milk using conventional digital imaging.

PubMed

Kucheryavskiy, Sergey; Melenteva, Anastasiia; Bogomolov, Andrey

2014-04-01

The applicability of conventional digital imaging to quantitative determination of fat and total protein in cow's milk, based on the phenomenon of light scatter, has been proved. A new algorithm for extracting features from digital images of milk samples has been developed. The algorithm takes into account spatial distribution of light, diffusely transmitted through a sample. The proposed method has been tested on two sample sets prepared from industrial raw milk standards, with variable fat and protein content. Partial Least-Squares (PLS) regression on the features calculated from images of monochromatically illuminated milk samples resulted in models with high prediction performance when analysed the sets separately (best models with cross-validated R(2)=0.974 for protein and R(2)=0.973 for fat content). However when analysed the sets jointly with the obtained results were significantly worse (best models with cross-validated R(2)=0.890 for fat content and R(2)=0.720 for protein content). The results have been compared with previously published Vis/SW-NIR spectroscopic study of similar samples. Copyright © 2013 Elsevier B.V. All rights reserved.

Decision tree modeling using R.

PubMed

Zhang, Zhongheng

2016-08-01

In machine learning field, decision tree learner is powerful and easy to interpret. It employs recursive binary partitioning algorithm that splits the sample in partitioning variable with the strongest association with the response variable. The process continues until some stopping criteria are met. In the example I focus on conditional inference tree, which incorporates tree-structured regression models into conditional inference procedures. While growing a single tree is subject to small changes in the training data, random forests procedure is introduced to address this problem. The sources of diversity for random forests come from the random sampling and restricted set of input variables to be selected. Finally, I introduce R functions to perform model based recursive partitioning. This method incorporates recursive partitioning into conventional parametric model building.

High-order fuzzy time-series based on multi-period adaptation model for forecasting stock markets

NASA Astrophysics Data System (ADS)

Chen, Tai-Liang; Cheng, Ching-Hsue; Teoh, Hia-Jong

2008-02-01

Stock investors usually make their short-term investment decisions according to recent stock information such as the late market news, technical analysis reports, and price fluctuations. To reflect these short-term factors which impact stock price, this paper proposes a comprehensive fuzzy time-series, which factors linear relationships between recent periods of stock prices and fuzzy logical relationships (nonlinear relationships) mined from time-series into forecasting processes. In empirical analysis, the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) and HSI (Heng Seng Index) are employed as experimental datasets, and four recent fuzzy time-series models, Chen’s (1996), Yu’s (2005), Cheng’s (2006) and Chen’s (2007), are used as comparison models. Besides, to compare with conventional statistic method, the method of least squares is utilized to estimate the auto-regressive models of the testing periods within the databases. From analysis results, the performance comparisons indicate that the multi-period adaptation model, proposed in this paper, can effectively improve the forecasting performance of conventional fuzzy time-series models which only factor fuzzy logical relationships in forecasting processes. From the empirical study, the traditional statistic method and the proposed model both reveal that stock price patterns in the Taiwan stock and Hong Kong stock markets are short-term.

Artificial neural network, genetic algorithm, and logistic regression applications for predicting renal colic in emergency settings.

PubMed

Eken, Cenker; Bilge, Ugur; Kartal, Mutlu; Eray, Oktay

2009-06-03

Logistic regression is the most common statistical model for processing multivariate data in the medical literature. Artificial intelligence models like an artificial neural network (ANN) and genetic algorithm (GA) may also be useful to interpret medical data. The purpose of this study was to perform artificial intelligence models on a medical data sheet and compare to logistic regression. ANN, GA, and logistic regression analysis were carried out on a data sheet of a previously published article regarding patients presenting to an emergency department with flank pain suspicious for renal colic. The study population was composed of 227 patients: 176 patients had a diagnosis of urinary stone, while 51 ultimately had no calculus. The GA found two decision rules in predicting urinary stones. Rule 1 consisted of being male, pain not spreading to back, and no fever. In rule 2, pelvicaliceal dilatation on bedside ultrasonography replaced no fever. ANN, GA rule 1, GA rule 2, and logistic regression had a sensitivity of 94.9, 67.6, 56.8, and 95.5%, a specificity of 78.4, 76.47, 86.3, and 47.1%, a positive likelihood ratio of 4.4, 2.9, 4.1, and 1.8, and a negative likelihood ratio of 0.06, 0.42, 0.5, and 0.09, respectively. The area under the curve was found to be 0.867, 0.720, 0.715, and 0.713 for all applications, respectively. Data mining techniques such as ANN and GA can be used for predicting renal colic in emergency settings and to constitute clinical decision rules. They may be an alternative to conventional multivariate analysis applications used in biostatistics.

Application of Rapid Visco Analyser (RVA) viscograms and chemometrics for maize hardness characterisation.

PubMed

Guelpa, Anina; Bevilacqua, Marta; Marini, Federico; O'Kennedy, Kim; Geladi, Paul; Manley, Marena

2015-04-15

It has been established in this study that the Rapid Visco Analyser (RVA) can describe maize hardness, irrespective of the RVA profile, when used in association with appropriate multivariate data analysis techniques. Therefore, the RVA can complement or replace current and/or conventional methods as a hardness descriptor. Hardness modelling based on RVA viscograms was carried out using seven conventional hardness methods (hectoliter mass (HLM), hundred kernel mass (HKM), particle size index (PSI), percentage vitreous endosperm (%VE), protein content, percentage chop (%chop) and near infrared (NIR) spectroscopy) as references and three different RVA profiles (hard, soft and standard) as predictors. An approach using locally weighted partial least squares (LW-PLS) was followed to build the regression models. The resulted prediction errors (root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP)) for the quantification of hardness values were always lower or in the same order of the laboratory error of the reference method. Copyright © 2014 Elsevier Ltd. All rights reserved.

Rex fortran 4 system for combinatorial screening or conventional analysis of multivariate regressions

Treesearch

L.R. Grosenbaugh

1967-01-01

Describes an expansible computerized system that provides data needed in regression or covariance analysis of as many as 50 variables, 8 of which may be dependent. Alternatively, it can screen variously generated combinations of independent variables to find the regression with the smallest mean-squared-residual, which will be fitted if desired. The user can easily...

Development of hybrid genetic-algorithm-based neural networks using regression trees for modeling air quality inside a public transportation bus.

PubMed

Kadiyala, Akhil; Kaur, Devinder; Kumar, Ashok

2013-02-01

The present study developed a novel approach to modeling indoor air quality (IAQ) of a public transportation bus by the development of hybrid genetic-algorithm-based neural networks (also known as evolutionary neural networks) with input variables optimized from using the regression trees, referred as the GART approach. This study validated the applicability of the GART modeling approach in solving complex nonlinear systems by accurately predicting the monitored contaminants of carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), sulfur dioxide (SO2), 0.3-0.4 microm sized particle numbers, 0.4-0.5 microm sized particle numbers, particulate matter (PM) concentrations less than 1.0 microm (PM10), and PM concentrations less than 2.5 microm (PM2.5) inside a public transportation bus operating on 20% grade biodiesel in Toledo, OH. First, the important variables affecting each monitored in-bus contaminant were determined using regression trees. Second, the analysis of variance was used as a complimentary sensitivity analysis to the regression tree results to determine a subset of statistically significant variables affecting each monitored in-bus contaminant. Finally, the identified subsets of statistically significant variables were used as inputs to develop three artificial neural network (ANN) models. The models developed were regression tree-based back-propagation network (BPN-RT), regression tree-based radial basis function network (RBFN-RT), and GART models. Performance measures were used to validate the predictive capacity of the developed IAQ models. The results from this approach were compared with the results obtained from using a theoretical approach and a generalized practicable approach to modeling IAQ that included the consideration of additional independent variables when developing the aforementioned ANN models. The hybrid GART models were able to capture majority of the variance in the monitored in-bus contaminants. The genetic-algorithm-based neural network IAQ models outperformed the traditional ANN methods of the back-propagation and the radial basis function networks. The novelty of this research is the development of a novel approach to modeling vehicular indoor air quality by integration of the advanced methods of genetic algorithms, regression trees, and the analysis of variance for the monitored in-vehicle gaseous and particulate matter contaminants, and comparing the results obtained from using the developed approach with conventional artificial intelligence techniques of back propagation networks and radial basis function networks. This study validated the newly developed approach using holdout and threefold cross-validation methods. These results are of great interest to scientists, researchers, and the public in understanding the various aspects of modeling an indoor microenvironment. This methodology can easily be extended to other fields of study also.

The contextual effects of social capital on health: a cross-national instrumental variable analysis.

PubMed

Kim, Daniel; Baum, Christopher F; Ganz, Michael L; Subramanian, S V; Kawachi, Ichiro

2011-12-01

Past research on the associations between area-level/contextual social capital and health has produced conflicting evidence. However, interpreting this rapidly growing literature is difficult because estimates using conventional regression are prone to major sources of bias including residual confounding and reverse causation. Instrumental variable (IV) analysis can reduce such bias. Using data on up to 167,344 adults in 64 nations in the European and World Values Surveys and applying IV and ordinary least squares (OLS) regression, we estimated the contextual effects of country-level social trust on individual self-rated health. We further explored whether these associations varied by gender and individual levels of trust. Using OLS regression, we found higher average country-level trust to be associated with better self-rated health in both women and men. Instrumental variable analysis yielded qualitatively similar results, although the estimates were more than double in size in both sexes when country population density and corruption were used as instruments. The estimated health effects of raising the percentage of a country's population that trusts others by 10 percentage points were at least as large as the estimated health effects of an individual developing trust in others. These findings were robust to alternative model specifications and instruments. Conventional regression and to a lesser extent IV analysis suggested that these associations are more salient in women and in women reporting social trust. In a large cross-national study, our findings, including those using instrumental variables, support the presence of beneficial effects of higher country-level trust on self-rated health. Previous findings for contextual social capital using traditional regression may have underestimated the true associations. Given the close linkages between self-rated health and all-cause mortality, the public health gains from raising social capital within and across countries may be large. Copyright © 2011 Elsevier Ltd. All rights reserved.

The contextual effects of social capital on health: a cross-national instrumental variable analysis

PubMed Central

Kim, Daniel; Baum, Christopher F; Ganz, Michael; Subramanian, S V; Kawachi, Ichiro

2011-01-01

Past observational studies of the associations of area-level/contextual social capital with health have revealed conflicting findings. However, interpreting this rapidly growing literature is difficult because estimates using conventional regression are prone to major sources of bias including residual confounding and reverse causation. Instrumental variable (IV) analysis can reduce such bias. Using data on up to 167 344 adults in 64 nations in the European and World Values Surveys and applying IV and ordinary least squares (OLS) regression, we estimated the contextual effects of country-level social trust on individual self-rated health. We further explored whether these associations varied by gender and individual levels of trust. Using OLS regression, we found higher average country-level trust to be associated with better self-rated health in both women and men. Instrumental variable analysis yielded qualitatively similar results, although the estimates were more than double in size in women and men using country population density and corruption as instruments. The estimated health effects of raising the percentage of a country's population that trusts others by 10 percentage points were at least as large as the estimated health effects of an individual developing trust in others. These findings were robust to alternative model specifications and instruments. Conventional regression and to a lesser extent IV analysis suggested that these associations are more salient in women and in women reporting social trust. In a large cross-national study, our findings, including those using instrumental variables, support the presence of beneficial effects of higher country-level trust on self-rated health. Past findings for contextual social capital using traditional regression may have underestimated the true associations. Given the close linkages between self-rated health and all-cause mortality, the public health gains from raising social capital within countries may be large. PMID:22078106

Acidity measurement of iron ore powders using laser-induced breakdown spectroscopy with partial least squares regression.

PubMed

Hao, Z Q; Li, C M; Shen, M; Yang, X Y; Li, K H; Guo, L B; Li, X Y; Lu, Y F; Zeng, X Y

2015-03-23

Laser-induced breakdown spectroscopy (LIBS) with partial least squares regression (PLSR) has been applied to measuring the acidity of iron ore, which can be defined by the concentrations of oxides: CaO, MgO, Al₂O₃, and SiO₂. With the conventional internal standard calibration, it is difficult to establish the calibration curves of CaO, MgO, Al₂O₃, and SiO₂ in iron ore due to the serious matrix effects. PLSR is effective to address this problem due to its excellent performance in compensating the matrix effects. In this work, fifty samples were used to construct the PLSR calibration models for the above-mentioned oxides. These calibration models were validated by the 10-fold cross-validation method with the minimum root-mean-square errors (RMSE). Another ten samples were used as a test set. The acidities were calculated according to the estimated concentrations of CaO, MgO, Al₂O₃, and SiO₂ using the PLSR models. The average relative error (ARE) and RMSE of the acidity achieved 3.65% and 0.0048, respectively, for the test samples.

Cascade Optimization Strategy with Neural Network and Regression Approximations Demonstrated on a Preliminary Aircraft Engine Design

NASA Technical Reports Server (NTRS)

Hopkins, Dale A.; Patnaik, Surya N.

2000-01-01

A preliminary aircraft engine design methodology is being developed that utilizes a cascade optimization strategy together with neural network and regression approximation methods. The cascade strategy employs different optimization algorithms in a specified sequence. The neural network and regression methods are used to approximate solutions obtained from the NASA Engine Performance Program (NEPP), which implements engine thermodynamic cycle and performance analysis models. The new methodology is proving to be more robust and computationally efficient than the conventional optimization approach of using a single optimization algorithm with direct reanalysis. The methodology has been demonstrated on a preliminary design problem for a novel subsonic turbofan engine concept that incorporates a wave rotor as a cycle-topping device. Computations of maximum thrust were obtained for a specific design point in the engine mission profile. The results (depicted in the figure) show a significant improvement in the maximum thrust obtained using the new methodology in comparison to benchmark solutions obtained using NEPP in a manual design mode.

A hybrid group method of data handling with discrete wavelet transform for GDP forecasting

NASA Astrophysics Data System (ADS)

Isa, Nadira Mohamed; Shabri, Ani

2013-09-01

This study is proposed the application of hybridization model using Group Method of Data Handling (GMDH) and Discrete Wavelet Transform (DWT) in time series forecasting. The objective of this paper is to examine the flexibility of the hybridization GMDH in time series forecasting by using Gross Domestic Product (GDP). A time series data set is used in this study to demonstrate the effectiveness of the forecasting model. This data are utilized to forecast through an application aimed to handle real life time series. This experiment compares the performances of a hybrid model and a single model of Wavelet-Linear Regression (WR), Artificial Neural Network (ANN), and conventional GMDH. It is shown that the proposed model can provide a promising alternative technique in GDP forecasting.

Use of Myocardial T1 Mapping at 3.0 T to Differentiate Anderson-Fabry Disease from Hypertrophic Cardiomyopathy.

PubMed

Karur, Gauri R; Robison, Sean; Iwanochko, Robert M; Morel, Chantal F; Crean, Andrew M; Thavendiranathan, Paaladinesh; Nguyen, Elsie T; Mathur, Shobhit; Wasim, Syed; Hanneman, Kate

2018-04-24

Purpose To compare left ventricular (LV) and right ventricular (RV) 3.0-T cardiac magnetic resonance (MR) imaging T1 values in Anderson-Fabry disease (AFD) and hypertrophic cardiomyopathy (HCM) and evaluate the diagnostic value of native T1 values beyond age, sex, and conventional imaging features. Materials and Methods For this prospective study, 30 patients with gene-positive AFD (37% male; mean age ± standard deviation, 45.0 years ± 14.1) and 30 patients with HCM (57% male; mean age, 49.3 years ± 13.5) were prospectively recruited between June 2016 and September 2017 to undergo cardiac MR imaging T1 mapping with a modified Look-Locker inversion recovery (MOLLI) acquisition scheme at 3.0 T (repetition time msec/echo time msec, 280/1.12; section thickness, 8 mm). LV and RV T1 values were evaluated. Statistical analysis included independent samples t test, receiver operating characteristic curve analysis, multivariable logistic regression, and likelihood ratio test. Results Septal LV, global LV, and RV native T1 values were significantly lower in AFD compared with those in HCM (1161 msec ± 47 vs 1296 msec ± 55, respectively [P < .001]; 1192 msec ± 52 vs 1268 msec ± 55 [P < .001]; and 1221 msec ± 54 vs 1271 msec ± 37 [P = .001], respectively). A septal LV native T1 cutoff point of 1220 msec or lower distinguished AFD from HCM with sensitivity of 97%, specificity of 93%, and accuracy of 95%. Septal LV native T1 values differentiated AFD from HCM after adjustment for age, sex, and conventional imaging features (odds ratio, 0.94; 95% confidence interval: 0.91, 0.98; P = < .001). In a nested logistic regression model with age, sex, and conventional imaging features, model fit was significantly improved by the addition of septal LV native T1 values (χ 2 [df = 1] = 33.4; P < .001). Conclusion Cardiac MR imaging native T1 values at 3.0 T are significantly lower in patients with AFD compared with those with HCM and provide independent and incremental diagnostic value beyond age, sex, and conventional imaging features. © RSNA, 2018.

Cerebrospinal fluid ferritin and albumin index: potential candidates for scoring system to differentiate between bacterial and viral meningitis in children.

PubMed

Jebamalar, Angelin A; Prabhat; Balakrishnapillai, Agiesh K; Parmeswaran, Narayanan; Dhiman, Pooja; Rajendiran, Soundravally

2016-07-01

To evaluate the diagnostic role of cerebrospinal fluid (CSF) ferritin and albumin index (AI = CSF albumin/serum albumin × 1000) in differentiating acute bacterial meningitis (ABM) from acute viral meningitis (AVM) in children. The study included 42 cases each of ABM and AVM in pediatric age group. Receiver operating characteristic (ROC) analysis was carried out for CSF ferritin and AI. Binary logistic regression was also done. CSF ferritin and AI were found significantly higher in ABM compared to AVM. Model obtained using AI and CSF ferritin along with conventional criteria is better than existing models.

Application of ERTS-1 data to the harvest model of the US menhaden fishery. [Gulf of Mexico

NASA Technical Reports Server (NTRS)

Maughan, P. M. (Principal Investigator); Marmelstein, A. D.

1974-01-01

The author has identified the following significant results. The project was conducted in Mississippi Sound in the north-central Gulf of Mexico. It utilized conventional surface data, obtained from fishing and other vessels, as well as aircraft and spacecraft remote data. A relationship was established between surface measured water transparency, temperature and salinity, and commercial fish-stock availability. Numerical models of the relationships were derived. A multiple regression was performed relating ERTS-1 MSS Band 5 image density to measured transparency and water depth. It is concluded that remotely acquired data can play a role in harvest decisions of commercial fisheries.

Exposure time independent summary statistics for assessment of drug dependent cell line growth inhibition.

PubMed

Falgreen, Steffen; Laursen, Maria Bach; Bødker, Julie Støve; Kjeldsen, Malene Krag; Schmitz, Alexander; Nyegaard, Mette; Johnsen, Hans Erik; Dybkær, Karen; Bøgsted, Martin

2014-06-05

In vitro generated dose-response curves of human cancer cell lines are widely used to develop new therapeutics. The curves are summarised by simplified statistics that ignore the conventionally used dose-response curves' dependency on drug exposure time and growth kinetics. This may lead to suboptimal exploitation of data and biased conclusions on the potential of the drug in question. Therefore we set out to improve the dose-response assessments by eliminating the impact of time dependency. First, a mathematical model for drug induced cell growth inhibition was formulated and used to derive novel dose-response curves and improved summary statistics that are independent of time under the proposed model. Next, a statistical analysis workflow for estimating the improved statistics was suggested consisting of 1) nonlinear regression models for estimation of cell counts and doubling times, 2) isotonic regression for modelling the suggested dose-response curves, and 3) resampling based method for assessing variation of the novel summary statistics. We document that conventionally used summary statistics for dose-response experiments depend on time so that fast growing cell lines compared to slowly growing ones are considered overly sensitive. The adequacy of the mathematical model is tested for doxorubicin and found to fit real data to an acceptable degree. Dose-response data from the NCI60 drug screen were used to illustrate the time dependency and demonstrate an adjustment correcting for it. The applicability of the workflow was illustrated by simulation and application on a doxorubicin growth inhibition screen. The simulations show that under the proposed mathematical model the suggested statistical workflow results in unbiased estimates of the time independent summary statistics. Variance estimates of the novel summary statistics are used to conclude that the doxorubicin screen covers a significant diverse range of responses ensuring it is useful for biological interpretations. Time independent summary statistics may aid the understanding of drugs' action mechanism on tumour cells and potentially renew previous drug sensitivity evaluation studies.

Exposure time independent summary statistics for assessment of drug dependent cell line growth inhibition

PubMed Central

2014-01-01

Background In vitro generated dose-response curves of human cancer cell lines are widely used to develop new therapeutics. The curves are summarised by simplified statistics that ignore the conventionally used dose-response curves’ dependency on drug exposure time and growth kinetics. This may lead to suboptimal exploitation of data and biased conclusions on the potential of the drug in question. Therefore we set out to improve the dose-response assessments by eliminating the impact of time dependency. Results First, a mathematical model for drug induced cell growth inhibition was formulated and used to derive novel dose-response curves and improved summary statistics that are independent of time under the proposed model. Next, a statistical analysis workflow for estimating the improved statistics was suggested consisting of 1) nonlinear regression models for estimation of cell counts and doubling times, 2) isotonic regression for modelling the suggested dose-response curves, and 3) resampling based method for assessing variation of the novel summary statistics. We document that conventionally used summary statistics for dose-response experiments depend on time so that fast growing cell lines compared to slowly growing ones are considered overly sensitive. The adequacy of the mathematical model is tested for doxorubicin and found to fit real data to an acceptable degree. Dose-response data from the NCI60 drug screen were used to illustrate the time dependency and demonstrate an adjustment correcting for it. The applicability of the workflow was illustrated by simulation and application on a doxorubicin growth inhibition screen. The simulations show that under the proposed mathematical model the suggested statistical workflow results in unbiased estimates of the time independent summary statistics. Variance estimates of the novel summary statistics are used to conclude that the doxorubicin screen covers a significant diverse range of responses ensuring it is useful for biological interpretations. Conclusion Time independent summary statistics may aid the understanding of drugs’ action mechanism on tumour cells and potentially renew previous drug sensitivity evaluation studies. PMID:24902483

Is complex allometry in field metabolic rates of mammals a statistical artifact?

PubMed

Packard, Gary C

2017-01-01

Recent reports indicate that field metabolic rates (FMRs) of mammals conform to a pattern of complex allometry in which the exponent in a simple, two-parameter power equation increases steadily as a dependent function of body mass. The reports were based, however, on indirect analyses performed on logarithmic transformations of the original data. I re-examined values for FMR and body mass for 114 species of mammal by the conventional approach to allometric analysis (to illustrate why the approach is unreliable) and by linear and nonlinear regression on untransformed variables (to illustrate the power and versatility of newer analytical methods). The best of the regression models fitted directly to untransformed observations is a three-parameter power equation with multiplicative, lognormal, heteroscedastic error and an allometric exponent of 0.82. The mean function is a good fit to data in graphical display. The significant intercept in the model may simply have gone undetected in prior analyses because conventional allometry assumes implicitly that the intercept is zero; or the intercept may be a spurious finding resulting from bias introduced by the haphazard sampling that underlies "exploratory" analyses like the one reported here. The aforementioned issues can be resolved only by gathering new data specifically intended to address the question of scaling of FMR with body mass in mammals. However, there is no support for the concept of complex allometry in the relationship between FMR and body size in mammals. Copyright © 2016 Elsevier Inc. All rights reserved.

Improving satellite-based PM2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting.

PubMed

Yu, Wenxi; Liu, Yang; Ma, Zongwei; Bi, Jun

2017-08-01

Using satellite-based aerosol optical depth (AOD) measurements and statistical models to estimate ground-level PM 2.5 is a promising way to fill the areas that are not covered by ground PM 2.5 monitors. The statistical models used in previous studies are primarily Linear Mixed Effects (LME) and Geographically Weighted Regression (GWR) models. In this study, we developed a new regression model between PM 2.5 and AOD using Gaussian processes in a Bayesian hierarchical setting. Gaussian processes model the stochastic nature of the spatial random effects, where the mean surface and the covariance function is specified. The spatial stochastic process is incorporated under the Bayesian hierarchical framework to explain the variation of PM 2.5 concentrations together with other factors, such as AOD, spatial and non-spatial random effects. We evaluate the results of our model and compare them with those of other, conventional statistical models (GWR and LME) by within-sample model fitting and out-of-sample validation (cross validation, CV). The results show that our model possesses a CV result (R 2  = 0.81) that reflects higher accuracy than that of GWR and LME (0.74 and 0.48, respectively). Our results indicate that Gaussian process models have the potential to improve the accuracy of satellite-based PM 2.5 estimates.

The geography of recreational open space: influence of neighborhood racial composition and neighborhood poverty.

PubMed

Duncan, Dustin T; Kawachi, Ichiro; White, Kellee; Williams, David R

2013-08-01

The geography of recreational open space might be inequitable in terms of minority neighborhood racial/ethnic composition and neighborhood poverty, perhaps due in part to residential segregation. This study evaluated the association between minority neighborhood racial/ethnic composition, neighborhood poverty, and recreational open space in Boston, Massachusetts (US). Across Boston census tracts, we computed percent non-Hispanic Black, percent Hispanic, and percent families in poverty as well as recreational open space density. We evaluated spatial autocorrelation in study variables and in the ordinary least squares (OLS) regression residuals via the Global Moran's I. We then computed Spearman correlations between the census tract socio-demographic characteristics and recreational open space density, including correlations adjusted for spatial autocorrelation. After this, we computed OLS regressions or spatial regressions as appropriate. Significant positive spatial autocorrelation was found for neighborhood socio-demographic characteristics (all p value = 0.001). We found marginally significant positive spatial autocorrelation in recreational open space (Global Moran's I = 0.082; p value = 0.053). However, we found no spatial autocorrelation in the OLS regression residuals, which indicated that spatial models were not appropriate. There was a negative correlation between census tract percent non-Hispanic Black and recreational open space density (r S = -0.22; conventional p value = 0.005; spatially adjusted p value = 0.019) as well as a negative correlation between predominantly non-Hispanic Black census tracts (>60 % non-Hispanic Black in a census tract) and recreational open space density (r S = -0.23; conventional p value = 0.003; spatially adjusted p value = 0.007). In bivariate and multivariate OLS models, percent non-Hispanic Black in a census tract and predominantly Black census tracts were associated with decreased density of recreational open space (p value < 0.001). Consistent with several previous studies in other geographic locales, we found that Black neighborhoods in Boston were less likely to have recreational open spaces, indicating the need for policy interventions promoting equitable access. Such interventions may contribute to reductions and disparities in obesity.

FAST INdiCATE Trial protocol. Clinical efficacy of functional strength training for upper limb motor recovery early after stroke: neural correlates and prognostic indicators.

PubMed

Pomeroy, Valerie M; Ward, Nick S; Johansen-Berg, Heidi; van Vliet, Paulette; Burridge, Jane; Hunter, Susan M; Lemon, Roger N; Rothwell, John; Weir, Christopher J; Wing, Alan; Walker, Andrew A; Kennedy, Niamh; Barton, Garry; Greenwood, Richard J; McConnachie, Alex

2014-02-01

Functional strength training in addition to conventional physical therapy could enhance upper limb recovery early after stroke more than movement performance therapy plus conventional physical therapy. To determine (a) the relative clinical efficacy of conventional physical therapy combined with functional strength training and conventional physical therapy combined with movement performance therapy for upper limb recovery; (b) the neural correlates of response to conventional physical therapy combined with functional strength training and conventional physical therapy combined with movement performance therapy; (c) whether any one or combination of baseline measures predict motor improvement in response to conventional physical therapy combined with functional strength training or conventional physical therapy combined with movement performance therapy. Randomized, controlled, observer-blind trial. The sample will consist of 288 participants with upper limb paresis resulting from a stroke that occurred within the previous 60 days. All will be allocated to conventional physical therapy combined with functional strength training or conventional physical therapy combined with movement performance therapy. Functional strength training and movement performance therapy will be undertaken for up to 1·5 h/day, five-days/week for six-weeks. Measurements will be undertaken before randomization, six-weeks thereafter, and six-months after stroke. Primary efficacy outcome will be the Action Research Arm Test. Explanatory measurements will include voxel-wise estimates of brain activity during hand movement, brain white matter integrity (fractional anisotropy), and brain-muscle connectivity (e.g. latency of motor evoked potentials). The primary clinical efficacy analysis will compare treatment groups using a multilevel normal linear model adjusting for stratification variables and for which therapist administered the treatment. Effect of conventional physical therapy combined with functional strength training versus conventional physical therapy combined with movement performance therapy will be summarized using the adjusted mean difference and 95% confidence interval. To identify the neural correlates of improvement in both groups, we will investigate associations between change from baseline in clinical outcomes and each explanatory measure. To identify baseline measurements that independently predict motor improvement, we will develop a multiple regression model. © 2013 The Authors. International Journal of Stroke published by John Wiley & Sons Ltd on behalf of World Stroke Organization.

Analysis of Longitudinal Studies With Repeated Outcome Measures: Adjusting for Time-Dependent Confounding Using Conventional Methods.

PubMed

Keogh, Ruth H; Daniel, Rhian M; VanderWeele, Tyler J; Vansteelandt, Stijn

2018-05-01

Estimation of causal effects of time-varying exposures using longitudinal data is a common problem in epidemiology. When there are time-varying confounders, which may include past outcomes, affected by prior exposure, standard regression methods can lead to bias. Methods such as inverse probability weighted estimation of marginal structural models have been developed to address this problem. However, in this paper we show how standard regression methods can be used, even in the presence of time-dependent confounding, to estimate the total effect of an exposure on a subsequent outcome by controlling appropriately for prior exposures, outcomes, and time-varying covariates. We refer to the resulting estimation approach as sequential conditional mean models (SCMMs), which can be fitted using generalized estimating equations. We outline this approach and describe how including propensity score adjustment is advantageous. We compare the causal effects being estimated using SCMMs and marginal structural models, and we compare the two approaches using simulations. SCMMs enable more precise inferences, with greater robustness against model misspecification via propensity score adjustment, and easily accommodate continuous exposures and interactions. A new test for direct effects of past exposures on a subsequent outcome is described.

Estimating methane emissions from landfills based on rainfall, ambient temperature, and waste composition: The CLEEN model.

PubMed

Karanjekar, Richa V; Bhatt, Arpita; Altouqui, Said; Jangikhatoonabad, Neda; Durai, Vennila; Sattler, Melanie L; Hossain, M D Sahadat; Chen, Victoria

2015-12-01

Accurately estimating landfill methane emissions is important for quantifying a landfill's greenhouse gas emissions and power generation potential. Current models, including LandGEM and IPCC, often greatly simplify treatment of factors like rainfall and ambient temperature, which can substantially impact gas production. The newly developed Capturing Landfill Emissions for Energy Needs (CLEEN) model aims to improve landfill methane generation estimates, but still require inputs that are fairly easy to obtain: waste composition, annual rainfall, and ambient temperature. To develop the model, methane generation was measured from 27 laboratory scale landfill reactors, with varying waste compositions (ranging from 0% to 100%); average rainfall rates of 2, 6, and 12 mm/day; and temperatures of 20, 30, and 37°C, according to a statistical experimental design. Refuse components considered were the major biodegradable wastes, food, paper, yard/wood, and textile, as well as inert inorganic waste. Based on the data collected, a multiple linear regression equation (R(2)=0.75) was developed to predict first-order methane generation rate constant values k as functions of waste composition, annual rainfall, and temperature. Because, laboratory methane generation rates exceed field rates, a second scale-up regression equation for k was developed using actual gas-recovery data from 11 landfills in high-income countries with conventional operation. The Capturing Landfill Emissions for Energy Needs (CLEEN) model was developed by incorporating both regression equations into the first-order decay based model for estimating methane generation rates from landfills. CLEEN model values were compared to actual field data from 6 US landfills, and to estimates from LandGEM and IPCC. For 4 of the 6 cases, CLEEN model estimates were the closest to actual. Copyright © 2015 Elsevier Ltd. All rights reserved.

Health need and the use of alternative medicine among adults who do not use conventional medicine

PubMed Central

2010-01-01

Background We hypothesize that a substantial portion of individuals who forgo conventional care in a given year turn to some form of alternative medicine. This study also examines whether individuals who use only alternative medicine will differ substantially in health and sociodemographic status from individuals using neither alternative medicine nor conventional care in a given year. To identify those factors that predict alternative medicine use in those not using conventional care, we employed the socio-behavioral model of healthcare utilization. Methods The current study is a cross-sectional regression analysis using data from the 2002 National Health Interview Survey. Data were collected in-person from 31,044 adults throughout the 50 states and the District of Columbia. Results 19.3% of adults (38.3 million) did not use conventional care in a 12 month period, although 39.5% of these individuals (14.7 million) reported having one or more problems with their health. Of those not using conventional care, 24.8% (9.5 million) used alternative medicine. Users of alternative medicine had more health needs and were more likely to delay conventional care because of both cost and non-cost factors compared to those not using alternative medicine. While individual predisposing factors (gender, education) were positively associated with alternative medicine use, enabling factors (poverty status, insurance coverage) were not. Conclusions We found that a quarter of individuals who forgo conventional care in a given year turn towards alternative medicine. Our study suggests that the potential determinants of using only alternative medicine are multifactorial. Future research is needed to examine the decision process behind an individual's choice to use alternative medicine but not conventional medicine and the clinical outcomes of this choice. PMID:20670418

A method of determining bending properties of poultry long bones using beam analysis and micro-CT data.

PubMed

Vaughan, Patrick E; Orth, Michael W; Haut, Roger C; Karcher, Darrin M

2016-01-01

While conventional mechanical testing has been regarded as a gold standard for the evaluation of bone heath in numerous studies, with recent advances in medical imaging, virtual methods of biomechanics are rapidly evolving in the human literature. The objective of the current study was to evaluate the feasibility of determining the elastic and failure properties of poultry long bones using established methods of analysis from the human literature. In order to incorporate a large range of bone sizes and densities, a small number of specimens were utilized from an ongoing study of Regmi et al. (2016) that involved humeri and tibiae from 3 groups of animals (10 from each) including aviary, enriched, and conventional housing systems. Half the animals from each group were used for 'training' that involved the development of a regression equation relating bone density and geometry to bending properties from conventional mechanical tests. The remaining specimens from each group were used for 'testing' in which the mechanical properties from conventional tests were compared to those predicted by the regression equations. Based on the regression equations, the coefficients of determination for the 'test' set of data were 0.798 for bending bone stiffness and 0.901 for the yield (or failure) moment of the bones. All regression slopes and intercepts values for the tests versus predicted plots were not significantly different from 1 and 0, respectively. The study showed the feasibility of developing future methods of virtual biomechanics for the evaluation of poultry long bones. With further development, virtual biomechanics may have utility in future in vivo studies to assess laying hen bone health over time without the need to sacrifice large groups of animals at each time point. © 2016 Poultry Science Association Inc.

Comparison of low-dose spinal anesthesia and single-shot femoral block combination with conventional dose spinal anesthesia in outpatient arthroscopic meniscus repair.

PubMed

Turhan, K S Cakar; Akmese, R; Ozkan, F; Okten, F F

2015-04-01

In the current prospective, randomized study, we aimed to compare the effects of low dose selective spinal anesthesia with 5 mg of hyperbaric bupivacaine and single-shot femoral nerve block combination with conventional dose selective spinal anesthesia in terms of intraoperative anesthesia characteristics, block recovery characteristics, and postoperative analgesic consumption. After obtaining institutional Ethics Committee approval, 52 ASA I-II patients aged 25-65, undergoing arthroscopic meniscus repair were randomly assigned to Group S (conventional dose selective spinal anesthesia with 10 mg bupivacaine) and Group FS (low-dose selective spinal anesthesia with 5mg bupivacaine +single-shot femoral block with 0.25% bupivacaine). Primary endpoints were time to reach T12 sensory block level, L2 regression, and complete motor block regression. Secondary endpoints were maximum sensory block level (MSBL); time to reach MSBL, time to first urination, time to first analgesic consumption and pain severity at the time of first mobilization. Demographic characteristics were similar in both groups (p > 0.05). MSBL and time to reach T12 sensory level were similar in both groups (p > 0.05). Time to reach L2 regression, complete motor block regression, and time to first micturition were significantly shorter; time to first analgesic consumption was significantly longer; and total analgesic consumption and severity of pain at time of first mobilization were significantly lower in Group FS (p < 0.05). The findings of the current study suggest that addition of single-shot femoral block to low dose spinal anesthesia could be an alternative to conventional dose spinal anesthesia in outpatient arthroscopic meniscus repair. NCT02322372.

The rapid measurement of soil carbon stock using near-infrared technology

NASA Astrophysics Data System (ADS)

Kusumo, B. H.; Sukartono; Bustan

2018-03-01

As a soil pool stores carbon (C) three times higher than an atmospheric pool, the depletion of C stock in the soil will significantly increase the concentration of CO2 in the atmosphere, causing global warming. However, the monitoring or measurement of soil C stock using conventional procedures is time-consuming and expensive. So it requires a rapid and non-destructive technique that is simple and does not need chemical substances. This research is aimed at testing whether near-infrared (NIR) technology is able to rapidly measure C stock in the soil. Soil samples were collected from an agricultural land at the sub-district of Kayangan, North Lombok, Indonesia. The coordinates of the samples were recorded. Parts of the samples were analyzed using conventional procedure (Walkley and Black) and some other parts were scanned using near-infrared spectroscopy (NIRS) for soil spectral collection. Partial Least Square Regression (PLSR) was used to develop models from soil C data measured by conventional analysis and from spectral data scanned by NIRS. The best model was moderately successful to measure soil C stock in the study area in North Lombok. This indicates that the NIR technology can be further used to monitor the change of soil C stock in the soil.

Exploring unobserved heterogeneity in bicyclists' red-light running behaviors at different crossing facilities.

PubMed

Guo, Yanyong; Li, Zhibin; Wu, Yao; Xu, Chengcheng

2018-06-01

Bicyclists running the red light at crossing facilities increase the potential of colliding with motor vehicles. Exploring the contributing factors could improve the prediction of running red-light probability and develop countermeasures to reduce such behaviors. However, individuals could have unobserved heterogeneities in running a red light, which make the accurate prediction more challenging. Traditional models assume that factor parameters are fixed and cannot capture the varying impacts on red-light running behaviors. In this study, we employed the full Bayesian random parameters logistic regression approach to account for the unobserved heterogeneous effects. Two types of crossing facilities were considered which were the signalized intersection crosswalks and the road segment crosswalks. Electric and conventional bikes were distinguished in the modeling. Data were collected from 16 crosswalks in urban area of Nanjing, China. Factors such as individual characteristics, road geometric design, environmental features, and traffic variables were examined. Model comparison indicates that the full Bayesian random parameters logistic regression approach is statistically superior to the standard logistic regression model. More red-light runners are predicted at signalized intersection crosswalks than at road segment crosswalks. Factors affecting red-light running behaviors are gender, age, bike type, road width, presence of raised median, separation width, signal type, green ratio, bike and vehicle volume, and average vehicle speed. Factors associated with the unobserved heterogeneity are gender, bike type, signal type, separation width, and bike volume. Copyright © 2018 Elsevier Ltd. All rights reserved.

Association between response rates and survival outcomes in patients with newly diagnosed multiple myeloma. A systematic review and meta-regression analysis.

PubMed

Mainou, Maria; Madenidou, Anastasia-Vasiliki; Liakos, Aris; Paschos, Paschalis; Karagiannis, Thomas; Bekiari, Eleni; Vlachaki, Efthymia; Wang, Zhen; Murad, Mohammad Hassan; Kumar, Shaji; Tsapas, Apostolos

2017-06-01

We performed a systematic review and meta-regression analysis of randomized control trials to investigate the association between response to initial treatment and survival outcomes in patients with newly diagnosed multiple myeloma (MM). Response outcomes included complete response (CR) and the combined outcome of CR or very good partial response (VGPR), while survival outcomes were overall survival (OS) and progression-free survival (PFS). We used random-effect meta-regression models and conducted sensitivity analyses based on definition of CR and study quality. Seventy-two trials were included in the systematic review, 63 of which contributed data in meta-regression analyses. There was no association between OS and CR in patients without autologous stem cell transplant (ASCT) (regression coefficient: .02, 95% confidence interval [CI] -0.06, 0.10), in patients undergoing ASCT (-.11, 95% CI -0.44, 0.22) and in trials comparing ASCT with non-ASCT patients (.04, 95% CI -0.29, 0.38). Similarly, OS did not correlate with the combined metric of CR or VGPR, and no association was evident between response outcomes and PFS. Sensitivity analyses yielded similar results. This meta-regression analysis suggests that there is no association between conventional response outcomes and survival in patients with newly diagnosed MM. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

On neural networks in identification and control of dynamic systems

NASA Technical Reports Server (NTRS)

Phan, Minh; Juang, Jer-Nan; Hyland, David C.

1993-01-01

This paper presents a discussion of the applicability of neural networks in the identification and control of dynamic systems. Emphasis is placed on the understanding of how the neural networks handle linear systems and how the new approach is related to conventional system identification and control methods. Extensions of the approach to nonlinear systems are then made. The paper explains the fundamental concepts of neural networks in their simplest terms. Among the topics discussed are feed forward and recurrent networks in relation to the standard state-space and observer models, linear and nonlinear auto-regressive models, linear, predictors, one-step ahead control, and model reference adaptive control for linear and nonlinear systems. Numerical examples are presented to illustrate the application of these important concepts.

Conditional parametric models for storm sewer runoff

NASA Astrophysics Data System (ADS)

Jonsdottir, H.; Nielsen, H. Aa; Madsen, H.; Eliasson, J.; Palsson, O. P.; Nielsen, M. K.

2007-05-01

The method of conditional parametric modeling is introduced for flow prediction in a sewage system. It is a well-known fact that in hydrological modeling the response (runoff) to input (precipitation) varies depending on soil moisture and several other factors. Consequently, nonlinear input-output models are needed. The model formulation described in this paper is similar to the traditional linear models like final impulse response (FIR) and autoregressive exogenous (ARX) except that the parameters vary as a function of some external variables. The parameter variation is modeled by local lines, using kernels for local linear regression. As such, the method might be referred to as a nearest neighbor method. The results achieved in this study were compared to results from the conventional linear methods, FIR and ARX. The increase in the coefficient of determination is substantial. Furthermore, the new approach conserves the mass balance better. Hence this new approach looks promising for various hydrological models and analysis.

The Type Ia Supernova Color-Magnitude Relation and Host Galaxy Dust: A Simple Hierarchical Bayesian Model

NASA Astrophysics Data System (ADS)

Mandel, Kaisey S.; Scolnic, Daniel M.; Shariff, Hikmatali; Foley, Ryan J.; Kirshner, Robert P.

2017-06-01

Conventional Type Ia supernova (SN Ia) cosmology analyses currently use a simplistic linear regression of magnitude versus color and light curve shape, which does not model intrinsic SN Ia variations and host galaxy dust as physically distinct effects, resulting in low color-magnitude slopes. We construct a probabilistic generative model for the dusty distribution of extinguished absolute magnitudes and apparent colors as the convolution of an intrinsic SN Ia color-magnitude distribution and a host galaxy dust reddening-extinction distribution. If the intrinsic color-magnitude (M B versus B - V) slope {β }{int} differs from the host galaxy dust law R B , this convolution results in a specific curve of mean extinguished absolute magnitude versus apparent color. The derivative of this curve smoothly transitions from {β }{int} in the blue tail to R B in the red tail of the apparent color distribution. The conventional linear fit approximates this effective curve near the average apparent color, resulting in an apparent slope {β }{app} between {β }{int} and R B . We incorporate these effects into a hierarchical Bayesian statistical model for SN Ia light curve measurements, and analyze a data set of SALT2 optical light curve fits of 248 nearby SNe Ia at z< 0.10. The conventional linear fit gives {β }{app}≈ 3. Our model finds {β }{int}=2.3+/- 0.3 and a distinct dust law of {R}B=3.8+/- 0.3, consistent with the average for Milky Way dust, while correcting a systematic distance bias of ˜0.10 mag in the tails of the apparent color distribution. Finally, we extend our model to examine the SN Ia luminosity-host mass dependence in terms of intrinsic and dust components.

Variation between hospitals in inpatient admission practices for self-harm patients and its impact on repeat presentation.

PubMed

Carroll, R; Corcoran, P; Griffin, E; Perry, I; Arensman, E; Gunnell, D; Metcalfe, C

2016-11-01

Self-harm patient management varies markedly between hospitals, with fourfold differences in the proportion of patients who are admitted to a medical or psychiatric inpatient bed. The current study aimed to investigate whether differences in admission practices are associated with patient outcomes (repeat self-harm) while accounting for differences in patient case mix. Data came from the National Self-Harm Registry Ireland. A prospective cohort of 43,595 self-harm patients presenting to hospital between 2007 and 2012 were included. As well as conventional regression analysis, instrumental variable (IV) methods utilising between hospital differences in rates of hospital admission were used in an attempt to gain unbiased estimates of the association of admission with risk of repeat self-harm. The proportion of self-harm patients admitted to a medical bed varied from 10 to 74 % between hospitals. Conventional regression and IV analysis suggested medical admission was not associated with risk of repeat self-harm. Psychiatric inpatient admission was associated with an increased risk of repeat self-harm in both conventional and IV analyses. This increased risk persisted in analyses stratified by gender and when restricted to self-poisoning patients only. No strong evidence was found to suggest medical admission reduces the risk of repeat self-harm. Models of health service provision that encourage prompt mental health assessment in the emergency department and avoid unnecessary medical admission of self-harm patients appear warranted. Psychiatric inpatient admission may be associated with a heightened risk of repeat self-harm in some patients, but these findings could be biased by residual confounding and require replication.

Slow walking model for children with multiple disabilities via an application of humanoid robot

NASA Astrophysics Data System (ADS)

Wang, ZeFeng; Peyrodie, Laurent; Cao, Hua; Agnani, Olivier; Watelain, Eric; Wang, HaoPing

2016-02-01

Walk training research with children having multiple disabilities is presented. Orthosis aid in walking for children with multiple disabilities such as Cerebral Palsy continues to be a clinical and technological challenge. In order to reduce pain and improve treatment strategies, an intermediate structure - humanoid robot NAO - is proposed as an assay platform to study walking training models, to be transferred to future special exoskeletons for children. A suitable and stable walking model is proposed for walk training. It would be simulated and tested on NAO. This comparative study of zero moment point (ZMP) supports polygons and energy consumption validates the model as more stable than the conventional NAO. Accordingly direction variation of the center of mass and the slopes of linear regression knee/ankle angles, the Slow Walk model faithfully emulates the gait pattern of children.

Developing a stroke severity index based on administrative data was feasible using data mining techniques.

PubMed

Sung, Sheng-Feng; Hsieh, Cheng-Yang; Kao Yang, Yea-Huei; Lin, Huey-Juan; Chen, Chih-Hung; Chen, Yu-Wei; Hu, Ya-Han

2015-11-01

Case-mix adjustment is difficult for stroke outcome studies using administrative data. However, relevant prescription, laboratory, procedure, and service claims might be surrogates for stroke severity. This study proposes a method for developing a stroke severity index (SSI) by using administrative data. We identified 3,577 patients with acute ischemic stroke from a hospital-based registry and analyzed claims data with plenty of features. Stroke severity was measured using the National Institutes of Health Stroke Scale (NIHSS). We used two data mining methods and conventional multiple linear regression (MLR) to develop prediction models, comparing the model performance according to the Pearson correlation coefficient between the SSI and the NIHSS. We validated these models in four independent cohorts by using hospital-based registry data linked to a nationwide administrative database. We identified seven predictive features and developed three models. The k-nearest neighbor model (correlation coefficient, 0.743; 95% confidence interval: 0.737, 0.749) performed slightly better than the MLR model (0.742; 0.736, 0.747), followed by the regression tree model (0.737; 0.731, 0.742). In the validation cohorts, the correlation coefficients were between 0.677 and 0.725 for all three models. The claims-based SSI enables adjusting for disease severity in stroke studies using administrative data. Copyright © 2015 Elsevier Inc. All rights reserved.

Dynamic Network Logistic Regression: A Logistic Choice Analysis of Inter- and Intra-Group Blog Citation Dynamics in the 2004 US Presidential Election

PubMed Central

2013-01-01

Methods for analysis of network dynamics have seen great progress in the past decade. This article shows how Dynamic Network Logistic Regression techniques (a special case of the Temporal Exponential Random Graph Models) can be used to implement decision theoretic models for network dynamics in a panel data context. We also provide practical heuristics for model building and assessment. We illustrate the power of these techniques by applying them to a dynamic blog network sampled during the 2004 US presidential election cycle. This is a particularly interesting case because it marks the debut of Internet-based media such as blogs and social networking web sites as institutionally recognized features of the American political landscape. Using a longitudinal sample of all Democratic National Convention/Republican National Convention–designated blog citation networks, we are able to test the influence of various strategic, institutional, and balance-theoretic mechanisms as well as exogenous factors such as seasonality and political events on the propensity of blogs to cite one another over time. Using a combination of deviance-based model selection criteria and simulation-based model adequacy tests, we identify the combination of processes that best characterizes the choice behavior of the contending blogs. PMID:24143060

Preoperative prediction of inpatient recovery of function after total hip arthroplasty using performance-based tests: a prospective cohort study.

PubMed

Oosting, Ellen; Hoogeboom, Thomas J; Appelman-de Vries, Suzan A; Swets, Adam; Dronkers, Jaap J; van Meeteren, Nico L U

2016-01-01

The aim of this study was to evaluate the value of conventional factors, the Risk Assessment and Predictor Tool (RAPT) and performance-based functional tests as predictors of delayed recovery after total hip arthroplasty (THA). A prospective cohort study in a regional hospital in the Netherlands with 315 patients was attending for THA in 2012. The dependent variable recovery of function was assessed with the Modified Iowa Levels of Assistance scale. Delayed recovery was defined as taking more than 3 days to walk independently. Independent variables were age, sex, BMI, Charnley score, RAPT score and scores for four performance-based tests [2-minute walk test, timed up and go test (TUG), 10-meter walking test (10 mW) and hand grip strength]. Regression analysis with all variables identified older age (>70 years), Charnley score C, slow walking speed (10 mW >10.0 s) and poor functional mobility (TUG >10.5 s) as the best predictors of delayed recovery of function. This model (AUC 0.85, 95% CI 0.79-0.91) performed better than a model with conventional factors and RAPT scores, and significantly better (p = 0.04) than a model with only conventional factors (AUC 0.81, 95% CI 0.74-0.87). The combination of performance-based tests and conventional factors predicted inpatient functional recovery after THA. Two simple functional performance-based tests have a significant added value to a more conventional screening with age and comorbidities to predict recovery of functioning immediately after total hip surgery. Patients over 70 years old, with comorbidities, with a TUG score >10.5 s and a walking speed >1.0 m/s are at risk for delayed recovery of functioning. Those high risk patients need an accurate discharge plan and could benefit from targeted pre- and postoperative therapeutic exercise programs.

Alternative approaches to predicting methane emissions from dairy cows.

PubMed

Mills, J A N; Kebreab, E; Yates, C M; Crompton, L A; Cammell, S B; Dhanoa, M S; Agnew, R E; France, J

2003-12-01

Previous attempts to apply statistical models, which correlate nutrient intake with methane production, have been of limited value where predictions are obtained for nutrient intakes and diet types outside those used in model construction. Dynamic mechanistic models have proved more suitable for extrapolation, but they remain computationally expensive and are not applied easily in practical situations. The first objective of this research focused on employing conventional techniques to generate statistical models of methane production appropriate to United Kingdom dairy systems. The second objective was to evaluate these models and a model published previously using both United Kingdom and North American data sets. Thirdly, nonlinear models were considered as alternatives to the conventional linear regressions. The United Kingdom calorimetry data used to construct the linear models also were used to develop the three nonlinear alternatives that were all of modified Mitscherlich (monomolecular) form. Of the linear models tested, an equation from the literature proved most reliable across the full range of evaluation data (root mean square prediction error = 21.3%). However, the Mitscherlich models demonstrated the greatest degree of adaptability across diet types and intake level. The most successful model for simulating the independent data was a modified Mitscherlich equation with the steepness parameter set to represent dietary starch-to-ADF ratio (root mean square prediction error = 20.6%). However, when such data were unavailable, simpler Mitscherlich forms relating dry matter or metabolizable energy intake to methane production remained better alternatives relative to their linear counterparts.

The comparison between several robust ridge regression estimators in the presence of multicollinearity and multiple outliers

NASA Astrophysics Data System (ADS)

Zahari, Siti Meriam; Ramli, Norazan Mohamed; Moktar, Balkiah; Zainol, Mohammad Said

2014-09-01

In the presence of multicollinearity and multiple outliers, statistical inference of linear regression model using ordinary least squares (OLS) estimators would be severely affected and produces misleading results. To overcome this, many approaches have been investigated. These include robust methods which were reported to be less sensitive to the presence of outliers. In addition, ridge regression technique was employed to tackle multicollinearity problem. In order to mitigate both problems, a combination of ridge regression and robust methods was discussed in this study. The superiority of this approach was examined when simultaneous presence of multicollinearity and multiple outliers occurred in multiple linear regression. This study aimed to look at the performance of several well-known robust estimators; M, MM, RIDGE and robust ridge regression estimators, namely Weighted Ridge M-estimator (WRM), Weighted Ridge MM (WRMM), Ridge MM (RMM), in such a situation. Results of the study showed that in the presence of simultaneous multicollinearity and multiple outliers (in both x and y-direction), the RMM and RIDGE are more or less similar in terms of superiority over the other estimators, regardless of the number of observation, level of collinearity and percentage of outliers used. However, when outliers occurred in only single direction (y-direction), the WRMM estimator is the most superior among the robust ridge regression estimators, by producing the least variance. In conclusion, the robust ridge regression is the best alternative as compared to robust and conventional least squares estimators when dealing with simultaneous presence of multicollinearity and outliers.

System Identification Applied to Dynamic CFD Simulation and Wind Tunnel Data

NASA Technical Reports Server (NTRS)

Murphy, Patrick C.; Klein, Vladislav; Frink, Neal T.; Vicroy, Dan D.

2011-01-01

Demanding aerodynamic modeling requirements for military and civilian aircraft have provided impetus for researchers to improve computational and experimental techniques. Model validation is a key component for these research endeavors so this study is an initial effort to extend conventional time history comparisons by comparing model parameter estimates and their standard errors using system identification methods. An aerodynamic model of an aircraft performing one-degree-of-freedom roll oscillatory motion about its body axes is developed. The model includes linear aerodynamics and deficiency function parameters characterizing an unsteady effect. For estimation of unknown parameters two techniques, harmonic analysis and two-step linear regression, were applied to roll-oscillatory wind tunnel data and to computational fluid dynamics (CFD) simulated data. The model used for this study is a highly swept wing unmanned aerial combat vehicle. Differences in response prediction, parameters estimates, and standard errors are compared and discussed

Low central venous pressure versus acute normovolemic hemodilution versus conventional fluid management for reducing blood loss in radical retropubic prostatectomy: a randomized controlled trial.

PubMed

Habib, Ashraf S; Moul, Judd W; Polascik, Thomas J; Robertson, Cary N; Roche, Anthony M; White, William D; Hill, Stephen E; Nosnick, Israel; Gan, Tong J

2014-05-01

To compare acute normovolemic hemodilution versus low central venous pressure strategy versus conventional fluid management in reducing intraoperative estimated blood loss, hematocrit drop and need for blood transfusion in patients undergoing radical retropubic prostatectomy under general anesthesia. Patients undergoing radical retropubic prostatectomy under general anesthesia were randomized to conventional fluid management, acute normovolemic hemodilution or low central venous pressure (≤5 mmHg). Treatment effects on estimated blood loss and hematocrit change were tested in multivariable regression models accounting for surgeon, prostate size, and all two-way interactions. Ninety-two patients completed the study. Estimated blood loss (mean ± SD) was significantly lower with low central venous pressure (706 ± 362 ml) compared to acute normovolemic hemodilution (1103 ± 635 ml) and conventional (1051 ± 714 ml) groups (p = 0.0134). There was no difference between the groups in need for blood transfusion, or hematocrit drop from preoperative values. The multivariate model predicting estimated blood loss showed a significant effect of treatment (p = 0.0028) and prostate size (p = 0.0323), accounting for surgeon (p = 0.0013). In the model predicting hematocrit change, accounting for surgeon difference (p = 0.0037), the treatment effect depended on prostate size (p = 0.0007) with the slope of low central venous pressure differing from the other two groups. Hematocrit was predicted to drop more with increased prostate size in acute normovolemic hemodilution and conventional groups but not with low central venous pressure. Limitations include the inability to blind providers to group assignment, possible variability between providers in estimation of blood loss, and the relatively small sample size that was not powered to detect differences between the groups in need for blood transfusion. Maintaining low central venous pressure reduced estimated blood loss compared to conventional fluid management and acute normovolemic hemodilution in patients undergoing radical retropubic prostatectomy but there was no difference in allogeneic blood transfusion between the groups.

Competencies for Young European Higher Education Graduates: Labor Market Mismatches and Their Payoffs

ERIC Educational Resources Information Center

Garcia-Aracil, Adela; Van der Velden, Rolf

2008-01-01

Labor market rewards based on competencies are analyzed using a sample of young European higher education (HE) graduates. Estimates of monetary rewards are obtained from conventional earnings regressions, while estimates total rewards are based on job satisfaction and derived through ordered probit regressions. Results for income show that jobs…

Complex regression Doppler optical coherence tomography

NASA Astrophysics Data System (ADS)

Elahi, Sahar; Gu, Shi; Thrane, Lars; Rollins, Andrew M.; Jenkins, Michael W.

2018-04-01

We introduce a new method to measure Doppler shifts more accurately and extend the dynamic range of Doppler optical coherence tomography (OCT). The two-point estimate of the conventional Doppler method is replaced with a regression that is applied to high-density B-scans in polar coordinates. We built a high-speed OCT system using a 1.68-MHz Fourier domain mode locked laser to acquire high-density B-scans (16,000 A-lines) at high enough frame rates (˜100 fps) to accurately capture the dynamics of the beating embryonic heart. Flow phantom experiments confirm that the complex regression lowers the minimum detectable velocity from 12.25 mm / s to 374 μm / s, whereas the maximum velocity of 400 mm / s is measured without phase wrapping. Complex regression Doppler OCT also demonstrates higher accuracy and precision compared with the conventional method, particularly when signal-to-noise ratio is low. The extended dynamic range allows monitoring of blood flow over several stages of development in embryos without adjusting the imaging parameters. In addition, applying complex averaging recovers hidden features in structural images.

What we have learned about minimized extracorporeal circulation versus conventional extracorporeal circulation: an updated meta-analysis.

PubMed

Sun, Yanhua; Gong, Bing; Yuan, Xin; Zheng, Zhe; Wang, Guyan; Chen, Guo; Zhou, Chenghui; Wang, Wei; Ji, Bingyang

2015-08-01

The benefits of minimized extracorporeal circulation (MECC) compared with conventional extracorporeal circulation (CECC) are still in debate. PubMed, EMBASE and the Cochrane Library were searched until November 10, 2014. After quality assessment, we chose a fixed-effects model when the trials showed low heterogeneity, otherwise a random-effects model was used. We performed univariate meta-regression and sensitivity analysis to search for the potential sources of heterogeneity. Cumulative meta-analysis was performed to access the evolution of outcome over time. 41 RCTs enrolling 3744 patients were included after independent article review by 2 authors. MECC significantly reduced atrial fibrillation (RR, 0.76; 95% CI, 0.66 to 0.89; P < 0.001; I2 = 0%), and myocardial infarction (RR, 0.43; 95% CI, 0.26 to 0.71; P = 0.001; I2 = 0%). In addition, the results regarding chest tube drainage, transfusion rate, blood loss, red blood cell transfusion volume, and platelet count favored MECC as well. MECC diminished morbidity of cardiovascular complications postoperatively, conserved blood cells, and reduced allogeneic blood transfusion.

Solubility of caffeine from green tea in supercritical CO2: a theoretical and empirical approach.

PubMed

Gadkari, Pravin Vasantrao; Balaraman, Manohar

2015-12-01

Decaffeination of fresh green tea was carried out with supercritical CO2 in the presence of ethanol as co-solvent. The solubility of caffeine in supercritical CO2 varied from 44.19 × 10(-6) to 149.55 × 10(-6) (mole fraction) over a pressure and temperature range of 15 to 35 MPa and 313 to 333 K, respectively. The maximum solubility of caffeine was obtained at 25 MPa and 323 K. Experimental solubility data were correlated with the theoretical equation of state models Peng-Robinson (PR), Soave Redlich-Kwong (SRK), and Redlich-Kwong (RK). The RK model had regressed experimental data with 15.52 % average absolute relative deviation (AARD). In contrast, Gordillo empirical model regressed the best to experimental data with only 0.96 % AARD. Under supercritical conditions, solubility of caffeine in tea matrix was lower than the solubility of pure caffeine. Further, solubility of caffeine in supercritical CO2 was compared with solubility of pure caffeine in conventional solvents and a maximum solubility 90 × 10(-3) mol fraction was obtained with chloroform.

Use of multispectral Ikonos imagery for discriminating between conventional and conservation agricultural tillage practices

USGS Publications Warehouse

Vina, Andres; Peters, Albert J.; Ji, Lei

2003-01-01

There is a global concern about the increase in atmospheric concentrations of greenhouse gases. One method being discussed to encourage greenhouse gas mitigation efforts is based on a trading system whereby carbon emitters can buy effective mitigation efforts from farmers implementing conservation tillage practices. These practices sequester carbon from the atmosphere, and such a trading system would require a low-cost and accurate method of verification. Remote sensing technology can offer such a verification technique. This paper is focused on the use of standard image processing procedures applied to a multispectral Ikonos image, to determine whether it is possible to validate that farmers have complied with agreements to implement conservation tillage practices. A principal component analysis (PCA) was performed in order to isolate image variance in cropped fields. Analyses of variance (ANOVA) statistical procedures were used to evaluate the capability of each Ikonos band and each principal component to discriminate between conventional and conservation tillage practices. A logistic regression model was implemented on the principal component most effective in discriminating between conventional and conservation tillage, in order to produce a map of the probability of conventional tillage. The Ikonos imagery, in combination with ground-reference information, proved to be a useful tool for verification of conservation tillage practices.

Dynamics modeling for sugar cane sucrose estimation using time series satellite imagery

NASA Astrophysics Data System (ADS)

Zhao, Yu; Justina, Diego Della; Kazama, Yoriko; Rocha, Jansle Vieira; Graziano, Paulo Sergio; Lamparelli, Rubens Augusto Camargo

2016-10-01

Sugarcane, as one of the most mainstay crop in Brazil, plays an essential role in ethanol production. To monitor sugarcane crop growth and predict sugarcane sucrose content, remote sensing technology plays an essential role while accurate and timely crop growth information is significant, in particularly for large scale farming. We focused on the issues of sugarcane sucrose content estimation using time-series satellite image. Firstly, we calculated the spectral features and vegetation indices to make them be correspondence to the sucrose accumulation biological mechanism. Secondly, we improved the statistical regression model considering more other factors. The evaluation was performed and we got precision of 90% which is about 20% higher than the conventional method. The validation results showed that prediction accuracy using our sugarcane growth modeling and improved mix model is satisfied.

M5 model tree based predictive modeling of road accidents on non-urban sections of highways in India.

PubMed

Singh, Gyanendra; Sachdeva, S N; Pal, Mahesh

2016-11-01

This work examines the application of M5 model tree and conventionally used fixed/random effect negative binomial (FENB/RENB) regression models for accident prediction on non-urban sections of highway in Haryana (India). Road accident data for a period of 2-6 years on different sections of 8 National and State Highways in Haryana was collected from police records. Data related to road geometry, traffic and road environment related variables was collected through field studies. Total two hundred and twenty two data points were gathered by dividing highways into sections with certain uniform geometric characteristics. For prediction of accident frequencies using fifteen input parameters, two modeling approaches: FENB/RENB regression and M5 model tree were used. Results suggest that both models perform comparably well in terms of correlation coefficient and root mean square error values. M5 model tree provides simple linear equations that are easy to interpret and provide better insight, indicating that this approach can effectively be used as an alternative to RENB approach if the sole purpose is to predict motor vehicle crashes. Sensitivity analysis using M5 model tree also suggests that its results reflect the physical conditions. Both models clearly indicate that to improve safety on Indian highways minor accesses to the highways need to be properly designed and controlled, the service roads to be made functional and dispersion of speeds is to be brought down. Copyright © 2016 Elsevier Ltd. All rights reserved.

Dairy calf management-A comparison of practices and producer attitudes among conventional and organic herds.

PubMed

Pempek, J A; Schuenemann, G M; Holder, E; Habing, G G

2017-10-01

Dairy calves are at high risk for morbidity and mortality early in life. Understanding producer attitudes is important for implementation of best management practices to improve calf health. The objectives of this study were to evaluate usage frequency and producer attitudes on key calf management practices between conventional and organic dairy operations. A cross-sectional survey was mailed to conventional and organic dairy producers in Ohio and Michigan that included questions on cow-calf separation, colostrum management, and vaccination use. The overall survey response rate was 49% (727/1,488); 449 and 172 conventional and organic producer respondents, respectively, were included in the final analysis. Binary, cumulative, and multinomial logistic regression models were used to test differences within and between herd types for management practices and producer attitudes. The majority of conventional (64%, 279/439) producers reported separating the calf from the dam 30 min to 6 h after birth. More organic (34%, 56/166) than conventional (18%, 80/439) producers reported separation 6 to 12 h after birth, and organic producers were more likely to agree time before separation is beneficial. Few conventional (10%, 44/448) and organic (3%, 5/171) producers reported measuring colostrum quality. Most conventional producers (68%, 304/448) hand-fed the first feeding of colostrum, whereas the majority of organic producers (38%, 69/171) allowed calves to nurse colostrum. Last, 44% (188/430) of conventional producers reported vaccinating their calves for respiratory disease, compared with 14% (22/162) of organic producers; organic producers were more likely to perceive vaccines as ineffective and harmful to calf health. Thus, the usage frequency and perceived risks and benefits of calf management practices vary considerably between conventional and organic dairy producers. These findings provide helpful information to understand decision making at the herd level regarding key calf management and health practices, regardless of production systems. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Statistical-learning strategies generate only modestly performing predictive models for urinary symptoms following external beam radiotherapy of the prostate: A comparison of conventional and machine-learning methods

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

Yahya, Noorazrul, E-mail: noorazrul.yahya@research.uwa.edu.au; Ebert, Martin A.; Bulsara, Max

Purpose: Given the paucity of available data concerning radiotherapy-induced urinary toxicity, it is important to ensure derivation of the most robust models with superior predictive performance. This work explores multiple statistical-learning strategies for prediction of urinary symptoms following external beam radiotherapy of the prostate. Methods: The performance of logistic regression, elastic-net, support-vector machine, random forest, neural network, and multivariate adaptive regression splines (MARS) to predict urinary symptoms was analyzed using data from 754 participants accrued by TROG03.04-RADAR. Predictive features included dose-surface data, comorbidities, and medication-intake. Four symptoms were analyzed: dysuria, haematuria, incontinence, and frequency, each with three definitions (grade ≥more » 1, grade ≥ 2 and longitudinal) with event rate between 2.3% and 76.1%. Repeated cross-validations producing matched models were implemented. A synthetic minority oversampling technique was utilized in endpoints with rare events. Parameter optimization was performed on the training data. Area under the receiver operating characteristic curve (AUROC) was used to compare performance using sample size to detect differences of ≥0.05 at the 95% confidence level. Results: Logistic regression, elastic-net, random forest, MARS, and support-vector machine were the highest-performing statistical-learning strategies in 3, 3, 3, 2, and 1 endpoints, respectively. Logistic regression, MARS, elastic-net, random forest, neural network, and support-vector machine were the best, or were not significantly worse than the best, in 7, 7, 5, 5, 3, and 1 endpoints. The best-performing statistical model was for dysuria grade ≥ 1 with AUROC ± standard deviation of 0.649 ± 0.074 using MARS. For longitudinal frequency and dysuria grade ≥ 1, all strategies produced AUROC>0.6 while all haematuria endpoints and longitudinal incontinence models produced AUROC<0.6. Conclusions: Logistic regression and MARS were most likely to be the best-performing strategy for the prediction of urinary symptoms with elastic-net and random forest producing competitive results. The predictive power of the models was modest and endpoint-dependent. New features, including spatial dose maps, may be necessary to achieve better models.« less

Influence of salinity and temperature on acute toxicity of cadmium to Mysidopsis bahia molenock

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

Voyer, R.A.; Modica, G.

1990-01-01

Acute toxicity tests were conducted to compare estimates of toxicity, as modified by salinity and temperature, based on response surface techniques with those derived using conventional test methods, and to compare effect of a single episodic exposure to cadmium as a function of salinity with that of continuous exposure. Regression analysis indicated that mortality following continuous 96-hr exposure is related to linear and quadratic effects of salinity and cadmium at 20 C, and to the linear and quadratic effects of cadmium only at 25C. LC50s decreased with increases in temperature and decreases in salinity. Based on the regression model developed,more » 96-hr LC50s ranged from 15.5 to 28.0 micro Cd/L at 10 and 30% salinities, respectively, at 25C; and from 47 to 85 microgram Cd/L at these salinities at 20C.« less

Prediction of bovine milk technological traits from mid-infrared spectroscopy analysis in dairy cows.

PubMed

Visentin, G; McDermott, A; McParland, S; Berry, D P; Kenny, O A; Brodkorb, A; Fenelon, M A; De Marchi, M

2015-09-01

Rapid, cost-effective monitoring of milk technological traits is a significant challenge for dairy industries specialized in cheese manufacturing. The objective of the present study was to investigate the ability of mid-infrared spectroscopy to predict rennet coagulation time, curd-firming time, curd firmness at 30 and 60min after rennet addition, heat coagulation time, casein micelle size, and pH in cow milk samples, and to quantify associations between these milk technological traits and conventional milk quality traits. Samples (n=713) were collected from 605 cows from multiple herds; the samples represented multiple breeds, stages of lactation, parities, and milking times. Reference analyses were undertaken in accordance with standardized methods, and mid-infrared spectra in the range of 900 to 5,000cm(-1) were available for all samples. Prediction models were developed using partial least squares regression, and prediction accuracy was based on both cross and external validation. The proportion of variance explained by the prediction models in external validation was greatest for pH (71%), followed by rennet coagulation time (55%) and milk heat coagulation time (46%). Models to predict curd firmness 60min from rennet addition and casein micelle size, however, were poor, explaining only 25 and 13%, respectively, of the total variance in each trait within external validation. On average, all prediction models tended to be unbiased. The linear regression coefficient of the reference value on the predicted value varied from 0.17 (casein micelle size regression model) to 0.83 (pH regression model) but all differed from 1. The ratio performance deviation of 1.07 (casein micelle size prediction model) to 1.79 (pH prediction model) for all prediction models in the external validation was <2, suggesting that none of the prediction models could be used for analytical purposes. With the exception of casein micelle size and curd firmness at 60min after rennet addition, the developed prediction models may be useful as a screening method, because the concordance correlation coefficient ranged from 0.63 (heat coagulation time prediction model) to 0.84 (pH prediction model) in the external validation. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Using data mining to predict success in a weight loss trial.

PubMed

Batterham, M; Tapsell, L; Charlton, K; O'Shea, J; Thorne, R

2017-08-01

Traditional methods for predicting weight loss success use regression approaches, which make the assumption that the relationships between the independent and dependent (or logit of the dependent) variable are linear. The aim of the present study was to investigate the relationship between common demographic and early weight loss variables to predict weight loss success at 12 months without making this assumption. Data mining methods (decision trees, generalised additive models and multivariate adaptive regression splines), in addition to logistic regression, were employed to predict: (i) weight loss success (defined as ≥5%) at the end of a 12-month dietary intervention using demographic variables [body mass index (BMI), sex and age]; percentage weight loss at 1 month; and (iii) the difference between actual and predicted weight loss using an energy balance model. The methods were compared by assessing model parsimony and the area under the curve (AUC). The decision tree provided the most clinically useful model and had a good accuracy (AUC 0.720 95% confidence interval = 0.600-0.840). Percentage weight loss at 1 month (≥0.75%) was the strongest predictor for successful weight loss. Within those individuals losing ≥0.75%, individuals with a BMI (≥27 kg m -2 ) were more likely to be successful than those with a BMI between 25 and 27 kg m -2 . Data mining methods can provide a more accurate way of assessing relationships when conventional assumptions are not met. In the present study, a decision tree provided the most parsimonious model. Given that early weight loss cannot be predicted before randomisation, incorporating this information into a post randomisation trial design may give better weight loss results. © 2017 The British Dietetic Association Ltd.

Real-Time Assessment of Fatigue in Patients With Multiple Sclerosis: How Does It Relate to Commonly Used Self-Report Fatigue Questionnaires?

PubMed

Heine, Martin; van den Akker, Lizanne Eva; Blikman, Lyan; Hoekstra, Trynke; van Munster, Erik; Verschuren, Olaf; Visser-Meily, Anne; Kwakkel, Gert

2016-11-01

(1) To assess real-time patterns of fatigue; (2) to assess the association between a real-time fatigue score and 3 commonly used questionnaires (Checklist Individual Strength [CIS] fatigue subscale, Modified Fatigue Impact Scale (MFIS), and Fatigue Severity Scale [FSS]); and (3) to establish factors that confound the association between the real-time fatigue score and the conventional fatigue questionnaires in patients with multiple sclerosis (MS). Cross-sectional study. MS-specialized outpatient facility. Ambulant patients with MS (N=165) experiencing severe self-reported fatigue. Not applicable. A real-time fatigue score was assessed by sending participants 4 text messages on a particular day (How fatigued do you feel at this moment?; score range, 0-10). Latent class growth mixed modeling was used to determine diurnal patterns of fatigue. Regression analyses were used to assess the association between the mean real-time fatigue score and the CIS fatigue subscale, MFIS, and FSS. Significant associations were tested for candidate confounders (eg, disease severity, work status, sleepiness). Four significantly different fatigue profiles were identified by the real-time fatigue score, namely a stable high (n=79), increasing (n=57), stable low (n=16), and decreasing (n=13). The conventional questionnaires correlated poorly (r<.300) with the real-time fatigue score. The Epworth Sleepiness Scale significantly reduced the regression coefficient between the real-time fatigue score and conventional questionnaires, ranging from 15.4% to 35%. Perceived fatigue showed 4 different diurnal patterns in patients with MS. Severity of sleepiness is an important confounder to take into account in the assessment of fatigue. Copyright © 2016 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Complementary and Conventional Medicine Use Among Youth With Recurrent Headaches

PubMed Central

Kemper, Kathi J.; Gombojav, Narangerel; Koch, Thomas K.

2013-01-01

OBJECTIVE: To identify prevalence and patterns of complementary and alternative medicine (CAM) use among youth with recurrent headaches (HA) and evaluate associations with co-occurring health problems and limitations as well as with the use and expenditures for conventional medical care. METHODS: Variables were constructed for youth aged 10 to 17 by using linked data from the 2007 National Health Interview Survey and the 2008 Medical Expenditures Panel Survey. Bivariate, logistic, and 2-part regression analyses were used. RESULTS: Of the 10.6% of youth experiencing HA, 29.6% used CAM, rising to 41% for the many HA sufferers who also experienced difficulties with emotions, concentration, behavior, school attendance, or daily activities. Biologically based products (16.2%) and mind-body therapies (13.3%) were most commonly used, especially by the 86.4% of youth with HA experiencing at least 1 other chronic condition. Compared with non-CAM users, youth with HA who used CAM also had higher expenditures for and use of most types of conventional care. CONCLUSIONS: CAM use is most common among youth with HA experiencing multiple chronic conditions and difficulties in daily functioning. Associations among CAM use, multiple chronic conditions, and higher use of conventional care highlight the need for medical providers to routinely ask about CAM use to meet the complex health needs of their patients and facilitate the optimal integration of care. Research is needed to identify models for coordinating complementary and conventional care within a medical home and to understand the health benefits or risks associated with CAM use in conjunction with conventional treatments for patients with HA. PMID:24127466

SST algorithms in ACSPO reanalysis of AVHRR GAC data from 2002-2013

NASA Astrophysics Data System (ADS)

Petrenko, B.; Ignatov, A.; Kihai, Y.; Zhou, X.; Stroup, J.

2014-05-01

In response to a request from the NOAA Coral Reef Watch Program, NOAA SST Team initiated reprocessing of 4 km resolution GAC data from AVHRRs flown onboard NOAA and MetOp satellites. The objective is to create a longterm Level 2 Advanced Clear-Sky Processor for Oceans (ACSPO) SST product, consistent with NOAA operations. ACSPO-Reanalysis (RAN) is used as input in the NOAA geo-polar blended Level 4 SST and potentially other Level 4 SST products. In the first stage of reprocessing (reanalysis 1, or RAN1), data from NOAA-15, -16, -17, -18, -19, and Metop-A and -B, from 2002-present have been processed with ACSPO v2.20, and matched up with quality controlled in situ data from in situ Quality Monitor (iQuam) version 1. The ~12 years time series of matchups were used to develop and explore the SST retrieval algorithms, with emphasis on minimizing spatial biases in retrieved SSTs, close reproduction of the magnitudes of true SST variations, and maximizing temporal, spatial and inter-platform stability of retrieval metrics. Two types of SST algorithms were considered: conventional SST regressions, and recently developed incremental regressions. The conventional equations were adopted in the EUMETSAT OSI-SAF formulation, which, according to our previous analyses, provide relatively small regional biases and well-balanced combination of precision and sensitivity, in its class. Incremental regression equations were specifically elaborated to automatically correct for model minus observation biases, always present when RTM simulations are employed. Improved temporal stability was achieved by recalculation of SST coefficients from matchups on a daily basis, with a +/-45 day window around the current date. This presentation describes the candidate SST algorithms considered for the next round of ACSPO reanalysis, RAN2.

A Competing Risk Model of First Failure Site after Definitive Chemoradiation Therapy for Locally Advanced Non-Small Cell Lung Cancer.

PubMed

Nygård, Lotte; Vogelius, Ivan R; Fischer, Barbara M; Kjær, Andreas; Langer, Seppo W; Aznar, Marianne C; Persson, Gitte F; Bentzen, Søren M

2018-04-01

The aim of the study was to build a model of first failure site- and lesion-specific failure probability after definitive chemoradiotherapy for inoperable NSCLC. We retrospectively analyzed 251 patients receiving definitive chemoradiotherapy for NSCLC at a single institution between 2009 and 2015. All patients were scanned by fludeoxyglucose positron emission tomography/computed tomography for radiotherapy planning. Clinical patient data and fludeoxyglucose positron emission tomography standardized uptake values from primary tumor and nodal lesions were analyzed by using multivariate cause-specific Cox regression. In patients experiencing locoregional failure, multivariable logistic regression was applied to assess risk of each lesion being the first site of failure. The two models were used in combination to predict probability of lesion failure accounting for competing events. Adenocarcinoma had a lower hazard ratio (HR) of locoregional failure than squamous cell carcinoma (HR = 0.45, 95% confidence interval [CI]: 0.26-0.76, p = 0.003). Distant failures were more common in the adenocarcinoma group (HR = 2.21, 95% CI: 1.41-3.48, p < 0.001). Multivariable logistic regression of individual lesions at the time of first failure showed that primary tumors were more likely to fail than lymph nodes (OR = 12.8, 95% CI: 5.10-32.17, p < 0.001). Increasing peak standardized uptake value was significantly associated with lesion failure (OR = 1.26 per unit increase, 95% CI: 1.12-1.40, p < 0.001). The electronic model is available at http://bit.ly/LungModelFDG. We developed a failure site-specific competing risk model based on patient- and lesion-level characteristics. Failure patterns differed between adenocarcinoma and squamous cell carcinoma, illustrating the limitation of aggregating them into NSCLC. Failure site-specific models add complementary information to conventional prognostic models. Copyright © 2018 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

Spatial modeling in ecology: the flexibility of eigenfunction spatial analyses.

PubMed

Griffith, Daniel A; Peres-Neto, Pedro R

2006-10-01

Recently, analytical approaches based on the eigenfunctions of spatial configuration matrices have been proposed in order to consider explicitly spatial predictors. The present study demonstrates the usefulness of eigenfunctions in spatial modeling applied to ecological problems and shows equivalencies of and differences between the two current implementations of this methodology. The two approaches in this category are the distance-based (DB) eigenvector maps proposed by P. Legendre and his colleagues, and spatial filtering based upon geographic connectivity matrices (i.e., topology-based; CB) developed by D. A. Griffith and his colleagues. In both cases, the goal is to create spatial predictors that can be easily incorporated into conventional regression models. One important advantage of these two approaches over any other spatial approach is that they provide a flexible tool that allows the full range of general and generalized linear modeling theory to be applied to ecological and geographical problems in the presence of nonzero spatial autocorrelation.

NASA Experimental Program to Stimulate Competitive Research: South Carolina

NASA Technical Reports Server (NTRS)

Sutton, Michael A.

2004-01-01

The use of an appropriate relationship model is critical for reliable prediction of future urban growth. Identification of proper variables and mathematic functions and determination of the weights or coefficients are the key tasks for building such a model. Although the conventional logistic regression model is appropriate for handing land use problems, it appears insufficient to address the issue of interdependency of the predictor variables. This study used an alternative approach to simulation and modeling urban growth using artificial neural networks. It developed an operational neural network model trained using a robust backpropagation method. The model was applied in the Myrtle Beach region of South Carolina, and tested with both global datasets and areal datasets to examine the strength of both regional models and areal models. The results indicate that the neural network model not only has many theoretic advantages over other conventional mathematic models in representing the complex urban systems, but also is practically superior to the logistic model in its capability to predict urban growth with better - accuracy and less variation. The neural network model is particularly effective in terms of successfully identifying urban patterns in the rural areas where the logistic model often falls short. It was also found from the area-based tests that there are significant intra-regional differentiations in urban growth with different rules and rates. This suggests that the global modeling approach, or one model for the entire region, may not be adequate for simulation of a urban growth at the regional scale. Future research should develop methods for identification and subdivision of these areas and use a set of area-based models to address the issues of multi-centered, intra- regionally differentiated urban growth.

Estimation of Mangrove Forest Aboveground Biomass Using Multispectral Bands, Vegetation Indices and Biophysical Variables Derived from Optical Satellite Imageries: Rapideye, Planetscope and SENTINEL-2

NASA Astrophysics Data System (ADS)

Balidoy Baloloy, Alvin; Conferido Blanco, Ariel; Gumbao Candido, Christian; Labadisos Argamosa, Reginal Jay; Lovern Caboboy Dumalag, John Bart; Carandang Dimapilis, Lee, , Lady; Camero Paringit, Enrico

2018-04-01

Aboveground biomass estimation (AGB) is essential in determining the environmental and economic values of mangrove forests. Biomass prediction models can be developed through integration of remote sensing, field data and statistical models. This study aims to assess and compare the biomass predictor potential of multispectral bands, vegetation indices and biophysical variables that can be derived from three optical satellite systems: the Sentinel-2 with 10 m, 20 m and 60 m resolution; RapidEye with 5m resolution and PlanetScope with 3m ground resolution. Field data for biomass were collected from a Rhizophoraceae-dominated mangrove forest in Masinloc, Zambales, Philippines where 30 test plots (1.2 ha) and 5 validation plots (0.2 ha) were established. Prior to the generation of indices, images from the three satellite systems were pre-processed using atmospheric correction tools in SNAP (Sentinel-2), ENVI (RapidEye) and python (PlanetScope). The major predictor bands tested are Blue, Green and Red, which are present in the three systems; and Red-edge band from Sentinel-2 and Rapideye. The tested vegetation index predictors are Normalized Differenced Vegetation Index (NDVI), Soil-adjusted Vegetation Index (SAVI), Green-NDVI (GNDVI), Simple Ratio (SR), and Red-edge Simple Ratio (SRre). The study generated prediction models through conventional linear regression and multivariate regression. Higher coefficient of determination (r2) values were obtained using multispectral band predictors for Sentinel-2 (r2 = 0.89) and Planetscope (r2 = 0.80); and vegetation indices for RapidEye (r2 = 0.92). Multivariate Adaptive Regression Spline (MARS) models performed better than the linear regression models with r2 ranging from 0.62 to 0.92. Based on the r2 and root-mean-square errors (RMSE's), the best biomass prediction model per satellite were chosen and maps were generated. The accuracy of predicted biomass maps were high for both Sentinel-2 (r2 = 0.92) and RapidEye data (r2 = 0.91).

Comparative UAV and Field Phenotyping to Assess Yield and Nitrogen Use Efficiency in Hybrid and Conventional Barley.

PubMed

Kefauver, Shawn C; Vicente, Rubén; Vergara-Díaz, Omar; Fernandez-Gallego, Jose A; Kerfal, Samir; Lopez, Antonio; Melichar, James P E; Serret Molins, María D; Araus, José L

2017-01-01

With the commercialization and increasing availability of Unmanned Aerial Vehicles (UAVs) multiple rotor copters have expanded rapidly in plant phenotyping studies with their ability to provide clear, high resolution images. As such, the traditional bottleneck of plant phenotyping has shifted from data collection to data processing. Fortunately, the necessarily controlled and repetitive design of plant phenotyping allows for the development of semi-automatic computer processing tools that may sufficiently reduce the time spent in data extraction. Here we present a comparison of UAV and field based high throughput plant phenotyping (HTPP) using the free, open-source image analysis software FIJI (Fiji is just ImageJ) using RGB (conventional digital cameras), multispectral and thermal aerial imagery in combination with a matching suite of ground sensors in a study of two hybrids and one conventional barely variety with ten different nitrogen treatments, combining different fertilization levels and application schedules. A detailed correlation network for physiological traits and exploration of the data comparing between treatments and varieties provided insights into crop performance under different management scenarios. Multivariate regression models explained 77.8, 71.6, and 82.7% of the variance in yield from aerial, ground, and combined data sets, respectively.

Prediction on carbon dioxide emissions based on fuzzy rules

NASA Astrophysics Data System (ADS)

Pauzi, Herrini; Abdullah, Lazim

2014-06-01

There are several ways to predict air quality, varying from simple regression to models based on artificial intelligence. Most of the conventional methods are not sufficiently able to provide good forecasting performances due to the problems with non-linearity uncertainty and complexity of the data. Artificial intelligence techniques are successfully used in modeling air quality in order to cope with the problems. This paper describes fuzzy inference system (FIS) to predict CO2 emissions in Malaysia. Furthermore, adaptive neuro-fuzzy inference system (ANFIS) is used to compare the prediction performance. Data of five variables: energy use, gross domestic product per capita, population density, combustible renewable and waste and CO2 intensity are employed in this comparative study. The results from the two model proposed are compared and it is clearly shown that the ANFIS outperforms FIS in CO2 prediction.

Temporal Drivers of Liking Based on Functional Data Analysis and Non-Additive Models for Multi-Attribute Time-Intensity Data of Fruit Chews.

PubMed

Kuesten, Carla; Bi, Jian

2018-06-03

Conventional drivers of liking analysis was extended with a time dimension into temporal drivers of liking (TDOL) based on functional data analysis methodology and non-additive models for multiple-attribute time-intensity (MATI) data. The non-additive models, which consider both direct effects and interaction effects of attributes to consumer overall liking, include Choquet integral and fuzzy measure in the multi-criteria decision-making, and linear regression based on variance decomposition. Dynamics of TDOL, i.e., the derivatives of the relative importance functional curves were also explored. Well-established R packages 'fda', 'kappalab' and 'relaimpo' were used in the paper for developing TDOL. Applied use of these methods shows that the relative importance of MATI curves offers insights for understanding the temporal aspects of consumer liking for fruit chews.

Development of hardware system using temperature and vibration maintenance models integration concepts for conventional machines monitoring: a case study

NASA Astrophysics Data System (ADS)

Adeyeri, Michael Kanisuru; Mpofu, Khumbulani; Kareem, Buliaminu

2016-03-01

This article describes the integration of temperature and vibration models for maintenance monitoring of conventional machinery parts in which their optimal and best functionalities are affected by abnormal changes in temperature and vibration values thereby resulting in machine failures, machines breakdown, poor quality of products, inability to meeting customers' demand, poor inventory control and just to mention a few. The work entails the use of temperature and vibration sensors as monitoring probes programmed in microcontroller using C language. The developed hardware consists of vibration sensor of ADXL345, temperature sensor of AD594/595 of type K thermocouple, microcontroller, graphic liquid crystal display, real time clock, etc. The hardware is divided into two: one is based at the workstation (majorly meant to monitor machines behaviour) and the other at the base station (meant to receive transmission of machines information sent from the workstation), working cooperatively for effective functionalities. The resulting hardware built was calibrated, tested using model verification and validated through principles pivoted on least square and regression analysis approach using data read from the gear boxes of extruding and cutting machines used for polyethylene bag production. The results got therein confirmed related correlation existing between time, vibration and temperature, which are reflections of effective formulation of the developed concept.

Joint effect of unlinked genotypes: application to type 2 diabetes in the EPIC-Potsdam case-cohort study.

PubMed

Knüppel, Sven; Meidtner, Karina; Arregui, Maria; Holzhütter, Hermann-Georg; Boeing, Heiner

2015-07-01

Analyzing multiple single nucleotide polymorphisms (SNPs) is a promising approach to finding genetic effects beyond single-locus associations. We proposed the use of multilocus stepwise regression (MSR) to screen for allele combinations as a method to model joint effects, and compared the results with the often used genetic risk score (GRS), conventional stepwise selection, and the shrinkage method LASSO. In contrast to MSR, the GRS, conventional stepwise selection, and LASSO model each genotype by the risk allele doses. We reanalyzed 20 unlinked SNPs related to type 2 diabetes (T2D) in the EPIC-Potsdam case-cohort study (760 cases, 2193 noncases). No SNP-SNP interactions and no nonlinear effects were found. Two SNP combinations selected by MSR (Nagelkerke's R² = 0.050 and 0.048) included eight SNPs with mean allele combination frequency of 2%. GRS and stepwise selection selected nearly the same SNP combinations consisting of 12 and 13 SNPs (Nagelkerke's R² ranged from 0.020 to 0.029). LASSO showed similar results. The MSR method showed the best model fit measured by Nagelkerke's R² suggesting that further improvement may render this method a useful tool in genetic research. However, our comparison suggests that the GRS is a simple way to model genetic effects since it does not consider linkage, SNP-SNP interactions, and no non-linear effects. © 2015 John Wiley & Sons Ltd/University College London.

[Probabilistic models of mortality for patients hospitalized in conventional units].

PubMed

Rué, M; Roqué, M; Solà, J; Macià, M

2001-09-29

We have developed a tool to measure disease severity of patients hospitalized in conventional units in order to evaluate and compare the effectiveness and quality of health care in our setting. A total of 2,274 adult patients admitted consecutively to inpatient units from the Medicine, Surgery and Orthopaedic Surgery, and Trauma Departments of the Corporació Sanitària Parc Taulí of Sabadell, Spain, between November 1, 1997 and September 30, 1998 were included. The following variables were collected: demographic data, previous health state, substance abuse, comorbidity prior to admission, characteristics of the admission, clinical parameters within the first 24 hours of admission, laboratory results and data from the Basic Minimum Data Set of hospital discharges. Multiple logistic regression analysis was used to develop mortality probability models during the hospital stay. The mortality probability model at admission (MPMHOS-0) contained 7 variables associated with mortality during hospital stay: age, urgent admission, chronic cardiac insufficiency, chronic respiratory insufficiency, chronic liver disease, neoplasm, and dementia syndrome. The mortality probability model at 24-48 hours from admission (MPMHOS-24) contained 9 variables: those included in the MPMHOS-0 plus two statistically significant laboratory variables: hemoglobin and creatinine. Severity measures, in particular those presented in this study, can be helpful for the interpretation of hospital mortality rates and can guide mortality or quality committees at the time of investigating health care-related problems.

Hyperspectral imaging for predicting the allicin and soluble solid content of garlic with variable selection algorithms and chemometric models.

PubMed

Rahman, Anisur; Faqeerzada, Mohammad A; Cho, Byoung-Kwan

2018-03-14

Allicin and soluble solid content (SSC) in garlic is the responsible for its pungent flavor and odor. However, current conventional methods such as the use of high-pressure liquid chromatography and a refractometer have critical drawbacks in that they are time-consuming, labor-intensive and destructive procedures. The present study aimed to predict allicin and SSC in garlic using hyperspectral imaging in combination with variable selection algorithms and calibration models. Hyperspectral images of 100 garlic cloves were acquired that covered two spectral ranges, from which the mean spectra of each clove were extracted. The calibration models included partial least squares (PLS) and least squares-support vector machine (LS-SVM) regression, as well as different spectral pre-processing techniques, from which the highest performing spectral preprocessing technique and spectral range were selected. Then, variable selection methods, such as regression coefficients, variable importance in projection (VIP) and the successive projections algorithm (SPA), were evaluated for the selection of effective wavelengths (EWs). Furthermore, PLS and LS-SVM regression methods were applied to quantitatively predict the quality attributes of garlic using the selected EWs. Of the established models, the SPA-LS-SVM model obtained an Rpred2 of 0.90 and standard error of prediction (SEP) of 1.01% for SSC prediction, whereas the VIP-LS-SVM model produced the best result with an Rpred2 of 0.83 and SEP of 0.19 mg g -1 for allicin prediction in the range 1000-1700 nm. Furthermore, chemical images of garlic were developed using the best predictive model to facilitate visualization of the spatial distributions of allicin and SSC. The present study clearly demonstrates that hyperspectral imaging combined with an appropriate chemometrics method can potentially be employed as a fast, non-invasive method to predict the allicin and SSC in garlic. © 2018 Society of Chemical Industry. © 2018 Society of Chemical Industry.

An automated ranking platform for machine learning regression models for meat spoilage prediction using multi-spectral imaging and metabolic profiling.

PubMed

Estelles-Lopez, Lucia; Ropodi, Athina; Pavlidis, Dimitris; Fotopoulou, Jenny; Gkousari, Christina; Peyrodie, Audrey; Panagou, Efstathios; Nychas, George-John; Mohareb, Fady

2017-09-01

Over the past decade, analytical approaches based on vibrational spectroscopy, hyperspectral/multispectral imagining and biomimetic sensors started gaining popularity as rapid and efficient methods for assessing food quality, safety and authentication; as a sensible alternative to the expensive and time-consuming conventional microbiological techniques. Due to the multi-dimensional nature of the data generated from such analyses, the output needs to be coupled with a suitable statistical approach or machine-learning algorithms before the results can be interpreted. Choosing the optimum pattern recognition or machine learning approach for a given analytical platform is often challenging and involves a comparative analysis between various algorithms in order to achieve the best possible prediction accuracy. In this work, "MeatReg", a web-based application is presented, able to automate the procedure of identifying the best machine learning method for comparing data from several analytical techniques, to predict the counts of microorganisms responsible of meat spoilage regardless of the packaging system applied. In particularly up to 7 regression methods were applied and these are ordinary least squares regression, stepwise linear regression, partial least square regression, principal component regression, support vector regression, random forest and k-nearest neighbours. MeatReg" was tested with minced beef samples stored under aerobic and modified atmosphere packaging and analysed with electronic nose, HPLC, FT-IR, GC-MS and Multispectral imaging instrument. Population of total viable count, lactic acid bacteria, pseudomonads, Enterobacteriaceae and B. thermosphacta, were predicted. As a result, recommendations of which analytical platforms are suitable to predict each type of bacteria and which machine learning methods to use in each case were obtained. The developed system is accessible via the link: www.sorfml.com. Copyright © 2017 Elsevier Ltd. All rights reserved.

Early Conventional MRI for Prediction of Neurodevelopmental Impairment in Extremely-Low-Birth-Weight Infants.

PubMed

Slaughter, Laurel A; Bonfante-Mejia, Eliana; Hintz, Susan R; Dvorchik, Igor; Parikh, Nehal A

2016-01-01

Extremely-low-birth-weight (ELBW; ≤1,000 g) infants are at high risk for neurodevelopmental impairments. Conventional brain MRI at term-equivalent age is increasingly used for prediction of outcomes. However, optimal prediction models remain to be determined, especially for cognitive outcomes. The aim was to evaluate the accuracy of a data-driven MRI scoring system to predict neurodevelopmental impairments. 122 ELBW infants had a brain MRI performed at term-equivalent age. Conventional MRI findings were scored with a standardized algorithm and tested using a multivariable regression model to predict neurodevelopmental impairment, defined as one or more of the following at 18-24 months' corrected age: cerebral palsy, bilateral blindness, bilateral deafness requiring amplification, and/or cognitive/language delay. Results were compared with a commonly cited scoring system. In multivariable analyses, only moderate-to-severe gyral maturational delay was a significant predictor of overall neurodevelopmental impairment (OR: 12.6, 95% CI: 2.6, 62.0; p < 0.001). Moderate-to-severe gyral maturational delay also predicted cognitive delay, cognitive delay/death, and neurodevelopmental impairment/death. Diffuse cystic abnormality was a significant predictor of cerebral palsy (OR: 33.6, 95% CI: 4.9, 229.7; p < 0.001). These predictors exhibited high specificity (range: 94-99%) but low sensitivity (30-67%) for the above outcomes. White or gray matter scores, determined using a commonly cited scoring system, did not show significant association with neurodevelopmental impairment. In our cohort, conventional MRI at term-equivalent age exhibited high specificity in predicting neurodevelopmental outcomes. However, sensitivity was suboptimal, suggesting additional clinical factors and biomarkers are needed to enable accurate prognostication. © 2016 S. Karger AG, Basel.

Estimation of lung tumor position from multiple anatomical features on 4D-CT using multiple regression analysis.

PubMed

Ono, Tomohiro; Nakamura, Mitsuhiro; Hirose, Yoshinori; Kitsuda, Kenji; Ono, Yuka; Ishigaki, Takashi; Hiraoka, Masahiro

2017-09-01

To estimate the lung tumor position from multiple anatomical features on four-dimensional computed tomography (4D-CT) data sets using single regression analysis (SRA) and multiple regression analysis (MRA) approach and evaluate an impact of the approach on internal target volume (ITV) for stereotactic body radiotherapy (SBRT) of the lung. Eleven consecutive lung cancer patients (12 cases) underwent 4D-CT scanning. The three-dimensional (3D) lung tumor motion exceeded 5 mm. The 3D tumor position and anatomical features, including lung volume, diaphragm, abdominal wall, and chest wall positions, were measured on 4D-CT images. The tumor position was estimated by SRA using each anatomical feature and MRA using all anatomical features. The difference between the actual and estimated tumor positions was defined as the root-mean-square error (RMSE). A standard partial regression coefficient for the MRA was evaluated. The 3D lung tumor position showed a high correlation with the lung volume (R = 0.92 ± 0.10). Additionally, ITVs derived from SRA and MRA approaches were compared with ITV derived from contouring gross tumor volumes on all 10 phases of the 4D-CT (conventional ITV). The RMSE of the SRA was within 3.7 mm in all directions. Also, the RMSE of the MRA was within 1.6 mm in all directions. The standard partial regression coefficient for the lung volume was the largest and had the most influence on the estimated tumor position. Compared with conventional ITV, average percentage decrease of ITV were 31.9% and 38.3% using SRA and MRA approaches, respectively. The estimation accuracy of lung tumor position was improved by the MRA approach, which provided smaller ITV than conventional ITV. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Two-Stage Residual Inclusion Estimation in Health Services Research and Health Economics.

PubMed

Terza, Joseph V

2018-06-01

Empirical analyses in health services research and health economics often require implementation of nonlinear models whose regressors include one or more endogenous variables-regressors that are correlated with the unobserved random component of the model. In such cases, implementation of conventional regression methods that ignore endogeneity will likely produce results that are biased and not causally interpretable. Terza et al. (2008) discuss a relatively simple estimation method that avoids endogeneity bias and is applicable in a wide variety of nonlinear regression contexts. They call this method two-stage residual inclusion (2SRI). In the present paper, I offer a 2SRI how-to guide for practitioners and a step-by-step protocol that can be implemented with any of the popular statistical or econometric software packages. We introduce the protocol and its Stata implementation in the context of a real data example. Implementation of 2SRI for a very broad class of nonlinear models is then discussed. Additional examples are given. We analyze cigarette smoking as a determinant of infant birthweight using data from Mullahy (1997). It is hoped that the discussion will serve as a practical guide to implementation of the 2SRI protocol for applied researchers. © Health Research and Educational Trust.

Alkaline phosphatase: the next independent predictor of the poor 90-day outcome in alcoholic hepatitis.

PubMed

Kasztelan-Szczerbinska, Beata; Slomka, Maria; Celinski, Krzysztof; Szczerbinski, Mariusz

2013-01-01

Determination of risk factors relevant to 90-day prognosis in AH. Comparison of the conventional prognostic models such as Maddrey's modified discriminant function (mDF) and Child-Pugh-Turcotte (CPT) score with newer ones: the Glasgow Alcoholic Hepatitis Score (GAHS); Age, Bilirubin, INR, Creatinine (ABIC) score, Model for End-Stage Liver Disease (MELD), and MELD-Na in the death prediction. The clinical and laboratory variables obtained at admission were assessed. The mDF, CPT, GAHS, ABIC, MELD, and MELD-Na scores' different areas under the curve (AUCs) and the best threshold values were compared. Logistic regression was used to assess predictors of the 90-day outcome. One hundred sixteen pts fulfilled the inclusion criteria. Twenty (17.4%) pts died and one underwent orthotopic liver transplantation (OLT) within 90 days of follow-up. No statistically significant differences in the models' performances were found. Multivariate logistic regression identified CPT score, alkaline phosphatase (AP) level higher than 1.5 times the upper limit of normal (ULN), and corticosteroids (CS) nonresponse as independent predictors of mortality. The CPT score, AP > 1.5 ULN, and the CS nonresponse had an independent impact on the 90-day survival in AH. Accuracy of all studied scoring systems was comparable.

A New Metric for Land-Atmosphere Coupling Strength: Applications on Observations and Modeling

NASA Astrophysics Data System (ADS)

Tang, Q.; Xie, S.; Zhang, Y.; Phillips, T. J.; Santanello, J. A., Jr.; Cook, D. R.; Riihimaki, L.; Gaustad, K.

2017-12-01

A new metric is proposed to quantify the land-atmosphere (LA) coupling strength and is elaborated by correlating the surface evaporative fraction and impacting land and atmosphere variables (e.g., soil moisture, vegetation, and radiation). Based upon multiple linear regression, this approach simultaneously considers multiple factors and thus represents complex LA coupling mechanisms better than existing single variable metrics. The standardized regression coefficients quantify the relative contributions from individual drivers in a consistent manner, avoiding the potential inconsistency in relative influence of conventional metrics. Moreover, the unique expendable feature of the new method allows us to verify and explore potentially important coupling mechanisms. Our observation-based application of the new metric shows moderate coupling with large spatial variations at the U.S. Southern Great Plains. The relative importance of soil moisture vs. vegetation varies by location. We also show that LA coupling strength is generally underestimated by single variable methods due to their incompleteness. We also apply this new metric to evaluate the representation of LA coupling in the Accelerated Climate Modeling for Energy (ACME) V1 Contiguous United States (CONUS) regionally refined model (RRM). This work is performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-ABS-734201

The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation

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

Jahandideh, Sepideh; Jahandideh, Samad; Asadabadi, Ebrahim Barzegari

2009-11-15

Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R{sup 2} were used to evaluate performancemore » of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R{sup 2} confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.« less

Are the clinical effects of homoeopathy placebo effects? Comparative study of placebo-controlled trials of homoeopathy and allopathy.

PubMed

Shang, Aijing; Huwiler-Müntener, Karin; Nartey, Linda; Jüni, Peter; Dörig, Stephan; Sterne, Jonathan A C; Pewsner, Daniel; Egger, Matthias

Homoeopathy is widely used, but specific effects of homoeopathic remedies seem implausible. Bias in the conduct and reporting of trials is a possible explanation for positive findings of trials of both homoeopathy and conventional medicine. We analysed trials of homoeopathy and conventional medicine and estimated treatment effects in trials least likely to be affected by bias. Placebo-controlled trials of homoeopathy were identified by a comprehensive literature search, which covered 19 electronic databases, reference lists of relevant papers, and contacts with experts. Trials in conventional medicine matched to homoeopathy trials for disorder and type of outcome were randomly selected from the Cochrane Controlled Trials Register (issue 1, 2003). Data were extracted in duplicate and outcomes coded so that odds ratios below 1 indicated benefit. Trials described as double-blind, with adequate randomisation, were assumed to be of higher methodological quality. Bias effects were examined in funnel plots and meta-regression models. 110 homoeopathy trials and 110 matched conventional-medicine trials were analysed. The median study size was 65 participants (range ten to 1573). 21 homoeopathy trials (19%) and nine (8%) conventional-medicine trials were of higher quality. In both groups, smaller trials and those of lower quality showed more beneficial treatment effects than larger and higher-quality trials. When the analysis was restricted to large trials of higher quality, the odds ratio was 0.88 (95% CI 0.65-1.19) for homoeopathy (eight trials) and 0.58 (0.39-0.85) for conventional medicine (six trials). Biases are present in placebo-controlled trials of both homoeopathy and conventional medicine. When account was taken for these biases in the analysis, there was weak evidence for a specific effect of homoeopathic remedies, but strong evidence for specific effects of conventional interventions. This finding is compatible with the notion that the clinical effects of homoeopathy are placebo effects.

Modeling Pathologic Response of Esophageal Cancer to Chemoradiation Therapy Using Spatial-Temporal {sup 18}F-FDG PET Features, Clinical Parameters, and Demographics

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

Zhang, Hao; Tan, Shan; Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan

2014-01-01

Purpose: To construct predictive models using comprehensive tumor features for the evaluation of tumor response to neoadjuvant chemoradiation therapy (CRT) in patients with esophageal cancer. Methods and Materials: This study included 20 patients who underwent trimodality therapy (CRT + surgery) and underwent {sup 18}F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) both before and after CRT. Four groups of tumor features were examined: (1) conventional PET/CT response measures (eg, standardized uptake value [SUV]{sub max}, tumor diameter); (2) clinical parameters (eg, TNM stage, histology) and demographics; (3) spatial-temporal PET features, which characterize tumor SUV intensity distribution, spatial patterns, geometry, and associated changesmore » resulting from CRT; and (4) all features combined. An optimal feature set was identified with recursive feature selection and cross-validations. Support vector machine (SVM) and logistic regression (LR) models were constructed for prediction of pathologic tumor response to CRT, cross-validations being used to avoid model overfitting. Prediction accuracy was assessed by area under the receiver operating characteristic curve (AUC), and precision was evaluated by confidence intervals (CIs) of AUC. Results: When applied to the 4 groups of tumor features, the LR model achieved AUCs (95% CI) of 0.57 (0.10), 0.73 (0.07), 0.90 (0.06), and 0.90 (0.06). The SVM model achieved AUCs (95% CI) of 0.56 (0.07), 0.60 (0.06), 0.94 (0.02), and 1.00 (no misclassifications). With the use of spatial-temporal PET features combined with conventional PET/CT measures and clinical parameters, the SVM model achieved very high accuracy (AUC 1.00) and precision (no misclassifications)—results that were significantly better than when conventional PET/CT measures or clinical parameters and demographics alone were used. For groups with many tumor features (groups 3 and 4), the SVM model achieved significantly higher accuracy than did the LR model. Conclusions: The SVM model that used all features including spatial-temporal PET features accurately and precisely predicted pathologic tumor response to CRT in esophageal cancer.« less

Reflexion on linear regression trip production modelling method for ensuring good model quality

NASA Astrophysics Data System (ADS)

Suprayitno, Hitapriya; Ratnasari, Vita

2017-11-01

Transport Modelling is important. For certain cases, the conventional model still has to be used, in which having a good trip production model is capital. A good model can only be obtained from a good sample. Two of the basic principles of a good sampling is having a sample capable to represent the population characteristics and capable to produce an acceptable error at a certain confidence level. It seems that this principle is not yet quite understood and used in trip production modeling. Therefore, investigating the Trip Production Modelling practice in Indonesia and try to formulate a better modeling method for ensuring the Model Quality is necessary. This research result is presented as follows. Statistics knows a method to calculate span of prediction value at a certain confidence level for linear regression, which is called Confidence Interval of Predicted Value. The common modeling practice uses R2 as the principal quality measure, the sampling practice varies and not always conform to the sampling principles. An experiment indicates that small sample is already capable to give excellent R2 value and sample composition can significantly change the model. Hence, good R2 value, in fact, does not always mean good model quality. These lead to three basic ideas for ensuring good model quality, i.e. reformulating quality measure, calculation procedure, and sampling method. A quality measure is defined as having a good R2 value and a good Confidence Interval of Predicted Value. Calculation procedure must incorporate statistical calculation method and appropriate statistical tests needed. A good sampling method must incorporate random well distributed stratified sampling with a certain minimum number of samples. These three ideas need to be more developed and tested.

Decision Tree based Prediction and Rule Induction for Groundwater Trichloroethene (TCE) Pollution Vulnerability

NASA Astrophysics Data System (ADS)

Park, J.; Yoo, K.

2013-12-01

For groundwater resource conservation, it is important to accurately assess groundwater pollution sensitivity or vulnerability. In this work, we attempted to use data mining approach to assess groundwater pollution vulnerability in a TCE (trichloroethylene) contaminated Korean industrial site. The conventional DRASTIC method failed to describe TCE sensitivity data with a poor correlation with hydrogeological properties. Among the different data mining methods such as Artificial Neural Network (ANN), Multiple Logistic Regression (MLR), Case Base Reasoning (CBR), and Decision Tree (DT), the accuracy and consistency of Decision Tree (DT) was the best. According to the following tree analyses with the optimal DT model, the failure of the conventional DRASTIC method in fitting with TCE sensitivity data may be due to the use of inaccurate weight values of hydrogeological parameters for the study site. These findings provide a proof of concept that DT based data mining approach can be used in predicting and rule induction of groundwater TCE sensitivity without pre-existing information on weights of hydrogeological properties.

Patient-Specific Instrumentation Affects Perioperative Blood Loss in Total Knee Arthroplasty.

PubMed

Cucchi, Davide; Menon, Alessandra; Zanini, Beatrice; Compagnoni, Riccardo; Ferrua, Paolo; Randelli, Pietro

2018-05-23

Patient-specific instrumentation (PSI) may contribute to reduced blood loss related to total knee arthroplasty (TKA). The purpose of this study was to compare the estimated hemoglobin (Hb) and red blood cell volume (RBC) losses in two groups of patients undergoing TKA with PSI and conventional instrumentation. Pre- and postoperative blood samples were collected from 22 patients randomly assigned to receive a PSI-assisted or conventional TKA. Post- to preoperative Hb difference was calculated and RBC loss was estimated according to Sehat et al. A significant difference in Hb reduction in favor of the PSI group was registered on the last day of stay ( p  = 0.0084) and significant treatment effect ( p  = 0.027) on Hb reduction after intervention was found with a regression model for longitudinal measurements. This study demonstrated that PSI leads to a significant trend in earlier Hb regain. These promising results suggest a beneficial effect of PSI in blood loss reduction. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Surrogate Model Application to the Identification of Optimal Groundwater Exploitation Scheme Based on Regression Kriging Method—A Case Study of Western Jilin Province

PubMed Central

An, Yongkai; Lu, Wenxi; Cheng, Weiguo

2015-01-01

This paper introduces a surrogate model to identify an optimal exploitation scheme, while the western Jilin province was selected as the study area. A numerical simulation model of groundwater flow was established first, and four exploitation wells were set in the Tongyu county and Qian Gorlos county respectively so as to supply water to Daan county. Second, the Latin Hypercube Sampling (LHS) method was used to collect data in the feasible region for input variables. A surrogate model of the numerical simulation model of groundwater flow was developed using the regression kriging method. An optimization model was established to search an optimal groundwater exploitation scheme using the minimum average drawdown of groundwater table and the minimum cost of groundwater exploitation as multi-objective functions. Finally, the surrogate model was invoked by the optimization model in the process of solving the optimization problem. Results show that the relative error and root mean square error of the groundwater table drawdown between the simulation model and the surrogate model for 10 validation samples are both lower than 5%, which is a high approximation accuracy. The contrast between the surrogate-based simulation optimization model and the conventional simulation optimization model for solving the same optimization problem, shows the former only needs 5.5 hours, and the latter needs 25 days. The above results indicate that the surrogate model developed in this study could not only considerably reduce the computational burden of the simulation optimization process, but also maintain high computational accuracy. This can thus provide an effective method for identifying an optimal groundwater exploitation scheme quickly and accurately. PMID:26264008

Estimation of Unsteady Aerodynamic Models from Dynamic Wind Tunnel Data

NASA Technical Reports Server (NTRS)

Murphy, Patrick; Klein, Vladislav

2011-01-01

Demanding aerodynamic modelling requirements for military and civilian aircraft have motivated researchers to improve computational and experimental techniques and to pursue closer collaboration in these areas. Model identification and validation techniques are key components for this research. This paper presents mathematical model structures and identification techniques that have been used successfully to model more general aerodynamic behaviours in single-degree-of-freedom dynamic testing. Model parameters, characterizing aerodynamic properties, are estimated using linear and nonlinear regression methods in both time and frequency domains. Steps in identification including model structure determination, parameter estimation, and model validation, are addressed in this paper with examples using data from one-degree-of-freedom dynamic wind tunnel and water tunnel experiments. These techniques offer a methodology for expanding the utility of computational methods in application to flight dynamics, stability, and control problems. Since flight test is not always an option for early model validation, time history comparisons are commonly made between computational and experimental results and model adequacy is inferred by corroborating results. An extension is offered to this conventional approach where more general model parameter estimates and their standard errors are compared.

Paradoxical Behavior of Granger Causality

NASA Astrophysics Data System (ADS)

Witt, Annette; Battaglia, Demian; Gail, Alexander

2013-03-01

Granger causality is a standard tool for the description of directed interaction of network components and is popular in many scientific fields including econometrics, neuroscience and climate science. For time series that can be modeled as bivariate auto-regressive processes we analytically derive an expression for spectrally decomposed Granger Causality (SDGC) and show that this quantity depends only on two out of four groups of model parameters. Then we present examples of such processes whose SDGC expose paradoxical behavior in the sense that causality is high for frequency ranges with low spectral power. For avoiding misinterpretations of Granger causality analysis we propose to complement it by partial spectral analysis. Our findings are illustrated by an example from brain electrophysiology. Finally, we draw implications for the conventional definition of Granger causality. Bernstein Center for Computational Neuroscience Goettingen

Prostate specific antigen and acinar density: a new dimension, the "Prostatocrit".

PubMed

Robinson, Simon; Laniado, Marc; Montgomery, Bruce

2017-01-01

Prostate-specific antigen densities have limited success in diagnosing prostate cancer. We emphasise the importance of the peripheral zone when considered with its cellular constituents, the "prostatocrit". Using zonal volumes and asymmetry of glandular acini, we generate a peripheral zone acinar volume and density. With the ratio to the whole gland, we can better predict high grade and all grade cancer. We can model the gland into its acinar and stromal elements. This new "prostatocrit" model could offer more accurate nomograms for biopsy. 674 patients underwent TRUS and biopsy. Whole gland and zonal volumes were recorded. We compared ratio and acinar volumes when added to a "clinic" model using traditional PSA density. Univariate logistic regression was used to find significant predictors for all and high grade cancer. Backwards multiple logistic regression was used to generate ROC curves comparing the new model to conventional density and PSA alone. Prediction of all grades of prostate cancer: significant variables revealed four significant "prostatocrit" parameters: log peripheral zone acinar density; peripheral zone acinar volume/whole gland acinar volume; peripheral zone acinar density/whole gland volume; peripheral zone acinar density. Acinar model (AUC 0.774), clinic model (AUC 0.745) (P=0.0105). Prediction of high grade prostate cancer: peripheral zone acinar density ("prostatocrit") was the only significant density predictor. Acinar model (AUC 0.811), clinic model (AUC 0.769) (P=0.0005). There is renewed use for ratio and "prostatocrit" density of the peripheral zone in predicting cancer. This outperforms all traditional density measurements. Copyright® by the International Brazilian Journal of Urology.

On estimating gravity anomalies - A comparison of least squares collocation with conventional least squares techniques

NASA Technical Reports Server (NTRS)

Argentiero, P.; Lowrey, B.

1977-01-01

The least squares collocation algorithm for estimating gravity anomalies from geodetic data is shown to be an application of the well known regression equations which provide the mean and covariance of a random vector (gravity anomalies) given a realization of a correlated random vector (geodetic data). It is also shown that the collocation solution for gravity anomalies is equivalent to the conventional least-squares-Stokes' function solution when the conventional solution utilizes properly weighted zero a priori estimates. The mathematical and physical assumptions underlying the least squares collocation estimator are described.

Preventive Screening of Women Who Use Complementary and Alternative Medicine Providers

PubMed Central

Tyree, Patrick T.; Lafferty, William E.

2009-01-01

Abstract Background Many women use complementary and alternative medicine (CAM). Although CAM use has been associated with reductions in conventionally recommended pediatric preventive care (e.g., vaccination), little is known about associations between CAM use and receipt of recommended preventive screening in women. Methods Using Washington State insurance data from 2000 to 2003, the authors generated clustered logistic regression models, examining associations between provider-based CAM use and receipt of screening tests for Chlamydia trachomatis, breast cancer, and cervical cancer: (1) contrasting women who used CAM providers only (alternative use) and women who used both conventional and CAM providers (complementary use) with women who used conventional care only and (2) testing associations between screening and use of four specific CAM provider types—naturopathic physicians, chiropractors, massage therapists, and acupuncturists. Results Both alternative and complementary use was associated with reduced Chlamydia screening. Cancer screening increased with complementary use but decreased with alternative use of CAM. Use of naturopathy was associated with decreased mammography, whereas all four CAM therapies were positively associated with Papanicolaou testing. Conclusions When used in conjunction with conventional care, use of provider-based CAM may signal high interest in various types of health-promoting behavior, including cancer screening. Negative associations between CAM and Chlamydia screening and between naturopathy and mammography require additional study. Interventions with CAM providers and their patients, aimed at improving rates of conventionally recommended screening, might encourage greater focus on preventive care, an important task when CAM providers serve as women's only contact with the healthcare system. PMID:19630554

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.

A novel method linking neural connectivity to behavioral fluctuations: Behavior-regressed connectivity.

PubMed

Passaro, Antony D; Vettel, Jean M; McDaniel, Jonathan; Lawhern, Vernon; Franaszczuk, Piotr J; Gordon, Stephen M

2017-03-01

During an experimental session, behavioral performance fluctuates, yet most neuroimaging analyses of functional connectivity derive a single connectivity pattern. These conventional connectivity approaches assume that since the underlying behavior of the task remains constant, the connectivity pattern is also constant. We introduce a novel method, behavior-regressed connectivity (BRC), to directly examine behavioral fluctuations within an experimental session and capture their relationship to changes in functional connectivity. This method employs the weighted phase lag index (WPLI) applied to a window of trials with a weighting function. Using two datasets, the BRC results are compared to conventional connectivity results during two time windows: the one second before stimulus onset to identify predictive relationships, and the one second after onset to capture task-dependent relationships. In both tasks, we replicate the expected results for the conventional connectivity analysis, and extend our understanding of the brain-behavior relationship using the BRC analysis, demonstrating subject-specific BRC maps that correspond to both positive and negative relationships with behavior. Comparison with Existing Method(s): Conventional connectivity analyses assume a consistent relationship between behaviors and functional connectivity, but the BRC method examines performance variability within an experimental session to understand dynamic connectivity and transient behavior. The BRC approach examines connectivity as it covaries with behavior to complement the knowledge of underlying neural activity derived from conventional connectivity analyses. Within this framework, BRC may be implemented for the purpose of understanding performance variability both within and between participants. Published by Elsevier B.V.

Numerical analysis of combustion characteristics of hybrid rocket motor with multi-section swirl injection

NASA Astrophysics Data System (ADS)

Li, Chengen; Cai, Guobiao; Tian, Hui

2016-06-01

This paper is aimed to analyse the combustion characteristics of hybrid rocket motor with multi-section swirl injection by simulating the combustion flow field. Numerical combustion flow field and combustion performance parameters are obtained through three-dimensional numerical simulations based on a steady numerical model proposed in this paper. The hybrid rocket motor adopts 98% hydrogen peroxide and polyethylene as the propellants. Multiple injection sections are set along the axis of the solid fuel grain, and the oxidizer enters the combustion chamber by means of tangential injection via the injector ports in the injection sections. Simulation results indicate that the combustion flow field structure of the hybrid rocket motor could be improved by multi-section swirl injection method. The transformation of the combustion flow field can greatly increase the fuel regression rate and the combustion efficiency. The average fuel regression rate of the motor with multi-section swirl injection is improved by 8.37 times compared with that of the motor with conventional head-end irrotational injection. The combustion efficiency is increased to 95.73%. Besides, the simulation results also indicate that (1) the additional injection sections can increase the fuel regression rate and the combustion efficiency; (2) the upstream offset of the injection sections reduces the combustion efficiency; and (3) the fuel regression rate and the combustion efficiency decrease with the reduction of the number of injector ports in each injection section.

Environmental influence on mussel (Mytilus edulis) growth - A quantile regression approach

NASA Astrophysics Data System (ADS)

Bergström, Per; Lindegarth, Mats

2016-03-01

The need for methods for sustainable management and use of coastal ecosystems has increased in the last century. A key aspect for obtaining ecologically and economically sustainable aquaculture in threatened coastal areas is the requirement of geographic information of growth and potential production capacity. Growth varies over time and space and depends on a complex pattern of interactions between the bivalve and a diverse range of environmental factors (e.g. temperature, salinity, food availability). Understanding these processes and modelling the environmental control of bivalve growth has been central in aquaculture. In contrast to the most conventional modelling techniques, quantile regression can handle cases where not all factors are measured and provide the possibility to estimate the effect at different levels of the response distribution and give therefore a more complete picture of the relationship between environmental factors and biological response. Observation of the relationships between environmental factors and growth of the bivalve Mytilus edulis revealed relationships that varied both among level of growth rate and within the range of environmental variables along the Swedish west coast. The strongest patterns were found for water oxygen concentration level which had a negative effect on growth for all oxygen levels and growth levels. However, these patterns coincided with differences in growth among periods and very little of the remaining variability within periods could be explained indicating that interactive processes masked the importance of the individual variables. By using quantile regression and local regression (LOESS) this study was able to provide valuable information on environmental factors influencing the growth of M. edulis and important insight for the development of ecosystem based management tools of aquaculture activities, its use in mitigation efforts and successful management of human use of coastal areas.

Cytopathologic differential diagnosis of low-grade urothelial carcinoma and reactive urothelial proliferation in bladder washings: a logistic regression analysis.

PubMed

Cakir, Ebru; Kucuk, Ulku; Pala, Emel Ebru; Sezer, Ozlem; Ekin, Rahmi Gokhan; Cakmak, Ozgur

2017-05-01

Conventional cytomorphologic assessment is the first step to establish an accurate diagnosis in urinary cytology. In cytologic preparations, the separation of low-grade urothelial carcinoma (LGUC) from reactive urothelial proliferation (RUP) can be exceedingly difficult. The bladder washing cytologies of 32 LGUC and 29 RUP were reviewed. The cytologic slides were examined for the presence or absence of the 28 cytologic features. The cytologic criteria showing statistical significance in LGUC were increased numbers of monotonous single (non-umbrella) cells, three-dimensional cellular papillary clusters without fibrovascular cores, irregular bordered clusters, atypical single cells, irregular nuclear overlap, cytoplasmic homogeneity, increased N/C ratio, pleomorphism, nuclear border irregularity, nuclear eccentricity, elongated nuclei, and hyperchromasia (p ˂ 0.05), and the cytologic criteria showing statistical significance in RUP were inflammatory background, mixture of small and large urothelial cells, loose monolayer aggregates, and vacuolated cytoplasm (p ˂ 0.05). When these variables were subjected to a stepwise logistic regression analysis, four features were selected to distinguish LGUC from RUP: increased numbers of monotonous single (non-umbrella) cells, increased nuclear cytoplasmic ratio, hyperchromasia, and presence of small and large urothelial cells (p = 0.0001). By this logistic model of the 32 cases with proven LGUC, the stepwise logistic regression analysis correctly predicted 31 (96.9%) patients with this diagnosis, and of the 29 patients with RUP, the logistic model correctly predicted 26 (89.7%) patients as having this disease. There are several cytologic features to separate LGUC from RUP. Stepwise logistic regression analysis is a valuable tool for determining the most useful cytologic criteria to distinguish these entities. © 2017 APMIS. Published by John Wiley & Sons Ltd.

Rescaling quality of life values from discrete choice experiments for use as QALYs: a cautionary tale

PubMed Central

Flynn, Terry N; Louviere, Jordan J; Marley, Anthony AJ; Coast, Joanna; Peters, Tim J

2008-01-01

Background Researchers are increasingly investigating the potential for ordinal tasks such as ranking and discrete choice experiments to estimate QALY health state values. However, the assumptions of random utility theory, which underpin the statistical models used to provide these estimates, have received insufficient attention. In particular, the assumptions made about the decisions between living states and the death state are not satisfied, at least for some people. Estimated values are likely to be incorrectly anchored with respect to death (zero) in such circumstances. Methods Data from the Investigating Choice Experiments for the preferences of older people CAPability instrument (ICECAP) valuation exercise were analysed. The values (previously anchored to the worst possible state) were rescaled using an ordinal model proposed previously to estimate QALY-like values. Bootstrapping was conducted to vary artificially the proportion of people who conformed to the conventional random utility model underpinning the analyses. Results Only 26% of respondents conformed unequivocally to the assumptions of conventional random utility theory. At least 14% of respondents unequivocally violated the assumptions. Varying the relative proportions of conforming respondents in sensitivity analyses led to large changes in the estimated QALY values, particularly for lower-valued states. As a result these values could be either positive (considered to be better than death) or negative (considered to be worse than death). Conclusion Use of a statistical model such as conditional (multinomial) regression to anchor quality of life values from ordinal data to death is inappropriate in the presence of respondents who do not conform to the assumptions of conventional random utility theory. This is clearest when estimating values for that group of respondents observed in valuation samples who refuse to consider any living state to be worse than death: in such circumstances the model cannot be estimated. Only a valuation task requiring respondents to make choices in which both length and quality of life vary can produce estimates that properly reflect the preferences of all respondents. PMID:18945358

A Small and Slim Coaxial Probe for Single Rice Grain Moisture Sensing

PubMed Central

You, Kok Yeow; Mun, Hou Kit; You, Li Ling; Salleh, Jamaliah; Abbas, Zulkifly

2013-01-01

A moisture detection of single rice grains using a slim and small open-ended coaxial probe is presented. The coaxial probe is suitable for the nondestructive measurement of moisture values in the rice grains ranging from from 9.5% to 26%. Empirical polynomial models are developed to predict the gravimetric moisture content of rice based on measured reflection coefficients using a vector network analyzer. The relationship between the reflection coefficient and relative permittivity were also created using a regression method and expressed in a polynomial model, whose model coefficients were obtained by fitting the data from Finite Element-based simulation. Besides, the designed single rice grain sample holder and experimental set-up were shown. The measurement of single rice grains in this study is more precise compared to the measurement in conventional bulk rice grains, as the random air gap present in the bulk rice grains is excluded. PMID:23493127

A machine learning model with human cognitive biases capable of learning from small and biased datasets.

PubMed

Taniguchi, Hidetaka; Sato, Hiroshi; Shirakawa, Tomohiro

2018-05-09

Human learners can generalize a new concept from a small number of samples. In contrast, conventional machine learning methods require large amounts of data to address the same types of problems. Humans have cognitive biases that promote fast learning. Here, we developed a method to reduce the gap between human beings and machines in this type of inference by utilizing cognitive biases. We implemented a human cognitive model into machine learning algorithms and compared their performance with the currently most popular methods, naïve Bayes, support vector machine, neural networks, logistic regression and random forests. We focused on the task of spam classification, which has been studied for a long time in the field of machine learning and often requires a large amount of data to obtain high accuracy. Our models achieved superior performance with small and biased samples in comparison with other representative machine learning methods.

Diagnostic efficiency of an ability-focused battery.

PubMed

Miller, Justin B; Fichtenberg, Norman L; Millis, Scott R

2010-05-01

An ability-focused battery (AFB) is a selected group of well-validated neuropsychological measures that assess the conventional range of cognitive domains. This study examined the diagnostic efficiency of an AFB for use in clinical decision making with a mixed sample composed of individuals with neurological brain dysfunction and individuals referred for cognitive assessment without evidence of neurological disorders. Using logistic regression analyses and ROC curve analysis, a five-domain model composed of attention, processing speed, visual-spatial reasoning, language/verbal reasoning, and memory domain scores was fitted that had an AUC of.89 (95% CI =.84-.95). A more parsimonious two-domain model using processing speed and memory was also fitted that had an AUC of.90 (95% confidence interval =.84-.95). A model composed of a global ability score calculated from the mean of the individual domain scores was also fitted with an AUC of.88 (95% CI =.82-.94).

Least Square Fast Learning Network for modeling the combustion efficiency of a 300WM coal-fired boiler.

PubMed

Li, Guoqiang; Niu, Peifeng; Wang, Huaibao; Liu, Yongchao

2014-03-01

This paper presents a novel artificial neural network with a very fast learning speed, all of whose weights and biases are determined by the twice Least Square method, so it is called Least Square Fast Learning Network (LSFLN). In addition, there is another difference from conventional neural networks, which is that the output neurons of LSFLN not only receive the information from the hidden layer neurons, but also receive the external information itself directly from the input neurons. In order to test the validity of LSFLN, it is applied to 6 classical regression applications, and also employed to build the functional relation between the combustion efficiency and operating parameters of a 300WM coal-fired boiler. Experimental results show that, compared with other methods, LSFLN with very less hidden neurons could achieve much better regression precision and generalization ability at a much faster learning speed. Copyright © 2013 Elsevier Ltd. All rights reserved.

Revisiting the 'Low BirthWeight paradox' using a model-based definition.

PubMed

Juárez, Sol; Ploubidis, George B; Clarke, Lynda

2014-01-01

Immigrant mothers in Spain have a lower risk of delivering Low BirthWeight (LBW) babies in comparison to Spaniards (LBW paradox). This study aimed at revisiting this finding by applying a model-based threshold as an alternative to the conventional definition of LBW. Vital information data from Madrid was used (2005-2006). LBW was defined in two ways (less than 2500g and Wilcox's proposal). Logistic and linear regression models were run. According to common definition of LBW (less than 2500g) there is evidence to support the LBW paradox in Spain. Nevertheless, when an alternative model-based definition of LBW is used, the paradox is only clearly present in mothers from the rest of Southern America, suggesting a possible methodological bias effect. In the future, any examination of the existence of the LBW paradox should incorporate model-based definitions of LBW in order to avoid methodological bias. Copyright © 2013 SESPAS. Published by Elsevier Espana. All rights reserved.

SkyFACT: high-dimensional modeling of gamma-ray emission with adaptive templates and penalized likelihoods

NASA Astrophysics Data System (ADS)

Storm, Emma; Weniger, Christoph; Calore, Francesca

2017-08-01

We present SkyFACT (Sky Factorization with Adaptive Constrained Templates), a new approach for studying, modeling and decomposing diffuse gamma-ray emission. Like most previous analyses, the approach relies on predictions from cosmic-ray propagation codes like GALPROP and DRAGON. However, in contrast to previous approaches, we account for the fact that models are not perfect and allow for a very large number (gtrsim 105) of nuisance parameters to parameterize these imperfections. We combine methods of image reconstruction and adaptive spatio-spectral template regression in one coherent hybrid approach. To this end, we use penalized Poisson likelihood regression, with regularization functions that are motivated by the maximum entropy method. We introduce methods to efficiently handle the high dimensionality of the convex optimization problem as well as the associated semi-sparse covariance matrix, using the L-BFGS-B algorithm and Cholesky factorization. We test the method both on synthetic data as well as on gamma-ray emission from the inner Galaxy, |l|<90o and |b|<20o, as observed by the Fermi Large Area Telescope. We finally define a simple reference model that removes most of the residual emission from the inner Galaxy, based on conventional diffuse emission components as well as components for the Fermi bubbles, the Fermi Galactic center excess, and extended sources along the Galactic disk. Variants of this reference model can serve as basis for future studies of diffuse emission in and outside the Galactic disk.

Statistical Learning Theory for High Dimensional Prediction: Application to Criterion-Keyed Scale Development

PubMed Central

Chapman, Benjamin P.; Weiss, Alexander; Duberstein, Paul

2016-01-01

Statistical learning theory (SLT) is the statistical formulation of machine learning theory, a body of analytic methods common in “big data” problems. Regression-based SLT algorithms seek to maximize predictive accuracy for some outcome, given a large pool of potential predictors, without overfitting the sample. Research goals in psychology may sometimes call for high dimensional regression. One example is criterion-keyed scale construction, where a scale with maximal predictive validity must be built from a large item pool. Using this as a working example, we first introduce a core principle of SLT methods: minimization of expected prediction error (EPE). Minimizing EPE is fundamentally different than maximizing the within-sample likelihood, and hinges on building a predictive model of sufficient complexity to predict the outcome well, without undue complexity leading to overfitting. We describe how such models are built and refined via cross-validation. We then illustrate how three common SLT algorithms–Supervised Principal Components, Regularization, and Boosting—can be used to construct a criterion-keyed scale predicting all-cause mortality, using a large personality item pool within a population cohort. Each algorithm illustrates a different approach to minimizing EPE. Finally, we consider broader applications of SLT predictive algorithms, both as supportive analytic tools for conventional methods, and as primary analytic tools in discovery phase research. We conclude that despite their differences from the classic null-hypothesis testing approach—or perhaps because of them–SLT methods may hold value as a statistically rigorous approach to exploratory regression. PMID:27454257

Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports.

PubMed

Kessler, R C; van Loo, H M; Wardenaar, K J; Bossarte, R M; Brenner, L A; Cai, T; Ebert, D D; Hwang, I; Li, J; de Jonge, P; Nierenberg, A A; Petukhova, M V; Rosellini, A J; Sampson, N A; Schoevers, R A; Wilcox, M A; Zaslavsky, A M

2016-10-01

Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. Although efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine-learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared with observed scores assessed 10-12 years after baseline. ML model prediction accuracy was also compared with that of conventional logistic regression models. Area under the receiver operating characteristic curve based on ML (0.63 for high chronicity and 0.71-0.76 for the other prospective outcomes) was consistently higher than for the logistic models (0.62-0.70) despite the latter models including more predictors. A total of 34.6-38.1% of respondents with subsequent high persistence chronicity and 40.8-55.8% with the severity indicators were in the top 20% of the baseline ML-predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML-predicted risk distribution. These results confirm that clinically useful MDD risk-stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models.

Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports

PubMed Central

Kessler, Ronald C.; van Loo, Hanna M.; Wardenaar, Klaas J.; Bossarte, Robert M.; Brenner, Lisa A.; Cai, Tianxi; Ebert, David Daniel; Hwang, Irving; Li, Junlong; de Jonge, Peter; Nierenberg, Andrew A.; Petukhova, Maria V.; Rosellini, Anthony J.; Sampson, Nancy A.; Schoevers, Robert A.; Wilcox, Marsha A.; Zaslavsky, Alan M.

2015-01-01

Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. While efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity, and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1,056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared to observed scores assessed 10–12 years after baseline. ML model prediction accuracy was also compared to that of conventional logistic regression models. Area under the receiver operating characteristic curve (AUC) based on ML (.63 for high chronicity and .71–.76 for the other prospective outcomes) was consistently higher than for the logistic models (.62–.70) despite the latter models including more predictors. 34.6–38.1% of respondents with subsequent high persistence-chronicity and 40.8–55.8% with the severity indicators were in the top 20% of the baseline ML predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML predicted risk distribution. These results confirm that clinically useful MDD risk stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models. PMID:26728563

Pain control in a randomized, controlled, clinical trial comparing moist exposed burn ointment and conventional methods in patients with partial-thickness burns.

PubMed

Ang, Erik; Lee, S-T; Gan, Christine S-G; Chan, Y-H; Cheung, Y-B; Machin, D

2003-01-01

Conventional management of partial-thickness burn wounds includes the use of paraffin gauze dressing, frequently with topical silver-based antibacterial creams. Some creams form an overlying slough that renders wound assessment difficult and are painful upon application. An alternative to conventional management, moist exposed burn ointment (MEBO), has been proposed as a topical agent that may accelerate wound healing and have antibacterial and analgesic properties. One hundred fifteen patients with partial-thickness burns were randomly assigned to conventional (n = 58) or MEBO treatment (n = 57). A verbal numerical rating score of pain was made in the morning, after burn dressing, and some 8 hours later. Patient pain profiles were summarized by locally weighted regression smoothing technique curves and the difference between treatments estimated using multilevel regression techniques. Mean verbal numerical rating scale pain levels (cm) in week 1 for all patients were highest at 3.2 for the after dressing assessment, lowest in the evening at 2.6, and intermediate in the morning at 3.0. This pattern continued at similar levels in week 2 and then declined by a mean of 0.5 in all groups in week 3. There was little evidence to suggest a difference in pain levels by treatment group with the exception of the postdressing pain levels in the first week when those receiving MEBO had a mean level of 0.7 cm (95% confidence interval, 0.2 to 1.1) lower than those on conventional therapy. MEBO appeared to bring greater pain relief for the postdressing assessment during the first week after burns. This initial relief, together with comparable pain levels experienced on other occasions, indicates that MEBO could be an alternative to conventional burns management.

Regression techniques for oceanographic parameter retrieval using space-borne microwave radiometry

NASA Technical Reports Server (NTRS)

Hofer, R.; Njoku, E. G.

1981-01-01

Variations of conventional multiple regression techniques are applied to the problem of remote sensing of oceanographic parameters from space. The techniques are specifically adapted to the scanning multichannel microwave radiometer (SMRR) launched on the Seasat and Nimbus 7 satellites to determine ocean surface temperature, wind speed, and atmospheric water content. The retrievals are studied primarily from a theoretical viewpoint, to illustrate the retrieval error structure, the relative importances of different radiometer channels, and the tradeoffs between spatial resolution and retrieval accuracy. Comparisons between regressions using simulated and actual SMMR data are discussed; they show similar behavior.

The potential of iRest in measuring the hand function performance of stroke patients.

PubMed

Abdul Rahman, Hisyam; Khor, Kang Xiang; Yeong, Che Fai; Su, Eileen Lee Ming; Narayanan, Aqilah Leela T

2017-01-01

Clinical scales such as Fugl-Meyer Assessment (FMA) and Motor Assessment Scale (MAS) are widely used to evaluate stroke patient's motor performance. However, there are several limitations with these assessment scales such as subjectivity, lack of repeatability, time-consuming and highly depend on the ability of the physiotherapy. In contrast, robot-based assessments are objective, repeatable, and could potentially reduce the assessment time. However, robot-based assessments are not as well established as conventional assessment scale and the correlation to conventional assessment scale is unclear. This study was carried out to identify important parameters in designing tasks that efficiently assess hand function of stroke patients and to quantify potential benefits of robotic assessment modules to predict the conventional assessment score with iRest. Twelve predictive variables were explored, relating to movement time, velocity, strategy, accuracy and smoothness from three robotic assessment modules which are Draw I, Draw Diamond and Draw Circle. Regression models using up to four predictors were developed to describe the MAS. Results show that the time given should be not too long and it would affect the trajectory error. Besides, result also shows that it is possible to use iRest in predicting MAS score. There is a potential of using iRest, a non-motorized device in predicting MAS score.

Contribution of Bacillus Isolates to the Flavor Profiles of Vanilla Beans Assessed through Aroma Analysis and Chemometrics.

PubMed

Gu, Fenglin; Chen, Yonggan; Fang, Yiming; Wu, Guiping; Tan, Lehe

2015-10-09

Colonizing Bacillus in vanilla (Vanilla planifolia Andrews) beans is involved in glucovanillin hydrolysis and vanillin formation during conventional curing. The flavor profiles of vanilla beans under Bacillus-assisted curing were analyzed through gas chromatography-mass spectrometry, electronic nose, and quantitative sensory analysis. The flavor profiles were analytically compared among the vanilla beans under Bacillus-assisted curing, conventional curing, and non-microorganism-assisted curing. Vanilla beans added with Bacillus vanillea XY18 and Bacillus subtilis XY20 contained higher vanillin (3.58%±0.05% and 3.48%±0.10%, respectively) than vanilla beans that underwent non-microorganism-assisted curing and conventional curing (3.09%±0.14% and 3.21%±0.15%, respectively). Forty-two volatiles were identified from endogenous vanilla metabolism. Five other compounds were identified from exogenous Bacillus metabolism. Electronic nose data confirmed that vanilla flavors produced through the different curing processes were easily distinguished. Quantitative sensory analysis confirmed that Bacillus-assisted curing increased vanillin production without generating any unpleasant sensory attribute. Partial least squares regression further provided a correlation model of different measurements. Overall, we comparatively analyzed the flavor profiles of vanilla beans under Bacillus-assisted curing, indirectly demonstrated the mechanism of vanilla flavor formation by microbes.

Assessing Risk Prediction Models Using Individual Participant Data From Multiple Studies

PubMed Central

Pennells, Lisa; Kaptoge, Stephen; White, Ian R.; Thompson, Simon G.; Wood, Angela M.; Tipping, Robert W.; Folsom, Aaron R.; Couper, David J.; Ballantyne, Christie M.; Coresh, Josef; Goya Wannamethee, S.; Morris, Richard W.; Kiechl, Stefan; Willeit, Johann; Willeit, Peter; Schett, Georg; Ebrahim, Shah; Lawlor, Debbie A.; Yarnell, John W.; Gallacher, John; Cushman, Mary; Psaty, Bruce M.; Tracy, Russ; Tybjærg-Hansen, Anne; Price, Jackie F.; Lee, Amanda J.; McLachlan, Stela; Khaw, Kay-Tee; Wareham, Nicholas J.; Brenner, Hermann; Schöttker, Ben; Müller, Heiko; Jansson, Jan-Håkan; Wennberg, Patrik; Salomaa, Veikko; Harald, Kennet; Jousilahti, Pekka; Vartiainen, Erkki; Woodward, Mark; D'Agostino, Ralph B.; Bladbjerg, Else-Marie; Jørgensen, Torben; Kiyohara, Yutaka; Arima, Hisatomi; Doi, Yasufumi; Ninomiya, Toshiharu; Dekker, Jacqueline M.; Nijpels, Giel; Stehouwer, Coen D. A.; Kauhanen, Jussi; Salonen, Jukka T.; Meade, Tom W.; Cooper, Jackie A.; Cushman, Mary; Folsom, Aaron R.; Psaty, Bruce M.; Shea, Steven; Döring, Angela; Kuller, Lewis H.; Grandits, Greg; Gillum, Richard F.; Mussolino, Michael; Rimm, Eric B.; Hankinson, Sue E.; Manson, JoAnn E.; Pai, Jennifer K.; Kirkland, Susan; Shaffer, Jonathan A.; Shimbo, Daichi; Bakker, Stephan J. L.; Gansevoort, Ron T.; Hillege, Hans L.; Amouyel, Philippe; Arveiler, Dominique; Evans, Alun; Ferrières, Jean; Sattar, Naveed; Westendorp, Rudi G.; Buckley, Brendan M.; Cantin, Bernard; Lamarche, Benoît; Barrett-Connor, Elizabeth; Wingard, Deborah L.; Bettencourt, Richele; Gudnason, Vilmundur; Aspelund, Thor; Sigurdsson, Gunnar; Thorsson, Bolli; Kavousi, Maryam; Witteman, Jacqueline C.; Hofman, Albert; Franco, Oscar H.; Howard, Barbara V.; Zhang, Ying; Best, Lyle; Umans, Jason G.; Onat, Altan; Sundström, Johan; Michael Gaziano, J.; Stampfer, Meir; Ridker, Paul M.; Michael Gaziano, J.; Ridker, Paul M.; Marmot, Michael; Clarke, Robert; Collins, Rory; Fletcher, Astrid; Brunner, Eric; Shipley, Martin; Kivimäki, Mika; Ridker, Paul M.; Buring, Julie; Cook, Nancy; Ford, Ian; Shepherd, James; Cobbe, Stuart M.; Robertson, Michele; Walker, Matthew; Watson, Sarah; Alexander, Myriam; Butterworth, Adam S.; Angelantonio, Emanuele Di; Gao, Pei; Haycock, Philip; Kaptoge, Stephen; Pennells, Lisa; Thompson, Simon G.; Walker, Matthew; Watson, Sarah; White, Ian R.; Wood, Angela M.; Wormser, David; Danesh, John

2014-01-01

Individual participant time-to-event data from multiple prospective epidemiologic studies enable detailed investigation into the predictive ability of risk models. Here we address the challenges in appropriately combining such information across studies. Methods are exemplified by analyses of log C-reactive protein and conventional risk factors for coronary heart disease in the Emerging Risk Factors Collaboration, a collation of individual data from multiple prospective studies with an average follow-up duration of 9.8 years (dates varied). We derive risk prediction models using Cox proportional hazards regression analysis stratified by study and obtain estimates of risk discrimination, Harrell's concordance index, and Royston's discrimination measure within each study; we then combine the estimates across studies using a weighted meta-analysis. Various weighting approaches are compared and lead us to recommend using the number of events in each study. We also discuss the calculation of measures of reclassification for multiple studies. We further show that comparison of differences in predictive ability across subgroups should be based only on within-study information and that combining measures of risk discrimination from case-control studies and prospective studies is problematic. The concordance index and discrimination measure gave qualitatively similar results throughout. While the concordance index was very heterogeneous between studies, principally because of differing age ranges, the increments in the concordance index from adding log C-reactive protein to conventional risk factors were more homogeneous. PMID:24366051

Assessing risk prediction models using individual participant data from multiple studies.

PubMed

Pennells, Lisa; Kaptoge, Stephen; White, Ian R; Thompson, Simon G; Wood, Angela M

2014-03-01

Individual participant time-to-event data from multiple prospective epidemiologic studies enable detailed investigation into the predictive ability of risk models. Here we address the challenges in appropriately combining such information across studies. Methods are exemplified by analyses of log C-reactive protein and conventional risk factors for coronary heart disease in the Emerging Risk Factors Collaboration, a collation of individual data from multiple prospective studies with an average follow-up duration of 9.8 years (dates varied). We derive risk prediction models using Cox proportional hazards regression analysis stratified by study and obtain estimates of risk discrimination, Harrell's concordance index, and Royston's discrimination measure within each study; we then combine the estimates across studies using a weighted meta-analysis. Various weighting approaches are compared and lead us to recommend using the number of events in each study. We also discuss the calculation of measures of reclassification for multiple studies. We further show that comparison of differences in predictive ability across subgroups should be based only on within-study information and that combining measures of risk discrimination from case-control studies and prospective studies is problematic. The concordance index and discrimination measure gave qualitatively similar results throughout. While the concordance index was very heterogeneous between studies, principally because of differing age ranges, the increments in the concordance index from adding log C-reactive protein to conventional risk factors were more homogeneous.

Are Early-Life Socioeconomic Conditions Directly Related to Birth Outcomes? Grandmaternal Education, Grandchild Birth Weight, and Associated Bias Analyses

PubMed Central

Huang, Jonathan Y.; Gavin, Amelia R.; Richardson, Thomas S.; Rowhani-Rahbar, Ali; Siscovick, David S.; Enquobahrie, Daniel A.

2015-01-01

Grandmaternal education may be related to grandchild birth weight (GBW) through maternal early-life development; however, conventional regression models may be endogenously confounded. Alternative models employing explicit structural assumptions may provide incrementally clearer evidence. We used data from the US National Longitudinal Study of Adolescent to Adult Health (1995–2009; 1,681 mother-child pairs) to estimate “direct effects” of grandmaternal educational level (less than high school, high school diploma or equivalent, or college degree) at the time of the mother's birth on GBW, adjusted for maternal life-course factors: maltreatment as a child, education and income as an adult, prepregnancy overweight, and prenatal smoking. Using conventional and marginal structural model (MSM) approaches, we estimated 54-g (95% confidence interval: −14.0, 122.1) and 87-g (95% confidence interval: 10.9, 162.5) higher GBWs per increase in educational level, respectively. The MSM allowed simultaneous mediation by and adjustment for prepregnancy overweight. Estimates were insensitive to alternate structural assumptions and mediator parameterizations. Bias analysis suggested that a single unmeasured confounder would have to have a strong influence on GBW (approximately 150 g) or be greatly imbalanced across exposure groups (approximately 25%) to completely explain the findings. Coupling an MSM with sensitivity analyses provides some evidence that maternal early-life socioeconomic environment is directly associated with offspring birth weight. PMID:26283086

Prediction of lethal/effective concentration/dose in the presence of multiple auxiliary covariates and components of variance

USGS Publications Warehouse

Gutreuter, S.; Boogaard, M.A.

2007-01-01

Predictors of the percentile lethal/effective concentration/dose are commonly used measures of efficacy and toxicity. Typically such quantal-response predictors (e.g., the exposure required to kill 50% of some population) are estimated from simple bioassays wherein organisms are exposed to a gradient of several concentrations of a single agent. The toxicity of an agent may be influenced by auxiliary covariates, however, and more complicated experimental designs may introduce multiple variance components. Prediction methods lag examples of those cases. A conventional two-stage approach consists of multiple bivariate predictions of, say, medial lethal concentration followed by regression of those predictions on the auxiliary covariates. We propose a more effective and parsimonious class of generalized nonlinear mixed-effects models for prediction of lethal/effective dose/concentration from auxiliary covariates. We demonstrate examples using data from a study regarding the effects of pH and additions of variable quantities 2???,5???-dichloro-4???- nitrosalicylanilide (niclosamide) on the toxicity of 3-trifluoromethyl-4- nitrophenol to larval sea lamprey (Petromyzon marinus). The new models yielded unbiased predictions and root-mean-squared errors (RMSEs) of prediction for the exposure required to kill 50 and 99.9% of some population that were 29 to 82% smaller, respectively, than those from the conventional two-stage procedure. The model class is flexible and easily implemented using commonly available software. ?? 2007 SETAC.

Predicting the graft survival for heart-lung transplantation patients: an integrated data mining methodology.

PubMed

Oztekin, Asil; Delen, Dursun; Kong, Zhenyu James

2009-12-01

Predicting the survival of heart-lung transplant patients has the potential to play a critical role in understanding and improving the matching procedure between the recipient and graft. Although voluminous data related to the transplantation procedures is being collected and stored, only a small subset of the predictive factors has been used in modeling heart-lung transplantation outcomes. The previous studies have mainly focused on applying statistical techniques to a small set of factors selected by the domain-experts in order to reveal the simple linear relationships between the factors and survival. The collection of methods known as 'data mining' offers significant advantages over conventional statistical techniques in dealing with the latter's limitations such as normality assumption of observations, independence of observations from each other, and linearity of the relationship between the observations and the output measure(s). There are statistical methods that overcome these limitations. Yet, they are computationally more expensive and do not provide fast and flexible solutions as do data mining techniques in large datasets. The main objective of this study is to improve the prediction of outcomes following combined heart-lung transplantation by proposing an integrated data-mining methodology. A large and feature-rich dataset (16,604 cases with 283 variables) is used to (1) develop machine learning based predictive models and (2) extract the most important predictive factors. Then, using three different variable selection methods, namely, (i) machine learning methods driven variables-using decision trees, neural networks, logistic regression, (ii) the literature review-based expert-defined variables, and (iii) common sense-based interaction variables, a consolidated set of factors is generated and used to develop Cox regression models for heart-lung graft survival. The predictive models' performance in terms of 10-fold cross-validation accuracy rates for two multi-imputed datasets ranged from 79% to 86% for neural networks, from 78% to 86% for logistic regression, and from 71% to 79% for decision trees. The results indicate that the proposed integrated data mining methodology using Cox hazard models better predicted the graft survival with different variables than the conventional approaches commonly used in the literature. This result is validated by the comparison of the corresponding Gains charts for our proposed methodology and the literature review based Cox results, and by the comparison of Akaike information criteria (AIC) values received from each. Data mining-based methodology proposed in this study reveals that there are undiscovered relationships (i.e. interactions of the existing variables) among the survival-related variables, which helps better predict the survival of the heart-lung transplants. It also brings a different set of variables into the scene to be evaluated by the domain-experts and be considered prior to the organ transplantation.

Reliability Analysis of a Green Roof Under Different Storm Scenarios

NASA Astrophysics Data System (ADS)

William, R. K.; Stillwell, A. S.

2015-12-01

Urban environments continue to face the challenges of localized flooding and decreased water quality brought on by the increasing amount of impervious area in the built environment. Green infrastructure provides an alternative to conventional storm sewer design by using natural processes to filter and store stormwater at its source. However, there are currently few consistent standards available in North America to ensure that installed green infrastructure is performing as expected. This analysis offers a method for characterizing green roof failure using a visual aid commonly used in earthquake engineering: fragility curves. We adapted the concept of the fragility curve based on the efficiency in runoff reduction provided by a green roof compared to a conventional roof under different storm scenarios. We then used the 2D distributed surface water-groundwater coupled model MIKE SHE to model the impact that a real green roof might have on runoff in different storm events. We then employed a multiple regression analysis to generate an algebraic demand model that was input into the Matlab-based reliability analysis model FERUM, which was then used to calculate the probability of failure. The use of reliability analysis as a part of green infrastructure design code can provide insights into green roof weaknesses and areas for improvement. It also supports the design of code that is more resilient than current standards and is easily testable for failure. Finally, the understanding of reliability of a single green roof module under different scenarios can support holistic testing of system reliability.

Evaluation of conventional and alternative monitoring methods for a recreational marine beach with nonpoint source of fecal contamination.

PubMed

Shibata, Tomoyuki; Solo-Gabriele, Helena M; Sinigalliano, Christopher D; Gidley, Maribeth L; Plano, Lisa R W; Fleisher, Jay M; Wang, John D; Elmir, Samir M; He, Guoqing; Wright, Mary E; Abdelzaher, Amir M; Ortega, Cristina; Wanless, David; Garza, Anna C; Kish, Jonathan; Scott, Troy; Hollenbeck, Julie; Backer, Lorraine C; Fleming, Lora E

2010-11-01

The objectives of this work were to compare enterococci (ENT) measurements based on the membrane filter, ENT(MF) with alternatives that can provide faster results including alternative enterococci methods (e.g., chromogenic substrate (CS), and quantitative polymerase chain reaction (qPCR)), and results from regression models based upon environmental parameters that can be measured in real-time. ENT(MF) were also compared to source tracking markers (Staphylococcus aureus, Bacteroidales human and dog markers, and Catellicoccus gull marker) in an effort to interpret the variability of the signal. Results showed that concentrations of enterococci based upon MF (<2 to 3320 CFU/100 mL) were significantly different from the CS and qPCR methods (p < 0.01). The correlations between MF and CS (r = 0.58, p < 0.01) were stronger than between MF and qPCR (r ≤ 0.36, p < 0.01). Enterococci levels by MF, CS, and qPCR methods were positively correlated with turbidity and tidal height. Enterococci by MF and CS were also inversely correlated with solar radiation but enterococci by qPCR was not. The regression model based on environmental variables provided fair qualitative predictions of enterococci by MF in real-time, for daily geometric mean levels, but not for individual samples. Overall, ENT(MF) was not significantly correlated with source tracking markers with the exception of samples collected during one storm event. The inability of the regression model to predict ENT(MF) levels for individual samples is likely due to the different sources of ENT impacting the beach at any given time, making it particularly difficult to to predict short-term variability of ENT(MF) for environmental parameters.

Prediction of soil organic carbon with different parent materials development using visible-near infrared spectroscopy.

PubMed

Liu, Jinbao; Han, Jichang; Zhang, Yang; Wang, Huanyuan; Kong, Hui; Shi, Lei

2018-06-05

The storage of soil organic carbon (SOC) should improve soil fertility. Conventional determination of SOC is expensive and tedious. Visible-near infrared reflectance spectroscopy is a practical and cost-effective approach that has been successfully used SOC concentration. Soil spectral inversion model could quickly and efficiently determine SOC content. This paper presents a study dealing with SOC estimation through the combination of soil spectroscopy and stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), principal component regression (PCR). Spectral measurements for 106 soil samples were acquired using an ASD FieldSpec 4 standard-res spectroradiometer (350-2500 nm). Six types of transformations and three regression methods were applied to build for the quantification of different parent materials development soil. The results show that (1)the basaltic volcanic clastics development of SOC spectral response bands located in 500 nm, 800 nm; Trachyte spectral response of the soil quality, and the volcanic clastics development at 405 nm, 465 nm, 575 nm, 1105 nm. (2) Basaltic volcanic debris soil development, first deviation of maximum correlation coefficient is 0.8898; thick surface soil of the development of rocky volcanic debris from bottom reflectivity logarithm of first deviation of maximum correlation coefficient is 0.9029. (3) Soil organic matter content of basaltic volcanic clastics development optimal prediction model based on spectral reflectance inverse logarithms of first deviation of SMLR. Independent variable number is 7, Rv 2  = 0.9720, RMSEP = 2.0590, sig = 0.003. Trachyte qualitative volcanic clastics developed soil organic matter content of the optimal prediction model based on spectral reflectance inverse logarithms of first deviation of PLSR. Model number of the independent variables Pc = 5, Rc = 0.9872, Rc 2  = 0.9745, RMSEC = 0.4821, SEC = 0.4906, forecasts determine coefficient Rv 2  = 0.9702, RMSEP = 0.9563, SEP = 0.9711, Bias = 0.0637. Copyright © 2018 Elsevier B.V. All rights reserved.

Integrating conventional and inverse representation for face recognition.

PubMed

Xu, Yong; Li, Xuelong; Yang, Jian; Lai, Zhihui; Zhang, David

2014-10-01

Representation-based classification methods are all constructed on the basis of the conventional representation, which first expresses the test sample as a linear combination of the training samples and then exploits the deviation between the test sample and the expression result of every class to perform classification. However, this deviation does not always well reflect the difference between the test sample and each class. With this paper, we propose a novel representation-based classification method for face recognition. This method integrates conventional and the inverse representation-based classification for better recognizing the face. It first produces conventional representation of the test sample, i.e., uses a linear combination of the training samples to represent the test sample. Then it obtains the inverse representation, i.e., provides an approximation representation of each training sample of a subject by exploiting the test sample and training samples of the other subjects. Finally, the proposed method exploits the conventional and inverse representation to generate two kinds of scores of the test sample with respect to each class and combines them to recognize the face. The paper shows the theoretical foundation and rationale of the proposed method. Moreover, this paper for the first time shows that a basic nature of the human face, i.e., the symmetry of the face can be exploited to generate new training and test samples. As these new samples really reflect some possible appearance of the face, the use of them will enable us to obtain higher accuracy. The experiments show that the proposed conventional and inverse representation-based linear regression classification (CIRLRC), an improvement to linear regression classification (LRC), can obtain very high accuracy and greatly outperforms the naive LRC and other state-of-the-art conventional representation based face recognition methods. The accuracy of CIRLRC can be 10% greater than that of LRC.

Phototherapy of the newborn: a predictive model for the outcome.

PubMed

Ossamu Osaku, Nelson; Silverio Lopes, Heitor

2005-01-01

Jaundice in one of the most common problems of the newborn. In most cases, jaundice is considered a physiological transient situation, but sometimes it can lead to death or serious injuries for the survivors. For decades, phototherapy has been used as the main method for prevention and treatment of hyperbilirubinaemia of the newborn. This work aims at finding a predictive model for the decrement of blood bilirubin followed conventional phototherapy. Data from 90 patients were collected and used in the multiple regression method. A rigorous statistical analysis was done in order to guarantee a correct and valid model. The obtained model was able to explain 78% of the variation of the dependent variable We found that it is possible to predict the total sugar bilirubin of the patient under phototherapy by knowing its birth weight, bilirubin level at the beginning of treatment, duration of exposition, and irradiance. Besides, it is possible to infer the time necessary for a given decrement of bilirubin, under approximately constant irradiance.

Complex networks untangle competitive advantage in Australian football

NASA Astrophysics Data System (ADS)

Braham, Calum; Small, Michael

2018-05-01

We construct player-based complex network models of Australian football teams for the 2014 Australian Football League season; modelling the passes between players as weighted, directed edges. We show that analysis of these measures can give an insight into the underlying structure and strategy of Australian football teams, quantitatively distinguishing different playing styles. The relationships observed between network properties and match outcomes suggest that successful teams exhibit well-connected passing networks with the passes distributed between all 22 players as evenly as possible. Linear regression models of team scores and match margins show significant improvements in R2 and Bayesian information criterion when network measures are added to models that use conventional measures, demonstrating that network analysis measures contain useful, extra information. Several measures, particularly the mean betweenness centrality, are shown to be useful in predicting the outcomes of future matches, suggesting they measure some aspect of the intrinsic strength of teams. In addition, several local centrality measures are shown to be useful in analysing individual players' differing contributions to the team's structure.

Complex networks untangle competitive advantage in Australian football.

PubMed

Braham, Calum; Small, Michael

2018-05-01

We construct player-based complex network models of Australian football teams for the 2014 Australian Football League season; modelling the passes between players as weighted, directed edges. We show that analysis of these measures can give an insight into the underlying structure and strategy of Australian football teams, quantitatively distinguishing different playing styles. The relationships observed between network properties and match outcomes suggest that successful teams exhibit well-connected passing networks with the passes distributed between all 22 players as evenly as possible. Linear regression models of team scores and match margins show significant improvements in R 2 and Bayesian information criterion when network measures are added to models that use conventional measures, demonstrating that network analysis measures contain useful, extra information. Several measures, particularly the mean betweenness centrality, are shown to be useful in predicting the outcomes of future matches, suggesting they measure some aspect of the intrinsic strength of teams. In addition, several local centrality measures are shown to be useful in analysing individual players' differing contributions to the team's structure.

Time Series Expression Analyses Using RNA-seq: A Statistical Approach

PubMed Central

Oh, Sunghee; Song, Seongho; Grabowski, Gregory; Zhao, Hongyu; Noonan, James P.

2013-01-01

RNA-seq is becoming the de facto standard approach for transcriptome analysis with ever-reducing cost. It has considerable advantages over conventional technologies (microarrays) because it allows for direct identification and quantification of transcripts. Many time series RNA-seq datasets have been collected to study the dynamic regulations of transcripts. However, statistically rigorous and computationally efficient methods are needed to explore the time-dependent changes of gene expression in biological systems. These methods should explicitly account for the dependencies of expression patterns across time points. Here, we discuss several methods that can be applied to model timecourse RNA-seq data, including statistical evolutionary trajectory index (SETI), autoregressive time-lagged regression (AR(1)), and hidden Markov model (HMM) approaches. We use three real datasets and simulation studies to demonstrate the utility of these dynamic methods in temporal analysis. PMID:23586021

Time series expression analyses using RNA-seq: a statistical approach.

PubMed

Oh, Sunghee; Song, Seongho; Grabowski, Gregory; Zhao, Hongyu; Noonan, James P

2013-01-01

RNA-seq is becoming the de facto standard approach for transcriptome analysis with ever-reducing cost. It has considerable advantages over conventional technologies (microarrays) because it allows for direct identification and quantification of transcripts. Many time series RNA-seq datasets have been collected to study the dynamic regulations of transcripts. However, statistically rigorous and computationally efficient methods are needed to explore the time-dependent changes of gene expression in biological systems. These methods should explicitly account for the dependencies of expression patterns across time points. Here, we discuss several methods that can be applied to model timecourse RNA-seq data, including statistical evolutionary trajectory index (SETI), autoregressive time-lagged regression (AR(1)), and hidden Markov model (HMM) approaches. We use three real datasets and simulation studies to demonstrate the utility of these dynamic methods in temporal analysis.

Decoding of finger trajectory from ECoG using deep learning.

PubMed

Xie, Ziqian; Schwartz, Odelia; Prasad, Abhishek

2018-06-01

Conventional decoding pipeline for brain-machine interfaces (BMIs) consists of chained different stages of feature extraction, time-frequency analysis and statistical learning models. Each of these stages uses a different algorithm trained in a sequential manner, which makes it difficult to make the whole system adaptive. The goal was to create an adaptive online system with a single objective function and a single learning algorithm so that the whole system can be trained in parallel to increase the decoding performance. Here, we used deep neural networks consisting of convolutional neural networks (CNN) and a special kind of recurrent neural network (RNN) called long short term memory (LSTM) to address these needs. We used electrocorticography (ECoG) data collected by Kubanek et al. The task consisted of individual finger flexions upon a visual cue. Our model combined a hierarchical feature extractor CNN and a RNN that was able to process sequential data and recognize temporal dynamics in the neural data. CNN was used as the feature extractor and LSTM was used as the regression algorithm to capture the temporal dynamics of the signal. We predicted the finger trajectory using ECoG signals and compared results for the least angle regression (LARS), CNN-LSTM, random forest, LSTM model (LSTM_HC, for using hard-coded features) and a decoding pipeline consisting of band-pass filtering, energy extraction, feature selection and linear regression. The results showed that the deep learning models performed better than the commonly used linear model. The deep learning models not only gave smoother and more realistic trajectories but also learned the transition between movement and rest state. This study demonstrated a decoding network for BMI that involved a convolutional and recurrent neural network model. It integrated the feature extraction pipeline into the convolution and pooling layer and used LSTM layer to capture the state transitions. The discussed network eliminated the need to separately train the model at each step in the decoding pipeline. The whole system can be jointly optimized using stochastic gradient descent and is capable of online learning.

Decoding of finger trajectory from ECoG using deep learning

NASA Astrophysics Data System (ADS)

Xie, Ziqian; Schwartz, Odelia; Prasad, Abhishek

2018-06-01

Objective. Conventional decoding pipeline for brain-machine interfaces (BMIs) consists of chained different stages of feature extraction, time-frequency analysis and statistical learning models. Each of these stages uses a different algorithm trained in a sequential manner, which makes it difficult to make the whole system adaptive. The goal was to create an adaptive online system with a single objective function and a single learning algorithm so that the whole system can be trained in parallel to increase the decoding performance. Here, we used deep neural networks consisting of convolutional neural networks (CNN) and a special kind of recurrent neural network (RNN) called long short term memory (LSTM) to address these needs. Approach. We used electrocorticography (ECoG) data collected by Kubanek et al. The task consisted of individual finger flexions upon a visual cue. Our model combined a hierarchical feature extractor CNN and a RNN that was able to process sequential data and recognize temporal dynamics in the neural data. CNN was used as the feature extractor and LSTM was used as the regression algorithm to capture the temporal dynamics of the signal. Main results. We predicted the finger trajectory using ECoG signals and compared results for the least angle regression (LARS), CNN-LSTM, random forest, LSTM model (LSTM_HC, for using hard-coded features) and a decoding pipeline consisting of band-pass filtering, energy extraction, feature selection and linear regression. The results showed that the deep learning models performed better than the commonly used linear model. The deep learning models not only gave smoother and more realistic trajectories but also learned the transition between movement and rest state. Significance. This study demonstrated a decoding network for BMI that involved a convolutional and recurrent neural network model. It integrated the feature extraction pipeline into the convolution and pooling layer and used LSTM layer to capture the state transitions. The discussed network eliminated the need to separately train the model at each step in the decoding pipeline. The whole system can be jointly optimized using stochastic gradient descent and is capable of online learning.

MATERNAL SELF-ESTEEM, EXPOSURE TO LEAD, AND CHILD NEURODEVELOPMENT

PubMed Central

Surkan, Pamela J.; Schnaas, Lourdes; Wright, Rosalind J.; Téllez-Rojo, Martha M.; Lamadrid-Figueroa, Héctor; Hu, Howard; Hernández-Avila, E. Mauricio; Bellinger, David C.; Schwartz, Joel; Perroni, Estela; Wright, Robert O.

2008-01-01

The notion that maternal personality characteristics influence cognitive development in their children has been grounded in stress moderation theory. Maternal personality traits, such as self-esteem, may buffer maternal stressors or lead to improved maternal-child interactions that directly impact neurodevelopment. This can be extended to suggest that maternal personality may serve to attenuate or exacerbate the effects of other neurotoxicants, although this has not been studied directly. We examined whether mothers’ self-esteem had a direct or main effect on their children's cognitive outcomes. We also explored the modifying effects of maternal self-esteem on the association between exposure to lead and neurodevelopment in these children. Study participants included 379 mother-child pairs from Mexico City. Data included the Coopersmith self-esteem scale in mothers, children's Bayley's Scale of Infant Development (BSID) scores, and sociodemographic information. Linear regression was used to model the relationship between maternal self-esteem and the Bayley's Mental Development Index (MDI) and Psychomotor Development Index (PDI) scores at age 24 months using regression models stratified by levels of maternal self-esteem. In adjusted models, each point increase in maternal self-esteem was associated with children having 0.2 higher score on the Bayley's MDI (p=0.04). Similar results were observed using the PDI outcome. Moreover, there was evidence that maternal self-esteem attenuated the negative effects of lead exposure, although the interaction fell short of conventional levels of statistical significance. PMID:18261800

When homogeneity meets heterogeneity: the geographically weighted regression with spatial lag approach to prenatal care utilization

PubMed Central

Shoff, Carla; Chen, Vivian Yi-Ju; Yang, Tse-Chuan

2014-01-01

Using geographically weighted regression (GWR), a recent study by Shoff and colleagues (2012) investigated the place-specific risk factors for prenatal care utilization in the US and found that most of the relationships between late or not prenatal care and its determinants are spatially heterogeneous. However, the GWR approach may be subject to the confounding effect of spatial homogeneity. The goal of this study is to address this concern by including both spatial homogeneity and heterogeneity into the analysis. Specifically, we employ an analytic framework where a spatially lagged (SL) effect of the dependent variable is incorporated into the GWR model, which is called GWR-SL. Using this innovative framework, we found evidence to argue that spatial homogeneity is neglected in the study by Shoff et al. (2012) and the results are changed after considering the spatially lagged effect of prenatal care utilization. The GWR-SL approach allows us to gain a place-specific understanding of prenatal care utilization in US counties. In addition, we compared the GWR-SL results with the results of conventional approaches (i.e., OLS and spatial lag models) and found that GWR-SL is the preferred modeling approach. The new findings help us to better estimate how the predictors are associated with prenatal care utilization across space, and determine whether and how the level of prenatal care utilization in neighboring counties matters. PMID:24893033

Accounting for the decrease of photosystem photochemical efficiency with increasing irradiance to estimate quantum yield of leaf photosynthesis.

PubMed

Yin, Xinyou; Belay, Daniel W; van der Putten, Peter E L; Struik, Paul C

2014-12-01

Maximum quantum yield for leaf CO2 assimilation under limiting light conditions (Φ CO2LL) is commonly estimated as the slope of the linear regression of net photosynthetic rate against absorbed irradiance over a range of low-irradiance conditions. Methodological errors associated with this estimation have often been attributed either to light absorptance by non-photosynthetic pigments or to some data points being beyond the linear range of the irradiance response, both causing an underestimation of Φ CO2LL. We demonstrate here that a decrease in photosystem (PS) photochemical efficiency with increasing irradiance, even at very low levels, is another source of error that causes a systematic underestimation of Φ CO2LL. A model method accounting for this error was developed, and was used to estimate Φ CO2LL from simultaneous measurements of gas exchange and chlorophyll fluorescence on leaves using various combinations of species, CO2, O2, or leaf temperature levels. The conventional linear regression method under-estimated Φ CO2LL by ca. 10-15%. Differences in the estimated Φ CO2LL among measurement conditions were generally accounted for by different levels of photorespiration as described by the Farquhar-von Caemmerer-Berry model. However, our data revealed that the temperature dependence of PSII photochemical efficiency under low light was an additional factor that should be accounted for in the model.

Improving causal inference with a doubly robust estimator that combines propensity score stratification and weighting.

PubMed

Linden, Ariel

2017-08-01

When a randomized controlled trial is not feasible, health researchers typically use observational data and rely on statistical methods to adjust for confounding when estimating treatment effects. These methods generally fall into 3 categories: (1) estimators based on a model for the outcome using conventional regression adjustment; (2) weighted estimators based on the propensity score (ie, a model for the treatment assignment); and (3) "doubly robust" (DR) estimators that model both the outcome and propensity score within the same framework. In this paper, we introduce a new DR estimator that utilizes marginal mean weighting through stratification (MMWS) as the basis for weighted adjustment. This estimator may prove more accurate than treatment effect estimators because MMWS has been shown to be more accurate than other models when the propensity score is misspecified. We therefore compare the performance of this new estimator to other commonly used treatment effects estimators. Monte Carlo simulation is used to compare the DR-MMWS estimator to regression adjustment, 2 weighted estimators based on the propensity score and 2 other DR methods. To assess performance under varied conditions, we vary the level of misspecification of the propensity score model as well as misspecify the outcome model. Overall, DR estimators generally outperform methods that model one or the other components (eg, propensity score or outcome). The DR-MMWS estimator outperforms all other estimators when both the propensity score and outcome models are misspecified and performs equally as well as other DR estimators when only the propensity score is misspecified. Health researchers should consider using DR-MMWS as the principal evaluation strategy in observational studies, as this estimator appears to outperform other estimators in its class. © 2017 John Wiley & Sons, Ltd.

Evaluating neighborhood structures for modeling intercity diffusion of large-scale dengue epidemics.

PubMed

Wen, Tzai-Hung; Hsu, Ching-Shun; Hu, Ming-Che

2018-05-03

Dengue fever is a vector-borne infectious disease that is transmitted by contact between vector mosquitoes and susceptible hosts. The literature has addressed the issue on quantifying the effect of individual mobility on dengue transmission. However, there are methodological concerns in the spatial regression model configuration for examining the effect of intercity-scale human mobility on dengue diffusion. The purposes of the study are to investigate the influence of neighborhood structures on intercity epidemic progression from pre-epidemic to epidemic periods and to compare definitions of different neighborhood structures for interpreting the spread of dengue epidemics. We proposed a framework for assessing the effect of model configurations on dengue incidence in 2014 and 2015, which were the most severe outbreaks in 70 years in Taiwan. Compared with the conventional model configuration in spatial regression analysis, our proposed model used a radiation model, which reflects population flow between townships, as a spatial weight to capture the structure of human mobility. The results of our model demonstrate better model fitting performance, indicating that the structure of human mobility has better explanatory power in dengue diffusion than the geometric structure of administration boundaries and geographic distance between centroids of cities. We also identified spatial-temporal hierarchy of dengue diffusion: dengue incidence would be influenced by its immediate neighboring townships during pre-epidemic and epidemic periods, and also with more distant neighbors (based on mobility) in pre-epidemic periods. Our findings suggest that the structure of population mobility could more reasonably capture urban-to-urban interactions, which implies that the hub cities could be a "bridge" for large-scale transmission and make townships that immediately connect to hub cities more vulnerable to dengue epidemics.

Improving land surface emissivty parameter for land surface models using portable FTIR and remote sensing observation in Taklimakan Desert

NASA Astrophysics Data System (ADS)

Liu, Yongqiang; Mamtimin, Ali; He, Qing

2014-05-01

Because land surface emissivity (ɛ) has not been reliably measured, global climate model (GCM) land surface schemes conventionally set this parameter as simply assumption, for example, 1 as in the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) model, 0.96 for soil and wetland in the Global and Regional Assimilation and Prediction System (GRAPES) Common Land Model (CoLM). This is the so-called emissivity assumption. Accurate broadband emissivity data are needed as model inputs to better simulate the land surface climate. It is demonstrated in this paper that the assumption of the emissivity induces errors in modeling the surface energy budget over Taklimakan Desert where ɛ is far smaller than original value. One feasible solution to this problem is to apply the accurate broadband emissivity into land surface models. The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument has routinely measured spectral emissivities in six thermal infrared bands. The empirical regression equations have been developed in this study to convert these spectral emissivities to broadband emissivity required by land surface models. In order to calibrate the regression equations, using a portable Fourier Transform infrared (FTIR) spectrometer instrument, crossing Taklimakan Desert along with highway from north to south, to measure the accurate broadband emissivity. The observed emissivity data show broadband ɛ around 0.89-0.92. To examine the impact of improved ɛ to radiative energy redistribution, simulation studies were conducted using offline CoLM. The results illustrate that large impacts of surface ɛ occur over desert, with changes up in surface skin temperature, as well as evident changes in sensible heat fluxes. Keywords: Taklimakan Desert, surface broadband emissivity, Fourier Transform infrared spectrometer, MODIS, CoLM

A preliminary study on postmortem interval estimation of suffocated rats by GC-MS/MS-based plasma metabolic profiling.

PubMed

Sato, Takako; Zaitsu, Kei; Tsuboi, Kento; Nomura, Masakatsu; Kusano, Maiko; Shima, Noriaki; Abe, Shuntaro; Ishii, Akira; Tsuchihashi, Hitoshi; Suzuki, Koichi

2015-05-01

Estimation of postmortem interval (PMI) is an important goal in judicial autopsy. Although many approaches can estimate PMI through physical findings and biochemical tests, accurate PMI calculation by these conventional methods remains difficult because PMI is readily affected by surrounding conditions, such as ambient temperature and humidity. In this study, Sprague-Dawley (SD) rats (10 weeks) were sacrificed by suffocation, and blood was collected by dissection at various time intervals (0, 3, 6, 12, 24, and 48 h; n = 6) after death. A total of 70 endogenous metabolites were detected in plasma by gas chromatography-tandem mass spectrometry (GC-MS/MS). Each time group was separated from each other on the principal component analysis (PCA) score plot, suggesting that the various endogenous metabolites changed with time after death. To prepare a prediction model of a PMI, a partial least squares (or projection to latent structure, PLS) regression model was constructed using the levels of significantly different metabolites determined by variable importance in the projection (VIP) score and the Kruskal-Wallis test (P < 0.05). Because the constructed PLS regression model could successfully predict each PMI, this model was validated with another validation set (n = 3). In conclusion, plasma metabolic profiling demonstrated its ability to successfully estimate PMI under a certain condition. This result can be considered to be the first step for using the metabolomics method in future forensic casework.

Improved Measurement of Blood Pressure by Extraction of Characteristic Features from the Cuff Oscillometric Waveform

PubMed Central

Lim, Pooi Khoon; Ng, Siew-Cheok; Jassim, Wissam A.; Redmond, Stephen J.; Zilany, Mohammad; Avolio, Alberto; Lim, Einly; Tan, Maw Pin; Lovell, Nigel H.

2015-01-01

We present a novel approach to improve the estimation of systolic (SBP) and diastolic blood pressure (DBP) from oscillometric waveform data using variable characteristic ratios between SBP and DBP with mean arterial pressure (MAP). This was verified in 25 healthy subjects, aged 28 ± 5 years. The multiple linear regression (MLR) and support vector regression (SVR) models were used to examine the relationship between the SBP and the DBP ratio with ten features extracted from the oscillometric waveform envelope (OWE). An automatic algorithm based on relative changes in the cuff pressure and neighbouring oscillometric pulses was proposed to remove outlier points caused by movement artifacts. Substantial reduction in the mean and standard deviation of the blood pressure estimation errors were obtained upon artifact removal. Using the sequential forward floating selection (SFFS) approach, we were able to achieve a significant reduction in the mean and standard deviation of differences between the estimated SBP values and the reference scoring (MLR: mean ± SD = −0.3 ± 5.8 mmHg; SVR and −0.6 ± 5.4 mmHg) with only two features, i.e., Ratio2 and Area3, as compared to the conventional maximum amplitude algorithm (MAA) method (mean ± SD = −1.6 ± 8.6 mmHg). Comparing the performance of both MLR and SVR models, our results showed that the MLR model was able to achieve comparable performance to that of the SVR model despite its simplicity. PMID:26087370

Evaluation of body weight of sea cucumber Apostichopus japonicus by computer vision

NASA Astrophysics Data System (ADS)

Liu, Hui; Xu, Qiang; Liu, Shilin; Zhang, Libin; Yang, Hongsheng

2015-01-01

A postichopus japonicus (Holothuroidea, Echinodermata) is an ecological and economic species in East Asia. Conventional biometric monitoring method includes diving for samples and weighing above water, with highly variable in weight measurement due to variation in the quantity of water in the respiratory tree and intestinal content of this species. Recently, video survey method has been applied widely in biometric detection on underwater benthos. However, because of the high flexibility of A. japonicus body, video survey method of monitoring is less used in sea cucumber. In this study, we designed a model to evaluate the wet weight of A. japonicus, using machine vision technology combined with a support vector machine (SVM) that can be used in field surveys on the A. japonicus population. Continuous dorsal images of free-moving A. japonicus individuals in seawater were captured, which also allows for the development of images of the core body edge as well as thorn segmentation. Parameters that include body length, body breadth, perimeter and area, were extracted from the core body edge images and used in SVM regression, to predict the weight of A. japonicus and for comparison with a power model. Results indicate that the use of SVM for predicting the weight of 33 A. japonicus individuals is accurate ( R 2=0.99) and compatible with the power model ( R 2 =0.96). The image-based analysis and size-weight regression models in this study may be useful in body weight evaluation of A. japonicus in lab and field study.

Sleep Stage Transition Dynamics Reveal Specific Stage 2 Vulnerability in Insomnia.

PubMed

Wei, Yishul; Colombo, Michele A; Ramautar, Jennifer R; Blanken, Tessa F; van der Werf, Ysbrand D; Spiegelhalder, Kai; Feige, Bernd; Riemann, Dieter; Van Someren, Eus J W

2017-09-01

Objective sleep impairments in insomnia disorder (ID) are insufficiently understood. The present study evaluated whether whole-night sleep stage dynamics derived from polysomnography (PSG) differ between people with ID and matched controls and whether sleep stage dynamic features discriminate them better than conventional sleep parameters. Eighty-eight participants aged 21-70 years, including 46 with ID and 42 age- and sex-matched controls without sleep complaints, were recruited through www.sleepregistry.nl and completed two nights of laboratory PSG. Data of 100 people with ID and 100 age- and sex-matched controls from a previously reported study were used to validate the generalizability of findings. The second night was used to obtain, in addition to conventional sleep parameters, probabilities of transitions between stages and bout duration distributions of each stage. Group differences were evaluated with nonparametric tests. People with ID showed higher empirical probabilities to transition from stage N2 to the lighter sleep stage N1 or wakefulness and a faster decaying stage N2 bout survival function. The increased transition probability from stage N2 to stage N1 discriminated people with ID better than any of their deviations in conventional sleep parameters, including less total sleep time, less sleep efficiency, more stage N1, and more wake after sleep onset. Moreover, adding this transition probability significantly improved the discriminating power of a multiple logistic regression model based on conventional sleep parameters. Quantification of sleep stage dynamics revealed a particular vulnerability of stage N2 in insomnia. The feature characterizes insomnia better than-and independently of-any conventional sleep parameter. © Sleep Research Society 2017. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.

Gender-Specific Correlates of Complementary and Alternative Medicine Use for Knee Osteoarthritis

PubMed Central

Yang, Shibing; Eaton, Charles B.; McAlindon, Timothy; Lapane, Kate L.

2012-01-01

Abstract Background Knee osteoarthritis (OA) increases healthcare use and cost. Women have higher pain and lower quality of life measures compared to men even after accounting for differences in age, body mass index (BMI), and radiographic OA severity. Our objective was to describe gender-specific correlates of complementary and alternative medicine (CAM) use among persons with radiographically confirmed knee OA. Methods Using data from the Osteoarthritis Initiative, 2,679 women and men with radiographic tibiofemoral OA in at least one knee were identified. Treatment approaches were classified as current CAM therapy (alternative medical systems, mind-body interventions, manipulation and body-based methods, energy therapies, and three types of biologically based therapies) or conventional medication use (over-the-counter or prescription). Gender-specific multivariable logistic regression models identified sociodemographic and clinical/functional correlates of CAM use. Results CAM use, either alone (23.9% women, 21.9% men) or with conventional medications (27.3% women, 19.0% men), was common. Glucosamine use (27.2% women, 28.2% men) and chondroitin sulfate use (24.8% women; 25.7% men) did not differ by gender. Compared to men, women were more likely to report use of mind-body interventions (14.1% vs. 5.7%), topical agents (16.1% vs. 9.5%), and concurrent CAM strategies (18.0% vs. 9.9%). Higher quality of life measures and physical function indices in women were inversely associated with any therapy, and higher pain scores were positively associated with conventional medication use. History of hip replacement was a strong correlate of conventional medication use in women but not in men. Conclusions Women were more likely than men to use CAM alone or concomitantly with conventional medications. PMID:22946630

Prevalence and Correlates of E-Cigarette Perceptions and Trial Among Early Adolescents in Mexico.

PubMed

Thrasher, James F; Abad-Vivero, Erika N; Barrientos-Gutíerrez, Inti; Pérez-Hernández, Rosaura; Reynales-Shigematsu, Luz Miriam; Mejía, Raúl; Arillo-Santillán, Edna; Hernández-Ávila, Mauricio; Sargent, James D

2016-03-01

Assess the prevalence and correlates of e-cigarette perceptions and trial among adolescents in Mexico, where e-cigarettes are banned. Cross-sectional data were collected in 2015 from a representative sample of middle-school students (n = 10,146). Prevalence of e-cigarette awareness, relative harm, and trial were estimated, adjusting for sampling weights and school-level clustering. Multilevel logistic regression models adjusted for school-level clustering to assess correlates of e-cigarette awareness and trial. Finally, students who had tried only e-cigarettes were compared with students who had tried: (1) conventional cigarettes only; (2) both e-cigarettes and conventional cigarettes (dual triers); and (3) neither cigarette type (never triers). Fifty-one percent of students had heard about e-cigarettes, 19% believed e-cigarettes were less harmful than conventional cigarettes, and 10% had tried them. Independent correlates of e-cigarette awareness and trial included established risk factors for smoking, as well as technophilia (i.e., use of more media technologies) and greater Internet tobacco advertising exposure. Exclusive e-cigarette triers (4%) had significantly higher technophilia, bedroom Internet access, and Internet tobacco advertising exposure compared to conventional cigarette triers (19%) and never triers (71%) but not compared to dual triers (6%), although dual triers had significantly stronger conventional cigarette risk factors. This study suggests that adolescent e-cigarette awareness and use is high in Mexico, in spite of its e-cigarette ban. A significant number of medium-risk youth have tried e-cigarettes only, suggesting that e-cigarettes could lead to more intensive substance use. Strategies to reduce e-cigarette use should consider reducing exposures to Internet marketing. Copyright © 2016 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Prevalence and correlates of e-cigarette perceptions and trial among Mexican adolescents

PubMed Central

Thrasher, James F.; Abad-Vivero, Erika N.; Barrientos-Gutíerrez, Inti; Pérez-Hernández, Rosaura; Reynales-Shigematsu, Luz Miriam; Mejía, Raúl; Arillo-Santillán, Edna; Hernández-Ávila, Mauricio; Sargent, James D.

2016-01-01

PURPOSE Assess the prevalence and correlates of e-cigarette perceptions and trial among adolescents in Mexico, where e-cigarettes are banned. METHODS Cross-sectional data were collected in 2015 from a representative sample of middle school students (n=10,146). Prevalence of e-cigarette awareness, relative harm, and trial were estimated, adjusting for sampling weights and school-level clustering. Multilevel logistic regression models adjusted for school-level clustering to assess correlates of e-cigarette awareness and trial. Finally, students who had tried only e-cigarettes were compared with students who had tried: 1) conventional cigarettes only; 2) both e-cigarettes and conventional cigarettes (dual triers); 3) neither cigarette type (never triers). RESULTS 51% of students had heard about e-cigarettes, 19% believed e-cigarettes were less harmful than conventional cigarettes, and 10% had tried them. Independent correlates of e-cigarette awareness and trial included established risk factors for smoking, as well as technophilia (i.e., use of more media technologies) and greater Internet tobacco advertising exposure. Exclusive e-cigarette triers (4%) had significantly higher technophilia, bedroom Internet access, and Internet tobacco advertising exposure compared to conventional cigarette triers (19%) and never triers (71%), but not compared to dual triers (6%), even though dual triers had significantly stronger conventional cigarette risk factors. CONCLUSIONS This study suggests that adolescent e-cigarette awareness and use is high in Mexico, in spite of its e-cigarette ban. A significant number of medium-risk youth have tried e-cigarettes only, suggesting that e-cigarettes could lead to more intensive substance use. Strategies to reduce e-cigarette use should consider reducing exposures to Internet marketing. PMID:26903433

E-cigarette use and disparities by race, citizenship status and language among adolescents.

PubMed

Alcalá, Héctor E; Albert, Stephanie L; Ortega, Alexander N

2016-06-01

E-cigarette use among adolescents is on the rise in the U.S. However, limited attention has been given to examining the role of race, citizenship status and language spoken at home in shaping e-cigarette use behavior. Data are from the 2014 Adolescent California Health Interview Survey, which interviewed 1052 adolescents ages 12-17. Lifetime e-cigarette use was examined by sociodemographic characteristics. Separate logistic regression models predicted odds of ever-smoking e-cigarettes from race, citizenship status and language spoken at home. Sociodemographic characteristics were then added to these models as control variables and a model with all three predictors and controls was run. Similar models were run with conventional smoking as an outcome. 10.3% of adolescents ever used e-cigarettes. E-cigarette use was higher among ever-smokers of conventional cigarettes, individuals above 200% of the Federal Poverty Level, US citizens and those who spoke English-only at home. Multivariate analyses demonstrated that citizenship status and language spoken at home were associated with lifetime e-cigarette use, after accounting for control variables. Only citizenship status was associated with e-cigarette use, when controls variables race and language spoken at home were all in the same model. Ever use of e-cigarettes in this study was higher than previously reported national estimates. Action is needed to curb the use of e-cigarettes among adolescents. Differences in lifetime e-cigarette use by citizenship status and language spoken at home suggest that less acculturated individuals use e-cigarettes at lower rates. Copyright © 2016 Elsevier Ltd. All rights reserved.

BagMOOV: A novel ensemble for heart disease prediction bootstrap aggregation with multi-objective optimized voting.

PubMed

Bashir, Saba; Qamar, Usman; Khan, Farhan Hassan

2015-06-01

Conventional clinical decision support systems are based on individual classifiers or simple combination of these classifiers which tend to show moderate performance. This research paper presents a novel classifier ensemble framework based on enhanced bagging approach with multi-objective weighted voting scheme for prediction and analysis of heart disease. The proposed model overcomes the limitations of conventional performance by utilizing an ensemble of five heterogeneous classifiers: Naïve Bayes, linear regression, quadratic discriminant analysis, instance based learner and support vector machines. Five different datasets are used for experimentation, evaluation and validation. The datasets are obtained from publicly available data repositories. Effectiveness of the proposed ensemble is investigated by comparison of results with several classifiers. Prediction results of the proposed ensemble model are assessed by ten fold cross validation and ANOVA statistics. The experimental evaluation shows that the proposed framework deals with all type of attributes and achieved high diagnosis accuracy of 84.16 %, 93.29 % sensitivity, 96.70 % specificity, and 82.15 % f-measure. The f-ratio higher than f-critical and p value less than 0.05 for 95 % confidence interval indicate that the results are extremely statistically significant for most of the datasets.

A Global Study of GPP focusing on Light Use Efficiency in a Random Forest Regression Model

NASA Astrophysics Data System (ADS)

Fang, W.; Wei, S.; Yi, C.; Hendrey, G. R.

2016-12-01

Light use efficiency (LUE) is at the core of mechanistic modeling of global gross primary production (GPP). However, most LUE estimates in global models are satellite-based and coarsely measured with emphasis on environmental variables. Others are from eddy covariance towers with much greater spatial and temporal data quality and emphasis on mechanistic processes, but in a limited number of sites. In this paper, we conducted a comprehensive global study of tower-based LUE from 237 FLUXNET towers, and scaled up LUEs from in-situ tower level to global biome level. We integrated key environmental and biological variables into the tower-based LUE estimates, at 0.5o x 0.5o grid-cell resolution, using a random forest regression (RFR) approach. We then developed an RFR-LUE-GPP model using the grid-cell LUE data, and compared it to a tower-LUE-GPP model by the conventional way of treating LUE as a series of biome-specific constants. In order to calibrate the LUE models, we developed a data-driven RFR-GPP model using a random forest regression method. Our results showed that LUE varies largely with latitude. We estimated a global area-weighted average of LUE at 1.21 gC m-2 MJ-1 APAR, which led to an estimated global GPP of 102.9 Gt C /year from 2000 to 2005. The tower-LUE-GPP model tended to overestimate forest GPP in tropical and boreal regions. Large uncertainties exist in GPP estimates over sparsely vegetated areas covered by savannas and woody savannas around the middle to low latitudes (i.g. 20oS to 40oS and 5oN to 15oN) due to lack of available data. Model results were improved by incorporating Köppen climate types to represent climate /meteorological information in machine learning modeling. This shed new light on the recognized issues of climate dependence of spring onset of photosynthesis and the challenges in modeling the biome GPP of evergreen broad leaf forests (EBF) accurately. The divergent responses of GPP to temperature and precipitation at mid-high latitudes and at mid-low latitudes echoed the necessity of modeling GPP separately by latitudes. This work provided a global distribution of LUE estimate, and developed a comprehensive algorithm modeling global terrestrial carbon with high spatial and temporal resolutions.

The impact of conventional surface data upon VAS regression retrievals in the lower troposphere

NASA Technical Reports Server (NTRS)

Lee, T. H.; Chesters, D.; Mostek, A.

1983-01-01

Surface temperature and dewpoint reports are added to the infrared radiances from the VISSR Atmospheric Sounder (VAS) in order to improve the retrieval of temperature and moisture profiles in the lower troposphere. The conventional (airways) surface data are combined with the twelve VAS channels as additional predictors in a ridge regression retrieval scheme, with the aim of using all available data to make high resolution space-time interpolations of the radiosonde network. For one day of VAS observations, retrievals using only VAS radiances are compared with retrievals using VAS radiances plus surface data. Temperature retrieval accuracy evaluated at coincident radiosonde sites shows a significant impact within the boundary layer. Dewpoint retrieval accuracy shows a broader improvement within the lowest tropospheric layers. The most dramatic impact of surface data is observed in the improved relative spatial and temporal continuity of low-level fields retrieved over the Midwestern United States.

Short-term forecasts gain in accuracy. [Regression technique using ''Box-Jenkins'' analysis

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

Not Available

Box-Jenkins time-series models offer accuracy for short-term forecasts that compare with large-scale macroeconomic forecasts. Utilities need to be able to forecast peak demand in order to plan their generating, transmitting, and distribution systems. This new method differs from conventional models by not assuming specific data patterns, but by fitting available data into a tentative pattern on the basis of auto-correlations. Three types of models (autoregressive, moving average, or mixed autoregressive/moving average) can be used according to which provides the most appropriate combination of autocorrelations and related derivatives. Major steps in choosing a model are identifying potential models, estimating the parametersmore » of the problem, and running a diagnostic check to see if the model fits the parameters. The Box-Jenkins technique is well suited for seasonal patterns, which makes it possible to have as short as hourly forecasts of load demand. With accuracy up to two years, the method will allow electricity price-elasticity forecasting that can be applied to facility planning and rate design. (DCK)« less

Can Functional Cardiac Age be Predicted from ECG in a Normal Healthy Population

NASA Technical Reports Server (NTRS)

Schlegel, Todd; Starc, Vito; Leban, Manja; Sinigoj, Petra; Vrhovec, Milos

2011-01-01

In a normal healthy population, we desired to determine the most age-dependent conventional and advanced ECG parameters. We hypothesized that changes in several ECG parameters might correlate with age and together reliably characterize the functional age of the heart. Methods: An initial study population of 313 apparently healthy subjects was ultimately reduced to 148 subjects (74 men, 84 women, in the range from 10 to 75 years of age) after exclusion criteria. In all subjects, ECG recordings (resting 5-minute 12-lead high frequency ECG) were evaluated via custom software programs to calculate up to 85 different conventional and advanced ECG parameters including beat-to-beat QT and RR variability, waveform complexity, and signal-averaged, high-frequency and spatial/spatiotemporal ECG parameters. The prediction of functional age was evaluated by multiple linear regression analysis using the best 5 univariate predictors. Results: Ignoring what were ultimately small differences between males and females, the functional age was found to be predicted (R2= 0.69, P < 0.001) from a linear combination of 5 independent variables: QRS elevation in the frontal plane (p<0.001), a new repolarization parameter QTcorr (p<0.001), mean high frequency QRS amplitude (p=0.009), the variability parameter % VLF of RRV (p=0.021) and the P-wave width (p=0.10). Here, QTcorr represents the correlation between the calculated QT and the measured QT signal. Conclusions: In apparently healthy subjects with normal conventional ECGs, functional cardiac age can be estimated by multiple linear regression analysis of mostly advanced ECG results. Because some parameters in the regression formula, such as QTcorr, high frequency QRS amplitude and P-wave width also change with disease in the same direction as with increased age, increased functional age of the heart may reflect subtle age-related pathologies in cardiac electrical function that are usually hidden on conventional ECG.

Assessing Advanced Airway Management Performance in a National Cohort of Emergency Medical Services Agencies.

PubMed

Wang, Henry E; Donnelly, John P; Barton, Dustin; Jarvis, Jeffrey L

2018-05-01

Although often the focus of quality improvement efforts, emergency medical services (EMS) advanced airway management performance has few national comparisons, nor are there many assessments with benchmarks accounting for differences in agency volume or patient mix. We seek to assess variations in advanced airway management and conventional intubation performance in a national cohort of EMS agencies. We used EMS data from ESO Solutions, a national EMS electronic health record system. We identified EMS emergency responses with attempted advanced airway management (conventional intubation, rapid sequence intubation, sedation-assisted intubation, supraglottic airway insertion, and cricothyroidotomy). We also separately examined cases with initial conventional intubation. We determined EMS agency risk-standardized advanced airway management and initial conventional intubation success rates by using mixed-effects regression models, fitting agency as a random intercept, adjusting for patient age, sex, race, cardiac arrest, or trauma status, and use of rapid sequence or sedation-assisted intubation, and accounting for reliability variations from EMS agency airway volume. We assessed changes in agency advanced airway management and initial conventional intubation performance rank after risk and reliability adjustment. We also identified high and low performers (reliability-adjusted and risk-standardized success confidence intervals falling outside the mean). During 2011 to 2015, 550 EMS agencies performed 57,209 advanced airway management procedures. Among 401 EMS agencies with greater than or equal to 10 advanced airway management procedures, there were a total of 56,636 procedures. Median reliability-adjusted and risk-standardized EMS agency advanced airway management success was 92.9% (interquartile range 90.1% to 94.8%; minimum 58.2%; maximum 99.0%). There were 56 advanced airway management low-performing and 38 high-performing EMS agencies. Among 342 agencies with greater than or equal to 10 initial conventional intubations, there were a total of 37,360 initial conventional intubations. Median reliability-adjusted and risk-standardized EMS agency initial conventional intubation success was 77.3% (interquartile range 70.9% to 83.6%; minimum 47.1%; maximum 95.8%). There were 64 initial conventional intubation low-performing and 45 high-performing EMS agencies. In this national series, EMS advanced airway management and initial conventional intubation performance varied widely. Reliability adjustment and risk standardization may influence EMS airway management performance assessments. Copyright © 2017 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.

A PDE approach for quantifying and visualizing tumor progression and regression

NASA Astrophysics Data System (ADS)

Sintay, Benjamin J.; Bourland, J. Daniel

2009-02-01

Quantification of changes in tumor shape and size allows physicians the ability to determine the effectiveness of various treatment options, adapt treatment, predict outcome, and map potential problem sites. Conventional methods are often based on metrics such as volume, diameter, or maximum cross sectional area. This work seeks to improve the visualization and analysis of tumor changes by simultaneously analyzing changes in the entire tumor volume. This method utilizes an elliptic partial differential equation (PDE) to provide a roadmap of boundary displacement that does not suffer from the discontinuities associated with other measures such as Euclidean distance. Streamline pathways defined by Laplace's equation (a commonly used PDE) are used to track tumor progression and regression at the tumor boundary. Laplace's equation is particularly useful because it provides a smooth, continuous solution that can be evaluated with sub-pixel precision on variable grid sizes. Several metrics are demonstrated including maximum, average, and total regression and progression. This method provides many advantages over conventional means of quantifying change in tumor shape because it is observer independent, stable for highly unusual geometries, and provides an analysis of the entire three-dimensional tumor volume.

Predicting active-layer soil thickness using topographic variables at a small watershed scale

PubMed Central

Li, Aidi; Tan, Xing; Wu, Wei; Liu, Hongbin; Zhu, Jie

2017-01-01

Knowledge about the spatial distribution of active-layer (AL) soil thickness is indispensable for ecological modeling, precision agriculture, and land resource management. However, it is difficult to obtain the details on AL soil thickness by using conventional soil survey method. In this research, the objective is to investigate the possibility and accuracy of mapping the spatial distribution of AL soil thickness through random forest (RF) model by using terrain variables at a small watershed scale. A total of 1113 soil samples collected from the slope fields were randomly divided into calibration (770 soil samples) and validation (343 soil samples) sets. Seven terrain variables including elevation, aspect, relative slope position, valley depth, flow path length, slope height, and topographic wetness index were derived from a digital elevation map (30 m). The RF model was compared with multiple linear regression (MLR), geographically weighted regression (GWR) and support vector machines (SVM) approaches based on the validation set. Model performance was evaluated by precision criteria of mean error (ME), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). Comparative results showed that RF outperformed MLR, GWR and SVM models. The RF gave better values of ME (0.39 cm), MAE (7.09 cm), and RMSE (10.85 cm) and higher R2 (62%). The sensitivity analysis demonstrated that the DEM had less uncertainty than the AL soil thickness. The outcome of the RF model indicated that elevation, flow path length and valley depth were the most important factors affecting the AL soil thickness variability across the watershed. These results demonstrated the RF model is a promising method for predicting spatial distribution of AL soil thickness using terrain parameters. PMID:28877196

Identifying pollution sources and predicting urban air quality using ensemble learning methods

NASA Astrophysics Data System (ADS)

Singh, Kunwar P.; Gupta, Shikha; Rai, Premanjali

2013-12-01

In this study, principal components analysis (PCA) was performed to identify air pollution sources and tree based ensemble learning models were constructed to predict the urban air quality of Lucknow (India) using the air quality and meteorological databases pertaining to a period of five years. PCA identified vehicular emissions and fuel combustion as major air pollution sources. The air quality indices revealed the air quality unhealthy during the summer and winter. Ensemble models were constructed to discriminate between the seasonal air qualities, factors responsible for discrimination, and to predict the air quality indices. Accordingly, single decision tree (SDT), decision tree forest (DTF), and decision treeboost (DTB) were constructed and their generalization and predictive performance was evaluated in terms of several statistical parameters and compared with conventional machine learning benchmark, support vector machines (SVM). The DT and SVM models discriminated the seasonal air quality rendering misclassification rate (MR) of 8.32% (SDT); 4.12% (DTF); 5.62% (DTB), and 6.18% (SVM), respectively in complete data. The AQI and CAQI regression models yielded a correlation between measured and predicted values and root mean squared error of 0.901, 6.67 and 0.825, 9.45 (SDT); 0.951, 4.85 and 0.922, 6.56 (DTF); 0.959, 4.38 and 0.929, 6.30 (DTB); 0.890, 7.00 and 0.836, 9.16 (SVR) in complete data. The DTF and DTB models outperformed the SVM both in classification and regression which could be attributed to the incorporation of the bagging and boosting algorithms in these models. The proposed ensemble models successfully predicted the urban ambient air quality and can be used as effective tools for its management.

SkyFACT: high-dimensional modeling of gamma-ray emission with adaptive templates and penalized likelihoods

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

Storm, Emma; Weniger, Christoph; Calore, Francesca, E-mail: e.m.storm@uva.nl, E-mail: c.weniger@uva.nl, E-mail: francesca.calore@lapth.cnrs.fr

We present SkyFACT (Sky Factorization with Adaptive Constrained Templates), a new approach for studying, modeling and decomposing diffuse gamma-ray emission. Like most previous analyses, the approach relies on predictions from cosmic-ray propagation codes like GALPROP and DRAGON. However, in contrast to previous approaches, we account for the fact that models are not perfect and allow for a very large number (∼> 10{sup 5}) of nuisance parameters to parameterize these imperfections. We combine methods of image reconstruction and adaptive spatio-spectral template regression in one coherent hybrid approach. To this end, we use penalized Poisson likelihood regression, with regularization functions that aremore » motivated by the maximum entropy method. We introduce methods to efficiently handle the high dimensionality of the convex optimization problem as well as the associated semi-sparse covariance matrix, using the L-BFGS-B algorithm and Cholesky factorization. We test the method both on synthetic data as well as on gamma-ray emission from the inner Galaxy, |ℓ|<90{sup o} and | b |<20{sup o}, as observed by the Fermi Large Area Telescope. We finally define a simple reference model that removes most of the residual emission from the inner Galaxy, based on conventional diffuse emission components as well as components for the Fermi bubbles, the Fermi Galactic center excess, and extended sources along the Galactic disk. Variants of this reference model can serve as basis for future studies of diffuse emission in and outside the Galactic disk.« less

Estimating increment-decrement life tables with multiple covariates from panel data: the case of active life expectancy.

PubMed

Land, K C; Guralnik, J M; Blazer, D G

1994-05-01

A fundamental limitation of current multistate life table methodology-evident in recent estimates of active life expectancy for the elderly-is the inability to estimate tables from data on small longitudinal panels in the presence of multiple covariates (such as sex, race, and socioeconomic status). This paper presents an approach to such an estimation based on an isomorphism between the structure of the stochastic model underlying a conventional specification of the increment-decrement life table and that of Markov panel regression models for simple state spaces. We argue that Markov panel regression procedures can be used to provide smoothed or graduated group-specific estimates of transition probabilities that are more stable across short age intervals than those computed directly from sample data. We then join these estimates with increment-decrement life table methods to compute group-specific total, active, and dependent life expectancy estimates. To illustrate the methods, we describe an empirical application to the estimation of such life expectancies specific to sex, race, and education (years of school completed) for a longitudinal panel of elderly persons. We find that education extends both total life expectancy and active life expectancy. Education thus may serve as a powerful social protective mechanism delaying the onset of health problems at older ages.

Retrieving aboveground biomass of wetland Phragmites australis (common reed) using a combination of airborne discrete-return LiDAR and hyperspectral data

NASA Astrophysics Data System (ADS)

Luo, Shezhou; Wang, Cheng; Xi, Xiaohuan; Pan, Feifei; Qian, Mingjie; Peng, Dailiang; Nie, Sheng; Qin, Haiming; Lin, Yi

2017-06-01

Wetland biomass is essential for monitoring the stability and productivity of wetland ecosystems. Conventional field methods to measure or estimate wetland biomass are accurate and reliable, but expensive, time consuming and labor intensive. This research explored the potential for estimating wetland reed biomass using a combination of airborne discrete-return Light Detection and Ranging (LiDAR) and hyperspectral data. To derive the optimal predictor variables of reed biomass, a range of LiDAR and hyperspectral metrics at different spatial scales were regressed against the field-observed biomasses. The results showed that the LiDAR-derived H_p99 (99th percentile of the LiDAR height) and hyperspectral-calculated modified soil-adjusted vegetation index (MSAVI) were the best metrics for estimating reed biomass using the single regression model. Although the LiDAR data yielded a higher estimation accuracy compared to the hyperspectral data, the combination of LiDAR and hyperspectral data produced a more accurate prediction model for reed biomass (R2 = 0.648, RMSE = 167.546 g/m2, RMSEr = 20.71%) than LiDAR data alone. Thus, combining LiDAR data with hyperspectral data has a great potential for improving the accuracy of aboveground biomass estimation.

Assessing the Causality between Blood Pressure and Retinal Vascular Caliber through Mendelian Randomisation

NASA Astrophysics Data System (ADS)

Li, Ling-Jun; Liao, Jiemin; Cheung, Carol Yim-Lui; Ikram, M. Kamran; Shyong, Tai E.; Wong, Tien-Yin; Cheng, Ching-Yu

2016-02-01

We aimed to determine the association between blood pressure (BP) and retinal vascular caliber changes that were free from confounders and reverse causation by using Mendelian randomisation. A total of 6528 participants from a multi-ethnic cohort (Chinese, Malays, and Indians) in Singapore were included in this study. Retinal arteriolar and venular caliber was measured by a semi-automated computer program. Genotyping was done using Illumina 610-quad chips. Meta-analysis of association between BP, and retinal arteriolar and venular caliber across three ethnic groups was performed both in conventional linear regression and Mendelian randomisation framework with a genetic risk score of BP as an instrumental variable. In multiple linear regression models, each 10 mm Hg increase in systolic BP, diastolic BP, and mean arterial BP (MAP) was associated with significant decreases in retinal arteriolar caliber of a 1.4, 3.0, and 2.6 μm, and significant decreases in retinal venular caliber of a 0.6, 0.7, and 0.9 μm, respectively. In a Mendelian randomisation model, only associations between DBP and MAP and retinal arteriolar narrowing remained yet its significance was greatly reduced. Our data showed weak evidence of a causal relationship between elevated BP and retinal arteriolar narrowing.

A crystal plasticity model for slip in hexagonal close packed metals based on discrete dislocation simulations

NASA Astrophysics Data System (ADS)

Messner, Mark C.; Rhee, Moono; Arsenlis, Athanasios; Barton, Nathan R.

2017-06-01

This work develops a method for calibrating a crystal plasticity model to the results of discrete dislocation (DD) simulations. The crystal model explicitly represents junction formation and annihilation mechanisms and applies these mechanisms to describe hardening in hexagonal close packed metals. The model treats these dislocation mechanisms separately from elastic interactions among populations of dislocations, which the model represents through a conventional strength-interaction matrix. This split between elastic interactions and junction formation mechanisms more accurately reproduces the DD data and results in a multi-scale model that better represents the lower scale physics. The fitting procedure employs concepts of machine learning—feature selection by regularized regression and cross-validation—to develop a robust, physically accurate crystal model. The work also presents a method for ensuring the final, calibrated crystal model respects the physical symmetries of the crystal system. Calibrating the crystal model requires fitting two linear operators: one describing elastic dislocation interactions and another describing junction formation and annihilation dislocation reactions. The structure of these operators in the final, calibrated model reflect the crystal symmetry and slip system geometry of the DD simulations.

Revision of laser-induced damage threshold evaluation from damage probability data

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

Bataviciute, Gintare; Grigas, Povilas; Smalakys, Linas

2013-04-15

In this study, the applicability of commonly used Damage Frequency Method (DFM) is addressed in the context of Laser-Induced Damage Threshold (LIDT) testing with pulsed lasers. A simplified computer model representing the statistical interaction between laser irradiation and randomly distributed damage precursors is applied for Monte Carlo experiments. The reproducibility of LIDT predicted from DFM is examined under both idealized and realistic laser irradiation conditions by performing numerical 1-on-1 tests. A widely accepted linear fitting resulted in systematic errors when estimating LIDT and its error bars. For the same purpose, a Bayesian approach was proposed. A novel concept of parametricmore » regression based on varying kernel and maximum likelihood fitting technique is introduced and studied. Such approach exhibited clear advantages over conventional linear fitting and led to more reproducible LIDT evaluation. Furthermore, LIDT error bars are obtained as a natural outcome of parametric fitting which exhibit realistic values. The proposed technique has been validated on two conventionally polished fused silica samples (355 nm, 5.7 ns).« less

Effect of Alternate Supply of Shielding Gases of Tungsten Inert Gas Welding on Mechanical Properties of Austenitic Stainless Steel

NASA Astrophysics Data System (ADS)

Shinde, Neelam Vilas; Telsang, Martand Tamanacharya

2016-07-01

In the present study, an attempt is made to study the effect of alternate supply of the shielding gas in comparison with the conventional method of TIG welding with pure argon gas. The two sets of combination are used as 10-10 and 40-20 s for alternate supply of the Argon and Helium shielding gas respectively. The effect of alternate supply of shielding gas is studied on the mechanical properties like bend test, tensile test and impact test. The full factorial experimental design is applied for three set of combinations. The ANOVA is used to find significant parameters for the process and regression analysis used to develop the mathematical model. The result shows that the alternate supply of the shielding gas for 10-10 s provides better result for the bend, tensile and impact test as compared with the conventional argon gas and the alternate supply of 40-20 s argon and helium gas respectively. Welding speed can be increased for alternate supply of the shielding gas that can reduce the total welding cost.

Education and inequalities in risk scores for coronary heart disease and body mass index: evidence for a population strategy.

PubMed

Liu, Sze Yan; Kawachi, Ichiro; Glymour, M Maria

2012-09-01

Concerns have been raised that education may have greater benefits for persons at high risk of coronary heart disease (CHD) than for those at low risk. We estimated the association of education (less than high school, high school, or college graduates) with 10-year CHD risk and body mass index (BMI), using linear and quantile regression models, in the following two nationally representative datasets: the 2006 wave of the Health and Retirement Survey and the 2003-2008 National Health and Nutrition Examination Survey (NHANES). Higher educational attainment was associated with lower 10-year CHD risk for all groups. However, the magnitude of this association varied considerably across quantiles for some subgroups. For example, among women in NHANES, a high school degree was associated with 4% (95% confidence interval = -9% to 1%) and 17% (-24% to -8%) lower CHD risk in the 10th and 90th percentiles, respectively. For BMI, a college degree was associated with uniform decreases across the distribution for women, but with varying increases for men. Compared with those who had not completed high school, male college graduates in the NHANES sample had a BMI that was 6% greater (2% to 11%) at the 10th percentile of the BMI distribution and 7% lower (-10% to -3%) at the 90th percentile (ie, overweight/obese). Estimates from the Health and Retirement Survey sample and the marginal quantile regression models showed similar patterns. Conventional regression methods may mask important variations in the associations between education and CHD risk.

Statistical learning theory for high dimensional prediction: Application to criterion-keyed scale development.

PubMed

Chapman, Benjamin P; Weiss, Alexander; Duberstein, Paul R

2016-12-01

Statistical learning theory (SLT) is the statistical formulation of machine learning theory, a body of analytic methods common in "big data" problems. Regression-based SLT algorithms seek to maximize predictive accuracy for some outcome, given a large pool of potential predictors, without overfitting the sample. Research goals in psychology may sometimes call for high dimensional regression. One example is criterion-keyed scale construction, where a scale with maximal predictive validity must be built from a large item pool. Using this as a working example, we first introduce a core principle of SLT methods: minimization of expected prediction error (EPE). Minimizing EPE is fundamentally different than maximizing the within-sample likelihood, and hinges on building a predictive model of sufficient complexity to predict the outcome well, without undue complexity leading to overfitting. We describe how such models are built and refined via cross-validation. We then illustrate how 3 common SLT algorithms-supervised principal components, regularization, and boosting-can be used to construct a criterion-keyed scale predicting all-cause mortality, using a large personality item pool within a population cohort. Each algorithm illustrates a different approach to minimizing EPE. Finally, we consider broader applications of SLT predictive algorithms, both as supportive analytic tools for conventional methods, and as primary analytic tools in discovery phase research. We conclude that despite their differences from the classic null-hypothesis testing approach-or perhaps because of them-SLT methods may hold value as a statistically rigorous approach to exploratory regression. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

On estimating gravity anomalies: A comparison of least squares collocation with least squares techniques

NASA Technical Reports Server (NTRS)

Argentiero, P.; Lowrey, B.

1976-01-01

The least squares collocation algorithm for estimating gravity anomalies from geodetic data is shown to be an application of the well known regression equations which provide the mean and covariance of a random vector (gravity anomalies) given a realization of a correlated random vector (geodetic data). It is also shown that the collocation solution for gravity anomalies is equivalent to the conventional least-squares-Stokes' function solution when the conventional solution utilizes properly weighted zero a priori estimates. The mathematical and physical assumptions underlying the least squares collocation estimator are described, and its numerical properties are compared with the numerical properties of the conventional least squares estimator.

Use of complementary and alternative medicine during pregnancy and the postpartum period: an analysis of the National Health Interview Survey.

PubMed

Birdee, Gurjeet S; Kemper, Kathi J; Rothman, Russell; Gardiner, Paula

2014-10-01

Complementary and alternative medicine (CAM) is commonly used among women, but few national data exist regarding CAM use during pregnancy or the postnatal period. Data from the 2007 National Health Interview Survey were analyzed for women ages between the ages of 18 and 49 years who were pregnant or had children less than 1 year old. CAM use was identified based on standard definitions of CAM from the National Institutes of Health's National Center for Complementary and Alternative Medicine. CAM use among women who were pregnant or with a child less than 1 year was compared with the other similarly aged female responders. CAM use was examined among these women stratified by sociodemographics, health conditions, and conventional medicine use through bivariable and multivariable logistic regression models. Among pregnant and postpartum women from the ages of 19 to 49 years in the United States, 37% of pregnant women and 28% of postpartum women reported using CAM in the last 12 months compared with 40% of nonpregnant/non-postpartum women. Mind-body practices were the most common CAM modality reported, with one out of four women reporting use. Biological therapies, excluding vitamins and minerals, during the postpartum period were used by only 8% of women. Using multivariable regression modeling, we report no significant difference in CAM use among pregnant compared with non-pregnant women (adjusted odds ratio [AOR], 0.88; [95% confidence interval 0.65-1.20]), but lower CAM use among postpartum women compared with non-pregnant women (AOR 0.67; [0.52-0.88]), while adjusting for sociodemographics. CAM use among pregnancy similar to women who are not pregnant, while postpartum CAM use decreases. Further evaluation of CAM therapies among pregnant and postpartum women is necessary to determine the costs and benefits of integrative CAM therapies in conventional care.

Emergency department patient perceptions and preferences on opt-in rapid HIV screening program components

PubMed Central

Merchant, Roland C.; Clark, Melissa A.; Seage, George R.; Mayer, Kenneth H.; DeGruttola, Victor G.; Becker, Bruce M.

2011-01-01

The aim of this investigation was to assess emergency department (ED) patients’ perceptions and preferences about an opt-in, universal, rapid HIV screening program and identify patient groups who expressed stronger beliefs about components of the testing program. From July 2005 to July 2006, ED patients in the opt-in, universal, rapid HIV screening program were interviewed in person. Multivariable regression models were used to compare participants on their beliefs about the program components. Of the 561 participants, 62.0% had previously been tested for HIV. The majority of participants (58.8%) believed the rapid and standard/conventional HIV tests to be equally accurate, 27.7% believed the rapid test to be less or much less accurate, and 8.7% believed the rapid test to be more or much more accurate. Almost two-thirds (65.1%) favored having a rapid instead of a standard/conventional HIV test, 94.6% wanted the test results within one hour, and 61.3% would be likely or very likely to undergo testing in the ED if it prolonged their ED visit. Almost all (92.5%) believed that their medical care was “not at all” delayed because of being tested, 94.1% believed that testing did “not at all” divert attention from the reason for their ED visit, and 80.9% thought that testing in the ED was “not at all” stressful. In multivariable logistic regression models, males and those with more than 12 years of formal education showed greater concerns about the rapid HIV test’s accuracy. Hispanic/Latinos, participants with governmental insurance, and those previously HIV tested were more apt to be screened for HIV even if testing delayed their ED departure. Overall, participants were highly accepting of the components of this opt-in rapid HIV screening program. However, concerns regarding the accuracy of the rapid HIV test might limit test acceptance and should be addressed during pre-test information procedures. PMID:19283644

Risk factors for e-cigarette, conventional cigarette, and dual use in German adolescents: a cohort study.

PubMed

Hanewinkel, Reiner; Isensee, Barbara

2015-05-01

Little is known about risk factors that are associated with e-cigarette use in adolescents. Multilevel mixed-effects regressions were performed to assess the relationship between factors that might be associated with e-cigarette, conventional cigarette and dual use in a cohort of 2693 German adolescents (mean age=12.5 years; SD=0.6). Risk factors were assessed in October 2010 and life time e-cigarette and conventional cigarette use were assessed 26 months later. Use of e-cigarettes as well as use of conventional cigarette and dual use were associated with higher sensation seeking scores, and higher odds of having friends and parents who smoke conventional cigarettes, with conventional cigarette use additionally with male gender, being older, having higher odds of siblings who smoke conventional cigarettes, and less likely for adolescents who attend a Gymnasium, secondary school with a strong emphasis on academic learning. The use of conventional cigarettes at baseline did not predict e-cigarette use at follow-up. Lifetime prevalence of e-cigarette use was 4.7%, of conventional cigarette use 18.4%. A quarter of e-cigarette users (23.8%) never smoked a conventional cigarette. Data indicate that e-cigarette and conventional cigarette use share many but not all risk factors. E-cigarettes could counteract the process of denormalization of smoking. Copyright © 2015 Elsevier Inc. All rights reserved.

Chronology of DIC technique based on the fundamental mathematical modeling and dehydration impact.

PubMed

Alias, Norma; Saipol, Hafizah Farhah Saipan; Ghani, Asnida Che Abd

2014-12-01

A chronology of mathematical models for heat and mass transfer equation is proposed for the prediction of moisture and temperature behavior during drying using DIC (Détente Instantanée Contrôlée) or instant controlled pressure drop technique. DIC technique has the potential as most commonly used dehydration method for high impact food value including the nutrition maintenance and the best possible quality for food storage. The model is governed by the regression model, followed by 2D Fick's and Fourier's parabolic equation and 2D elliptic-parabolic equation in a rectangular slice. The models neglect the effect of shrinkage and radiation effects. The simulations of heat and mass transfer equations with parabolic and elliptic-parabolic types through some numerical methods based on finite difference method (FDM) have been illustrated. Intel®Core™2Duo processors with Linux operating system and C programming language have been considered as a computational platform for the simulation. Qualitative and quantitative differences between DIC technique and the conventional drying methods have been shown as a comparative.

The Satellite Clock Bias Prediction Method Based on Takagi-Sugeno Fuzzy Neural Network

NASA Astrophysics Data System (ADS)

Cai, C. L.; Yu, H. G.; Wei, Z. C.; Pan, J. D.

2017-05-01

The continuous improvement of the prediction accuracy of Satellite Clock Bias (SCB) is the key problem of precision navigation. In order to improve the precision of SCB prediction and better reflect the change characteristics of SCB, this paper proposes an SCB prediction method based on the Takagi-Sugeno fuzzy neural network. Firstly, the SCB values are pre-treated based on their characteristics. Then, an accurate Takagi-Sugeno fuzzy neural network model is established based on the preprocessed data to predict SCB. This paper uses the precise SCB data with different sampling intervals provided by IGS (International Global Navigation Satellite System Service) to realize the short-time prediction experiment, and the results are compared with the ARIMA (Auto-Regressive Integrated Moving Average) model, GM(1,1) model, and the quadratic polynomial model. The results show that the Takagi-Sugeno fuzzy neural network model is feasible and effective for the SCB short-time prediction experiment, and performs well for different types of clocks. The prediction results for the proposed method are better than the conventional methods obviously.

Correcting for Blood Arrival Time in Global Mean Regression Enhances Functional Connectivity Analysis of Resting State fMRI-BOLD Signals.

PubMed

Erdoğan, Sinem B; Tong, Yunjie; Hocke, Lia M; Lindsey, Kimberly P; deB Frederick, Blaise

2016-01-01

Resting state functional connectivity analysis is a widely used method for mapping intrinsic functional organization of the brain. Global signal regression (GSR) is commonly employed for removing systemic global variance from resting state BOLD-fMRI data; however, recent studies have demonstrated that GSR may introduce spurious negative correlations within and between functional networks, calling into question the meaning of anticorrelations reported between some networks. In the present study, we propose that global signal from resting state fMRI is composed primarily of systemic low frequency oscillations (sLFOs) that propagate with cerebral blood circulation throughout the brain. We introduce a novel systemic noise removal strategy for resting state fMRI data, "dynamic global signal regression" (dGSR), which applies a voxel-specific optimal time delay to the global signal prior to regression from voxel-wise time series. We test our hypothesis on two functional systems that are suggested to be intrinsically organized into anticorrelated networks: the default mode network (DMN) and task positive network (TPN). We evaluate the efficacy of dGSR and compare its performance with the conventional "static" global regression (sGSR) method in terms of (i) explaining systemic variance in the data and (ii) enhancing specificity and sensitivity of functional connectivity measures. dGSR increases the amount of BOLD signal variance being modeled and removed relative to sGSR while reducing spurious negative correlations introduced in reference regions by sGSR, and attenuating inflated positive connectivity measures. We conclude that incorporating time delay information for sLFOs into global noise removal strategies is of crucial importance for optimal noise removal from resting state functional connectivity maps.

Perceptions of the Relative Harm of Cigarettes and E-cigarettes Among U.S. Youth

PubMed Central

Ambrose, Bridget K.; Rostron, Brian L.; Johnson, Sarah E.; Portnoy, David B.; Apelberg, Benjamin J.; Kaufman, Annette R.; Choiniere, Conrad J.

2015-01-01

Background Despite progress in reducing youth smoking, adolescents remain highly susceptible to tobacco use. Of concern is whether youth perceive electronic cigarettes (e-cigarettes) as a preferable alternative to conventional cigarettes. Purpose To describe cigarette harm perception patterns among youth based on the frequency and intensity of cigarette smoking, and examine the relative harm perceptions of conventional versus e-cigarettes, using data from a large, nationally representative sample of U.S. youth. Methods Data from the 2012 National Youth Tobacco Survey (N=24,658) were analyzed in 2013 to identify patterns of cigarette harm perceptions. Multinomial logistic regression was conducted to identify associations between demographic and tobacco use characteristics and cigarette harm perception patterns. Logistic regression was conducted to examine the relationship between cigarette harm perceptions and the perception of e-cigarettes as less harmful than cigarettes for current, ever, and never cigarette smokers. Results The majority of youth (64.2%) perceived the harmfulness of cigarettes as dose-dependent. Approximately one in three students perceived e-cigarettes as less harmful than conventional cigarettes. Regardless of cigarette smoking status, ever users of e-cigarettes and those with “dose-dependent” cigarette harm perceptions consistently were more likely to perceive e-cigarettes as less harmful than conventional cigarettes. Conclusions Many youth perceive tobacco use on a continuum of harm. Youth who perceive gradations in harm—both by frequency and intensity of cigarette use and by type of product—may be particularly susceptible to e-cigarette use. PMID:25044196

Income or living standard and health in Germany: different ways of measurement of relative poverty with regard to self-rated health.

PubMed

Pfoertner, Timo-Kolja; Andress, Hans-Juergen; Janssen, Christian

2011-08-01

Current study introduces the living standard concept as an alternative approach of measuring poverty and compares its explanatory power to an income-based poverty measure with regard to subjective health status of the German population. Analyses are based on the German Socio-Economic Panel (2001, 2003 and 2005) and refer to binary logistic regressions of poor subjective health status with regard to each poverty condition, their duration and their causal influence from a previous time point. To calculate the discriminate power of both poverty indicators, initially the indicators were considered separately in regression models and subsequently, both were included simultaneously. The analyses reveal a stronger poverty-health relationship for the living standard indicator. An inadequate living standard in 2005, longer spells of an inadequate living standard between 2001, 2003 and 2005 as well as an inadequate living standard at a previous time point is significantly strongly associated with subjective health than income poverty. Our results challenge conventional measurements of the relationship between poverty and health that probably has been underestimated by income measures so far.

Political democracy, economic liberalization, and macro-sociological models of intergenerational mobility.

PubMed

Gugushvili, Alexi

2017-08-01

Building on the previously investigated macro-sociological models which analyze the consequences of economic development, income inequality, and international migration on social mobility, this article studies the specific contextual covariates of intergenerational reproduction of occupational status in post-communist societies. It is theorized that social mobility is higher in societies with democratic political regimes and less liberalized economies. The outlined hypotheses are tested by using micro- and macro-level datasets for 21 post-communist societies which are fitted into multilevel mixed-effects linear regressions. The derived findings suggest that factors specific to transition societies, conventional macro-level variables, and the legacy of the Soviet Union explain variation in intergenerational social mobility, but the results vary depending which birth cohorts survey participants belong to and whether or not they stem from advantaged or disadvantaged social origins. These findings are robust to various alternative data, sample, and method specifications. Copyright © 2017 Elsevier Inc. All rights reserved.

A multi-criteria index for ecological evaluation of tropical agriculture in southeastern Mexico.

PubMed

Huerta, Esperanza; Kampichler, Christian; Ochoa-Gaona, Susana; De Jong, Ben; Hernandez-Daumas, Salvador; Geissen, Violette

2014-01-01

The aim of this study was to generate an easy to use index to evaluate the ecological state of agricultural land from a sustainability perspective. We selected environmental indicators, such as the use of organic soil amendments (green manure) versus chemical fertilizers, plant biodiversity (including crop associations), variables which characterize soil conservation of conventional agricultural systems, pesticide use, method and frequency of tillage. We monitored the ecological state of 52 agricultural plots to test the performance of the index. The variables were hierarchically aggregated with simple mathematical algorithms, if-then rules, and rule-based fuzzy models, yielding the final multi-criteria index with values from 0 (worst) to 1 (best conditions). We validated the model through independent evaluation by experts, and we obtained a linear regression with an r2 = 0.61 (p = 2.4e-06, d.f. = 49) between index output and the experts' evaluation.

A Multi-Criteria Index for Ecological Evaluation of Tropical Agriculture in Southeastern Mexico

PubMed Central

Huerta, Esperanza; Kampichler, Christian; Ochoa-Gaona, Susana; De Jong, Ben; Hernandez-Daumas, Salvador; Geissen, Violette

2014-01-01

The aim of this study was to generate an easy to use index to evaluate the ecological state of agricultural land from a sustainability perspective. We selected environmental indicators, such as the use of organic soil amendments (green manure) versus chemical fertilizers, plant biodiversity (including crop associations), variables which characterize soil conservation of conventional agricultural systems, pesticide use, method and frequency of tillage. We monitored the ecological state of 52 agricultural plots to test the performance of the index. The variables were hierarchically aggregated with simple mathematical algorithms, if-then rules, and rule-based fuzzy models, yielding the final multi-criteria index with values from 0 (worst) to 1 (best conditions). We validated the model through independent evaluation by experts, and we obtained a linear regression with an r2 = 0.61 (p = 2.4e-06, d.f. = 49) between index output and the experts’ evaluation. PMID:25405980

Poisson Mixture Regression Models for Heart Disease Prediction.

PubMed

Mufudza, Chipo; Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

Poisson Mixture Regression Models for Heart Disease Prediction

PubMed Central

Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-)radiotherapy.

PubMed

Dankers, Frank; Wijsman, Robin; Troost, Esther G C; Monshouwer, René; Bussink, Johan; Hoffmann, Aswin L

2017-05-07

In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade  ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79-0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC  =  0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.

Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-)radiotherapy

NASA Astrophysics Data System (ADS)

Dankers, Frank; Wijsman, Robin; Troost, Esther G. C.; Monshouwer, René; Bussink, Johan; Hoffmann, Aswin L.

2017-05-01

In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade  ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79-0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC  =  0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.

Use of a Machine-learning Method for Predicting Highly Cited Articles Within General Radiology Journals.

PubMed

Rosenkrantz, Andrew B; Doshi, Ankur M; Ginocchio, Luke A; Aphinyanaphongs, Yindalon

2016-12-01

This study aimed to assess the performance of a text classification machine-learning model in predicting highly cited articles within the recent radiological literature and to identify the model's most influential article features. We downloaded from PubMed the title, abstract, and medical subject heading terms for 10,065 articles published in 25 general radiology journals in 2012 and 2013. Three machine-learning models were applied to predict the top 10% of included articles in terms of the number of citations to the article in 2014 (reflecting the 2-year time window in conventional impact factor calculations). The model having the highest area under the curve was selected to derive a list of article features (words) predicting high citation volume, which was iteratively reduced to identify the smallest possible core feature list maintaining predictive power. Overall themes were qualitatively assigned to the core features. The regularized logistic regression (Bayesian binary regression) model had highest performance, achieving an area under the curve of 0.814 in predicting articles in the top 10% of citation volume. We reduced the initial 14,083 features to 210 features that maintain predictivity. These features corresponded with topics relating to various imaging techniques (eg, diffusion-weighted magnetic resonance imaging, hyperpolarized magnetic resonance imaging, dual-energy computed tomography, computed tomography reconstruction algorithms, tomosynthesis, elastography, and computer-aided diagnosis), particular pathologies (prostate cancer; thyroid nodules; hepatic adenoma, hepatocellular carcinoma, non-alcoholic fatty liver disease), and other topics (radiation dose, electroporation, education, general oncology, gadolinium, statistics). Machine learning can be successfully applied to create specific feature-based models for predicting articles likely to achieve high influence within the radiological literature. Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Sequential experimental design based generalised ANOVA

NASA Astrophysics Data System (ADS)

Chakraborty, Souvik; Chowdhury, Rajib

2016-07-01

Over the last decade, surrogate modelling technique has gained wide popularity in the field of uncertainty quantification, optimization, model exploration and sensitivity analysis. This approach relies on experimental design to generate training points and regression/interpolation for generating the surrogate. In this work, it is argued that conventional experimental design may render a surrogate model inefficient. In order to address this issue, this paper presents a novel distribution adaptive sequential experimental design (DA-SED). The proposed DA-SED has been coupled with a variant of generalised analysis of variance (G-ANOVA), developed by representing the component function using the generalised polynomial chaos expansion. Moreover, generalised analytical expressions for calculating the first two statistical moments of the response, which are utilized in predicting the probability of failure, have also been developed. The proposed approach has been utilized in predicting probability of failure of three structural mechanics problems. It is observed that the proposed approach yields accurate and computationally efficient estimate of the failure probability.

The relative effect of noise at different times of day: An analysis of existing survey data

NASA Technical Reports Server (NTRS)

Fields, J. M.

1986-01-01

This report examines survey evidence on the relative impact of noise at different times of day and assesses the survey methodology which produces that evidence. Analyses of the regression of overall (24-hour) annoyance on noise levels in different time periods can provide direct estimates of the value of the parameters in human reaction models which are used in environmental noise indices such as LDN and CNEL. In this report these analyses are based on the original computer tapes containing the responses of 22,000 respondents from ten studies of response to noise in residential areas. The estimates derived from these analyses are found to be so inaccurate that they do not provide useful information for policy or scientific purposes. The possibility that the type of questionnaire item could be biasing the estimates of the time-of-day weightings is considered but not supported by the data. Two alternatives to the conventional noise reaction model (adjusted energy model) are considered but not supported by the data.

Extraction of anthocyanins from red cabbage using high pressure CO2.

PubMed

Xu, Zhenzhen; Wu, Jihong; Zhang, Yan; Hu, Xiaosong; Liao, Xiaojun; Wang, Zhengfu

2010-09-01

The extraction kinetics of anthocyanins from red cabbage using high pressure CO(2) (HPCD) against conventional acidified water (CAW) was investigated. The HPCD time, temperature, pressure and volume ratio of solid-liquid mixture vs. pressurized CO(2) (R((S+L)/G)) exhibited important roles on the extraction kinetics of anthocyanins. The extraction kinetics showed two phases, the yield increased with increasing the time in the first phase, the yield defined as steady-state yield (y(*)) was constant in the second phase. The y(*) of anthocyanins using HPCD increased with higher temperature, higher pressure and lower R((S+L)/G). The general mass transfer model with higher regression coefficients (R(2)>0.97) fitted the kinetic data better than the Fick's second law diffusion model. As compared with CAW, the time (t(*)) to reach the y(*) of anthocyanins using HPCD was reduced by half while its corresponding overall volumetric mass transfer coefficients k(L)xa from the general mass transfer model increased by two folds. Copyright 2010 Elsevier Ltd. All rights reserved.

Sequential experimental design based generalised ANOVA

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

Chakraborty, Souvik, E-mail: csouvik41@gmail.com; Chowdhury, Rajib, E-mail: rajibfce@iitr.ac.in

Over the last decade, surrogate modelling technique has gained wide popularity in the field of uncertainty quantification, optimization, model exploration and sensitivity analysis. This approach relies on experimental design to generate training points and regression/interpolation for generating the surrogate. In this work, it is argued that conventional experimental design may render a surrogate model inefficient. In order to address this issue, this paper presents a novel distribution adaptive sequential experimental design (DA-SED). The proposed DA-SED has been coupled with a variant of generalised analysis of variance (G-ANOVA), developed by representing the component function using the generalised polynomial chaos expansion. Moreover,more » generalised analytical expressions for calculating the first two statistical moments of the response, which are utilized in predicting the probability of failure, have also been developed. The proposed approach has been utilized in predicting probability of failure of three structural mechanics problems. It is observed that the proposed approach yields accurate and computationally efficient estimate of the failure probability.« less

The relative effect of noise at different times of day: An analysis of existing survey data

NASA Astrophysics Data System (ADS)

Fields, J. M.

1986-04-01

This report examines survey evidence on the relative impact of noise at different times of day and assesses the survey methodology which produces that evidence. Analyses of the regression of overall (24-hour) annoyance on noise levels in different time periods can provide direct estimates of the value of the parameters in human reaction models which are used in environmental noise indices such as LDN and CNEL. In this report these analyses are based on the original computer tapes containing the responses of 22,000 respondents from ten studies of response to noise in residential areas. The estimates derived from these analyses are found to be so inaccurate that they do not provide useful information for policy or scientific purposes. The possibility that the type of questionnaire item could be biasing the estimates of the time-of-day weightings is considered but not supported by the data. Two alternatives to the conventional noise reaction model (adjusted energy model) are considered but not supported by the data.

Modeling and optimization of red currants vacuum drying process by response surface methodology (RSM).

PubMed

Šumić, Zdravko; Vakula, Anita; Tepić, Aleksandra; Čakarević, Jelena; Vitas, Jasmina; Pavlić, Branimir

2016-07-15

Fresh red currants were dried by vacuum drying process under different drying conditions. Box-Behnken experimental design with response surface methodology was used for optimization of drying process in terms of physical (moisture content, water activity, total color change, firmness and rehydratation power) and chemical (total phenols, total flavonoids, monomeric anthocyanins and ascorbic acid content and antioxidant activity) properties of dried samples. Temperature (48-78 °C), pressure (30-330 mbar) and drying time (8-16 h) were investigated as independent variables. Experimental results were fitted to a second-order polynomial model where regression analysis and analysis of variance were used to determine model fitness and optimal drying conditions. The optimal conditions of simultaneously optimized responses were temperature of 70.2 °C, pressure of 39 mbar and drying time of 8 h. It could be concluded that vacuum drying provides samples with good physico-chemical properties, similar to lyophilized sample and better than conventionally dried sample. Copyright © 2016 Elsevier Ltd. All rights reserved.

TIMSS 2011 Student and Teacher Predictors for Mathematics Achievement Explored and Identified via Elastic Net.

PubMed

Yoo, Jin Eun

2018-01-01

A substantial body of research has been conducted on variables relating to students' mathematics achievement with TIMSS. However, most studies have employed conventional statistical methods, and have focused on selected few indicators instead of utilizing hundreds of variables TIMSS provides. This study aimed to find a prediction model for students' mathematics achievement using as many TIMSS student and teacher variables as possible. Elastic net, the selected machine learning technique in this study, takes advantage of both LASSO and ridge in terms of variable selection and multicollinearity, respectively. A logistic regression model was also employed to predict TIMSS 2011 Korean 4th graders' mathematics achievement. Ten-fold cross-validation with mean squared error was employed to determine the elastic net regularization parameter. Among 162 TIMSS variables explored, 12 student and 5 teacher variables were selected in the elastic net model, and the prediction accuracy, sensitivity, and specificity were 76.06, 70.23, and 80.34%, respectively. This study showed that the elastic net method can be successfully applied to educational large-scale data by selecting a subset of variables with reasonable prediction accuracy and finding new variables to predict students' mathematics achievement. Newly found variables via machine learning can shed light on the existing theories from a totally different perspective, which in turn propagates creation of a new theory or complement of existing ones. This study also examined the current scale development convention from a machine learning perspective.

TIMSS 2011 Student and Teacher Predictors for Mathematics Achievement Explored and Identified via Elastic Net

PubMed Central

Yoo, Jin Eun

2018-01-01

A substantial body of research has been conducted on variables relating to students' mathematics achievement with TIMSS. However, most studies have employed conventional statistical methods, and have focused on selected few indicators instead of utilizing hundreds of variables TIMSS provides. This study aimed to find a prediction model for students' mathematics achievement using as many TIMSS student and teacher variables as possible. Elastic net, the selected machine learning technique in this study, takes advantage of both LASSO and ridge in terms of variable selection and multicollinearity, respectively. A logistic regression model was also employed to predict TIMSS 2011 Korean 4th graders' mathematics achievement. Ten-fold cross-validation with mean squared error was employed to determine the elastic net regularization parameter. Among 162 TIMSS variables explored, 12 student and 5 teacher variables were selected in the elastic net model, and the prediction accuracy, sensitivity, and specificity were 76.06, 70.23, and 80.34%, respectively. This study showed that the elastic net method can be successfully applied to educational large-scale data by selecting a subset of variables with reasonable prediction accuracy and finding new variables to predict students' mathematics achievement. Newly found variables via machine learning can shed light on the existing theories from a totally different perspective, which in turn propagates creation of a new theory or complement of existing ones. This study also examined the current scale development convention from a machine learning perspective. PMID:29599736

Overcoming bias in estimating the volume-outcome relationship.

PubMed

Tsai, Alexander C; Votruba, Mark; Bridges, John F P; Cebul, Randall D

2006-02-01

To examine the effect of hospital volume on 30-day mortality for patients with congestive heart failure (CHF) using administrative and clinical data in conventional regression and instrumental variables (IV) estimation models. The primary data consisted of longitudinal information on comorbid conditions, vital signs, clinical status, and laboratory test results for 21,555 Medicare-insured patients aged 65 years and older hospitalized for CHF in northeast Ohio in 1991-1997. The patient was the primary unit of analysis. We fit a linear probability model to the data to assess the effects of hospital volume on patient mortality within 30 days of admission. Both administrative and clinical data elements were included for risk adjustment. Linear distances between patients and hospitals were used to construct the instrument, which was then used to assess the endogeneity of hospital volume. When only administrative data elements were included in the risk adjustment model, the estimated volume-outcome effect was statistically significant (p=.029) but small in magnitude. The estimate was markedly attenuated in magnitude and statistical significance when clinical data were added to the model as risk adjusters (p=.39). IV estimation shifted the estimate in a direction consistent with selective referral, but we were unable to reject the consistency of the linear probability estimates. Use of only administrative data for volume-outcomes research may generate spurious findings. The IV analysis further suggests that conventional estimates of the volume-outcome relationship may be contaminated by selective referral effects. Taken together, our results suggest that efforts to concentrate hospital-based CHF care in high-volume hospitals may not reduce mortality among elderly patients.

Impacts of land use change on watershed streamflow and sediment yield: An assessment using hydrologic modelling and partial least squares regression

NASA Astrophysics Data System (ADS)

Yan, B.; Fang, N. F.; Zhang, P. C.; Shi, Z. H.

2013-03-01

SummaryUnderstanding how changes in individual land use types influence the dynamics of streamflow and sediment yield would greatly improve the predictability of the hydrological consequences of land use changes and could thus help stakeholders to make better decisions. Multivariate statistics are commonly used to compare individual land use types to control the dynamics of streamflow or sediment yields. However, one issue with the use of conventional statistical methods to address relationships between land use types and streamflow or sediment yield is multicollinearity. In this study, an integrated approach involving hydrological modelling and partial least squares regression (PLSR) was used to quantify the contributions of changes in individual land use types to changes in streamflow and sediment yield. In a case study, hydrological modelling was conducted using land use maps from four time periods (1978, 1987, 1999, and 2007) for the Upper Du watershed (8973 km2) in China using the Soil and Water Assessment Tool (SWAT). Changes in streamflow and sediment yield across the two simulations conducted using the land use maps from 2007 to 1978 were found to be related to land use changes according to a PLSR, which was used to quantify the effect of this influence at the sub-basin scale. The major land use changes that affected streamflow in the studied catchment areas were related to changes in the farmland, forest and urban areas between 1978 and 2007; the corresponding regression coefficients were 0.232, -0.147 and 1.256, respectively, and the Variable Influence on Projection (VIP) was greater than 1. The dominant first-order factors affecting the changes in sediment yield in our study were: farmland (the VIP and regression coefficient were 1.762 and 14.343, respectively) and forest (the VIP and regression coefficient were 1.517 and -7.746, respectively). The PLSR methodology presented in this paper is beneficial and novel, as it partially eliminates the co-dependency of the variables and facilitates a more unbiased view of the contribution of the changes in individual land use types to changes in streamflow and sediment yield. This practicable and simple approach could be applied to a variety of other watersheds for which time-sequenced digital land use maps are available.

Model-checking techniques based on cumulative residuals.

PubMed

Lin, D Y; Wei, L J; Ying, Z

2002-03-01

Residuals have long been used for graphical and numerical examinations of the adequacy of regression models. Conventional residual analysis based on the plots of raw residuals or their smoothed curves is highly subjective, whereas most numerical goodness-of-fit tests provide little information about the nature of model misspecification. In this paper, we develop objective and informative model-checking techniques by taking the cumulative sums of residuals over certain coordinates (e.g., covariates or fitted values) or by considering some related aggregates of residuals, such as moving sums and moving averages. For a variety of statistical models and data structures, including generalized linear models with independent or dependent observations, the distributions of these stochastic processes tinder the assumed model can be approximated by the distributions of certain zero-mean Gaussian processes whose realizations can be easily generated by computer simulation. Each observed process can then be compared, both graphically and numerically, with a number of realizations from the Gaussian process. Such comparisons enable one to assess objectively whether a trend seen in a residual plot reflects model misspecification or natural variation. The proposed techniques are particularly useful in checking the functional form of a covariate and the link function. Illustrations with several medical studies are provided.

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

Introduction to the use of regression models in epidemiology.

PubMed

Bender, Ralf

2009-01-01

Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.

Stability of Early EEG Background Patterns After Pediatric Cardiac Arrest.

PubMed

Abend, Nicholas S; Xiao, Rui; Kessler, Sudha Kilaru; Topjian, Alexis A

2018-05-01

We aimed to determine whether EEG background characteristics remain stable across discrete time periods during the acute period after resuscitation from pediatric cardiac arrest. Children resuscitated from cardiac arrest underwent continuous conventional EEG monitoring. The EEG was scored in 12-hour epochs for up to 72 hours after return of circulation by an electroencephalographer using a Background Category with 4 levels (normal, slow-disorganized, discontinuous/burst-suppression, or attenuated-featureless) or 2 levels (normal/slow-disorganized or discontinuous/burst-suppression/attenuated-featureless). Survival analyses and mixed-effects ordinal logistic regression models evaluated whether the EEG remained stable across epochs. EEG monitoring was performed in 89 consecutive children. When EEG was assessed as the 4-level Background Category, 30% of subjects changed category over time. Based on initial Background Category, one quarter of the subjects changed EEG category by 24 hours if the initial EEG was attenuated-featureless, by 36 hours if the initial EEG was discontinuous or burst-suppression, by 48 hours if the initial EEG was slow-disorganized, and never if the initial EEG was normal. However, regression modeling for the 4-level Background Category indicated that the EEG did not change over time (odds ratio = 1.06, 95% confidence interval = 0.96-1.17, P = 0.26). Similarly, when EEG was assessed as the 2-level Background Category, 8% of subjects changed EEG category over time. However, regression modeling for the 2-level category indicated that the EEG did not change over time (odds ratio = 1.02, 95% confidence interval = 0.91-1.13, P = 0.75). The EEG Background Category changes over time whether analyzed as 4 levels (30% of subjects) or 2 levels (8% of subjects), although regression analyses indicated that no significant changes occurred over time for the full cohort. These data indicate that the Background Category is often stable during the acute 72 hours after pediatric cardiac arrest and thus may be a useful EEG assessment metric in future studies, but that some subjects do have EEG changes over time and therefore serial EEG assessments may be informative.

Added prognostic value of CT characteristics and IASLC/ATS/ERS histologic subtype in surgically resected lung adenocarcinomas.

PubMed

Suh, Young Joo; Lee, Hyun-Ju; Kim, Young Tae; Kang, Chang Hyun; Park, In Kyu; Jeon, Yoon Kyung; Chung, Doo Hyun

2018-06-01

Our study investigates the added value of computed tomography (CT) characteristics, histologic subtype classification of the International Association for the Study of Lung Cancer (IASLC)/the American Thoracic Society (ATS)/the European Respiratory Society (ERS), and genetic mutation for predicting postoperative prognoses of patients who received curative surgical resections for lung adenocarcinoma. We retrospectively enrolled 988 patients who underwent curative resection for invasive lung adenocarcinoma between October 2007 and December 2013. Cox's proportional hazard model was used to explore the risk of recurrence-free survival, based on the combination of conventional prognostic factors, CT characteristics, IASLC/ATS/ERS histologic subtype, and epidermal growth factor receptor (EGFR) mutations. Incremental prognostic values of CT characteristics, histologic subtype, and EGFR mutations over conventional risk factors were measured by C-statistics. During median follow-up period of 44.7 months (25th to 75th percentile 24.6-59.7 months), postoperative recurrence occurred in 248 patients (25.1%). In univariate Cox proportion hazard model, female sex, tumor size and stage, CT characteristics, and predominant histologic subtype were associated with tumor recurrence (P < 0.05). In multivariate Cox regression model adjusted for tumor size and stage, both CT characteristics and histologic subtype were independent tumor recurrence predictors (P < 0.05). Cox proportion hazard models combining CT characteristics or histologic subtype with size and tumor stage showed higher C-indices (0.763 and 0.767, respectively) than size and stage-only models (C-index 0.759, P > 0.05). CT characteristics and histologic subtype have relatively limited added prognostic values over tumor size and stage in surgically resected lung adenocarcinomas. Copyright © 2018 Elsevier B.V. All rights reserved.

Application of Low Melting Point Thermoplastics to Hybrid Rocket Fuel

NASA Astrophysics Data System (ADS)

Wada, Yutaka; Jikei, Mitsutoshi; Kato, Ryuichi; Kato, Nobuji; Hori, Keiichi

This paper introduces the application of low melting point thermoplastics (LT) to hybrid rocket fuel. LT made by Katazen Corporation has an excellent mechanical property comparing with other thermoplastics and prospect of high surface regression rate because it has a similar physical property with low melting point of paraffin fuel which has high regression rate probably due to the entrainment mass transfer mechanism that droplets continuously depart out of the surface melt layer. Several different types of LT developed by Katazen Corporation for this use have been evaluated in the measurements of regression rate, mechanical properties These results show the LTs have the higher regression rate and better mechanical properties comparing with conventional hybrid rocket fuels. Observation was also made using a small 2D combustor, and the entrainment mass transfer mechanism is confirmed with the LT fuels.

Interpretation of commonly used statistical regression models.

PubMed

Kasza, Jessica; Wolfe, Rory

2014-01-01

A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.

Predicting acute toxicity of traditional Chinese medicine wastewater using UV absorption and volatile fatty acids as surrogates.

PubMed

Lv, Longyi; Li, Weiguang; Yu, Yang; Meng, Liqiang; Qin, Wen; Wu, Chuandong

2018-03-01

In this study, the applicability of UV absorbance at 254 nm (UV 254 ) and volatile fatty acids (VFAs) to serve as reliable surrogates to predict acute toxicity of traditional Chinese medicine (TCM) wastewater was investigated. The medicine residues and VFAs were identified as main components of the TCM wastewater, and their individual and joint toxicity assays were operated with luminescent bacteria. The median effective concentration (EC 50 ) values of medicine residues and VFAs were in the range of 26.46-165.55 mg/L and 11.45-20.58 g/L, respectively. The joint toxicity action modes of medicine residues, VFAs and medicine residues-VFAs were identified as additive, additive and synergistic respectively. UV 254 and VFAs showed better correlations with acute toxicity according to the correlation analysis, compared with other conventional parameters. The regression model was a good fit for toxic unit (TU 50 ) as a function of UV 254 and VFAs according to the stepwise regression method (adjusted R 2  = 0.836). Validation of the model to the pilot-scale samples provided satisfactory prediction results in the influent and hydrolysis acidification effluent samples tests, but for EGSB effluent and final effluent samples, the model needed further optimization. Surrogates prediction using UV 254 and VFAs provided a valuable and cost-saving tool for rapid or on-line monitoring of acute toxicity of TCM wastewater. Copyright © 2017 Elsevier Ltd. All rights reserved.

Utility of a New Model to Diagnose an Alcohol Basis for Steatohepatitis

PubMed Central

Dunn, Winston; Angulo, Paul; Sanderson, Schuyler; Jamil, Laith H.; Stadheim, Linda; Rosen, Charles; Malinchoc, Michael; Kamath, Patrick S.; Shah, Vijay

2007-01-01

Background and Aims Distinguishing an alcohol basis from a nonalcoholic basis for the clinical and histological spectrum of steatohepatitic liver disease is difficult owing to unreliability of alcohol consumption history. Unfortunately, various biomarkers have had limited utility in distinguishing alcoholic liver disease (ALD) from nonalcoholic fatty liver disease (NAFLD). Thus, the aim of our study was to create and validate a model to diagnose ALD in patients with steatohepatitis. Methods Cross-sectional cohort study was performed at Mayo Clinic; Rochester, Minnesota to create a model using multivariable logistic regression analysis. This model was validated in three independent data-sets comprising patients of varying severity of steatohepatitis spanning over 10 years. Results Logistic regression identified mean corpuscular volume, AST/ALT ratio, body-mass index, and gender as the most important variables that separated patients with ALD from NAFLD. These variables were used to generate the ALD/NAFLD Index (ANI); with ANI of greater than 0 incrementally favoring ALD, and ANI of less than 0 incrementally favoring a diagnosis of NAFLD, thus making ALD unlikely. ANI had a c-statistic of 0.989 in the derivation sample, and 0.974, 0.989, 0.767 in the three validation samples. ANI performance characteristics were significantly better than several conventional and recently proposed biomarkers used to differentiate ALD from NAFLD including the histopathological marker Protein Tyrosine Phosphatase 1b, AST/ALT ratio, gamma-glutamyl transferase and Carbohydrate Deficient Transferrin. Conclusion ANI, derived from easily available objective variables, accurately differentiates ALD from NAFLD in hospitalized, ambulatory and pre-transplant patients and compares favorably to other traditional and proposed biomarkers. PMID:17030176

Adaptive smoothing based on Gaussian processes regression increases the sensitivity and specificity of fMRI data.

PubMed

Strappini, Francesca; Gilboa, Elad; Pitzalis, Sabrina; Kay, Kendrick; McAvoy, Mark; Nehorai, Arye; Snyder, Abraham Z

2017-03-01

Temporal and spatial filtering of fMRI data is often used to improve statistical power. However, conventional methods, such as smoothing with fixed-width Gaussian filters, remove fine-scale structure in the data, necessitating a tradeoff between sensitivity and specificity. Specifically, smoothing may increase sensitivity (reduce noise and increase statistical power) but at the cost loss of specificity in that fine-scale structure in neural activity patterns is lost. Here, we propose an alternative smoothing method based on Gaussian processes (GP) regression for single subjects fMRI experiments. This method adapts the level of smoothing on a voxel by voxel basis according to the characteristics of the local neural activity patterns. GP-based fMRI analysis has been heretofore impractical owing to computational demands. Here, we demonstrate a new implementation of GP that makes it possible to handle the massive data dimensionality of the typical fMRI experiment. We demonstrate how GP can be used as a drop-in replacement to conventional preprocessing steps for temporal and spatial smoothing in a standard fMRI pipeline. We present simulated and experimental results that show the increased sensitivity and specificity compared to conventional smoothing strategies. Hum Brain Mapp 38:1438-1459, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Development of a Screening Tool for Predicting Adverse Outcomes of Gestational Diabetes Mellitus

PubMed Central

Park, Jee Soo; Kim, Deok Won; Kwon, Ja-Young; Park, Yong Won; Kim, Young Han; Cho, Hee Young

2016-01-01

Abstract Gestational diabetes mellitus (GDM) is a common disease in pregnancy causing maternal and fetal complications. To prevent these adverse outcomes, optimal screening and diagnostic criteria must be adequate, timely, and efficient. This study suggests a novel approach that is practical, efficient, and patient- and clinician-friendly in predicting adverse outcomes of GDM. The authors conducted a retrospective cohort study via medical record review of patients admitted between March 2001 and April 2013 at the Severance Hospital, Seoul, South Korea. Patients diagnosed by a conventional 2-step method were evaluated according to the presence of adverse outcomes (neonatal hypoglycemia, hyperbilirubinemia, and hyperinsulinemia; admission to the neonatal intensive care unit; large for gestational age; gestational insulin therapy; and gestational hypertension). Of 802 women who had an abnormal 50-g, 1-hour glucose challenge test, 306 were diagnosed with GDM and 496 did not have GDM (false-positive group). In the GDM group, 218 women (71.2%) had adverse outcomes. In contrast, 240 women (48.4%) in the false-positive group had adverse outcomes. Women with adverse outcomes had a significantly higher body mass index (BMI) at entry (P = 0.03) and fasting blood glucose (FBG) (P = 0.03). Our logistic regression model derived from 2 variables, BMI at entry and FBG, predicted GDM adverse outcome with an area under the curve of 0.642, accuracy of 61.3%, sensitivity of 57.2%, and specificity of 66.9% compared with the conventional 2-step method with an area under the curve of 0.610, accuracy of 59.1%, sensitivity of 47.6%, and specificity of 74.4%. Our model performed better in predicting GDM adverse outcomes than the conventional 2-step method using only BMI at entry and FBG. Moreover, our model represents a practical, inexpensive, efficient, reproducible, easy, and patient- and clinician-friendly approach. PMID:26735528

Development of a Screening Tool for Predicting Adverse Outcomes of Gestational Diabetes Mellitus: A Retrospective Cohort Study.

PubMed

Park, Jee Soo; Kim, Deok Won; Kwon, Ja-Young; Park, Yong Won; Kim, Young Han; Cho, Hee Young

2016-01-01

Gestational diabetes mellitus (GDM) is a common disease in pregnancy causing maternal and fetal complications. To prevent these adverse outcomes, optimal screening and diagnostic criteria must be adequate, timely, and efficient. This study suggests a novel approach that is practical, efficient, and patient- and clinician-friendly in predicting adverse outcomes of GDM. The authors conducted a retrospective cohort study via medical record review of patients admitted between March 2001 and April 2013 at the Severance Hospital, Seoul, South Korea. Patients diagnosed by a conventional 2-step method were evaluated according to the presence of adverse outcomes (neonatal hypoglycemia, hyperbilirubinemia, and hyperinsulinemia; admission to the neonatal intensive care unit; large for gestational age; gestational insulin therapy; and gestational hypertension). Of 802 women who had an abnormal 50-g, 1-hour glucose challenge test, 306 were diagnosed with GDM and 496 did not have GDM (false-positive group). In the GDM group, 218 women (71.2%) had adverse outcomes. In contrast, 240 women (48.4%) in the false-positive group had adverse outcomes. Women with adverse outcomes had a significantly higher body mass index (BMI) at entry (P = 0.03) and fasting blood glucose (FBG) (P = 0.03). Our logistic regression model derived from 2 variables, BMI at entry and FBG, predicted GDM adverse outcome with an area under the curve of 0.642, accuracy of 61.3%, sensitivity of 57.2%, and specificity of 66.9% compared with the conventional 2-step method with an area under the curve of 0.610, accuracy of 59.1%, sensitivity of 47.6%, and specificity of 74.4%. Our model performed better in predicting GDM adverse outcomes than the conventional 2-step method using only BMI at entry and FBG. Moreover, our model represents a practical, inexpensive, efficient, reproducible, easy, and patient- and clinician-friendly approach.

Application of Artificial Neural Network and Support Vector Machines in Predicting Metabolizable Energy in Compound Feeds for Pigs.

PubMed

Ahmadi, Hamed; Rodehutscord, Markus

2017-01-01

In the nutrition literature, there are several reports on the use of artificial neural network (ANN) and multiple linear regression (MLR) approaches for predicting feed composition and nutritive value, while the use of support vector machines (SVM) method as a new alternative approach to MLR and ANN models is still not fully investigated. The MLR, ANN, and SVM models were developed to predict metabolizable energy (ME) content of compound feeds for pigs based on the German energy evaluation system from analyzed contents of crude protein (CP), ether extract (EE), crude fiber (CF), and starch. A total of 290 datasets from standardized digestibility studies with compound feeds was provided from several institutions and published papers, and ME was calculated thereon. Accuracy and precision of developed models were evaluated, given their produced prediction values. The results revealed that the developed ANN [ R 2  = 0.95; root mean square error (RMSE) = 0.19 MJ/kg of dry matter] and SVM ( R 2  = 0.95; RMSE = 0.21 MJ/kg of dry matter) models produced better prediction values in estimating ME in compound feed than those produced by conventional MLR ( R 2  = 0.89; RMSE = 0.27 MJ/kg of dry matter). The developed ANN and SVM models produced better prediction values in estimating ME in compound feed than those produced by conventional MLR; however, there were not obvious differences between performance of ANN and SVM models. Thus, SVM model may also be considered as a promising tool for modeling the relationship between chemical composition and ME of compound feeds for pigs. To provide the readers and nutritionist with the easy and rapid tool, an Excel ® calculator, namely, SVM_ME_pig, was created to predict the metabolizable energy values in compound feeds for pigs using developed support vector machine model.

Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses

PubMed Central

Nickerson, Lisa D.; Smith, Stephen M.; Öngür, Döst; Beckmann, Christian F.

2017-01-01

Independent Component Analysis (ICA) is one of the most popular techniques for the analysis of resting state FMRI data because it has several advantageous properties when compared with other techniques. Most notably, in contrast to a conventional seed-based correlation analysis, it is model-free and multivariate, thus switching the focus from evaluating the functional connectivity of single brain regions identified a priori to evaluating brain connectivity in terms of all brain resting state networks (RSNs) that simultaneously engage in oscillatory activity. Furthermore, typical seed-based analysis characterizes RSNs in terms of spatially distributed patterns of correlation (typically by means of simple Pearson's coefficients) and thereby confounds together amplitude information of oscillatory activity and noise. ICA and other regression techniques, on the other hand, retain magnitude information and therefore can be sensitive to both changes in the spatially distributed nature of correlations (differences in the spatial pattern or “shape”) as well as the amplitude of the network activity. Furthermore, motion can mimic amplitude effects so it is crucial to use a technique that retains such information to ensure that connectivity differences are accurately localized. In this work, we investigate the dual regression approach that is frequently applied with group ICA to assess group differences in resting state functional connectivity of brain networks. We show how ignoring amplitude effects and how excessive motion corrupts connectivity maps and results in spurious connectivity differences. We also show how to implement the dual regression to retain amplitude information and how to use dual regression outputs to identify potential motion effects. Two key findings are that using a technique that retains magnitude information, e.g., dual regression, and using strict motion criteria are crucial for controlling both network amplitude and motion-related amplitude effects, respectively, in resting state connectivity analyses. We illustrate these concepts using realistic simulated resting state FMRI data and in vivo data acquired in healthy subjects and patients with bipolar disorder and schizophrenia. PMID:28348512

Belief in complementary and alternative medicine is related to age and paranormal beliefs in adults.

PubMed

Van den Bulck, Jan; Custers, Kathleen

2010-04-01

The use of complementary and alternative medicine (CAM) is widespread, even among people who use conventional medicine. Positive beliefs about CAM are common among physicians and medical students. Little is known about the beliefs regarding CAM among the general public. Among science students, belief in CAM was predicted by belief in the paranormal. In a cross-sectional study, 712 randomly selected adults (>18 years old) responded to the CAM Health Belief Questionnaire (CHBQ) and a paranormal beliefs scale. CAM beliefs were very prevalent in this sample of adult Flemish men and women. Zero-order correlations indicated that belief in CAM was associated with age (r = 0.173 P < 0.001) level of education (r = -0.079 P = 0.039) social desirability (r = -0.119 P = 0.002) and paranormal belief (r = 0.365 P < 0.001). In a multivariate model, two variables predicted CAM beliefs. Support for CAM increased with age (regression coefficient: 0.01; 95% confidence interval (CI): 0.006 to 0.014), but the strongest relationship existed between support for CAM and beliefs in the paranormal. Paranormal beliefs accounted for 14% of the variance of the CAM beliefs (regression coefficient: 0.376; 95%: CI 0.30-0.44). The level of education (regression coefficient: 0.06; 95% CI: -0.014-0.129) and social desirability (regression coefficient: -0.023; 95% CI: -0.048-0.026) did not make a significant contribution to the explained variance (<0.1%, P = 0.867). Support of CAM was very prevalent in this Flemish adult population. CAM beliefs were strongly associated with paranormal beliefs.

A new approach to correct the QT interval for changes in heart rate using a nonparametric regression model in beagle dogs.

PubMed

Watanabe, Hiroyuki; Miyazaki, Hiroyasu

2006-01-01

Over- and/or under-correction of QT intervals for changes in heart rate may lead to misleading conclusions and/or masking the potential of a drug to prolong the QT interval. This study examines a nonparametric regression model (Loess Smoother) to adjust the QT interval for differences in heart rate, with an improved fitness over a wide range of heart rates. 240 sets of (QT, RR) observations collected from each of 8 conscious and non-treated beagle dogs were used as the materials for investigation. The fitness of the nonparametric regression model to the QT-RR relationship was compared with four models (individual linear regression, common linear regression, and Bazett's and Fridericia's correlation models) with reference to Akaike's Information Criterion (AIC). Residuals were visually assessed. The bias-corrected AIC of the nonparametric regression model was the best of the models examined in this study. Although the parametric models did not fit, the nonparametric regression model improved the fitting at both fast and slow heart rates. The nonparametric regression model is the more flexible method compared with the parametric method. The mathematical fit for linear regression models was unsatisfactory at both fast and slow heart rates, while the nonparametric regression model showed significant improvement at all heart rates in beagle dogs.

The Tax Exclusion for Employer-Sponsored Insurance Is Not Regressive-But What Is It?

PubMed

White, Joseph

2017-08-01

Conventional wisdom says that the tax exclusion for employer-sponsored health insurance (ESI) is "regressive and therefore unfair." Yet, by the standard definition of regressive tax policy, the conventional view is almost certainly false. It confuses the absolute size of the tax exclusion with its proportional effect on income. The error results from paying attention only to the marginal tax rate applied to ESI benefits as a portion of income and ignoring the fact that benefits are normally a much larger share of income for people with lower wages. This article explains the difference and then considers other distributional effects of ESI. It suggests that ESI-for those who receive it-further redistributes toward those with lesser means or greater need. The most evident effect is by need, favoring employees with families over those without. Yet there is good reason to believe there is also a redistribution by income, with the package of wages plus benefits being less unequal than wages alone would be. Therefore reformers should be much more careful before criticizing either ESI or its subsidy through the tax code as "unfair," especially as the likelihood of enacting something better in the United States seems quite low. Copyright © 2017 by Duke University Press.

Modified Regression Correlation Coefficient for Poisson Regression Model

NASA Astrophysics Data System (ADS)

Kaengthong, Nattacha; Domthong, Uthumporn

2017-09-01

This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).

Spontaneous complete regression of a brain stem glioma pathologically diagnosed as a high-grade glioma.

PubMed

Ishihara, Masahiro; Yamamoto, Kazumi; Miwa, Hideaki; Nishi, Masaya

2017-12-01

Spontaneous regressions of brain stem gliomas are extremely rare. Only six cases have been reported in the literature. We describe the case of a patient who was diagnosed with a pontomedullary dorsal brain stem glioma at the age of 15 years. An open biopsy showed the presence of an anaplastic glioma. Because the patient and her parents refused conventional therapies, including radiation and chemotherapy, we followed up the patient by performing magnetic resonance imaging scans on her every 3 months. At 3 months after biopsy, we observed the radiological disappearance of her tumor. One year after biopsy, the tumor retained the spontaneous complete regression observed earlier. In this case report, we present the first report of the spontaneous complete regression of a brain stem glioma that was histologically proven to be a high-grade glioma and we believe that this regression was the natural progression of this case, as may be the scenario in a few other cases of brain stem gliomas.

Linear mixed-effects modeling approach to FMRI group analysis

PubMed Central

Chen, Gang; Saad, Ziad S.; Britton, Jennifer C.; Pine, Daniel S.; Cox, Robert W.

2013-01-01

Conventional group analysis is usually performed with Student-type t-test, regression, or standard AN(C)OVA in which the variance–covariance matrix is presumed to have a simple structure. Some correction approaches are adopted when assumptions about the covariance structure is violated. However, as experiments are designed with different degrees of sophistication, these traditional methods can become cumbersome, or even be unable to handle the situation at hand. For example, most current FMRI software packages have difficulty analyzing the following scenarios at group level: (1) taking within-subject variability into account when there are effect estimates from multiple runs or sessions; (2) continuous explanatory variables (covariates) modeling in the presence of a within-subject (repeated measures) factor, multiple subject-grouping (between-subjects) factors, or the mixture of both; (3) subject-specific adjustments in covariate modeling; (4) group analysis with estimation of hemodynamic response (HDR) function by multiple basis functions; (5) various cases of missing data in longitudinal studies; and (6) group studies involving family members or twins. Here we present a linear mixed-effects modeling (LME) methodology that extends the conventional group analysis approach to analyze many complicated cases, including the six prototypes delineated above, whose analyses would be otherwise either difficult or unfeasible under traditional frameworks such as AN(C)OVA and general linear model (GLM). In addition, the strength of the LME framework lies in its flexibility to model and estimate the variance–covariance structures for both random effects and residuals. The intraclass correlation (ICC) values can be easily obtained with an LME model with crossed random effects, even at the presence of confounding fixed effects. The simulations of one prototypical scenario indicate that the LME modeling keeps a balance between the control for false positives and the sensitivity for activation detection. The importance of hypothesis formulation is also illustrated in the simulations. Comparisons with alternative group analysis approaches and the limitations of LME are discussed in details. PMID:23376789

Linear mixed-effects modeling approach to FMRI group analysis.

PubMed

Chen, Gang; Saad, Ziad S; Britton, Jennifer C; Pine, Daniel S; Cox, Robert W

2013-06-01

Conventional group analysis is usually performed with Student-type t-test, regression, or standard AN(C)OVA in which the variance-covariance matrix is presumed to have a simple structure. Some correction approaches are adopted when assumptions about the covariance structure is violated. However, as experiments are designed with different degrees of sophistication, these traditional methods can become cumbersome, or even be unable to handle the situation at hand. For example, most current FMRI software packages have difficulty analyzing the following scenarios at group level: (1) taking within-subject variability into account when there are effect estimates from multiple runs or sessions; (2) continuous explanatory variables (covariates) modeling in the presence of a within-subject (repeated measures) factor, multiple subject-grouping (between-subjects) factors, or the mixture of both; (3) subject-specific adjustments in covariate modeling; (4) group analysis with estimation of hemodynamic response (HDR) function by multiple basis functions; (5) various cases of missing data in longitudinal studies; and (6) group studies involving family members or twins. Here we present a linear mixed-effects modeling (LME) methodology that extends the conventional group analysis approach to analyze many complicated cases, including the six prototypes delineated above, whose analyses would be otherwise either difficult or unfeasible under traditional frameworks such as AN(C)OVA and general linear model (GLM). In addition, the strength of the LME framework lies in its flexibility to model and estimate the variance-covariance structures for both random effects and residuals. The intraclass correlation (ICC) values can be easily obtained with an LME model with crossed random effects, even at the presence of confounding fixed effects. The simulations of one prototypical scenario indicate that the LME modeling keeps a balance between the control for false positives and the sensitivity for activation detection. The importance of hypothesis formulation is also illustrated in the simulations. Comparisons with alternative group analysis approaches and the limitations of LME are discussed in details. Published by Elsevier Inc.

Landscape characteristics influence pond occupancy by frogs after accounting for detectability

USGS Publications Warehouse

Mazerolle, M.J.; Desrochers, A.; Rochefort, L.

2005-01-01

Many investigators have hypothesized that landscape attributes such as the amount and proximity of habitat are important for amphibian spatial patterns. This has produced a number of studies focusing on the effects of landscape characteristics on amphibian patterns of occurrence in patches or ponds, most of which conclude that the landscape is important. We identified two concerns associated with these studies: one deals with their applicability to other landscape types, as most have been conducted in agricultural landscapes; the other highlights the need to account for the probability of detection. We tested the hypothesis that landscape characteristics influence spatial patterns of amphibian occurrence at ponds after accounting for the probability of detection in little-studied peatland landscapes undergoing peat mining. We also illustrated the costs of not accounting for the probability of detection by comparing our results to conventional logistic regression analyses. Results indicate that frog occurrence increased with the percent cover of ponds within 100, 250, and 1000 m, as well as the amount of forest cover within 1000 m. However, forest cover at 250 m had a negative influence on frog presence at ponds. Not accounting for the probability of detection resulted in underestimating the influence of most variables on frog occurrence, whereas a few were overestimated. Regardless, we show that conventional logistic regression can lead to different conclusions than analyses accounting for detectability. Our study is consistent with the hypothesis that landscape characteristics are important in determining the spatial patterns of frog occurrence at ponds. We strongly recommend estimating the probability of detection in field surveys, as this will increase the quality and conservation potential of models derived from such data. ?? 2005 by the Ecological Society of America.

Comparison of conventional study model measurements and 3D digital study model measurements from laser scanned dental impressions

NASA Astrophysics Data System (ADS)

Nugrahani, F.; Jazaldi, F.; Noerhadi, N. A. I.

2017-08-01

The field of orthodontics is always evolving,and this includes the use of innovative technology. One type of orthodontic technology is the development of three-dimensional (3D) digital study models that replace conventional study models made by stone. This study aims to compare the mesio-distal teeth width, intercanine width, and intermolar width measurements between a 3D digital study model and a conventional study model. Twelve sets of upper arch dental impressions were taken from subjects with non-crowding teeth. The impressions were taken twice, once with alginate and once with polivinylsiloxane. The alginate impressions used in the conventional study model and the polivinylsiloxane impressions were scanned to obtain the 3D digital study model. Scanning was performed using a laser triangulation scanner device assembled by the School of Electrical Engineering and Informatics at the Institut Teknologi Bandung and David Laser Scan software. For the conventional model, themesio-distal width, intercanine width, and intermolar width were measured using digital calipers; in the 3D digital study model they were measured using software. There were no significant differences between the mesio-distal width, intercanine width, and intermolar width measurments between the conventional and 3D digital study models (p>0.05). Thus, measurements using 3D digital study models are as accurate as those obtained from conventional study models

Quantitative Analysis of {sup 18}F-Fluorodeoxyglucose Positron Emission Tomography Identifies Novel Prognostic Imaging Biomarkers in Locally Advanced Pancreatic Cancer Patients Treated With Stereotactic Body Radiation Therapy

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

Cui, Yi; Global Institution for Collaborative Research and Education, Hokkaido University, Sapporo; Song, Jie

Purpose: To identify prognostic biomarkers in pancreatic cancer using high-throughput quantitative image analysis. Methods and Materials: In this institutional review board–approved study, we retrospectively analyzed images and outcomes for 139 locally advanced pancreatic cancer patients treated with stereotactic body radiation therapy (SBRT). The overall population was split into a training cohort (n=90) and a validation cohort (n=49) according to the time of treatment. We extracted quantitative imaging characteristics from pre-SBRT {sup 18}F-fluorodeoxyglucose positron emission tomography, including statistical, morphologic, and texture features. A Cox proportional hazard regression model was built to predict overall survival (OS) in the training cohort using 162more » robust image features. To avoid over-fitting, we applied the elastic net to obtain a sparse set of image features, whose linear combination constitutes a prognostic imaging signature. Univariate and multivariate Cox regression analyses were used to evaluate the association with OS, and concordance index (CI) was used to evaluate the survival prediction accuracy. Results: The prognostic imaging signature included 7 features characterizing different tumor phenotypes, including shape, intensity, and texture. On the validation cohort, univariate analysis showed that this prognostic signature was significantly associated with OS (P=.002, hazard ratio 2.74), which improved upon conventional imaging predictors including tumor volume, maximum standardized uptake value, and total legion glycolysis (P=.018-.028, hazard ratio 1.51-1.57). On multivariate analysis, the proposed signature was the only significant prognostic index (P=.037, hazard ratio 3.72) when adjusted for conventional imaging and clinical factors (P=.123-.870, hazard ratio 0.53-1.30). In terms of CI, the proposed signature scored 0.66 and was significantly better than competing prognostic indices (CI 0.48-0.64, Wilcoxon rank sum test P<1e-6). Conclusion: Quantitative analysis identified novel {sup 18}F-fluorodeoxyglucose positron emission tomography image features that showed improved prognostic value over conventional imaging metrics. If validated in large, prospective cohorts, the new prognostic signature might be used to identify patients for individualized risk-adaptive therapy.« less

[Evaluation of continuous education: from the satisfaction to the impact. With regard to a formative programme in minor surgery in a health area].

PubMed

Martín Fernández, J; Martínez Marcos, M; Ferrándiz Santos, J

2001-04-30

To compare the evaluation of reaction of an activity of continuous education (CE) in minor surgery (MS), with the impact in the realization of MS in a health area. Observational cross-sectional study. Setting. 27 centers in a health area that offer MS between their services. The valuation was studied in a scale from 1 to 10, of 9 theoretical-practical activities of CE in MS and the consideration of its utility. The number of activities of MS (NMS) carried out in all the units, was picked up during one year, and a model of lineal regression was built. The independent variable was the NMS, and the explanatory ones the assistance pressure (AP), the postgraduate formation (PF), the staff of the unit, the equipment (E), and the carried out CE. The valuation of the CE had a median of 8 (with interquartile range 1), 85.1% of the people who realized CE in MS said that this would be of utility. However in the explanatory regression model the PF was the only significant variable (beta = 6.7; 95% CI, 0.12-12.22). Neither the CE, nor the AP, nor the E, explained the variability among the NMS. The CE in MS with conventional methodology has a very positive reaction evaluation, but its impact in the later realization of MS is not significant.

Proton-pump inhibitor use does not affect semen quality in subfertile men.

PubMed

Keihani, Sorena; Craig, James R; Zhang, Chong; Presson, Angela P; Myers, Jeremy B; Brant, William O; Aston, Kenneth I; Emery, Benjamin R; Jenkins, Timothy G; Carrell, Douglas T; Hotaling, James M

2018-01-01

Proton-pump inhibitors (PPIs) are among the most widely used drugs worldwide. PPI use has recently been linked to adverse changes in semen quality in healthy men; however, the effects of PPI use on semen parameters remain largely unknown specifically in cases with male factor infertility. We examined whether PPI use was associated with detrimental effects on semen parameters in a large population of subfertile men. We retrospectively reviewed data from 12 257 subfertile men who had visited our fertility clinic from 2003 to 2013. Patients who reported using any PPIs for >3 months before semen sample collection were included; 7698 subfertile men taking no medication served as controls. Data were gathered on patient age, medication use, and conventional semen parameters; patients taking any known spermatotoxic medication were excluded. Linear mixed-effect regression models were used to test the effect of PPI use on semen parameters adjusting for age. A total of 248 patients (258 samples) used PPIs for at least 3 months before semen collection. In regression models, PPI use (either as the only medication or when used in combination with other nonspermatotoxic medications) was not associated with statistically significant changes in semen parameters. To our knowledge, this is the largest study to compare PPI use with semen parameters in subfertile men. Using PPIs was not associated with detrimental effects on semen quality in this retrospective study.

Structure-activity relationships for chloro- and nitrophenol toxicity in the pollen tube growth test

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

Schueuermann, G.; Somashekar, R.K.; Kristen, U.

Acute toxicity of 10 chlorophenols and 10 nitrophenols with identical substitution patterns is analyzed with the pollen tube growth (PTG) test. Concentration values of 50% growth inhibition (IC50) between 0.1 and 300 mg/L indicate that the absolute sensitivity of this alternative biotest is comparable to conventional aquatic test systems. Analysis of quantitative structure-activity relationships using lipophilicity (log K{sub ow}), acidity (pK{sub a}), and quantum chemical parameters to model intrinsic acidity, solvation interactions, and nucleophilicity reveals substantial differences between the intraseries trends of log IC50. With chlorophenols, a narcotic-type relationship is derived, which, however, shows marked differences in slope and interceptmore » when compared to reference regression equations for polar narcosis. Regression analysis of nitrophenol toxicity suggests interpretation in terms of two modes of action: oxidative uncoupling activity is associated with a pK{sub a} window from 3.8 to 8.5, and more acidic congeners with diortho-substitution show a transition from uncoupling to a narcotic mode of action with decreasing pK{sub a} and log K{sub ow}. Model calculations for phenol nucleophilicity suggest that differences in the phenol readiness for glucuronic acid conjugation as a major phase-II detoxication pathway have no direct influence on acute PTG toxicity of the compounds.« less

Proteomic Screening and Lasso Regression Reveal Differential Signaling in Insulin and Insulin-like Growth Factor I (IGF1) Pathways *

PubMed Central

Erdem, Cemal; Nagle, Alison M.; Casa, Angelo J.; Litzenburger, Beate C.; Wang, Yu-fen; Taylor, D. Lansing; Lee, Adrian V.; Lezon, Timothy R.

2016-01-01

Insulin and insulin-like growth factor I (IGF1) influence cancer risk and progression through poorly understood mechanisms. To better understand the roles of insulin and IGF1 signaling in breast cancer, we combined proteomic screening with computational network inference to uncover differences in IGF1 and insulin induced signaling. Using reverse phase protein array, we measured the levels of 134 proteins in 21 breast cancer cell lines stimulated with IGF1 or insulin for up to 48 h. We then constructed directed protein expression networks using three separate methods: (i) lasso regression, (ii) conventional matrix inversion, and (iii) entropy maximization. These networks, named here as the time translation models, were analyzed and the inferred interactions were ranked by differential magnitude to identify pathway differences. The two top candidates, chosen for experimental validation, were shown to regulate IGF1/insulin induced phosphorylation events. First, acetyl-CoA carboxylase (ACC) knock-down was shown to increase the level of mitogen-activated protein kinase (MAPK) phosphorylation. Second, stable knock-down of E-Cadherin increased the phospho-Akt protein levels. Both of the knock-down perturbations incurred phosphorylation responses stronger in IGF1 stimulated cells compared with insulin. Overall, the time-translation modeling coupled to wet-lab experiments has proven to be powerful in inferring differential interactions downstream of IGF1 and insulin signaling, in vitro. PMID:27364358

Prevalence of Tobacco Smoking and Factors Associated with the Initiation of Smoking among University Students in Dhaka, Bangladesh.

PubMed

Hossain, Sahadat; Hossain, Shakhaoat; Ahmed, Fahad; Islam, Rabiul; Sikder, Tajuddin; Rahman, Abdur

2017-01-01

Tobacco smoking is considered to be the key preventable risk factor for morbidity and mortality at the global level. The aim of this study was to determine the prevalence of tobacco smoking and factors associated with the initiation of smoking among university students in Dhaka, Bangladesh. A cross-sectional survey study was conducted with 264 students of Jahangirnagar University, Dhaka, Bangladesh in 2015. A standard, self-administered questionnaire consisting of questions on socio-demographic variables, tobacco smoking status, family and peer tobacco smoking history, attitudes and beliefs about tobacco smoking, as well as knowledge about the negative health consequences of tobacco smoking was administered to participants. Data were analyzed using logistic regression models, chi square, and Fisher exact tests. The overall prevalence of tobacco smoking was 60.2%, where males smoked at higher rates than females (68.81% and 19.56%, respectively). The influence of friends was the most significant reason for initiating tobacco smoking (OR: 0.862; CI: 0.810-0.917). Perception regarding tobacco smoking was significantly related to continuing tobacco use. Logistic regression models identified that smoking-related attitudes, potential health problems, and family members dying from cardiovascular disease and cancer were significantly associated with tobacco smoking. The current tobacco smoking prevalence among university students in Bangladesh is over 60%. We suggest adopting WHO Framework Convention on Tobacco Control (FCTC) policies, especially for university students.

Regression modeling of ground-water flow

USGS Publications Warehouse

Cooley, R.L.; Naff, R.L.

1985-01-01

Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)

Wired: Energy Drinks, Jock Identity, Masculine Norms, and Risk Taking

ERIC Educational Resources Information Center

Miller, Kathleen E.

2008-01-01

Objective: The author examined gendered links among sport-related identity, endorsement of conventional masculine norms, risk taking, and energy-drink consumption. Participants: The author surveyed 795 undergraduate students enrolled in introductory-level courses at a public university. Methods: The author conducted linear regression analyses of…

Estimation of diffusion coefficients from voltammetric signals by support vector and gaussian process regression

PubMed Central

2014-01-01

Background Support vector regression (SVR) and Gaussian process regression (GPR) were used for the analysis of electroanalytical experimental data to estimate diffusion coefficients. Results For simulated cyclic voltammograms based on the EC, Eqr, and EqrC mechanisms these regression algorithms in combination with nonlinear kernel/covariance functions yielded diffusion coefficients with higher accuracy as compared to the standard approach of calculating diffusion coefficients relying on the Nicholson-Shain equation. The level of accuracy achieved by SVR and GPR is virtually independent of the rate constants governing the respective reaction steps. Further, the reduction of high-dimensional voltammetric signals by manual selection of typical voltammetric peak features decreased the performance of both regression algorithms compared to a reduction by downsampling or principal component analysis. After training on simulated data sets, diffusion coefficients were estimated by the regression algorithms for experimental data comprising voltammetric signals for three organometallic complexes. Conclusions Estimated diffusion coefficients closely matched the values determined by the parameter fitting method, but reduced the required computational time considerably for one of the reaction mechanisms. The automated processing of voltammograms according to the regression algorithms yields better results than the conventional analysis of peak-related data. PMID:24987463

The Application of the Cumulative Logistic Regression Model to Automated Essay Scoring

ERIC Educational Resources Information Center

Haberman, Shelby J.; Sinharay, Sandip

2010-01-01

Most automated essay scoring programs use a linear regression model to predict an essay score from several essay features. This article applied a cumulative logit model instead of the linear regression model to automated essay scoring. Comparison of the performances of the linear regression model and the cumulative logit model was performed on a…

Fluoride release and recharge behavior of a nano-filled resin-modified glass ionomer compared with that of other fluoride releasing materials.

PubMed

Mitra, Sumita B; Oxman, Joe D; Falsafi, Afshin; Ton, Tiffany T

2011-12-01

To compare the long-term fluoride release kinetics of a novel nano-filled two-paste resin-modified glass-ionomer (RMGI), Ketac Nano (KN) with that of two powder-liquid resin-modified glass-ionomers, Fuji II LC (FLC) and Vitremer (VT) and one conventional glass-ionomer, Fuji IX (FIX). Fluoride release was measured in vitro using ion-selective electrodes. Kinetic analysis was done using regression analysis and compared with existing models for GIs and compomers. In a separate experiment the samples of KN and two conventional glass-ionomers, FIX and Ketac Molar (KM) were subjected to a treatment with external fluoride source (Oral-B Neutra-Foam) after 3 months of fluoride release and the recharge behavior studied for an additional 7-day period. The cumulative amount of fluoride released from KN, VT and FLC and the release profiles were statistically similar but greater than that for FIX at P < 0.05. All four materials, including KN, showed a burst of fluoride ions at shorter times (t) and an overall rate dependence on t1/2 typical for glass-ionomers. The coating of KN with its primer and of DY with its adhesive did not significantly alter the fluoride release behavior of the respective materials. The overall rate for KN was significantly higher than for the compomer DY. DY showed a linear rate of release vs. t and no burst effect as expected for compomers. The nanoionomer KN showed fluoride recharge behavior similar to the conventional glass ionomers FIX and KM. Thus, it was concluded that the new RMGI KN exhibits fluoride ion release behavior similar to typical conventional and RMGIs and that the primer does not impede the release of fluoride.

TU-C-12A-09: Modeling Pathologic Response of Locally Advanced Esophageal Cancer to Chemo-Radiotherapy Using Quantitative PET/CT Features, Clinical Parameters and Demographics

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

Zhang, H; Chen, W; Kligerman, S

2014-06-15

Purpose: To develop predictive models using quantitative PET/CT features for the evaluation of tumor response to neoadjuvant chemo-radiotherapy (CRT) in patients with locally advanced esophageal cancer. Methods: This study included 20 patients who underwent tri-modality therapy (CRT + surgery) and had {sup 18}F-FDG PET/CT scans before initiation of CRT and 4-6 weeks after completion of CRT but prior to surgery. Four groups of tumor features were examined: (1) conventional PET/CT response measures (SUVmax, tumor diameter, etc.); (2) clinical parameters (TNM stage, histology, etc.) and demographics; (3) spatial-temporal PET features, which characterize tumor SUV intensity distribution, spatial patterns, geometry, and associatedmore » changes resulting from CRT; and (4) all features combined. An optimal feature set was identified with recursive feature selection and cross-validations. Support vector machine (SVM) and logistic regression (LR) models were constructed for prediction of pathologic tumor response to CRT, using cross-validations to avoid model over-fitting. Prediction accuracy was assessed via area under the receiver operating characteristic curve (AUC), and precision was evaluated via confidence intervals (CIs) of AUC. Results: When applied to the 4 groups of tumor features, the LR model achieved AUCs (95% CI) of 0.57 (0.10), 0.73 (0.07), 0.90 (0.06), and 0.90 (0.06). The SVM model achieved AUCs (95% CI) of 0.56 (0.07), 0.60 (0.06), 0.94 (0.02), and 1.00 (no misclassifications). Using spatial-temporal PET features combined with conventional PET/CT measures and clinical parameters, the SVM model achieved very high accuracy (AUC 1.00) and precision (no misclassifications), significantly better than using conventional PET/CT measures or clinical parameters and demographics alone. For groups with a large number of tumor features (groups 3 and 4), the SVM model achieved significantly higher accuracy than the LR model. Conclusion: The SVM model using all features including quantitative PET/CT features accurately and precisely predicted pathologic tumor response to CRT in esophageal cancer. This work was supported in part by National Cancer Institute Grant R21 CA131979 and R01 CA172638. Shan Tan was supported in part by the National Natural Science Foundation of China 60971112 and 61375018, and by Fundamental Research Funds for the Central Universities 2012QN086.« less

Dendroclimatic transfer functions revisited: Little Ice Age and Medieval Warm Period summer temperatures reconstructed using artificial neural networks and linear algorithms

NASA Astrophysics Data System (ADS)

Helama, S.; Makarenko, N. G.; Karimova, L. M.; Kruglun, O. A.; Timonen, M.; Holopainen, J.; Meriläinen, J.; Eronen, M.

2009-03-01

Tree-rings tell of past climates. To do so, tree-ring chronologies comprising numerous climate-sensitive living-tree and subfossil time-series need to be "transferred" into palaeoclimate estimates using transfer functions. The purpose of this study is to compare different types of transfer functions, especially linear and nonlinear algorithms. Accordingly, multiple linear regression (MLR), linear scaling (LSC) and artificial neural networks (ANN, nonlinear algorithm) were compared. Transfer functions were built using a regional tree-ring chronology and instrumental temperature observations from Lapland (northern Finland and Sweden). In addition, conventional MLR was compared with a hybrid model whereby climate was reconstructed separately for short- and long-period timescales prior to combining the bands of timescales into a single hybrid model. The fidelity of the different reconstructions was validated against instrumental climate data. The reconstructions by MLR and ANN showed reliable reconstruction capabilities over the instrumental period (AD 1802-1998). LCS failed to reach reasonable verification statistics and did not qualify as a reliable reconstruction: this was due mainly to exaggeration of the low-frequency climatic variance. Over this instrumental period, the reconstructed low-frequency amplitudes of climate variability were rather similar by MLR and ANN. Notably greater differences between the models were found over the actual reconstruction period (AD 802-1801). A marked temperature decline, as reconstructed by MLR, from the Medieval Warm Period (AD 931-1180) to the Little Ice Age (AD 1601-1850), was evident in all the models. This decline was approx. 0.5°C as reconstructed by MLR. Different ANN based palaeotemperatures showed simultaneous cooling of 0.2 to 0.5°C, depending on algorithm. The hybrid MLR did not seem to provide further benefit above conventional MLR in our sample. The robustness of the conventional MLR over the calibration, verification and reconstruction periods qualified it as a reasonable transfer function for our forest-limit (i.e., timberline) dataset. ANN appears a potential tool for other environments and/or proxies having more complex and noisier climatic relationships.

Modeling Math Growth Trajectory--An Application of Conventional Growth Curve Model and Growth Mixture Model to ECLS K-5 Data

ERIC Educational Resources Information Center

Lu, Yi

2016-01-01

To model students' math growth trajectory, three conventional growth curve models and three growth mixture models are applied to the Early Childhood Longitudinal Study Kindergarten-Fifth grade (ECLS K-5) dataset in this study. The results of conventional growth curve model show gender differences on math IRT scores. When holding socio-economic…

Abundance of Soil-Borne Entomopathogenic Fungi in Organic and Conventional Fields in the Midwestern USA with an Emphasis on the Effect of Herbicides and Fungicides on Fungal Persistence

PubMed Central

Clifton, Eric H.; Jaronski, Stefan T.; Hodgson, Erin W.; Gassmann, Aaron J.

2015-01-01

Entomopathogenic fungi (EPF) are widespread in agricultural fields and help suppress crop pests. These natural enemies may be hindered by certain agronomic practices associated with conventional agriculture including the use of pesticides. We tested whether the abundance of EPF differed between organic and conventional fields, and whether specific cropping practices and soil properties were correlated with their abundance. In one year of the survey, soil from organic fields and accompanying margins had significantly more EPF than conventional fields and accompanying margins. Regression analysis revealed that the percentage of silt and the application of organic fertilizer were positively correlated with EPF abundance; but nitrogen concentration, tillage, conventional fields, and margins of conventional fields were negatively correlated with EPF abundance. A greenhouse experiment in which fungicides and herbicides were applied to the soil surface showed no significant effect on EPF. Though organic fields were perceived to be more suitable environments for EPF, abiotic factors and cropping practices such as tillage may have greater impacts on the abundance of EPF. Also, fungicides and herbicides may not be as toxic to soil-borne EPF as originally thought. PMID:26191815

Abundance of Soil-Borne Entomopathogenic Fungi in Organic and Conventional Fields in the Midwestern USA with an Emphasis on the Effect of Herbicides and Fungicides on Fungal Persistence.

PubMed

Clifton, Eric H; Jaronski, Stefan T; Hodgson, Erin W; Gassmann, Aaron J

2015-01-01

Entomopathogenic fungi (EPF) are widespread in agricultural fields and help suppress crop pests. These natural enemies may be hindered by certain agronomic practices associated with conventional agriculture including the use of pesticides. We tested whether the abundance of EPF differed between organic and conventional fields, and whether specific cropping practices and soil properties were correlated with their abundance. In one year of the survey, soil from organic fields and accompanying margins had significantly more EPF than conventional fields and accompanying margins. Regression analysis revealed that the percentage of silt and the application of organic fertilizer were positively correlated with EPF abundance; but nitrogen concentration, tillage, conventional fields, and margins of conventional fields were negatively correlated with EPF abundance. A greenhouse experiment in which fungicides and herbicides were applied to the soil surface showed no significant effect on EPF. Though organic fields were perceived to be more suitable environments for EPF, abiotic factors and cropping practices such as tillage may have greater impacts on the abundance of EPF. Also, fungicides and herbicides may not be as toxic to soil-borne EPF as originally thought.

BODY SIZE-SPECIFIC EFFECTIVE DOSE CONVERSION COEFFICIENTS FOR CT SCANS.

PubMed

Romanyukha, Anna; Folio, Les; Lamart, Stephanie; Simon, Steven L; Lee, Choonsik

2016-12-01

Effective dose from computed tomography (CT) examinations is usually estimated using the scanner-provided dose-length product and using conversion factors, also known as k-factors, which correspond to scan regions and differ by age according to five categories: 0, 1, 5, 10 y and adult. However, patients often deviate from the standard body size on which the conversion factor is based. In this study, a method for deriving body size-specific k-factors is presented, which can be determined from a simple regression curve based on patient diameter at the centre of the scan range. Using the International Commission on Radiological Protection reference paediatric and adult computational phantoms paired with Monte Carlo simulation of CT X-ray beams, the authors derived a regression-based k-factor model for the following CT scan types: head-neck, head, neck, chest, abdomen, pelvis, abdomen-pelvis (AP) and chest-abdomen-pelvis (CAP). The resulting regression functions were applied to a total of 105 paediatric and 279 adult CT scans randomly sampled from patients who underwent chest, AP and CAP scans at the National Institutes of Health Clinical Center. The authors have calculated and compared the effective doses derived from the conventional age-specific k-factors with the values computed using their body size-specific k-factor. They found that by using the age-specific k-factor, paediatric patients tend to have underestimates (up to 3-fold) of effective dose, while underweight and overweight adult patients tend to have underestimates (up to 2.6-fold) and overestimates (up to 4.6-fold) of effective dose, respectively, compared with the effective dose determined from their body size-dependent factors. The authors present these size-specific k-factors as an alternative to the existing age-specific factors. The body size-specific k-factor will assess effective dose more precisely and on a more individual level than the conventional age-specific k-factors and, hence, improve awareness of the true exposure, which is important for the clinical community to understand. Published by Oxford University Press 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Moderation analysis using a two-level regression model.

PubMed

Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott

2014-10-01

Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.

The Scholarly Influence of Orthopaedic Research According to Conventional and Alternative Metrics: A Systematic Review.

PubMed

Evaniew, Nathan; Adili, Anthony F; Ghert, Michelle; Khan, Moin; Madden, Kim; Smith, Christopher; Bhandari, Mohit

2017-05-01

Researchers are experiencing an innovative shift toward online distribution of their work, and metrics related to online scholarly influence are gaining importance. Our objectives were to determine which types of online activity are most prevalent in orthopaedics, to identify associated factors, and to explore a complementary approach to measuring overall scholarly influence using online activity and conventional citations. We performed a systematic review of randomized controlled trials of surgical or nonsurgical interventions in participants with, or at specific risk for, injuries and diseases of the musculoskeletal system. We collected data on online activity in social media, mainstream media, blogs, forums, and other sources from a commercial provider of alternative metric data for medical journals. We tested associations with use of negative binomial regression. We identified 1,697 trials, published between 2011 and 2014, that had a total of 12,995 conventional citations and 15,068 online mentions. The median number of online mentions of each trial was 2 (interquartile range, 0 to 5). Twitter (82%) and Facebook (13%) mentions were the most prevalent types of online activity. Counts of online mentions correlated with conventional citations (r = 0.11, p < 0.01) but accumulated more rapidly. Higher total counts of online mentions were consistently associated with longer time since publication, higher journal impact factor, higher author h-index values, and less risk of bias (p < 0.01 for each). We found the best model fit for a complementary approach by weighting citations and online mentions equally. Online activity in orthopaedics is dominated by activity on Twitter and Facebook and is associated with increasing time since publication, journal impact factor, and author h-index values, and less risk of bias. Institutions, publishers, funding agencies, and clinicians may consider a complementary approach to measuring scholarly influence that weights online mentions and conventional citations equally.

Adiponectin Provides Additional Information to Conventional Cardiovascular Risk Factors for Assessing the Risk of Atherosclerosis in Both Genders

PubMed Central

Yoon, Jin-Ha; Kim, Sung-Kyung; Choi, Ho-June; Choi, Soo-In; Cha, So-Youn; Koh, Sang-Baek

2013-01-01

Background This study evaluated the relation between adiponectin and atherosclerosis in both genders, and investigated whether adiponectin provides useful additional information for assessing the risk of atherosclerosis. Methods We measured serum adiponectin levels and other cardiovascular risk factors in 1033 subjects (454 men, 579 women) from the Korean Genomic Rural Cohort study. Carotid intima–media-thickness (CIMT) was used as measure of atherosclerosis. Odds ratios (ORs) with 95% confidence intervals (95% CI) were calculated using multiple logistic regression, and receiver operating characteristic curves (ROC), the category-free net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated. Results After adjustment for conventional cardiovascular risk factors, such as age, waist circumference, smoking history, low-density and high-density lipoprotein cholesterol, triglycerides, systolic blood pressure and insulin resistance, the ORs (95%CI) of the third tertile adiponectin group were 0.42 (0.25–0.72) in men and 0.47 (0.29–0.75) in women. The area under the curve (AUC) on the ROC analysis increased significantly by 0.025 in men and 0.022 in women when adiponectin was added to the logistic model of conventional cardiovascular risk factors (AUC in men: 0.655 to 0.680, p = 0.038; AUC in women: 0.654 to 0.676, p = 0.041). The NRI was 0.32 (95%CI: 0.13–0.50, p<0.001), and the IDI was 0.03 (95%CI: 0.01–0.04, p<0.001) for men. For women, the category-free NRI was 0.18 (95%CI: 0.02–0.34, p = 0.031) and the IDI was 0.003 (95%CI: −0.002–0.008, p = 0.189). Conclusion Adiponectin and atherosclerosis were significantly related in both genders, and these relationships were independent of conventional cardiovascular risk factors. Furthermore, adiponectin provided additional information to conventional cardiovascular risk factors regarding the risk of atherosclerosis. PMID:24116054

Adiponectin provides additional information to conventional cardiovascular risk factors for assessing the risk of atherosclerosis in both genders.

PubMed

Yoon, Jin-Ha; Kim, Sung-Kyung; Choi, Ho-June; Choi, Soo-In; Cha, So-Youn; Koh, Sang-Baek; Kang, Hee-Taik; Ahn, Song Vogue

2013-01-01

This study evaluated the relation between adiponectin and atherosclerosis in both genders, and investigated whether adiponectin provides useful additional information for assessing the risk of atherosclerosis. We measured serum adiponectin levels and other cardiovascular risk factors in 1033 subjects (454 men, 579 women) from the Korean Genomic Rural Cohort study. Carotid intima-media-thickness (CIMT) was used as measure of atherosclerosis. Odds ratios (ORs) with 95% confidence intervals (95% CI) were calculated using multiple logistic regression, and receiver operating characteristic curves (ROC), the category-free net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated. After adjustment for conventional cardiovascular risk factors, such as age, waist circumference, smoking history, low-density and high-density lipoprotein cholesterol, triglycerides, systolic blood pressure and insulin resistance, the ORs (95%CI) of the third tertile adiponectin group were 0.42 (0.25-0.72) in men and 0.47 (0.29-0.75) in women. The area under the curve (AUC) on the ROC analysis increased significantly by 0.025 in men and 0.022 in women when adiponectin was added to the logistic model of conventional cardiovascular risk factors (AUC in men: 0.655 to 0.680, p = 0.038; AUC in women: 0.654 to 0.676, p = 0.041). The NRI was 0.32 (95%CI: 0.13-0.50, p<0.001), and the IDI was 0.03 (95%CI: 0.01-0.04, p<0.001) for men. For women, the category-free NRI was 0.18 (95%CI: 0.02-0.34, p = 0.031) and the IDI was 0.003 (95%CI: -0.002-0.008, p = 0.189). Adiponectin and atherosclerosis were significantly related in both genders, and these relationships were independent of conventional cardiovascular risk factors. Furthermore, adiponectin provided additional information to conventional cardiovascular risk factors regarding the risk of atherosclerosis.

Gender roles, suicidal ideation, and self-harming in young adults.

PubMed

Straiton, Melanie L; Roen, Katrina; Hjelmeland, Heidi

2012-01-01

This study investigates whether positive and negative conventional gender roles relate to suicidal ideation and self-harming in different ways among young adults. Participants completed an online survey about previous self-harm, recent suicidal ideation, and positive and negative aspects of conventional masculinity and femininity. Logistic regression analyses showed that negative femininity positively predicted self-harm and recent suicidal ideation status. Positive femininity was unrelated. Positive masculinity was negatively related to suicidal ideation and self-harming while negative masculinity was negatively related to self-harming only. The findings suggest that it is not the conventional feminine gender role per se that is associated with suicidality but specific negatively evaluated aspects. Conceptualizing gender as a multivariate construct may be useful in the gender socialization theory of suicidal behavior.

The microcomputer scientific software series 2: general linear model--regression.

Treesearch

Harold M. Rauscher

1983-01-01

The general linear model regression (GLMR) program provides the microcomputer user with a sophisticated regression analysis capability. The output provides a regression ANOVA table, estimators of the regression model coefficients, their confidence intervals, confidence intervals around the predicted Y-values, residuals for plotting, a check for multicollinearity, a...

Extending local canonical correlation analysis to handle general linear contrasts for FMRI data.

PubMed

Jin, Mingwu; Nandy, Rajesh; Curran, Tim; Cordes, Dietmar

2012-01-01

Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic.

On-Board Real-Time Optimization Control for Turbo-Fan Engine Life Extending

NASA Astrophysics Data System (ADS)

Zheng, Qiangang; Zhang, Haibo; Miao, Lizhen; Sun, Fengyong

2017-11-01

A real-time optimization control method is proposed to extend turbo-fan engine service life. This real-time optimization control is based on an on-board engine mode, which is devised by a MRR-LSSVR (multi-input multi-output recursive reduced least squares support vector regression method). To solve the optimization problem, a FSQP (feasible sequential quadratic programming) algorithm is utilized. The thermal mechanical fatigue is taken into account during the optimization process. Furthermore, to describe the engine life decaying, a thermal mechanical fatigue model of engine acceleration process is established. The optimization objective function not only contains the sub-item which can get fast response of the engine, but also concludes the sub-item of the total mechanical strain range which has positive relationship to engine fatigue life. Finally, the simulations of the conventional optimization control which just consider engine acceleration performance or the proposed optimization method have been conducted. The simulations demonstrate that the time of the two control methods from idle to 99.5 % of the maximum power are equal. However, the engine life using the proposed optimization method could be surprisingly increased by 36.17 % compared with that using conventional optimization control.

Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data

PubMed Central

Jin, Mingwu; Nandy, Rajesh; Curran, Tim; Cordes, Dietmar

2012-01-01

Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic. PMID:22461786

Differences in Multi-Modal Ultrasound Imaging between Triple Negative and Non-Triple Negative Breast Cancer.

PubMed

Li, Ziyao; Tian, Jiawei; Wang, Xiaowei; Wang, Ying; Wang, Zhenzhen; Zhang, Lei; Jing, Hui; Wu, Tong

2016-04-01

The objective of this study was to identify multi-modal ultrasound imaging parameters that could potentially help to differentiate between triple negative breast cancer (TNBC) and non-TNBC. Conventional ultrasonography, ultrasound strain elastography and 3-D ultrasound (3-D-US) findings from 50 TNBC and 179 non-TNBC patients were retrospectively reviewed. Immunohistochemical examination was used as the reference gold standard for cancer subtyping. Different ultrasound modalities were initially analyzed to define TNBC-related features. Subsequently, logistic regression analysis was applied to TNBC-related features to establish models for predicting TNBC. TNBCs often presented as micro-lobulated, markedly hypo-echoic masses with an abrupt interface (p = 0.015, 0.0015 and 0.004, compared with non-TNBCs, respectively) on conventional ultrasound, and showed a diminished retraction pattern phenomenon in the coronal plane (p = 0.035) on 3-D-US. Our findings suggest that B-mode ultrasound and 3-D-US in multi-modality ultrasonography could be a useful non-invasive technique for differentiating TNBCs from non-TNBCs. Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Point focusing using loudspeaker arrays from the perspective of optimal beamforming.

PubMed

Bai, Mingsian R; Hsieh, Yu-Hao

2015-06-01

Sound focusing is to create a concentrated acoustic field in the region surrounded by a loudspeaker array. This problem was tackled in the previous research via the Helmholtz integral approach, brightness control, acoustic contrast control, etc. In this paper, the same problem was revisited from the perspective of beamforming. A source array model is reformulated in terms of the steering matrix between the source and the field points, which lends itself to the use of beamforming algorithms such as minimum variance distortionless response (MVDR) and linearly constrained minimum variance (LCMV) originally intended for sensor arrays. The beamforming methods are compared with the conventional methods in terms of beam pattern, directional index, and control effort. Objective tests are conducted to assess the audio quality by using perceptual evaluation of audio quality (PEAQ). Experiments of produced sound field and listening tests are conducted in a listening room, with results processed using analysis of variance and regression analysis. In contrast to the conventional energy-based methods, the results have shown that the proposed methods are phase-sensitive in light of the distortionless constraint in formulating the array filters, which helps enhance audio quality and focusing performance.

Wavelength selection-based nonlinear calibration for transcutaneous blood glucose sensing using Raman spectroscopy

PubMed Central

Dingari, Narahara Chari; Barman, Ishan; Kang, Jeon Woong; Kong, Chae-Ryon; Dasari, Ramachandra R.; Feld, Michael S.

2011-01-01

While Raman spectroscopy provides a powerful tool for noninvasive and real time diagnostics of biological samples, its translation to the clinical setting has been impeded by the lack of robustness of spectroscopic calibration models and the size and cumbersome nature of conventional laboratory Raman systems. Linear multivariate calibration models employing full spectrum analysis are often misled by spurious correlations, such as system drift and covariations among constituents. In addition, such calibration schemes are prone to overfitting, especially in the presence of external interferences that may create nonlinearities in the spectra-concentration relationship. To address both of these issues we incorporate residue error plot-based wavelength selection and nonlinear support vector regression (SVR). Wavelength selection is used to eliminate uninformative regions of the spectrum, while SVR is used to model the curved effects such as those created by tissue turbidity and temperature fluctuations. Using glucose detection in tissue phantoms as a representative example, we show that even a substantial reduction in the number of wavelengths analyzed using SVR lead to calibration models of equivalent prediction accuracy as linear full spectrum analysis. Further, with clinical datasets obtained from human subject studies, we also demonstrate the prospective applicability of the selected wavelength subsets without sacrificing prediction accuracy, which has extensive implications for calibration maintenance and transfer. Additionally, such wavelength selection could substantially reduce the collection time of serial Raman acquisition systems. Given the reduced footprint of serial Raman systems in relation to conventional dispersive Raman spectrometers, we anticipate that the incorporation of wavelength selection in such hardware designs will enhance the possibility of miniaturized clinical systems for disease diagnosis in the near future. PMID:21895336

Flexible modeling of exposure-response relationship between long-term average levels of particulate air pollution and mortality in the American Cancer Society study.

PubMed

Abrahamowicz, Michal; Schopflocher, Tom; Leffondré, Karen; du Berger, Roxane; Krewski, Daniel

Accurate estimation of the exposure-response relationship between environmental particulate air pollution and mortality is important from both an etiologic and regulatory perspective. However, little is known about the actual shapes of these exposure-response curves. The objective of this study was to estimate the exposure-response relationships between mortality and long-term average city-specific levels of sulfates and fine particulate matter (PM(2.5)). We reanalyzed the data derived from the American Cancer Society (ACS) Cancer Prevention Study II, a large prospective study conducted in the United States between 1982 and 1989. Exposure to particulate air pollution was assessed prior to entry into the cohort. Mean sulfate concentrations for 1980 were available in 151 cities, and median PM(2.5) levels between 1979 and 1983 were available in 50 cities. Two sampling strategies were employed to reduce the computational burden. The modified case-cohort approach combined a random subcohort of 1200 individuals with an additional 1300 cases (i.e., deaths). The second strategy involved pooling the results of separate analyses of 10 disjoint random subsets, each with about 2200 participants. To assess the independent effect of the particulate levels on all-causes mortality, we relied on flexible, nonparametric survival analytical methods. To eliminate potentially restrictive assumptions underlying the conventional models, we employed a flexible regression spline generalization of the Cox proportional-hazards (PH) model. The regression spline method allowed us to model simultaneously the time-dependent changes in the effect of particulate matter on the hazard and a possibly nonlinear exposure-response relationship. The PH and linearity hypotheses were tested using likelihood ratio tests. In all analyses, we stratified by age and 5-yr age groups and adjusted for the subject's age, lifetime smoking exposure, obesity, and education. For both fine particles (PM(2.5)) and sulfates, there was a statistically significant (at.05 level) departure from the conventional linearity assumption. The adjusted effect of fine particles on mortality indicated a stronger relationship in the lower (up to about 16 microg/m(3)) than in the higher range of their values. Increasing levels of sulfates in the lower range (up to about 12 microg/m(3)) had little impact on mortality, suggesting a possible "no-effect threshold." For body mass index (BMI), the risks were lowest in the middle range and increased for both very obese and very lean individuals. It was concluded that flexible modeling yields new insights about the effect of long-term air pollution on mortality.

Climate variations and salmonellosis transmission in Adelaide, South Australia: a comparison between regression models

NASA Astrophysics Data System (ADS)

Zhang, Ying; Bi, Peng; Hiller, Janet

2008-01-01

This is the first study to identify appropriate regression models for the association between climate variation and salmonellosis transmission. A comparison between different regression models was conducted using surveillance data in Adelaide, South Australia. By using notified salmonellosis cases and climatic variables from the Adelaide metropolitan area over the period 1990-2003, four regression methods were examined: standard Poisson regression, autoregressive adjusted Poisson regression, multiple linear regression, and a seasonal autoregressive integrated moving average (SARIMA) model. Notified salmonellosis cases in 2004 were used to test the forecasting ability of the four models. Parameter estimation, goodness-of-fit and forecasting ability of the four regression models were compared. Temperatures occurring 2 weeks prior to cases were positively associated with cases of salmonellosis. Rainfall was also inversely related to the number of cases. The comparison of the goodness-of-fit and forecasting ability suggest that the SARIMA model is better than the other three regression models. Temperature and rainfall may be used as climatic predictors of salmonellosis cases in regions with climatic characteristics similar to those of Adelaide. The SARIMA model could, thus, be adopted to quantify the relationship between climate variations and salmonellosis transmission.

Are Early-Life Socioeconomic Conditions Directly Related to Birth Outcomes? Grandmaternal Education, Grandchild Birth Weight, and Associated Bias Analyses.

PubMed

Huang, Jonathan Y; Gavin, Amelia R; Richardson, Thomas S; Rowhani-Rahbar, Ali; Siscovick, David S; Enquobahrie, Daniel A

2015-10-01

Grandmaternal education may be related to grandchild birth weight (GBW) through maternal early-life development; however, conventional regression models may be endogenously confounded. Alternative models employing explicit structural assumptions may provide incrementally clearer evidence. We used data from the US National Longitudinal Study of Adolescent to Adult Health (1995-2009; 1,681 mother-child pairs) to estimate "direct effects" of grandmaternal educational level (less than high school, high school diploma or equivalent, or college degree) at the time of the mother's birth on GBW, adjusted for maternal life-course factors: maltreatment as a child, education and income as an adult, prepregnancy overweight, and prenatal smoking. Using conventional and marginal structural model (MSM) approaches, we estimated 54-g (95% confidence interval: -14.0, 122.1) and 87-g (95% confidence interval: 10.9, 162.5) higher GBWs per increase in educational level, respectively. The MSM allowed simultaneous mediation by and adjustment for prepregnancy overweight. Estimates were insensitive to alternate structural assumptions and mediator parameterizations. Bias analysis suggested that a single unmeasured confounder would have to have a strong influence on GBW (approximately 150 g) or be greatly imbalanced across exposure groups (approximately 25%) to completely explain the findings. Coupling an MSM with sensitivity analyses provides some evidence that maternal early-life socioeconomic environment is directly associated with offspring birth weight. © The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

On the move: Exploring the impact of residential mobility on cannabis use.

PubMed

Morris, Tim; Manley, David; Northstone, Kate; Sabel, Clive E

2016-11-01

A large literature exists suggesting that residential mobility leads to increased participation in risky health behaviours such as cannabis use amongst youth. However, much of this work fails to account for the impact that underlying differences between mobile and non-mobile youth have on this relationship. In this study we utilise multilevel models with longitudinal data to simultaneously estimate between-child and within-child effects in the relationship between residential mobility and cannabis use, allowing us to determine the extent to which cannabis use in adolescence is driven by residential mobility and unobserved confounding. Data come from a UK cohort, The Avon Longitudinal Study of Parents and Children. Consistent with previous research we find a positive association between cumulative residential mobility and cannabis use when using multilevel extensions of conventional logistic regression models (log odds: 0.94, standard error: 0.42), indicating that children who move houses are more likely to use cannabis than those who remain residentially stable. However, decomposing this relationship into within- and between-child components reveals that the conventional model is underspecified and misleading; we find that differences in cannabis use between mobile and non-mobile children are due to underlying differences between these groups (between-child log odds: 3.56, standard error: 1.22), not by a change in status of residential mobility (within-child log odds: 1.33, standard error: 1.02). Our findings suggest that residential mobility in the teenage years does not place children at an increased risk of cannabis use throughout these years. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Osteoporosis risk prediction for bone mineral density assessment of postmenopausal women using machine learning.

PubMed

Yoo, Tae Keun; Kim, Sung Kean; Kim, Deok Won; Choi, Joon Yul; Lee, Wan Hyung; Oh, Ein; Park, Eun-Cheol

2013-11-01

A number of clinical decision tools for osteoporosis risk assessment have been developed to select postmenopausal women for the measurement of bone mineral density. We developed and validated machine learning models with the aim of more accurately identifying the risk of osteoporosis in postmenopausal women compared to the ability of conventional clinical decision tools. We collected medical records from Korean postmenopausal women based on the Korea National Health and Nutrition Examination Surveys. The training data set was used to construct models based on popular machine learning algorithms such as support vector machines (SVM), random forests, artificial neural networks (ANN), and logistic regression (LR) based on simple surveys. The machine learning models were compared to four conventional clinical decision tools: osteoporosis self-assessment tool (OST), osteoporosis risk assessment instrument (ORAI), simple calculated osteoporosis risk estimation (SCORE), and osteoporosis index of risk (OSIRIS). SVM had significantly better area under the curve (AUC) of the receiver operating characteristic than ANN, LR, OST, ORAI, SCORE, and OSIRIS for the training set. SVM predicted osteoporosis risk with an AUC of 0.827, accuracy of 76.7%, sensitivity of 77.8%, and specificity of 76.0% at total hip, femoral neck, or lumbar spine for the testing set. The significant factors selected by SVM were age, height, weight, body mass index, duration of menopause, duration of breast feeding, estrogen therapy, hyperlipidemia, hypertension, osteoarthritis, and diabetes mellitus. Considering various predictors associated with low bone density, the machine learning methods may be effective tools for identifying postmenopausal women at high risk for osteoporosis.

Exploiting cancer cell vulnerabilities to develop a combination therapy for Ras-driven tumors

PubMed Central

De Raedt, Thomas; Walton, Zandra; Yecies, Jessica L.; Li, Danan; Chen, Yimei; Malone, Clare F.; Maertens, Ophelia; Jeong, Seung Min; Bronson, Roderick T.; Lebleu, Valerie; Kalluri, Raghu; Normant, Emmanuel; Haigis, Marcia C.; Manning, Brendan D.; Wong, Kwok-Kin; Macleod, Kay F; Cichowski, Karen

2011-01-01

Summary Ras-driven tumors are often refractory to conventional therapies. Here we identify a promising targeted therapeutic strategy for two Ras-driven cancers: Nf1-deficient malignancies and KRas/p53-mutant lung cancer. We show that agents that enhance proteotoxic stress, including the HSP90 inhibitor IPI-504, induce tumor regression in aggressive mouse models, but only when combined with rapamycin. These agents synergize by promoting irresolvable ER stress, resulting in catastrophic ER and mitochondrial damage. This process is fueled by oxidative stress, which is caused by IPI-504-dependent production of reactive oxygen species, and the rapamycin-dependent suppression of glutathione, an important endogenous antioxidant. Notably, the mechanism by which these agents cooperate reveals a therapeutic paradigm that can be expanded to develop additional combinations. PMID:21907929

Application of two neural network paradigms to the study of voluntary employee turnover.

PubMed

Somers, M J

1999-04-01

Two neural network paradigms--multilayer perceptron and learning vector quantization--were used to study voluntary employee turnover with a sample of 577 hospital employees. The objectives of the study were twofold. The 1st was to assess whether neural computing techniques offered greater predictive accuracy than did conventional turnover methodologies. The 2nd was to explore whether computer models of turnover based on neural network technologies offered new insights into turnover processes. When compared with logistic regression analysis, both neural network paradigms provided considerably more accurate predictions of turnover behavior, particularly with respect to the correct classification of leavers. In addition, these neural network paradigms captured nonlinear relationships that are relevant for theory development. Results are discussed in terms of their implications for future research.

Comparison of Bolton analysis and Little’s irregularity index on laser scanned three-dimensional digital study models with conventional study models

NASA Astrophysics Data System (ADS)

Kurnia, H.; Noerhadi, N. A. I.

2017-08-01

Three-dimensional digital study models were introduced following advances in digital technology. This study was carried out to assess the reliability of digital study models scanned by a laser scanning device newly assembled. The aim of this study was to compare the digital study models and conventional models. Twelve sets of dental impressions were taken from patients with mild-to-moderate crowding. The impressions were taken twice, one with alginate and the other with polyvinylsiloxane. The alginate impressions were made into conventional models, and the polyvinylsiloxane impressions were scanned to produce digital models. The mesiodistal tooth width and Little’s irregularity index (LII) were measured manually with digital calipers on the conventional models and digitally on the digital study models. Bolton analysis was performed on each study models. Each method was carried out twice to check for intra-observer variability. The reproducibility (comparison of the methods) was assessed using independent-sample t-tests. The mesiodistal tooth width between conventional and digital models did not significantly differ (p > 0.05). Independent-sample t-tests did not identify statistically significant differences for Bolton analysis and LII (p = 0.603 for Bolton and p = 0894 for LII). The measurements of the digital study models are as accurate as those of the conventional models.

Evaluation of weighted regression and sample size in developing a taper model for loblolly pine

Treesearch

Kenneth L. Cormier; Robin M. Reich; Raymond L. Czaplewski; William A. Bechtold

1992-01-01

A stem profile model, fit using pseudo-likelihood weighted regression, was used to estimate merchantable volume of loblolly pine (Pinus taeda L.) in the southeast. The weighted regression increased model fit marginally, but did not substantially increase model performance. In all cases, the unweighted regression models performed as well as the...

Error-in-variables models in calibration

NASA Astrophysics Data System (ADS)

Lira, I.; Grientschnig, D.

2017-12-01

In many calibration operations, the stimuli applied to the measuring system or instrument under test are derived from measurement standards whose values may be considered to be perfectly known. In that case, it is assumed that calibration uncertainty arises solely from inexact measurement of the responses, from imperfect control of the calibration process and from the possible inaccuracy of the calibration model. However, the premise that the stimuli are completely known is never strictly fulfilled and in some instances it may be grossly inadequate. Then, error-in-variables (EIV) regression models have to be employed. In metrology, these models have been approached mostly from the frequentist perspective. In contrast, not much guidance is available on their Bayesian analysis. In this paper, we first present a brief summary of the conventional statistical techniques that have been developed to deal with EIV models in calibration. We then proceed to discuss the alternative Bayesian framework under some simplifying assumptions. Through a detailed example about the calibration of an instrument for measuring flow rates, we provide advice on how the user of the calibration function should employ the latter framework for inferring the stimulus acting on the calibrated device when, in use, a certain response is measured.

Automatic age and gender classification using supervised appearance model

NASA Astrophysics Data System (ADS)

Bukar, Ali Maina; Ugail, Hassan; Connah, David

2016-11-01

Age and gender classification are two important problems that recently gained popularity in the research community, due to their wide range of applications. Research has shown that both age and gender information are encoded in the face shape and texture, hence the active appearance model (AAM), a statistical model that captures shape and texture variations, has been one of the most widely used feature extraction techniques for the aforementioned problems. However, AAM suffers from some drawbacks, especially when used for classification. This is primarily because principal component analysis (PCA), which is at the core of the model, works in an unsupervised manner, i.e., PCA dimensionality reduction does not take into account how the predictor variables relate to the response (class labels). Rather, it explores only the underlying structure of the predictor variables, thus, it is no surprise if PCA discards valuable parts of the data that represent discriminatory features. Toward this end, we propose a supervised appearance model (sAM) that improves on AAM by replacing PCA with partial least-squares regression. This feature extraction technique is then used for the problems of age and gender classification. Our experiments show that sAM has better predictive power than the conventional AAM.

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.

Modelling fourier regression for time series data- a case study: modelling inflation in foods sector in Indonesia

NASA Astrophysics Data System (ADS)

Prahutama, Alan; Suparti; Wahyu Utami, Tiani

2018-03-01

Regression analysis is an analysis to model the relationship between response variables and predictor variables. The parametric approach to the regression model is very strict with the assumption, but nonparametric regression model isn’t need assumption of model. Time series data is the data of a variable that is observed based on a certain time, so if the time series data wanted to be modeled by regression, then we should determined the response and predictor variables first. Determination of the response variable in time series is variable in t-th (yt), while the predictor variable is a significant lag. In nonparametric regression modeling, one developing approach is to use the Fourier series approach. One of the advantages of nonparametric regression approach using Fourier series is able to overcome data having trigonometric distribution. In modeling using Fourier series needs parameter of K. To determine the number of K can be used Generalized Cross Validation method. In inflation modeling for the transportation sector, communication and financial services using Fourier series yields an optimal K of 120 parameters with R-square 99%. Whereas if it was modeled by multiple linear regression yield R-square 90%.

Modeling abundance effects in distance sampling

USGS Publications Warehouse

Royle, J. Andrew; Dawson, D.K.; Bates, S.

2004-01-01

Distance-sampling methods are commonly used in studies of animal populations to estimate population density. A common objective of such studies is to evaluate the relationship between abundance or density and covariates that describe animal habitat or other environmental influences. However, little attention has been focused on methods of modeling abundance covariate effects in conventional distance-sampling models. In this paper we propose a distance-sampling model that accommodates covariate effects on abundance. The model is based on specification of the distance-sampling likelihood at the level of the sample unit in terms of local abundance (for each sampling unit). This model is augmented with a Poisson regression model for local abundance that is parameterized in terms of available covariates. Maximum-likelihood estimation of detection and density parameters is based on the integrated likelihood, wherein local abundance is removed from the likelihood by integration. We provide an example using avian point-transect data of Ovenbirds (Seiurus aurocapillus) collected using a distance-sampling protocol and two measures of habitat structure (understory cover and basal area of overstory trees). The model yields a sensible description (positive effect of understory cover, negative effect on basal area) of the relationship between habitat and Ovenbird density that can be used to evaluate the effects of habitat management on Ovenbird populations.

Twelve-Year Follow-Up of Laser In Situ Keratomileusis for Moderate to High Myopia

PubMed Central

Ikeda, Tetsuya; Igarashi, Akihito; Kasahara, Sumie

2017-01-01

Purpose To assess the long-term clinical outcomes of conventional laser in situ keratomileusis (LASIK) for moderate to high myopia. Methods We retrospectively examined sixty-eight eyes of 37 consecutive patients who underwent conventional LASIK for the correction of myopia (−3.00 to −12.75 diopters (D)). At 3 months and 1, 4, 8, and 12 years postoperatively, we assessed the safety, efficacy, predictability, stability, mean keratometry, central corneal thickness, and adverse events. Results The safety and efficacy indices were 0.82 ± 0.29 and 0.67 ± 0.37, respectively, 12 years postoperatively. At 12 years, 53% and 75% of the eyes were within 0.5 and 1.0 D, respectively, of the targeted correction. Manifest refraction changes of −0.74 ± 0.99 D occurred from 3 months to 12 years after LASIK (p < 0.001). We found a significant correlation of refractive regression with the changes in keratometric readings from 3 months to 12 years postoperatively (Pearson correlation coefficient, r = −0.28, p = 0.02), but not with the changes in central corneal thickness (r = −0.08, p = 0.63). No vision-threatening complications occurred in any case. Conclusions Conventional LASIK offered good safety outcomes during the 12-year observation period. However, the efficacy and the predictability gradually decreased with time owing to myopic regression in relation to corneal steepening. PMID:28596969

Twelve-Year Follow-Up of Laser In Situ Keratomileusis for Moderate to High Myopia.

PubMed

Ikeda, Tetsuya; Shimizu, Kimiya; Igarashi, Akihito; Kasahara, Sumie; Kamiya, Kazutaka

2017-01-01

To assess the long-term clinical outcomes of conventional laser in situ keratomileusis (LASIK) for moderate to high myopia. We retrospectively examined sixty-eight eyes of 37 consecutive patients who underwent conventional LASIK for the correction of myopia (-3.00 to -12.75 diopters (D)). At 3 months and 1, 4, 8, and 12 years postoperatively, we assessed the safety, efficacy, predictability, stability, mean keratometry, central corneal thickness, and adverse events. The safety and efficacy indices were 0.82 ± 0.29 and 0.67 ± 0.37, respectively, 12 years postoperatively. At 12 years, 53% and 75% of the eyes were within 0.5 and 1.0 D, respectively, of the targeted correction. Manifest refraction changes of -0.74 ± 0.99 D occurred from 3 months to 12 years after LASIK ( p < 0.001). We found a significant correlation of refractive regression with the changes in keratometric readings from 3 months to 12 years postoperatively (Pearson correlation coefficient, r = -0.28, p = 0.02), but not with the changes in central corneal thickness ( r = -0.08, p = 0.63). No vision-threatening complications occurred in any case. Conventional LASIK offered good safety outcomes during the 12-year observation period. However, the efficacy and the predictability gradually decreased with time owing to myopic regression in relation to corneal steepening.

Hydrostratigraphy and hydrogeology of the western part of Maira area, Khyber Pakhtunkhwa, Pakistan: a case study by using electrical resistivity.

PubMed

Farid, Asam; Jadoon, Khanzaib; Akhter, Gulraiz; Iqbal, Muhammad Asim

2013-03-01

Hydrostratigraphy and hydrogeology of the Maira vicinity is important for the characterization of aquifer system and developing numerical groundwater flow models to predict the future availability of the water resource. Conventionally, the aquifer parameters are obtained by the analysis of pumping tests data which provide limited spatial information and turn out to be costly and time consuming. Vertical electrical soundings and pump testing of boreholes were conducted to delineate the aquifer system at the western part of the Maira area, Khyber Pakhtun Khwa, Pakistan. Aquifer lithology in the eastern part of the study area is dominated by coarse sand and gravel whereas the western part is characterized by fine sand. An attempt has been made to estimate the hydraulic conductivity of the aquifer system by establishing a relationship between the pumping test results and vertical electrical soundings by using regression technique. The relationship is applied to the area along the resistivity profiles where boreholes are not drilled. Our findings show a good match between pumped hydraulic conductivity and estimated hydraulic conductivity. In case of sparse borehole data, regression technique is useful in estimating hydraulic properties for aquifers with varying lithology.

Bumps in river profiles: uncertainty assessment and smoothing using quantile regression techniques

NASA Astrophysics Data System (ADS)

Schwanghart, Wolfgang; Scherler, Dirk

2017-12-01

The analysis of longitudinal river profiles is an important tool for studying landscape evolution. However, characterizing river profiles based on digital elevation models (DEMs) suffers from errors and artifacts that particularly prevail along valley bottoms. The aim of this study is to characterize uncertainties that arise from the analysis of river profiles derived from different, near-globally available DEMs. We devised new algorithms - quantile carving and the CRS algorithm - that rely on quantile regression to enable hydrological correction and the uncertainty quantification of river profiles. We find that globally available DEMs commonly overestimate river elevations in steep topography. The distributions of elevation errors become increasingly wider and right skewed if adjacent hillslope gradients are steep. Our analysis indicates that the AW3D DEM has the highest precision and lowest bias for the analysis of river profiles in mountainous topography. The new 12 m resolution TanDEM-X DEM has a very low precision, most likely due to the combined effect of steep valley walls and the presence of water surfaces in valley bottoms. Compared to the conventional approaches of carving and filling, we find that our new approach is able to reduce the elevation bias and errors in longitudinal river profiles.

Entropy-Based TOA Estimation and SVM-Based Ranging Error Mitigation in UWB Ranging Systems

PubMed Central

Yin, Zhendong; Cui, Kai; Wu, Zhilu; Yin, Liang

2015-01-01

The major challenges for Ultra-wide Band (UWB) indoor ranging systems are the dense multipath and non-line-of-sight (NLOS) problems of the indoor environment. To precisely estimate the time of arrival (TOA) of the first path (FP) in such a poor environment, a novel approach of entropy-based TOA estimation and support vector machine (SVM) regression-based ranging error mitigation is proposed in this paper. The proposed method can estimate the TOA precisely by measuring the randomness of the received signals and mitigate the ranging error without the recognition of the channel conditions. The entropy is used to measure the randomness of the received signals and the FP can be determined by the decision of the sample which is followed by a great entropy decrease. The SVM regression is employed to perform the ranging-error mitigation by the modeling of the regressor between the characteristics of received signals and the ranging error. The presented numerical simulation results show that the proposed approach achieves significant performance improvements in the CM1 to CM4 channels of the IEEE 802.15.4a standard, as compared to conventional approaches. PMID:26007726

CerebroMatic: A Versatile Toolbox for Spline-Based MRI Template Creation

PubMed Central

Wilke, Marko; Altaye, Mekibib; Holland, Scott K.

2017-01-01

Brain image spatial normalization and tissue segmentation rely on prior tissue probability maps. Appropriately selecting these tissue maps becomes particularly important when investigating “unusual” populations, such as young children or elderly subjects. When creating such priors, the disadvantage of applying more deformation must be weighed against the benefit of achieving a crisper image. We have previously suggested that statistically modeling demographic variables, instead of simply averaging images, is advantageous. Both aspects (more vs. less deformation and modeling vs. averaging) were explored here. We used imaging data from 1914 subjects, aged 13 months to 75 years, and employed multivariate adaptive regression splines to model the effects of age, field strength, gender, and data quality. Within the spm/cat12 framework, we compared an affine-only with a low- and a high-dimensional warping approach. As expected, more deformation on the individual level results in lower group dissimilarity. Consequently, effects of age in particular are less apparent in the resulting tissue maps when using a more extensive deformation scheme. Using statistically-described parameters, high-quality tissue probability maps could be generated for the whole age range; they are consistently closer to a gold standard than conventionally-generated priors based on 25, 50, or 100 subjects. Distinct effects of field strength, gender, and data quality were seen. We conclude that an extensive matching for generating tissue priors may model much of the variability inherent in the dataset which is then not contained in the resulting priors. Further, the statistical description of relevant parameters (using regression splines) allows for the generation of high-quality tissue probability maps while controlling for known confounds. The resulting CerebroMatic toolbox is available for download at http://irc.cchmc.org/software/cerebromatic.php. PMID:28275348

CerebroMatic: A Versatile Toolbox for Spline-Based MRI Template Creation.

PubMed

Wilke, Marko; Altaye, Mekibib; Holland, Scott K

2017-01-01

Brain image spatial normalization and tissue segmentation rely on prior tissue probability maps. Appropriately selecting these tissue maps becomes particularly important when investigating "unusual" populations, such as young children or elderly subjects. When creating such priors, the disadvantage of applying more deformation must be weighed against the benefit of achieving a crisper image. We have previously suggested that statistically modeling demographic variables, instead of simply averaging images, is advantageous. Both aspects (more vs. less deformation and modeling vs. averaging) were explored here. We used imaging data from 1914 subjects, aged 13 months to 75 years, and employed multivariate adaptive regression splines to model the effects of age, field strength, gender, and data quality. Within the spm/cat12 framework, we compared an affine-only with a low- and a high-dimensional warping approach. As expected, more deformation on the individual level results in lower group dissimilarity. Consequently, effects of age in particular are less apparent in the resulting tissue maps when using a more extensive deformation scheme. Using statistically-described parameters, high-quality tissue probability maps could be generated for the whole age range; they are consistently closer to a gold standard than conventionally-generated priors based on 25, 50, or 100 subjects. Distinct effects of field strength, gender, and data quality were seen. We conclude that an extensive matching for generating tissue priors may model much of the variability inherent in the dataset which is then not contained in the resulting priors. Further, the statistical description of relevant parameters (using regression splines) allows for the generation of high-quality tissue probability maps while controlling for known confounds. The resulting CerebroMatic toolbox is available for download at http://irc.cchmc.org/software/cerebromatic.php.

Gaussian process regression for tool wear prediction

NASA Astrophysics Data System (ADS)

Kong, Dongdong; Chen, Yongjie; Li, Ning

2018-05-01

To realize and accelerate the pace of intelligent manufacturing, this paper presents a novel tool wear assessment technique based on the integrated radial basis function based kernel principal component analysis (KPCA_IRBF) and Gaussian process regression (GPR) for real-timely and accurately monitoring the in-process tool wear parameters (flank wear width). The KPCA_IRBF is a kind of new nonlinear dimension-increment technique and firstly proposed for feature fusion. The tool wear predictive value and the corresponding confidence interval are both provided by utilizing the GPR model. Besides, GPR performs better than artificial neural networks (ANN) and support vector machines (SVM) in prediction accuracy since the Gaussian noises can be modeled quantitatively in the GPR model. However, the existence of noises will affect the stability of the confidence interval seriously. In this work, the proposed KPCA_IRBF technique helps to remove the noises and weaken its negative effects so as to make the confidence interval compressed greatly and more smoothed, which is conducive for monitoring the tool wear accurately. Moreover, the selection of kernel parameter in KPCA_IRBF can be easily carried out in a much larger selectable region in comparison with the conventional KPCA_RBF technique, which helps to improve the efficiency of model construction. Ten sets of cutting tests are conducted to validate the effectiveness of the presented tool wear assessment technique. The experimental results show that the in-process flank wear width of tool inserts can be monitored accurately by utilizing the presented tool wear assessment technique which is robust under a variety of cutting conditions. This study lays the foundation for tool wear monitoring in real industrial settings.

The Association between Triglyceride/High-Density Lipoprotein Cholesterol Ratio and All-Cause Mortality in Acute Coronary Syndrome after Coronary Revascularization

PubMed Central

Wan, Ke; Zhao, Jianxun; Huang, Hao; Zhang, Qing; Chen, Xi; Zeng, Zhi; Zhang, Li; Chen, Yucheng

2015-01-01

Aims High triglycerides (TG) and low high-density lipoprotein cholesterol (HDL-C) are cardiovascular risk factors. A positive correlation between elevated TG/HDL-C ratio and all-cause mortality and cardiovascular events exists in women. However, utility of TG to HDL-C ratio for prediction is unknown among acute coronary syndrome (ACS). Methods Fasting lipid profiles, detailed demographic data, and clinical data were obtained at baseline from 416 patients with ACS after coronary revascularization. Subjects were stratified into three levels of TG/HDL-C. We constructed multivariate Cox-proportional hazard models for all-cause mortality over a median follow-up of 3 years using log TG to HDL-C ratio as a predictor variable and analyzing traditional cardiovascular risk factors. We constructed a logistic regression model for major adverse cardiovascular events (MACEs) to prove that the TG/HDL-C ratio is a risk factor. Results The subject’s mean age was 64 ± 11 years; 54.5% were hypertensive, 21.8% diabetic, and 61.0% current or prior smokers. TG/HDL-C ratio ranged from 0.27 to 14.33. During the follow-up period, there were 43 deaths. In multivariate Cox models after adjusting for age, smoking, hypertension, diabetes, and severity of angiographic coronary disease, patients in the highest tertile of ACS had a 5.32-fold increased risk of mortality compared with the lowest tertile. After adjusting for conventional coronary heart disease risk factors by the logistic regression model, the TG/HDL-C ratio was associated with MACEs. Conclusion The TG to HDL-C ratio is a powerful independent predictor of all-cause mortality and is a risk factor of cardiovascular events. PMID:25880982

Development and Validation of an Empiric Tool to Predict Favorable Neurologic Outcomes Among PICU Patients.

PubMed

Gupta, Punkaj; Rettiganti, Mallikarjuna; Gossett, Jeffrey M; Daufeldt, Jennifer; Rice, Tom B; Wetzel, Randall C

2018-01-01

To create a novel tool to predict favorable neurologic outcomes during ICU stay among children with critical illness. Logistic regression models using adaptive lasso methodology were used to identify independent factors associated with favorable neurologic outcomes. A mixed effects logistic regression model was used to create the final prediction model including all predictors selected from the lasso model. Model validation was performed using a 10-fold internal cross-validation approach. Virtual Pediatric Systems (VPS, LLC, Los Angeles, CA) database. Patients less than 18 years old admitted to one of the participating ICUs in the Virtual Pediatric Systems database were included (2009-2015). None. A total of 160,570 patients from 90 hospitals qualified for inclusion. Of these, 1,675 patients (1.04%) were associated with a decline in Pediatric Cerebral Performance Category scale by at least 2 between ICU admission and ICU discharge (unfavorable neurologic outcome). The independent factors associated with unfavorable neurologic outcome included higher weight at ICU admission, higher Pediatric Index of Morality-2 score at ICU admission, cardiac arrest, stroke, seizures, head/nonhead trauma, use of conventional mechanical ventilation and high-frequency oscillatory ventilation, prolonged hospital length of ICU stay, and prolonged use of mechanical ventilation. The presence of chromosomal anomaly, cardiac surgery, and utilization of nitric oxide were associated with favorable neurologic outcome. The final online prediction tool can be accessed at https://soipredictiontool.shinyapps.io/GNOScore/. Our model predicted 139,688 patients with favorable neurologic outcomes in an internal validation sample when the observed number of patients with favorable neurologic outcomes was among 139,591 patients. The area under the receiver operating curve for the validation model was 0.90. This proposed prediction tool encompasses 20 risk factors into one probability to predict favorable neurologic outcome during ICU stay among children with critical illness. Future studies should seek external validation and improved discrimination of this prediction tool.

Applying Kaplan-Meier to Item Response Data

ERIC Educational Resources Information Center

McNeish, Daniel

2018-01-01

Some IRT models can be equivalently modeled in alternative frameworks such as logistic regression. Logistic regression can also model time-to-event data, which concerns the probability of an event occurring over time. Using the relation between time-to-event models and logistic regression and the relation between logistic regression and IRT, this…

Temporally-Constrained Group Sparse Learning for Longitudinal Data Analysis in Alzheimer’s Disease

PubMed Central

Jie, Biao; Liu, Mingxia; Liu, Jun

2016-01-01

Sparse learning has been widely investigated for analysis of brain images to assist the diagnosis of Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). However, most existing sparse learning-based studies only adopt cross-sectional analysis methods, where the sparse model is learned using data from a single time-point. Actually, multiple time-points of data are often available in brain imaging applications, which can be used in some longitudinal analysis methods to better uncover the disease progression patterns. Accordingly, in this paper we propose a novel temporally-constrained group sparse learning method aiming for longitudinal analysis with multiple time-points of data. Specifically, we learn a sparse linear regression model by using the imaging data from multiple time-points, where a group regularization term is first employed to group the weights for the same brain region across different time-points together. Furthermore, to reflect the smooth changes between data derived from adjacent time-points, we incorporate two smoothness regularization terms into the objective function, i.e., one fused smoothness term which requires that the differences between two successive weight vectors from adjacent time-points should be small, and another output smoothness term which requires the differences between outputs of two successive models from adjacent time-points should also be small. We develop an efficient optimization algorithm to solve the proposed objective function. Experimental results on ADNI database demonstrate that, compared with conventional sparse learning-based methods, our proposed method can achieve improved regression performance and also help in discovering disease-related biomarkers. PMID:27093313

Learning-based deformable image registration for infant MR images in the first year of life.

PubMed

Hu, Shunbo; Wei, Lifang; Gao, Yaozong; Guo, Yanrong; Wu, Guorong; Shen, Dinggang

2017-01-01

Many brain development studies have been devoted to investigate dynamic structural and functional changes in the first year of life. To quantitatively measure brain development in such a dynamic period, accurate image registration for different infant subjects with possible large age gap is of high demand. Although many state-of-the-art image registration methods have been proposed for young and elderly brain images, very few registration methods work for infant brain images acquired in the first year of life, because of (a) large anatomical changes due to fast brain development and (b) dynamic appearance changes due to white-matter myelination. To address these two difficulties, we propose a learning-based registration method to not only align the anatomical structures but also alleviate the appearance differences between two arbitrary infant MR images (with large age gap) by leveraging the regression forest to predict both the initial displacement vector and appearance changes. Specifically, in the training stage, two regression models are trained separately, with (a) one model learning the relationship between local image appearance (of one development phase) and its displacement toward the template (of another development phase) and (b) another model learning the local appearance changes between the two brain development phases. Then, in the testing stage, to register a new infant image to the template, we first predict both its voxel-wise displacement and appearance changes by the two learned regression models. Since such initializations can alleviate significant appearance and shape differences between new infant image and the template, it is easy to just use a conventional registration method to refine the remaining registration. We apply our proposed registration method to align 24 infant subjects at five different time points (i.e., 2-week-old, 3-month-old, 6-month-old, 9-month-old, and 12-month-old), and achieve more accurate and robust registration results, compared to the state-of-the-art registration methods. The proposed learning-based registration method addresses the challenging task of registering infant brain images and achieves higher registration accuracy compared with other counterpart registration methods. © 2016 American Association of Physicists in Medicine.

Multi-decadal trend and space-time variability of sea level over the Indian Ocean since the 1950s: impact of decadal climate modes

NASA Astrophysics Data System (ADS)

Han, W.; Stammer, D.; Meehl, G. A.; Hu, A.; Sienz, F.

2016-12-01

Sea level varies on decadal and multi-decadal timescales over the Indian Ocean. The variations are not spatially uniform, and can deviate considerably from the global mean sea level rise (SLR) due to various geophysical processes. One of these processes is the change of ocean circulation, which can be partly attributed to natural internal modes of climate variability. Over the Indian Ocean, the most influential climate modes on decadal and multi-decadal timescales are the Interdecadal Pacific Oscillation (IPO) and decadal variability of the Indian Ocean dipole (IOD). Here, we first analyze observational datasets to investigate the impacts of IPO and IOD on spatial patterns of decadal and interdecadal (hereafter decal) sea level variability & multi-decadal trend over the Indian Ocean since the 1950s, using a new statistical approach of Bayesian Dynamical Linear regression Model (DLM). The Bayesian DLM overcomes the limitation of "time-constant (static)" regression coefficients in conventional multiple linear regression model, by allowing the coefficients to vary with time and therefore measuring "time-evolving (dynamical)" relationship between climate modes and sea level. For the multi-decadal sea level trend since the 1950s, our results show that climate modes and non-climate modes (the part that cannot be explained by climate modes) have comparable contributions in magnitudes but with different spatial patterns, with each dominating different regions of the Indian Ocean. For decadal variability, climate modes are the major contributors for sea level variations over most region of the tropical Indian Ocean. The relative importance of IPO and decadal variability of IOD, however, varies spatially. For example, while IOD decadal variability dominates IPO in the eastern equatorial basin (85E-100E, 5S-5N), IPO dominates IOD in causing sea level variations in the tropical southwest Indian Ocean (45E-65E, 12S-2S). To help decipher the possible contribution of external forcing to the multi-decadal sea level trend and decadal variability, we also analyze the model outputs from NCAR's Community Earth System Model (CESM) Large Ensemble Experiments, and compare the results with our observational analyses.

Physicians' prescribing preferences were a potential instrument for patients' actual prescriptions of antidepressants☆

PubMed Central

Davies, Neil M.; Gunnell, David; Thomas, Kyla H.; Metcalfe, Chris; Windmeijer, Frank; Martin, Richard M.

2013-01-01

Objectives To investigate whether physicians' prescribing preferences were valid instrumental variables for the antidepressant prescriptions they issued to their patients. Study Design and Setting We investigated whether physicians' previous prescriptions of (1) tricyclic antidepressants (TCAs) vs. selective serotonin reuptake inhibitors (SSRIs) and (2) paroxetine vs. other SSRIs were valid instruments. We investigated whether the instrumental variable assumptions are likely to hold and whether TCAs (vs. SSRIs) were associated with hospital admission for self-harm or death by suicide using both conventional and instrumental variable regressions. The setting for the study was general practices in the United Kingdom. Results Prior prescriptions were strongly associated with actual prescriptions: physicians who previously prescribed TCAs were 14.9 percentage points (95% confidence interval [CI], 14.4, 15.4) more likely to prescribe TCAs, and those who previously prescribed paroxetine were 27.7 percentage points (95% CI, 26.7, 28.8) more likely to prescribe paroxetine, to their next patient. Physicians' previous prescriptions were less strongly associated with patients' baseline characteristics than actual prescriptions. We found no evidence that the estimated association of TCAs with self-harm/suicide using instrumental variable regression differed from conventional regression estimates (P-value = 0.45). Conclusion The main instrumental variable assumptions held, suggesting that physicians' prescribing preferences are valid instruments for evaluating the short-term effects of antidepressants. PMID:24075596

JEDI Conventional Hydropower Model | Jobs and Economic Development Impact

Science.gov Websites

Economic Development Impacts (JEDI) Conventional Hydropower Model allows users to estimate economic development impacts from conventional hydropower projects and includes default information that can be

The impact of covariance misspecification in multivariate Gaussian mixtures on estimation and inference: an application to longitudinal modeling.

PubMed

Heggeseth, Brianna C; Jewell, Nicholas P

2013-07-20

Multivariate Gaussian mixtures are a class of models that provide a flexible parametric approach for the representation of heterogeneous multivariate outcomes. When the outcome is a vector of repeated measurements taken on the same subject, there is often inherent dependence between observations. However, a common covariance assumption is conditional independence-that is, given the mixture component label, the outcomes for subjects are independent. In this paper, we study, through asymptotic bias calculations and simulation, the impact of covariance misspecification in multivariate Gaussian mixtures. Although maximum likelihood estimators of regression and mixing probability parameters are not consistent under misspecification, they have little asymptotic bias when mixture components are well separated or if the assumed correlation is close to the truth even when the covariance is misspecified. We also present a robust standard error estimator and show that it outperforms conventional estimators in simulations and can indicate that the model is misspecified. Body mass index data from a national longitudinal study are used to demonstrate the effects of misspecification on potential inferences made in practice. Copyright © 2013 John Wiley & Sons, Ltd.

Visual fatigue modeling for stereoscopic video shot based on camera motion

NASA Astrophysics Data System (ADS)

Shi, Guozhong; Sang, Xinzhu; Yu, Xunbo; Liu, Yangdong; Liu, Jing

2014-11-01

As three-dimensional television (3-DTV) and 3-D movie become popular, the discomfort of visual feeling limits further applications of 3D display technology. The cause of visual discomfort from stereoscopic video conflicts between accommodation and convergence, excessive binocular parallax, fast motion of objects and so on. Here, a novel method for evaluating visual fatigue is demonstrated. Influence factors including spatial structure, motion scale and comfortable zone are analyzed. According to the human visual system (HVS), people only need to converge their eyes to the specific objects for static cameras and background. Relative motion should be considered for different camera conditions determining different factor coefficients and weights. Compared with the traditional visual fatigue prediction model, a novel visual fatigue predicting model is presented. Visual fatigue degree is predicted using multiple linear regression method combining with the subjective evaluation. Consequently, each factor can reflect the characteristics of the scene, and the total visual fatigue score can be indicated according to the proposed algorithm. Compared with conventional algorithms which ignored the status of the camera, our approach exhibits reliable performance in terms of correlation with subjective test results.

Changes in flowering phenology of woody plants in North China

NASA Astrophysics Data System (ADS)

Dai, Junhu

2016-04-01

Over the past several decades, abundant evidences proved that the first flowering date of plants in northern hemisphere became earlier in response to climate warming. However, the existing results about impact of climate change on flowering duration are controversial. In this study, we studied temporal trends in first flowering date (FFD), end of flowering date (EFD) and flowering duration (FD) of 94 woody plants from 1963 to 2014 at three stations (Harbin, Beijing and Xi'an) in North China. Meanwhile, we analyzed the relationship between length of flowering periods and temperature using two phenological models (including regression model and growing degree day model). At all stations, more than 90% of observed species showed earlier flowering over time from 1963 to 2014. The average trends in FFD were 1.33, 1.77 and 3.01 days decade-1 at Harbin, Beijing and Xi'an, respectively. During the same period, EFD also became earlier by a mean rate of 2.19, 1.39 and 2.00 days decade-1, respectively. Regarding FD, a significant shortening of FD was observed at Harbin (-0.86 days decade-1), but FD extended by 0.37 and 1.01 days decade-1 at Beijing and Xi'an, respectively. At interspecific level, the plant species with longer FD tend to have stronger trends of FD extension. Through regression analyses, we found more than 85% of time series revealed a significant negative relationship between FFD (or EFD) and preseason temperature. The regression model could simulate the interannual changes in FFD and EFD with the mean goodness of fit (R2) ranging from 0.38 to 0.67, but failed to simulate the FD accurately, as R2 ranging from 0.09 to 0.18. Regarding to FFD and EFD, the growing degree day model could improved R2 of simuation, but also could not simulate FD accurately. Therefore, we concluded that the FFD and EFD advanced notably in recent six decades as a result of climate warming, but the direction of FD changes depended on locations and the species involved. In addition, the conventional phenological models could not explain most parts of interannual variance in FD, partly due superposition of errors caused by simultaneously simulating FFD and EFD. Therefore, the mechanism of FD changes and more drivers of FD such as soil moisture and light need to be further studied.

Preoperative short hookwire placement for small pulmonary lesions: evaluation of technical success and risk factors for initial placement failure.

PubMed

Iguchi, Toshihiro; Hiraki, Takao; Matsui, Yusuke; Fujiwara, Hiroyasu; Masaoka, Yoshihisa; Tanaka, Takashi; Sato, Takuya; Gobara, Hideo; Toyooka, Shinichi; Kanazawa, Susumu

2018-05-01

To retrospectively evaluate the technical success of computed tomography fluoroscopy-guided short hookwire placement before video-assisted thoracoscopic surgery and to identify the risk factors for initial placement failure. In total, 401 short hookwire placements for 401 lesions (mean diameter 9.3 mm) were reviewed. Technical success was defined as correct positioning of the hookwire. Possible risk factors for initial placement failure (i.e., requirement for placement of an additional hookwire or to abort the attempt) were evaluated using logistic regression analysis for all procedures, and for procedures performed via the conventional route separately. Of the 401 initial placements, 383 were successful and 18 failed. Short hookwires were finally placed for 399 of 401 lesions (99.5%). Univariate logistic regression analyses revealed that in all 401 procedures only the transfissural approach was a significant independent predictor of initial placement failure (odds ratio, OR, 15.326; 95% confidence interval, CI, 5.429-43.267; p < 0.001) and for the 374 procedures performed via the conventional route only lesion size was a significant independent predictor of failure (OR 0.793, 95% CI 0.631-0.996; p = 0.046). The technical success of preoperative short hookwire placement was extremely high. The transfissural approach was a predictor initial placement failure for all procedures and small lesion size was a predictor of initial placement failure for procedures performed via the conventional route. • Technical success of preoperative short hookwire placement was extremely high. • The transfissural approach was a significant independent predictor of initial placement failure for all procedures. • Small lesion size was a significant independent predictor of initial placement failure for procedures performed via the conventional route.

An Enhanced MEMS Error Modeling Approach Based on Nu-Support Vector Regression

PubMed Central

Bhatt, Deepak; Aggarwal, Priyanka; Bhattacharya, Prabir; Devabhaktuni, Vijay

2012-01-01

Micro Electro Mechanical System (MEMS)-based inertial sensors have made possible the development of a civilian land vehicle navigation system by offering a low-cost solution. However, the accurate modeling of the MEMS sensor errors is one of the most challenging tasks in the design of low-cost navigation systems. These sensors exhibit significant errors like biases, drift, noises; which are negligible for higher grade units. Different conventional techniques utilizing the Gauss Markov model and neural network method have been previously utilized to model the errors. However, Gauss Markov model works unsatisfactorily in the case of MEMS units due to the presence of high inherent sensor errors. On the other hand, modeling the random drift utilizing Neural Network (NN) is time consuming, thereby affecting its real-time implementation. We overcome these existing drawbacks by developing an enhanced Support Vector Machine (SVM) based error model. Unlike NN, SVMs do not suffer from local minimisation or over-fitting problems and delivers a reliable global solution. Experimental results proved that the proposed SVM approach reduced the noise standard deviation by 10–35% for gyroscopes and 61–76% for accelerometers. Further, positional error drifts under static conditions improved by 41% and 80% in comparison to NN and GM approaches. PMID:23012552

Fine-spatial scale predictions of understory species using climate- and LiDAR-derived terrain and canopy metrics

NASA Astrophysics Data System (ADS)

Nijland, Wiebe; Nielsen, Scott E.; Coops, Nicholas C.; Wulder, Michael A.; Stenhouse, Gordon B.

2014-01-01

Food and habitat resources are critical components of wildlife management and conservation efforts. The grizzly bear (Ursus arctos) has diverse diets and habitat requirements particularly for understory plant species, which are impacted by human developments and forest management activities. We use light detection and ranging (LiDAR) data to predict the occurrence of 14 understory plant species relevant to bear forage and compare our predictions with more conventional climate- and land cover-based models. We use boosted regression trees to model each of the 14 understory species across 4435 km2 using occurrence (presence-absence) data from 1941 field plots. Three sets of models were fitted: climate only, climate and basic land and forest covers from Landsat 30-m imagery, and a climate- and LiDAR-derived model describing both the terrain and forest canopy. Resulting model accuracies varied widely among species. Overall, 8 of 14 species models were improved by including the LiDAR-derived variables. For climate-only models, mean annual precipitation and frost-free periods were the most important variables. With inclusion of LiDAR-derived attributes, depth-to-water table, terrain-intercepted annual radiation, and elevation were most often selected. This suggests that fine-scale terrain conditions affect the distribution of the studied species more than canopy conditions.

CO2 forcing induces semi-direct effects with consequences for climate feedback interpretations

NASA Astrophysics Data System (ADS)

Andrews, Timothy; Forster, Piers M.

2008-02-01

Climate forcing and feedbacks are diagnosed from seven slab-ocean GCMs for 2 × CO2 using a regression method. Results are compared to those using conventional methodologies to derive a semi-direct forcing due to tropospheric adjustment, analogous to the semi-direct effect of absorbing aerosols. All models show a cloud semi-direct effect, indicating a rapid cloud response to CO2; cloud typically decreases, enhancing the warming. Similarly there is evidence of semi-direct effects from water-vapour, lapse-rate, ice and snow. Previous estimates of climate feedbacks are unlikely to have taken these semi-direct effects into account and so misinterpret processes as feedbacks that depend only on the forcing, but not the global surface temperature. We show that the actual cloud feedback is smaller than what previous methods suggest and that a significant part of the cloud response and the large spread between previous model estimates of cloud feedback is due to the semi-direct forcing.

A Model Comparison for Count Data with a Positively Skewed Distribution with an Application to the Number of University Mathematics Courses Completed

ERIC Educational Resources Information Center

Liou, Pey-Yan

2009-01-01

The current study examines three regression models: OLS (ordinary least square) linear regression, Poisson regression, and negative binomial regression for analyzing count data. Simulation results show that the OLS regression model performed better than the others, since it did not produce more false statistically significant relationships than…

Three-dimensional self-gated cardiac MR imaging for the evaluation of myocardial infarction in mouse model on a 3T clinical MR system.

PubMed

Zhang, Xiaoyong; Qiu, Bensheng; Wei, Zijun; Yan, Fei; Shi, Caiyun; Su, Shi; Liu, Xin; Ji, Jim X; Xie, Guoxi

2017-01-01

To develop and assess a three-dimensional (3D) self-gated technique for the evaluation of myocardial infarction (MI) in mouse model without the use of external electrocardiogram (ECG) trigger and respiratory motion sensor on a 3T clinical MR system. A 3D T1-weighted GRE sequence with stack-of-stars sampling trajectories was developed and performed on six mice with MIs that were injected with a gadolinium-based contrast agent at a 3T clinical MR system. Respiratory and cardiac self-gating signals were derived from the Cartesian mapping of the k-space center along the partition encoding direction by bandpass filtering in image domain. The data were then realigned according to the predetermined self-gating signals for the following image reconstruction. In order to accelerate the data acquisition, image reconstruction was based on compressed sensing (CS) theory by exploiting temporal sparsity of the reconstructed images. In addition, images were also reconstructed from the same realigned data by conventional regridding method for demonstrating the advantageous of the proposed reconstruction method. Furthermore, the accuracy of detecting MI by the proposed method was assessed using histological analysis as the standard reference. Linear regression and Bland-Altman analysis were used to assess the agreement between the proposed method and the histological analysis. Compared to the conventional regridding method, the proposed CS method reconstructed images with much less streaking artifact, as well as a better contrast-to-noise ratio (CNR) between the blood and myocardium (4.1 ± 2.1 vs. 2.9 ± 1.1, p = 0.031). Linear regression and Bland-Altman analysis demonstrated that excellent correlation was obtained between infarct sizes derived from the proposed method and histology analysis. A 3D T1-weighted self-gating technique for mouse cardiac imaging was developed, which has potential for accurately evaluating MIs in mice at 3T clinical MR system without the use of external ECG trigger and respiratory motion sensor.

Breeding value accuracy estimates for growth traits using random regression and multi-trait models in Nelore cattle.

PubMed

Boligon, A A; Baldi, F; Mercadante, M E Z; Lobo, R B; Pereira, R J; Albuquerque, L G

2011-06-28

We quantified the potential increase in accuracy of expected breeding value for weights of Nelore cattle, from birth to mature age, using multi-trait and random regression models on Legendre polynomials and B-spline functions. A total of 87,712 weight records from 8144 females were used, recorded every three months from birth to mature age from the Nelore Brazil Program. For random regression analyses, all female weight records from birth to eight years of age (data set I) were considered. From this general data set, a subset was created (data set II), which included only nine weight records: at birth, weaning, 365 and 550 days of age, and 2, 3, 4, 5, and 6 years of age. Data set II was analyzed using random regression and multi-trait models. The model of analysis included the contemporary group as fixed effects and age of dam as a linear and quadratic covariable. In the random regression analyses, average growth trends were modeled using a cubic regression on orthogonal polynomials of age. Residual variances were modeled by a step function with five classes. Legendre polynomials of fourth and sixth order were utilized to model the direct genetic and animal permanent environmental effects, respectively, while third-order Legendre polynomials were considered for maternal genetic and maternal permanent environmental effects. Quadratic polynomials were applied to model all random effects in random regression models on B-spline functions. Direct genetic and animal permanent environmental effects were modeled using three segments or five coefficients, and genetic maternal and maternal permanent environmental effects were modeled with one segment or three coefficients in the random regression models on B-spline functions. For both data sets (I and II), animals ranked differently according to expected breeding value obtained by random regression or multi-trait models. With random regression models, the highest gains in accuracy were obtained at ages with a low number of weight records. The results indicate that random regression models provide more accurate expected breeding values than the traditionally finite multi-trait models. Thus, higher genetic responses are expected for beef cattle growth traits by replacing a multi-trait model with random regression models for genetic evaluation. B-spline functions could be applied as an alternative to Legendre polynomials to model covariance functions for weights from birth to mature age.

Evaluating differential effects using regression interactions and regression mixture models

PubMed Central

Van Horn, M. Lee; Jaki, Thomas; Masyn, Katherine; Howe, George; Feaster, Daniel J.; Lamont, Andrea E.; George, Melissa R. W.; Kim, Minjung

2015-01-01

Research increasingly emphasizes understanding differential effects. This paper focuses on understanding regression mixture models, a relatively new statistical methods for assessing differential effects by comparing results to using an interactive term in linear regression. The research questions which each model answers, their formulation, and their assumptions are compared using Monte Carlo simulations and real data analysis. The capabilities of regression mixture models are described and specific issues to be addressed when conducting regression mixtures are proposed. The paper aims to clarify the role that regression mixtures can take in the estimation of differential effects and increase awareness of the benefits and potential pitfalls of this approach. Regression mixture models are shown to be a potentially effective exploratory method for finding differential effects when these effects can be defined by a small number of classes of respondents who share a typical relationship between a predictor and an outcome. It is also shown that the comparison between regression mixture models and interactions becomes substantially more complex as the number of classes increases. It is argued that regression interactions are well suited for direct tests of specific hypotheses about differential effects and regression mixtures provide a useful approach for exploring effect heterogeneity given adequate samples and study design. PMID:26556903

Evaluating Differential Effects Using Regression Interactions and Regression Mixture Models

ERIC Educational Resources Information Center

Van Horn, M. Lee; Jaki, Thomas; Masyn, Katherine; Howe, George; Feaster, Daniel J.; Lamont, Andrea E.; George, Melissa R. W.; Kim, Minjung

2015-01-01

Research increasingly emphasizes understanding differential effects. This article focuses on understanding regression mixture models, which are relatively new statistical methods for assessing differential effects by comparing results to using an interactive term in linear regression. The research questions which each model answers, their…

Beyond Rating Curves: Time Series Models for in-Stream Turbidity Prediction

NASA Astrophysics Data System (ADS)

Wang, L.; Mukundan, R.; Zion, M.; Pierson, D. C.

2012-12-01

The New York City Department of Environmental Protection (DEP) manages New York City's water supply, which is comprised of over 20 reservoirs and supplies over 1 billion gallons of water per day to more than 9 million customers. DEP's "West of Hudson" reservoirs located in the Catskill Mountains are unfiltered per a renewable filtration avoidance determination granted by the EPA. While water quality is usually pristine, high volume storm events occasionally cause the reservoirs to become highly turbid. A logical strategy for turbidity control is to temporarily remove the turbid reservoirs from service. While effective in limiting delivery of turbid water and reducing the need for in-reservoir alum flocculation, this strategy runs the risk of negatively impacting water supply reliability. Thus, it is advantageous for DEP to understand how long a particular turbidity event will affect their system. In order to understand the duration, intensity and total load of a turbidity event, predictions of future in-stream turbidity values are important. Traditionally, turbidity predictions have been carried out by applying streamflow observations/forecasts to a flow-turbidity rating curve. However, predictions from rating curves are often inaccurate due to inter- and intra-event variability in flow-turbidity relationships. Predictions can be improved by applying an autoregressive moving average (ARMA) time series model in combination with a traditional rating curve. Since 2003, DEP and the Upstate Freshwater Institute have compiled a relatively consistent set of 15-minute turbidity observations at various locations on Esopus Creek above Ashokan Reservoir. Using daily averages of this data and streamflow observations at nearby USGS gauges, flow-turbidity rating curves were developed via linear regression. Time series analysis revealed that the linear regression residuals may be represented using an ARMA(1,2) process. Based on this information, flow-turbidity regressions with ARMA(1,2) errors were fit to the observations. Preliminary model validation exercises at a 30-day forecast horizon show that the ARMA error models generally improve the predictive skill of the linear regression rating curves. Skill seems to vary based on the ambient hydrologic conditions at the onset of the forecast. For example, ARMA error model forecasts issued before a high flow/turbidity event do not show significant improvements over the rating curve approach. However, ARMA error model forecasts issued during the "falling limb" of the hydrograph are significantly more accurate than rating curves for both single day and accumulated event predictions. In order to assist in reservoir operations decisions associated with turbidity events and general water supply reliability, DEP has initiated design of an Operations Support Tool (OST). OST integrates a reservoir operations model with 2D hydrodynamic water quality models and a database compiling near-real-time data sources and hydrologic forecasts. Currently, OST uses conventional flow-turbidity rating curves and hydrologic forecasts for predictive turbidity inputs. Given the improvements in predictive skill over traditional rating curves, the ARMA error models are currently being evaluated as an addition to DEP's Operations Support Tool.

Seat Integrated and Conventional Restraints: A Study of Crash Injury/Fatality Rates in Rollovers

PubMed Central

Padmanaban, Jeya; Burnett, Roger A.

2008-01-01

This study used police-reported motor vehicle crash data from eleven states to determine ejection, fatality, and fatal/serious injury risks for belted drivers in vehicles with conventional seatbelts compared to belted drivers in vehicles with seat integrated restraint systems (SIRS). Risks were compared for 11,159 belted drivers involved in single- or multiple-vehicle rollover crashes. Simple driver ejection (partial and complete), fatality, and injury rates were derived, and logistic regression analyses were used to determine relative contribution of factors (including event calendar year, vehicle age, driver age/gender/alcohol use) that significantly influence the likelihood of fatality and fatal/serious injury to belted drivers in rollovers. Results show no statistically significant difference in driver ejection, fatality, or fatal/serious injury rates between vehicles with conventional belts and vehicles with SIRS. PMID:19026243

Meta-regression approximations to reduce publication selection bias.

PubMed

Stanley, T D; Doucouliagos, Hristos

2014-03-01

Publication selection bias is a serious challenge to the integrity of all empirical sciences. We derive meta-regression approximations to reduce this bias. Our approach employs Taylor polynomial approximations to the conditional mean of a truncated distribution. A quadratic approximation without a linear term, precision-effect estimate with standard error (PEESE), is shown to have the smallest bias and mean squared error in most cases and to outperform conventional meta-analysis estimators, often by a great deal. Monte Carlo simulations also demonstrate how a new hybrid estimator that conditionally combines PEESE and the Egger regression intercept can provide a practical solution to publication selection bias. PEESE is easily expanded to accommodate systematic heterogeneity along with complex and differential publication selection bias that is related to moderator variables. By providing an intuitive reason for these approximations, we can also explain why the Egger regression works so well and when it does not. These meta-regression methods are applied to several policy-relevant areas of research including antidepressant effectiveness, the value of a statistical life, the minimum wage, and nicotine replacement therapy. Copyright © 2013 John Wiley & Sons, Ltd.

The Type Ia Supernova Color-Magnitude Relation and Host Galaxy Dust: A Simple Hierarchical Bayesian Model

NASA Astrophysics Data System (ADS)

Mandel, Kaisey; Scolnic, Daniel; Shariff, Hikmatali; Foley, Ryan; Kirshner, Robert

2017-01-01

Inferring peak optical absolute magnitudes of Type Ia supernovae (SN Ia) from distance-independent measures such as their light curve shapes and colors underpins the evidence for cosmic acceleration. SN Ia with broader, slower declining optical light curves are more luminous (“broader-brighter”) and those with redder colors are dimmer. But the “redder-dimmer” color-luminosity relation widely used in cosmological SN Ia analyses confounds its two separate physical origins. An intrinsic correlation arises from the physics of exploding white dwarfs, while interstellar dust in the host galaxy also makes SN Ia appear dimmer and redder. Conventional SN Ia cosmology analyses currently use a simplistic linear regression of magnitude versus color and light curve shape, which does not model intrinsic SN Ia variations and host galaxy dust as physically distinct effects, resulting in low color-magnitude slopes. We construct a probabilistic generative model for the dusty distribution of extinguished absolute magnitudes and apparent colors as the convolution of an intrinsic SN Ia color-magnitude distribution and a host galaxy dust reddening-extinction distribution. If the intrinsic color-magnitude (MB vs. B-V) slope βint differs from the host galaxy dust law RB, this convolution results in a specific curve of mean extinguished absolute magnitude vs. apparent color. The derivative of this curve smoothly transitions from βint in the blue tail to RB in the red tail of the apparent color distribution. The conventional linear fit approximates this effective curve near the average apparent color, resulting in an apparent slope βapp between βint and RB. We incorporate these effects into a hierarchical Bayesian statistical model for SN Ia light curve measurements, and analyze a dataset of SALT2 optical light curve fits of 277 nearby SN Ia at z < 0.10. The conventional linear fit obtains βapp ≈ 3. Our model finds a βint = 2.2 ± 0.3 and a distinct dust law of RB = 3.7 ± 0.3, consistent with the average for Milky Way dust, while correcting a systematic distance bias of ~0.10 mag in the tails of the apparent color distribution. This research is supported by NSF grants AST-156854, AST-1211196, and NASA grant NNX15AJ55G.

Dose-Dependent Effects of Sirolimus on mTOR Signaling and Polycystic Kidney Disease

PubMed Central

Novalic, Zlata; van der Wal, Annemieke M.; Leonhard, Wouter N.; Koehl, Gudrun; Breuning, Martijn H.; Geissler, Edward K.; de Heer, Emile

2012-01-01

Inhibition of the mammalian target of rapamycin (mTOR) shows beneficial effects in animal models of polycystic kidney disease (PKD); however, two clinical trials in patients with autosomal dominant PKD failed to demonstrate a short-term benefit in either the early or progressive stages of disease. The stage of disease during treatment and the dose of mTOR inhibitors may account for these differing results. Here, we studied the effects of a conventional low dose and a higher dose of sirolimus (blood levels of 3 ng/ml and 30–60 ng/ml, respectively) on mTOR activity and renal cystic disease in two Pkd1-mutant mouse models at different stages of the disease. When initiated at early but not late stages of disease, high-dose treatment strongly reduced mTOR signaling in renal tissues, inhibited cystogenesis, accelerated cyst regression, and abrogated fibrosis and the infiltration of immune cells. In contrast, low-dose treatment did not significantly reduce renal cystic disease. Levels of p-S6RpSer240/244, which marks mTOR activity, varied between kidneys; severity of the renal cystic phenotype correlated with the level of mTOR activity. Taken together, these data suggest that long-term treatment with conventional doses of sirolimus is insufficient to inhibit mTOR activity in renal cystic tissue. Mechanisms to increase bioavailability or to target mTOR inhibitors more specifically to kidneys, alone or in combination with other compounds, may improve the potential for these therapies in PKD. PMID:22343118

Real-time ultrasonography as a monitoring technique for interstitial Nd:YAG laser treatment of voluminous hemangiomas and vascular malformations

NASA Astrophysics Data System (ADS)

Werner, Jochen A.; Gottschlich, Stefan; Lippert, Burkard M.; Folz, Benedikt J.

1998-01-01

Voluminous vascular anomalies of the head and neck region are still treated with conventional surgery although Neodymium:Yttrium-Aluminum-Garnet (Nd:YAG) laser therapy is an effective treatment method. One hundred thirty give patients with voluminous hemangiomas and vascular malformations were treated with interstitial Nd:YAG laser therapy, partly complemented by a non-contact mode Nd:YAG laser light application. The vascular tumors had a diameter of more than 3 cm in two or all three dimensions. Treatment was carried out under ultrasound and manual control. Nearly 60% of the patients showed a complete clinical regression of the vascular tumor, a third of the patients had a partial regression and were satisfied with the treatment outcome. Four patients were treated unsuccessfully with the laser and three of them subsequently underwent conventional surgery. Only 10 patients showed cosmetic and functional deficits. These results on the interstitial Nd:YAG laser therapy of voluminous hemangiomas and vascular malformations in a large patient group demonstrated the high effectiveness of this novel and innovative therapy modality.

Relational event models for longitudinal network data with an application to interhospital patient transfers.

PubMed

Vu, Duy; Lomi, Alessandro; Mascia, Daniele; Pallotti, Francesca

2017-06-30

The main objective of this paper is to introduce and illustrate relational event models, a new class of statistical models for the analysis of time-stamped data with complex temporal and relational dependencies. We outline the main differences between recently proposed relational event models and more conventional network models based on the graph-theoretic formalism typically adopted in empirical studies of social networks. Our main contribution involves the definition and implementation of a marked point process extension of currently available models. According to this approach, the sequence of events of interest is decomposed into two components: (a) event time and (b) event destination. This decomposition transforms the problem of selection of event destination in relational event models into a conditional multinomial logistic regression problem. The main advantages of this formulation are the possibility of controlling for the effect of event-specific data and a significant reduction in the estimation time of currently available relational event models. We demonstrate the empirical value of the model in an analysis of interhospital patient transfers within a regional community of health care organizations. We conclude with a discussion of how the models we presented help to overcome some the limitations of statistical models for networks that are currently available. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

10 CFR 429.23 - Conventional cooking tops, conventional ovens, microwave ovens.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 10 Energy 3 2013-01-01 2013-01-01 false Conventional cooking tops, conventional ovens, microwave... Conventional cooking tops, conventional ovens, microwave ovens. (a) Sampling plan for selection of units for... and microwave ovens; and (2) For each basic model of conventional cooking tops, conventional ovens and...

10 CFR 429.23 - Conventional cooking tops, conventional ovens, microwave ovens.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 10 Energy 3 2014-01-01 2014-01-01 false Conventional cooking tops, conventional ovens, microwave... Conventional cooking tops, conventional ovens, microwave ovens. (a) Sampling plan for selection of units for... and microwave ovens; and (2) For each basic model of conventional cooking tops, conventional ovens and...

10 CFR 429.23 - Conventional cooking tops, conventional ovens, microwave ovens.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 10 Energy 3 2012-01-01 2012-01-01 false Conventional cooking tops, conventional ovens, microwave... Conventional cooking tops, conventional ovens, microwave ovens. (a) Sampling plan for selection of units for... and microwave ovens; and (2) For each basic model of conventional cooking tops, conventional ovens and...

Modeling absolute differences in life expectancy with a censored skew-normal regression approach

PubMed Central

Clough-Gorr, Kerri; Zwahlen, Marcel

2015-01-01

Parameter estimates from commonly used multivariable parametric survival regression models do not directly quantify differences in years of life expectancy. Gaussian linear regression models give results in terms of absolute mean differences, but are not appropriate in modeling life expectancy, because in many situations time to death has a negative skewed distribution. A regression approach using a skew-normal distribution would be an alternative to parametric survival models in the modeling of life expectancy, because parameter estimates can be interpreted in terms of survival time differences while allowing for skewness of the distribution. In this paper we show how to use the skew-normal regression so that censored and left-truncated observations are accounted for. With this we model differences in life expectancy using data from the Swiss National Cohort Study and from official life expectancy estimates and compare the results with those derived from commonly used survival regression models. We conclude that a censored skew-normal survival regression approach for left-truncated observations can be used to model differences in life expectancy across covariates of interest. PMID:26339544

Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure.

PubMed

Allegrini, Franco; Braga, Jez W B; Moreira, Alessandro C O; Olivieri, Alejandro C

2018-06-29

A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error structure of the system, using the error covariance matrix (ECM) as a penalization term. Results are reported from both simulations and experimental data based on replicate mid and near infrared (MIR and NIR) spectral measurements. The results for ECPR are better under non-iid conditions when compared with traditional first-order multivariate methods such as ridge regression (RR), principal component regression (PCR) and partial least-squares regression (PLS). Copyright © 2018 Elsevier B.V. All rights reserved.

Analyzing Student Learning Outcomes: Usefulness of Logistic and Cox Regression Models. IR Applications, Volume 5

ERIC Educational Resources Information Center

Chen, Chau-Kuang

2005-01-01

Logistic and Cox regression methods are practical tools used to model the relationships between certain student learning outcomes and their relevant explanatory variables. The logistic regression model fits an S-shaped curve into a binary outcome with data points of zero and one. The Cox regression model allows investigators to study the duration…

Robust geographically weighted regression of modeling the Air Polluter Standard Index (APSI)

NASA Astrophysics Data System (ADS)

Warsito, Budi; Yasin, Hasbi; Ispriyanti, Dwi; Hoyyi, Abdul

2018-05-01

The Geographically Weighted Regression (GWR) model has been widely applied to many practical fields for exploring spatial heterogenity of a regression model. However, this method is inherently not robust to outliers. Outliers commonly exist in data sets and may lead to a distorted estimate of the underlying regression model. One of solution to handle the outliers in the regression model is to use the robust models. So this model was called Robust Geographically Weighted Regression (RGWR). This research aims to aid the government in the policy making process related to air pollution mitigation by developing a standard index model for air polluter (Air Polluter Standard Index - APSI) based on the RGWR approach. In this research, we also consider seven variables that are directly related to the air pollution level, which are the traffic velocity, the population density, the business center aspect, the air humidity, the wind velocity, the air temperature, and the area size of the urban forest. The best model is determined by the smallest AIC value. There are significance differences between Regression and RGWR in this case, but Basic GWR using the Gaussian kernel is the best model to modeling APSI because it has smallest AIC.

Perioperative surgical outcome of conventional and robot-assisted total laparoscopic hysterectomy.

PubMed

van Weelden, W J; Gordon, B B M; Roovers, E A; Kraayenbrink, A A; Aalders, C I M; Hartog, F; Dijkhuizen, F P H L J

2017-01-01

To evaluate surgical outcome in a consecutive series of patients with conventional and robot assisted total laparoscopic hysterectomy. A retrospective cohort study was performed among patients with benign and malignant indications for a laparoscopic hysterectomy. Main surgical outcomes were operation room time and skin to skin operating time, complications, conversions, rehospitalisation and reoperation, estimated blood loss and length of hospital stay. A total of 294 patients were evaluated: 123 in the conventional total laparoscopic hysterectomy (TLH) group and 171 in the robot TLH group. After correction for differences in basic demographics with a multivariate linear regression analysis, the skin to skin operating time was a significant 18 minutes shorter in robot assisted TLH compared to conventional TLH (robot assisted TLH 92m, conventional TLH 110m, p0.001). The presence or absence of previous abdominal surgery had a significant influence on the skin to skin operating time as did the body mass index and the weight of the uterus. Complications were not significantly different. The robot TLH group had significantly less blood loss and lower rehospitalisation and reoperation rates. This study compares conventional TLH with robot assisted TLH and shows shorter operating times, less blood loss and lower rehospitalisation and reoperation rates in the robot TLH group.

Bayesian Unimodal Density Regression for Causal Inference

ERIC Educational Resources Information Center

Karabatsos, George; Walker, Stephen G.

2011-01-01

Karabatsos and Walker (2011) introduced a new Bayesian nonparametric (BNP) regression model. Through analyses of real and simulated data, they showed that the BNP regression model outperforms other parametric and nonparametric regression models of common use, in terms of predictive accuracy of the outcome (dependent) variable. The other,…

Bayesian Estimation of Multivariate Latent Regression Models: Gauss versus Laplace

ERIC Educational Resources Information Center

Culpepper, Steven Andrew; Park, Trevor

2017-01-01

A latent multivariate regression model is developed that employs a generalized asymmetric Laplace (GAL) prior distribution for regression coefficients. The model is designed for high-dimensional applications where an approximate sparsity condition is satisfied, such that many regression coefficients are near zero after accounting for all the model…

A simple approach to power and sample size calculations in logistic regression and Cox regression models.

PubMed

Vaeth, Michael; Skovlund, Eva

2004-06-15

For a given regression problem it is possible to identify a suitably defined equivalent two-sample problem such that the power or sample size obtained for the two-sample problem also applies to the regression problem. For a standard linear regression model the equivalent two-sample problem is easily identified, but for generalized linear models and for Cox regression models the situation is more complicated. An approximately equivalent two-sample problem may, however, also be identified here. In particular, we show that for logistic regression and Cox regression models the equivalent two-sample problem is obtained by selecting two equally sized samples for which the parameters differ by a value equal to the slope times twice the standard deviation of the independent variable and further requiring that the overall expected number of events is unchanged. In a simulation study we examine the validity of this approach to power calculations in logistic regression and Cox regression models. Several different covariate distributions are considered for selected values of the overall response probability and a range of alternatives. For the Cox regression model we consider both constant and non-constant hazard rates. The results show that in general the approach is remarkably accurate even in relatively small samples. Some discrepancies are, however, found in small samples with few events and a highly skewed covariate distribution. Comparison with results based on alternative methods for logistic regression models with a single continuous covariate indicates that the proposed method is at least as good as its competitors. The method is easy to implement and therefore provides a simple way to extend the range of problems that can be covered by the usual formulas for power and sample size determination. Copyright 2004 John Wiley & Sons, Ltd.

Predicting infant cortical surface development using a 4D varifold-based learning framework and local topography-based shape morphing.

PubMed

Rekik, Islem; Li, Gang; Lin, Weili; Shen, Dinggang

2016-02-01

Longitudinal neuroimaging analysis methods have remarkably advanced our understanding of early postnatal brain development. However, learning predictive models to trace forth the evolution trajectories of both normal and abnormal cortical shapes remains broadly absent. To fill this critical gap, we pioneered the first prediction model for longitudinal developing cortical surfaces in infants using a spatiotemporal current-based learning framework solely from the baseline cortical surface. In this paper, we detail this prediction model and even further improve its performance by introducing two key variants. First, we use the varifold metric to overcome the limitations of the current metric for surface registration that was used in our preliminary study. We also extend the conventional varifold-based surface registration model for pairwise registration to a spatiotemporal surface regression model. Second, we propose a morphing process of the baseline surface using its topographic attributes such as normal direction and principal curvature sign. Specifically, our method learns from longitudinal data both the geometric (vertices positions) and dynamic (temporal evolution trajectories) features of the infant cortical surface, comprising a training stage and a prediction stage. In the training stage, we use the proposed varifold-based shape regression model to estimate geodesic cortical shape evolution trajectories for each training subject. We then build an empirical mean spatiotemporal surface atlas. In the prediction stage, given an infant, we select the best learnt features from training subjects to simultaneously predict the cortical surface shapes at all later timepoints, based on similarity metrics between this baseline surface and the learnt baseline population average surface atlas. We used a leave-one-out cross validation method to predict the inner cortical surface shape at 3, 6, 9 and 12 months of age from the baseline cortical surface shape at birth. Our method attained a higher prediction accuracy and better captured the spatiotemporal dynamic change of the highly folded cortical surface than the previous proposed prediction method. Copyright © 2015 Elsevier B.V. All rights reserved.

Comparative evaluation of urban storm water quality models

NASA Astrophysics Data System (ADS)

Vaze, J.; Chiew, Francis H. S.

2003-10-01

The estimation of urban storm water pollutant loads is required for the development of mitigation and management strategies to minimize impacts to receiving environments. Event pollutant loads are typically estimated using either regression equations or "process-based" water quality models. The relative merit of using regression models compared to process-based models is not clear. A modeling study is carried out here to evaluate the comparative ability of the regression equations and process-based water quality models to estimate event diffuse pollutant loads from impervious surfaces. The results indicate that, once calibrated, both the regression equations and the process-based model can estimate event pollutant loads satisfactorily. In fact, the loads estimated using the regression equation as a function of rainfall intensity and runoff rate are better than the loads estimated using the process-based model. Therefore, if only estimates of event loads are required, regression models should be used because they are simpler and require less data compared to process-based models.

Comparison of Artificial Neural Networks and ARIMA statistical models in simulations of target wind time series

NASA Astrophysics Data System (ADS)

Kolokythas, Kostantinos; Vasileios, Salamalikis; Athanassios, Argiriou; Kazantzidis, Andreas

2015-04-01

The wind is a result of complex interactions of numerous mechanisms taking place in small or large scales, so, the better knowledge of its behavior is essential in a variety of applications, especially in the field of power production coming from wind turbines. In the literature there is a considerable number of models, either physical or statistical ones, dealing with the problem of simulation and prediction of wind speed. Among others, Artificial Neural Networks (ANNs) are widely used for the purpose of wind forecasting and, in the great majority of cases, outperform other conventional statistical models. In this study, a number of ANNs with different architectures, which have been created and applied in a dataset of wind time series, are compared to Auto Regressive Integrated Moving Average (ARIMA) statistical models. The data consist of mean hourly wind speeds coming from a wind farm on a hilly Greek region and cover a period of one year (2013). The main goal is to evaluate the models ability to simulate successfully the wind speed at a significant point (target). Goodness-of-fit statistics are performed for the comparison of the different methods. In general, the ANN showed the best performance in the estimation of wind speed prevailing over the ARIMA models.

A generalized right truncated bivariate Poisson regression model with applications to health data.

PubMed

Islam, M Ataharul; Chowdhury, Rafiqul I

2017-01-01

A generalized right truncated bivariate Poisson regression model is proposed in this paper. Estimation and tests for goodness of fit and over or under dispersion are illustrated for both untruncated and right truncated bivariate Poisson regression models using marginal-conditional approach. Estimation and test procedures are illustrated for bivariate Poisson regression models with applications to Health and Retirement Study data on number of health conditions and the number of health care services utilized. The proposed test statistics are easy to compute and it is evident from the results that the models fit the data very well. A comparison between the right truncated and untruncated bivariate Poisson regression models using the test for nonnested models clearly shows that the truncated model performs significantly better than the untruncated model.

A generalized right truncated bivariate Poisson regression model with applications to health data

PubMed Central

Islam, M. Ataharul; Chowdhury, Rafiqul I.

2017-01-01

A generalized right truncated bivariate Poisson regression model is proposed in this paper. Estimation and tests for goodness of fit and over or under dispersion are illustrated for both untruncated and right truncated bivariate Poisson regression models using marginal-conditional approach. Estimation and test procedures are illustrated for bivariate Poisson regression models with applications to Health and Retirement Study data on number of health conditions and the number of health care services utilized. The proposed test statistics are easy to compute and it is evident from the results that the models fit the data very well. A comparison between the right truncated and untruncated bivariate Poisson regression models using the test for nonnested models clearly shows that the truncated model performs significantly better than the untruncated model. PMID:28586344

A novel multi-target regression framework for time-series prediction of drug efficacy.

PubMed

Li, Haiqing; Zhang, Wei; Chen, Ying; Guo, Yumeng; Li, Guo-Zheng; Zhu, Xiaoxin

2017-01-18

Excavating from small samples is a challenging pharmacokinetic problem, where statistical methods can be applied. Pharmacokinetic data is special due to the small samples of high dimensionality, which makes it difficult to adopt conventional methods to predict the efficacy of traditional Chinese medicine (TCM) prescription. The main purpose of our study is to obtain some knowledge of the correlation in TCM prescription. Here, a novel method named Multi-target Regression Framework to deal with the problem of efficacy prediction is proposed. We employ the correlation between the values of different time sequences and add predictive targets of previous time as features to predict the value of current time. Several experiments are conducted to test the validity of our method and the results of leave-one-out cross-validation clearly manifest the competitiveness of our framework. Compared with linear regression, artificial neural networks, and partial least squares, support vector regression combined with our framework demonstrates the best performance, and appears to be more suitable for this task.

A novel multi-target regression framework for time-series prediction of drug efficacy

PubMed Central

Li, Haiqing; Zhang, Wei; Chen, Ying; Guo, Yumeng; Li, Guo-Zheng; Zhu, Xiaoxin

2017-01-01

Excavating from small samples is a challenging pharmacokinetic problem, where statistical methods can be applied. Pharmacokinetic data is special due to the small samples of high dimensionality, which makes it difficult to adopt conventional methods to predict the efficacy of traditional Chinese medicine (TCM) prescription. The main purpose of our study is to obtain some knowledge of the correlation in TCM prescription. Here, a novel method named Multi-target Regression Framework to deal with the problem of efficacy prediction is proposed. We employ the correlation between the values of different time sequences and add predictive targets of previous time as features to predict the value of current time. Several experiments are conducted to test the validity of our method and the results of leave-one-out cross-validation clearly manifest the competitiveness of our framework. Compared with linear regression, artificial neural networks, and partial least squares, support vector regression combined with our framework demonstrates the best performance, and appears to be more suitable for this task. PMID:28098186

REGRES: A FORTRAN-77 program to calculate nonparametric and ``structural'' parametric solutions to bivariate regression equations

NASA Astrophysics Data System (ADS)

Rock, N. M. S.; Duffy, T. R.

REGRES allows a range of regression equations to be calculated for paired sets of data values in which both variables are subject to error (i.e. neither is the "independent" variable). Nonparametric regressions, based on medians of all possible pairwise slopes and intercepts, are treated in detail. Estimated slopes and intercepts are output, along with confidence limits, Spearman and Kendall rank correlation coefficients. Outliers can be rejected with user-determined stringency. Parametric regressions can be calculated for any value of λ (the ratio of the variances of the random errors for y and x)—including: (1) major axis ( λ = 1); (2) reduced major axis ( λ = variance of y/variance of x); (3) Y on Xλ = infinity; or (4) X on Y ( λ = 0) solutions. Pearson linear correlation coefficients also are output. REGRES provides an alternative to conventional isochron assessment techniques where bivariate normal errors cannot be assumed, or weighting methods are inappropriate.

A Technique of Fuzzy C-Mean in Multiple Linear Regression Model toward Paddy Yield

NASA Astrophysics Data System (ADS)

Syazwan Wahab, Nur; Saifullah Rusiman, Mohd; Mohamad, Mahathir; Amira Azmi, Nur; Che Him, Norziha; Ghazali Kamardan, M.; Ali, Maselan

2018-04-01

In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used were from the multi-location trials for rice carried out by MARDI at major paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing observations were estimated using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear regression model and fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is normally scattered without multicollinearity among independent variables. Analysis of fuzzy c-means cluster the yield of paddy into two clusters before the multiple linear regression model can be used. The comparison between two method indicate that the hybrid of multiple linear regression model and fuzzy c-means method outperform the multiple linear regression model with lower value of mean square error.

Genomic prediction based on data from three layer lines using non-linear regression models.

PubMed

Huang, Heyun; Windig, Jack J; Vereijken, Addie; Calus, Mario P L

2014-11-06

Most studies on genomic prediction with reference populations that include multiple lines or breeds have used linear models. Data heterogeneity due to using multiple populations may conflict with model assumptions used in linear regression methods. In an attempt to alleviate potential discrepancies between assumptions of linear models and multi-population data, two types of alternative models were used: (1) a multi-trait genomic best linear unbiased prediction (GBLUP) model that modelled trait by line combinations as separate but correlated traits and (2) non-linear models based on kernel learning. These models were compared to conventional linear models for genomic prediction for two lines of brown layer hens (B1 and B2) and one line of white hens (W1). The three lines each had 1004 to 1023 training and 238 to 240 validation animals. Prediction accuracy was evaluated by estimating the correlation between observed phenotypes and predicted breeding values. When the training dataset included only data from the evaluated line, non-linear models yielded at best a similar accuracy as linear models. In some cases, when adding a distantly related line, the linear models showed a slight decrease in performance, while non-linear models generally showed no change in accuracy. When only information from a closely related line was used for training, linear models and non-linear radial basis function (RBF) kernel models performed similarly. The multi-trait GBLUP model took advantage of the estimated genetic correlations between the lines. Combining linear and non-linear models improved the accuracy of multi-line genomic prediction. Linear models and non-linear RBF models performed very similarly for genomic prediction, despite the expectation that non-linear models could deal better with the heterogeneous multi-population data. This heterogeneity of the data can be overcome by modelling trait by line combinations as separate but correlated traits, which avoids the occasional occurrence of large negative accuracies when the evaluated line was not included in the training dataset. Furthermore, when using a multi-line training dataset, non-linear models provided information on the genotype data that was complementary to the linear models, which indicates that the underlying data distributions of the three studied lines were indeed heterogeneous.

A systematic review of methodology: time series regression analysis for environmental factors and infectious diseases.

PubMed

Imai, Chisato; Hashizume, Masahiro

2015-03-01

Time series analysis is suitable for investigations of relatively direct and short-term effects of exposures on outcomes. In environmental epidemiology studies, this method has been one of the standard approaches to assess impacts of environmental factors on acute non-infectious diseases (e.g. cardiovascular deaths), with conventionally generalized linear or additive models (GLM and GAM). However, the same analysis practices are often observed with infectious diseases despite of the substantial differences from non-infectious diseases that may result in analytical challenges. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, systematic review was conducted to elucidate important issues in assessing the associations between environmental factors and infectious diseases using time series analysis with GLM and GAM. Published studies on the associations between weather factors and malaria, cholera, dengue, and influenza were targeted. Our review raised issues regarding the estimation of susceptible population and exposure lag times, the adequacy of seasonal adjustments, the presence of strong autocorrelations, and the lack of a smaller observation time unit of outcomes (i.e. daily data). These concerns may be attributable to features specific to infectious diseases, such as transmission among individuals and complicated causal mechanisms. The consequence of not taking adequate measures to address these issues is distortion of the appropriate risk quantifications of exposures factors. Future studies should pay careful attention to details and examine alternative models or methods that improve studies using time series regression analysis for environmental determinants of infectious diseases.

Aquatic insect ecophysiological traits reveal phylogenetically based differences in dissolved cadmium susceptibility.

PubMed

Buchwalter, David B; Cain, Daniel J; Martin, Caitrin A; Xie, Lingtian; Luoma, Samuel N; Garland, Theodore

2008-06-17

We used a phylogenetically based comparative approach to evaluate the potential for physiological studies to reveal patterns of diversity in traits related to susceptibility to an environmental stressor, the trace metal cadmium (Cd). Physiological traits related to Cd bioaccumulation, compartmentalization, and ultimately susceptibility were measured in 21 aquatic insect species representing the orders Ephemeroptera, Plecoptera, and Trichoptera. We mapped these experimentally derived physiological traits onto a phylogeny and quantified the tendency for related species to be similar (phylogenetic signal). All traits related to Cd bioaccumulation and susceptibility exhibited statistically significant phylogenetic signal, although the signal strength varied among traits. Conventional and phylogenetically based regression models were compared, revealing great variability within orders but consistent, strong differences among insect families. Uptake and elimination rate constants were positively correlated among species, but only when effects of body size and phylogeny were incorporated in the analysis. Together, uptake and elimination rates predicted dramatic Cd bioaccumulation differences among species that agreed with field-based measurements. We discovered a potential tradeoff between the ability to eliminate Cd and the ability to detoxify it across species, particularly mayflies. The best-fit regression models were driven by phylogenetic parameters (especially differences among families) rather than functional traits, suggesting that it may eventually be possible to predict a taxon's physiological performance based on its phylogenetic position, provided adequate physiological information is available for close relatives. There appears to be great potential for evolutionary physiological approaches to augment our understanding of insect responses to environmental stressors in nature.

Aquatic insect ecophysiological traits reveal phylogenetically based differences in dissolved cadmium susceptibility

PubMed Central

Buchwalter, David B.; Cain, Daniel J.; Martin, Caitrin A.; Xie, Lingtian; Luoma, Samuel N.; Garland, Theodore

2008-01-01

We used a phylogenetically based comparative approach to evaluate the potential for physiological studies to reveal patterns of diversity in traits related to susceptibility to an environmental stressor, the trace metal cadmium (Cd). Physiological traits related to Cd bioaccumulation, compartmentalization, and ultimately susceptibility were measured in 21 aquatic insect species representing the orders Ephemeroptera, Plecoptera, and Trichoptera. We mapped these experimentally derived physiological traits onto a phylogeny and quantified the tendency for related species to be similar (phylogenetic signal). All traits related to Cd bioaccumulation and susceptibility exhibited statistically significant phylogenetic signal, although the signal strength varied among traits. Conventional and phylogenetically based regression models were compared, revealing great variability within orders but consistent, strong differences among insect families. Uptake and elimination rate constants were positively correlated among species, but only when effects of body size and phylogeny were incorporated in the analysis. Together, uptake and elimination rates predicted dramatic Cd bioaccumulation differences among species that agreed with field-based measurements. We discovered a potential tradeoff between the ability to eliminate Cd and the ability to detoxify it across species, particularly mayflies. The best-fit regression models were driven by phylogenetic parameters (especially differences among families) rather than functional traits, suggesting that it may eventually be possible to predict a taxon's physiological performance based on its phylogenetic position, provided adequate physiological information is available for close relatives. There appears to be great potential for evolutionary physiological approaches to augment our understanding of insect responses to environmental stressors in nature. PMID:18559853

Proteomic Screening and Lasso Regression Reveal Differential Signaling in Insulin and Insulin-like Growth Factor I (IGF1) Pathways.

PubMed

Erdem, Cemal; Nagle, Alison M; Casa, Angelo J; Litzenburger, Beate C; Wang, Yu-Fen; Taylor, D Lansing; Lee, Adrian V; Lezon, Timothy R

2016-09-01

Insulin and insulin-like growth factor I (IGF1) influence cancer risk and progression through poorly understood mechanisms. To better understand the roles of insulin and IGF1 signaling in breast cancer, we combined proteomic screening with computational network inference to uncover differences in IGF1 and insulin induced signaling. Using reverse phase protein array, we measured the levels of 134 proteins in 21 breast cancer cell lines stimulated with IGF1 or insulin for up to 48 h. We then constructed directed protein expression networks using three separate methods: (i) lasso regression, (ii) conventional matrix inversion, and (iii) entropy maximization. These networks, named here as the time translation models, were analyzed and the inferred interactions were ranked by differential magnitude to identify pathway differences. The two top candidates, chosen for experimental validation, were shown to regulate IGF1/insulin induced phosphorylation events. First, acetyl-CoA carboxylase (ACC) knock-down was shown to increase the level of mitogen-activated protein kinase (MAPK) phosphorylation. Second, stable knock-down of E-Cadherin increased the phospho-Akt protein levels. Both of the knock-down perturbations incurred phosphorylation responses stronger in IGF1 stimulated cells compared with insulin. Overall, the time-translation modeling coupled to wet-lab experiments has proven to be powerful in inferring differential interactions downstream of IGF1 and insulin signaling, in vitro. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

Geometrical quality evaluation in laser cutting of Inconel-718 sheet by using Taguchi based regression analysis and particle swarm optimization

NASA Astrophysics Data System (ADS)

Shrivastava, Prashant Kumar; Pandey, Arun Kumar

2018-03-01

The Inconel-718 is one of the most demanding advanced engineering materials because of its superior quality. The conventional machining techniques are facing many problems to cut intricate profiles on these materials due to its minimum thermal conductivity, minimum elastic property and maximum chemical affinity at magnified temperature. The laser beam cutting is one of the advanced cutting method that may be used to achieve the geometrical accuracy with more precision by the suitable management of input process parameters. In this research work, the experimental investigation during the pulsed Nd:YAG laser cutting of Inconel-718 has been carried out. The experiments have been conducted by using the well planned orthogonal array L27. The experimentally measured values of different quality characteristics have been used for developing the second order regression models of bottom kerf deviation (KD), bottom kerf width (KW) and kerf taper (KT). The developed models of different quality characteristics have been utilized as a quality function for single-objective optimization by using particle swarm optimization (PSO) method. The optimum results obtained by the proposed hybrid methodology have been compared with experimental results. The comparison of optimized results with the experimental results shows that an individual improvement of 75%, 12.67% and 33.70% in bottom kerf deviation, bottom kerf width, and kerf taper has been observed. The parametric effects of different most significant input process parameters on quality characteristics have also been discussed.

A comparative Thermal Analysis of conventional parabolic receiver tube and Cavity model tube in a Solar Parabolic Concentrator

NASA Astrophysics Data System (ADS)

Arumugam, S.; Ramakrishna, P.; Sangavi, S.

2018-02-01

Improvements in heating technology with solar energy is gaining focus, especially solar parabolic collectors. Solar heating in conventional parabolic collectors is done with the help of radiation concentration on receiver tubes. Conventional receiver tubes are open to atmosphere and loose heat by ambient air currents. In order to reduce the convection losses and also to improve the aperture area, we designed a tube with cavity. This study is a comparative performance behaviour of conventional tube and cavity model tube. The performance formulae were derived for the cavity model based on conventional model. Reduction in overall heat loss coefficient was observed for cavity model, though collector heat removal factor and collector efficiency were nearly same for both models. Improvement in efficiency was also observed in the cavity model’s performance. The approach towards the design of a cavity model tube as the receiver tube in solar parabolic collectors gave improved results and proved as a good consideration.

Spatial Assessment of Model Errors from Four Regression Techniques

Treesearch

Lianjun Zhang; Jeffrey H. Gove; Jeffrey H. Gove

2005-01-01

Fomst modelers have attempted to account for the spatial autocorrelations among trees in growth and yield models by applying alternative regression techniques such as linear mixed models (LMM), generalized additive models (GAM), and geographicalIy weighted regression (GWR). However, the model errors are commonly assessed using average errors across the entire study...

Can We Use Regression Modeling to Quantify Mean Annual Streamflow at a Global-Scale?

NASA Astrophysics Data System (ADS)

Barbarossa, V.; Huijbregts, M. A. J.; Hendriks, J. A.; Beusen, A.; Clavreul, J.; King, H.; Schipper, A.

2016-12-01

Quantifying mean annual flow of rivers (MAF) at ungauged sites is essential for a number of applications, including assessments of global water supply, ecosystem integrity and water footprints. MAF can be quantified with spatially explicit process-based models, which might be overly time-consuming and data-intensive for this purpose, or with empirical regression models that predict MAF based on climate and catchment characteristics. Yet, regression models have mostly been developed at a regional scale and the extent to which they can be extrapolated to other regions is not known. In this study, we developed a global-scale regression model for MAF using observations of discharge and catchment characteristics from 1,885 catchments worldwide, ranging from 2 to 106 km2 in size. In addition, we compared the performance of the regression model with the predictive ability of the spatially explicit global hydrological model PCR-GLOBWB [van Beek et al., 2011] by comparing results from both models to independent measurements. We obtained a regression model explaining 89% of the variance in MAF based on catchment area, mean annual precipitation and air temperature, average slope and elevation. The regression model performed better than PCR-GLOBWB for the prediction of MAF, as root-mean-square error values were lower (0.29 - 0.38 compared to 0.49 - 0.57) and the modified index of agreement was higher (0.80 - 0.83 compared to 0.72 - 0.75). Our regression model can be applied globally at any point of the river network, provided that the input parameters are within the range of values employed in the calibration of the model. The performance is reduced for water scarce regions and further research should focus on improving such an aspect for regression-based global hydrological models.

Developing a predictive tropospheric ozone model for Tabriz

NASA Astrophysics Data System (ADS)

Khatibi, Rahman; Naghipour, Leila; Ghorbani, Mohammad A.; Smith, Michael S.; Karimi, Vahid; Farhoudi, Reza; Delafrouz, Hadi; Arvanaghi, Hadi

2013-04-01

Predictive ozone models are becoming indispensable tools by providing a capability for pollution alerts to serve people who are vulnerable to the risks. We have developed a tropospheric ozone prediction capability for Tabriz, Iran, by using the following five modeling strategies: three regression-type methods: Multiple Linear Regression (MLR), Artificial Neural Networks (ANNs), and Gene Expression Programming (GEP); and two auto-regression-type models: Nonlinear Local Prediction (NLP) to implement chaos theory and Auto-Regressive Integrated Moving Average (ARIMA) models. The regression-type modeling strategies explain the data in terms of: temperature, solar radiation, dew point temperature, and wind speed, by regressing present ozone values to their past values. The ozone time series are available at various time intervals, including hourly intervals, from August 2010 to March 2011. The results for MLR, ANN and GEP models are not overly good but those produced by NLP and ARIMA are promising for the establishing a forecasting capability.

Unitary Response Regression Models

ERIC Educational Resources Information Center

Lipovetsky, S.

2007-01-01

The dependent variable in a regular linear regression is a numerical variable, and in a logistic regression it is a binary or categorical variable. In these models the dependent variable has varying values. However, there are problems yielding an identity output of a constant value which can also be modelled in a linear or logistic regression with…

Modelling infant mortality rate in Central Java, Indonesia use generalized poisson regression method

NASA Astrophysics Data System (ADS)

Prahutama, Alan; Sudarno

2018-05-01

The infant mortality rate is the number of deaths under one year of age occurring among the live births in a given geographical area during a given year, per 1,000 live births occurring among the population of the given geographical area during the same year. This problem needs to be addressed because it is an important element of a country’s economic development. High infant mortality rate will disrupt the stability of a country as it relates to the sustainability of the population in the country. One of regression model that can be used to analyze the relationship between dependent variable Y in the form of discrete data and independent variable X is Poisson regression model. Recently The regression modeling used for data with dependent variable is discrete, among others, poisson regression, negative binomial regression and generalized poisson regression. In this research, generalized poisson regression modeling gives better AIC value than poisson regression. The most significant variable is the Number of health facilities (X1), while the variable that gives the most influence to infant mortality rate is the average breastfeeding (X9).

[From clinical judgment to linear regression model.

PubMed

Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O

2013-01-01

When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R 2 ) indicates the importance of independent variables in the outcome.

Impact of multicollinearity on small sample hydrologic regression models

NASA Astrophysics Data System (ADS)

Kroll, Charles N.; Song, Peter

2013-06-01

Often hydrologic regression models are developed with ordinary least squares (OLS) procedures. The use of OLS with highly correlated explanatory variables produces multicollinearity, which creates highly sensitive parameter estimators with inflated variances and improper model selection. It is not clear how to best address multicollinearity in hydrologic regression models. Here a Monte Carlo simulation is developed to compare four techniques to address multicollinearity: OLS, OLS with variance inflation factor screening (VIF), principal component regression (PCR), and partial least squares regression (PLS). The performance of these four techniques was observed for varying sample sizes, correlation coefficients between the explanatory variables, and model error variances consistent with hydrologic regional regression models. The negative effects of multicollinearity are magnified at smaller sample sizes, higher correlations between the variables, and larger model error variances (smaller R2). The Monte Carlo simulation indicates that if the true model is known, multicollinearity is present, and the estimation and statistical testing of regression parameters are of interest, then PCR or PLS should be employed. If the model is unknown, or if the interest is solely on model predictions, is it recommended that OLS be employed since using more complicated techniques did not produce any improvement in model performance. A leave-one-out cross-validation case study was also performed using low-streamflow data sets from the eastern United States. Results indicate that OLS with stepwise selection generally produces models across study regions with varying levels of multicollinearity that are as good as biased regression techniques such as PCR and PLS.

Accounting for the dissociating properties of organic chemicals in LCIA: an uncertainty analysis applied to micropollutants in the assessment of freshwater ecotoxicity.

PubMed

Morais, Sérgio Alberto; Delerue-Matos, Cristina; Gabarrell, Xavier

2013-03-15

In life cycle impact assessment (LCIA) models, the sorption of the ionic fraction of dissociating organic chemicals is not adequately modeled because conventional non-polar partitioning models are applied. Therefore, high uncertainties are expected when modeling the mobility, as well as the bioavailability for uptake by exposed biota and degradation, of dissociating organic chemicals. Alternative regressions that account for the ionized fraction of a molecule to estimate fate parameters were applied to the USEtox model. The most sensitive model parameters in the estimation of ecotoxicological characterization factors (CFs) of micropollutants were evaluated by Monte Carlo analysis in both the default USEtox model and the alternative approach. Negligible differences of CFs values and 95% confidence limits between the two approaches were estimated for direct emissions to the freshwater compartment; however the default USEtox model overestimates CFs and the 95% confidence limits of basic compounds up to three orders and four orders of magnitude, respectively, relatively to the alternative approach for emissions to the agricultural soil compartment. For three emission scenarios, LCIA results show that the default USEtox model overestimates freshwater ecotoxicity impacts for the emission scenarios to agricultural soil by one order of magnitude, and larger confidence limits were estimated, relatively to the alternative approach. Copyright © 2013 Elsevier B.V. All rights reserved.

Influence of Fluidized Bed Quenching on the Mechanical Properties and Quality Index of T6 Tempered B319.2-Type Aluminum Alloys

NASA Astrophysics Data System (ADS)

Ragab, Kh. A.; Samuel, A. M.; Al-Ahmari, A. M. A.; Samuel, F. H.; Doty, H. W.

2013-11-01

The current study aimed to investigate the effect of fluidized sand bed (FB) quenching on the mechanical performance of B319.2 aluminum cast alloys. Traditional water and conventional hot air (CF) quenching media were used to establish a relevant comparison with FB quenching. Quality charts were generated using two models of quality indices to support the selection of material conditions on the basis of the proposed quality indices. The use of an FB for the direct quenching-aging treatment of B319.2 casting alloys yields greater UTS and YS values compared to conventional furnace quenched alloys. The strength values of T6 tempered B319 alloys are greater when quenched in water compared with those quenched in an FB or CF. For the same aging conditions (170°C/4h), the fluidized bed quenched-aged 319 alloys show nearly the same or better strength values than those quenched in water and then aged in a CF or an FB. Based on the quality charts developed for alloys subjected to different quenching media, higher quality index values are obtained by conventional furnace quenched-aged T6-tempered B319 alloys. The modification factor has the most significant effect on the quality results of the alloys investigated, for all heat treatment cycles, as compared to other metallurgical parameters. The results of alloys subjected to multi-temperature aging cycles reveal that the optimum strength properties of B319.2 alloys, however, is obtained by applying multi-temperature aging cycles such as, for example, 240 °C/2 h followed by 170 °C/8 h, rather than T6 aging treatments. The regression models indicate that the mean quality values of B319 alloys are highly quench sensitive due to the formation of a larger percent of clusters in Al-Si-Cu-Mg alloys. These clusters act as heterogeneous nucleation sites for precipitation and enhance the aging process.

Real estate value prediction using multivariate regression models

NASA Astrophysics Data System (ADS)

Manjula, R.; Jain, Shubham; Srivastava, Sharad; Rajiv Kher, Pranav

2017-11-01

The real estate market is one of the most competitive in terms of pricing and the same tends to vary significantly based on a lot of factors, hence it becomes one of the prime fields to apply the concepts of machine learning to optimize and predict the prices with high accuracy. Therefore in this paper, we present various important features to use while predicting housing prices with good accuracy. We have described regression models, using various features to have lower Residual Sum of Squares error. While using features in a regression model some feature engineering is required for better prediction. Often a set of features (multiple regressions) or polynomial regression (applying a various set of powers in the features) is used for making better model fit. For these models are expected to be susceptible towards over fitting ridge regression is used to reduce it. This paper thus directs to the best application of regression models in addition to other techniques to optimize the result.

Diffusion parameter mapping with the combined intravoxel incoherent motion and kurtosis model using artificial neural networks at 3 T.

PubMed

Bertleff, Marco; Domsch, Sebastian; Weingärtner, Sebastian; Zapp, Jascha; O'Brien, Kieran; Barth, Markus; Schad, Lothar R

2017-12-01

Artificial neural networks (ANNs) were used for voxel-wise parameter estimation with the combined intravoxel incoherent motion (IVIM) and kurtosis model facilitating robust diffusion parameter mapping in the human brain. The proposed ANN approach was compared with conventional least-squares regression (LSR) and state-of-the-art multi-step fitting (LSR-MS) in Monte-Carlo simulations and in vivo in terms of estimation accuracy and precision, number of outliers and sensitivity in the distinction between grey (GM) and white (WM) matter. Both the proposed ANN approach and LSR-MS yielded visually increased parameter map quality. Estimations of all parameters (perfusion fraction f, diffusion coefficient D, pseudo-diffusion coefficient D*, kurtosis K) were in good agreement with the literature using ANN, whereas LSR-MS resulted in D* overestimation and LSR yielded increased values for f and D*, as well as decreased values for K. Using ANN, outliers were reduced for the parameters f (ANN, 1%; LSR-MS, 19%; LSR, 8%), D* (ANN, 21%; LSR-MS, 25%; LSR, 23%) and K (ANN, 0%; LSR-MS, 0%; LSR, 15%). Moreover, ANN enabled significant distinction between GM and WM based on all parameters, whereas LSR facilitated this distinction only based on D and LSR-MS on f, D and K. Overall, the proposed ANN approach was found to be superior to conventional LSR, posing a powerful alternative to the state-of-the-art method LSR-MS with several advantages in the estimation of IVIM-kurtosis parameters, which might facilitate increased applicability of enhanced diffusion models at clinical scan times. Copyright © 2017 John Wiley & Sons, Ltd.

A potential to monitor nutrients as an indicator of rangeland quality using space borne remote sensing

NASA Astrophysics Data System (ADS)

Ramoelo, A.; Cho, M. A.; Madonsela, S.; Mathieu, R.; van der Korchove, R.; Kaszta, Z.; Wolf, E.

2014-02-01

Global change consisting of land use and climate change could have huge impacts on food security and the health of various ecosystems. Leaf nitrogen (N) is one of the key factors limiting agricultural production and ecosystem functioning. Leaf N can be used as an indicator of rangeland quality which could provide information for the farmers, decision makers, land planners and managers. Leaf N plays a crucial role in understanding the feeding patterns and distribution of wildlife and livestock. Assessment of this vegetation parameter using conventional methods at landscape scale level is time consuming and tedious. Remote sensing provides a synoptic view of the landscape, which engenders an opportunity to assess leaf N over wider rangeland areas from protected to communal areas. Estimation of leaf N has been successful during peak productivity or high biomass and limited studies estimated leaf N in dry season. The objective of this study is to monitor leaf N as an indicator of rangeland quality using WorldView 2 satellite images in the north-eastern part of South Africa. Series of field work to collect samples for leaf N were undertaken in the beginning of May (end of wet season) and July (dry season). Several conventional and red edge based vegetation indices were computed. Simple regression was used to develop prediction model for leaf N. Using bootstrapping, indicator of precision and accuracy were analyzed to select a best model for the combined data sets (May and July). The may model for red edge based simple ratio explained over 90% of leaf N variations. The model developed from the combined data sets with normalized difference vegetation index explained 62% of leaf N variation, and this is a model used to estimate and map leaf N for two seasons. The study demonstrated that leaf N could be monitored using high spatial resolution with the red edge band capability.

Sequential cohort design applying propensity score matching to analyze the comparative effectiveness of atorvastatin and simvastatin in preventing cardiovascular events.

PubMed

Helin-Salmivaara, Arja; Lavikainen, Piia; Aarnio, Emma; Huupponen, Risto; Korhonen, Maarit Jaana

2014-01-01

Sequential cohort design (SCD) applying matching for propensity scores (PS) in accrual periods has been proposed to mitigate bias caused by channeling when calendar time is a proxy for strong confounders. We studied the channeling of patients according to atorvastatin and simvastatin initiation in Finland, starting from the market introduction of atorvastatin in 1998, and explored the SCD PS approach to analyzing the comparative effectiveness of atorvastatin versus simvastatin in the prevention of cardiovascular events (CVE). Initiators of atorvastatin or simvastatin use in the 45-75-year age range in 1998-2006 were characterized by their propensity of receiving atorvastatin over simvastatin, as estimated for 17 six-month periods. Atorvastatin (10 mg) and simvastatin (20 mg) initiators were matched 1∶1 on the PS, as estimated for the whole cohort and within each period. Cox regression models were fitted conventionally, and also for the PS matched cohort and the periodically PS matched cohort, to estimate the hazard ratios (HR) for CVEs. Atorvastatin (10 mg) was associated with a 11%-12% lower incidence of CVE in comparison with simvastatin (20 mg). The HR estimates were the same for a conventional Cox model (0.88, 95% confidence interval 0.85-0.91), for the analysis in which the PS was used to match across all periods and the Cox model was adjusted for strong confounders (0.89, 0.85-0.92), and for the analysis in which PS matching was applied within sequential periods (0.88, 0.84-0.92). The HR from a traditional PS matched analysis was 0.80 (0.77-0.83). The SCD PS approach produced effect estimates similar to those obtained in matching for PS within the whole cohort and adjusting the outcome model for strong confounders, but at the cost of efficiency. A traditional PS matched analysis without further adjustment in the outcome model produced estimates further away from unity.

Effectiveness of a coordinated and tailored return-to-work intervention for sickness absence beneficiaries with mental health problems.

PubMed

Martin, Marie H T; Nielsen, Maj Britt D; Madsen, Ida E H; Petersen, Signe M A; Lange, Theis; Rugulies, Reiner

2013-12-01

Sickness absence and exclusion from the labour market due to mental health problems (MHPs) is a growing concern in many countries. Knowledge about effective return-to-work (RTW) intervention models is still limited, but a multidisciplinary, coordinated and tailored approach has shown promising results in the context of musculoskeletal disorders. The purpose of this study was to assess the effectiveness of this approach as implemented among sickness absence beneficiaries with MHPs. In a quasi-randomised, controlled trial, we assessed the intervention's effect in terms of time to RTW and labour market status after 1 year. We used two different analytical strategies to compare time to RTW between participants receiving the intervention (n = 88) and those receiving conventional case management (n = 80): (1) a traditional multivariable regression analysis controlling for measured confounding, and (2) an instrumental variable (IV) analysis controlling for unmeasured confounding. The two analytical approaches provided similar results in terms of a longer time to RTW among recipients of the intervention (HR = 0.50; 95 % CI 0.34-0.75), although the estimate provided by the IV-analysis was non-significant (HR = 0.70; 95 % CI 0.23-2.12). After 1 year, more recipients of the intervention than of conventional case management were receiving sickness absence benefits (p = 0.031). The intervention delayed RTW compared to conventional case management, after accounting for measured confounding. The delayed RTW may be due to either implementation or program failure, or both. It may also reflect the complexity of retaining employees with mental health problems in the workplace.

The Dialysis Sodium Gradient: A Modifiable Risk Factor for Fluid Overload.

PubMed

Trinh, Emilie; Weber, Catherine

2017-01-01

Fluid overload in patients on conventional hemodialysis is a frequent complication, associated with increased cardiovascular morbidity and mortality. The dialysate sodium prescription is a potential modifiable risk factor. Our primary objective was to describe associations between dialysate-to-serum sodium gradient and parameters of fluid status. A secondary objective was to evaluate the 6-month risk of hospitalization and mortality in relation to sodium gradient. We performed a cross-sectional study of 110 prevalent conventional hemodialysis patients at a single center. The associations of sodium gradient with interdialytic weight gain index (IDWG%), ultrafiltration (UF) rate, and blood pressure (BP) were analyzed. The mean serum sodium gradient was 4.6 ± 3.6 mEq/L. There was a direct correlation between sodium gradient and IDWG% ( r = 0.48, p < 0.01) as well as UF rate ( r = 0.44, p < 0.01). In a logistic regression model, a 1 mEq/L higher sodium gradient was associated with increased risk of IDWG% >3% (OR 1.33, p < 0.01) and increased risk of UF rate >10 mL/kg/h (OR 1.16, p = 0.03), but there were no associations with intradialytic hypotension, intradialytic hypertension or BP. No significant differences were found with 6-month hospitalization or mortality risk in relation to sodium gradient. A higher sodium gradient was associated with significant increases in IDWG and UF rates, known to be associated with poor outcomes, but was not associated with intradialytic hypotension. Individualizing the dialysate sodium prescription to minimize sodium gap may lead to less fluid overload in conventional hemodialysis patients.

Comparison Between Linear and Non-parametric Regression Models for Genome-Enabled Prediction in Wheat

PubMed Central

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-01-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models. PMID:23275882

Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat.

PubMed

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-12-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.

Regression-Based Norms for a Bi-factor Model for Scoring the Brief Test of Adult Cognition by Telephone (BTACT).

PubMed

Gurnani, Ashita S; John, Samantha E; Gavett, Brandon E

2015-05-01

The current study developed regression-based normative adjustments for a bi-factor model of the The Brief Test of Adult Cognition by Telephone (BTACT). Archival data from the Midlife Development in the United States-II Cognitive Project were used to develop eight separate linear regression models that predicted bi-factor BTACT scores, accounting for age, education, gender, and occupation-alone and in various combinations. All regression models provided statistically significant fit to the data. A three-predictor regression model fit best and accounted for 32.8% of the variance in the global bi-factor BTACT score. The fit of the regression models was not improved by gender. Eight different regression models are presented to allow the user flexibility in applying demographic corrections to the bi-factor BTACT scores. Occupation corrections, while not widely used, may provide useful demographic adjustments for adult populations or for those individuals who have attained an occupational status not commensurate with expected educational attainment. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Regression Model Term Selection for the Analysis of Strain-Gage Balance Calibration Data

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert Manfred; Volden, Thomas R.

2010-01-01

The paper discusses the selection of regression model terms for the analysis of wind tunnel strain-gage balance calibration data. Different function class combinations are presented that may be used to analyze calibration data using either a non-iterative or an iterative method. The role of the intercept term in a regression model of calibration data is reviewed. In addition, useful algorithms and metrics originating from linear algebra and statistics are recommended that will help an analyst (i) to identify and avoid both linear and near-linear dependencies between regression model terms and (ii) to make sure that the selected regression model of the calibration data uses only statistically significant terms. Three different tests are suggested that may be used to objectively assess the predictive capability of the final regression model of the calibration data. These tests use both the original data points and regression model independent confirmation points. Finally, data from a simplified manual calibration of the Ames MK40 balance is used to illustrate the application of some of the metrics and tests to a realistic calibration data set.

Panel regressions to estimate low-flow response to rainfall variability in ungaged basins

USGS Publications Warehouse

Bassiouni, Maoya; Vogel, Richard M.; Archfield, Stacey A.

2016-01-01

Multicollinearity and omitted-variable bias are major limitations to developing multiple linear regression models to estimate streamflow characteristics in ungaged areas and varying rainfall conditions. Panel regression is used to overcome limitations of traditional regression methods, and obtain reliable model coefficients, in particular to understand the elasticity of streamflow to rainfall. Using annual rainfall and selected basin characteristics at 86 gaged streams in the Hawaiian Islands, regional regression models for three stream classes were developed to estimate the annual low-flow duration discharges. Three panel-regression structures (random effects, fixed effects, and pooled) were compared to traditional regression methods, in which space is substituted for time. Results indicated that panel regression generally was able to reproduce the temporal behavior of streamflow and reduce the standard errors of model coefficients compared to traditional regression, even for models in which the unobserved heterogeneity between streams is significant and the variance inflation factor for rainfall is much greater than 10. This is because both spatial and temporal variability were better characterized in panel regression. In a case study, regional rainfall elasticities estimated from panel regressions were applied to ungaged basins on Maui, using available rainfall projections to estimate plausible changes in surface-water availability and usable stream habitat for native species. The presented panel-regression framework is shown to offer benefits over existing traditional hydrologic regression methods for developing robust regional relations to investigate streamflow response in a changing climate.

Panel regressions to estimate low-flow response to rainfall variability in ungaged basins

NASA Astrophysics Data System (ADS)

Bassiouni, Maoya; Vogel, Richard M.; Archfield, Stacey A.

2016-12-01

Multicollinearity and omitted-variable bias are major limitations to developing multiple linear regression models to estimate streamflow characteristics in ungaged areas and varying rainfall conditions. Panel regression is used to overcome limitations of traditional regression methods, and obtain reliable model coefficients, in particular to understand the elasticity of streamflow to rainfall. Using annual rainfall and selected basin characteristics at 86 gaged streams in the Hawaiian Islands, regional regression models for three stream classes were developed to estimate the annual low-flow duration discharges. Three panel-regression structures (random effects, fixed effects, and pooled) were compared to traditional regression methods, in which space is substituted for time. Results indicated that panel regression generally was able to reproduce the temporal behavior of streamflow and reduce the standard errors of model coefficients compared to traditional regression, even for models in which the unobserved heterogeneity between streams is significant and the variance inflation factor for rainfall is much greater than 10. This is because both spatial and temporal variability were better characterized in panel regression. In a case study, regional rainfall elasticities estimated from panel regressions were applied to ungaged basins on Maui, using available rainfall projections to estimate plausible changes in surface-water availability and usable stream habitat for native species. The presented panel-regression framework is shown to offer benefits over existing traditional hydrologic regression methods for developing robust regional relations to investigate streamflow response in a changing climate.

Height-age relationships for regeneration-size trees in the northern Rocky Mountains, USA

Treesearch

Dennis E. Ferguson; Clinton E. Carlson

2010-01-01

Regression equations were developed to predict heights of 10 conifer species inregenerating stands in central and northern Idaho, western Montana, and eastern Washington. Most sample trees were natural regeneration that became established after conventional harvest and site preparation methods. Heights are predicted as a function of tree age, residual overstory density...

Multiresponse semiparametric regression for modelling the effect of regional socio-economic variables on the use of information technology

NASA Astrophysics Data System (ADS)

Wibowo, Wahyu; Wene, Chatrien; Budiantara, I. Nyoman; Permatasari, Erma Oktania

2017-03-01

Multiresponse semiparametric regression is simultaneous equation regression model and fusion of parametric and nonparametric model. The regression model comprise several models and each model has two components, parametric and nonparametric. The used model has linear function as parametric and polynomial truncated spline as nonparametric component. The model can handle both linearity and nonlinearity relationship between response and the sets of predictor variables. The aim of this paper is to demonstrate the application of the regression model for modeling of effect of regional socio-economic on use of information technology. More specific, the response variables are percentage of households has access to internet and percentage of households has personal computer. Then, predictor variables are percentage of literacy people, percentage of electrification and percentage of economic growth. Based on identification of the relationship between response and predictor variable, economic growth is treated as nonparametric predictor and the others are parametric predictors. The result shows that the multiresponse semiparametric regression can be applied well as indicate by the high coefficient determination, 90 percent.