Sample records for linear models glms
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Guisan, Antoine; Edwards, T.C.; Hastie, T.
2002-01-01
An important statistical development of the last 30 years has been the advance in regression analysis provided by generalized linear models (GLMs) and generalized additive models (GAMs). Here we introduce a series of papers prepared within the framework of an international workshop entitled: Advances in GLMs/GAMs modeling: from species distribution to environmental management, held in Riederalp, Switzerland, 6-11 August 2001. We first discuss some general uses of statistical models in ecology, as well as provide a short review of several key examples of the use of GLMs and GAMs in ecological modeling efforts. We next present an overview of GLMs and GAMs, and discuss some of their related statistics used for predictor selection, model diagnostics, and evaluation. Included is a discussion of several new approaches applicable to GLMs and GAMs, such as ridge regression, an alternative to stepwise selection of predictors, and methods for the identification of interactions by a combined use of regression trees and several other approaches. We close with an overview of the papers and how we feel they advance our understanding of their application to ecological modeling. ?? 2002 Elsevier Science B.V. All rights reserved.
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Application of conditional moment tests to model checking for generalized linear models.
Pan, Wei
2002-06-01
Generalized linear models (GLMs) are increasingly being used in daily data analysis. However, model checking for GLMs with correlated discrete response data remains difficult. In this paper, through a case study on marginal logistic regression using a real data set, we illustrate the flexibility and effectiveness of using conditional moment tests (CMTs), along with other graphical methods, to do model checking for generalized estimation equation (GEE) analyses. Although CMTs provide an array of powerful diagnostic tests for model checking, they were originally proposed in the econometrics literature and, to our knowledge, have never been applied to GEE analyses. CMTs cover many existing tests, including the (generalized) score test for an omitted covariate, as special cases. In summary, we believe that CMTs provide a class of useful model checking tools.
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Bayesian Inference for Generalized Linear Models for Spiking Neurons
Gerwinn, Sebastian; Macke, Jakob H.; Bethge, Matthias
2010-01-01
Generalized Linear Models (GLMs) are commonly used statistical methods for modelling the relationship between neural population activity and presented stimuli. When the dimension of the parameter space is large, strong regularization has to be used in order to fit GLMs to datasets of realistic size without overfitting. By imposing properly chosen priors over parameters, Bayesian inference provides an effective and principled approach for achieving regularization. Here we show how the posterior distribution over model parameters of GLMs can be approximated by a Gaussian using the Expectation Propagation algorithm. In this way, we obtain an estimate of the posterior mean and posterior covariance, allowing us to calculate Bayesian confidence intervals that characterize the uncertainty about the optimal solution. From the posterior we also obtain a different point estimate, namely the posterior mean as opposed to the commonly used maximum a posteriori estimate. We systematically compare the different inference techniques on simulated as well as on multi-electrode recordings of retinal ganglion cells, and explore the effects of the chosen prior and the performance measure used. We find that good performance can be achieved by choosing an Laplace prior together with the posterior mean estimate. PMID:20577627
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Truccolo, Wilson
2017-01-01
Point process generalized linear models (PP-GLMs) provide an important statistical framework for modeling spiking activity in single-neurons and neuronal networks. Stochastic stability is essential when sampling from these models, as done in computational neuroscience to analyze statistical properties of neuronal dynamics and in neuro-engineering to implement closed-loop applications. Here we show, however, that despite passing common goodness-of-fit tests, PP-GLMs estimated from data are often unstable, leading to divergent firing rates. The inclusion of absolute refractory periods is not a satisfactory solution since the activity then typically settles into unphysiological rates. To address these issues, we derive a framework for determining the existence and stability of fixed points of the expected conditional intensity function (CIF) for general PP-GLMs. Specifically, in nonlinear Hawkes PP-GLMs, the CIF is expressed as a function of the previous spike history and exogenous inputs. We use a mean-field quasi-renewal (QR) approximation that decomposes spike history effects into the contribution of the last spike and an average of the CIF over all spike histories prior to the last spike. Fixed points for stationary rates are derived as self-consistent solutions of integral equations. Bifurcation analysis and the number of fixed points predict that the original models can show stable, divergent, and metastable (fragile) dynamics. For fragile models, fluctuations of the single-neuron dynamics predict expected divergence times after which rates approach unphysiologically high values. This metric can be used to estimate the probability of rates to remain physiological for given time periods, e.g., for simulation purposes. We demonstrate the use of the stability framework using simulated single-neuron examples and neurophysiological recordings. Finally, we show how to adapt PP-GLM estimation procedures to guarantee model stability. Overall, our results provide a stability framework for data-driven PP-GLMs and shed new light on the stochastic dynamics of state-of-the-art statistical models of neuronal spiking activity. PMID:28234899
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Gerhard, Felipe; Deger, Moritz; Truccolo, Wilson
2017-02-01
Point process generalized linear models (PP-GLMs) provide an important statistical framework for modeling spiking activity in single-neurons and neuronal networks. Stochastic stability is essential when sampling from these models, as done in computational neuroscience to analyze statistical properties of neuronal dynamics and in neuro-engineering to implement closed-loop applications. Here we show, however, that despite passing common goodness-of-fit tests, PP-GLMs estimated from data are often unstable, leading to divergent firing rates. The inclusion of absolute refractory periods is not a satisfactory solution since the activity then typically settles into unphysiological rates. To address these issues, we derive a framework for determining the existence and stability of fixed points of the expected conditional intensity function (CIF) for general PP-GLMs. Specifically, in nonlinear Hawkes PP-GLMs, the CIF is expressed as a function of the previous spike history and exogenous inputs. We use a mean-field quasi-renewal (QR) approximation that decomposes spike history effects into the contribution of the last spike and an average of the CIF over all spike histories prior to the last spike. Fixed points for stationary rates are derived as self-consistent solutions of integral equations. Bifurcation analysis and the number of fixed points predict that the original models can show stable, divergent, and metastable (fragile) dynamics. For fragile models, fluctuations of the single-neuron dynamics predict expected divergence times after which rates approach unphysiologically high values. This metric can be used to estimate the probability of rates to remain physiological for given time periods, e.g., for simulation purposes. We demonstrate the use of the stability framework using simulated single-neuron examples and neurophysiological recordings. Finally, we show how to adapt PP-GLM estimation procedures to guarantee model stability. Overall, our results provide a stability framework for data-driven PP-GLMs and shed new light on the stochastic dynamics of state-of-the-art statistical models of neuronal spiking activity.
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Anderson-Cook, Christine M.; Burke, Sarah E.
2016-10-18
First, we would like to commend Dr. Woods on his thought-provoking paper and insightful presentation at the 4th Annual Stu Hunter conference. We think that the material presented highlights some important needs in the area of design of experiments for generalized linear models (GLMs). In addition, we agree with Dr. Woods that design of experiements of GLMs does implicitly require expert judgement about model parameters, and hence using a Bayesian approach to capture this knowledge is a natural strategy to summarize what is known with the opportunity to incorporate associated uncertainty about that information.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Anderson-Cook, Christine M.; Burke, Sarah E.
First, we would like to commend Dr. Woods on his thought-provoking paper and insightful presentation at the 4th Annual Stu Hunter conference. We think that the material presented highlights some important needs in the area of design of experiments for generalized linear models (GLMs). In addition, we agree with Dr. Woods that design of experiements of GLMs does implicitly require expert judgement about model parameters, and hence using a Bayesian approach to capture this knowledge is a natural strategy to summarize what is known with the opportunity to incorporate associated uncertainty about that information.
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NASA Astrophysics Data System (ADS)
Elliott, J.; de Souza, R. S.; Krone-Martins, A.; Cameron, E.; Ishida, E. E. O.; Hilbe, J.; COIN Collaboration
2015-04-01
Machine learning techniques offer a precious tool box for use within astronomy to solve problems involving so-called big data. They provide a means to make accurate predictions about a particular system without prior knowledge of the underlying physical processes of the data. In this article, and the companion papers of this series, we present the set of Generalized Linear Models (GLMs) as a fast alternative method for tackling general astronomical problems, including the ones related to the machine learning paradigm. To demonstrate the applicability of GLMs to inherently positive and continuous physical observables, we explore their use in estimating the photometric redshifts of galaxies from their multi-wavelength photometry. Using the gamma family with a log link function we predict redshifts from the PHoto-z Accuracy Testing simulated catalogue and a subset of the Sloan Digital Sky Survey from Data Release 10. We obtain fits that result in catastrophic outlier rates as low as ∼1% for simulated and ∼2% for real data. Moreover, we can easily obtain such levels of precision within a matter of seconds on a normal desktop computer and with training sets that contain merely thousands of galaxies. Our software is made publicly available as a user-friendly package developed in Python, R and via an interactive web application. This software allows users to apply a set of GLMs to their own photometric catalogues and generates publication quality plots with minimum effort. By facilitating their ease of use to the astronomical community, this paper series aims to make GLMs widely known and to encourage their implementation in future large-scale projects, such as the Large Synoptic Survey Telescope.
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Johnston, K M; Gustafson, P; Levy, A R; Grootendorst, P
2008-04-30
A major, often unstated, concern of researchers carrying out epidemiological studies of medical therapy is the potential impact on validity if estimates of treatment are biased due to unmeasured confounders. One technique for obtaining consistent estimates of treatment effects in the presence of unmeasured confounders is instrumental variables analysis (IVA). This technique has been well developed in the econometrics literature and is being increasingly used in epidemiological studies. However, the approach to IVA that is most commonly used in such studies is based on linear models, while many epidemiological applications make use of non-linear models, specifically generalized linear models (GLMs) such as logistic or Poisson regression. Here we present a simple method for applying IVA within the class of GLMs using the generalized method of moments approach. We explore some of the theoretical properties of the method and illustrate its use within both a simulation example and an epidemiological study where unmeasured confounding is suspected to be present. We estimate the effects of beta-blocker therapy on one-year all-cause mortality after an incident hospitalization for heart failure, in the absence of data describing disease severity, which is believed to be a confounder. 2008 John Wiley & Sons, Ltd
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Evaluating atmospheric blocking in the global climate model EC-Earth
NASA Astrophysics Data System (ADS)
Hartung, Kerstin; Hense, Andreas; Kjellström, Erik
2013-04-01
Atmospheric blocking is a phenomenon of the midlatitudal troposphere, which plays an important role in climate variability. Therefore a correct representation of blocking in climate models is necessary, especially for evaluating the results of climate projections. In my master's thesis a validation of blocking in the coupled climate model EC-Earth is performed. Blocking events are detected based on the Tibaldi-Molteni Index. At first, a comparison with the reanalysis dataset ERA-Interim is conducted. The blocking frequency depending on longitude shows a small general underestimation of blocking in the model - a well known problem. Scaife et al. (2011) proposed the correction of model bias as a way to solve this problem. However, applying the correction to the higher resolution EC-Earth model does not yield any improvement. Composite maps show a link between blocking events and surface variables. One example is the formation of a positive surface temperature anomaly north and a negative anomaly south of the blocking anticyclone. In winter the surface temperature in EC-Earth can be reproduced quite well, but in summer a cold bias over the inner-European ocean is present. Using generalized linear models (GLMs) I want to study the connection between regional blocking and global atmospheric variables further. GLMs have the advantage of being applicable to non-Gaussian variables. Therefore the blocking index at each longitude, which is Bernoulli distributed, can be analysed statistically with GLMs. I applied a logistic regression between the blocking index and the geopotential height at 500 hPa to study the teleconnection of blocking events at midlatitudes with global geopotential height. GLMs also offer the possibility of quantifying the connections shown in composite maps. The implementation of the logistic regression can even be expanded to a search for trends in blocking frequency, for example in the scenario simulations.
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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.
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NASA Astrophysics Data System (ADS)
Kenabatho, P. K.; Parida, B. P.; Moalafhi, D. B.
2017-08-01
In semi-arid catchments, hydrological modeling, water resources planning and management are hampered by insufficient spatial rainfall data which is usually derived from limited rain gauge networks. Satellite products are potential candidates to augment the limited spatial rainfall data in these areas. In this paper, the utility of the Tropical Rainfall Measuring Mission (TRMM) product (3B42 v7) is evaluated using data from the Notwane catchment in Botswana. In addition, rainfall simulations obtained from a multi-site stochastic rainfall model based on the generalised linear models (GLMs) were used as additional spatial rainfall estimates. These rainfall products were compared to the observed rainfall data obtained from six (6) rainfall stations available in the catchment for the period 1998-2012. The results show that in general the two approaches produce reasonable spatial rainfall estimates. However, the TRMM products provided better spatial rainfall estimates compared to the GLM rainfall outputs on an average, as more than 90% of the monthly rainfall variations were explained by the TRMM compared to 80% from the GLMs. However, there is still uncertainty associated mainly with limited rainfall stations, and the inability of the two products to capture unusually high rainfall values in the data sets. Despite this observation, rainfall indices computed to further assess the daily rainfall products (i.e. rainfall occurrence and amounts, length of dry spells) were adequately represented by the TRMM data compared to the GLMs. Performance from the GLMs is expected to improve with addition of further rainfall predictors. A combination of these rainfall products allows for reasonable spatial rainfall estimates and temporal (short term future) rainfall simulations from the TRMM and GLMs, respectively. The results have significant implications on water resources planning and management in the catchment which has, for the past three years, been experiencing prolonged droughts as shown by the drying of Gaborone dam (currently at a record low of 1.6% full), which is the main source of water supply to the city of Gaborone and neighbouring townships in Botswana.
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The glmS Ribozyme Cofactor is a General Acid-Base Catalyst
Viladoms, Julia; Fedor, Martha J.
2012-01-01
The glmS ribozyme is the first natural self-cleaving ribozyme known to require a cofactor. The D-glucosamine-6-phosphate (GlcN6P) cofactor has been proposed to serve as a general acid, but its role in the catalytic mechanism has not been established conclusively. We surveyed GlcN6P-like molecules for their ability to support self-cleavage of the glmS ribozyme and found a strong correlation between the pH dependence of the cleavage reaction and the intrinsic acidity of the cofactors. For cofactors with low binding affinities the contribution to rate enhancement was proportional to their intrinsic acidity. This linear free-energy relationship between cofactor efficiency and acid dissociation constants is consistent with a mechanism in which the cofactors participate directly in the reaction as general acid-base catalysts. A high value for the Brønsted coefficient (β ~ 0.7) indicates that a significant amount of proton transfer has already occurred in the transition state. The glmS ribozyme is the first self-cleaving RNA to use an exogenous acid-base catalyst. PMID:23113700
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The glmS ribozyme cofactor is a general acid-base catalyst.
Viladoms, Júlia; Fedor, Martha J
2012-11-21
The glmS ribozyme is the first natural self-cleaving ribozyme known to require a cofactor. The d-glucosamine-6-phosphate (GlcN6P) cofactor has been proposed to serve as a general acid, but its role in the catalytic mechanism has not been established conclusively. We surveyed GlcN6P-like molecules for their ability to support self-cleavage of the glmS ribozyme and found a strong correlation between the pH dependence of the cleavage reaction and the intrinsic acidity of the cofactors. For cofactors with low binding affinities, the contribution to rate enhancement was proportional to their intrinsic acidity. This linear free-energy relationship between cofactor efficiency and acid dissociation constants is consistent with a mechanism in which the cofactors participate directly in the reaction as general acid-base catalysts. A high value for the Brønsted coefficient (β ~ 0.7) indicates that a significant amount of proton transfer has already occurred in the transition state. The glmS ribozyme is the first self-cleaving RNA to use an exogenous acid-base catalyst.
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Local-aggregate modeling for big data via distributed optimization: Applications to neuroimaging.
Hu, Yue; Allen, Genevera I
2015-12-01
Technological advances have led to a proliferation of structured big data that have matrix-valued covariates. We are specifically motivated to build predictive models for multi-subject neuroimaging data based on each subject's brain imaging scans. This is an ultra-high-dimensional problem that consists of a matrix of covariates (brain locations by time points) for each subject; few methods currently exist to fit supervised models directly to this tensor data. We propose a novel modeling and algorithmic strategy to apply generalized linear models (GLMs) to this massive tensor data in which one set of variables is associated with locations. Our method begins by fitting GLMs to each location separately, and then builds an ensemble by blending information across locations through regularization with what we term an aggregating penalty. Our so called, Local-Aggregate Model, can be fit in a completely distributed manner over the locations using an Alternating Direction Method of Multipliers (ADMM) strategy, and thus greatly reduces the computational burden. Furthermore, we propose to select the appropriate model through a novel sequence of faster algorithmic solutions that is similar to regularization paths. We will demonstrate both the computational and predictive modeling advantages of our methods via simulations and an EEG classification problem. © 2015, The International Biometric Society.
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Sung, Yao-Ting; Chen, Ju-Ling; Cha, Ji-Her; Tseng, Hou-Chiang; Chang, Tao-Hsing; Chang, Kuo-En
2015-06-01
Multilevel linguistic features have been proposed for discourse analysis, but there have been few applications of multilevel linguistic features to readability models and also few validations of such models. Most traditional readability formulae are based on generalized linear models (GLMs; e.g., discriminant analysis and multiple regression), but these models have to comply with certain statistical assumptions about data properties and include all of the data in formulae construction without pruning the outliers in advance. The use of such readability formulae tends to produce a low text classification accuracy, while using a support vector machine (SVM) in machine learning can enhance the classification outcome. The present study constructed readability models by integrating multilevel linguistic features with SVM, which is more appropriate for text classification. Taking the Chinese language as an example, this study developed 31 linguistic features as the predicting variables at the word, semantic, syntax, and cohesion levels, with grade levels of texts as the criterion variable. The study compared four types of readability models by integrating unilevel and multilevel linguistic features with GLMs and an SVM. The results indicate that adopting a multilevel approach in readability analysis provides a better representation of the complexities of both texts and the reading comprehension process.
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Statistical Modeling of Fire Occurrence Using Data from the Tōhoku, Japan Earthquake and Tsunami.
Anderson, Dana; Davidson, Rachel A; Himoto, Keisuke; Scawthorn, Charles
2016-02-01
In this article, we develop statistical models to predict the number and geographic distribution of fires caused by earthquake ground motion and tsunami inundation in Japan. Using new, uniquely large, and consistent data sets from the 2011 Tōhoku earthquake and tsunami, we fitted three types of models-generalized linear models (GLMs), generalized additive models (GAMs), and boosted regression trees (BRTs). This is the first time the latter two have been used in this application. A simple conceptual framework guided identification of candidate covariates. Models were then compared based on their out-of-sample predictive power, goodness of fit to the data, ease of implementation, and relative importance of the framework concepts. For the ground motion data set, we recommend a Poisson GAM; for the tsunami data set, a negative binomial (NB) GLM or NB GAM. The best models generate out-of-sample predictions of the total number of ignitions in the region within one or two. Prefecture-level prediction errors average approximately three. All models demonstrate predictive power far superior to four from the literature that were also tested. A nonlinear relationship is apparent between ignitions and ground motion, so for GLMs, which assume a linear response-covariate relationship, instrumental intensity was the preferred ground motion covariate because it captures part of that nonlinearity. Measures of commercial exposure were preferred over measures of residential exposure for both ground motion and tsunami ignition models. This may vary in other regions, but nevertheless highlights the value of testing alternative measures for each concept. Models with the best predictive power included two or three covariates. © 2015 Society for Risk Analysis.
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Lord, Dominique; Guikema, Seth D; Geedipally, Srinivas Reddy
2008-05-01
This paper documents the application of the Conway-Maxwell-Poisson (COM-Poisson) generalized linear model (GLM) for modeling motor vehicle crashes. The COM-Poisson distribution, originally developed in 1962, has recently been re-introduced by statisticians for analyzing count data subjected to over- and under-dispersion. This innovative distribution is an extension of the Poisson distribution. The objectives of this study were to evaluate the application of the COM-Poisson GLM for analyzing motor vehicle crashes and compare the results with the traditional negative binomial (NB) model. The comparison analysis was carried out using the most common functional forms employed by transportation safety analysts, which link crashes to the entering flows at intersections or on segments. To accomplish the objectives of the study, several NB and COM-Poisson GLMs were developed and compared using two datasets. The first dataset contained crash data collected at signalized four-legged intersections in Toronto, Ont. The second dataset included data collected for rural four-lane divided and undivided highways in Texas. Several methods were used to assess the statistical fit and predictive performance of the models. The results of this study show that COM-Poisson GLMs perform as well as NB models in terms of GOF statistics and predictive performance. Given the fact the COM-Poisson distribution can also handle under-dispersed data (while the NB distribution cannot or has difficulties converging), which have sometimes been observed in crash databases, the COM-Poisson GLM offers a better alternative over the NB model for modeling motor vehicle crashes, especially given the important limitations recently documented in the safety literature about the latter type of model.
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Canary, Jana D; Blizzard, Leigh; Barry, Ronald P; Hosmer, David W; Quinn, Stephen J
2016-05-01
Generalized linear models (GLM) with a canonical logit link function are the primary modeling technique used to relate a binary outcome to predictor variables. However, noncanonical links can offer more flexibility, producing convenient analytical quantities (e.g., probit GLMs in toxicology) and desired measures of effect (e.g., relative risk from log GLMs). Many summary goodness-of-fit (GOF) statistics exist for logistic GLM. Their properties make the development of GOF statistics relatively straightforward, but it can be more difficult under noncanonical links. Although GOF tests for logistic GLM with continuous covariates (GLMCC) have been applied to GLMCCs with log links, we know of no GOF tests in the literature specifically developed for GLMCCs that can be applied regardless of link function chosen. We generalize the Tsiatis GOF statistic originally developed for logistic GLMCCs, (TG), so that it can be applied under any link function. Further, we show that the algebraically related Hosmer-Lemeshow (HL) and Pigeon-Heyse (J(2) ) statistics can be applied directly. In a simulation study, TG, HL, and J(2) were used to evaluate the fit of probit, log-log, complementary log-log, and log models, all calculated with a common grouping method. The TG statistic consistently maintained Type I error rates, while those of HL and J(2) were often lower than expected if terms with little influence were included. Generally, the statistics had similar power to detect an incorrect model. An exception occurred when a log GLMCC was incorrectly fit to data generated from a logistic GLMCC. In this case, TG had more power than HL or J(2) . © 2015 John Wiley & Sons Ltd/London School of Economics.
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The Spike-and-Slab Lasso Generalized Linear Models for Prediction and Associated Genes Detection.
Tang, Zaixiang; Shen, Yueping; Zhang, Xinyan; Yi, Nengjun
2017-01-01
Large-scale "omics" data have been increasingly used as an important resource for prognostic prediction of diseases and detection of associated genes. However, there are considerable challenges in analyzing high-dimensional molecular data, including the large number of potential molecular predictors, limited number of samples, and small effect of each predictor. We propose new Bayesian hierarchical generalized linear models, called spike-and-slab lasso GLMs, for prognostic prediction and detection of associated genes using large-scale molecular data. The proposed model employs a spike-and-slab mixture double-exponential prior for coefficients that can induce weak shrinkage on large coefficients, and strong shrinkage on irrelevant coefficients. We have developed a fast and stable algorithm to fit large-scale hierarchal GLMs by incorporating expectation-maximization (EM) steps into the fast cyclic coordinate descent algorithm. The proposed approach integrates nice features of two popular methods, i.e., penalized lasso and Bayesian spike-and-slab variable selection. The performance of the proposed method is assessed via extensive simulation studies. The results show that the proposed approach can provide not only more accurate estimates of the parameters, but also better prediction. We demonstrate the proposed procedure on two cancer data sets: a well-known breast cancer data set consisting of 295 tumors, and expression data of 4919 genes; and the ovarian cancer data set from TCGA with 362 tumors, and expression data of 5336 genes. Our analyses show that the proposed procedure can generate powerful models for predicting outcomes and detecting associated genes. The methods have been implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/). Copyright © 2017 by the Genetics Society of America.
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Ecological Relationships of Meso-Scale Distribution in 25 Neotropical Vertebrate Species
Michalski, Lincoln José; Norris, Darren; de Oliveira, Tadeu Gomes; Michalski, Fernanda
2015-01-01
Vertebrates are a vital ecological component of Amazon forest biodiversity. Although vertebrates are a functionally important part of various ecosystem services they continue to be threatened by anthropogenic impacts throughout the Amazon. Here we use a standardized, regularly spaced arrangement of camera traps within 25km2 to provide a baseline assessment of vertebrate species diversity in a sustainable use protected area in the eastern Brazilian Amazon. We examined seasonal differences in the per species encounter rates (number of photos per camera trap and number of cameras with photos). Generalized linear models (GLMs) were then used to examine the influence of five variables (altitude, canopy cover, basal area, distance to nearest river and distance to nearest large river) on the number of photos per species and on functional groups. GLMs were also used to examine the relationships between large predators [Jaguar (Panthera onca) and Puma (Puma concolor)] and their prey. A total of 649 independent photos of 25 species were obtained from 1,800 camera trap days (900 each during wet and dry seasons). Only ungulates and rodents showed significant seasonal differences in the number of photos per camera. The number of photos differed between seasons for only three species (Mazama americana, Dasyprocta leporina and Myoprocta acouchy) all of which were photographed more (3 to 10 fold increase) during the wet season. Mazama americana was the only species where a significant difference was found in occupancy, with more photos in more cameras during the wet season. For most groups and species variation in the number of photos per camera was only explained weakly by the GLMs (deviance explained ranging from 10.3 to 54.4%). Terrestrial birds (Crax alector, Psophia crepitans and Tinamus major) and rodents (Cuniculus paca, Dasyprocta leporina and M. acouchy) were the notable exceptions, with our GLMs significantly explaining variation in the distribution of all species (deviance explained ranging from 21.0 to 54.5%). The group and species GLMs showed some novel ecological information from this relatively pristine area. We found no association between large cats and their potential prey. We also found that rodent and bird species were more often recorded closer to streams. As hunters gain access via rivers this finding suggests that there is currently little anthropogenic impact on the species. Our findings provide a standardized baseline for comparison with other sites and with which planned management and extractive activities can be evaluated. PMID:25938582
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Ecological relationships of meso-scale distribution in 25 neotropical vertebrate species.
Michalski, Lincoln José; Norris, Darren; de Oliveira, Tadeu Gomes; Michalski, Fernanda
2015-01-01
Vertebrates are a vital ecological component of Amazon forest biodiversity. Although vertebrates are a functionally important part of various ecosystem services they continue to be threatened by anthropogenic impacts throughout the Amazon. Here we use a standardized, regularly spaced arrangement of camera traps within 25km2 to provide a baseline assessment of vertebrate species diversity in a sustainable use protected area in the eastern Brazilian Amazon. We examined seasonal differences in the per species encounter rates (number of photos per camera trap and number of cameras with photos). Generalized linear models (GLMs) were then used to examine the influence of five variables (altitude, canopy cover, basal area, distance to nearest river and distance to nearest large river) on the number of photos per species and on functional groups. GLMs were also used to examine the relationships between large predators [Jaguar (Panthera onca) and Puma (Puma concolor)] and their prey. A total of 649 independent photos of 25 species were obtained from 1,800 camera trap days (900 each during wet and dry seasons). Only ungulates and rodents showed significant seasonal differences in the number of photos per camera. The number of photos differed between seasons for only three species (Mazama americana, Dasyprocta leporina and Myoprocta acouchy) all of which were photographed more (3 to 10 fold increase) during the wet season. Mazama americana was the only species where a significant difference was found in occupancy, with more photos in more cameras during the wet season. For most groups and species variation in the number of photos per camera was only explained weakly by the GLMs (deviance explained ranging from 10.3 to 54.4%). Terrestrial birds (Crax alector, Psophia crepitans and Tinamus major) and rodents (Cuniculus paca, Dasyprocta leporina and M. acouchy) were the notable exceptions, with our GLMs significantly explaining variation in the distribution of all species (deviance explained ranging from 21.0 to 54.5%). The group and species GLMs showed some novel ecological information from this relatively pristine area. We found no association between large cats and their potential prey. We also found that rodent and bird species were more often recorded closer to streams. As hunters gain access via rivers this finding suggests that there is currently little anthropogenic impact on the species. Our findings provide a standardized baseline for comparison with other sites and with which planned management and extractive activities can be evaluated.
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NASA Astrophysics Data System (ADS)
Silva, Claudio; Yáñez, Eleuterio; Barbieri, María Angela; Bernal, Claudio; Aranis, Antonio
2015-05-01
Recent studies have demonstrated the effects of climate change on both oceanographic conditions and the relative abundance and distribution of fisheries resources. In this study, we investigated the impacts of climate change on swordfish (Xiphias gladius) and common sardine (Strangomera bentincki) fisheries using predictions of changes from global models (according to the NCAR model and IPCC emissions scenario A2), bioclimate envelope models and satellite-based sea surface temperature (SST) estimates from high-resolution regional models for the simulation period 2015-2065. Predictions of SST from global climate models were regionalised using the Delta statistical downscaling technique. The results show an SST trend of 0.0196 °C per year in the study area, equivalent to 0.98 °C for the simulation horizon and for a high CO2 emission scenario (A2). The bioclimate envelope models were developed using historical (2001-2011) monthly environmental and fisheries data. These data included the local relative abundance index of fish catch per unit effort (CPUE), corresponding to the total catch (kg) by 1000 hooks in a 1° latitude × 1° longitude fishing grid for swordfish and to the total catch (ton) by hold capacity (100 m3) in a 10‧ latitude × 10‧ longitude grid for common sardine. The environmental data included temporal (month), spatial (latitude) and thermal conditions (SST). In the first step of the bioclimate modelling performed in this study, generalised additive models (GAMs) were used as an exploratory tool to identify the functional relationships between the environmental variables and CPUE. These relationships were then parameterised using general linear models (GLMs) to provide a robust forecasting tool. With this modelling approach, environmental variables explained 58.7% of the variation in the CPUE of swordfish and 60.6% of the variation in the CPUE of common sardine in the final GLMs. Using IDRISI GIS, these GLMs simulated monthly changes in the relative abundance and distribution of the studied species forced by changes in the regionalised SST projected by the NCAR model under the A2 emission scenario. The simulations predicted a slight decline of 6% (17 kg/1000 hooks) and 7% (3.8 ton/100 m3) for swordfish and common sardine, respectively, in the spatial mean of the potential relative abundance (CPUE) by 2065.
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Stormwater dissolved organic matter: influence of land cover and environmental factors.
McElmurry, Shawn P; Long, David T; Voice, Thomas C
2014-01-01
Dissolved organic matter (DOM) plays a major role in defining biological systems and it influences the fate and transport of many pollutants. Despite the importance of DOM, understanding of how environmental and anthropogenic factors influence its composition and characteristics is limited. This study focuses on DOM exported as stormwater from suburban and urban sources. Runoff was collected before entering surface waters and DOM was characterized using specific ultraviolet absorbance at 280 nm (a proxy for aromaticity), molecular weight, polydispersity and the fraction of DOM removed from solution via hydrophobic and H-bonding mechanisms. General linear models (GLMs) incorporating land cover, precipitation, solar radiation and selected aqueous chemical measurements explained variations in DOM properties. Results show (1) molecular characteristics of DOM differ as a function of land cover, (2) DOM produced by forested land is significantly different from other landscapes, particularly urban and suburban areas, and (3) DOM from land cover that contains paved surfaces and sewers is more hydrophobic than from other types of land cover. GLMs incorporating environmental factors and land cover accounted for up to 86% of the variability observed in DOM characteristics. Significant variables (p < 0.05) included solar radiation, water temperature and water conductivity.
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Kawada-Matsuo, Miki; Oogai, Yuichi; Komatsuzawa, Hitoshi
2016-01-01
Bacteria take up and metabolize sugar as a carbohydrate source for survival. Most bacteria can utilize many sugars, including glucose, sucrose, and galactose, as well as amino sugars, such as glucosamine and N-acetylglucosamine. After entering the cytoplasm, the sugars are mainly allocated to the glycolysis pathway (energy production) and to various bacterial component biosynthesis pathways, including the cell wall, nucleic acids and amino acids. Sugars are also utilized to produce several virulence factors, such as capsule and lipoteichoic acid. Glutamine-fructose-6-phosphate aminotransferase (GlmS) and glucosamine-6-phosphate deaminase (NagB) have crucial roles in sugar distribution to the glycolysis pathway and to cell wall biosynthesis. In Streptococcus mutans, a cariogenic pathogen, the expression levels of glmS and nagB are coordinately regulated in response to the presence or absence of amino sugars. In addition, the disruption of this regulation affects the virulence of S. mutans. The expression of nagB and glmS is regulated by NagR in S. mutans, but the precise mechanism underlying glmS regulation is not clear. In Staphylococcus aureus and Bacillus subtilis, the mRNA of glmS has ribozyme activity and undergoes self-degradation at the mRNA level. However, there is no ribozyme activity region on glmS mRNA in S. mutans. In this review article, we summarize the sugar distribution, particularly the coordinated regulation of GlmS and NagB expression, and its relationship with the virulence of S. mutans. PMID:28036052
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Effects of climate on the productivity of desert truffles beneath hyper-arid conditions.
Bradai, Lyès; Bissati, Samia; Chenchouni, Haroun; Amrani, Khaled
2015-07-01
Desert truffles are edible hypogenous fungi that are very well adapted to conditions of aridity in arid and semi-arid regions. This study aims to highlight the influence of climatic factors on the productivity of desert truffles under hyper-arid climatic conditions of the Sahara Desert in Algeria, with assumptions that the more varying climatic factors, mainly rainfall, are more crucial for the development and production of desert truffles. At seven separate sites, desert truffles were collected by systematic sampling between 2006 and 2012. The effects of climate parameters of each site on the productivities (g/ha/year) of desert truffle species were tested using generalized linear models (GLMs). The annual mean of the total production recorded for all three harvested species (Terfezia arenaria, Terfezia claveryi, and Tirmania nivea) was 785.43 ± 743.39 g/ha. Tirmania nivea was commonly present over the sampled sites with an occurrence of 70 ± 10.1%. GLMs revealed that total and specific productivities were closely positively related to autumnal precipitations occurring during October-December, which is the critical pre-breeding period for both desert truffles and host plant species. The other climatic parameters have statistically no effect on the annual variation of desert truffle productivity.
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Gao, Jie; Zhang, Zhijie; Hu, Yi; Bian, Jianchao; Jiang, Wen; Wang, Xiaoming; Sun, Liqian; Jiang, Qingwu
2014-05-19
County-based spatial distribution characteristics and the related geological factors for iodine in drinking-water were studied in Shandong Province (China). Spatial autocorrelation analysis and spatial scan statistic were applied to analyze the spatial characteristics. Generalized linear models (GLMs) and geographically weighted regression (GWR) studies were conducted to explore the relationship between water iodine level and its related geological factors. The spatial distribution of iodine in drinking-water was significantly heterogeneous in Shandong Province (Moran's I = 0.52, Z = 7.4, p < 0.001). Two clusters for high iodine in drinking-water were identified in the south-western and north-western parts of Shandong Province by the purely spatial scan statistic approach. Both GLMs and GWR indicated a significantly global association between iodine in drinking-water and geological factors. Furthermore, GWR showed obviously spatial variability across the study region. Soil type and distance to Yellow River were statistically significant at most areas of Shandong Province, confirming the hypothesis that the Yellow River causes iodine deposits in Shandong Province. Our results suggested that the more effective regional monitoring plan and water improvement strategies should be strengthened targeting at the cluster areas based on the characteristics of geological factors and the spatial variability of local relationships between iodine in drinking-water and geological factors.
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Upatising, Benjavan; Wood, Douglas L; Kremers, Walter K; Christ, Sharon L; Yih, Yuehwern; Hanson, Gregory J; Takahashi, Paul Y
2015-01-01
From 1992 to 2008, older adults in the United States incurred more healthcare expense per capita than any other age group. Home telemonitoring has emerged as a potential solution to reduce these costs, but evidence is mixed. The primary aim of the study was to evaluate whether the mean difference in total direct medical cost consequence between older adults receiving additional home telemonitoring care (TELE) (n=102) and those receiving usual medical care (UC) (n=103) were significant. Inpatient, outpatient, emergency department, decedents, survivors, and 30-day readmission costs were evaluated as secondary aim. Multivariate generalized linear models (GLMs) and parametric bootstrapping method were used to model cost and to determine significance of the cost differences. We also compared the differences in arithmetic mean costs. From the conditional GLMs, the estimated mean cost differences (TELE versus UC) for total, inpatient, outpatient, and ED were -$9,537 (p=0.068), -$8,482 (p =0.098), -$1,160 (p=0.177), and $106 (p=0.619), respectively. Mean postenrollment cost was 11% lower than the prior year for TELE versus 22% higher for UC. The ratio of mean cost for decedents to survivors was 2.1:1 (TELE) versus 12.7:1 (UC). There were no significant differences in the mean total cost between the two treatment groups. The TELE group had less variability in cost of care, lower decedents to survivors cost ratio, and lower total 30-day readmission cost than the UC group.
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Nóbrega, M F; Kinas, P G; Lessa, R; Ferrandis, E
2015-02-01
The sampling of fish from the artisanal fleet operating with surface lines off north-eastern Brazil was carried out between 1998 and 2000. Generalized linear models (GLMs) were used to standardize mean abundance indices using catch and fishing effort data on dolphinfish Coryphaena hippurus and to identify abundance trends in time and space, using 1215 surface line deployments. A standard relative abundance index (catch per unit effort, CPUE) was estimated for the most frequent vessels used in the sets, employing factors and coefficients generated in the GLMs. According to the models, C. hippurus catches are affected by the operating characteristics and power of different fishing vessels. These differences highlight the need for standardization of catch and effort data for artisanal fisheries. The highest mean abundance values for C. hippurus were off the state of Rio Grande do Norte, with an increasing tendency in areas with greater depths and more distant from the coast, reaching maximal values in areas whose depths range from 200 to 500 m. The highest mean abundance values occurred between April and June. The higher estimated abundance of C. hippurus in this period off the state of Rio Grande do Norte and within the 200-500 m depth range may be related to a migration pattern of food sources, as its main prey, the flying fish Hirundichthys affinis, uses floating algae as refuge and to deposit its pelagic eggs. © 2015 The Fisheries Society of the British Isles.
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NASA Astrophysics Data System (ADS)
Fikrika, H.; Ambarsari, L.; Sumaryada, T.
2016-01-01
Molecular docking simulation of catechin and its derivatives on Glucosamine-6- Phosphate Synthase (GlmS) has been performed in this research. GlmS inhibition by a particular ligand will suppress the production of bacterial cell wall and significantly reduce the population of invading bacteria. In this study, catechin derivatives i.e epicatechin, galloatechin and epigalloatechin were found to have stronger binding affinities as compared to natural ligand of GlmS, Fructose-6-Phosphate (F6P). Those three ligands were docked on the same pocket in GlmS target as F6P, with 70% binding sites similarity. Based on the docking results, gallocatechin turns out to be the most potent ligand for anti-bacterial agent with ΔG= -8.00 kcal/mol. The docking between GlmS and catechin derivatives are characterized by a constant present of a strong hydrogen bond between functional group O3 and Ser-349. This hydrogen bond most likely plays a significant role in the docking mechanism and binding modes selection. The surprising result is catechin itself exhibited a quite strong binding with GlmS (ΔG= -7.80 kcal.mol), but docked on a completely different pocket compared to other ligands. This results suggest that catechin might still have a curing effect but with a completely different pathway and mechanism as compared to its derivatives.
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Gao, Jie; Zhang, Zhijie; Hu, Yi; Bian, Jianchao; Jiang, Wen; Wang, Xiaoming; Sun, Liqian; Jiang, Qingwu
2014-01-01
County-based spatial distribution characteristics and the related geological factors for iodine in drinking-water were studied in Shandong Province (China). Spatial autocorrelation analysis and spatial scan statistic were applied to analyze the spatial characteristics. Generalized linear models (GLMs) and geographically weighted regression (GWR) studies were conducted to explore the relationship between water iodine level and its related geological factors. The spatial distribution of iodine in drinking-water was significantly heterogeneous in Shandong Province (Moran’s I = 0.52, Z = 7.4, p < 0.001). Two clusters for high iodine in drinking-water were identified in the south-western and north-western parts of Shandong Province by the purely spatial scan statistic approach. Both GLMs and GWR indicated a significantly global association between iodine in drinking-water and geological factors. Furthermore, GWR showed obviously spatial variability across the study region. Soil type and distance to Yellow River were statistically significant at most areas of Shandong Province, confirming the hypothesis that the Yellow River causes iodine deposits in Shandong Province. Our results suggested that the more effective regional monitoring plan and water improvement strategies should be strengthened targeting at the cluster areas based on the characteristics of geological factors and the spatial variability of local relationships between iodine in drinking-water and geological factors. PMID:24852390
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Smoothing two-dimensional Malaysian mortality data using P-splines indexed by age and year
NASA Astrophysics Data System (ADS)
Kamaruddin, Halim Shukri; Ismail, Noriszura
2014-06-01
Nonparametric regression implements data to derive the best coefficient of a model from a large class of flexible functions. Eilers and Marx (1996) introduced P-splines as a method of smoothing in generalized linear models, GLMs, in which the ordinary B-splines with a difference roughness penalty on coefficients is being used in a single dimensional mortality data. Modeling and forecasting mortality rate is a problem of fundamental importance in insurance company calculation in which accuracy of models and forecasts are the main concern of the industry. The original idea of P-splines is extended to two dimensional mortality data. The data indexed by age of death and year of death, in which the large set of data will be supplied by Department of Statistics Malaysia. The extension of this idea constructs the best fitted surface and provides sensible prediction of the underlying mortality rate in Malaysia mortality case.
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Upatising, Benjavan; Wood, Douglas L.; Kremers, Walter K.; Christ, Sharon L.; Yih, Yuehwern; Hanson, Gregory J.
2015-01-01
Abstract Background: From 1992 to 2008, older adults in the United States incurred more healthcare expense per capita than any other age group. Home telemonitoring has emerged as a potential solution to reduce these costs, but evidence is mixed. The primary aim of the study was to evaluate whether the mean difference in total direct medical cost consequence between older adults receiving additional home telemonitoring care (TELE) (n=102) and those receiving usual medical care (UC) (n=103) were significant. Inpatient, outpatient, emergency department, decedents, survivors, and 30-day readmission costs were evaluated as secondary aim. Materials and Methods: Multivariate generalized linear models (GLMs) and parametric bootstrapping method were used to model cost and to determine significance of the cost differences. We also compared the differences in arithmetic mean costs. Results: From the conditional GLMs, the estimated mean cost differences (TELE versus UC) for total, inpatient, outpatient, and ED were −$9,537 (p=0.068), −$8,482 (p =0.098), −$1,160 (p=0.177), and $106 (p=0.619), respectively. Mean postenrollment cost was 11% lower than the prior year for TELE versus 22% higher for UC. The ratio of mean cost for decedents to survivors was 2.1:1 (TELE) versus 12.7:1 (UC). Conclusions: There were no significant differences in the mean total cost between the two treatment groups. The TELE group had less variability in cost of care, lower decedents to survivors cost ratio, and lower total 30-day readmission cost than the UC group. PMID:25453392
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Phosphatase-inert glucosamine 6-phosphate mimics serve as actuators of the glmS riboswitch.
Fei, Xiang; Holmes, Thomas; Diddle, Julianna; Hintz, Lauren; Delaney, Dan; Stock, Alex; Renner, Danielle; McDevitt, Molly; Berkowitz, David B; Soukup, Juliane K
2014-12-19
The glmS riboswitch is unique among gene-regulating riboswitches and catalytic RNAs. This is because its own metabolite, glucosamine-6-phosphate (GlcN6P), binds to the riboswitch and catalytically participates in the RNA self-cleavage reaction, thereby providing a novel negative feedback mechanism. Given that a number of pathogens harbor the glmS riboswitch, artificial actuators of this potential RNA target are of great interest. Structural/kinetic studies point to the 2-amino and 6-phosphate ester functionalities in GlcN6P as being crucial for this actuation. As a first step toward developing artificial actuators, we have synthesized a series of nine GlcN6P analogs bearing phosphatase-inert surrogates in place of the natural phosphate ester. Self-cleavage assays with the Bacillus cereus glmS riboswitch give a broad SAR. Two analogs display significant activity, namely, the 6-deoxy-6-phosphonomethyl analog (5) and the 6-O-malonyl ether (13). Kinetic profiles show a 22-fold and a 27-fold higher catalytic efficiency, respectively, for these analogs vs glucosamine (GlcN). Given their nonhydrolyzable phosphate surrogate functionalities, these analogs are arguably the most robust artificial glmS riboswitch actuators yet reported. Interestingly, the malonyl ether (13, extra O atom) is much more effective than the simple malonate (17), and the "sterically true" phosphonate (5) is far superior to the chain-truncated (7) or chain-extended (11) analogs, suggesting that positioning via Mg coordination is important for activity. Docking results are consistent with this view. Indeed, the viability of the phosphonate and 6-O-malonyl ether mimics of GlcN6P points to a potential new strategy for artificial actuation of the glmS riboswitch in a biological setting, wherein phosphatase-resistance is paramount.
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Paredes, Omar Stalin Landázuri; Norris, Darren; de Oliveira, Tadeu Gomes
2017-01-01
Terrestrial vertebrate frugivores constitute one of the major guilds in tropical forests. Previous studies show that the meso-scale distribution of this group is only weakly explained by variables such as altitude and tree basal area in lowland Amazon forests. For the first time we test whether seasonally limiting resources (water and fallen fruit) affect the dry season distribution in 25 species of terrestrial vertebrates. To examine the effects of the spatial availability of fruit and water on terrestrial vertebrates we used a standardized, regularly spaced arrangement of camera-traps within 25km2 of lowland Amazon forest. Generalized linear models (GLMs) were then used to examine the influence of four variables (altitude, distance to large rivers, distance to nearest water, and presence vs absence of fruits) on the number of photos on five functional groups (all frugivores, small, medium, large and very large frugivores) and on seven of the most abundant frugivore species (Cuniculus paca, Dasyprocta leporina, Mazama americana, Mazama nemorivaga, Myoprocta acouchy, Pecari tajacu and Psophia crepitans). A total of 279 independent photos of 25 species were obtained from 900 camera-trap days. For most species and three functional groups, the variation in the number of photos per camera was significantly but weakly explained by the GLMs (deviance explained ranging from 6.2 to 48.8%). Generally, we found that the presence of water availability was more important than the presence of fallen fruit for the groups and species studied. Medium frugivores, large-bodied frugivores, and two of the more abundant species (C. paca and P. crepitans) were recorded more frequently closer to water bodies; while none of the functional groups nor the most abundant species showed any significant relationship with the presence of fallen fruit. Two functional groups and two of the seven most common frugivore species assessed in the GLMs showed significant results with species-specific responses to altitude. Our findings provide a more detailed understanding of how frugivorous vertebrates cope with periods of water and fruit scarcity in lowland Amazon forests. PMID:28301589
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Paredes, Omar Stalin Landázuri; Norris, Darren; Oliveira, Tadeu Gomes de; Michalski, Fernanda
2017-01-01
Terrestrial vertebrate frugivores constitute one of the major guilds in tropical forests. Previous studies show that the meso-scale distribution of this group is only weakly explained by variables such as altitude and tree basal area in lowland Amazon forests. For the first time we test whether seasonally limiting resources (water and fallen fruit) affect the dry season distribution in 25 species of terrestrial vertebrates. To examine the effects of the spatial availability of fruit and water on terrestrial vertebrates we used a standardized, regularly spaced arrangement of camera-traps within 25km2 of lowland Amazon forest. Generalized linear models (GLMs) were then used to examine the influence of four variables (altitude, distance to large rivers, distance to nearest water, and presence vs absence of fruits) on the number of photos on five functional groups (all frugivores, small, medium, large and very large frugivores) and on seven of the most abundant frugivore species (Cuniculus paca, Dasyprocta leporina, Mazama americana, Mazama nemorivaga, Myoprocta acouchy, Pecari tajacu and Psophia crepitans). A total of 279 independent photos of 25 species were obtained from 900 camera-trap days. For most species and three functional groups, the variation in the number of photos per camera was significantly but weakly explained by the GLMs (deviance explained ranging from 6.2 to 48.8%). Generally, we found that the presence of water availability was more important than the presence of fallen fruit for the groups and species studied. Medium frugivores, large-bodied frugivores, and two of the more abundant species (C. paca and P. crepitans) were recorded more frequently closer to water bodies; while none of the functional groups nor the most abundant species showed any significant relationship with the presence of fallen fruit. Two functional groups and two of the seven most common frugivore species assessed in the GLMs showed significant results with species-specific responses to altitude. Our findings provide a more detailed understanding of how frugivorous vertebrates cope with periods of water and fruit scarcity in lowland Amazon forests.
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Paavonen, Vesa; Luoto, Kaisa; Koivukangas, Antti; Lassila, Antero; Leinonen, Esa; Kampman, Olli
2016-11-30
There is limited knowledge on the relationship between temperament and character profiles and substance abuse comorbidity in depressed patients. We recruited 127 depressed patients without alcohol use problems (non-AUP) and 89 depressed patients with alcohol use problems (AUP). We assessed all patients using the Temperament and Character Inventory (TCI-R) at baseline and after 6 weeks of treatment. Using univariate general linear models (GLMs), we analyzed differences in TCI-R between AUP and non-AUP. GLMs were also used in analyzing the associations between TCI-R changes and antidepressive treatment responses measured with changes in Montgomery Åsberg Depression Rating Scale score (ΔMADRS). Alcohol use explained independently significant proportions of the variation in Novelty Seeking, Self-Directedness, and Persistence. Reward Dependence score change explained 14.1% of the ΔMADRS in AUP, but was non-significant in non-AUP. Character score changes in Self-Directedness and Self-Transcendence explained together 14.1% of ΔMADRS in non-AUP, whereas they were all non-significant in AUP. AUP compared with non-AUP patients had lower Self-Directedness and Persistence and higher Novelty Seeking scores. Detected changes in Reward Dependence and lower Self-Directedness in AUP patients could be reflective of different biological mechanisms associated with depressive symptomatology in alcohol abuse. Changes in character are associated with acute treatment response in non-AUP. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
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Hu, Meng; Clark, Kelsey L.; Gong, Xiajing; Noudoost, Behrad; Li, Mingyao; Moore, Tirin
2015-01-01
Inferotemporal (IT) neurons are known to exhibit persistent, stimulus-selective activity during the delay period of object-based working memory tasks. Frontal eye field (FEF) neurons show robust, spatially selective delay period activity during memory-guided saccade tasks. We present a copula regression paradigm to examine neural interaction of these two types of signals between areas IT and FEF of the monkey during a working memory task. This paradigm is based on copula models that can account for both marginal distribution over spiking activity of individual neurons within each area and joint distribution over ensemble activity of neurons between areas. Considering the popular GLMs as marginal models, we developed a general and flexible likelihood framework that uses the copula to integrate separate GLMs into a joint regression analysis. Such joint analysis essentially leads to a multivariate analog of the marginal GLM theory and hence efficient model estimation. In addition, we show that Granger causality between spike trains can be readily assessed via the likelihood ratio statistic. The performance of this method is validated by extensive simulations, and compared favorably to the widely used GLMs. When applied to spiking activity of simultaneously recorded FEF and IT neurons during working memory task, we observed significant Granger causality influence from FEF to IT, but not in the opposite direction, suggesting the role of the FEF in the selection and retention of visual information during working memory. The copula model has the potential to provide unique neurophysiological insights about network properties of the brain. PMID:26063909
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N-acetylglucosamine-Mediated Expression of nagA and nagB in Streptococcus pneumoniae.
Afzal, Muhammad; Shafeeq, Sulman; Manzoor, Irfan; Henriques-Normark, Birgitta; Kuipers, Oscar P
2016-01-01
In this study, we have explored the transcriptomic response of Streptococcus pneumoniae D39 to N-acetylglucosamine (NAG). Transcriptome comparison of S. pneumoniae D39 wild-type grown in chemically defined medium (CDM) in the presence of 0.5% NAG to that grown in the presence of 0.5% glucose revealed elevated expression of many genes/operons, including nagA, nagB, manLMN , and nanP . We have further confirmed the NAG-dependent expression of nagA, nagB, manLMN , and nanP by β-galactosidase assays. nagA, nagB and glmS are putatively regulated by a transcriptional regulator NagR. We predicted the operator site of NagR ( dre site) in P nagA , P nagB , and P glmS , which was further confirmed by mutating the predicted dre site in the respective promoters ( nagA, nagB , and glmS ). Growth comparison of Δ nagA , Δ nagB , and Δ glmS with the D39 wild-type demonstrates that nagA and nagB are essential for S. pneumoniae D39 to grow in the presence of NAG as a sole carbon source. Furthermore, deletion of ccpA shows that CcpA has no effect on the expression of nagA, nagB , and glmS in the presence of NAG in S . pneumoniae .
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NASA Astrophysics Data System (ADS)
Fleury, Manon; Charron, Dominique F.; Holt, John D.; Allen, O. Brian; Maarouf, Abdel R.
2006-07-01
The incidence of enteric infections in the Canadian population varies seasonally, and may be expected to be change in response to global climate changes. To better understand any potential impact of warmer temperature on enteric infections in Canada, we investigated the relationship between ambient temperature and weekly reports of confirmed cases of three pathogens in Canada: Salmonella, pathogenic Escherichia coli and Campylobacter, between 1992 and 2000 in two Canadian provinces. We used generalized linear models (GLMs) and generalized additive models (GAMs) to estimate the effect of seasonal adjustments on the estimated models. We found a strong non-linear association between ambient temperature and the occurrence of all three enteric pathogens in Alberta, Canada, and of Campylobacter in Newfoundland-Labrador. Threshold models were used to quantify the relationship of disease and temperature with thresholds chosen from 0 to -10°C depending on the pathogen modeled. For Alberta, the log relative risk of Salmonella weekly case counts increased by 1.2%, Campylobacter weekly case counts increased by 2.2%, and E. coli weekly case counts increased by 6.0% for every degree increase in weekly mean temperature. For Newfoundland-Labrador the log relative risk increased by 4.5% for Campylobacter for every degree increase in weekly mean temperature.
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Regulatory insights into the production of UDP-N-acetylglucosamine by Lactobacillus casei
Rodríguez-Díaz, Jesús; Rubio-del-Campo, Antonio; Yebra, María J.
2012-01-01
UDP-N-acetylglucosamine (UDP-GlcNAc) is an important sugar nucleotide used as a precursor of cell wall components in bacteria, and as a substrate in the synthesis of oligosaccharides in eukaryotes. In bacteria UDP-GlcNAc is synthesized from the glycolytic intermediate D-fructose-6-phosphate (fructose-6P) by four successive reactions catalyzed by three enzymes: glucosamine-6-phosphate synthase (GlmS), phosphoglucosamine mutase (GlmM) and the bi-functional enzyme glucosamine-1-phosphate acetyltransferase/ N-acetylglucosamine-1-phosphate uridyltransferase (GlmU). We have previously reported a metabolic engineering strategy in Lactobacillus casei directed to increase the intracellular levels of UDP-GlcNAc by homologous overexpression of the genes glmS, glmM and glmU. One of the most remarkable features regarding the production of UDP-GlcNAc in L. casei was to find multiple regulation points on its biosynthetic pathway: (1) regulation by the NagB enzyme, (2) glmS RNA specific degradation through the possible participation of a glmS riboswitch mechanism, (3) regulation of the GlmU activity probably by end product inhibition and (4) transcription of glmU. PMID:22825354
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Comparison of the hedonic general Labeled Magnitude Scale with the hedonic 9-point scale.
Kalva, Jaclyn J; Sims, Charles A; Puentes, Lorenzo A; Snyder, Derek J; Bartoshuk, Linda M
2014-02-01
The hedonic 9-point scale was designed to compare palatability among different food items; however, it has also been used occasionally to compare individuals and groups. Such comparisons can be invalid because scale labels (for example, "like extremely") can denote systematically different hedonic intensities across some groups. Addressing this problem, the hedonic general Labeled Magnitude Scale (gLMS) frames affective experience in terms of the strongest imaginable liking/disliking of any kind, which can yield valid group comparisons of food palatability provided extreme hedonic experiences are unrelated to food. For each scale, 200 panelists rated affect for remembered food products (including favorite and least favorite foods) and sampled foods; they also sampled taste stimuli (quinine, sucrose, NaCl, citric acid) and rated their intensity. Finally, subjects identified experiences representing the endpoints of the hedonic gLMS. Both scales were similar in their ability to detect within-subject hedonic differences across a range of food experiences, but group comparisons favored the hedonic gLMS. With the 9-point scale, extreme labels were strongly associated with extremes in food affect. In contrast, gLMS data showed that scale extremes referenced nonfood experiences. Perceived taste intensity significantly influenced differences in food liking/disliking (for example, those experiencing the most intense tastes, called supertasters, showed more extreme liking and disliking for their favorite and least favorite foods). Scales like the hedonic gLMS are suitable for across-group comparisons of food palatability. © 2014 Institute of Food Technologists®
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Garcia, J M; Teodoro, F; Cerdeira, R; Coelho, L M R; Kumar, Prashant; Carvalho, M G
2016-09-01
A methodology to predict PM10 concentrations in urban outdoor environments is developed based on the generalized linear models (GLMs). The methodology is based on the relationship developed between atmospheric concentrations of air pollutants (i.e. CO, NO2, NOx, VOCs, SO2) and meteorological variables (i.e. ambient temperature, relative humidity (RH) and wind speed) for a city (Barreiro) of Portugal. The model uses air pollution and meteorological data from the Portuguese monitoring air quality station networks. The developed GLM considers PM10 concentrations as a dependent variable, and both the gaseous pollutants and meteorological variables as explanatory independent variables. A logarithmic link function was considered with a Poisson probability distribution. Particular attention was given to cases with air temperatures both below and above 25°C. The best performance for modelled results against the measured data was achieved for the model with values of air temperature above 25°C compared with the model considering all ranges of air temperatures and with the model considering only temperature below 25°C. The model was also tested with similar data from another Portuguese city, Oporto, and results found to behave similarly. It is concluded that this model and the methodology could be adopted for other cities to predict PM10 concentrations when these data are not available by measurements from air quality monitoring stations or other acquisition means.
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Major depressive disorder subtypes to predict long-term course
van Loo, Hanna M.; Cai, Tianxi; Gruber, Michael J.; Li, Junlong; de Jonge, Peter; Petukhova, Maria; Rose, Sherri; Sampson, Nancy A.; Schoevers, Robert A.; Wardenaar, Klaas J.; Wilcox, Marsha A.; Al-Hamzawi, Ali Obaid; Andrade, Laura Helena; Bromet, Evelyn J.; Bunting, Brendan; Fayyad, John; Florescu, Silvia E.; Gureje, Oye; Hu, Chiyi; Huang, Yueqin; Levinson, Daphna; Medina-Mora, Maria Elena; Nakane, Yoshibumi; Posada-Villa, Jose; Scott, Kate M.; Xavier, Miguel; Zarkov, Zahari; Kessler, Ronald C.
2016-01-01
Background Variation in course of major depressive disorder (MDD) is not strongly predicted by existing subtype distinctions. A new subtyping approach is considered here. Methods Two data mining techniques, ensemble recursive partitioning and Lasso generalized linear models (GLMs) followed by k-means cluster analysis, are used to search for subtypes based on index episode symptoms predicting subsequent MDD course in the World Mental Health (WMH) Surveys. The WMH surveys are community surveys in 16 countries. Lifetime DSM-IV MDD was reported by 8,261 respondents. Retrospectively reported outcomes included measures of persistence (number of years with an episode; number of with an episode lasting most of the year) and severity (hospitalization for MDD; disability due to MDD). Results Recursive partitioning found significant clusters defined by the conjunctions of early onset, suicidality, and anxiety (irritability, panic, nervousness-worry-anxiety) during the index episode. GLMs found additional associations involving a number of individual symptoms. Predicted values of the four outcomes were strongly correlated. Cluster analysis of these predicted values found three clusters having consistently high, intermediate, or low predicted scores across all outcomes. The high-risk cluster (30.0% of respondents) accounted for 52.9-69.7% of high persistence and severity and was most strongly predicted by index episode severe dysphoria, suicidality, anxiety, and early onset. A total symptom count, in comparison, was not a significant predictor. Conclusions Despite being based on retrospective reports, results suggest that useful MDD subtyping distinctions can be made using data mining methods. Further studies are needed to test and expand these results with prospective data. PMID:24425049
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Population decoding of motor cortical activity using a generalized linear model with hidden states.
Lawhern, Vernon; Wu, Wei; Hatsopoulos, Nicholas; Paninski, Liam
2010-06-15
Generalized linear models (GLMs) have been developed for modeling and decoding population neuronal spiking activity in the motor cortex. These models provide reasonable characterizations between neural activity and motor behavior. However, they lack a description of movement-related terms which are not observed directly in these experiments, such as muscular activation, the subject's level of attention, and other internal or external states. Here we propose to include a multi-dimensional hidden state to address these states in a GLM framework where the spike count at each time is described as a function of the hand state (position, velocity, and acceleration), truncated spike history, and the hidden state. The model can be identified by an Expectation-Maximization algorithm. We tested this new method in two datasets where spikes were simultaneously recorded using a multi-electrode array in the primary motor cortex of two monkeys. It was found that this method significantly improves the model-fitting over the classical GLM, for hidden dimensions varying from 1 to 4. This method also provides more accurate decoding of hand state (reducing the mean square error by up to 29% in some cases), while retaining real-time computational efficiency. These improvements on representation and decoding over the classical GLM model suggest that this new approach could contribute as a useful tool to motor cortical decoding and prosthetic applications. Copyright (c) 2010 Elsevier B.V. All rights reserved.
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Population Decoding of Motor Cortical Activity using a Generalized Linear Model with Hidden States
Lawhern, Vernon; Wu, Wei; Hatsopoulos, Nicholas G.; Paninski, Liam
2010-01-01
Generalized linear models (GLMs) have been developed for modeling and decoding population neuronal spiking activity in the motor cortex. These models provide reasonable characterizations between neural activity and motor behavior. However, they lack a description of movement-related terms which are not observed directly in these experiments, such as muscular activation, the subject's level of attention, and other internal or external states. Here we propose to include a multi-dimensional hidden state to address these states in a GLM framework where the spike count at each time is described as a function of the hand state (position, velocity, and acceleration), truncated spike history, and the hidden state. The model can be identified by an Expectation-Maximization algorithm. We tested this new method in two datasets where spikes were simultaneously recorded using a multi-electrode array in the primary motor cortex of two monkeys. It was found that this method significantly improves the model-fitting over the classical GLM, for hidden dimensions varying from 1 to 4. This method also provides more accurate decoding of hand state (lowering the Mean Square Error by up to 29% in some cases), while retaining real-time computational efficiency. These improvements on representation and decoding over the classical GLM model suggest that this new approach could contribute as a useful tool to motor cortical decoding and prosthetic applications. PMID:20359500
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Permutation inference for the general linear model
Winkler, Anderson M.; Ridgway, Gerard R.; Webster, Matthew A.; Smith, Stephen M.; Nichols, Thomas E.
2014-01-01
Permutation methods can provide exact control of false positives and allow the use of non-standard statistics, making only weak assumptions about the data. With the availability of fast and inexpensive computing, their main limitation would be some lack of flexibility to work with arbitrary experimental designs. In this paper we report on results on approximate permutation methods that are more flexible with respect to the experimental design and nuisance variables, and conduct detailed simulations to identify the best method for settings that are typical for imaging research scenarios. We present a generic framework for permutation inference for complex general linear models (glms) when the errors are exchangeable and/or have a symmetric distribution, and show that, even in the presence of nuisance effects, these permutation inferences are powerful while providing excellent control of false positives in a wide range of common and relevant imaging research scenarios. We also demonstrate how the inference on glm parameters, originally intended for independent data, can be used in certain special but useful cases in which independence is violated. Detailed examples of common neuroimaging applications are provided, as well as a complete algorithm – the “randomise” algorithm – for permutation inference with the glm. PMID:24530839
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NASA Astrophysics Data System (ADS)
Tong, Cheuk Hei Marcus; Yim, Steve Hung Lam; Rothenberg, Daniel; Wang, Chien; Lin, Chuan-Yao; Chen, Yongqin David; Lau, Ngar Cheung
2018-05-01
Air pollution is an increasingly concerning problem in many metropolitan areas due to its adverse public health and environmental impacts. Vertical atmospheric conditions have strong effects on vertical mixing of air pollutants, which directly affects surface air quality. The characteristics and magnitude of how vertical atmospheric conditions affect surface air quality, which are critical to future air quality projections, have not yet been fully understood. This study aims to enhance understanding of the annual and seasonal sensitivities of air pollution to both surface and vertical atmospheric conditions. Based on both surface and vertical meteorological characteristics provided by 1994-2003 monthly dynamic downscaling data from the Weather and Research Forecast Model, we develop generalized linear models (GLMs) to study the relationships between surface air pollutants (ozone, respirable suspended particulates, and sulfur dioxide) and atmospheric conditions in the Pearl River Delta (PRD) region. Applying Principal Component Regression (PCR) to address multi-collinearity, we study the contributions of various meteorological variables to pollutants' concentration levels based on the loading and model coefficient of major principal components. Our results show that relatively high pollutant concentration occurs under relatively low mid-level troposphere temperature gradients, low relative humidity, weak southerly wind (or strong northerly wind) and weak westerly wind (or strong easterly wind). Moreover, the correlations vary among pollutant species, seasons, and meteorological variables at various altitudes. In general, pollutant sensitivity to meteorological variables is found to be greater in winter than in other seasons, and the sensitivity of ozone to meteorology differs from that of the other two pollutants. Applying our GLMs to anomalous air pollution episodes, we find that meteorological variables up to mid troposphere (∼700 mb) play an important role in influencing surface air quality, pinpointing the significant and unique associations between meteorological variables at higher altitudes and surface air quality.
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Valença-Barbosa, Carolina; Lima, Marli M.; Sarquis, Otília; Bezerra, Claudia M.; Abad-Franch, Fernando
2014-01-01
Background Understanding the drivers of habitat selection by insect disease vectors is instrumental to the design and operation of rational control-surveillance systems. One pervasive yet often overlooked drawback of vector studies is that detection failures result in some sites being misclassified as uninfested; naïve infestation indices are therefore biased, and this can confound our view of vector habitat preferences. Here, we present an initial attempt at applying methods that explicitly account for imperfect detection to investigate the ecology of Chagas disease vectors in man-made environments. Methodology We combined triplicate-sampling of individual ecotopes (n = 203) and site-occupancy models (SOMs) to test a suite of pre-specified hypotheses about habitat selection by Triatoma brasiliensis. SOM results were compared with those of standard generalized linear models (GLMs) that assume perfect detection even with single bug-searches. Principal Findings Triatoma brasiliensis was strongly associated with key hosts (native rodents, goats/sheep and, to a lesser extent, fowl) in peridomestic environments; ecotope structure had, in comparison, small to negligible effects, although wooden ecotopes were slightly preferred. We found evidence of dwelling-level aggregation of infestation foci; when there was one such focus, same-dwelling ecotopes, whether houses or peridomestic structures, were more likely to become infested too. GLMs yielded negatively-biased covariate effect estimates and standard errors; both were, on average, about four times smaller than those derived from SOMs. Conclusions/Significance Our results confirm substantial population-level ecological heterogeneity in T. brasiliensis. They also suggest that, at least in some sites, control of this species may benefit from peridomestic rodent control and changes in goat/sheep husbandry practices. Finally, our comparative analyses highlight the importance of accounting for the various sources of uncertainty inherent to vector studies, including imperfect detection. We anticipate that future research on infectious disease ecology will increasingly rely on approaches akin to those described here. PMID:24811125
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Aroonsri, Aiyada; Akinola, Olugbenga; Posayapisit, Navaporn; Songsungthong, Warangkhana; Uthaipibull, Chairat; Kamchonwongpaisan, Sumalee; Gbotosho, Grace O; Yuthavong, Yongyuth; Shaw, Philip J
2016-07-01
The mode of action of many antimalarial drugs is unknown. Chemogenomic profiling is a powerful method to address this issue. This experimental approach entails disruption of gene function and phenotypic screening for changes in sensitivity to bioactive compounds. Here, we describe the application of reverse genetics for chemogenomic profiling in Plasmodium. Plasmodium falciparum parasites harbouring a transgenic insertion of the glmS ribozyme downstream of the dihydrofolate reductase-thymidylate synthase (DHFR-TS) gene were used for chemogenomic profiling of antimalarial compounds to identify those which target DHFR-TS. DHFR-TS expression can be attenuated by exposing parasites to glucosamine. Parasites with attenuated DHFR-TS expression were significantly more sensitive to antifolate drugs known to target DHFR-TS. In contrast, no change in sensitivity to other antimalarial drugs with different modes of action was observed. Chemogenomic profiling was performed using the Medicines for Malaria Venture (Switzerland) Malaria Box compound library, and two compounds were identified as novel DHFR-TS inhibitors. We also tested the glmS ribozyme in Plasmodium berghei, a rodent malaria parasite. The expression of reporter genes with downstream glmS ribozyme could be attenuated in transgenic parasites comparable with that obtained in P. falciparum. The chemogenomic profiling method was applied in a P. berghei line expressing a pyrimethamine-resistant Toxoplasma gondii DHFR-TS reporter gene under glmS ribozyme control. Parasites with attenuated expression of this gene were significantly sensitised to antifolates targeting DHFR-TS, but not other drugs with different modes of action. In conclusion, these data show that the glmS ribozyme reverse genetic tool can be applied for identifying primary targets of antimalarial compounds in human and rodent malaria parasites. Copyright © 2016 Australian Society for Parasitology. Published by Elsevier Ltd. All rights reserved.
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Bush, Nicholas E; Schroeder, Christopher L; Hobbs, Jennifer A; Yang, Anne ET; Huet, Lucie A; Solla, Sara A; Hartmann, Mitra JZ
2016-01-01
Tactile information available to the rat vibrissal system begins as external forces that cause whisker deformations, which in turn excite mechanoreceptors in the follicle. Despite the fundamental mechanical origin of tactile information, primary sensory neurons in the trigeminal ganglion (Vg) have often been described as encoding the kinematics (geometry) of object contact. Here we aimed to determine the extent to which Vg neurons encode the kinematics vs. mechanics of contact. We used models of whisker bending to quantify mechanical signals (forces and moments) at the whisker base while simultaneously monitoring whisker kinematics and recording single Vg units in both anesthetized rats and awake, body restrained rats. We employed a novel manual stimulation technique to deflect whiskers in a way that decouples kinematics from mechanics, and used Generalized Linear Models (GLMs) to show that Vg neurons more directly encode mechanical signals when the whisker is deflected in this decoupled stimulus space. DOI: http://dx.doi.org/10.7554/eLife.13969.001 PMID:27348221
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Truccolo, Wilson
2017-01-01
This review presents a perspective on capturing collective dynamics in recorded neuronal ensembles based on multivariate point process models, inference of low-dimensional dynamics and coarse graining of spatiotemporal measurements. A general probabilistic framework for continuous time point processes reviewed, with an emphasis on multivariate nonlinear Hawkes processes with exogenous inputs. A point process generalized linear model (PP-GLM) framework for the estimation of discrete time multivariate nonlinear Hawkes processes is described. The approach is illustrated with the modeling of collective dynamics in neocortical neuronal ensembles recorded in human and non-human primates, and prediction of single-neuron spiking. A complementary approach to capture collective dynamics based on low-dimensional dynamics (“order parameters”) inferred via latent state-space models with point process observations is presented. The approach is illustrated by inferring and decoding low-dimensional dynamics in primate motor cortex during naturalistic reach and grasp movements. Finally, we briefly review hypothesis tests based on conditional inference and spatiotemporal coarse graining for assessing collective dynamics in recorded neuronal ensembles. PMID:28336305
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Truccolo, Wilson
2016-11-01
This review presents a perspective on capturing collective dynamics in recorded neuronal ensembles based on multivariate point process models, inference of low-dimensional dynamics and coarse graining of spatiotemporal measurements. A general probabilistic framework for continuous time point processes reviewed, with an emphasis on multivariate nonlinear Hawkes processes with exogenous inputs. A point process generalized linear model (PP-GLM) framework for the estimation of discrete time multivariate nonlinear Hawkes processes is described. The approach is illustrated with the modeling of collective dynamics in neocortical neuronal ensembles recorded in human and non-human primates, and prediction of single-neuron spiking. A complementary approach to capture collective dynamics based on low-dimensional dynamics ("order parameters") inferred via latent state-space models with point process observations is presented. The approach is illustrated by inferring and decoding low-dimensional dynamics in primate motor cortex during naturalistic reach and grasp movements. Finally, we briefly review hypothesis tests based on conditional inference and spatiotemporal coarse graining for assessing collective dynamics in recorded neuronal ensembles. Published by Elsevier Ltd.
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Mediterranean dunes on the go: Evidence from a short term study on coastal herbaceous vegetation
NASA Astrophysics Data System (ADS)
Prisco, Irene; Stanisci, Angela; Acosta, Alicia T. R.
2016-12-01
Detailed monitoring studies on permanent sites are a promising tool for an accurate evaluation of short, medium or long term vegetation dynamics. This work aims to evaluate short-term changes in coastal dune herbaceous plant species and EU Habitats through a multi-temporal analysis using permanent vegetation transects. In particular, (I) we analyze changes in species richness of coastal habitats; (II) we identify changes in plant cover of selected focal plants; and (III) we relate the changes to selected climatic variables and erosion/accretion processes. We selected one of the Italian's peninsula best preserved coastal dune areas (ca. 50 km along the Adriatic sea) with a relatively homogeneous coastal zonation and low anthropic pressure but with different erosion/accretion processes. We explored changes in richness over time using generalized linear models (GLMs). We identified different ecological guilds: focal, ruderal and alien plant species and investigated temporal trends in these guilds' species richness. We also applied GLMs to determine how plant cover of the most important focal species have changed over time. Overall, in this study we observed that the influence of climatic variables was relatively small. However, we found remarkable different trends in response to erosion/accretion processes both at community and at species level. Thus, our results highlight the importance of coastal dynamics in preserving not only coastal vegetation zonation, but also species richness and focal species cover. Moreover, we identified the dune grasslands as the most sensitive habitat for detecting the influence of climatic variables throughout a short term monitoring survey. Information from this study provides useful insights for detecting changes in vegetation, for establishing habitat protection priorities and for improving conservation efforts for these fragile ecosystems.
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Weirich, Scott R; Silverstein, Joann; Rajagopalan, Balaji
2011-08-01
There is increasing interest in decentralization of wastewater collection and treatment systems. However, there have been no systematic studies of the performance of small treatment facilities compared with larger plants. A statistical analysis of 4 years of discharge monthly report (DMR) data from 210 operating wastewater treatment facilities was conducted to determine the effect of average flow rate and capacity utilization on effluent biochemical oxygen demand (BOD), total suspended solids (TSS), ammonia, and fecal coliforms relative to permitted values. Relationships were quantified using generalized linear models (GLMs). Small facilities (40 m³/d) had violation rates greater than 10 times that of the largest facilities (400,000 m³/d) for BOD, TSS, and ammonia. For facilities with average flows less than 40,000 m³/d, increasing capacity utilization was correlated with increased effluent levels of BOD and TSS. Larger facilities tended to operate at flows closer to their design capacity while maintaining treatment suggesting greater efficiency. Copyright © 2011 Elsevier Ltd. All rights reserved.
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Göpel, Yvonne; Papenfort, Kai; Reichenbach, Birte; Vogel, Jörg; Görke, Boris
2013-01-01
Bacterial small RNAs (sRNAs) are well established to regulate diverse cellular processes, but how they themselves are regulated is less understood. Recently, we identified a regulatory circuit wherein the GlmY and GlmZ sRNAs of Escherichia coli act hierarchically to activate mRNA glmS, which encodes glucosamine-6-phosphate (GlcN6P) synthase. Although the two sRNAs are highly similar, only GlmZ is a direct activator that base-pairs with the glmS mRNA, aided by protein Hfq. GlmY, however, does not bind Hfq and activates glmS indirectly by protecting GlmZ from RNA cleavage. This complex regulation feedback controls the levels of GlmS protein in response to its product, GlcN6P, a key metabolite in cell wall biosynthesis. Here, we reveal the molecular basis for the regulated turnover of GlmZ, identifying RapZ (RNase adaptor protein for sRNA GlmZ; formerly YhbJ) as a novel type of RNA-binding protein that recruits the major endoribonuclease RNase E to GlmZ. This involves direct interaction of RapZ with the catalytic domain of RNase E. GlmY binds RapZ through a secondary structure shared by both sRNAs and therefore acts by molecular mimicry as a specific decoy for RapZ. Thus, in analogy to regulated proteolysis, RapZ is an adaptor, and GlmY is an anti-adaptor in regulated turnover of a regulatory small RNA. PMID:23475961
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Input-output mapping reconstruction of spike trains at dorsal horn evoked by manual acupuncture
NASA Astrophysics Data System (ADS)
Wei, Xile; Shi, Dingtian; Yu, Haitao; Deng, Bin; Lu, Meili; Han, Chunxiao; Wang, Jiang
2016-12-01
In this study, a generalized linear model (GLM) is used to reconstruct mapping from acupuncture stimulation to spike trains driven by action potential data. The electrical signals are recorded in spinal dorsal horn after manual acupuncture (MA) manipulations with different frequencies being taken at the “Zusanli” point of experiment rats. Maximum-likelihood method is adopted to estimate the parameters of GLM and the quantified value of assumed model input. Through validating the accuracy of firings generated from the established GLM, it is found that the input-output mapping of spike trains evoked by acupuncture can be successfully reconstructed for different frequencies. Furthermore, via comparing the performance of several GLMs based on distinct inputs, it suggests that input with the form of half-sine with noise can well describe the generator potential induced by acupuncture mechanical action. Particularly, the comparison of reproducing the experiment spikes for five selected inputs is in accordance with the phenomenon found in Hudgkin-Huxley (H-H) model simulation, which indicates the mapping from half-sine with noise input to experiment spikes meets the real encoding scheme to some extent. These studies provide us a new insight into coding processes and information transfer of acupuncture.
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On the interpretation of weight vectors of linear models in multivariate neuroimaging.
Haufe, Stefan; Meinecke, Frank; Görgen, Kai; Dähne, Sven; Haynes, John-Dylan; Blankertz, Benjamin; Bießmann, Felix
2014-02-15
The increase in spatiotemporal resolution of neuroimaging devices is accompanied by a trend towards more powerful multivariate analysis methods. Often it is desired to interpret the outcome of these methods with respect to the cognitive processes under study. Here we discuss which methods allow for such interpretations, and provide guidelines for choosing an appropriate analysis for a given experimental goal: For a surgeon who needs to decide where to remove brain tissue it is most important to determine the origin of cognitive functions and associated neural processes. In contrast, when communicating with paralyzed or comatose patients via brain-computer interfaces, it is most important to accurately extract the neural processes specific to a certain mental state. These equally important but complementary objectives require different analysis methods. Determining the origin of neural processes in time or space from the parameters of a data-driven model requires what we call a forward model of the data; such a model explains how the measured data was generated from the neural sources. Examples are general linear models (GLMs). Methods for the extraction of neural information from data can be considered as backward models, as they attempt to reverse the data generating process. Examples are multivariate classifiers. Here we demonstrate that the parameters of forward models are neurophysiologically interpretable in the sense that significant nonzero weights are only observed at channels the activity of which is related to the brain process under study. In contrast, the interpretation of backward model parameters can lead to wrong conclusions regarding the spatial or temporal origin of the neural signals of interest, since significant nonzero weights may also be observed at channels the activity of which is statistically independent of the brain process under study. As a remedy for the linear case, we propose a procedure for transforming backward models into forward models. This procedure enables the neurophysiological interpretation of the parameters of linear backward models. We hope that this work raises awareness for an often encountered problem and provides a theoretical basis for conducting better interpretable multivariate neuroimaging analyses. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.
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Profound and pervasive degradation of Madagascar's freshwater wetlands and links with biodiversity.
Bamford, Andrew J; Razafindrajao, Felix; Young, Richard P; Hilton, Geoff M
2017-01-01
Reflecting a global trend, freshwater wetlands in Madagascar have received little conservation or research attention. Madagascar is a global conservation priority due to its high level of species endemism but most work has focused on protecting forests. For the first time, we investigated the state of wetlands across the country to determine the effects of human disturbance. We conducted a rapid survey of 37 wetlands, using waterbirds and benthic invertebrates as ecological indicators. We recorded nine variables relating to human disturbance, revealing widespread wetland destruction. Principal Components Analysis reduced the nine variables to a single Principal Component (PC) that explained 50% of the dataset variance, demonstrating that different forms of human disturbance are ubiquitous and inseparable. The disturbance PC provides an index of how pristine a lake is and in Generalized Linear Models (GLMs) was significantly inversely related to the number of waterbird species present and the density of Chironomidae. The disturbance PC was estimated for every wetland in a GIS-derived dataset of wetland locations in Madagascar, giving a country-wide frequency distribution of disturbance. To validate the estimated PC values, we used the GLMs to predict the number of endemic bird species at an independent sample of 22 lakes. The predicted values correlated with the observed number of species, demonstrating that our procedure can identify lakes with high biodiversity value. The disturbance PC provides a convenient method for ranking sites, and a country-wide ranking demonstrates that the only near-pristine lakes in Madagascar are small sites that have been preserved by remoteness from human activity and not conservation management. The strategy of conserving high biodiversity remnants is insufficient because existing remnants suffer some degree of degradation and only support small populations of threatened species. Large-scale restoration of degraded wetlands is required for the long-term conservation of Madagascar's freshwater biodiversity.
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Profound and pervasive degradation of Madagascar’s freshwater wetlands and links with biodiversity
Razafindrajao, Felix; Young, Richard P.; Hilton, Geoff M.
2017-01-01
Reflecting a global trend, freshwater wetlands in Madagascar have received little conservation or research attention. Madagascar is a global conservation priority due to its high level of species endemism but most work has focused on protecting forests. For the first time, we investigated the state of wetlands across the country to determine the effects of human disturbance. We conducted a rapid survey of 37 wetlands, using waterbirds and benthic invertebrates as ecological indicators. We recorded nine variables relating to human disturbance, revealing widespread wetland destruction. Principal Components Analysis reduced the nine variables to a single Principal Component (PC) that explained 50% of the dataset variance, demonstrating that different forms of human disturbance are ubiquitous and inseparable. The disturbance PC provides an index of how pristine a lake is and in Generalized Linear Models (GLMs) was significantly inversely related to the number of waterbird species present and the density of Chironomidae. The disturbance PC was estimated for every wetland in a GIS-derived dataset of wetland locations in Madagascar, giving a country-wide frequency distribution of disturbance. To validate the estimated PC values, we used the GLMs to predict the number of endemic bird species at an independent sample of 22 lakes. The predicted values correlated with the observed number of species, demonstrating that our procedure can identify lakes with high biodiversity value. The disturbance PC provides a convenient method for ranking sites, and a country-wide ranking demonstrates that the only near-pristine lakes in Madagascar are small sites that have been preserved by remoteness from human activity and not conservation management. The strategy of conserving high biodiversity remnants is insufficient because existing remnants suffer some degree of degradation and only support small populations of threatened species. Large-scale restoration of degraded wetlands is required for the long-term conservation of Madagascar’s freshwater biodiversity. PMID:28792516
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A Land-Use Perspective for Birdstrike Risk Assessment: The Attraction Risk Index.
Coccon, Francesca; Zucchetta, Matteo; Bossi, Giulia; Borrotti, Matteo; Torricelli, Patrizia; Franzoi, Piero
2015-01-01
Collisions between aircraft and birds, birdstrikes, pose a serious threat to aviation safety. The occurrence of these events is influenced by land-uses in the surroundings of airports. Airports located in the same region might have different trends for birdstrike risk, due to differences in the surrounding habitats. Here we developed a quantitative tool that assesses the risk of birdstrike based on the habitats within a 13-km buffer from the airport. For this purpose, we developed Generalized Linear Models (GLMs) with binomial distribution to estimate the contribution of habitats to wildlife use of the study area, depending on season. These GLMs predictions were combined to the flight altitude of birds within the 13-km buffer, the airport traffic pattern and the severity indices associated with impacts. Our approach was developed at Venice Marco Polo International airport (VCE), located in northeast Italy and then tested at Treviso Antonio Canova International airport (TSF), which is 20 km inland. Results from the two airports revealed that both the surrounding habitats and the season had a significant influence to the pattern of risk. With regard to VCE, agricultural fields, wetlands and urban areas contributed most to the presence of birds in the study area. Furthermore, the key role of distance of land-uses from the airport on the probability of presence of birds was highlighted. The reliability of developed risk index was demonstrated since at VCE it was significantly correlated with bird strike rate. This study emphasizes the importance of the territory near airports and the wildlife use of its habitats, as factors in need of consideration for birdstrike risk assessment procedures. Information on the contribution of habitats in attracting birds, depending on season, can be used by airport managers and local authorities to plan specific interventions in the study area in order to lower the risk.
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A Land-Use Perspective for Birdstrike Risk Assessment: The Attraction Risk Index
Coccon, Francesca; Zucchetta, Matteo; Bossi, Giulia; Borrotti, Matteo; Torricelli, Patrizia; Franzoi, Piero
2015-01-01
Collisions between aircraft and birds, birdstrikes, pose a serious threat to aviation safety. The occurrence of these events is influenced by land-uses in the surroundings of airports. Airports located in the same region might have different trends for birdstrike risk, due to differences in the surrounding habitats. Here we developed a quantitative tool that assesses the risk of birdstrike based on the habitats within a 13-km buffer from the airport. For this purpose, we developed Generalized Linear Models (GLMs) with binomial distribution to estimate the contribution of habitats to wildlife use of the study area, depending on season. These GLMs predictions were combined to the flight altitude of birds within the 13-km buffer, the airport traffic pattern and the severity indices associated with impacts. Our approach was developed at Venice Marco Polo International airport (VCE), located in northeast Italy and then tested at Treviso Antonio Canova International airport (TSF), which is 20 km inland. Results from the two airports revealed that both the surrounding habitats and the season had a significant influence to the pattern of risk. With regard to VCE, agricultural fields, wetlands and urban areas contributed most to the presence of birds in the study area. Furthermore, the key role of distance of land-uses from the airport on the probability of presence of birds was highlighted. The reliability of developed risk index was demonstrated since at VCE it was significantly correlated with bird strike rate. This study emphasizes the importance of the territory near airports and the wildlife use of its habitats, as factors in need of consideration for birdstrike risk assessment procedures. Information on the contribution of habitats in attracting birds, depending on season, can be used by airport managers and local authorities to plan specific interventions in the study area in order to lower the risk. PMID:26114958
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Predicting stem borer density in maize using RapidEye data and generalized linear models
NASA Astrophysics Data System (ADS)
Abdel-Rahman, Elfatih M.; Landmann, Tobias; Kyalo, Richard; Ong'amo, George; Mwalusepo, Sizah; Sulieman, Saad; Ru, Bruno Le
2017-05-01
Average maize yield in eastern Africa is 2.03 t ha-1 as compared to global average of 6.06 t ha-1 due to biotic and abiotic constraints. Amongst the biotic production constraints in Africa, stem borers are the most injurious. In eastern Africa, maize yield losses due to stem borers are currently estimated between 12% and 21% of the total production. The objective of the present study was to explore the possibility of RapidEye spectral data to assess stem borer larva densities in maize fields in two study sites in Kenya. RapidEye images were acquired for the Bomet (western Kenya) test site on the 9th of December 2014 and on 27th of January 2015, and for Machakos (eastern Kenya) a RapidEye image was acquired on the 3rd of January 2015. Five RapidEye spectral bands as well as 30 spectral vegetation indices (SVIs) were utilized to predict per field maize stem borer larva densities using generalized linear models (GLMs), assuming Poisson ('Po') and negative binomial ('NB') distributions. Root mean square error (RMSE) and ratio prediction to deviation (RPD) statistics were used to assess the models performance using a leave-one-out cross-validation approach. The Zero-inflated NB ('ZINB') models outperformed the 'NB' models and stem borer larva densities could only be predicted during the mid growing season in December and early January in both study sites, respectively (RMSE = 0.69-1.06 and RPD = 8.25-19.57). Overall, all models performed similar when all the 30 SVIs (non-nested) and only the significant (nested) SVIs were used. The models developed could improve decision making regarding controlling maize stem borers within integrated pest management (IPM) interventions.
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Moraes, Gleiciane Leal; Gomes, Guelber Cardoso; de Sousa, Paulo Robson Monteiro; Alves, Cláudio Nahum; Govender, Thavendran; Kruger, Hendrik G.; Maguire, Glenn E.M.; Lamichhane, Gyanu; Lameira, Jerônimo
2015-01-01
SUMMARY Tuberculosis (TB) is the second leading cause of human mortality from infectious diseases worldwide. The WHO reported 1.3 million deaths and 8.6 million new cases of TB in 2012. Mycobacterium tuberculosis (M. tuberculosis), the infectious bacteria that causes TB, is encapsulated by a thick and robust cell wall. The innermost segment of the cell wall is comprised of peptidoglycan, a layer that is required for survival and growth of the pathogen. Enzymes that catalyse biosynthesis of the peptidoglycan are essential and are therefore attractive targets for discovery of novel antibiotics as humans lack similar enzymes making it possible to selectively target bacteria only. In this paper, we have reviewed the structures and functions of enzymes GlmS, GlmM, GlmU, MurA, MurB, MurC, MurD, MurE and MurF from M. tuberculosis that are involved in peptidoglycan biosynthesis. In addition, we report homology modelled 3D structures of those key enzymes from M. tuberculosis of which the structures are still unknown. We demonstrated that natural substrates can be successfully docked into the active sites of the GlmS and GlmU respectively. It is therefore expected that the models and the data provided herein will facilitate translational research to develop new drugs to treat TB. PMID:25701501
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Review of Statistical Methods for Analysing Healthcare Resources and Costs
Mihaylova, Borislava; Briggs, Andrew; O'Hagan, Anthony; Thompson, Simon G
2011-01-01
We review statistical methods for analysing healthcare resource use and costs, their ability to address skewness, excess zeros, multimodality and heavy right tails, and their ease for general use. We aim to provide guidance on analysing resource use and costs focusing on randomised trials, although methods often have wider applicability. Twelve broad categories of methods were identified: (I) methods based on the normal distribution, (II) methods following transformation of data, (III) single-distribution generalized linear models (GLMs), (IV) parametric models based on skewed distributions outside the GLM family, (V) models based on mixtures of parametric distributions, (VI) two (or multi)-part and Tobit models, (VII) survival methods, (VIII) non-parametric methods, (IX) methods based on truncation or trimming of data, (X) data components models, (XI) methods based on averaging across models, and (XII) Markov chain methods. Based on this review, our recommendations are that, first, simple methods are preferred in large samples where the near-normality of sample means is assured. Second, in somewhat smaller samples, relatively simple methods, able to deal with one or two of above data characteristics, may be preferable but checking sensitivity to assumptions is necessary. Finally, some more complex methods hold promise, but are relatively untried; their implementation requires substantial expertise and they are not currently recommended for wider applied work. Copyright © 2010 John Wiley & Sons, Ltd. PMID:20799344
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Lloyd-Smith, Patrick
2017-12-01
Decisions regarding the optimal provision of infection prevention and control resources depend on accurate estimates of the attributable costs of health care-associated infections. This is challenging given the skewed nature of health care cost data and the endogeneity of health care-associated infections. The objective of this study is to determine the hospital costs attributable to vancomycin-resistant enterococci (VRE) while accounting for endogeneity. This study builds on an attributable cost model conducted by a retrospective cohort study including 1,292 patients admitted to an urban hospital in Vancouver, Canada. Attributable hospital costs were estimated with multivariate generalized linear models (GLMs). To account for endogeneity, a control function approach was used. The analysis sample included 217 patients with health care-associated VRE. In the standard GLM, the costs attributable to VRE are $17,949 (SEM, $2,993). However, accounting for endogeneity, the attributable costs were estimated to range from $14,706 (SEM, $7,612) to $42,101 (SEM, $15,533). Across all model specifications, attributable costs are 76% higher on average when controlling for endogeneity. VRE was independently associated with increased hospital costs, and controlling for endogeneity lead to higher attributable cost estimates. Copyright © 2017 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.
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Miller, Robert; Plessow, Franziska
2013-06-01
Endocrine time series often lack normality and homoscedasticity most likely due to the non-linear dynamics of their natural determinants and the immanent characteristics of the biochemical analysis tools, respectively. As a consequence, data transformation (e.g., log-transformation) is frequently applied to enable general linear model-based analyses. However, to date, data transformation techniques substantially vary across studies and the question of which is the optimum power transformation remains to be addressed. The present report aims to provide a common solution for the analysis of endocrine time series by systematically comparing different power transformations with regard to their impact on data normality and homoscedasticity. For this, a variety of power transformations of the Box-Cox family were applied to salivary cortisol data of 309 healthy participants sampled in temporal proximity to a psychosocial stressor (the Trier Social Stress Test). Whereas our analyses show that un- as well as log-transformed data are inferior in terms of meeting normality and homoscedasticity, they also provide optimum transformations for both, cross-sectional cortisol samples reflecting the distributional concentration equilibrium and longitudinal cortisol time series comprising systematically altered hormone distributions that result from simultaneously elicited pulsatile change and continuous elimination processes. Considering these dynamics of endocrine oscillations, data transformation prior to testing GLMs seems mandatory to minimize biased results. Copyright © 2012 Elsevier Ltd. All rights reserved.
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Moraes, Gleiciane Leal; Gomes, Guelber Cardoso; Monteiro de Sousa, Paulo Robson; Alves, Cláudio Nahum; Govender, Thavendran; Kruger, Hendrik G; Maguire, Glenn E M; Lamichhane, Gyanu; Lameira, Jerônimo
2015-03-01
Tuberculosis (TB) is the second leading cause of human mortality from infectious diseases worldwide. The WHO reported 1.3 million deaths and 8.6 million new cases of TB in 2012. Mycobacterium tuberculosis (M. tuberculosis), the infectious bacteria that causes TB, is encapsulated by a thick and robust cell wall. The innermost segment of the cell wall is comprised of peptidoglycan, a layer that is required for survival and growth of the pathogen. Enzymes that catalyse biosynthesis of the peptidoglycan are essential and are therefore attractive targets for discovery of novel antibiotics as humans lack similar enzymes making it possible to selectively target bacteria only. In this paper, we have reviewed the structures and functions of enzymes GlmS, GlmM, GlmU, MurA, MurB, MurC, MurD, MurE and MurF from M. tuberculosis that are involved in peptidoglycan biosynthesis. In addition, we report homology modelled 3D structures of those key enzymes from M. tuberculosis of which the structures are still unknown. We demonstrated that natural substrates can be successfully docked into the active sites of the GlmS and GlmU respectively. It is therefore expected that the models and the data provided herein will facilitate translational research to develop new drugs to treat TB. Copyright © 2015. Published by Elsevier Ltd.
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N-acetylglucosamine-Mediated Expression of nagA and nagB in Streptococcus pneumoniae
Afzal, Muhammad; Shafeeq, Sulman; Manzoor, Irfan; Henriques-Normark, Birgitta; Kuipers, Oscar P.
2016-01-01
In this study, we have explored the transcriptomic response of Streptococcus pneumoniae D39 to N-acetylglucosamine (NAG). Transcriptome comparison of S. pneumoniae D39 wild-type grown in chemically defined medium (CDM) in the presence of 0.5% NAG to that grown in the presence of 0.5% glucose revealed elevated expression of many genes/operons, including nagA, nagB, manLMN, and nanP. We have further confirmed the NAG-dependent expression of nagA, nagB, manLMN, and nanP by β-galactosidase assays. nagA, nagB and glmS are putatively regulated by a transcriptional regulator NagR. We predicted the operator site of NagR (dre site) in PnagA, PnagB, and PglmS, which was further confirmed by mutating the predicted dre site in the respective promoters (nagA, nagB, and glmS). Growth comparison of ΔnagA, ΔnagB, and ΔglmS with the D39 wild-type demonstrates that nagA and nagB are essential for S. pneumoniae D39 to grow in the presence of NAG as a sole carbon source. Furthermore, deletion of ccpA shows that CcpA has no effect on the expression of nagA, nagB, and glmS in the presence of NAG in S. pneumoniae. PMID:27900287
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Vázquez, Nuria; Bruno, Elizabeth Perez; Márquez, Federico; Van der Molen, Silvina; Gilardoni, Carmen; Cremonte, Florencia
2013-03-01
This is the first study performed to determine the health status of the razor clam, Ensis macha, including six different populations along Argentina Patagonian coast and one of Chile. The parasites and pathologies affecting E. macha were analyzed and their prevalence and mean intensity values were calculated. To establish which factors affect the presence and intensity of infection, Generalized Linear Models (GLMs) were applied. Basophilic inclusions, ciliates, coccidians protozoans and turbellarians were found. We report an Aporocotylidae digenean and hemocyte infiltrations. None of the parasites is OIE (World Organisation for Animal Health) notifiable, and none seemed to be pathogenic, with the exception of the digenean. The prevalence of the parasites was affected mainly by environmental factors (such as site of sampling and season) instead of intrinsic conditions of the clam (such as size, condition index, sex and gonadal stage). On the other hand, the maximum intensity of parasites was not only related with cold seasons but also with the partially spawned gonadal stage of E. macha. During this stage, the clams would need to store energy for the next gametogenesis cycle, might be more susceptible to infection by the parasites. Copyright © 2012 Elsevier Inc. All rights reserved.
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Mazumdar, Subhendu; Ghose, Dipankar; Saha, Goutam Kumar
2017-12-14
Although Black Kites (Milvus migrans govinda) serve as major scavenging raptor in most of the urban areas, scientific studies on this important ecosystem service provider are almost non-existent in Indian context. The present study was carried out in a metropolis in eastern India to find out the factors influencing relative abundance and roosting site selection of Black Kites. Separate generalized linear models (GLMs) were performed considering encounter rate and roosting Black Kite abundance as response variables. The study conclusively indicated that encounter rates of Black Kites were significantly influenced by the presence of garbage dumps in its vicinity. Numbers of Black Kites were also higher in the roosting sites situated closer to garbage dumps and open spaces. In addition, expected counts of Black Kites significantly increased in roosting sites situated away from buildings and water bodies. However, built-up area and tree cover around the roosting sites had no influence on the abundance of Black Kites therein. With rapid urbanization and changing offal disposal patterns, our findings would be useful to ensure continued availability of food and roosting sites of Black Kites in urban areas.
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NASA Astrophysics Data System (ADS)
Carus, Jana; Heuner, Maike; Paul, Maike; Schröder, Boris
2017-09-01
Due to increasing pressure on estuarine marshes from sea level rise and river training, there is a growing need to understand how species-environment relationships influence the zonation and growth of tidal marsh vegetation. In the present study, we investigated the distribution and stand characteristics of the two key brackish marsh species Bolboschoenus maritimus and Phragmites australis in the Elbe estuary together with several abiotic habitat factors. We then tested the effect of these habitat factors on plant growth and zonation with generalised linear models (GLMs). Our study provides detailed information on the importance of single habitat factors and their interactions for controlling the distribution patterns and stand characteristics of two key marsh species. Our results suggest that flow velocity is the main factor influencing species distribution and stand characteristics and together with soil-water salinity even affects the inundation tolerance of the two specie investigated here. Additionally, inundation height and duration as well as interspecific competition helped explain the distribution patterns and stand characteristics. By identifying the drivers of marsh zonation and stand characteristics and quantifying their effects, this study provides useful information for evaluating a future contribution of tidal marsh vegetation to ecosystem-based shore protection.
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Nakae, Ken; Ikegaya, Yuji; Ishikawa, Tomoe; Oba, Shigeyuki; Urakubo, Hidetoshi; Koyama, Masanori; Ishii, Shin
2014-01-01
Crosstalk between neurons and glia may constitute a significant part of information processing in the brain. We present a novel method of statistically identifying interactions in a neuron–glia network. We attempted to identify neuron–glia interactions from neuronal and glial activities via maximum-a-posteriori (MAP)-based parameter estimation by developing a generalized linear model (GLM) of a neuron–glia network. The interactions in our interest included functional connectivity and response functions. We evaluated the cross-validated likelihood of GLMs that resulted from the addition or removal of connections to confirm the existence of specific neuron-to-glia or glia-to-neuron connections. We only accepted addition or removal when the modification improved the cross-validated likelihood. We applied the method to a high-throughput, multicellular in vitro Ca2+ imaging dataset obtained from the CA3 region of a rat hippocampus, and then evaluated the reliability of connectivity estimates using a statistical test based on a surrogate method. Our findings based on the estimated connectivity were in good agreement with currently available physiological knowledge, suggesting our method can elucidate undiscovered functions of neuron–glia systems. PMID:25393874
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Generalized structural equations improve sexual-selection analyses
Santini, Giacomo; Marchetti, Giovanni Maria; Focardi, Stefano
2017-01-01
Sexual selection is an intense evolutionary force, which operates through competition for the access to breeding resources. There are many cases where male copulatory success is highly asymmetric, and few males are able to sire most females. Two main hypotheses were proposed to explain this asymmetry: “female choice” and “male dominance”. The literature reports contrasting results. This variability may reflect actual differences among studied populations, but it may also be generated by methodological differences and statistical shortcomings in data analysis. A review of the statistical methods used so far in lek studies, shows a prevalence of Linear Models (LM) and Generalized Linear Models (GLM) which may be affected by problems in inferring cause-effect relationships; multi-collinearity among explanatory variables and erroneous handling of non-normal and non-continuous distributions of the response variable. In lek breeding, selective pressure is maximal, because large numbers of males and females congregate in small arenas. We used a dataset on lekking fallow deer (Dama dama), to contrast the methods and procedures employed so far, and we propose a novel approach based on Generalized Structural Equations Models (GSEMs). GSEMs combine the power and flexibility of both SEM and GLM in a unified modeling framework. We showed that LMs fail to identify several important predictors of male copulatory success and yields very imprecise parameter estimates. Minor variations in data transformation yield wide changes in results and the method appears unreliable. GLMs improved the analysis, but GSEMs provided better results, because the use of latent variables decreases the impact of measurement errors. Using GSEMs, we were able to test contrasting hypotheses and calculate both direct and indirect effects, and we reached a high precision of the estimates, which implies a high predictive ability. In synthesis, we recommend the use of GSEMs in studies on lekking behaviour, and we provide guidelines to implement these models. PMID:28809923
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Wijeakumar, Sobanawartiny; Ambrose, Joseph P.; Spencer, John P.; Curtu, Rodica
2017-01-01
A fundamental challenge in cognitive neuroscience is to develop theoretical frameworks that effectively span the gap between brain and behavior, between neuroscience and psychology. Here, we attempt to bridge this divide by formalizing an integrative cognitive neuroscience approach using dynamic field theory (DFT). We begin by providing an overview of how DFT seeks to understand the neural population dynamics that underlie cognitive processes through previous applications and comparisons to other modeling approaches. We then use previously published behavioral and neural data from a response selection Go/Nogo task as a case study for model simulations. Results from this study served as the ‘standard’ for comparisons with a model-based fMRI approach using dynamic neural fields (DNF). The tutorial explains the rationale and hypotheses involved in the process of creating the DNF architecture and fitting model parameters. Two DNF models, with similar structure and parameter sets, are then compared. Both models effectively simulated reaction times from the task as we varied the number of stimulus-response mappings and the proportion of Go trials. Next, we directly simulated hemodynamic predictions from the neural activation patterns from each model. These predictions were tested using general linear models (GLMs). Results showed that the DNF model that was created by tuning parameters to capture simultaneously trends in neural activation and behavioral data quantitatively outperformed a Standard GLM analysis of the same dataset. Further, by using the GLM results to assign functional roles to particular clusters in the brain, we illustrate how DNF models shed new light on the neural populations’ dynamics within particular brain regions. Thus, the present study illustrates how an interactive cognitive neuroscience model can be used in practice to bridge the gap between brain and behavior. PMID:29118459
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Haslinger, Robert; Pipa, Gordon; Brown, Emery
2010-01-01
One approach for understanding the encoding of information by spike trains is to fit statistical models and then test their goodness of fit. The time rescaling theorem provides a goodness of fit test consistent with the point process nature of spike trains. The interspike intervals (ISIs) are rescaled (as a function of the model’s spike probability) to be independent and exponentially distributed if the model is accurate. A Kolmogorov Smirnov (KS) test between the rescaled ISIs and the exponential distribution is then used to check goodness of fit. This rescaling relies upon assumptions of continuously defined time and instantaneous events. However spikes have finite width and statistical models of spike trains almost always discretize time into bins. Here we demonstrate that finite temporal resolution of discrete time models prevents their rescaled ISIs from being exponentially distributed. Poor goodness of fit may be erroneously indicated even if the model is exactly correct. We present two adaptations of the time rescaling theorem to discrete time models. In the first we propose that instead of assuming the rescaled times to be exponential, the reference distribution be estimated through direct simulation by the fitted model. In the second, we prove a discrete time version of the time rescaling theorem which analytically corrects for the effects of finite resolution. This allows us to define a rescaled time which is exponentially distributed, even at arbitrary temporal discretizations. We demonstrate the efficacy of both techniques by fitting Generalized Linear Models (GLMs) to both simulated spike trains and spike trains recorded experimentally in monkey V1 cortex. Both techniques give nearly identical results, reducing the false positive rate of the KS test and greatly increasing the reliability of model evaluation based upon the time rescaling theorem. PMID:20608868
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NASA Astrophysics Data System (ADS)
Bobrowski, Maria; Schickhoff, Udo
2017-04-01
Betula utilis is a major constituent of alpine treeline ecotones in the western and central Himalayan region. The objective of this study is to provide first time analysis of the potential distribution of Betula utilis in the subalpine and alpine belts of the Himalayan region using species distribution modelling. Using Generalized Linear Models (GLM) we aim at examining climatic factors controlling the species distribution under current climate conditions. Furthermore we evaluate the prediction ability of climate data derived from different statistical methods. GLMs were created using least correlated bioclimatic variables derived from two different climate models: 1) interpolated climate data (i.e. Worldclim, Hijmans et al., 2005) and 2) quasi-mechanistical statistical downscaling (i.e. Chelsa; Karger et al., 2016). Model accuracy was evaluated by the ability to predict the potential species distribution range. We found that models based on variables of Chelsa climate data had higher predictive power, whereas models using Worldclim climate data consistently overpredicted the potential suitable habitat for Betula utilis. Although climatic variables of Worldclim are widely used in modelling species distribution, our results suggest to treat them with caution when remote regions like the Himalayan mountains are in focus. Unmindful usage of climatic variables for species distribution models potentially cause misleading projections and may lead to wrong implications and recommendations for nature conservation. References: Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. & Jarvis, A. (2005) Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25, 1965-1978. Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N., Linder, H.P. & Kessler, M. (2016) Climatologies at high resolution for the earth land surface areas. arXiv:1607.00217 [physics].
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Advantages of Synthetic Noise and Machine Learning for Analyzing Radioecological Data Sets.
Shuryak, Igor
2017-01-01
The ecological effects of accidental or malicious radioactive contamination are insufficiently understood because of the hazards and difficulties associated with conducting studies in radioactively-polluted areas. Data sets from severely contaminated locations can therefore be small. Moreover, many potentially important factors, such as soil concentrations of toxic chemicals, pH, and temperature, can be correlated with radiation levels and with each other. In such situations, commonly-used statistical techniques like generalized linear models (GLMs) may not be able to provide useful information about how radiation and/or these other variables affect the outcome (e.g. abundance of the studied organisms). Ensemble machine learning methods such as random forests offer powerful alternatives. We propose that analysis of small radioecological data sets by GLMs and/or machine learning can be made more informative by using the following techniques: (1) adding synthetic noise variables to provide benchmarks for distinguishing the performances of valuable predictors from irrelevant ones; (2) adding noise directly to the predictors and/or to the outcome to test the robustness of analysis results against random data fluctuations; (3) adding artificial effects to selected predictors to test the sensitivity of the analysis methods in detecting predictor effects; (4) running a selected machine learning method multiple times (with different random-number seeds) to test the robustness of the detected "signal"; (5) using several machine learning methods to test the "signal's" sensitivity to differences in analysis techniques. Here, we applied these approaches to simulated data, and to two published examples of small radioecological data sets: (I) counts of fungal taxa in samples of soil contaminated by the Chernobyl nuclear power plan accident (Ukraine), and (II) bacterial abundance in soil samples under a ruptured nuclear waste storage tank (USA). We show that the proposed techniques were advantageous compared with the methodology used in the original publications where the data sets were presented. Specifically, our approach identified a negative effect of radioactive contamination in data set I, and suggested that in data set II stable chromium could have been a stronger limiting factor for bacterial abundance than the radionuclides 137Cs and 99Tc. This new information, which was extracted from these data sets using the proposed techniques, can potentially enhance the design of radioactive waste bioremediation.
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Advantages of Synthetic Noise and Machine Learning for Analyzing Radioecological Data Sets
Shuryak, Igor
2017-01-01
The ecological effects of accidental or malicious radioactive contamination are insufficiently understood because of the hazards and difficulties associated with conducting studies in radioactively-polluted areas. Data sets from severely contaminated locations can therefore be small. Moreover, many potentially important factors, such as soil concentrations of toxic chemicals, pH, and temperature, can be correlated with radiation levels and with each other. In such situations, commonly-used statistical techniques like generalized linear models (GLMs) may not be able to provide useful information about how radiation and/or these other variables affect the outcome (e.g. abundance of the studied organisms). Ensemble machine learning methods such as random forests offer powerful alternatives. We propose that analysis of small radioecological data sets by GLMs and/or machine learning can be made more informative by using the following techniques: (1) adding synthetic noise variables to provide benchmarks for distinguishing the performances of valuable predictors from irrelevant ones; (2) adding noise directly to the predictors and/or to the outcome to test the robustness of analysis results against random data fluctuations; (3) adding artificial effects to selected predictors to test the sensitivity of the analysis methods in detecting predictor effects; (4) running a selected machine learning method multiple times (with different random-number seeds) to test the robustness of the detected “signal”; (5) using several machine learning methods to test the “signal’s” sensitivity to differences in analysis techniques. Here, we applied these approaches to simulated data, and to two published examples of small radioecological data sets: (I) counts of fungal taxa in samples of soil contaminated by the Chernobyl nuclear power plan accident (Ukraine), and (II) bacterial abundance in soil samples under a ruptured nuclear waste storage tank (USA). We show that the proposed techniques were advantageous compared with the methodology used in the original publications where the data sets were presented. Specifically, our approach identified a negative effect of radioactive contamination in data set I, and suggested that in data set II stable chromium could have been a stronger limiting factor for bacterial abundance than the radionuclides 137Cs and 99Tc. This new information, which was extracted from these data sets using the proposed techniques, can potentially enhance the design of radioactive waste bioremediation. PMID:28068401
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Kraan, Casper; Aarts, Geert; Van der Meer, Jaap; Piersma, Theunis
2010-06-01
Ongoing statistical sophistication allows a shift from describing species' spatial distributions toward statistically disentangling the possible roles of environmental variables in shaping species distributions. Based on a landscape-scale benthic survey in the Dutch Wadden Sea, we show the merits of spatially explicit generalized estimating equations (GEE). The intertidal macrozoobenthic species, Macoma balthica, Cerastoderma edule, Marenzelleria viridis, Scoloplos armiger, Corophium volutator, and Urothoe poseidonis served as test cases, with median grain-size and inundation time as typical environmental explanatory variables. GEEs outperformed spatially naive generalized linear models (GLMs), and removed much residual spatial structure, indicating the importance of median grain-size and inundation time in shaping landscape-scale species distributions in the intertidal. GEE regression coefficients were smaller than those attained with GLM, and GEE standard errors were larger. The best fitting GEE for each species was used to predict species' density in relation to median grain-size and inundation time. Although no drastic changes were noted compared to previous work that described habitat suitability for benthic fauna in the Wadden Sea, our predictions provided more detailed and unbiased estimates of the determinants of species-environment relationships. We conclude that spatial GEEs offer the necessary methodological advances to further steps toward linking pattern to process.
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Noel, Corinna A; Sugrue, Meaghan; Dando, Robin
2017-10-01
Research suggests a weaker sense of taste in people with obesity, with the assumption that a debilitated taste response increases the desire for more intensely tasting stimuli to compensate for decreased taste input. However, empirical testing of this supposition remains largely absent. In a randomized, repeated measures design, 51 healthy subjects were treated with varying concentrations of a tea containing Gymnema sylvestre (GS), to temporarily and selectively diminish sweet taste perception, or a control tea. Following treatment in the four testing sessions, taste intensity ratings for various sweet stimuli were captured on the generalized Labeled Magnitude Scale (gLMS), liking for real foods assessed on the hedonic gLMS, and optimal level of sweetness quantified via an ad-libitum mixing task. Data were analyzed with mixed models assessing both treatment condition and each subject's resultant sweet response with various taste-related outcomes, controlling for covariates. GS treatment diminished sweet intensity perception (p < 0.001), reduced liking for sweet foods (p < 0.001), and increased the desired sucrose content of these foods (p < 0.001). Regression modeling revealed a 1% reduction in sweet taste response was associated with a 0.40 g/L increase in optimal concentration of sucrose (p < 0.001). Our results show that an attenuation in the perceived taste intensity of sweeteners correlates with shifted preference and altered hedonic response to select sweet foods. This suggests that those with a diminished sense of taste may desire more intense stimuli to attain a satisfactory level of reward, potentially influencing eating habits to compensate for a lower gustatory input. Copyright © 2017. Published by Elsevier Ltd.
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The GlcN6P cofactor serves multiple catalytic roles in the glmS ribozyme
Bingaman, Jamie L.; Zhang, Sixue; Stevens, David R.; Yennawar, Neela H.; Hammes-Schiffer, Sharon; Bevilacqua, Philip C.
2017-01-01
RNA enzymes have remarkably diverse biological roles despite having limited chemical diversity. Protein enzymes enhance their reactivity through recruitment of cofactors. The naturally occurring glmS ribozyme uses the glucosamine-6-phosphate (GlcN6P) organic cofactor for phosphodiester bond cleavage. Prior structural and biochemical studies implicated GlcN6P as the general acid. Here we describe new catalytic roles for GlcN6P through experiments and calculations. Large stereospecific normal thio effects and lack of metal ion rescue in the holoribozyme show that nucleobases and the cofactor play direct chemical roles and align the active site for self-cleavage. Large stereospecific inverse thio effects in the aporibozyme suggest that the GlcN6P cofactor disrupts an inhibitory interaction of the nucleophile. Strong metal ion rescue in the aporibozyme reveals this cofactor also provides electrostatic stabilization. Ribozyme organic cofactors thus perform myriad catalytic roles, allowing RNA to compensate for its limited functional diversity. PMID:28192411
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Jerrett, M; Burnett, R; Brook, J; Kanaroglou, P; Giovis, C; Finkelstein, N; Hutchison, B
2004-01-01
Study objective: To assess the short term association between air pollution and mortality in different zones of an industrial city. An intra-urban study design is used to test the hypothesis that socioeconomic characteristics modify the acute health effects of ambient air pollution exposure. Design: The City of Hamilton, Canada, was divided into five zones based on proximity to fixed site air pollution monitors. Within each zone, daily counts of non-trauma mortality and air pollution estimates were combined. Generalised linear models (GLMs) were used to test mortality associations with sulphur dioxide (SO2) and with particulate air pollution measured by the coefficient of haze (CoH). Main results: Increased mortality was associated with air pollution exposure in a citywide model and in intra-urban zones with lower socioeconomic characteristics. Low educational attainment and high manufacturing employment in the zones significantly and positively modified the acute mortality effects of air pollution exposure. Discussion: Three possible explanations are proposed for the observed effect modification by education and manufacturing: (1) those in manufacturing receive higher workplace exposures that combine with ambient exposures to produce larger health effects; (2) persons with lower education are less mobile and experience less exposure measurement error, which reduces bias toward the null; or (3) manufacturing and education proxy for many social variables representing material deprivation, and poor material conditions increase susceptibility to health risks from air pollution. PMID:14684724
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Human access and landscape structure effects on Andean forest bird richness
NASA Astrophysics Data System (ADS)
Aubad, Jorge; Aragón, Pedro; Rodríguez, Miguel Á.
2010-07-01
We analyzed the influence of human access and landscape structure on forest bird species richness in a fragmented landscape of the Colombian Andes. In Latin America, habitat loss and fragmentation are considered as the greatest threats to biodiversity because a large number of countryside villagers complement their food and incomes with the extraction of forest resources. Anthropogenic actions may also affect forest species by bird hunting or indirectly through modifying the structure of forest habitats. We surveyed 14 secondary cloud forest remnants to generate bird species richness data for each of them. We also quantified six landscape structure descriptors of forest patch size (patch area and core area), shape (perimeter of each fragment and the Patton's shape index) and isolation (nearest neighbor distance and edge contrast), and generated (using principal components analysis) a synthetic human influence variable based on the distance of each fragment to roads and villages, as well as the total slope of the fragments. Species richness was related to these variables using generalized linear models (GLMs) complemented with model selection techniques based on information theory and partial regression analysis. We found that forest patch size and accessibility were key drivers of bird richness, which increased toward largest patches, but decreased in those more accessible to humans and their potential disturbances. Both patch area and human access effects on forest bird species richness were complementary and similar in magnitude. Our results provide a basis for biodiversity conservation plans and initiatives of Andean forest diversity.
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Sojda, Richard S.; Towler, Erin; Roberts, Mike; Rajagopalan, Balaji
2013-01-01
[1] Despite the influence of hydroclimate on river ecosystems, most efforts to date have focused on using climate information to predict streamflow for water supply. However, as water demands intensify and river systems are increasingly stressed, research is needed to explicitly integrate climate into streamflow forecasts that are relevant to river ecosystem management. To this end, we present a five step risk-based framework: (1) define risk tolerance, (2) develop a streamflow forecast model, (3) generate climate forecast ensembles, (4) estimate streamflow ensembles and associated risk, and (5) manage for climate risk. The framework is successfully demonstrated for an unregulated watershed in southwest Montana, where the combination of recent drought and water withdrawals has made it challenging to maintain flows needed for healthy fisheries. We put forth a generalized linear modeling (GLM) approach to develop a suite of tools that skillfully model decision-relevant low flow characteristics in terms of climate predictors. Probabilistic precipitation forecasts are used in conjunction with the GLMs, resulting in season-ahead prediction ensembles that provide the full risk profile. These tools are embedded in an end-to-end risk management framework that directly supports proactive fish conservation efforts. Results show that the use of forecasts can be beneficial to planning, especially in wet years, but historical precipitation forecasts are quite conservative (i.e., not very “sharp”). Synthetic forecasts show that a modest “sharpening” can strongly impact risk and improve skill. We emphasize that use in management depends on defining relevant environmental flows and risk tolerance, requiring local stakeholder involvement.
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NASA Astrophysics Data System (ADS)
Nambu, Ryogen; Saito, Hajime; Tanaka, Yoshio; Higano, Junya; Kuwahara, Hisami
2012-03-01
There are many studies on spatial distributions of Asari clam Ruditapes philippinarum adults on tidal flats but few have dealt with spatial distributions of newly settled Asari clam (<0.3 mm shell length, indicative of settlement patterns) in relation to physical/topographical conditions on tidal flats. We examined small-scale spatial distributions of newly settled individuals on the Matsunase tidal flat, central Japan, during the low spring tides on two days 29th-30th June 2007, together with the shear stress from waves and currents on the flat. The characteristics of spatial distribution of newly settled Asari clam markedly varied depending on both of hydrodynamic and topographical conditions on the tidal flat. Using generalized linear models (GLMs), factors responsible for affecting newly settled Asari clam density and its spatial distribution were distinguished between sampling days, with "crest" sites always having a negative influence each on the density and the distribution on both sampling days. The continuously recorded data for the wave-current flows at the "crest" site on the tidal flat showed that newly settled Asari clam, as well as bottom sediment particles, at the "crest" site to be easily displaced. Small-scale spatial distributions of newly settled Asari clam changed with more advanced benthic stages in relation to the wave shear stress.
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Haakonsson, Signe; Rodríguez-Gallego, Lorena; Somma, Andrea; Bonilla, Sylvia
2017-12-31
Cyanobacterial blooms are expected to become more frequent in freshwaters globally due to eutrophication and climate change effects. However, our knowledge about cyanobacterial biogeography in the subtropics, particularly in lotic ecosystems, is still very limited and the relationship of blooms to temperature and precipitation remains unclear. We took advantage of a comprehensive database of field data compiled over several years (1997 to 2015) to compare cyanobacteria biomass and distribution between lentic and lotic subtropical freshwaters (36 ecosystems, 30°-35°S) and to investigate the role of water temperature and precipitation as significant predictors in eutrophic ecosystems. A filamentous Nostocales, Dolichospermum (Anabaena), was the most widely distributed and frequent genus in the region of the study, followed by the colonial Microcystis, supporting observations of a global latitudinal pattern. Similar total cyanobacteria biovolumes (TCB) were found in lentic and lotic ecosystems, but the proportion of Dolichospermum was higher in lotic ecosystems. Using generalized linear models (GLMs), we found that temperature and rainfall explained 27% of the variation in TCB in lotic ecosystems, while temperature explained 19 and 28% of Dolichospermum and Microcystis biovolume, respectively. In lentic ecosystems, accumulated rainfall explained 34% of the variation of Microcystis biovolume while temperature explained 64%. Our results imply that the increase in extreme meteorological events and temperature predicted by climate models will promote increasingly severe cyanobacterial blooms in eutrophic subtropical freshwaters. Our analysis provides new information about the occurrence of bloom-forming cyanobacteria for southeastern South America and thus fills an important knowledge gap for subtropical freshwaters. Copyright © 2017 Elsevier B.V. All rights reserved.
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NASA Astrophysics Data System (ADS)
Manzanas, R., Sr.; Brands, S.; San Martin, D., Sr.; Gutiérrez, J. M., Sr.
2014-12-01
This work shows that local-scale climate projections obtained by means of statistical downscaling are sensitive to the choice of reanalysis used for calibration. To this aim, a Generalized Linear Model (GLM) approach is applied to downscale daily precipitation in the Philippines. First, the GLMs are trained and tested -under a cross-validation scheme- separately for two distinct reanalyses (ERA-Interim and JRA-25) for the period 1981-2000. When the observed and downscaled time-series are compared, the attained performance is found to be sensitive to the reanalysis considered if climate change signal bearing variables (temperature and/or specific humidity) are included in the predictor field. Moreover, performance differences are shown to be in correspondence with the disagreement found between the raw predictors from the two reanalyses. Second, the regression coefficients calibrated either with ERA-Interim or JRA-25 are subsequently applied to the output of a Global Climate Model (MPI-ECHAM5) in order to assess the sensitivity of local-scale climate change projections (up to 2100) to reanalysis choice. In this case, the differences detected in present climate conditions are considerably amplified, leading to "delta-change" estimates differing by up to a 35% (on average for the entire country) depending on the reanalysis used for calibration. Therefore, reanalysis choice is shown to importantly contribute to the uncertainty of local-scale climate change projections, and, consequently, should be treated with equal care as other, well-known, sources of uncertainty -e.g., the choice of the GCM and/or downscaling method.- Implications of the results for the entire tropics, as well as for the Model Output Statistics downscaling approach are also briefly discussed.
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NASA Astrophysics Data System (ADS)
Liu, Yonghe; Feng, Jinming; Liu, Xiu; Zhao, Yadi
2017-12-01
Statistical downscaling (SD) is a method that acquires the local information required for hydrological impact assessment from large-scale atmospheric variables. Very few statistical and deterministic downscaling models for daily precipitation have been conducted for local sites influenced by the East Asian monsoon. In this study, SD models were constructed by selecting the best predictors and using generalized linear models (GLMs) for Feixian, a site in the Yishu River Basin and Shandong Province. By calculating and mapping Spearman rank correlation coefficients between the gridded standardized values of five large-scale variables and daily observed precipitation, different cyclonic circulation patterns were found for monsoonal precipitation in summer (June-September) and winter (November-December and January-March); the values of the gridded boxes with the highest absolute correlations for observed precipitation were selected as predictors. Data for predictors and predictands covered the period 1979-2015, and different calibration and validation periods were divided when fitting and validating the models. Meanwhile, the bootstrap method was also used to fit the GLM. All the above thorough validations indicated that the models were robust and not sensitive to different samples or different periods. Pearson's correlations between downscaled and observed precipitation (logarithmically transformed) on a daily scale reached 0.54-0.57 in summer and 0.56-0.61 in winter, and the Nash-Sutcliffe efficiency between downscaled and observed precipitation reached 0.1 in summer and 0.41 in winter. The downscaled precipitation partially reflected exact variations in winter and main trends in summer for total interannual precipitation. For the number of wet days, both winter and summer models were able to reflect interannual variations. Other comparisons were also made in this study. These results demonstrated that when downscaling, it is appropriate to combine a correlation-based predictor selection across a spatial domain with GLM modeling.
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2010-01-01
Background There is growing concern in communities surrounding airports regarding the contribution of various emission sources (such as aircraft and ground support equipment) to nearby ambient concentrations. We used extensive monitoring of nitrogen dioxide (NO2) in neighborhoods surrounding T.F. Green Airport in Warwick, RI, and land-use regression (LUR) modeling techniques to determine the impact of proximity to the airport and local traffic on these concentrations. Methods Palmes diffusion tube samplers were deployed along the airport's fence line and within surrounding neighborhoods for one to two weeks. In total, 644 measurements were collected over three sampling campaigns (October 2007, March 2008 and June 2008) and each sampling location was geocoded. GIS-based variables were created as proxies for local traffic and airport activity. A forward stepwise regression methodology was employed to create general linear models (GLMs) of NO2 variability near the airport. The effect of local meteorology on associations with GIS-based variables was also explored. Results Higher concentrations of NO2 were seen near the airport terminal, entrance roads to the terminal, and near major roads, with qualitatively consistent spatial patterns between seasons. In our final multivariate model (R2 = 0.32), the local influences of highways and arterial/collector roads were statistically significant, as were local traffic density and distance to the airport terminal (all p < 0.001). Local meteorology did not significantly affect associations with principal GIS variables, and the regression model structure was robust to various model-building approaches. Conclusion Our study has shown that there are clear local variations in NO2 in the neighborhoods that surround an urban airport, which are spatially consistent across seasons. LUR modeling demonstrated a strong influence of local traffic, except the smallest roads that predominate in residential areas, as well as proximity to the airport terminal. PMID:21083910
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Adamkiewicz, Gary; Hsu, Hsiao-Hsien; Vallarino, Jose; Melly, Steven J; Spengler, John D; Levy, Jonathan I
2010-11-17
There is growing concern in communities surrounding airports regarding the contribution of various emission sources (such as aircraft and ground support equipment) to nearby ambient concentrations. We used extensive monitoring of nitrogen dioxide (NO2) in neighborhoods surrounding T.F. Green Airport in Warwick, RI, and land-use regression (LUR) modeling techniques to determine the impact of proximity to the airport and local traffic on these concentrations. Palmes diffusion tube samplers were deployed along the airport's fence line and within surrounding neighborhoods for one to two weeks. In total, 644 measurements were collected over three sampling campaigns (October 2007, March 2008 and June 2008) and each sampling location was geocoded. GIS-based variables were created as proxies for local traffic and airport activity. A forward stepwise regression methodology was employed to create general linear models (GLMs) of NO2 variability near the airport. The effect of local meteorology on associations with GIS-based variables was also explored. Higher concentrations of NO2 were seen near the airport terminal, entrance roads to the terminal, and near major roads, with qualitatively consistent spatial patterns between seasons. In our final multivariate model (R2 = 0.32), the local influences of highways and arterial/collector roads were statistically significant, as were local traffic density and distance to the airport terminal (all p < 0.001). Local meteorology did not significantly affect associations with principal GIS variables, and the regression model structure was robust to various model-building approaches. Our study has shown that there are clear local variations in NO2 in the neighborhoods that surround an urban airport, which are spatially consistent across seasons. LUR modeling demonstrated a strong influence of local traffic, except the smallest roads that predominate in residential areas, as well as proximity to the airport terminal.
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Predicting motor vehicle collisions using Bayesian neural network models: an empirical analysis.
Xie, Yuanchang; Lord, Dominique; Zhang, Yunlong
2007-09-01
Statistical models have frequently been used in highway safety studies. They can be utilized for various purposes, including establishing relationships between variables, screening covariates and predicting values. Generalized linear models (GLM) and hierarchical Bayes models (HBM) have been the most common types of model favored by transportation safety analysts. Over the last few years, researchers have proposed the back-propagation neural network (BPNN) model for modeling the phenomenon under study. Compared to GLMs and HBMs, BPNNs have received much less attention in highway safety modeling. The reasons are attributed to the complexity for estimating this kind of model as well as the problem related to "over-fitting" the data. To circumvent the latter problem, some statisticians have proposed the use of Bayesian neural network (BNN) models. These models have been shown to perform better than BPNN models while at the same time reducing the difficulty associated with over-fitting the data. The objective of this study is to evaluate the application of BNN models for predicting motor vehicle crashes. To accomplish this objective, a series of models was estimated using data collected on rural frontage roads in Texas. Three types of models were compared: BPNN, BNN and the negative binomial (NB) regression models. The results of this study show that in general both types of neural network models perform better than the NB regression model in terms of data prediction. Although the BPNN model can occasionally provide better or approximately equivalent prediction performance compared to the BNN model, in most cases its prediction performance is worse than the BNN model. In addition, the data fitting performance of the BPNN model is consistently worse than the BNN model, which suggests that the BNN model has better generalization abilities than the BPNN model and can effectively alleviate the over-fitting problem without significantly compromising the nonlinear approximation ability. The results also show that BNNs could be used for other useful analyses in highway safety, including the development of accident modification factors and for improving the prediction capabilities for evaluating different highway design alternatives.
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Tackenberg, Oliver
2007-01-01
Background and Aims Biomass is an important trait in functional ecology and growth analysis. The typical methods for measuring biomass are destructive. Thus, they do not allow the development of individual plants to be followed and they require many individuals to be cultivated for repeated measurements. Non-destructive methods do not have these limitations. Here, a non-destructive method based on digital image analysis is presented, addressing not only above-ground fresh biomass (FBM) and oven-dried biomass (DBM), but also vertical biomass distribution as well as dry matter content (DMC) and growth rates. Methods Scaled digital images of the plants silhouettes were taken for 582 individuals of 27 grass species (Poaceae). Above-ground biomass and DMC were measured using destructive methods. With image analysis software Zeiss KS 300, the projected area and the proportion of greenish pixels were calculated, and generalized linear models (GLMs) were developed with destructively measured parameters as dependent variables and parameters derived from image analysis as independent variables. A bootstrap analysis was performed to assess the number of individuals required for re-calibration of the models. Key Results The results of the developed models showed no systematic errors compared with traditionally measured values and explained most of their variance (R2 ≥ 0·85 for all models). The presented models can be directly applied to herbaceous grasses without further calibration. Applying the models to other growth forms might require a re-calibration which can be based on only 10–20 individuals for FBM or DMC and on 40–50 individuals for DBM. Conclusions The methods presented are time and cost effective compared with traditional methods, especially if development or growth rates are to be measured repeatedly. Hence, they offer an alternative way of determining biomass, especially as they are non-destructive and address not only FBM and DBM, but also vertical biomass distribution and DMC. PMID:17353204
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Efficient Synthesis and Bioactivity of Novel Triazole Derivatives.
Hu, Boyang; Zhao, Hanqing; Chen, Zili; Xu, Chen; Zhao, Jianzhuang; Zhao, Wenting
2018-03-21
Triazole pesticides are organic nitrogen-containing heterocyclic compounds, which contain 1,2,3-triazole ring. In order to develop potential glucosamine-6-phosphate synthase (GlmS) inhibitor fungicides, forty compounds of triazole derivatives were synthesized in an efficient way, thirty nine of them were new compounds. The structures of all the compounds were confirmed by high resolution mass spectrometer (HRMS), ¹H-NMR and 13 C-NMR. The fungicidal activities results based on means of mycelium growth rate method indicated that some of the compounds exhibited good fungicidal activities against P. CapasiciLeonian , Sclerotinia sclerotiorum (Lib.) de Bary, Pyricularia oryzae Cav. and Fusarium oxysporum Schl. F.sp. vasinfectum (Atk.) Snyd. & Hans. at the concentration of 50 µg/mL, especially the inhibitory rates of compounds 1-d and 1-f were over 80%. At the same time, the preliminary studies based on the Elson-Morgan method indicated that the compounds exhibited some inhibitory activity toward glucosamine-6-phosphate synthase (GlmS). These compounds will be further studied as potential antifungal lead compounds. The structure-activity relationships (SAR) were discussed in terms of the effects of the substituents on both the benzene and the sugar ring.
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Predicting the Invasion Potential of a Puerto Rican Frog in Hawaii using MODIS Satellite Imagery
NASA Astrophysics Data System (ADS)
Bisrat, S. A.; White, M. A.
2008-12-01
The Puerto Rican coqui frog (Eleutherodactylus coqui, hereafter coqui), which was introduced into Hawaii accidentally via commercial nurseries, is an aggressive invasive species in Hawaii. The coqui threatens Hawaii's unique ecological communities because it predates upon endemic invertebrates, which comprise the large majority of Hawaii's endemic fauna. Coqui frogs also affect real estate valuations because of their loud mating calls. Despite this widespread problem, the potential coqui range in Hawaii is currently unknown, making control and management efforts difficult. We fitted linear discriminant analysis (LDA), logistic regression (LR) via generalized linear models (GLMs), generalized additive models (GAMs), classification trees (CTs), random forests (RF), and support vector machine (SVM) to model the species distribution and map their invasion potential. We used five MODIS satellite imagery-derived biophysical variables as explanatory variables: leaf area index (LAI), fraction of photosynthetically active radiation absorbed by vegetation (FPAR), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and land surface temperature (LST) from three MODIS products: MOD11 (LST), MOD13 (LAI and FPAR), and MOD15 (Vegetation Index) (collection 4). We used 2000-2005 MODIS data from Aqua and Terra satellites to generate monthly climatologies for each biophysical variable. We collected presence/absence data from Puerto Rico and Hawaii using a 1 km grid overlaid over the entire islands of Puerto Rico and the Island of Hawaii by sampling every other pixel of the grid intersecting with the road network. We then used the dataset from Puerto Rico to train the six models while the Hawaii dataset was used as a test set. All six models predicted the invasion potential of coqui frogs in Hawaii with a moderate success with mean Kappa value of 0.31, mean area under the curve of receiver operating characteristics (AUC) of 0.75 and mean classification accuracy (CA) of 0.69. RF and SVM outperformed the other classifiers with Kappa value of 0.4, AUC value of 0.79 and CA of 0.71 for RF and Kappa value of 0.4, AUC value of 0.71 and CA value of 0.72 for SVM. These results suggest climate matching between the native and the introduced habitats of coqui frogs is not strong. Further, the results suggest coqui frogs in their introduced habitat are not showing strong niche conservation.
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Masi, Laura; Ottanelli, Silva; Berni, Rossella; Cacudi, Ettore; Giusti, Francesca; Marcucci, Gemma; Cavalli, Loredana; Fossi, Caterina; Marini, Francesca; Ciuffi, Simone; Tanini, Annalisa; Brandi, Maria Luisa
2014-01-01
Sex steroids are important regulators of bone physiology and play an essential role in the maintenance of bone health throughout the life. Hormonal replacement therapy (HRT) is a treatment commonly used to relieve symptoms and some undesirable consequences of menopause such as osteoporosis. Osteoporosis, characterized by the loss of bone mass and deterioration of microarchitecture with a consequent higher risk of fragility fractures, is under genetic influence. A tetranucleotide (TTTA)n microsatellite repeat polymorphism, at intron 4 of the CYP19 (aromatase) gene, has been previously associated with higher lumbar spine bone mineral density (LS-BMD) and lower risk of spine fracture in postmenopausal women. Moreover, the ERα encoded by the ESR1 gene is another important candidate for the regulation of bone mass of menopause. Moreover prospective analysis from >18.000 subjects at the GENOMOS study indicated that XX homozygotes genotype had a reduced risk of fracture independently from BMD. In the present study, we investigated in postmenopausal Italian women, at baseline and after 1 year of HRT, whether ESR1 and CYP19 gene polymorphisms could affect BMD through different statistical models. This study has been performed on 100 post-menopausal Italian women, from a larger group of 250. The study group was administred HRT and LS-BMD was measured at baseline and after 1 year of therapy. Genetic analysis evaluating ESR1 and CYP19 gene polymorphisms was performed. Generalized Linear Models (GLMs) test showed that women with normal LS-BMD at the baseline had a major statistically significant BMD increase of 0.1426 gr/cm(2) (p= 0.0001) with respect to the osteoporotic patients. In addition, subjects with genotype 1 and 2 of CYP19 gene had a lower modification in LS-BMD after 1 year of HRT (0.0837 gr/cm(2) and 0,076 g/cm(2); p=0.0470 and 0,0547 respectively) when compared to genotype 3. No influences of the aromatase genotypes were observed in the variable difference using both Anova and GLMs test. Regarding the ESR1 gene polymorphism, the LS-BMD after 1 year of HRT was influenced by the diagnosis at the baseline and height and ERα genotypes were able to influence difference with statistical significant results with both test. In the present study, we have demonstrated that CYP19 gene polymorphism is able to influence the effect of 1 year HRT on LS-BMD with no influence on pre-/ and post-/HRT LS-BMD differences. Although ESR1 gene polymorphism is not able to influence the LS-BMD after 1 year HRT, it influences the observed modifications during the year of therapy. These data underlie the complexity of the genetics of the bone mass and its importance in influencing the response to HRT.
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How common is self-incompatibility across species of the herkogamous genus Ariocarpus?
Martínez-Peralta, Concepción; Márquez-Guzmán, Judith; Mandujano, María C
2014-03-01
Self-incompatibility (SI), the most effective mechanism to prevent selfing, may limit the number of compatible mates in populations. The seven species of Ariocarpus are endangered and predominantly outcrossers but fruit set may reach 1-20% after selfing. We aimed to determine whether SI is the underlying mechanism influencing mating in Ariocarpus species. We characterized the presence/absence of SI using pollination treatments (self-pollination, cross-pollination, natural pollination) in one population per species. We assessed SI using epifluorescence and generalized linear models (GLMs) to compare the presence of pollen tubes in the stigma, stylar transmitting tissue, and ovary among self- and cross-pollinated pistils 48 h after pollination. Following the same treatments, production of fruit set was noted and related to pollen tube growth. Pollen tubes were found more frequently in the ovaries of natural and cross-pollinated flowers than in ovaries of self-pollinated. Stylar rejection of self-pollen indicated gametophytic SI, although pollen tubes reached the ovaries in six species (4-33% of pistils). Fruit set was lower after hand-pollinations than expected from pollen tube observations. The low percentages of self-compatibility in all species in pollen tube growth and pollination experiments indicated that no species had complete self-sterility, suggesting the presence of partial SI. Reduced fruit set relative to pollen tube production could result from a threshold of insufficient pollination, early-acting inbreeding depression, or resource limitation. The origin of partial SI in Ariocarpus could respond to pressures such as pollen limitation and population size.
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Mistletoe Infection in an Oak Forest Is Influenced by Competition and Host Size
Matula, Radim; Svátek, Martin; Pálková, Marcela; Volařík, Daniel; Vrška, Tomáš
2015-01-01
Host size and distance from an infected plant have been previously found to affect mistletoe occurrence in woody vegetation but the effect of host plant competition on mistletoe infection has not been empirically tested. For an individual tree, increasing competition from neighbouring trees decreases its resource availability, and resource availability is also known to affect the establishment of mistletoes on host trees. Therefore, competition is likely to affect mistletoe infection but evidence for such a mechanism is lacking. Based on this, we hypothesised that the probability of occurrence as well as the abundance of mistletoes on a tree would increase not only with increasing host size and decreasing distance from an infected tree but also with decreasing competition by neighbouring trees. Our hypothesis was tested using generalized linear models (GLMs) with data on Loranthus europaeus Jacq., one of the two most common mistletoes in Europe, on 1015 potential host stems collected in a large fully mapped plot in the Czech Republic. Because many trees were multi-stemmed, we ran the analyses for both individual stems and whole trees. We found that the probability of mistletoe occurrence on individual stems was affected mostly by stem size, whereas competition had the most important effects on the probability of mistletoe occurrence on whole trees as well as on mistletoe abundance. Therefore, we confirmed our hypothesis that competition among trees has a negative effect on mistletoe occurrence. PMID:25992920
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NASA Astrophysics Data System (ADS)
Michelot, Candice; Pinaud, David; Fortin, Matthieu; Maes, Philippe; Callard, Benjamin; Leicher, Marine; Barbraud, Christophe
2017-07-01
Studies of habitat selection by higher trophic level species are necessary for using top predator species as indicators of ecosystem functioning. However, contrary to terrestrial ecosystems, few habitat selection studies have been conducted at a fine scale for coastal marine top predator species, and fewer have coupled diet data with habitat selection modeling to highlight a link between prey selection and habitat use. The aim of this study was to characterize spatially and oceanographically, at a fine scale, the habitats used by the European Shag Phalacrocorax aristotelis in the Special Protection Area (SPA) of Houat-Hœdic in the Mor Braz Bay during its foraging activity. Habitat selection models were built using in situ observation data of foraging shags (transect sampling) and spatially explicit environmental data to characterize marine benthic habitats. Observations were first adjusted for detectability biases and shag abundance was subsequently spatialized. The influence of habitat variables on shag abundance was tested using Generalized Linear Models (GLMs). Diet data were finally confronted to habitat selection models. Results showed that European shags breeding in the Mor Braz Bay changed foraging habitats according to the season and to the different environmental and energetic constraints. The proportion of the main preys also varied seasonally. Rocky and coarse sand habitats were clearly preferred compared to fine or muddy sand habitats. Shags appeared to be more selective in their foraging habitats during the breeding period and the rearing of chicks, using essentially rocky areas close to the colony and consuming preferentially fish from the Labridae family and three other fish families in lower proportions. During the post-breeding period shags used a broader range of habitats and mainly consumed Gadidae. Thus, European shags seem to adjust their feeding strategy to minimize energetic costs, to avoid intra-specific competition and to maximize access to suitable habitats and preys.
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Davis, Jill R; Kern, David M; Williams, Setareh A; Tunceli, Ozgur; Wu, Bingcao; Hollis, Sally; Strange, Charlie; Trudo, Frank
2016-03-01
Chronic obstructive pulmonary disease (COPD) affects approximately 15 million people in the United States and accounts for approximately $36 billion in economic burden, primarily due to medical costs. To address the increasing clinical and economic burden, the Global Initiative for Chronic Obstructive Lung Disease emphasizes the use of therapies that help prevent COPD exacerbations, including inhaled corticosteroid/long-acting beta2-agonist (ICS/LABA). To evaluate health care costs and utilization among COPD patients newly initiating ICS/LABA combination therapy with budesonide/formoterol (BFC) or fluticasone/salmeterol (FSC) in a managed care system. COPD patients aged 40 years and older who initiated BFC (160/4.5 μg) or FSC (250/50 μg) treatment between March 1, 2009, and March 31, 2012, were identified using claims data from major U.S. health plans. BFC and FSC patients were propensity score matched (1:1) on age, sex, prior asthma diagnosis, prior COPD-related health care utilization, and respiratory medication use. COPD-related, pneumonia-related, and all-cause costs and utilization were analyzed during the 12-month follow-up period. Post-index costs were assessed with generalized linear models (GLMs) with gamma distribution. Health care utilization data were analyzed via logistic regression (any event vs. none) and GLMs with negative binomial distribution (number of visits) and were adjusted for the analogous pre-index variable as well as pre-index characteristics that remained imbalanced after matching. After matching, each cohort had 3,697 patients balanced on age (mean 64 years), sex (female 52% BFC and 54% FSC), asthma and other comorbid conditions, prior COPD-related health care utilization, and respiratory medication use. During the 12-month follow-up, COPD-related costs averaged $316 less for BFC versus FSC patients ($4,326 vs. $4,846; P = 0.003), reflecting lower inpatient ($966 vs. $1,202; P < 0.001), pharmacy ($1,482 vs. $1,609; P = 0.002), and outpatient/office ($1,378 vs. $1,436; P = 0.048) costs, but higher emergency department ($257 vs. $252; P = 0.033) costs. Pneumonia-related health care costs were also lower on average for BFC patients ($2,855 vs. $3,605; P < 0.001). Similarly, initiating BFC was associated with lower all-use health care costs versus initiating FSC ($21,580 vs. $24,483; P < 0.001, respectively). No differences in health care utilization were found between the 2 groups. In this study, although no difference was observed in rates of health care utilization, COPD patients initiating BFC treatment incurred lower average COPD-related, pneumonia-related, and all-cause costs versus FSC initiators, which was driven by cumulative differences in inpatient, outpatient, and pharmacy costs.
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Moran, John L; Solomon, Patricia J
2012-05-16
For the analysis of length-of-stay (LOS) data, which is characteristically right-skewed, a number of statistical estimators have been proposed as alternatives to the traditional ordinary least squares (OLS) regression with log dependent variable. Using a cohort of patients identified in the Australian and New Zealand Intensive Care Society Adult Patient Database, 2008-2009, 12 different methods were used for estimation of intensive care (ICU) length of stay. These encompassed risk-adjusted regression analysis of firstly: log LOS using OLS, linear mixed model [LMM], treatment effects, skew-normal and skew-t models; and secondly: unmodified (raw) LOS via OLS, generalised linear models [GLMs] with log-link and 4 different distributions [Poisson, gamma, negative binomial and inverse-Gaussian], extended estimating equations [EEE] and a finite mixture model including a gamma distribution. A fixed covariate list and ICU-site clustering with robust variance were utilised for model fitting with split-sample determination (80%) and validation (20%) data sets, and model simulation was undertaken to establish over-fitting (Copas test). Indices of model specification using Bayesian information criterion [BIC: lower values preferred] and residual analysis as well as predictive performance (R2, concordance correlation coefficient (CCC), mean absolute error [MAE]) were established for each estimator. The data-set consisted of 111663 patients from 131 ICUs; with mean(SD) age 60.6(18.8) years, 43.0% were female, 40.7% were mechanically ventilated and ICU mortality was 7.8%. ICU length-of-stay was 3.4(5.1) (median 1.8, range (0.17-60)) days and demonstrated marked kurtosis and right skew (29.4 and 4.4 respectively). BIC showed considerable spread, from a maximum of 509801 (OLS-raw scale) to a minimum of 210286 (LMM). R2 ranged from 0.22 (LMM) to 0.17 and the CCC from 0.334 (LMM) to 0.149, with MAE 2.2-2.4. Superior residual behaviour was established for the log-scale estimators. There was a general tendency for over-prediction (negative residuals) and for over-fitting, the exception being the GLM negative binomial estimator. The mean-variance function was best approximated by a quadratic function, consistent with log-scale estimation; the link function was estimated (EEE) as 0.152(0.019, 0.285), consistent with a fractional-root function. For ICU length of stay, log-scale estimation, in particular the LMM, appeared to be the most consistently performing estimator(s). Neither the GLM variants nor the skew-regression estimators dominated.
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Zeng, Qing; Shi, Linlu; Wen, Li; Chen, Junzhu; Duo, Hairui; Lei, Guangchun
2015-01-01
Gravel bars are characteristic components of river landscapes and are increasingly recognized as key sites for many waterbirds, though detailed studies on the ecological function of gravel bars for waterbirds are rare. In this study, we surveyed the endangered Scaly-sided Merganser Mergus squamatus along a 40 km river section of Yuan River, in Central China, for three consecutive winters. We derived the landscape metrics of river gravel bars from geo-rectified fine resolution (0.6 m) aerial image data. We then built habitat suitability models (Generalized Linear Models—GLMs) to study the effects of landscape metrics and human disturbance on Scaly-sided Merganser presence probability. We found that 1) the Scaly-sided Merganser tended to congregate at river segments with more gravel patches; 2) the Scaly-sided Merganser preferred areas with larger and more contiguous gravel patches; and 3) the number of houses along the river bank (a proxy for anthropogenic disturbance) had significantly negative impacts on the occurrence of the Scaly-sided Merganser. Our results suggest that gravel bars are vital to the Scaly-sided Merganser as shelters from disturbance, as well as sites for feeding and roosting. Therefore, maintaining the exposure of gravel bars in regulated rivers during the low water period in winter might be the key for the conservation of the endangered species. These findings have important implications for understanding behavioral evolution and distribution of the species and for delineating between habitats of different quality for conservation and management. PMID:25996671
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Predictive Tools for Severe Dengue Conforming to World Health Organization 2009 Criteria
Carrasco, Luis R.; Leo, Yee Sin; Cook, Alex R.; Lee, Vernon J.; Thein, Tun L.; Go, Chi Jong; Lye, David C.
2014-01-01
Background Dengue causes 50 million infections per year, posing a large disease and economic burden in tropical and subtropical regions. Only a proportion of dengue cases require hospitalization, and predictive tools to triage dengue patients at greater risk of complications may optimize usage of limited healthcare resources. For severe dengue (SD), proposed by the World Health Organization (WHO) 2009 dengue guidelines, predictive tools are lacking. Methods We undertook a retrospective study of adult dengue patients in Tan Tock Seng Hospital, Singapore, from 2006 to 2008. Demographic, clinical and laboratory variables at presentation from dengue polymerase chain reaction-positive and serology-positive patients were used to predict the development of SD after hospitalization using generalized linear models (GLMs). Principal findings Predictive tools compatible with well-resourced and resource-limited settings – not requiring laboratory measurements – performed acceptably with optimism-corrected specificities of 29% and 27% respectively for 90% sensitivity. Higher risk of severe dengue (SD) was associated with female gender, lower than normal hematocrit level, abdominal distension, vomiting and fever on admission. Lower risk of SD was associated with more years of age (in a cohort with an interquartile range of 27–47 years of age), leucopenia and fever duration on admission. Among the warning signs proposed by WHO 2009, we found support for abdominal pain or tenderness and vomiting as predictors of combined forms of SD. Conclusions The application of these predictive tools in the clinical setting may reduce unnecessary admissions by 19% allowing the allocation of scarce public health resources to patients according to the severity of outcomes. PMID:25010515
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Secular trends in the prevalence of dementia and depression in Swedish septuagenarians 1976-2006.
Wiberg, P; Waern, M; Billstedt, E; Ostling, S; Skoog, I
2013-12-01
It is not clear whether the prevalence of dementia and depression among the elderly has changed during the past 30 years. Population-based samples from Gothenburg, Sweden were examined with identical psychiatric and neuropsychiatric examinations at age 70 years in 1976-1977 (n = 404, response rate 78.8%) and 2000-2001 (n = 579, response rate 66.4%), and at age 75 in 1976-1977 (n = 303, response rate 78%) and 2005-2006 (n = 753, response rate 63.4%). Depression was diagnosed according to DSM-IV and dementia according to Kay's criteria. General linear models (GLMs) were used to test for differences between groups. Dementia was related to age but not to birth cohort or sex. Major depression was related to sex (higher in women) but not to birth cohort or age. Minor depression was related to birth cohort, sex (higher in women), age (higher at age 75) and the interaction effect of birth cohort × age; that is, the prevalence of minor depression increased with age in the 2000s but not in the 1970s. Thus, the prevalence of minor depression was higher in 2005-2006 than in 1976-1977 among 75-year-olds for both men (12.4% v. 3.7%) and women (19.1% v. 5.6%) whereas there were no birth cohort differences at age 70. Secular changes were observed only for minor depression, which is considered to be related more to psychosocial factors than major depression. The high prevalence of minor depression in later-born birth cohorts emphasizes the importance of detecting minor depression in the elderly.
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Association mapping of seed and disease resistance traits in Theobroma cacao L.
Motilal, Lambert A; Zhang, Dapeng; Mischke, Sue; Meinhardt, Lyndel W; Boccara, Michel; Fouet, Olivier; Lanaud, Claire; Umaharan, Pathmanathan
2016-12-01
Microsatellite and single nucleotide polymorphism markers that could be used in marker assisted breeding of cacao were identified for number of filled seeds, black pod resistance and witches' broom disease resistance. An association mapping approach was employed to identify markers for seed number and resistance to black pod and witches' broom disease (WBD) in cacao (Theobroma cacao L.). Ninety-five microsatellites (SSRs) and 775 single nucleotide polymorphisms (SNPs) were assessed on 483 unique trees in the International Cocoa Genebank Trinidad (ICGT). Linkage disequilibrium (LD) and association mapping studies were conducted to identify markers to tag the phenotypic traits. Decay of LD occurred over an average 9.3 cM for chromosomes 1-9 and 2.5 cM for chromosome 10. Marker/trait associations were generally identified based on general linear models (GLMs) that incorporated principal components from molecular information on relatedness factor. Seven markers (mTcCIR 8, 66, 126, 212; TcSNP368, 697, 1370) on chromosomes 1 and 9 were identified for number of filled seeds (NSEED). A single marker was found for black pod resistance (mTcCIR280) on chromosome 3, whereas six markers on chromosomes 4, 5, 6, 8, and 10 were detected for WBD (mTcCIR91, 183; TcSNP375, 720, 1230 and 1374). It is expected that this association mapping study in cacao would contribute to the knowledge of the genetic determinism of cocoa traits and that the markers identified herein would prove useful in marker assisted breeding of cacao.
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Rabies in the Baltic States: Decoding a Process of Control and Elimination
Robardet, Emmanuelle; Picard-Meyer, Evelyne; Dobroštana, Marianna; Jaceviciene, Ingrida; Mähar, Katrin; Muižniece, Zita; Pridotkas, Gediminas; Masiulis, Marius; Niin, Enel; Olševskis, Edvīns; Cliquet, Florence
2016-01-01
Rabies is a fatal zoonosis that still causes nearly 70, 000 human deaths every year. In Europe, the oral rabies vaccination (ORV) of red foxes (Vulpes vulpes) was developed in the late 1970s and has demonstrated its effectiveness in the eradication of the disease in Western and some Central European countries. Following the accession of the three Baltic countries—Estonia, Latvia and Lithuania—to the European Union in 2004, subsequent financial support has allowed the implementation of regular ORV campaigns since 2005–2006. This paper reviews ten years of surveillance efforts and ORV campaigns in these countries resulting in the near eradication of the disease. The various factors that may have influenced the results of vaccination monitoring were assessed using generalized linear models (GLMs) on bait uptake and on herd immunity. As shown in previous studies, juveniles had lower bait uptake level than adults. For the first time, raccoon dogs (Nyctereutes procyonoides) were shown to have significantly lower bait uptake proportion compared with red foxes. This result suggests potentially altered ORV effectiveness in this invasive species compared to the red foxes. An extensive phylogenetic analysis demonstrated that the North-East European (NEE) rabies phylogroup is endemic in all three Baltic countries. Although successive oral vaccination campaigns have substantially reduced the number of detected rabies cases, sporadic detection of the C lineage (European part of Russian phylogroup) underlines the risk of reintroduction via westward spread from bordering countries. Vaccine induced cases were also reported for the first time in non-target species (Martes martes and Meles meles). PMID:26849358
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Abdulrahman, Hunar; Henson, Richard N.
2016-01-01
Functional magnetic resonance imaging (fMRI) studies typically employ rapid, event-related designs for behavioral reasons and for reasons associated with statistical efficiency. Efficiency is calculated from the precision of the parameters (Betas) estimated from a General Linear Model (GLM) in which trial onsets are convolved with a Hemodynamic Response Function (HRF). However, previous calculations of efficiency have ignored likely variability in the neural response from trial to trial, for example due to attentional fluctuations, or different stimuli across trials. Here we compare three GLMs in their efficiency for estimating average and individual Betas across trials as a function of trial variability, scan noise and Stimulus Onset Asynchrony (SOA): “Least Squares All” (LSA), “Least Squares Separate” (LSS) and “Least Squares Unitary” (LSU). Estimation of responses to individual trials in particular is important for both functional connectivity using “Beta-series correlation” and “multi-voxel pattern analysis” (MVPA). Our simulations show that the ratio of trial-to-trial variability to scan noise impacts both the optimal SOA and optimal GLM, especially for short SOAs < 5 s: LSA is better when this ratio is high, whereas LSS and LSU are better when the ratio is low. For MVPA, the consistency across voxels of trial variability and of scan noise is also critical. These findings not only have important implications for design of experiments using Beta-series regression and MVPA, but also statistical parametric mapping studies that seek only efficient estimation of the mean response across trials. PMID:26549299
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NASA Astrophysics Data System (ADS)
Maguire, A.; Boelman, N.; Griffin, K. L.; Jensen, J.; Hiers, E.; Johnson, D. M.; Vierling, L. A.; Eitel, J.
2017-12-01
The effect of climate change on treeline position at the latitudinal Forest-Tundra ecotone (FTE) is poorly understood. While the FTE is expansive (stretching 13,000 km acros the panarctic), understanding relationships between climate and tree function may depend on very fine scale processes. High resolution tools are therefore needed to appropriately characterize the leading (northernmost) edge of the FTE. We hypothesized that microstructural metrics obtainable from lidar remote sensing may explain variation in the physical growth environment that governs sapling establishment. To test our hypothesis, we used terrestrial laser scanning (TLS) to collect highly spatially resolved 3-D structural information of white spruce (Picea glauca) saplings and their aboveground growth environment at the leading edge of a FTE in northern Alaska and Northwest Territories, Canada. Coordinates of sapling locations were extracted from the 3-D TLS data. Within each sampling plot, 20 sets of coordinates were randomly selected from regions where no saplings were present. Ground roughness, canopy roughness, average aspect, average slope, average curvature, wind shelter index, and wetness indexwere extracted from point clouds within a variable radius from all coordinates. Generalized linear models (GLM) were fit to determine which microstructural metrics were most strongly associated with sapling establishment. Preliminary analyses of three plots suggest that vegetation roughness, wetness index, ground roughness, and slope were the most important terrain metrics governing sapling presence (Figure 1). Comprehensive analyses will include eight plots and GLMs optimized for scale at which structural parameters affect sapling establishment. Spatial autocorrelation of sample locations will be accounted for in models. Because these analyses address how the physical growth environment affects sapling establishment, model outputs will provide information for improving understanding of the ecological processes that regulate treeline dynamics. Moreover, establishing relationships between the remotely sensed structural growth environment and tree establishment provides new ways of spatially scaling across larger areas to study ecological change at the FTE.
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Nieto-Lugilde, Diego; Lenoir, Jonathan; Abdulhak, Sylvain; Aeschimann, David; Dullinger, Stefan; Gégout, Jean-Claude; Guisan, Antoine; Pauli, Harald; Renaud, Julien; Theurillat, Jean-Paul; Thuiller, Wilfried; Van Es, Jérémie; Vittoz, Pascal; Willner, Wolfgang; Wohlgemuth, Thomas; Zimmermann, Niklaus E.; Svenning, Jens-Christian
2015-01-01
The role of competition for light among plants has long been recognised at local scales, but its importance for plant species distributions at larger spatial scales has generally been ignored. Tree cover modifies the local abiotic conditions below the canopy, notably by reducing light availability, and thus, also the performance of species that are not adapted to low-light conditions. However, this local effect may propagate to coarser spatial grains, by affecting colonisation probabilities and local extinction risks of herbs and shrubs. To assess the effect of tree cover at both the plot- and landscape-grain sizes (approximately 10-m and 1-km), we fit Generalised Linear Models (GLMs) for the plot-level distributions of 960 species of herbs and shrubs using 6,935 vegetation plots across the European Alps. We ran four models with different combinations of variables (climate, soil and tree cover) at both spatial grains for each species. We used partial regressions to evaluate the independent effects of plot- and landscape-grain tree cover on plot-level plant communities. Finally, the effects on species-specific elevational range limits were assessed by simulating a removal experiment comparing the species distributions under high and low tree cover. Accounting for tree cover improved the model performance, with the probability of the presence of shade-tolerant species increasing with increasing tree cover, whereas shade-intolerant species showed the opposite pattern. The tree cover effect occurred consistently at both the plot and landscape spatial grains, albeit most strongly at the former. Importantly, tree cover at the two grain sizes had partially independent effects on plot-level plant communities. With high tree cover, shade-intolerant species exhibited narrower elevational ranges than with low tree cover whereas shade-tolerant species showed wider elevational ranges at both limits. These findings suggest that forecasts of climate-related range shifts for herb and shrub species may be modified by tree cover dynamics. PMID:26290621
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Possibilities of fish passage through the block ramp: Model-based estimation of permeability.
Plesiński, Karol; Bylak, Aneta; Radecki-Pawlik, Artur; Mikołajczyk, Tomasz; Kukuła, Krzysztof
2018-08-01
Block ramps offer an opportunity to combine hydrotechnical structures with fish passages. The primary study objective was to evaluate the effectiveness of a block ramp for upstream fish movement in a mountain stream. Geodetic measurements of the bottom surface and water level were taken for three cross-sections. The description of the geometric and hydrodynamic parameters of the block ramp was supplemented with information on the width and length of crevices between boulders. Measurements of the geometric and hydrodynamic parameters of the block ramp were performed at 76 measurement sites, at three different types of discharge. Ichthyological data were collected in the analyzed stream. Measurements covered among others total length, width, and height of caught fish. Salmonid, cottid, balitorid, and cyprinid fish were studied. The determination of the main effects of the geometric and hydrodynamic parameters of the block ramp on the possibilities of use by target fish species employed generalized linear models (GLMs). The study shows that the block ramp cannot provide longitudinal connectivity and migration of fish occurring in the mountain stream. According to estimates, the block ramp did not meet the permeability expectations. The reason for low usefulness of the ramp for fish is particularly excessively strong water current. The stream concentration constituted an unsurmountable velocity barrier for fish moving upstream for each of the analyzed discharges. The developed model suggests that some crevices in the side zones of the ramp could be parts of the migration corridor, but only for small and medium-sized fish. At medium and high water stages, movement of fish in crevices was difficult due to fast water current, and at low and very low discharges, some crevices lost their permeability, and could become ecological traps for fish. The necessity of estimation of ramp permeability during pre-construction phase was emphasized. Copyright © 2018 Elsevier B.V. All rights reserved.
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Muenchhoff, Maximilian; Healy, Michael; Singh, Ravesh; Roider, Julia; Groll, Andreas; Kindra, Chirjeev; Sibaya, Thobekile; Moonsamy, Angeline; McGregor, Callum; Phan, Michelle Q; Palma, Alejandro; Kloverpris, Henrik; Leslie, Alasdair; Bobat, Raziya; LaRussa, Philip; Ndung'u, Thumbi; Goulder, Philip; Sobieszczyk, Magdalena E; Archary, Mohendran
2018-01-01
This observational study aimed to describe immunopathogenesis and treatment outcomes in children with and without severe acute malnutrition (SAM) and HIV-infection. We studied markers of microbial translocation (16sDNA), intestinal damage (iFABP), monocyte activation (sCD14), T-cell activation (CD38, HLA-DR) and immune exhaustion (PD1) in 32 HIV-infected children with and 41 HIV-infected children without SAM prior to initiation of antiretroviral therapy (ART) and cross-sectionally compared these children to 15 HIV-uninfected children with and 19 HIV-uninfected children without SAM. We then prospectively measured these markers and correlated them to treatment outcomes in the HIV-infected children at 48 weeks following initiation of ART. Plasma levels of 16sDNA, iFABP and sCD14 were measured by quantitative real time PCR, ELISA and Luminex, respectively. T cell phenotype markers were measured by flow cytometry. Multiple regression analysis was performed using generalized linear models (GLMs) and the least absolute shrinkage and selection operator (LASSO) approach for variable selection. Microbial translocation, T cell activation and exhaustion were increased in HIV-uninfected children with SAM compared to HIV-uninfected children without SAM. In HIV-infected children microbial translocation, immune activation, and exhaustion was strongly increased but did not differ by SAM-status. SAM was associated with increased mortality rates early after ART initiation. Malnutrition, age, microbial translocation, monocyte, and CD8 T cell activation were independently associated with decreased rates of CD4% immune recovery after 48 weeks of ART. SAM is associated with increased microbial translocation, immune activation, and immune exhaustion in HIV-uninfected children and with worse prognosis and impaired immune recovery in HIV-infected children on ART.
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Sui, Xueyan; Wu, Zhipeng; Lin, Chen; Zhou, Shenglu
2017-07-01
Glomalin, which sequesters substantial amounts of carbon, plays a critical role in sustaining terrestrial biome functions and contributes to the fate of many pollutants from terrestrial to aquatic ecosystems. Despite having focused on the amount of glomalin produced, very few attempts have been made to understand how landscapes and environmental conditions influence glomalin composition and characteristics. This study focused on glomalin-related soil protein (GRSP) exported as storm runoff including eroded sediment and water that was collected before flowing to surface waters in a peri-urban watershed. GRSP characteristics were assessed by Bradford protein analysis, fluorescence spectroscopy combined with parallel factor analysis (PARAFAC), and the determination of aromaticity based on the specific ultraviolet absorption value (280 nm) and molecular weight. General linear models (GLMs) was established by integrating microbial activity, land cover, water temperature, precipitation, and other solution chemical properties to explain the variations in GRSP characteristics. Results showed that a higher GRSP concentration in agricultural reference sites was produced in the form of specific materials with low molecular weight and aromaticity, as well as high percentage of C1 and C5 components which indicate microbial-processed sources, relative to urbanized and forested sites. Compared with forested land, urbanized land clearly produced runoff GRSP with low molecular weight and aromaticity, as well as more degradation of humic-like materials (C3 component). The highest GLM explaining 89% of the variables, including significant variables (p < 0.05) such as microbial activity, water temperature, and water conductivity, was observed for GRSP characteristics. Therefore, changes in eroded soil GRSP quality can serve as an indicator for improving watershed management and thus protecting aquatic ecosystems.
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Chen, Stephanie; Swallow, Elyse; Li, Nanxin; Faust, Elizabeth; Kelley, Caroline; Xie, Jipan; Wu, Eric
2015-04-01
To assess the association between medical costs and persistence with beta blockers among hypertensive patients, and to quantify persistence related medical cost differences with nebivolol, which is associated with improved tolerability, versus other beta blockers. Adults who initiated hypertension treatment with a beta blocker were identified from the MarketScan * claims database (2008-2012). Patients were classified based on their first beta blocker use: nebivolol, atenolol, carvedilol, metoprolol, and other beta blockers. Patients with compelling indications for atenolol, carvedilol or metoprolol (acute coronary syndrome and congestive heart failure) were excluded. Patients enrolled in health maintenance organization or capitated point of service insurance plans were also excluded. Persistence was defined as continuous use of the index drug (<60 day gap). The average effect of persistence on medical costs (2012 USD) was estimated using generalized linear models (GLMs). Regression estimates were used to predict medical cost differences associated with persistence between nebivolol and the other cohorts. A total of 587,424 hypertensive patients met the inclusion criteria. Each additional month of persistence with any one beta blocker was associated with $152.51 in all-cause medical cost savings; continuous treatment for 1 year was associated with $1585.98 in all-cause medical cost savings. Patients treated with nebivolol had longer persistence during the 1 year study period (median: 315 days) than all other beta blockers (median: 156-292 days). Longer persistence with nebivolol translated into $305.74 all-cause medical cost savings relative to all other beta blockers. The results may not be generalizable to hypertensive patients with acute coronary syndrome or congestive heart failure. Longer persistence with beta blockers for the treatment of hypertension was associated with lower medical costs. There may be greater cost savings due to better persistence with nebivolol than other beta blockers.
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HeFPipe: a complete analytical pipeline for heterozygosity-fitness correlation studies.
Fisher, Mark A
2014-01-01
As the body of heterozygosity-fitness correlation (HFC) research grows, more and increasingly complicated tests have become an integral part of a typical HFC analysis (Chapman et al. 2009). Currently, no software is available to undertake conversion between the file formats required to conduct all of these tests and to conduct the main regression analyses at the core of all HFCs. Heterozygosity-Fitness Pipeline (HeFPipe) is a script written in Python that accomplishes both of these tasks for studies based on microsatellite data. HeFPipe is designed to be used from the command line terminal and will run on any Mac OSX computer. The script takes input in the form of allele reports from either the genotype-calling software, GeneMapper or GeneMarker, and reconfigures the data into GENEPOP (Raymond & Rousset 1995), Rhh (Alho et al. 2010), RMES (David et al. 2007) and GEPHAST (Amos & Acevedo-Whitehouse 2009) formats. The script is also equipped to reformat the output from GENEPOP on the Web (option 5) and Rhh into csv spreadsheets that can be incorporated into downstream analyses. HeFPipe accommodates user-provided lists of samples and markers to be included in or excluded from analyses. HeFPipe is equipped to create generalized linear models (GLMs) from both the main data set and subsets of the data. Finally, HeFPipe allows users to explore single-marker effects and conduct correlation analyses. The script, a comprehensive manual, a link to a series of video tutorials, and an example data set are available from GitHub (http://github.com/Atticus29/HeFPipe_rpos). © 2013 John Wiley & Sons Ltd.
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Prado, Patricia; Alcaraz, Carles; Jornet, Lluis; Caiola, Nuno; Ibáñez, Carles
2017-01-01
The hydrological connectivity between the salt marsh and the sea was partially restored in a Mediterranean wetland containing isolated ponds resulting from former salt extraction and aquaculture activities. A preliminary assessment provided evidence that ponds farther from the sea hosted very large numbers of the endangered Spanish toothcarp, Aphanius iberus , suggesting that individuals had been trapped and consequently reach unnaturally high densities. In order to achieve both habitat rehabilitation and toothcarp conservation, efforts were made to create a gradient of hydrologically connected areas, including isolated fish reservoirs, semi-isolated, and connected salt marsh-sea areas that could allow migratory movements of fish and provide some protection for A. iberus . The fish community was monitored prior to, and for three years after rehabilitation. Results showed an increase in the number of fish species within semi-isolated areas (Zone A), whereas areas adjacent to the sea (Zone B) increased the number of marine species and decreased that of estuarine species (ES). Yet overall differences in fish assemblages were much higher between zones than among study years. Generalized linear models (GLMs) evidenced that distance to the sea was the most important variable explaining the local diversity of the fish community after restoration, with occasional influence of other factors such as temperature, and depth. The abundance of A. iberus was consistently higher in semi-isolated areas at greater distances from the sea, but a decline occurred in both zones and in isolated reservoir ponds after restoration efforts, which may be attributable to interannual differences in recruitment success and, to a lesser extent, to dispersal into adjacent habitats. A negative effect of restoration works on fish population cannot be excluded, but the final outcome of the intervention likely needs a longer period.
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Does bathymetry drive coastal whale shark (Rhincodon typus) aggregations?
Stewart, Bryce D.; McClean, Colin J.; Hancock, James; Rees, Richard
2018-01-01
Background The whale shark (Rhincodon typus) is known to aggregate in a number of coastal locations globally, however what causes these aggregations to form where they do is largely unknown. This study examines whether bathymetry is an important driver of coastal aggregation locations for R. typus through bathymetry’s effect on primary productivity and prey availability. This is a global study taking into account all coastal areas within R. typus’ range. Methods R. typus aggregation locations were identified through an extensive literature review. Global bathymetric data were compared at R. typus aggregation locations and a large random selection of non-aggregation areas. Generalised linear models were used to assess which bathymetric characteristic had the biggest influence on aggregation presence. Results Aggregation sites were significantly shallower than non-aggregation sites and in closer proximity to deep water (the mesopelagic zone) by two orders of magnitude. Slope at aggregation sites was significantly steeper than non-aggregation sites. These three bathymetric variables were shown to have the biggest association with aggregation sites, with up to 88% of deviation explained by the GLMs. Discussion The three key bathymetric characteristics similar at the aggregation sites are known to induce upwelling events, increase primary productivity and consequently attract numerous other filter feeding species. The location of aggregation sites in these key areas can be attributed to this increased prey availability, thought to be the main reason R. typus aggregations occur, extensively outlined in the literature. The proximity of aggregations to shallow areas such as reefs could also be an important factor why whale sharks thermoregulate after deep dives to feed. These findings increase our understanding of whale shark behaviour and may help guide the identification and conservation of further aggregation sites.
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Healy, Michael; Singh, Ravesh; Roider, Julia; Groll, Andreas; Kindra, Chirjeev; Sibaya, Thobekile; Moonsamy, Angeline; McGregor, Callum; Phan, Michelle Q.; Palma, Alejandro; Kloverpris, Henrik; Leslie, Alasdair; Bobat, Raziya; LaRussa, Philip; Ndung'u, Thumbi; Goulder, Philip; Sobieszczyk, Magdalena E.; Archary, Mohendran
2018-01-01
Abstract This observational study aimed to describe immunopathogenesis and treatment outcomes in children with and without severe acute malnutrition (SAM) and HIV-infection. We studied markers of microbial translocation (16sDNA), intestinal damage (iFABP), monocyte activation (sCD14), T-cell activation (CD38, HLA-DR) and immune exhaustion (PD1) in 32 HIV-infected children with and 41 HIV-infected children without SAM prior to initiation of antiretroviral therapy (ART) and cross-sectionally compared these children to 15 HIV-uninfected children with and 19 HIV-uninfected children without SAM. We then prospectively measured these markers and correlated them to treatment outcomes in the HIV-infected children at 48 weeks following initiation of ART. Plasma levels of 16sDNA, iFABP and sCD14 were measured by quantitative real time PCR, ELISA and Luminex, respectively. T cell phenotype markers were measured by flow cytometry. Multiple regression analysis was performed using generalized linear models (GLMs) and the least absolute shrinkage and selection operator (LASSO) approach for variable selection. Microbial translocation, T cell activation and exhaustion were increased in HIV-uninfected children with SAM compared to HIV-uninfected children without SAM. In HIV-infected children microbial translocation, immune activation, and exhaustion was strongly increased but did not differ by SAM-status. SAM was associated with increased mortality rates early after ART initiation. Malnutrition, age, microbial translocation, monocyte, and CD8 T cell activation were independently associated with decreased rates of CD4% immune recovery after 48 weeks of ART. SAM is associated with increased microbial translocation, immune activation, and immune exhaustion in HIV-uninfected children and with worse prognosis and impaired immune recovery in HIV-infected children on ART. PMID:28670966
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NASA Astrophysics Data System (ADS)
Ivory, J.; Steinberg, D. K.; Latour, R. J.
2016-02-01
Temporal changes in mesozooplankton community structure affect planktonic food web interactions and biogeochemical cycling. Epipelagic mesozooplankton biomass in the Sargasso Sea has increased over the last two decades, with a related increase in zooplankton-mediated carbon export. Unknown, however, is what are the patterns and variability at different temporal scales (diel, seasonal, and interannual) in abundance of each major zooplankton taxon, and how do these patterns relate to physical and other environmental changes? We enumerated major taxa of mesozooplankton collected from monthly day and night net tows in the epipelagic zone at the Bermuda Atlantic Time-series Study (BATS) site in the Sargasso Sea from 1999 to 2010. Abundances of each taxon were determined using a ZooScan optical imaging system and microscopy. Generalized Linear Models (GLMs) were used to determine what environmental parameters best explain abundance of major taxa. We used annual averages to consider broader patterns. Zooplankton taxa with the most pronounced diel vertical migration (i.e., night:day ratio, N:D, >>1) included Limacina spp. pteropods (N:D=2.02), euphausiids (1.93), calanoid copepods (1.34), and heteropods (1.34). Taxa with a pronounced spring abundance peak included chaetognaths, larvaceans, and Limacina spp. pteropods, while harpacticoid copepods peaked in late summer, and calanoid copepods in both spring and summer. Environmental variables affecting abundance differed amongst taxa. For example, calanoid copepod density was highly influenced by the abundance of a major predator- chaetognaths. Multi-year densities of calanoid copepods and ostracods both increased with increasing Water Column Stratification Index and the Atlantic Multidecadal Oscillation (AMO) index, indicating warmer sea surface temperatures are favorable for these taxa. We discuss how these temporal patterns at different scales help predict effects of global climate change on the zooplankton community.
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Ribeiro, Manuel C; Pinho, P; Branquinho, C; Llop, Esteve; Pereira, Maria J
2016-08-15
In most studies correlating health outcomes with air pollution, personal exposure assignments are based on measurements collected at air-quality monitoring stations not coinciding with health data locations. In such cases, interpolators are needed to predict air quality in unsampled locations and to assign personal exposures. Moreover, a measure of the spatial uncertainty of exposures should be incorporated, especially in urban areas where concentrations vary at short distances due to changes in land use and pollution intensity. These studies are limited by the lack of literature comparing exposure uncertainty derived from distinct spatial interpolators. Here, we addressed these issues with two interpolation methods: regression Kriging (RK) and ordinary Kriging (OK). These methods were used to generate air-quality simulations with a geostatistical algorithm. For each method, the geostatistical uncertainty was drawn from generalized linear model (GLM) analysis. We analyzed the association between air quality and birth weight. Personal health data (n=227) and exposure data were collected in Sines (Portugal) during 2007-2010. Because air-quality monitoring stations in the city do not offer high-spatial-resolution measurements (n=1), we used lichen data as an ecological indicator of air quality (n=83). We found no significant difference in the fit of GLMs with any of the geostatistical methods. With RK, however, the models tended to fit better more often and worse less often. Moreover, the geostatistical uncertainty results showed a marginally higher mean and precision with RK. Combined with lichen data and land-use data of high spatial resolution, RK is a more effective geostatistical method for relating health outcomes with air quality in urban areas. This is particularly important in small cities, which generally do not have expensive air-quality monitoring stations with high spatial resolution. Further, alternative ways of linking human activities with their environment are needed to improve human well-being. Copyright © 2016 Elsevier B.V. All rights reserved.
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Posakony, Jeffrey J.; Ferré-D'Amaré, Adrian R.
2013-01-01
Two analogues of glucosamine-6-phosphate (GlcN6P, 1) and five of glucosamine (GlcN, 2) were prepared for evaluation as catalytic cofactor of the glmS ribozyme, a bacterial gene-regulatory RNA that controls cell wall biosynthesis. Glucosamine and allosamine with 3-azido substitutions were prepared by SN2 reactions of the respective 1,2,4,6-protected sugars; final acidic hydrolysis afforded the fully deprotected compounds as their TFA salts. A 6-phospho-2-aminoglucolactam (31) was prepared from glucosamine in a 13-step synthesis, which included a late-stage POCl3-phosphorylation. A simple and widely applicable 2-step procedure with the triethylsilyl (TES) protecting group was developed to selectively expose the 6-OH group in N-protected glucosamine analogs, which provided another route to chemical phosphorylation. Mitsunobu chemistry afforded 6-cyano (35) and 6-azido (36) analogues of GlcN-(Cbz) and the selectivity for the 6-position was confirmed by NMR (COSY, HMBC, HMQC) experiments. Compound 36 was converted to the fully deprotected 6-azido-GlcN (37) and 2,6-diaminoglucose (38) analogs. A 2-hydroxylamino glucose (42) analogue was prepared via an oxaziridine (41). Enzymatic phosphorylation of 42 and chemical phosphorylation of its 6-OH precursor (43) were possible, but 42 and the 6-phospho product (44) were unstable under neutral or basic conditions. Chemical phosphorylation of the previously described 2-guanidinyl-glucose (46) afforded its 6-phospho analogue (49) after final deprotection. PMID:23578404
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Sediment rating curve & Co. - a contest of prediction methods
NASA Astrophysics Data System (ADS)
Francke, T.; Zimmermann, A.
2012-04-01
In spite of the recent technological progress in sediment monitoring, often the calculation of sediment yield (SSY) still relies on intermittent measurements because of the use of historic records, instrument-failure in continuous recording or financial constraints. Therefore, available measurements are usually inter- and even extrapolated using the sediment rating curve approach, which uses continuously available discharge data to predict sediment concentrations. Extending this idea by further aspects like the inclusion of other predictors (e.g. rainfall, discharge-characteristics, etc.), or the consideration of prediction uncertainty led to a variety of new methods. Now, with approaches such as Fuzzy Logic, Artificial Neural Networks, Tree-based regression, GLMs, etc., the user is left to decide which method to apply. Trying multiple approaches is usually not an option, as considerable effort and expertise may be needed for their application. To establish a helpful guideline in selecting the most appropriate method for SSY-computation, we initiated a study to compare and rank available methods. Depending on problem attributes like hydrological and sediment regime, number of samples, sampling scheme, and availability of ancillary predictors, the performance of different methods is compared. Our expertise allowed us to "register" Random Forests, Quantile Regression Forests and GLMs for the contest. To include many different methods and ensure their sophisticated use we invite scientists that are willing to benchmark their favourite method(s) with us. The more diverse the participating methods are, the more exciting the contest will be.
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Assessing the Impact of Climate Change on Stream Temperatures in the Methow River Basin, Washington
NASA Astrophysics Data System (ADS)
Gangopadhyay, S.; Caldwell, R. J.; Lai, Y.; Bountry, J.
2011-12-01
The Methow River in Washington offers prime spawning habitat for salmon and other cold-water fishes. During the summer months, low streamflows on the Methow result in cutoff side channels that limit the habitat available to these fishes. Future climate scenarios of increasing air temperature and decreasing precipitation suggest the potential for increasing loss of habitat and fish mortality as stream temperatures rise in response to lower flows and additional heating. To assess the impacts of climate change on stream temperature in the Methow River, the US Bureau of Reclamation is developing an hourly time-step, two-dimensional hydraulic model of the confluence of the Methow and Chewuch Rivers above Winthrop. The model will be coupled with a physical stream temperature model to generate spatial representations of stream conditions conducive for fish habitat. In this study, we develop a statistical framework for generating stream temperature time series from global climate model (GCM) and hydrologic model outputs. Regional observations of stream temperature and hydrometeorological conditions are used to develop statistical models of daily mean stream temperature for the Methow River at Winthrop, WA. Temperature and precipitation projections from 10 global climate models (GCMs) are coupled with the streamflow generated using the University of Washington Variable Infiltration Capacity model. The projections serve as input to the statistical models to generate daily time series of mean daily stream temperature. Since the output from the GCM, VIC, and statistical models offer only daily data, a k-nearest neighbor (k-nn) resampling technique is employed to select appropriate proportion vectors for disaggregating the Winthrop daily flow and temperature to an upstream location on each of the rivers above the confluence. Hourly proportion vectors are then used to disaggregate the daily flow and temperature to hourly values to be used in the hydraulic model. Historical meteorological variables are also selected using the k-nn method. We present the statistical modeling framework using Generalized Linear Models (GLMs), along with diagnostics and measurements of skill. We will also provide a comparison of the stream temperature projections from the future years of 2020, 2040, and 2080 and discuss the potential implications on fish habitat in the Methow River. Future integration of the hourly climate scenarios in the hydraulic model will provide the ability to assess the spatial extent of habitat impacts and allow the USBR to evaluate the effectiveness of various river restoration projects in maintaining or improving habitat in a changing climate.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Perk, T; Bradshaw, T; Muzahir, S
2014-06-15
Purpose: [F-18]NaF PET can be used to image bone metastases; however, tracer uptake in degenerative joint disease (DJD) often appears similar to metastases. This study aims to develop and compare different machine learning algorithms to automatically identify regions of [F-18]NaF scans that correspond to DJD. Methods: 10 metastatic prostate cancer patients received whole body [F-18]NaF PET/CT scans prior to treatment. Image segmentation resulted in 852 ROIs, 69 of which were identified by a nuclear medicine physician as DJD. For all ROIs, various PET and CT textural features were computed. ROIs were divided into training and testing sets used to trainmore » eight different machine learning classifiers. Classifiers were evaluated based on receiver operating characteristics area under the curve (AUC), sensitivity, specificity, and positive predictive value (PPV). We also assessed the added value of including CT features in addition to PET features for training classifiers. Results: The training set consisted of 37 DJD ROIs with 475 non-DJD ROIs, and the testing set consisted of 32 DJD ROIs with 308 non-DJD ROIs. Of all classifiers, generalized linear models (GLM), decision forests (DF), and support vector machines (SVM) had the best performance. AUCs of GLM (0.929), DF (0.921), and SVM (0.889) were significantly higher than the other models (p<0.001). GLM and DF, overall, had the best sensitivity, specificity, and PPV, and gave a significantly better performance (p<0.01) than all other models. PET/CT GLM classifiers had higher AUC than just PET or just CT. GLMs built using PET/CT information had superior or comparable sensitivities, specificities and PPVs to just PET or just CT. Conclusion: Machine learning algorithms trained with PET/CT features were able to identify some cases of DJD. GLM outperformed the other classification algorithms. Using PET and CT information together was shown to be superior to using PET or CT features alone. Research supported by the Prostate Cancer Foundation.« less
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Semenov, Alexander V; Elsas, Jan Dirk; Glandorf, Debora C M; Schilthuizen, Menno; Boer, Willem F
2013-01-01
Abstract To fulfill existing guidelines, applicants that aim to place their genetically modified (GM) insect-resistant crop plants on the market are required to provide data from field experiments that address the potential impacts of the GM plants on nontarget organisms (NTO's). Such data may be based on varied experimental designs. The recent EFSA guidance document for environmental risk assessment (2010) does not provide clear and structured suggestions that address the statistics of field trials on effects on NTO's. This review examines existing practices in GM plant field testing such as the way of randomization, replication, and pseudoreplication. Emphasis is placed on the importance of design features used for the field trials in which effects on NTO's are assessed. The importance of statistical power and the positive and negative aspects of various statistical models are discussed. Equivalence and difference testing are compared, and the importance of checking the distribution of experimental data is stressed to decide on the selection of the proper statistical model. While for continuous data (e.g., pH and temperature) classical statistical approaches – for example, analysis of variance (ANOVA) – are appropriate, for discontinuous data (counts) only generalized linear models (GLM) are shown to be efficient. There is no golden rule as to which statistical test is the most appropriate for any experimental situation. In particular, in experiments in which block designs are used and covariates play a role GLMs should be used. Generic advice is offered that will help in both the setting up of field testing and the interpretation and data analysis of the data obtained in this testing. The combination of decision trees and a checklist for field trials, which are provided, will help in the interpretation of the statistical analyses of field trials and to assess whether such analyses were correctly applied. We offer generic advice to risk assessors and applicants that will help in both the setting up of field testing and the interpretation and data analysis of the data obtained in field testing. PMID:24567836
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Semenov, Alexander V; Elsas, Jan Dirk; Glandorf, Debora C M; Schilthuizen, Menno; Boer, Willem F
2013-08-01
To fulfill existing guidelines, applicants that aim to place their genetically modified (GM) insect-resistant crop plants on the market are required to provide data from field experiments that address the potential impacts of the GM plants on nontarget organisms (NTO's). Such data may be based on varied experimental designs. The recent EFSA guidance document for environmental risk assessment (2010) does not provide clear and structured suggestions that address the statistics of field trials on effects on NTO's. This review examines existing practices in GM plant field testing such as the way of randomization, replication, and pseudoreplication. Emphasis is placed on the importance of design features used for the field trials in which effects on NTO's are assessed. The importance of statistical power and the positive and negative aspects of various statistical models are discussed. Equivalence and difference testing are compared, and the importance of checking the distribution of experimental data is stressed to decide on the selection of the proper statistical model. While for continuous data (e.g., pH and temperature) classical statistical approaches - for example, analysis of variance (ANOVA) - are appropriate, for discontinuous data (counts) only generalized linear models (GLM) are shown to be efficient. There is no golden rule as to which statistical test is the most appropriate for any experimental situation. In particular, in experiments in which block designs are used and covariates play a role GLMs should be used. Generic advice is offered that will help in both the setting up of field testing and the interpretation and data analysis of the data obtained in this testing. The combination of decision trees and a checklist for field trials, which are provided, will help in the interpretation of the statistical analyses of field trials and to assess whether such analyses were correctly applied. We offer generic advice to risk assessors and applicants that will help in both the setting up of field testing and the interpretation and data analysis of the data obtained in field testing.
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Measuring taste impairment in epidemiologic studies: the Beaver Dam Offspring Study.
Cruickshanks, K J; Schubert, C R; Snyder, D J; Bartoshuk, L M; Huang, G H; Klein, B E K; Klein, R; Nieto, F J; Pankow, J S; Tweed, T S; Krantz, E M; Moy, G S
2009-07-01
Taste or gustatory function may play an important role in determining diet and nutritional status and therefore indirectly impact health. Yet there have been few attempts to study the spectrum of taste function and dysfunction in human populations. Epidemiologic studies are needed to understand the impact of taste function and dysfunction on public health, to identify modifiable risk factors, and to develop and test strategies to prevent clinically significant dysfunction. However, measuring taste function in epidemiologic studies is challenging and requires repeatable, efficient methods that can measure change over time. Insights gained from translating laboratory-based methods to a population-based study, the Beaver Dam Offspring Study (BOSS) will be shared. In this study, a generalized labeled magnitude scale (gLMS) method was used to measure taste intensity of filter paper disks saturated with salt, sucrose, citric acid, quinine, or 6-n-propylthiouracil, and a gLMS measure of taste preferences was administered. In addition, a portable, inexpensive camera system to capture digital images of fungiform papillae and a masked grading system to measure the density of fungiform papillae were developed. Adult children of participants in the population-based Epidemiology of Hearing Loss Study in Beaver Dam, Wisconsin, are eligible for this ongoing study. The parents were residents of Beaver Dam and 43-84 years of age in 1987-1988; offspring ranged in age from 21-84 years in 2005-2008. Methods will be described in detail and preliminary results about the distributions of taste function in the BOSS cohort will be presented.
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Non-nutritive sweeteners are not super-normal stimuli
Antenucci, Rachel G.; Hayes, John E.
2014-01-01
Background It is often claimed that non-nutritive sweeteners (NNS) are ‘sweeter than sugar’, with the implicit implication high potency sweeteners are super-normal stimuli that encourage exaggerated responses. This study aimed to investigate the perceived sweetness intensity of a variety of nutritive (Sucrose, Maple Syrup, and Agave Nectar) and NNS (Acesulfame-K (AceK), Rebaudioside A (RebA), Aspartame, and Sucralose) in a large cohort of untrained participants using contemporary psychophysical methods. Methods Participants (n=401 total) rated the intensity of sweet, bitter, and metallic sensations for nutritive and NNS in water using the general labeled magnitude scale (gLMS). Results Sigmoidal Dose-Response functions were observed for all stimuli except AceK. That is, sucrose follows a sigmoidal function if the data are not artifactually linearized via prior training. More critically, there is no evidence that NNS have a maximal sweetness (intensity) greater than sucrose; indeed, the maximal sweetness for AceK, RebA and Sucralose were significantly lower than for concentrated sucrose. For these sweeteners, mixture suppression due to endogenous dose-dependent bitter or metallic sensations appears to limit maximal perceived sweetness. Conclusions In terms of perceived sweetness, non-nutritive sweeteners cannot be considered super-normal stimuli. These data do not support the view that non-nutritive sweeteners hijack or over-stimulate sweet receptors to product elevated sweet sensations. PMID:24942868
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Anguita, Cristóbal; Simeone, Alejandro
2015-01-01
The formation of multi-species feeding flocks (MSFFs) through visual recruitment is considered an important strategy for obtaining food in seabirds and its functionality has been ascribed to enhanced foraging efficiency. Its use has been demonstrated in much of the world's oceans and includes numerous species. However, there is scant information on the temporal stability of the composition and abundance of MSFFs as well as the effect of seasonal food availability on their dynamics. Between July 2006 and September 2014, we conducted monthly at-sea seabird counts at Valparaiso Bay (32°56′ to 33°01′S, 71°36′ to 71°46′W) within the area of influence of the Humboldt Current in central Chile. This area is characterized by a marked seasonality in primary and secondary production associated with upwelling, mainly during austral spring-summer. Based on studies that provide evidence that flocking is most frequent when food is both scarce and patchy, we hypothesized that seabird MSFF attributes (i.e. frequency of occurrence, abundance and composition) will be modified according to the seasonal availability of food. Using generalized linear models (GLMs), our results show that the contrasting seasonality in food availability of the study area (using chlorophyll-a concentration as a proxy) had no significant influence on MSFF attributes, sparsely explaining their variations (P>0.05). Rather than seasonal food availability, the observed pattern for MSFF attributes at Valparaiso Bay suggests a substantial influence of reproductive and migratory (boreal and austral migrants) habits of birds that modulates MSFF dynamics consistently throughout the whole year in this highly variable and patchy environment. We highlight the importance of visual recruitment as a mechanism by which migratory and resident birds interact. This would allow them to reduce resource unpredictability, which in turn has a major impact on structuring seabird’s MSFF dynamics. PMID:26125630
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Chung, Jane; Demiris, George; Thompson, Hilaire J; Chen, Ke-Yu; Burr, Robert; Patel, Shwetak; Fogarty, James
2017-03-01
This study aimed to test feasibility of a home-based sensor system that is designed to assess mobility and daily activity patterns among Korean American older adults (KAOAs; n = 6) and explore sensor technology acceptance among participants. Home-based sensors have the potential to support older adults' desire to remain at home as long as possible. Despite a growing interest in using home-based sensors for older adults, there have been no documented attempts to apply this type of technology to a group of ethnic minority older adults. The study employed descriptive, quantitative and qualitative approaches. The system was deployed for 2 months in four homes of KAOAs. Study procedures included (i) sensor-based data collection, (ii) self-report mobility instruments, (iii) activity logs and (iv) interviews. To explore changes in activity patterns, line graphs and sequence plots were applied to data obtained from a set of sensors. General linear models (GLMs) were used for motion in each space of the home to examine how much variability of activities is explained by several time variables. Sensor data had natural fluctuation over time. Different 24-hr patterns were observed across homes. The GLM estimates showed that effect sizes of the time variables vary across individuals. A hydro sensor deployed in one participant's bathroom inferred various water usage activities. Overall, sensors were acceptable for all participants, despite some privacy concerns. Study findings demonstrate that sensor technology applications could be successfully used longitudinally in a minority population of older adults that is not often targeted as an end-user group for the use of innovative technologies. The use of home-based sensors provides nurses with a useful tool to detect deviations from normal patterns and to achieve proactive care for some groups of older adults. © 2016 John Wiley & Sons Ltd.
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2010-01-01
Background Candida represents the most common cause of invasive fungal disease, and candidal blood stream infections (CBSI) are prevalent in the ICU. Inappropriate antifungal therapy (IAT) is known to increase a patient's risk for death. We hypothesized that in an ICU cohort it would also adversely affect resource utilization. Methods We retrospectively identified all patients with candidemia on or before hospital day 14 and requiring an ICU stay at Barnes-Jewish Hospital between 2004 and 2007. Hospital length of stay following culture-proven onset of CBSI (post-CBSI HLOS) was primary and hospital costs secondary endpoints. IAT was defined as treatment delay of ≥24 hours from candidemia onset or inadequate dose of antifungal agent active against the pathogen. We developed generalized linear models (GLM) to assess independent impact of inappropriate therapy on LOS and costs. Results Ninety patients met inclusion criteria. IAT was frequent (88.9%). In the IAT group antifungal delay ≥24 hours occurred in 95.0% and inappropriate dosage in 26.3%. Unadjusted hospital mortality was greater among IAT (28.8%) than non-IAT (0%) patients, p = 0.059. Both crude post-CBSI HLOS (18.4 ± 17.0 vs. 10.7 ± 9.4, p = 0.062) and total costs ($66,584 ± $49,120 vs. $33,526 ± $27,244, p = 0.006) were higher in IAT than in non-IAT. In GLMs adjusting for confounders IAT-attributable excess post-CBSI HLOS was 7.7 days (95% CI 0.6-13.5) and attributable total costs were $13,398 (95% CI $1,060-$26,736). Conclusions IAT of CBSI, such as delays and incorrect dosing, occurs commonly. In addition to its adverse impact on clinical outcomes, IAT results in substantial prolongation of hospital LOS and increase in hospital costs. Efforts to enhance rates of appropriate therapy for candidemia may improve resource use. PMID:20525301
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Factors affecting spruce establishment and recruitment near western treeline, Alaska
NASA Astrophysics Data System (ADS)
Miller, A. E.; Sherriff, R.; Wilson, T. L.
2015-12-01
Regional warming and increases in tree growth are contributing to increased productivity near the western forest margin in Alaska. The effects of warming on seedling recruitment has received little attention, in spite of forecasted forest expansion near western treeline. Here, we used stand structure and environmental data from white spruce (Picea glauca) stands (n = 95) sampled across a longitudinal gradient to explore factors influencing white spruce growth, establishment and recruitment in southwest Alaska. Using tree-ring chronologies developed from a subset of the plots (n = 30), we estimated establishment dates and basal area increment (BAI) for trees of all age classes across a range of site conditions. We used GLMs (generalized linear models) to explore the relationship between tree growth and temperature in undisturbed, low elevation sites along the gradient, using BAI averaged over the years 1975-2000. In addition, we examined the relationship between growing degree days (GDD) and seedling establishment over the previous three decades. We used total counts of live seedlings, saplings and live and dead trees, representing four cohorts, to evaluate whether geospatial, climate, and measured plot covariates predicted abundance of the different size classes. We hypothesized that the relationship between abundance and longitude would vary by size class, and that this relationship would be mediated by growing season temperature. We found that mean BAI for trees in undisturbed, low elevation sites increased with July maximum temperature, and that the slope of the relationship with temperature changed with longitude (interaction significant with 90% confidence). White spruce establishment was positively associated with longer summers and/or greater heat accumulation, as inferred from GDD. Seedling, sapling and tree abundance were also positively correlated with temperature across the study area. The response to longitude was mixed, with smaller size classes (seedlings, small saplings) most abundant at the western end of the gradient, and larger size classes (trees) most abundant to the east, suggesting a moving front of white spruce establishment near western treeline.
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Amato-Lourenco, Luís Fernando; Moreira, Tiana Carla Lopes; de Oliveira Souza, Vanessa Cristina; Barbosa, Fernando; Saiki, Mitiko; Saldiva, Paulo Hilário Nascimento; Mauad, Thais
2016-09-01
Although urban horticulture provides multiple benefits to society, the extent to which these vegetables are contaminated by the absorption of chemical elements derived from atmospheric deposition is unclear. This study was designed to evaluate the influence of air pollution on leafy vegetables in community gardens of Sao Paulo, Brazil. Vegetable seedlings of Brassica oleracea var. acephala (collard greens) and Spinacia oleracea (spinach) obtained in a non-polluted rural area and growing in vessels containing standard uncontaminated soil were exposed for three consecutive periods of 30, 60 and 90 days in 10 community gardens in Sao Paulo and in one control site. The concentrations of 17 chemical elements (traffic-related elements and those essential to plant biology) were quantified by Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Tillandsia usneoides L. specimens were used as air plant biomonitors. The concentrations of As, Cd, Cr and Pb found in vegetables were compared to the recommended values for consumption. Principal Component Analysis (PCA) was used to cluster the elemental concentrations, and Generalized Linear Models (GLMs) were employed to evaluate the association of the factor scores from each PCA component with variables such as local weather, traffic burden and vertical barriers adjacent to the gardens. We found significant differences in the elemental concentrations of the vegetables in the different community gardens. These differences were related to the overall traffic burden, vertical obstacles and local weather. The Pb and Cd concentrations in both vegetables exceeded the limit values for consumption after 60 days of exposure. A strong correlation was observed between the concentration of traffic-related elements in vegetables and in Tillandsia usneoides L. An exposure response was observed between traffic burden and traffic-derived particles absorbed in the vegetables. Traffic-derived air pollution directly influences the absorption of chemical elements in leafy vegetables, and the levels of these elements may exceed the recommended values for consumption. Copyright © 2016 Elsevier Ltd. All rights reserved.
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Sagra, J; Ferrandis, P; Plaza-Álvarez, P A; Lucas-Borja, M E; González-Romero, J; Alfaro-Sánchez, R; De Las Heras, J; Moya, D
2018-05-17
Prescribed fires are used as a fuel reduction tool, but heat alter microsite conditions affecting the natural regeneration of Mediterranean pine forests. Our study tested the hypothesis that implementing prescription before or after pine seed release may influence the composition of tree communities by changing the regeneration patterns of Pinus pinaster Aiton across a climatic gradient in the eastern Iberian Peninsula. We ran a seed-sowing experiment to analyse the recruitment patterns of this pine species in prescribed-burned stands, in two different biogeographical seed provenances from wetter and drier areas than the local seeding site. Survival of seedlings was through one year, until the end of the first drought and winter period, respectively. >5400 seeds were sown during the study distributed in sixty plots (30 burned, 30 unburned) per site and treatment, with 10 seeding units per plot. General linear models (GLMs) and ANOVA analyses indicated higher performance for the Drier seed provenance in burned areas, whereas a similar performance was recorded in the control area. Control areas showed higher germination and success rates for plant establishment throughout the study period. Total germination and survival after one year were slightly higher, respectively, at northern sites due to massive mortality during summer in the southern stands. At the burned sites, the mean germination time was significantly longer in those seeds sown before fire passage than those sown after fire. Total germination and successful establishment were significantly higher in the individuals sown before the passage of the fire than in those sown after fire. Most of the mortality occurred in summer for the southern stand, while winter was the most constraining period at the northern sites. The understanding of the dynamics in this species' establishment can help managers to perform a better management planning according to the species' ecology. Copyright © 2018 Elsevier B.V. All rights reserved.
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Preliminary assessment of factors influencing riverine fish communities in Massachusetts.
Armstrong, David S.; Richards, Todd A.; Brandt, Sara L.
2010-01-01
The U.S. Geological Survey, in cooperation with the Massachusetts Department of Conservation and Recreation (MDCR), Massachusetts Department of Environmental Protection (MDEP), and the Massachusetts Department of Fish and Game (MDFG), conducted a preliminary investigation of fish communities in small- to medium-sized Massachusetts streams. The objective of this investigation was to determine relations between fish-community characteristics and anthropogenic alteration, including flow alteration and impervious cover, relative to the effect of physical basin and land-cover (environmental) characteristics. Fish data were obtained for 756 fish-sampling sites from the Massachusetts Division of Fisheries and Wildlife fish-community database. A review of the literature was used to select a set of fish metrics responsive to flow alteration. Fish metrics tested include two fish-community metrics (fluvial-fish relative abundance and fluvial-fish species richness), and five indicator species metrics (relative abundance of brook trout, blacknose dace, fallfish, white sucker, and redfin pickerel). Streamflows were simulated for each fish-sampling site using the Sustainable Yield Estimator application (SYE). Daily streamflows and the SYE water-use database were used to determine a set of indicators of flow alteration, including percent alteration of August median flow, water-use intensity, and withdrawal and return-flow fraction. The contributing areas to the fish-sampling sites were delineated and used with a Geographic Information System (GIS) to determine a set of environmental characteristics, including elevation, basin slope, percent sand and gravel, percent wetland, and percent open water, and a set of anthropogenic-alteration variables, including impervious cover and dam density. Two analytical techniques, quantile regression and generalized linear modeling, were applied to determine the association between fish-response variables and the selected environmental and anthropogenic explanatory variables. Quantile regression indicated that flow alteration and impervious cover were negatively associated with both fluvial-fish relative abundance and fluvial-fish species richness. Three generalized linear models (GLMs) were developed to quantify the response of fish communities to multiple environmental and anthropogenic variables. Flow-alteration variables are statistically significant for the fluvial-fish relative-abundance model. Impervious cover is statistically significant for the fluvial-fish relative-abundance, fluvial-fish species richness, and brook trout relative-abundance models. The variables in the equations were demonstrated to be significant, and the variability explained by the models, as measured by the correlation between observed and predicted values, ranges from 39 to 65 percent. The GLM models indicated that, keeping all other variables the same, a one-unit (1 percent) increase in the percent depletion or percent surcharging of August median flow would result in a 0.4-percent decrease in the relative abundance (in counts per hour) of fluvial fish and that the relative abundance of fluvial fish was expected to be about 55 percent lower in net-depleted streams than in net-surcharged streams. The GLM models also indicated that a unit increase in impervious cover resulted in a 5.5-percent decrease in the relative abundance of fluvial fish and a 2.5-percent decrease in fluvial-fish species richness.
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Social power, conflict policing, and the role of subordination signals in rhesus macaque society
Beisner, Brianne A.; Hannibal, Darcy L.; Finn, Kelly R.; Fushing, Hsieh; McCowan, Brenda
2017-01-01
Objectives Policing is a conflict-limiting mechanism observed in many primate species. It is thought to require a skewed distribution of social power for some individuals to have sufficiently high social power to stop others’ fights, yet social power has not been examined in most species with policing behavior. We examined networks of subordination signals as a source of social power that permits policing behavior in rhesus macaques. Materials and Methods For each of seven captive groups of rhesus macaques, we (a) examined the structure of subordination signal networks and used GLMs to examine the relationship between (b) pairwise dominance certainty and subordination network pathways and (c) policing frequency and social power (group-level convergence in subordination signaling pathways). Results Networks of subordination signals had perfect linear transitivity, and pairs connected by both direct and indirect pathways of signals had more certain dominance relationships than pairs with no such network connection. Social power calculated using both direct and indirect network pathways showed a heavy-tailed distribution and positively predicted conflict policing. Conclusions Our results empirically substantiate that subordination signaling is associated with greater dominance relationship certainty and further show that pairs who signal rarely (or not at all) may use information from others’ signaling interactions to infer or reaffirm the relative certainty of their own relationships. We argue that the network of formal dominance relationships is central to societal stability because it is important for relationship stability and also supports the additional stabilizing mechanism of policing. PMID:26801956
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Social power, conflict policing, and the role of subordination signals in rhesus macaque society.
Beisner, Brianne A; Hannibal, Darcy L; Finn, Kelly R; Fushing, Hsieh; McCowan, Brenda
2016-05-01
Policing is a conflict-limiting mechanism observed in many primate species. It is thought to require a skewed distribution of social power for some individuals to have sufficiently high social power to stop others' fights, yet social power has not been examined in most species with policing behavior. We examined networks of subordination signals as a source of social power that permits policing behavior in rhesus macaques. For each of seven captive groups of rhesus macaques, we (a) examined the structure of subordination signal networks and used GLMs to examine the relationship between (b) pairwise dominance certainty and subordination network pathways and (c) policing frequency and social power (group-level convergence in subordination signaling pathways). Networks of subordination signals had perfect linear transitivity, and pairs connected by both direct and indirect pathways of signals had more certain dominance relationships than pairs with no such network connection. Social power calculated using both direct and indirect network pathways showed a heavy-tailed distribution and positively predicted conflict policing. Our results empirically substantiate that subordination signaling is associated with greater dominance relationship certainty and further show that pairs who signal rarely (or not at all) may use information from others' signaling interactions to infer or reaffirm the relative certainty of their own relationships. We argue that the network of formal dominance relationships is central to societal stability because it is important for relationship stability and also supports the additional stabilizing mechanism of policing. © 2016 Wiley Periodicals, Inc.
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High-Voltage, Low-Power BNC Feedthrough Terminator
NASA Technical Reports Server (NTRS)
Bearden, Douglas
2012-01-01
This innovation is a high-voltage, lowpower BNC (Bayonet Neill-Concelman) feedthrough that enables the user to terminate an instrumentation cable properly while connected to a high voltage, without the use of a voltage divider. This feedthrough is low power, which will not load the source, and will properly terminate the instrumentation cable to the instrumentation, even if the cable impedance is not constant. The Space Shuttle Program had a requirement to measure voltage transients on the orbiter bus through the Ground Lightning Measurement System (GLMS). This measurement has a bandwidth requirement of 1 MHz. The GLMS voltage measurement is connected to the orbiter through a DC panel. The DC panel is connected to the bus through a nonuniform cable that is approximately 75 ft (approximately equal to 23 m) long. A 15-ft (approximately equal to 5-m), 50-ohm triaxial cable is connected between the DC panel and the digitizer. Based on calculations and simulations, cable resonances and reflections due to mismatched impedances of the cable connecting the orbiter bus and the digitizer causes the output not to reflect accurately what is on the bus. A voltage divider at the DC panel, and terminating the 50-ohm cable properly, would eliminate this issue. Due to implementation issues, an alternative design was needed to terminate the cable properly without the use of a voltage divider. Analysis shows how the cable resonances and reflections due to the mismatched impedances of the cable connecting the orbiter bus and the digitizer causes the output not to reflect accurately what is on the bus. After simulating a dampening circuit located at the digitizer, simulations were performed to show how the cable resonances were dampened and the accuracy was improved significantly. Test cables built to verify simulations were accurate. Since the dampening circuit is low power, it can be packaged in a BNC feedthrough.
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2015-01-01
The glmS ribozyme catalyzes a self-cleavage reaction at the phosphodiester bond between residues A-1 and G1. This reaction is thought to occur by an acid–base mechanism involving the glucosamine-6-phosphate cofactor and G40 residue. Herein quantum mechanical/molecular mechanical free energy simulations and pKa calculations, as well as experimental measurements of the rate constant for self-cleavage, are utilized to elucidate the mechanism, particularly the role of G40. Our calculations suggest that an external base deprotonates either G40(N1) or possibly A-1(O2′), which would be followed by proton transfer from G40(N1) to A-1(O2′). After this initial deprotonation, A-1(O2′) starts attacking the phosphate as a hydroxyl group, which is hydrogen-bonded to deprotonated G40, concurrent with G40(N1) moving closer to the hydroxyl group and directing the in-line attack. Proton transfer from A-1(O2′) to G40 is concomitant with attack of the scissile phosphate, followed by the remainder of the cleavage reaction. A mechanism in which an external base does not participate, but rather the proton transfers from A-1(O2′) to a nonbridging oxygen during nucleophilic attack, was also considered but deemed to be less likely due to its higher effective free energy barrier. The calculated rate constant for the favored mechanism is in agreement with the experimental rate constant measured at biological Mg2+ ion concentration. According to these calculations, catalysis is optimal when G40 has an elevated pKa rather than a pKa shifted toward neutrality, although a balance among the pKa’s of A-1, G40, and the nonbridging oxygen is essential. These results have general implications, as the hammerhead, hairpin, and twister ribozymes have guanines at a similar position as G40. PMID:25526516
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Combining Automated Theorem Provers with Symbolic Algebraic Systems: Position Paper
NASA Technical Reports Server (NTRS)
Schumann, Johann; Koga, Dennis (Technical Monitor)
1999-01-01
In contrast to pure mathematical applications where automated theorem provers (ATPs) are quite capable, proof tasks arising form real-world applications from the area of Software Engineering show quite different characteristics: they usually do not only contain much arithmetic (albeit often quite simple one), but they also often contain reasoning about specific structures (e.g. graphics, sets). Thus, an ATP must be capable of performing reasoning together with a fair amount of simplification, calculation and solving. Therefore, powerful simplifiers and other (symbolic and semi-symbolic) algorithms seem to be ideally suited to augment ATPs. In the following we shortly describe two major points of interest in combining SASs (symbolic algebraic systems) with top-down automated theorem provers (here: SETHEO [Let92, GLMS94]).
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Margolis, Jay M; Deitelzweig, Steven; Kline, Jeffrey; Tran, Oth; Smith, David M; Bookhart, Brahim; Crivera, Concetta; Schein, Jeff
2016-10-06
Venous thromboembolism, including deep vein thrombosis and pulmonary embolism, results in a substantial healthcare system burden. This retrospective observational study compared hospital length of stay (LOS) and hospitalization costs for patients with venous thromboembolism treated with rivaroxaban versus those treated with warfarin. Hospitalizations for adult patients with a primary diagnosis of deep vein thrombosis or pulmonary embolism who were initiated on rivaroxaban or warfarin were selected from MarketScan's Hospital Drug Database between November 1, 2012, and December 31, 2013. Patients treated with warfarin were matched 1:1 to patients treated with rivaroxaban using exact and propensity score matching. Hospital LOS, time from first dose to discharge, and hospitalization costs were reported descriptively and with generalized linear models (GLMs). The final study cohorts each included 1223 patients (751 with pulmonary embolism and 472 with deep vein thrombosis). Cohorts were well matched for demographic and clinical characteristics. Mean (±SD) LOS was 3.7±3.1 days for patients taking rivaroxaban and 5.2±3.7 days for patients taking warfarin, confirmed by GLM-adjusted results (rivaroxaban 3.7 days, warfarin 5.3 days, P<0.001). Patients with provoked venous thromboembolism admissions showed longer LOSs (rivaroxaban 5.1±4.5 days, warfarin 6.5±5.6 days, P<0.001) than those with unprovoked venous thromboembolism (rivaroxaban 3.3±2.4 days, warfarin 4.8±2.8 days, P<0.001). Days from first dose to discharge were 2.4±1.7 for patients treated with rivaroxaban and 3.9±3.7 for patients treated with warfarin when initiated with parenteral anticoagulants (P<0.001), and 2.7±1.7 and 3.7±2.1, respectively, when initiated without parenteral anticoagulants (P<0.001). Patients initiated on rivaroxaban incurred significantly lower mean total hospitalization costs ($8688±$9927 versus $9823±$9319, P=0.004), confirmed by modeling (rivaroxaban $8387 [95% confidence interval, $8035-$8739]; warfarin $10 275 [95% confidence interval, $9842-$10 708]). Rivaroxaban was associated with significantly shorter hospital LOS and lower hospitalization costs compared with warfarin. © 2016 The Authors, Janssen Scientific Affairs, LLC, and Truven Health Analytics. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.
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Synoptic weather typing applied to air pollution mortality among the elderly in 10 Canadian cities.
Vanos, Jennifer K; Cakmak, Sabit; Bristow, Corben; Brion, Vladislav; Tremblay, Neil; Martin, Sara L; Sheridan, Scott S
2013-10-01
Synoptic circulation patterns (large-scale weather systems) affect ambient levels of air pollution, as well as the relationship between air pollution and human health. To investigate the air pollution-mortality relationship within weather types and seasons, and to determine which combination of atmospheric conditions may pose increased health threats in the elderly age categories. The relative risk of mortality (RR) due to air pollution was examined using Poisson generalized linear models (GLMs) within specific weather types. Analysis was completed by weather type and age group (all ages, ≤64, 65-74, 75-84, ≥85 years) in ten Canadian cities from 1981 to 1999. There was significant modification of RR by weather type and age. When examining the entire population, weather type was shown to have the greatest modifying effect on the risk of dying due to ozone (O3). This effect was highest on average for the dry tropical (DT) weather type, with the all-age RR of mortality at a population weighted mean (PWM) found to be 1.055 (95% CI 1.026-1.085). All-weather type risk estimates increased with age due to exposure to carbon monoxide (CO), nitrogen dioxide (NO2), and sulphur dioxide (SO2). On average, RR increased by 2.6, 3.8 and 1.5% for the respective pollutants between the ≤64 and ≥85 age categories. Conversely, mean ozone estimates remained relatively consistent with age. Elevated levels of air pollution were found to be detrimental to the health of elderly individuals for all weather types. However, the entire population was negatively effected by air pollution on the hot dry (DT) and hot humid (MT) days. We identified a significant modification of RR for mortality due to air pollution by age, which is enhanced under specific weather types. Efforts should be targeted at minimizing pollutant exposure to the elderly and/or all age groups with respect to weather type in question. Crown Copyright © 2013 Published by Elsevier Inc. All rights reserved.
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Predicting plant attractiveness to pollinators with passive crowdsourcing.
Bahlai, Christie A; Landis, Douglas A
2016-06-01
Global concern regarding pollinator decline has intensified interest in enhancing pollinator resources in managed landscapes. These efforts frequently emphasize restoration or planting of flowering plants to provide pollen and nectar resources that are highly attractive to the desired pollinators. However, determining exactly which plant species should be used to enhance a landscape is difficult. Empirical screening of plants for such purposes is logistically daunting, but could be streamlined by crowdsourcing data to create lists of plants most probable to attract the desired pollinator taxa. People frequently photograph plants in bloom and the Internet has become a vast repository of such images. A proportion of these images also capture floral visitation by arthropods. Here, we test the hypothesis that the abundance of floral images containing identifiable pollinator and other beneficial insects is positively associated with the observed attractiveness of the same species in controlled field trials from previously published studies. We used Google Image searches to determine the correlation of pollinator visitation captured by photographs on the Internet relative to the attractiveness of the same species in common-garden field trials for 43 plant species. From the first 30 photographs, which successfully identified the plant, we recorded the number of Apis (managed honeybees), non-Apis (exclusively wild bees) and the number of bee-mimicking syrphid flies. We used these observations from search hits as well as bloom period (BP) as predictor variables in Generalized Linear Models (GLMs) for field-observed abundances of each of these groups. We found that non-Apis bees observed in controlled field trials were positively associated with observations of these taxa in Google Image searches (pseudo-R (2) of 0.668). Syrphid fly observations in the field were also associated with the frequency they were observed in images, but this relationship was weak. Apis bee observations were not associated with Internet images, but were slightly associated with BP. Our results suggest that passively crowdsourced image data can potentially be a useful screening tool to identify candidate plants for pollinator habitat restoration efforts directed at wild bee conservation. Increasing our understanding of the attractiveness of a greater diversity of plants increases the potential for more rapid and efficient research in creating pollinator-supportive landscapes.
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Lopes, Laís Fernanda de Palma; Agostini, Vanessa Ochi; Muxagata, Erik
2018-04-15
Many organizations have suggested the use of the Calanoid copepod Acartia tonsa in protocols for acute toxicity tests. Nevertheless, these protocols present some problems, such as using 60-180µm meshes to separate specific stages of A. tonsa or carrying out the tests using small volumes that reflect high densities of A. tonsa that do not occur in nature, which could lead to distorted results. In addition, ecotoxicological studies may use statistical approaches that are inadequate for the type of data being analysed. For these reasons, some methodological approaches for bioassays using A. tonsa need to be clarified and revised. In this study, we present information about (i) the retention of copepodite stages of A. tonsa on 180, 330 and 500µm net meshes; (ii) tested storage volumes of 1 organism per 5, 10 or 20mL in each test container (TC); and (iii) considerations about the statistics employed. The results demonstrated that a net mesh of 180µm is capable of retaining all copepodite stages (CI to CVI), contrasting with the recommendation of using a 180µm mesh to separate out adults only. Coarser meshes (330 and 500µm) can also retain different proportions of all copepodite stages, but cannot separate out one developmental stage only. Twenty-five millilitres of medium in an open TC, commonly employed in bioassays simulating densities of 1 organism 5mL -1 , completely evaporated, and the results showed that the TCs need to be covered (e.g., PVC film) and filled with a minimum of 100mL of culture medium (simulating densities of 1 organism 20mL -1 ) to avoid evaporation and increases in salinity. The current use of ANOVA in ecotoxicological studies with proportions of surviving organisms should also be reconsidered since the data are discrete and have a binomial distribution; general linear models (GLMs) are considered more adequate. The information presented here suggests some adjustments that hopefully will enable the improvement of the procedures and methods employed in studies of acute toxicity using the copepod A. tonsa. Copyright © 2017 Elsevier Inc. All rights reserved.
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Heigl, Florian; Horvath, Kathrin; Laaha, Gregor; Zaller, Johann G
2017-06-26
Amphibians and reptiles are among the most endangered vertebrate species worldwide. However, little is known how they are affected by road-kills on tertiary roads and whether the surrounding landscape structure can explain road-kill patterns. The aim of our study was to examine the applicability of open-access remote sensing data for a large-scale citizen science approach to describe spatial patterns of road-killed amphibians and reptiles on tertiary roads. Using a citizen science app we monitored road-kills of amphibians and reptiles along 97.5 km of tertiary roads covering agricultural, municipal and interurban roads as well as cycling paths in eastern Austria over two seasons. Surrounding landscape was assessed using open access land cover classes for the region (Coordination of Information on the Environment, CORINE). Hotspot analysis was performed using kernel density estimation (KDE+). Relations between land cover classes and amphibian and reptile road-kills were analysed with conditional probabilities and general linear models (GLM). We also estimated the potential cost-efficiency of a large scale citizen science monitoring project. We recorded 180 amphibian and 72 reptile road-kills comprising eight species mainly occurring on agricultural roads. KDE+ analyses revealed a significant clustering of road-killed amphibians and reptiles, which is an important information for authorities aiming to mitigate road-kills. Overall, hotspots of amphibian and reptile road-kills were next to the land cover classes arable land, suburban areas and vineyards. Conditional probabilities and GLMs identified road-kills especially next to preferred habitats of green toad, common toad and grass snake, the most often found road-killed species. A citizen science approach appeared to be more cost-efficient than monitoring by professional researchers only when more than 400 km of road are monitored. Our findings showed that freely available remote sensing data in combination with a citizen science approach would be a cost-efficient method aiming to identify and monitor road-kill hotspots of amphibians and reptiles on a larger scale.
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Farivar, Tanaz; Burne, Robert A.
2016-01-01
ABSTRACT Biochemical and genetic aspects of the metabolism of the amino sugars N-acetylglucosamine (GlcNAc) and glucosamine (GlcN) by commensal oral streptococci and the effects of these sugars on interspecies competition with the dental caries pathogen Streptococcus mutans were explored. Multiple S. mutans wild-type isolates displayed long lag phases when transferred from glucose-containing medium to medium with GlcNAc as the primary carbohydrate source, but commensal streptococci did not. Competition in liquid coculture or dual-species biofilms between S. mutans and Streptococcus gordonii showed that S. gordonii was particularly dominant when the primary carbohydrate was GlcN or GlcNAc. Transcriptional and enzymatic assays showed that the catabolic pathway for GlcNAc was less highly induced in S. mutans than in S. gordonii. Exposure to H2O2, which is produced by S. gordonii and antagonizes the growth of S. mutans, led to reduced mRNA levels of nagA and nagB in S. mutans. When the gene for the transcriptional regulatory NagR was deleted in S. gordonii, the strain produced constitutively high levels of nagA (GlcNAc-6-P deacetylase), nagB (GlcN-6-P deaminase), and glmS (GlcN-6-P synthase) mRNA. Similar to NagR of S. mutans (NagRSm), the S. gordonii NagR protein (NagRSg) could bind to consensus binding sites (dre) in the nagA, nagB, and glmS promoter regions of S. gordonii. Notably, NagRSg binding was inhibited by GlcN-6-P, but G-6-P had no effect, unlike for NagRSm. This study expands the understanding of amino sugar metabolism and NagR-dependent gene regulation in streptococci and highlights the potential for therapeutic applications of amino sugars to prevent dental caries. IMPORTANCE Amino sugars are abundant in the biosphere, so the relative efficiency of particular bacteria in a given microbiota to metabolize these sources of carbon and nitrogen might have a profound impact on the ecology of the community. Our investigation reveals that several oral commensal bacteria have a much greater capacity to utilize amino sugars than the dental pathogen Streptococcus mutans and that the ability of the model commensal Streptococcus gordonii to compete against S. mutans is substantively enhanced by the presence of amino sugars commonly found in the oral cavity. The mechanisms underlying the greater capacity and competitive enhancements of the commensal are shown to depend on how the genes for the catabolic enzymes are regulated, the role of the allosteric modulators affecting such regulation, and the ability of amino sugars to enhance certain activities of the commensal that are antagonistic to S. mutans. PMID:27084009
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Zeng, Lin; Farivar, Tanaz; Burne, Robert A
2016-06-15
Biochemical and genetic aspects of the metabolism of the amino sugars N-acetylglucosamine (GlcNAc) and glucosamine (GlcN) by commensal oral streptococci and the effects of these sugars on interspecies competition with the dental caries pathogen Streptococcus mutans were explored. Multiple S. mutans wild-type isolates displayed long lag phases when transferred from glucose-containing medium to medium with GlcNAc as the primary carbohydrate source, but commensal streptococci did not. Competition in liquid coculture or dual-species biofilms between S. mutans and Streptococcus gordonii showed that S. gordonii was particularly dominant when the primary carbohydrate was GlcN or GlcNAc. Transcriptional and enzymatic assays showed that the catabolic pathway for GlcNAc was less highly induced in S. mutans than in S. gordonii Exposure to H2O2, which is produced by S. gordonii and antagonizes the growth of S. mutans, led to reduced mRNA levels of nagA and nagB in S. mutans When the gene for the transcriptional regulatory NagR was deleted in S. gordonii, the strain produced constitutively high levels of nagA (GlcNAc-6-P deacetylase), nagB (GlcN-6-P deaminase), and glmS (GlcN-6-P synthase) mRNA. Similar to NagR of S. mutans (NagRSm), the S. gordonii NagR protein (NagRSg) could bind to consensus binding sites (dre) in the nagA, nagB, and glmS promoter regions of S. gordonii Notably, NagRSg binding was inhibited by GlcN-6-P, but G-6-P had no effect, unlike for NagRSm This study expands the understanding of amino sugar metabolism and NagR-dependent gene regulation in streptococci and highlights the potential for therapeutic applications of amino sugars to prevent dental caries. Amino sugars are abundant in the biosphere, so the relative efficiency of particular bacteria in a given microbiota to metabolize these sources of carbon and nitrogen might have a profound impact on the ecology of the community. Our investigation reveals that several oral commensal bacteria have a much greater capacity to utilize amino sugars than the dental pathogen Streptococcus mutans and that the ability of the model commensal Streptococcus gordonii to compete against S. mutans is substantively enhanced by the presence of amino sugars commonly found in the oral cavity. The mechanisms underlying the greater capacity and competitive enhancements of the commensal are shown to depend on how the genes for the catabolic enzymes are regulated, the role of the allosteric modulators affecting such regulation, and the ability of amino sugars to enhance certain activities of the commensal that are antagonistic to S. mutans. Copyright © 2016, American Society for Microbiology. All Rights Reserved.
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NASA Astrophysics Data System (ADS)
Foglini, Federica; Bargain, Annaëlle; Angeletti, Lorenzo; Bonaldo, Davide; Carniel, Sandro; Taviani, Marco
2017-04-01
Predictive habitat modeling is gaining momentum because of its usefulness to recognize potential distributional patterns of ecosystems thus facilitating their proper governance when required, as it is for instance the case of the Marine Strategy Framework Directive (MSFD). This holds particularly true for the deep-sea in front of its overwhelming areal extent on a global scale and intrinsic technological difficulties (with related costs) for its direct exploration. Cold Water Corals (CWC) is one emblematic, virtually cosmopolitan, ecosystem in the deep, that is under international attention because of its multifaceted ecological importance. CWC is currently represented in the Mediterranean basin by habitats engineered by the arborescent scleractinians Madrepora oculata and Lophelia pertusa associated with a number of other benthic invertebrates. One major CWC hotspot located on the southwestern Adriatic margin, the Bari Canyon cold water coral province, has been targeted for producing habitat suitability maps. Initially the evaluation of the theoretical distribution of CWC in this area has been based upon visual observations, mainly extracted from geo-referenced underwater ROV imagery, coupled with the eco-geographic information derived from bathymetry. This approach relies upon the compilation and comparison of presence-only models (MaxEnt and ENFA), but also presence-absence model (GLMs). However, the pivotal role played by oceanographic factors has been soon added in order to achieve more robust predictive models. In fact, the Bari Canyon CWC province is situated on the main path of the North Adriatic Dense Water cascading, and hypothesized to be sensitive to hydrological factors. Accordingly, the statistical models to assess potential habitat extent have been implemented using hydrodynamic fields provided by ROMS for ocean currents, coupled with SWAN within the COAWST modelling system to account for wave-current interactions. The integration of results is beneficial to the production of more sophisticated habitat suitability maps.
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Predicting plant attractiveness to pollinators with passive crowdsourcing
DOE Office of Scientific and Technical Information (OSTI.GOV)
Bahlai, Christie A.; Landis, Douglas A.
Global concern regarding pollinator decline has intensified interest in enhancing pollinator resources in managed landscapes. These efforts frequently emphasize restoration or planting of flowering plants to provide pollen and nectar resources that are highly attractive to the desired pollinators. However, determining exactly which plant species should be used to enhance a landscape is difficult. Empirical screening of plants for such purposes is logistically daunting, but could be streamlined by crowdsourcing data to create lists of plants most probable to attract the desired pollinator taxa. People frequently photograph plants in bloom and the Internet has become a vast repository of suchmore » images. A proportion of these images also capture floral visitation by arthropods. Here, we test the hypothesis that the abundance of floral images containing identifiable pollinator and other beneficial insects is positively associated with the observed attractiveness of the same species in controlled field trials from previously published studies. We used Google Image searches to determine the correlation of pollinator visitation captured by photographs on the Internet relative to the attractiveness of the same species in common-garden field trials for 43 plant species. From the first 30 photographs, which successfully identified the plant, we recorded the number of Apis (managed honeybees), non-Apis (exclusively wild bees) and the number of bee-mimicking syrphid flies. We used these observations from search hits as well as bloom period (BP) as predictor variables in Generalized Linear Models (GLMs) for field-observed abundances of each of these groups. We found that non-Apis bees observed in controlled field trials were positively associated with observations of these taxa in Google Image searches (pseudo-R 2 of 0.668). Syrphid fly observations in the field were also associated with the frequency they were observed in images, but this relationship was weak. Apis bee observations were not associated with Internet images, but were slightly associated with BP. Our results suggest that passively crowdsourced image data can potentially be a useful screening tool to identify candidate plants for pollinator habitat restoration efforts directed at wild bee conservation. Increasing our understanding of the attractiveness of a greater diversity of plants increases the potential for more rapid and efficient research in creating pollinator-supportive landscapes.« less
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Predicting plant attractiveness to pollinators with passive crowdsourcing
Bahlai, Christie A.; Landis, Douglas A.
2016-06-01
Global concern regarding pollinator decline has intensified interest in enhancing pollinator resources in managed landscapes. These efforts frequently emphasize restoration or planting of flowering plants to provide pollen and nectar resources that are highly attractive to the desired pollinators. However, determining exactly which plant species should be used to enhance a landscape is difficult. Empirical screening of plants for such purposes is logistically daunting, but could be streamlined by crowdsourcing data to create lists of plants most probable to attract the desired pollinator taxa. People frequently photograph plants in bloom and the Internet has become a vast repository of suchmore » images. A proportion of these images also capture floral visitation by arthropods. Here, we test the hypothesis that the abundance of floral images containing identifiable pollinator and other beneficial insects is positively associated with the observed attractiveness of the same species in controlled field trials from previously published studies. We used Google Image searches to determine the correlation of pollinator visitation captured by photographs on the Internet relative to the attractiveness of the same species in common-garden field trials for 43 plant species. From the first 30 photographs, which successfully identified the plant, we recorded the number of Apis (managed honeybees), non-Apis (exclusively wild bees) and the number of bee-mimicking syrphid flies. We used these observations from search hits as well as bloom period (BP) as predictor variables in Generalized Linear Models (GLMs) for field-observed abundances of each of these groups. We found that non-Apis bees observed in controlled field trials were positively associated with observations of these taxa in Google Image searches (pseudo-R 2 of 0.668). Syrphid fly observations in the field were also associated with the frequency they were observed in images, but this relationship was weak. Apis bee observations were not associated with Internet images, but were slightly associated with BP. Our results suggest that passively crowdsourced image data can potentially be a useful screening tool to identify candidate plants for pollinator habitat restoration efforts directed at wild bee conservation. Increasing our understanding of the attractiveness of a greater diversity of plants increases the potential for more rapid and efficient research in creating pollinator-supportive landscapes.« less
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Factors associated with blood lead concentrations of children in Jamaica
RAHBAR, MOHAMMAD H.; SAMMS-VAUGHAN, MAUREEN; DICKERSON, AISHA S.; LOVELAND, KATHERINE A.; ARDJOMAND-HESSABI, MANOUCHEHR; BRESSLER, JAN; SHAKESPEARE-PELLINGTON, SYDONNIE; GROVE, MEGAN L.; BOERWINKLE, ERIC
2015-01-01
Lead is a heavy metal known to be detrimental to neurologic, physiologic, and behavioral health of children. Previous studies from Jamaica reported that mean lead levels in soil are four times that of lead levels in some other parts of the world. Other studies detected lead levels in fruits and root vegetables, which were grown in areas with lead contaminated soil. In this study, we investigate environmental factors associated with blood lead concentrations in Jamaican children. The participants in this study comprised 125 typically developing (TD) children (ages 2–8 years) who served as controls in an age- and sex-matched case-control study that enrolled children from 2009 – 2012 in Jamaica. We administered a questionnaire to assess demographic and socioeconomic information as well as potential exposures to lead through food. Using General Linear Models (GLMs), we identified factors associated with blood lead concentrations in Jamaican children. The geometric mean blood lead concentration (GMBLC) in the sample of children in this study was 2.80 μg/dL. In univariable GLM analyses, GMBLC was higher for children whose parents did not have education beyond high school compared to those whose parents had attained this level (3.00 μg/dL vs. 2.31 μg/dL; P = 0.05), children living near a high traffic road compared to those who did not (3.43 μg/dL vs. 2.52 μg/dL; P < 0.01), and children who reported eating ackee compared to those who did not eat this fruit (2.89 μg/dL vs. 1.65 μg/dL; P < 0.05). In multivariable analysis, living near a high traffic road was identified as an independent risk factor for higher adjusted GMBLC (3.05 μg/dL vs. 2.19 μg/dL; P = 0.01). While our findings indicate that GMBLC in Jamaican children has dropped by at least 62% during the past two decades, children living in Jamaica still have GMBLC that is twice that of children in more developed countries. In addition, we have identified significant risk factors for higher blood lead concentrations in Jamaican children. We believe increasing awareness among parents regarding these risk factors could potentially lead to a lower level of lead exposure in Jamaican children. PMID:25837555
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NASA Astrophysics Data System (ADS)
Liang, Pengfei; Zhu, Tong; Fang, Yanhua; Li, Yingruo; Han, Yiqun; Wu, Yusheng; Hu, Min; Wang, Junxia
2017-11-01
To control severe air pollution in China, comprehensive pollution control strategies have been implemented throughout the country in recent years. To evaluate the effectiveness of these strategies, the influence of meteorological conditions on levels of air pollution needs to be determined. Using the intensive air pollution control strategies implemented during the Asia-Pacific Economic Cooperation Forum in 2014 (APEC 2014) and the 2015 China Victory Day Parade (Victory Parade 2015) as examples, we estimated the role of meteorological conditions and pollution control strategies in reducing air pollution levels in Beijing. Atmospheric particulate matter of aerodynamic diameter ≤ 2.5 µm (PM2.5) samples were collected and gaseous pollutants (SO2, NO, NOx, and O3) were measured online at a site in Peking University (PKU). To determine the influence of meteorological conditions on the levels of air pollution, we first compared the air pollutant concentrations during days with stable meteorological conditions. However, there were few days with stable meteorological conditions during the Victory Parade. As such, we were unable to estimate the level of emission reduction efforts during this period. Finally, a generalized linear regression model (GLM) based only on meteorological parameters was built to predict air pollutant concentrations, which could explain more than 70 % of the variation in air pollutant concentration levels, after incorporating the nonlinear relationships between certain meteorological parameters and the concentrations of air pollutants. Evaluation of the GLM performance revealed that the GLM, even based only on meteorological parameters, could be satisfactory to estimate the contribution of meteorological conditions in reducing air pollution and, hence, the contribution of control strategies in reducing air pollution. Using the GLM, we found that the meteorological conditions and pollution control strategies contributed 30 and 28 % to the reduction of the PM2.5 concentration during APEC and 38 and 25 % during the Victory Parade, respectively, based on the assumption that the concentrations of air pollutants are only determined by meteorological conditions and emission intensities. We also estimated the contribution of meteorological conditions and control strategies in reducing the concentrations of gaseous pollutants and PM2.5 components with the GLMs, revealing the effective control of anthropogenic emissions.
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Predicting plant attractiveness to pollinators with passive crowdsourcing
Bahlai, Christie A.; Landis, Douglas A.
2016-01-01
Global concern regarding pollinator decline has intensified interest in enhancing pollinator resources in managed landscapes. These efforts frequently emphasize restoration or planting of flowering plants to provide pollen and nectar resources that are highly attractive to the desired pollinators. However, determining exactly which plant species should be used to enhance a landscape is difficult. Empirical screening of plants for such purposes is logistically daunting, but could be streamlined by crowdsourcing data to create lists of plants most probable to attract the desired pollinator taxa. People frequently photograph plants in bloom and the Internet has become a vast repository of such images. A proportion of these images also capture floral visitation by arthropods. Here, we test the hypothesis that the abundance of floral images containing identifiable pollinator and other beneficial insects is positively associated with the observed attractiveness of the same species in controlled field trials from previously published studies. We used Google Image searches to determine the correlation of pollinator visitation captured by photographs on the Internet relative to the attractiveness of the same species in common-garden field trials for 43 plant species. From the first 30 photographs, which successfully identified the plant, we recorded the number of Apis (managed honeybees), non-Apis (exclusively wild bees) and the number of bee-mimicking syrphid flies. We used these observations from search hits as well as bloom period (BP) as predictor variables in Generalized Linear Models (GLMs) for field-observed abundances of each of these groups. We found that non-Apis bees observed in controlled field trials were positively associated with observations of these taxa in Google Image searches (pseudo-R2 of 0.668). Syrphid fly observations in the field were also associated with the frequency they were observed in images, but this relationship was weak. Apis bee observations were not associated with Internet images, but were slightly associated with BP. Our results suggest that passively crowdsourced image data can potentially be a useful screening tool to identify candidate plants for pollinator habitat restoration efforts directed at wild bee conservation. Increasing our understanding of the attractiveness of a greater diversity of plants increases the potential for more rapid and efficient research in creating pollinator-supportive landscapes. PMID:27429762
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The evolution of labile traits in sex- and age-structured populations.
Childs, Dylan Z; Sheldon, Ben C; Rees, Mark
2016-03-01
Many quantitative traits are labile (e.g. somatic growth rate, reproductive timing and investment), varying over the life cycle as a result of behavioural adaptation, developmental processes and plastic responses to the environment. At the population level, selection can alter the distribution of such traits across age classes and among generations. Despite a growing body of theoretical research exploring the evolutionary dynamics of labile traits, a data-driven framework for incorporating such traits into demographic models has not yet been developed. Integral projection models (IPMs) are increasingly being used to understand the interplay between changes in labile characters, life histories and population dynamics. One limitation of the IPM approach is that it relies on phenotypic associations between parents and offspring traits to capture inheritance. However, it is well-established that many different processes may drive these associations, and currently, no clear consensus has emerged on how to model micro-evolutionary dynamics in an IPM framework. We show how to embed quantitative genetic models of inheritance of labile traits into age-structured, two-sex models that resemble standard IPMs. Commonly used statistical tools such as GLMs and their mixed model counterparts can then be used for model parameterization. We illustrate the methodology through development of a simple model of egg-laying date evolution, parameterized using data from a population of Great tits (Parus major). We demonstrate how our framework can be used to project the joint dynamics of species' traits and population density. We then develop a simple extension of the age-structured Price equation (ASPE) for two-sex populations, and apply this to examine the age-specific contributions of different processes to change in the mean phenotype and breeding value. The data-driven framework we outline here has the potential to facilitate greater insight into the nature of selection and its consequences in settings where focal traits vary over the lifetime through ontogeny, behavioural adaptation and phenotypic plasticity, as well as providing a potential bridge between theoretical and empirical studies of labile trait variation. © 2016 The Authors Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society.
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Progressive deterioration of thalamic nuclei relates to cortical network decline in schizophrenia
Cobia, Derin J.; Smith, Matthew J.; Salinas, Ilse; Ng, Charlene; Gado, Mohktar; Csernansky, John G.; Wang, Lei
2016-01-01
Thalamic abnormalities are considered part of the complex pathophysiology of schizophrenia, particularly the involvement of specific thalamic nuclei. The goals of this study were to: introduce a novel atlas-based parcellation scheme for defining various thalamic nuclei; compare their integrity in a schizophrenia sample against healthy individuals at baseline and follow-up time points, as well as rates of change over time; examine relationships between the nuclei and abnormalities in known connected cortical regions; and finally, to determine if schizophrenia-related thalamic nuclei changes relate to cognitive functioning and clinical symptoms. Subjects were from a larger longitudinal 2-year follow-up study, schizophrenia (n=20) and healthy individuals (n=20) were group-matched for age, gender, and recent-alcohol use. We used high-dimensional brain mapping to obtain thalamic morphology, and applied a novel atlas-based method for delineating anterior, mediodorsal, and pulvinar nuclei. Results from cross sectional GLMs revealed group differences in bilateral mediodorsal and anterior nuclei, while longitudinal models revealed significant group-by-time interactions for the mediodorsal and pulvinar nuclei. Cortical correlations were the strongest for the pulvinar in frontal, temporal and parietal regions, followed by the mediodorsal nucleus in frontal regions, but none in the anterior nucleus. Thalamic measures did not correlate with cognitive and clinical scores at any time point or longitudinally. Overall, findings revealed a pattern of persistent progressive abnormalities in thalamic nuclei that relate to advancing cortical decline in schizophrenia, but not with measures of behavior. PMID:27613507
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Tummaruk, Padet; Tantasuparuk, Wichai; Techakumphu, Mongkol; Kunavongkrit, Annop
2009-01-01
The present study aims to investigate the association between growth rate (GR), body weight (BW), backfat thickness (BF) and age at first observed oestrus in crossbred Landrace x Yorkshire (LY) replacement gilts in the tropics. The study was carried out on five commercial swine herds in Thailand between 2004 and 2006. A total of 6946 LY gilts were included. The gilts entered the herd at about 163 days of age. The BW (kg) and BF (mm) of the gilts were measured when the gilts entered the gilt pools and again when the gilts were sent to the breeding house. The GR from birth to entry into the gilt pools (birth to 90 kg BW) (GRe), the GR from entry into to exit from the gilt pools (91-134 kg BW) (GRi) and the GR from birth until the gilts were sent to the breeding house (birth to 134 kg BW) (GRs) were calculated. The relationship between age at first observed oestrus and GRe, GRs, GRi, BW and BF were analyzed. Pearson's correlation and four general linear models (GLMs) were conducted. On average, the gilts showed first observed oestrus at 200+/-28 days of age. The means of age at first observed oestrus varied from 188 to 251 days (P<0.001) among the herds. The GRs of the gilts significantly correlated with the BW (r=0.55, P<0.001) of the gilts when they were sent to the breeding house and the age at first observed oestrus (r=-0.40, P<0.001). Gilts with a high GRe and GRs were younger at first observed oestrus compared to gilts with a low GRe and GRs. On average, the gilts with GRs of over 604 g/day showed first observed oestrus before 5 months of age. GRi was not correlated with the age at first observed oestrus (P>0.05). Neither the BF of the gilts at entry nor the BF that the gilts gained within the gilt pools significantly correlated with age at first observed oestrus (P=0.29 and P=0.69, respectively). But the gilts with a higher BF at entry tended to have a higher BW when they were sent to the breeding house (r=0.44, P<0.001). The present study indicates that replacement gilts with a high GR (both GRe and GRs) tend to show sign of oestrus earlier than gilts with a low GR (both GRe and GRs).
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Genomic prediction based on data from three layer lines using non-linear regression models.
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.
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LINEAR - DERIVATION AND DEFINITION OF A LINEAR AIRCRAFT MODEL
NASA Technical Reports Server (NTRS)
Duke, E. L.
1994-01-01
The Derivation and Definition of a Linear Model program, LINEAR, provides the user with a powerful and flexible tool for the linearization of aircraft aerodynamic models. LINEAR was developed to provide a standard, documented, and verified tool to derive linear models for aircraft stability analysis and control law design. Linear system models define the aircraft system in the neighborhood of an analysis point and are determined by the linearization of the nonlinear equations defining vehicle dynamics and sensors. LINEAR numerically determines a linear system model using nonlinear equations of motion and a user supplied linear or nonlinear aerodynamic model. The nonlinear equations of motion used are six-degree-of-freedom equations with stationary atmosphere and flat, nonrotating earth assumptions. LINEAR is capable of extracting both linearized engine effects, such as net thrust, torque, and gyroscopic effects and including these effects in the linear system model. The point at which this linear model is defined is determined either by completely specifying the state and control variables, or by specifying an analysis point on a trajectory and directing the program to determine the control variables and the remaining state variables. The system model determined by LINEAR consists of matrices for both the state and observation equations. The program has been designed to provide easy selection of state, control, and observation variables to be used in a particular model. Thus, the order of the system model is completely under user control. Further, the program provides the flexibility of allowing alternate formulations of both the state and observation equations. Data describing the aircraft and the test case is input to the program through a terminal or formatted data files. All data can be modified interactively from case to case. The aerodynamic model can be defined in two ways: a set of nondimensional stability and control derivatives for the flight point of interest, or a full non-linear aerodynamic model as used in simulations. LINEAR is written in FORTRAN and has been implemented on a DEC VAX computer operating under VMS with a virtual memory requirement of approximately 296K of 8 bit bytes. Both an interactive and batch version are included. LINEAR was developed in 1988.
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Gaucher-Wieczorek, Florence; Guérineau, Vincent; Touboul, David; Thétiot-Laurent, Sophie; Pelissier, Franck; Badet-Denisot, Marie-Ange; Badet, Bernard; Durand, Philippe
2014-08-01
Glucosamine-6-phosphate synthase (GlmS, EC 2.6.1.16) catalyzes the first and rate-limiting step in the hexosamine biosynthetic pathway, leading to the synthesis of uridine-5'-diphospho-N-acetyl-D-glucosamine, the major building block for the edification of peptidoglycan in bacteria, chitin in fungi, and glycoproteins in mammals. This bisubstrate enzyme converts D-fructose-6-phosphate (Fru-6P) and L-glutamine (Gln) into D-glucosamine-6-phosphate (GlcN-6P) and L-glutamate (Glu), respectively. We previously demonstrated that matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) allows determination of the kinetic parameters of the synthase activity. We propose here to refine the experimental protocol to quantify Glu and GlcN-6P, allowing determination of both hemisynthase and synthase parameters from a single assay kinetic experiment, while avoiding interferences encountered in other assays. It is the first time that MALDI-MS is used to survey the activity of a bisubstrate enzyme. Copyright © 2014 Elsevier Inc. All rights reserved.
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The Yale Craving Scale: Development and psychometric properties.
Rojewski, Alana M; Morean, Meghan E; Toll, Benjamin A; McKee, Sherry A; Krishnan-Sarin, Suchitra; Green, Barry G; Bartoshuk, Linda M; O'Malley, Stephanie S
2015-09-01
The current study presents a psychometric evaluation of the Yale Craving Scale (YCS), a novel measure of craving for cigarettes and alcohol, respectively. The YCS is the first craving measure to use a generalized Labeled Magnitude Scale (gLMS) as the scoring format, which facilitates between-group comparisons of subjective craving and eliminates ceiling effects by assessing the full range of imaginable sensation intensities. Psychometric evaluations of the YCS for use with cigarettes (YCS Smoking) and alcohol (YCS Drinking) included assessments of latent factor structure, internal consistency, ceiling effects, and test-criterion relationships. Study samples included 493 treatment-seeking smokers and 213 heavy drinkers. Factor analyses of the 5-item YCS Smoking and Drinking scores confirmed a 1-factor scale. The YCS Smoking and Drinking scores evidenced: (1) good internal consistency, (2) scalar measurement invariance within several subgroups (e.g., smoking/drinking status; nicotine/alcohol dependence), (3) convergent relationships with extant craving measures, and (4) concurrent relationships with smoking/drinking outcomes. These results suggest that the YCS represents a psychometrically sound scale for assessing smoking and drinking urges in dependent populations. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
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Progressive deterioration of thalamic nuclei relates to cortical network decline in schizophrenia.
Cobia, Derin J; Smith, Matthew J; Salinas, Ilse; Ng, Charlene; Gado, Mokhtar; Csernansky, John G; Wang, Lei
2017-02-01
Thalamic abnormalities are considered part of the complex pathophysiology of schizophrenia, particularly the involvement of specific thalamic nuclei. The goals of this study were to: introduce a novel atlas-based parcellation scheme for defining various thalamic nuclei; compare their integrity in a schizophrenia sample against healthy individuals at baseline and follow-up time points, as well as rates of change over time; examine relationships between the nuclei and abnormalities in known connected cortical regions; and finally, to determine if schizophrenia-related thalamic nuclei changes relate to cognitive functioning and clinical symptoms. Subjects were from a larger longitudinal 2-year follow-up study, schizophrenia (n=20) and healthy individuals (n=20) were group-matched for age, gender, and recent-alcohol use. We used high-dimensional brain mapping to obtain thalamic morphology, and applied a novel atlas-based method for delineating anterior, mediodorsal, and pulvinar nuclei. Results from cross sectional GLMs revealed group differences in bilateral mediodorsal and anterior nuclei, while longitudinal models revealed significant group-by-time interactions for the mediodorsal and pulvinar nuclei. Cortical correlations were the strongest for the pulvinar in frontal, temporal and parietal regions, followed by the mediodorsal nucleus in frontal regions, but none in the anterior nucleus. Thalamic measures did not correlate with cognitive and clinical scores at any time point or longitudinally. Overall, findings revealed a pattern of persistent progressive abnormalities in thalamic nuclei that relate to advancing cortical decline in schizophrenia, but not with measures of behavior. Copyright © 2016 Elsevier B.V. All rights reserved.
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Montoye, Alexander H K; Begum, Munni; Henning, Zachary; Pfeiffer, Karin A
2017-02-01
This study had three purposes, all related to evaluating energy expenditure (EE) prediction accuracy from body-worn accelerometers: (1) compare linear regression to linear mixed models, (2) compare linear models to artificial neural network models, and (3) compare accuracy of accelerometers placed on the hip, thigh, and wrists. Forty individuals performed 13 activities in a 90 min semi-structured, laboratory-based protocol. Participants wore accelerometers on the right hip, right thigh, and both wrists and a portable metabolic analyzer (EE criterion). Four EE prediction models were developed for each accelerometer: linear regression, linear mixed, and two ANN models. EE prediction accuracy was assessed using correlations, root mean square error (RMSE), and bias and was compared across models and accelerometers using repeated-measures analysis of variance. For all accelerometer placements, there were no significant differences for correlations or RMSE between linear regression and linear mixed models (correlations: r = 0.71-0.88, RMSE: 1.11-1.61 METs; p > 0.05). For the thigh-worn accelerometer, there were no differences in correlations or RMSE between linear and ANN models (ANN-correlations: r = 0.89, RMSE: 1.07-1.08 METs. Linear models-correlations: r = 0.88, RMSE: 1.10-1.11 METs; p > 0.05). Conversely, one ANN had higher correlations and lower RMSE than both linear models for the hip (ANN-correlation: r = 0.88, RMSE: 1.12 METs. Linear models-correlations: r = 0.86, RMSE: 1.18-1.19 METs; p < 0.05), and both ANNs had higher correlations and lower RMSE than both linear models for the wrist-worn accelerometers (ANN-correlations: r = 0.82-0.84, RMSE: 1.26-1.32 METs. Linear models-correlations: r = 0.71-0.73, RMSE: 1.55-1.61 METs; p < 0.01). For studies using wrist-worn accelerometers, machine learning models offer a significant improvement in EE prediction accuracy over linear models. Conversely, linear models showed similar EE prediction accuracy to machine learning models for hip- and thigh-worn accelerometers and may be viable alternative modeling techniques for EE prediction for hip- or thigh-worn accelerometers.
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Estimation of suspended-sediment rating curves and mean suspended-sediment loads
Crawford, Charles G.
1991-01-01
A simulation study was done to evaluate: (1) the accuracy and precision of parameter estimates for the bias-corrected, transformed-linear and non-linear models obtained by the method of least squares; (2) the accuracy of mean suspended-sediment loads calculated by the flow-duration, rating-curve method using model parameters obtained by the alternative methods. Parameter estimates obtained by least squares for the bias-corrected, transformed-linear model were considerably more precise than those obtained for the non-linear or weighted non-linear model. The accuracy of parameter estimates obtained for the biascorrected, transformed-linear and weighted non-linear model was similar and was much greater than the accuracy obtained by non-linear least squares. The improved parameter estimates obtained by the biascorrected, transformed-linear or weighted non-linear model yield estimates of mean suspended-sediment load calculated by the flow-duration, rating-curve method that are more accurate and precise than those obtained for the non-linear model.
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User's manual for LINEAR, a FORTRAN program to derive linear aircraft models
NASA Technical Reports Server (NTRS)
Duke, Eugene L.; Patterson, Brian P.; Antoniewicz, Robert F.
1987-01-01
This report documents a FORTRAN program that provides a powerful and flexible tool for the linearization of aircraft models. The program LINEAR numerically determines a linear system model using nonlinear equations of motion and a user-supplied nonlinear aerodynamic model. The system model determined by LINEAR consists of matrices for both state and observation equations. The program has been designed to allow easy selection and definition of the state, control, and observation variables to be used in a particular model.
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Latent log-linear models for handwritten digit classification.
Deselaers, Thomas; Gass, Tobias; Heigold, Georg; Ney, Hermann
2012-06-01
We present latent log-linear models, an extension of log-linear models incorporating latent variables, and we propose two applications thereof: log-linear mixture models and image deformation-aware log-linear models. The resulting models are fully discriminative, can be trained efficiently, and the model complexity can be controlled. Log-linear mixture models offer additional flexibility within the log-linear modeling framework. Unlike previous approaches, the image deformation-aware model directly considers image deformations and allows for a discriminative training of the deformation parameters. Both are trained using alternating optimization. For certain variants, convergence to a stationary point is guaranteed and, in practice, even variants without this guarantee converge and find models that perform well. We tune the methods on the USPS data set and evaluate on the MNIST data set, demonstrating the generalization capabilities of our proposed models. Our models, although using significantly fewer parameters, are able to obtain competitive results with models proposed in the literature.
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Simplified large African carnivore density estimators from track indices.
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.
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Population response to climate change: linear vs. non-linear modeling approaches.
Ellis, Alicia M; Post, Eric
2004-03-31
Research on the ecological consequences of global climate change has elicited a growing interest in the use of time series analysis to investigate population dynamics in a changing climate. Here, we compare linear and non-linear models describing the contribution of climate to the density fluctuations of the population of wolves on Isle Royale, Michigan from 1959 to 1999. The non-linear self excitatory threshold autoregressive (SETAR) model revealed that, due to differences in the strength and nature of density dependence, relatively small and large populations may be differentially affected by future changes in climate. Both linear and non-linear models predict a decrease in the population of wolves with predicted changes in climate. Because specific predictions differed between linear and non-linear models, our study highlights the importance of using non-linear methods that allow the detection of non-linearity in the strength and nature of density dependence. Failure to adopt a non-linear approach to modelling population response to climate change, either exclusively or in addition to linear approaches, may compromise efforts to quantify ecological consequences of future warming.
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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
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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.
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Computing Linear Mathematical Models Of Aircraft
NASA Technical Reports Server (NTRS)
Duke, Eugene L.; Antoniewicz, Robert F.; Krambeer, Keith D.
1991-01-01
Derivation and Definition of Linear Aircraft Model (LINEAR) computer program provides user with powerful, and flexible, standard, documented, and verified software tool for linearization of mathematical models of aerodynamics of aircraft. Intended for use in software tool to drive linear analysis of stability and design of control laws for aircraft. Capable of both extracting such linearized engine effects as net thrust, torque, and gyroscopic effects, and including these effects in linear model of system. Designed to provide easy selection of state, control, and observation variables used in particular model. Also provides flexibility of allowing alternate formulations of both state and observation equations. Written in FORTRAN.
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Modelling female fertility traits in beef cattle using linear and non-linear models.
Naya, H; Peñagaricano, F; Urioste, J I
2017-06-01
Female fertility traits are key components of the profitability of beef cattle production. However, these traits are difficult and expensive to measure, particularly under extensive pastoral conditions, and consequently, fertility records are in general scarce and somehow incomplete. Moreover, fertility traits are usually dominated by the effects of herd-year environment, and it is generally assumed that relatively small margins are kept for genetic improvement. New ways of modelling genetic variation in these traits are needed. Inspired in the methodological developments made by Prof. Daniel Gianola and co-workers, we assayed linear (Gaussian), Poisson, probit (threshold), censored Poisson and censored Gaussian models to three different kinds of endpoints, namely calving success (CS), number of days from first calving (CD) and number of failed oestrus (FE). For models involving FE and CS, non-linear models overperformed their linear counterparts. For models derived from CD, linear versions displayed better adjustment than the non-linear counterparts. Non-linear models showed consistently higher estimates of heritability and repeatability in all cases (h 2 < 0.08 and r < 0.13, for linear models; h 2 > 0.23 and r > 0.24, for non-linear models). While additive and permanent environment effects showed highly favourable correlations between all models (>0.789), consistency in selecting the 10% best sires showed important differences, mainly amongst the considered endpoints (FE, CS and CD). In consequence, endpoints should be considered as modelling different underlying genetic effects, with linear models more appropriate to describe CD and non-linear models better for FE and CS. © 2017 Blackwell Verlag GmbH.
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User's manual for interactive LINEAR: A FORTRAN program to derive linear aircraft models
NASA Technical Reports Server (NTRS)
Antoniewicz, Robert F.; Duke, Eugene L.; Patterson, Brian P.
1988-01-01
An interactive FORTRAN program that provides the user with a powerful and flexible tool for the linearization of aircraft aerodynamic models is documented in this report. The program LINEAR numerically determines a linear system model using nonlinear equations of motion and a user-supplied linear or nonlinear aerodynamic model. The nonlinear equations of motion used are six-degree-of-freedom equations with stationary atmosphere and flat, nonrotating earth assumptions. The system model determined by LINEAR consists of matrices for both the state and observation equations. The program has been designed to allow easy selection and definition of the state, control, and observation variables to be used in a particular model.
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Development and validation of a general purpose linearization program for rigid aircraft models
NASA Technical Reports Server (NTRS)
Duke, E. L.; Antoniewicz, R. F.
1985-01-01
A FORTRAN program that provides the user with a powerful and flexible tool for the linearization of aircraft models is discussed. The program LINEAR numerically determines a linear systems model using nonlinear equations of motion and a user-supplied, nonlinear aerodynamic model. The system model determined by LINEAR consists of matrices for both the state and observation equations. The program has been designed to allow easy selection and definition of the state, control, and observation variables to be used in a particular model. Also, included in the report is a comparison of linear and nonlinear models for a high performance aircraft.
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The determination of third order linear models from a seventh order nonlinear jet engine model
NASA Technical Reports Server (NTRS)
Lalonde, Rick J.; Hartley, Tom T.; De Abreu-Garcia, J. Alex
1989-01-01
Results are presented that demonstrate how good reduced-order models can be obtained directly by recursive parameter identification using input/output (I/O) data of high-order nonlinear systems. Three different methods of obtaining a third-order linear model from a seventh-order nonlinear turbojet engine model are compared. The first method is to obtain a linear model from the original model and then reduce the linear model by standard reduction techniques such as residualization and balancing. The second method is to identify directly a third-order linear model by recursive least-squares parameter estimation using I/O data of the original model. The third method is to obtain a reduced-order model from the original model and then linearize the reduced model. Frequency responses are used as the performance measure to evaluate the reduced models. The reduced-order models along with their Bode plots are presented for comparison purposes.
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Development of a Linear Stirling Model with Varying Heat Inputs
NASA Technical Reports Server (NTRS)
Regan, Timothy F.; Lewandowski, Edward J.
2007-01-01
The linear model of the Stirling system developed by NASA Glenn Research Center (GRC) has been extended to include a user-specified heat input. Previously developed linear models were limited to the Stirling convertor and electrical load. They represented the thermodynamic cycle with pressure factors that remained constant. The numerical values of the pressure factors were generated by linearizing GRC s non-linear System Dynamic Model (SDM) of the convertor at a chosen operating point. The pressure factors were fixed for that operating point, thus, the model lost accuracy if a transition to a different operating point were simulated. Although the previous linear model was used in developing controllers that manipulated current, voltage, and piston position, it could not be used in the development of control algorithms that regulated hot-end temperature. This basic model was extended to include the thermal dynamics associated with a hot-end temperature that varies over time in response to external changes as well as to changes in the Stirling cycle. The linear model described herein includes not only dynamics of the piston, displacer, gas, and electrical circuit, but also the transient effects of the heater head thermal inertia. The linear version algebraically couples two separate linear dynamic models, one model of the Stirling convertor and one model of the thermal system, through the pressure factors. The thermal system model includes heat flow of heat transfer fluid, insulation loss, and temperature drops from the heat source to the Stirling convertor expansion space. The linear model was compared to a nonlinear model, and performance was very similar. The resulting linear model can be implemented in a variety of computing environments, and is suitable for analysis with classical and state space controls analysis techniques.
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Factors influencing riverine fish assemblages in Massachusetts
Armstrong, David S.; Richards, Todd A.; Levin, Sara B.
2011-01-01
The U.S. Geological Survey, in cooperation with the Massachusetts Department of Conservation and Recreation, Massachusetts Department of Environmental Protection, and the Massachusetts Department of Fish and Game, conducted an investigation of fish assemblages in small- to medium-sized Massachusetts streams. The objective of this study was to determine relations between fish-assemblage characteristics and anthropogenic factors, including impervious cover and estimated flow alteration, relative to the effects of environmental factors, including physical-basin characteristics and land use. The results of this investigation supersede those of a preliminary analysis published in 2010. Fish data were obtained for 669 fish-sampling sites from the Massachusetts Division of Fisheries and Wildlife fish-community database. A review of the literature was used to select fish metrics - species richness, abundance of individual species, and abundances of species grouped on life history traits - responsive to flow alteration. The contributing areas to the fish-sampling sites were delineated and used with a geographic information system to determine a set of environmental and anthropogenic factors that were tested for use as explanatory variables in regression models. Reported and estimated withdrawals and return flows were used together with simulated unaltered streamflows to estimate altered streamflows and indicators of flow alteration for each fish-sampling site. Altered streamflows and indicators of flow alteration were calculated on the basis of methods developed in a previous U.S. Geological Survey study in which unaltered daily streamflows were simulated for a 44-year period (water years 1961-2004), and streamflow alterations were estimated by use of water-withdrawal and wastewater-return data previously reported to the State for the 2000-04 period and estimated domestic-well withdrawals and septic-system discharges. A variable selection process, conducted using principal components analysis and Spearman rank correlation, was used to select a set of 15 non-redundant environmental and anthropogenic factors to test for use as explanatory variables in the regression analyses. Twenty-one fish species were used in a multivariate analysis of fish-assemblage patterns. Results of nonmetric multidimensional scaling and hierarchical cluster analysis were used to group fish species into fluvial and macrohabitat generalist habitat-use classes. Two analytical techniques, quantile regression and generalized linear modeling, were applied to characterize the association between fish-response variables and environmental and anthropogenic explanatory variables. Quantile regression demonstrated that as percent impervious cover and an indicator of percent alteration of August median flow from groundwater withdrawals increase, the relative abundance and species richness of fluvial fish decrease. The quantile regression plots indicate that (1) as many as seven fluvial fish species are expected in streams with little flow alteration or impervious cover, (2) no more than four fluvial fish species are expected in streams where flow alterations from groundwater withdrawals exceed 50 percent of the August median flow or the percent area of impervious cover exceeds 15 percent, and (3) few fluvial fish remain at high rates of withdrawal (approaching 100 percent) or high rates of impervious cover (between 25 and 30 percent). Three generalized linear models (GLMs) were developed to quantify the response of fluvial fish to multiple environmental and anthropogenic variables. All variables in the GLM equations were demonstrated to be significant (p less than 0.05, with most less than 0.01). Variables in the fluvial-fish relative-abundance model were channel slope, estimated percent alteration of August median flow from groundwater withdrawals, percent wetland in a 240-meter buffer strip, and percent impervious cover. Variables in the fluvial-fish species-richness model were drainage area, channel slope, total undammed reach length, percent wetland in a 240-meter buffer strip, and percent impervious cover. Variables in the brook trout relativeabundance model were drainage area, percent open water, and percent impervious cover. The variability explained by the GLM models, as measured by the pseudo R2, ranged from 18.2 to 34.6, and correlations between observed and predicted values ranged from 0.50 to 0.60. Results of GLM models indicated that, keeping all other variables the same, a one-unit (1 percent) increase in the percent depletion of August median flow would result in a 0.9-percent decrease in the relative abundance (in counts per hour) of fluvial fish. The results of GLM models also indicated that a unit increase in impervious cover (1 percent) resulted in a 3.7-percent decrease in the relative abundance of fluvial fish, a 5.4-percent decrease in fluvial-fish species richness, and an 8.7-percent decrease in brook trout relative abundance.
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ERIC Educational Resources Information Center
Ker, H. W.
2014-01-01
Multilevel data are very common in educational research. Hierarchical linear models/linear mixed-effects models (HLMs/LMEs) are often utilized to analyze multilevel data nowadays. This paper discusses the problems of utilizing ordinary regressions for modeling multilevel educational data, compare the data analytic results from three regression…
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NASA Astrophysics Data System (ADS)
Takahashi, Takuya; Sugiura, Junnnosuke; Nagayama, Kuniaki
2002-05-01
To investigate the role hydration plays in the electrostatic interactions of proteins, the time-averaged electrostatic potential of the B1 domain of protein G in an aqueous solution was calculated with full atomic molecular dynamics simulations that explicitly considers every atom (i.e., an all atom model). This all atom calculated potential was compared with the potential obtained from an electrostatic continuum model calculation. In both cases, the charge-screening effect was fairly well formulated with an effective relative dielectric constant which increased linearly with increasing charge-charge distance. This simulated linear dependence agrees with the experimentally determined linear relation proposed by Pickersgill. Cut-off approximations for Coulomb interactions failed to reproduce this linear relation. Correlation between the all atom model and the continuum models was found to be better than the respective correlation calculated for linear fitting to the two models. This confirms that the continuum model is better at treating the complicated shapes of protein conformations than the simple linear fitting empirical model. We have tried a sigmoid fitting empirical model in addition to the linear one. When weights of all data were treated equally, the sigmoid model, which requires two fitting parameters, fits results of both the all atom and the continuum models less accurately than the linear model which requires only one fitting parameter. When potential values are chosen as weighting factors, the fitting error of the sigmoid model became smaller, and the slope of both linear fitting curves became smaller. This suggests the screening effect of an aqueous medium within a short range, where potential values are relatively large, is smaller than that expected from the linear fitting curve whose slope is almost 4. To investigate the linear increase of the effective relative dielectric constant, the Poisson equation of a low-dielectric sphere in a high-dielectric medium was solved and charges distributed near the molecular surface were indicated as leading to the apparent linearity.
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ERIC Educational Resources Information Center
Esteley, Cristina B.; Villarreal, Monica E.; Alagia, Humberto R.
2010-01-01
Over the past several years, we have been exploring and researching a phenomenon that occurs among undergraduate students that we called extension of linear models to non-linear contexts or overgeneralization of linear models. This phenomenon appears when some students use linear representations in situations that are non-linear. In a first phase,…
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Piecewise Linear-Linear Latent Growth Mixture Models with Unknown Knots
ERIC Educational Resources Information Center
Kohli, Nidhi; Harring, Jeffrey R.; Hancock, Gregory R.
2013-01-01
Latent growth curve models with piecewise functions are flexible and useful analytic models for investigating individual behaviors that exhibit distinct phases of development in observed variables. As an extension of this framework, this study considers a piecewise linear-linear latent growth mixture model (LGMM) for describing segmented change of…
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Yock, Adam D; Rao, Arvind; Dong, Lei; Beadle, Beth M; Garden, Adam S; Kudchadker, Rajat J; Court, Laurence E
2014-05-01
The purpose of this work was to develop and evaluate the accuracy of several predictive models of variation in tumor volume throughout the course of radiation therapy. Nineteen patients with oropharyngeal cancers were imaged daily with CT-on-rails for image-guided alignment per an institutional protocol. The daily volumes of 35 tumors in these 19 patients were determined and used to generate (1) a linear model in which tumor volume changed at a constant rate, (2) a general linear model that utilized the power fit relationship between the daily and initial tumor volumes, and (3) a functional general linear model that identified and exploited the primary modes of variation between time series describing the changing tumor volumes. Primary and nodal tumor volumes were examined separately. The accuracy of these models in predicting daily tumor volumes were compared with those of static and linear reference models using leave-one-out cross-validation. In predicting the daily volume of primary tumors, the general linear model and the functional general linear model were more accurate than the static reference model by 9.9% (range: -11.6%-23.8%) and 14.6% (range: -7.3%-27.5%), respectively, and were more accurate than the linear reference model by 14.2% (range: -6.8%-40.3%) and 13.1% (range: -1.5%-52.5%), respectively. In predicting the daily volume of nodal tumors, only the 14.4% (range: -11.1%-20.5%) improvement in accuracy of the functional general linear model compared to the static reference model was statistically significant. A general linear model and a functional general linear model trained on data from a small population of patients can predict the primary tumor volume throughout the course of radiation therapy with greater accuracy than standard reference models. These more accurate models may increase the prognostic value of information about the tumor garnered from pretreatment computed tomography images and facilitate improved treatment management.
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Frequency Response of Synthetic Vocal Fold Models with Linear and Nonlinear Material Properties
Shaw, Stephanie M.; Thomson, Scott L.; Dromey, Christopher; Smith, Simeon
2014-01-01
Purpose The purpose of this study was to create synthetic vocal fold models with nonlinear stress-strain properties and to investigate the effect of linear versus nonlinear material properties on fundamental frequency during anterior-posterior stretching. Method Three materially linear and three materially nonlinear models were created and stretched up to 10 mm in 1 mm increments. Phonation onset pressure (Pon) and fundamental frequency (F0) at Pon were recorded for each length. Measurements were repeated as the models were relaxed in 1 mm increments back to their resting lengths, and tensile tests were conducted to determine the stress-strain responses of linear versus nonlinear models. Results Nonlinear models demonstrated a more substantial frequency response than did linear models and a more predictable pattern of F0 increase with respect to increasing length (although range was inconsistent across models). Pon generally increased with increasing vocal fold length for nonlinear models, whereas for linear models, Pon decreased with increasing length. Conclusions Nonlinear synthetic models appear to more accurately represent the human vocal folds than linear models, especially with respect to F0 response. PMID:22271874
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Frequency response of synthetic vocal fold models with linear and nonlinear material properties.
Shaw, Stephanie M; Thomson, Scott L; Dromey, Christopher; Smith, Simeon
2012-10-01
The purpose of this study was to create synthetic vocal fold models with nonlinear stress-strain properties and to investigate the effect of linear versus nonlinear material properties on fundamental frequency (F0) during anterior-posterior stretching. Three materially linear and 3 materially nonlinear models were created and stretched up to 10 mm in 1-mm increments. Phonation onset pressure (Pon) and F0 at Pon were recorded for each length. Measurements were repeated as the models were relaxed in 1-mm increments back to their resting lengths, and tensile tests were conducted to determine the stress-strain responses of linear versus nonlinear models. Nonlinear models demonstrated a more substantial frequency response than did linear models and a more predictable pattern of F0 increase with respect to increasing length (although range was inconsistent across models). Pon generally increased with increasing vocal fold length for nonlinear models, whereas for linear models, Pon decreased with increasing length. Nonlinear synthetic models appear to more accurately represent the human vocal folds than do linear models, especially with respect to F0 response.
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ERIC Educational Resources Information Center
Esteley, Cristina; Villarreal, Monica; Alagia, Humberto
2004-01-01
This research report presents a study of the work of agronomy majors in which an extension of linear models to non-linear contexts can be observed. By linear models we mean the model y=a.x+b, some particular representations of direct proportionality and the diagram for the rule of three. Its presence and persistence in different types of problems…
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Kaiyala, Karl J
2014-01-01
Mathematical models for the dependence of energy expenditure (EE) on body mass and composition are essential tools in metabolic phenotyping. EE scales over broad ranges of body mass as a non-linear allometric function. When considered within restricted ranges of body mass, however, allometric EE curves exhibit 'local linearity.' Indeed, modern EE analysis makes extensive use of linear models. Such models typically involve one or two body mass compartments (e.g., fat free mass and fat mass). Importantly, linear EE models typically involve a non-zero (usually positive) y-intercept term of uncertain origin, a recurring theme in discussions of EE analysis and a source of confounding in traditional ratio-based EE normalization. Emerging linear model approaches quantify whole-body resting EE (REE) in terms of individual organ masses (e.g., liver, kidneys, heart, brain). Proponents of individual organ REE modeling hypothesize that multi-organ linear models may eliminate non-zero y-intercepts. This could have advantages in adjusting REE for body mass and composition. Studies reveal that individual organ REE is an allometric function of total body mass. I exploit first-order Taylor linearization of individual organ REEs to model the manner in which individual organs contribute to whole-body REE and to the non-zero y-intercept in linear REE models. The model predicts that REE analysis at the individual organ-tissue level will not eliminate intercept terms. I demonstrate that the parameters of a linear EE equation can be transformed into the parameters of the underlying 'latent' allometric equation. This permits estimates of the allometric scaling of EE in a diverse variety of physiological states that are not represented in the allometric EE literature but are well represented by published linear EE analyses.
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Development of a Linear Stirling System Model with Varying Heat Inputs
NASA Technical Reports Server (NTRS)
Regan, Timothy F.; Lewandowski, Edward J.
2007-01-01
The linear model of the Stirling system developed by NASA Glenn Research Center (GRC) has been extended to include a user-specified heat input. Previously developed linear models were limited to the Stirling convertor and electrical load. They represented the thermodynamic cycle with pressure factors that remained constant. The numerical values of the pressure factors were generated by linearizing GRC's nonlinear System Dynamic Model (SDM) of the convertor at a chosen operating point. The pressure factors were fixed for that operating point, thus, the model lost accuracy if a transition to a different operating point were simulated. Although the previous linear model was used in developing controllers that manipulated current, voltage, and piston position, it could not be used in the development of control algorithms that regulated hot-end temperature. This basic model was extended to include the thermal dynamics associated with a hot-end temperature that varies over time in response to external changes as well as to changes in the Stirling cycle. The linear model described herein includes not only dynamics of the piston, displacer, gas, and electrical circuit, but also the transient effects of the heater head thermal inertia. The linear version algebraically couples two separate linear dynamic models, one model of the Stirling convertor and one model of the thermal system, through the pressure factors. The thermal system model includes heat flow of heat transfer fluid, insulation loss, and temperature drops from the heat source to the Stirling convertor expansion space. The linear model was compared to a nonlinear model, and performance was very similar. The resulting linear model can be implemented in a variety of computing environments, and is suitable for analysis with classical and state space controls analysis techniques.
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Kumar, K Vasanth
2007-04-02
Kinetic experiments were carried out for the sorption of safranin onto activated carbon particles. The kinetic data were fitted to pseudo-second order model of Ho, Sobkowsk and Czerwinski, Blanchard et al. and Ritchie by linear and non-linear regression methods. Non-linear method was found to be a better way of obtaining the parameters involved in the second order rate kinetic expressions. Both linear and non-linear regression showed that the Sobkowsk and Czerwinski and Ritchie's pseudo-second order models were the same. Non-linear regression analysis showed that both Blanchard et al. and Ho have similar ideas on the pseudo-second order model but with different assumptions. The best fit of experimental data in Ho's pseudo-second order expression by linear and non-linear regression method showed that Ho pseudo-second order model was a better kinetic expression when compared to other pseudo-second order kinetic expressions.
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Linearized aerodynamic and control law models of the X-29A airplane and comparison with flight data
NASA Technical Reports Server (NTRS)
Bosworth, John T.
1992-01-01
Flight control system design and analysis for aircraft rely on mathematical models of the vehicle dynamics. In addition to a six degree of freedom nonlinear simulation, the X-29A flight controls group developed a set of programs that calculate linear perturbation models throughout the X-29A flight envelope. The models include the aerodynamics as well as flight control system dynamics and were used for stability, controllability, and handling qualities analysis. These linear models were compared to flight test results to help provide a safe flight envelope expansion. A description is given of the linear models at three flight conditions and two flight control system modes. The models are presented with a level of detail that would allow the reader to reproduce the linear results if desired. Comparison between the response of the linear model and flight measured responses are presented to demonstrate the strengths and weaknesses of the linear models' ability to predict flight dynamics.
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From Spiking Neuron Models to Linear-Nonlinear Models
Ostojic, Srdjan; Brunel, Nicolas
2011-01-01
Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN) cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF), exponential integrate-and-fire (EIF) and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates. PMID:21283777
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From spiking neuron models to linear-nonlinear models.
Ostojic, Srdjan; Brunel, Nicolas
2011-01-20
Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN) cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF), exponential integrate-and-fire (EIF) and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates.
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Ho, Yuh-Shan
2006-01-01
A comparison was made of the linear least-squares method and a trial-and-error non-linear method of the widely used pseudo-second-order kinetic model for the sorption of cadmium onto ground-up tree fern. Four pseudo-second-order kinetic linear equations are discussed. Kinetic parameters obtained from the four kinetic linear equations using the linear method differed but they were the same when using the non-linear method. A type 1 pseudo-second-order linear kinetic model has the highest coefficient of determination. Results show that the non-linear method may be a better way to obtain the desired parameters.
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Effects of Stochastic Traffic Flow Model on Expected System Performance
2012-12-01
NSWC-PCD has made considerable improvements to their pedestrian flow modeling . In addition to the linear paths, the 2011 version now includes...using stochastic paths. 2.2 Linear Paths vs. Stochastic Paths 2.2.1 Linear Paths and Direct Maximum Pd Calculation Modeling pedestrian traffic flow...as a stochastic process begins with the linear path model . Let the detec- tion area be R x C voxels. This creates C 2 total linear paths, path(Cs
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Statistical Methodology for the Analysis of Repeated Duration Data in Behavioral Studies.
Letué, Frédérique; Martinez, Marie-José; Samson, Adeline; Vilain, Anne; Vilain, Coriandre
2018-03-15
Repeated duration data are frequently used in behavioral studies. Classical linear or log-linear mixed models are often inadequate to analyze such data, because they usually consist of nonnegative and skew-distributed variables. Therefore, we recommend use of a statistical methodology specific to duration data. We propose a methodology based on Cox mixed models and written under the R language. This semiparametric model is indeed flexible enough to fit duration data. To compare log-linear and Cox mixed models in terms of goodness-of-fit on real data sets, we also provide a procedure based on simulations and quantile-quantile plots. We present two examples from a data set of speech and gesture interactions, which illustrate the limitations of linear and log-linear mixed models, as compared to Cox models. The linear models are not validated on our data, whereas Cox models are. Moreover, in the second example, the Cox model exhibits a significant effect that the linear model does not. We provide methods to select the best-fitting models for repeated duration data and to compare statistical methodologies. In this study, we show that Cox models are best suited to the analysis of our data set.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Adcock, T. A. A.; Taylor, P. H.
2016-01-15
The non-linear Schrödinger equation and its higher order extensions are routinely used for analysis of extreme ocean waves. This paper compares the evolution of individual wave-packets modelled using non-linear Schrödinger type equations with packets modelled using fully non-linear potential flow models. The modified non-linear Schrödinger Equation accurately models the relatively large scale non-linear changes to the shape of wave-groups, with a dramatic contraction of the group along the mean propagation direction and a corresponding extension of the width of the wave-crests. In addition, as extreme wave form, there is a local non-linear contraction of the wave-group around the crest whichmore » leads to a localised broadening of the wave spectrum which the bandwidth limited non-linear Schrödinger Equations struggle to capture. This limitation occurs for waves of moderate steepness and a narrow underlying spectrum.« less
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NASA Technical Reports Server (NTRS)
Armstrong, Jeffrey B.; Simon, Donald L.
2012-01-01
Self-tuning aircraft engine models can be applied for control and health management applications. The self-tuning feature of these models minimizes the mismatch between any given engine and the underlying engineering model describing an engine family. This paper provides details of the construction of a self-tuning engine model centered on a piecewise linear Kalman filter design. Starting from a nonlinear transient aerothermal model, a piecewise linear representation is first extracted. The linearization procedure creates a database of trim vectors and state-space matrices that are subsequently scheduled for interpolation based on engine operating point. A series of steady-state Kalman gains can next be constructed from a reduced-order form of the piecewise linear model. Reduction of the piecewise linear model to an observable dimension with respect to available sensed engine measurements can be achieved using either a subset or an optimal linear combination of "health" parameters, which describe engine performance. The resulting piecewise linear Kalman filter is then implemented for faster-than-real-time processing of sensed engine measurements, generating outputs appropriate for trending engine performance, estimating both measured and unmeasured parameters for control purposes, and performing on-board gas-path fault diagnostics. Computational efficiency is achieved by designing multidimensional interpolation algorithms that exploit the shared scheduling of multiple trim vectors and system matrices. An example application illustrates the accuracy of a self-tuning piecewise linear Kalman filter model when applied to a nonlinear turbofan engine simulation. Additional discussions focus on the issue of transient response accuracy and the advantages of a piecewise linear Kalman filter in the context of validation and verification. The techniques described provide a framework for constructing efficient self-tuning aircraft engine models from complex nonlinear simulations.Self-tuning aircraft engine models can be applied for control and health management applications. The self-tuning feature of these models minimizes the mismatch between any given engine and the underlying engineering model describing an engine family. This paper provides details of the construction of a self-tuning engine model centered on a piecewise linear Kalman filter design. Starting from a nonlinear transient aerothermal model, a piecewise linear representation is first extracted. The linearization procedure creates a database of trim vectors and state-space matrices that are subsequently scheduled for interpolation based on engine operating point. A series of steady-state Kalman gains can next be constructed from a reduced-order form of the piecewise linear model. Reduction of the piecewise linear model to an observable dimension with respect to available sensed engine measurements can be achieved using either a subset or an optimal linear combination of "health" parameters, which describe engine performance. The resulting piecewise linear Kalman filter is then implemented for faster-than-real-time processing of sensed engine measurements, generating outputs appropriate for trending engine performance, estimating both measured and unmeasured parameters for control purposes, and performing on-board gas-path fault diagnostics. Computational efficiency is achieved by designing multidimensional interpolation algorithms that exploit the shared scheduling of multiple trim vectors and system matrices. An example application illustrates the accuracy of a self-tuning piecewise linear Kalman filter model when applied to a nonlinear turbofan engine simulation. Additional discussions focus on the issue of transient response accuracy and the advantages of a piecewise linear Kalman filter in the context of validation and verification. The techniques described provide a framework for constructing efficient self-tuning aircraft engine models from complex nonlinear simulatns.
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Non-linear scaling of a musculoskeletal model of the lower limb using statistical shape models.
Nolte, Daniel; Tsang, Chui Kit; Zhang, Kai Yu; Ding, Ziyun; Kedgley, Angela E; Bull, Anthony M J
2016-10-03
Accurate muscle geometry for musculoskeletal models is important to enable accurate subject-specific simulations. Commonly, linear scaling is used to obtain individualised muscle geometry. More advanced methods include non-linear scaling using segmented bone surfaces and manual or semi-automatic digitisation of muscle paths from medical images. In this study, a new scaling method combining non-linear scaling with reconstructions of bone surfaces using statistical shape modelling is presented. Statistical Shape Models (SSMs) of femur and tibia/fibula were used to reconstruct bone surfaces of nine subjects. Reference models were created by morphing manually digitised muscle paths to mean shapes of the SSMs using non-linear transformations and inter-subject variability was calculated. Subject-specific models of muscle attachment and via points were created from three reference models. The accuracy was evaluated by calculating the differences between the scaled and manually digitised models. The points defining the muscle paths showed large inter-subject variability at the thigh and shank - up to 26mm; this was found to limit the accuracy of all studied scaling methods. Errors for the subject-specific muscle point reconstructions of the thigh could be decreased by 9% to 20% by using the non-linear scaling compared to a typical linear scaling method. We conclude that the proposed non-linear scaling method is more accurate than linear scaling methods. Thus, when combined with the ability to reconstruct bone surfaces from incomplete or scattered geometry data using statistical shape models our proposed method is an alternative to linear scaling methods. Copyright © 2016 The Author. Published by Elsevier Ltd.. All rights reserved.
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Hossain, Ahmed; Beyene, Joseph
2014-01-01
This article compares baseline, average, and longitudinal data analysis methods for identifying genetic variants in genome-wide association study using the Genetic Analysis Workshop 18 data. We apply methods that include (a) linear mixed models with baseline measures, (b) random intercept linear mixed models with mean measures outcome, and (c) random intercept linear mixed models with longitudinal measurements. In the linear mixed models, covariates are included as fixed effects, whereas relatedness among individuals is incorporated as the variance-covariance structure of the random effect for the individuals. The overall strategy of applying linear mixed models decorrelate the data is based on Aulchenko et al.'s GRAMMAR. By analyzing systolic and diastolic blood pressure, which are used separately as outcomes, we compare the 3 methods in identifying a known genetic variant that is associated with blood pressure from chromosome 3 and simulated phenotype data. We also analyze the real phenotype data to illustrate the methods. We conclude that the linear mixed model with longitudinal measurements of diastolic blood pressure is the most accurate at identifying the known single-nucleotide polymorphism among the methods, but linear mixed models with baseline measures perform best with systolic blood pressure as the outcome.
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A phenomenological biological dose model for proton therapy based on linear energy transfer spectra.
Rørvik, Eivind; Thörnqvist, Sara; Stokkevåg, Camilla H; Dahle, Tordis J; Fjaera, Lars Fredrik; Ytre-Hauge, Kristian S
2017-06-01
The relative biological effectiveness (RBE) of protons varies with the radiation quality, quantified by the linear energy transfer (LET). Most phenomenological models employ a linear dependency of the dose-averaged LET (LET d ) to calculate the biological dose. However, several experiments have indicated a possible non-linear trend. Our aim was to investigate if biological dose models including non-linear LET dependencies should be considered, by introducing a LET spectrum based dose model. The RBE-LET relationship was investigated by fitting of polynomials from 1st to 5th degree to a database of 85 data points from aerobic in vitro experiments. We included both unweighted and weighted regression, the latter taking into account experimental uncertainties. Statistical testing was performed to decide whether higher degree polynomials provided better fits to the data as compared to lower degrees. The newly developed models were compared to three published LET d based models for a simulated spread out Bragg peak (SOBP) scenario. The statistical analysis of the weighted regression analysis favored a non-linear RBE-LET relationship, with the quartic polynomial found to best represent the experimental data (P = 0.010). The results of the unweighted regression analysis were on the borderline of statistical significance for non-linear functions (P = 0.053), and with the current database a linear dependency could not be rejected. For the SOBP scenario, the weighted non-linear model estimated a similar mean RBE value (1.14) compared to the three established models (1.13-1.17). The unweighted model calculated a considerably higher RBE value (1.22). The analysis indicated that non-linear models could give a better representation of the RBE-LET relationship. However, this is not decisive, as inclusion of the experimental uncertainties in the regression analysis had a significant impact on the determination and ranking of the models. As differences between the models were observed for the SOBP scenario, both non-linear LET spectrum- and linear LET d based models should be further evaluated in clinically realistic scenarios. © 2017 American Association of Physicists in Medicine.
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Kaiyala, Karl J.
2014-01-01
Mathematical models for the dependence of energy expenditure (EE) on body mass and composition are essential tools in metabolic phenotyping. EE scales over broad ranges of body mass as a non-linear allometric function. When considered within restricted ranges of body mass, however, allometric EE curves exhibit ‘local linearity.’ Indeed, modern EE analysis makes extensive use of linear models. Such models typically involve one or two body mass compartments (e.g., fat free mass and fat mass). Importantly, linear EE models typically involve a non-zero (usually positive) y-intercept term of uncertain origin, a recurring theme in discussions of EE analysis and a source of confounding in traditional ratio-based EE normalization. Emerging linear model approaches quantify whole-body resting EE (REE) in terms of individual organ masses (e.g., liver, kidneys, heart, brain). Proponents of individual organ REE modeling hypothesize that multi-organ linear models may eliminate non-zero y-intercepts. This could have advantages in adjusting REE for body mass and composition. Studies reveal that individual organ REE is an allometric function of total body mass. I exploit first-order Taylor linearization of individual organ REEs to model the manner in which individual organs contribute to whole-body REE and to the non-zero y-intercept in linear REE models. The model predicts that REE analysis at the individual organ-tissue level will not eliminate intercept terms. I demonstrate that the parameters of a linear EE equation can be transformed into the parameters of the underlying ‘latent’ allometric equation. This permits estimates of the allometric scaling of EE in a diverse variety of physiological states that are not represented in the allometric EE literature but are well represented by published linear EE analyses. PMID:25068692
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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.
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Circuit transients due to negative bias arcs-II. [on solar cell power systems in low earth orbit
NASA Technical Reports Server (NTRS)
Metz, R. N.
1986-01-01
Two new models of negative-bias arcing on a solar cell power system in Low Earth Orbit are presented. One is an extended, analytical model and the other is a non-linear, numerical model. The models are based on an earlier analytical model in which the interactions between solar cell interconnects and the space plasma as well as the parameters of the power circuit are approximated linearly. Transient voltages due to arcs struck at the negative thermal of the solar panel are calculated in the time domain. The new models treat, respectively, further linear effects within the solar panel load circuit and non-linear effects associated with the plasma interactions. Results of computer calculations with the models show common-mode voltage transients of the electrically floating solar panel struck by an arc comparable to the early model but load transients that differ substantially from the early model. In particular, load transients of the non-linear model can be more than twice as great as those of the early model and more than twenty times as great as the extended, linear model.
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State Estimation for Humanoid Robots
2015-07-01
21 2.2.1 Linear Inverted Pendulum Model . . . . . . . . . . . . . . . . . . . 21 2.2.2 Planar Five-link Model...Linear Inverted Pendulum Model. LVDT Linear Variable Differential Transformers. MEMS Microelectromechanical Systems. MHE Moving Horizon Estimator. QP...
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1974-01-01
REGRESSION MODEL - THE UNCONSTRAINED, LINEAR EQUALITY AND INEQUALITY CONSTRAINED APPROACHES January 1974 Nelson Delfino d’Avila Mascarenha;? Image...Report 520 DIGITAL IMAGE RESTORATION UNDER A REGRESSION MODEL THE UNCONSTRAINED, LINEAR EQUALITY AND INEQUALITY CONSTRAINED APPROACHES January...a two- dimensional form adequately describes the linear model . A dis- cretization is performed by using quadrature methods. By trans
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Defining a Family of Cognitive Diagnosis Models Using Log-Linear Models with Latent Variables
ERIC Educational Resources Information Center
Henson, Robert A.; Templin, Jonathan L.; Willse, John T.
2009-01-01
This paper uses log-linear models with latent variables (Hagenaars, in "Loglinear Models with Latent Variables," 1993) to define a family of cognitive diagnosis models. In doing so, the relationship between many common models is explicitly defined and discussed. In addition, because the log-linear model with latent variables is a general model for…
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Three-dimensional modeling of flexible pavements : research implementation plan.
DOT National Transportation Integrated Search
2006-02-14
Many of the asphalt pavement analysis programs are based on linear elastic models. A linear viscoelastic models : would be superior to linear elastic models for analyzing the response of asphalt concrete pavements to loads. There : is a need to devel...
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Equivalent linear damping characterization in linear and nonlinear force-stiffness muscle models.
Ovesy, Marzieh; Nazari, Mohammad Ali; Mahdavian, Mohammad
2016-02-01
In the current research, the muscle equivalent linear damping coefficient which is introduced as the force-velocity relation in a muscle model and the corresponding time constant are investigated. In order to reach this goal, a 1D skeletal muscle model was used. Two characterizations of this model using a linear force-stiffness relationship (Hill-type model) and a nonlinear one have been implemented. The OpenSim platform was used for verification of the model. The isometric activation has been used for the simulation. The equivalent linear damping and the time constant of each model were extracted by using the results obtained from the simulation. The results provide a better insight into the characteristics of each model. It is found that the nonlinear models had a response rate closer to the reality compared to the Hill-type models.
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A Constrained Linear Estimator for Multiple Regression
ERIC Educational Resources Information Center
Davis-Stober, Clintin P.; Dana, Jason; Budescu, David V.
2010-01-01
"Improper linear models" (see Dawes, Am. Psychol. 34:571-582, "1979"), such as equal weighting, have garnered interest as alternatives to standard regression models. We analyze the general circumstances under which these models perform well by recasting a class of "improper" linear models as "proper" statistical models with a single predictor. We…
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NASA Astrophysics Data System (ADS)
Kusumawati, Rosita; Subekti, Retno
2017-04-01
Fuzzy bi-objective linear programming (FBOLP) model is bi-objective linear programming model in fuzzy number set where the coefficients of the equations are fuzzy number. This model is proposed to solve portfolio selection problem which generate an asset portfolio with the lowest risk and the highest expected return. FBOLP model with normal fuzzy numbers for risk and expected return of stocks is transformed into linear programming (LP) model using magnitude ranking function.
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Sahin, Rubina; Tapadia, Kavita
2015-01-01
The three widely used isotherms Langmuir, Freundlich and Temkin were examined in an experiment using fluoride (F⁻) ion adsorption on a geo-material (limonite) at four different temperatures by linear and non-linear models. Comparison of linear and non-linear regression models were given in selecting the optimum isotherm for the experimental results. The coefficient of determination, r², was used to select the best theoretical isotherm. The four Langmuir linear equations (1, 2, 3, and 4) are discussed. Langmuir isotherm parameters obtained from the four Langmuir linear equations using the linear model differed but they were the same when using the nonlinear model. Langmuir-2 isotherm is one of the linear forms, and it had the highest coefficient of determination (r² = 0.99) compared to the other Langmuir linear equations (1, 3 and 4) in linear form, whereas, for non-linear, Langmuir-4 fitted best among all the isotherms because it had the highest coefficient of determination (r² = 0.99). The results showed that the non-linear model may be a better way to obtain the parameters. In the present work, the thermodynamic parameters show that the absorption of fluoride onto limonite is both spontaneous (ΔG < 0) and endothermic (ΔH > 0). Scanning electron microscope and X-ray diffraction images also confirm the adsorption of F⁻ ion onto limonite. The isotherm and kinetic study reveals that limonite can be used as an adsorbent for fluoride removal. In future we can develop new technology for fluoride removal in large scale by using limonite which is cost-effective, eco-friendly and is easily available in the study area.
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Investigation on Constrained Matrix Factorization for Hyperspectral Image Analysis
2005-07-25
analysis. Keywords: matrix factorization; nonnegative matrix factorization; linear mixture model ; unsupervised linear unmixing; hyperspectral imagery...spatial resolution permits different materials present in the area covered by a single pixel. The linear mixture model says that a pixel reflectance in...in r. In the linear mixture model , r is considered as the linear mixture of m1, m2, …, mP as nMαr += (1) where n is included to account for
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Valuation of financial models with non-linear state spaces
NASA Astrophysics Data System (ADS)
Webber, Nick
2001-02-01
A common assumption in valuation models for derivative securities is that the underlying state variables take values in a linear state space. We discuss numerical implementation issues in an interest rate model with a simple non-linear state space, formulating and comparing Monte Carlo, finite difference and lattice numerical solution methods. We conclude that, at least in low dimensional spaces, non-linear interest rate models may be viable.
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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…
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Estimation of the linear mixed integrated Ornstein–Uhlenbeck model
Hughes, Rachael A.; Kenward, Michael G.; Sterne, Jonathan A. C.; Tilling, Kate
2017-01-01
ABSTRACT The linear mixed model with an added integrated Ornstein–Uhlenbeck (IOU) process (linear mixed IOU model) allows for serial correlation and estimation of the degree of derivative tracking. It is rarely used, partly due to the lack of available software. We implemented the linear mixed IOU model in Stata and using simulations we assessed the feasibility of fitting the model by restricted maximum likelihood when applied to balanced and unbalanced data. We compared different (1) optimization algorithms, (2) parameterizations of the IOU process, (3) data structures and (4) random-effects structures. Fitting the model was practical and feasible when applied to large and moderately sized balanced datasets (20,000 and 500 observations), and large unbalanced datasets with (non-informative) dropout and intermittent missingness. Analysis of a real dataset showed that the linear mixed IOU model was a better fit to the data than the standard linear mixed model (i.e. independent within-subject errors with constant variance). PMID:28515536
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A comparison of linear and nonlinear statistical techniques in performance attribution.
Chan, N H; Genovese, C R
2001-01-01
Performance attribution is usually conducted under the linear framework of multifactor models. Although commonly used by practitioners in finance, linear multifactor models are known to be less than satisfactory in many situations. After a brief survey of nonlinear methods, nonlinear statistical techniques are applied to performance attribution of a portfolio constructed from a fixed universe of stocks using factors derived from some commonly used cross sectional linear multifactor models. By rebalancing this portfolio monthly, the cumulative returns for procedures based on standard linear multifactor model and three nonlinear techniques-model selection, additive models, and neural networks-are calculated and compared. It is found that the first two nonlinear techniques, especially in combination, outperform the standard linear model. The results in the neural-network case are inconclusive because of the great variety of possible models. Although these methods are more complicated and may require some tuning, toolboxes are developed and suggestions on calibration are proposed. This paper demonstrates the usefulness of modern nonlinear statistical techniques in performance attribution.
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NASA Astrophysics Data System (ADS)
See, J. J.; Jamaian, S. S.; Salleh, R. M.; Nor, M. E.; Aman, F.
2018-04-01
This research aims to estimate the parameters of Monod model of microalgae Botryococcus Braunii sp growth by the Least-Squares method. Monod equation is a non-linear equation which can be transformed into a linear equation form and it is solved by implementing the Least-Squares linear regression method. Meanwhile, Gauss-Newton method is an alternative method to solve the non-linear Least-Squares problem with the aim to obtain the parameters value of Monod model by minimizing the sum of square error ( SSE). As the result, the parameters of the Monod model for microalgae Botryococcus Braunii sp can be estimated by the Least-Squares method. However, the estimated parameters value obtained by the non-linear Least-Squares method are more accurate compared to the linear Least-Squares method since the SSE of the non-linear Least-Squares method is less than the linear Least-Squares method.
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Linear Logistic Test Modeling with R
ERIC Educational Resources Information Center
Baghaei, Purya; Kubinger, Klaus D.
2015-01-01
The present paper gives a general introduction to the linear logistic test model (Fischer, 1973), an extension of the Rasch model with linear constraints on item parameters, along with eRm (an R package to estimate different types of Rasch models; Mair, Hatzinger, & Mair, 2014) functions to estimate the model and interpret its parameters. The…
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NASA Astrophysics Data System (ADS)
Fukuda, Jun'ichi; Johnson, Kaj M.
2010-06-01
We present a unified theoretical framework and solution method for probabilistic, Bayesian inversions of crustal deformation data. The inversions involve multiple data sets with unknown relative weights, model parameters that are related linearly or non-linearly through theoretic models to observations, prior information on model parameters and regularization priors to stabilize underdetermined problems. To efficiently handle non-linear inversions in which some of the model parameters are linearly related to the observations, this method combines both analytical least-squares solutions and a Monte Carlo sampling technique. In this method, model parameters that are linearly and non-linearly related to observations, relative weights of multiple data sets and relative weights of prior information and regularization priors are determined in a unified Bayesian framework. In this paper, we define the mixed linear-non-linear inverse problem, outline the theoretical basis for the method, provide a step-by-step algorithm for the inversion, validate the inversion method using synthetic data and apply the method to two real data sets. We apply the method to inversions of multiple geodetic data sets with unknown relative data weights for interseismic fault slip and locking depth. We also apply the method to the problem of estimating the spatial distribution of coseismic slip on faults with unknown fault geometry, relative data weights and smoothing regularization weight.
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Anomaly General Circulation Models.
NASA Astrophysics Data System (ADS)
Navarra, Antonio
The feasibility of the anomaly model is assessed using barotropic and baroclinic models. In the barotropic case, both a stationary and a time-dependent model has been formulated and constructed, whereas only the stationary, linear case is considered in the baroclinic case. Results from the barotropic model indicate that a relation between the stationary solution and the time-averaged non-linear solution exists. The stationary linear baroclinic solution can therefore be considered with some confidence. The linear baroclinic anomaly model poses a formidable mathematical problem because it is necessary to solve a gigantic linear system to obtain the solution. A new method to find solution of large linear system, based on a projection on the Krylov subspace is shown to be successful when applied to the linearized baroclinic anomaly model. The scheme consists of projecting the original linear system on the Krylov subspace, thereby reducing the dimensionality of the matrix to be inverted to obtain the solution. With an appropriate setting of the damping parameters, the iterative Krylov method reaches a solution even using a Krylov subspace ten times smaller than the original space of the problem. This generality allows the treatment of the important problem of linear waves in the atmosphere. A larger class (nonzonally symmetric) of basic states can now be treated for the baroclinic primitive equations. These problem leads to large unsymmetrical linear systems of order 10000 and more which can now be successfully tackled by the Krylov method. The (R7) linear anomaly model is used to investigate extensively the linear response to equatorial and mid-latitude prescribed heating. The results indicate that the solution is deeply affected by the presence of the stationary waves in the basic state. The instability of the asymmetric flows, first pointed out by Simmons et al. (1983), is active also in the baroclinic case. However, the presence of baroclinic processes modifies the dominant response. The most sensitive areas are identified; they correspond to north Japan, the Pole and Greenland regions. A limited set of higher resolution (R15) experiments indicate that this situation is still present and enhanced at higher resolution. The linear anomaly model is also applied to a realistic case. (Abstract shortened with permission of author.).
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ERIC Educational Resources Information Center
Xu, Xueli; von Davier, Matthias
2008-01-01
The general diagnostic model (GDM) utilizes located latent classes for modeling a multidimensional proficiency variable. In this paper, the GDM is extended by employing a log-linear model for multiple populations that assumes constraints on parameters across multiple groups. This constrained model is compared to log-linear models that assume…
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Linear models: permutation methods
Cade, B.S.; Everitt, B.S.; Howell, D.C.
2005-01-01
Permutation tests (see Permutation Based Inference) for the linear model have applications in behavioral studies when traditional parametric assumptions about the error term in a linear model are not tenable. Improved validity of Type I error rates can be achieved with properly constructed permutation tests. Perhaps more importantly, increased statistical power, improved robustness to effects of outliers, and detection of alternative distributional differences can be achieved by coupling permutation inference with alternative linear model estimators. For example, it is well-known that estimates of the mean in linear model are extremely sensitive to even a single outlying value of the dependent variable compared to estimates of the median [7, 19]. Traditionally, linear modeling focused on estimating changes in the center of distributions (means or medians). However, quantile regression allows distributional changes to be estimated in all or any selected part of a distribution or responses, providing a more complete statistical picture that has relevance to many biological questions [6]...
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Comparing The Effectiveness of a90/95 Calculations (Preprint)
2006-09-01
Nachtsheim, John Neter, William Li, Applied Linear Statistical Models , 5th ed., McGraw-Hill/Irwin, 2005 5. Mood, Graybill and Boes, Introduction...curves is based on methods that are only valid for ordinary linear regression. Requirements for a valid Ordinary Least-Squares Regression Model There... linear . For example is a linear model ; is not. 2. Uniform variance (homoscedasticity
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Non-Linear Approach in Kinesiology Should Be Preferred to the Linear--A Case of Basketball.
Trninić, Marko; Jeličić, Mario; Papić, Vladan
2015-07-01
In kinesiology, medicine, biology and psychology, in which research focus is on dynamical self-organized systems, complex connections exist between variables. Non-linear nature of complex systems has been discussed and explained by the example of non-linear anthropometric predictors of performance in basketball. Previous studies interpreted relations between anthropometric features and measures of effectiveness in basketball by (a) using linear correlation models, and by (b) including all basketball athletes in the same sample of participants regardless of their playing position. In this paper the significance and character of linear and non-linear relations between simple anthropometric predictors (AP) and performance criteria consisting of situation-related measures of effectiveness (SE) in basketball were determined and evaluated. The sample of participants consisted of top-level junior basketball players divided in three groups according to their playing time (8 minutes and more per game) and playing position: guards (N = 42), forwards (N = 26) and centers (N = 40). Linear (general model) and non-linear (general model) regression models were calculated simultaneously and separately for each group. The conclusion is viable: non-linear regressions are frequently superior to linear correlations when interpreting actual association logic among research variables.
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Waveform Design for Wireless Power Transfer
NASA Astrophysics Data System (ADS)
Clerckx, Bruno; Bayguzina, Ekaterina
2016-12-01
Far-field Wireless Power Transfer (WPT) has attracted significant attention in recent years. Despite the rapid progress, the emphasis of the research community in the last decade has remained largely concentrated on improving the design of energy harvester (so-called rectenna) and has left aside the effect of transmitter design. In this paper, we study the design of transmit waveform so as to enhance the DC power at the output of the rectenna. We derive a tractable model of the non-linearity of the rectenna and compare with a linear model conventionally used in the literature. We then use those models to design novel multisine waveforms that are adaptive to the channel state information (CSI). Interestingly, while the linear model favours narrowband transmission with all the power allocated to a single frequency, the non-linear model favours a power allocation over multiple frequencies. Through realistic simulations, waveforms designed based on the non-linear model are shown to provide significant gains (in terms of harvested DC power) over those designed based on the linear model and over non-adaptive waveforms. We also compute analytically the theoretical scaling laws of the harvested energy for various waveforms as a function of the number of sinewaves and transmit antennas. Those scaling laws highlight the benefits of CSI knowledge at the transmitter in WPT and of a WPT design based on a non-linear rectenna model over a linear model. Results also motivate the study of a promising architecture relying on large-scale multisine multi-antenna waveforms for WPT. As a final note, results stress the importance of modeling and accounting for the non-linearity of the rectenna in any system design involving wireless power.
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1975-09-30
systems a linear model results in an object f being mappad into an image _ by a point spread function matrix H. Thus with noise j +Hf +n (1) The simplest... linear models for imaging systems are given by space invariant point spread functions (SIPSF) in which case H is block circulant. If the linear model is...Ij,...,k-IM1 is a set of two dimensional indices each distinct and prior to k. Modeling Procedare: To derive the linear predictor (block LP of figure
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Kim, Jongrae; Bates, Declan G; Postlethwaite, Ian; Heslop-Harrison, Pat; Cho, Kwang-Hyun
2008-05-15
Inherent non-linearities in biomolecular interactions make the identification of network interactions difficult. One of the principal problems is that all methods based on the use of linear time-invariant models will have fundamental limitations in their capability to infer certain non-linear network interactions. Another difficulty is the multiplicity of possible solutions, since, for a given dataset, there may be many different possible networks which generate the same time-series expression profiles. A novel algorithm for the inference of biomolecular interaction networks from temporal expression data is presented. Linear time-varying models, which can represent a much wider class of time-series data than linear time-invariant models, are employed in the algorithm. From time-series expression profiles, the model parameters are identified by solving a non-linear optimization problem. In order to systematically reduce the set of possible solutions for the optimization problem, a filtering process is performed using a phase-portrait analysis with random numerical perturbations. The proposed approach has the advantages of not requiring the system to be in a stable steady state, of using time-series profiles which have been generated by a single experiment, and of allowing non-linear network interactions to be identified. The ability of the proposed algorithm to correctly infer network interactions is illustrated by its application to three examples: a non-linear model for cAMP oscillations in Dictyostelium discoideum, the cell-cycle data for Saccharomyces cerevisiae and a large-scale non-linear model of a group of synchronized Dictyostelium cells. The software used in this article is available from http://sbie.kaist.ac.kr/software
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NASA Astrophysics Data System (ADS)
Pipkins, Daniel Scott
Two diverse topics of relevance in modern computational mechanics are treated. The first involves the modeling of linear and non-linear wave propagation in flexible, lattice structures. The technique used combines the Laplace Transform with the Finite Element Method (FEM). The procedure is to transform the governing differential equations and boundary conditions into the transform domain where the FEM formulation is carried out. For linear problems, the transformed differential equations can be solved exactly, hence the method is exact. As a result, each member of the lattice structure is modeled using only one element. In the non-linear problem, the method is no longer exact. The approximation introduced is a spatial discretization of the transformed non-linear terms. The non-linear terms are represented in the transform domain by making use of the complex convolution theorem. A weak formulation of the resulting transformed non-linear equations yields a set of element level matrix equations. The trial and test functions used in the weak formulation correspond to the exact solution of the linear part of the transformed governing differential equation. Numerical results are presented for both linear and non-linear systems. The linear systems modeled are longitudinal and torsional rods and Bernoulli-Euler and Timoshenko beams. For non-linear systems, a viscoelastic rod and Von Karman type beam are modeled. The second topic is the analysis of plates and shallow shells under-going finite deflections by the Field/Boundary Element Method. Numerical results are presented for two plate problems. The first is the bifurcation problem associated with a square plate having free boundaries which is loaded by four, self equilibrating corner forces. The results are compared to two existing numerical solutions of the problem which differ substantially.
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Descriptive Linear modeling of steady-state visual evoked response
NASA Technical Reports Server (NTRS)
Levison, W. H.; Junker, A. M.; Kenner, K.
1986-01-01
A study is being conducted to explore use of the steady state visual-evoke electrocortical response as an indicator of cognitive task loading. Application of linear descriptive modeling to steady state Visual Evoked Response (VER) data is summarized. Two aspects of linear modeling are reviewed: (1) unwrapping the phase-shift portion of the frequency response, and (2) parsimonious characterization of task-loading effects in terms of changes in model parameters. Model-based phase unwrapping appears to be most reliable in applications, such as manual control, where theoretical models are available. Linear descriptive modeling of the VER has not yet been shown to provide consistent and readily interpretable results.
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Peñagaricano, F; Urioste, J I; Naya, H; de los Campos, G; Gianola, D
2011-04-01
Black skin spots are associated with pigmented fibres in wool, an important quality fault. Our objective was to assess alternative models for genetic analysis of presence (BINBS) and number (NUMBS) of black spots in Corriedale sheep. During 2002-08, 5624 records from 2839 animals in two flocks, aged 1 through 6 years, were taken at shearing. Four models were considered: linear and probit for BINBS and linear and Poisson for NUMBS. All models included flock-year and age as fixed effects and animal and permanent environmental as random effects. Models were fitted to the whole data set and were also compared based on their predictive ability in cross-validation. Estimates of heritability ranged from 0.154 to 0.230 for BINBS and 0.269 to 0.474 for NUMBS. For BINBS, the probit model fitted slightly better to the data than the linear model. Predictions of random effects from these models were highly correlated, and both models exhibited similar predictive ability. For NUMBS, the Poisson model, with a residual term to account for overdispersion, performed better than the linear model in goodness of fit and predictive ability. Predictions of random effects from the Poisson model were more strongly correlated with those from BINBS models than those from the linear model. Overall, the use of probit or linear models for BINBS and of a Poisson model with a residual for NUMBS seems a reasonable choice for genetic selection purposes in Corriedale sheep. © 2010 Blackwell Verlag GmbH.
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Hamid, Ka; Yusoff, An; Rahman, Mza; Mohamad, M; Hamid, Aia
2012-04-01
This fMRI study is about modelling the effective connectivity between Heschl's gyrus (HG) and the superior temporal gyrus (STG) in human primary auditory cortices. MATERIALS #ENTITYSTARTX00026; Ten healthy male participants were required to listen to white noise stimuli during functional magnetic resonance imaging (fMRI) scans. Statistical parametric mapping (SPM) was used to generate individual and group brain activation maps. For input region determination, two intrinsic connectivity models comprising bilateral HG and STG were constructed using dynamic causal modelling (DCM). The models were estimated and inferred using DCM while Bayesian Model Selection (BMS) for group studies was used for model comparison and selection. Based on the winning model, six linear and six non-linear causal models were derived and were again estimated, inferred, and compared to obtain a model that best represents the effective connectivity between HG and the STG, balancing accuracy and complexity. Group results indicated significant asymmetrical activation (p(uncorr) < 0.001) in bilateral HG and STG. Model comparison results showed strong evidence of STG as the input centre. The winning model is preferred by 6 out of 10 participants. The results were supported by BMS results for group studies with the expected posterior probability, r = 0.7830 and exceedance probability, ϕ = 0.9823. One-sample t-tests performed on connection values obtained from the winning model indicated that the valid connections for the winning model are the unidirectional parallel connections from STG to bilateral HG (p < 0.05). Subsequent model comparison between linear and non-linear models using BMS prefers non-linear connection (r = 0.9160, ϕ = 1.000) from which the connectivity between STG and the ipsi- and contralateral HG is gated by the activity in STG itself. We are able to demonstrate that the effective connectivity between HG and STG while listening to white noise for the respective participants can be explained by a non-linear dynamic causal model with the activity in STG influencing the STG-HG connectivity non-linearly.
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The Simplest Complete Model of Choice Response Time: Linear Ballistic Accumulation
ERIC Educational Resources Information Center
Brown, Scott D.; Heathcote, Andrew
2008-01-01
We propose a linear ballistic accumulator (LBA) model of decision making and reaction time. The LBA is simpler than other models of choice response time, with independent accumulators that race towards a common response threshold. Activity in the accumulators increases in a linear and deterministic manner. The simplicity of the model allows…
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Determining Predictor Importance in Hierarchical Linear Models Using Dominance Analysis
ERIC Educational Resources Information Center
Luo, Wen; Azen, Razia
2013-01-01
Dominance analysis (DA) is a method used to evaluate the relative importance of predictors that was originally proposed for linear regression models. This article proposes an extension of DA that allows researchers to determine the relative importance of predictors in hierarchical linear models (HLM). Commonly used measures of model adequacy in…
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A network model of successive partitioning-limited solute diffusion through the stratum corneum.
Schumm, Phillip; Scoglio, Caterina M; van der Merwe, Deon
2010-02-07
As the most exposed point of contact with the external environment, the skin is an important barrier to many chemical exposures, including medications, potentially toxic chemicals and cosmetics. Traditional dermal absorption models treat the stratum corneum lipids as a homogenous medium through which solutes diffuse according to Fick's first law of diffusion. This approach does not explain non-linear absorption and irregular distribution patterns within the stratum corneum lipids as observed in experimental data. A network model, based on successive partitioning-limited solute diffusion through the stratum corneum, where the lipid structure is represented by a large, sparse, and regular network where nodes have variable characteristics, offers an alternative, efficient, and flexible approach to dermal absorption modeling that simulates non-linear absorption data patterns. Four model versions are presented: two linear models, which have unlimited node capacities, and two non-linear models, which have limited node capacities. The non-linear model outputs produce absorption to dose relationships that can be best characterized quantitatively by using power equations, similar to the equations used to describe non-linear experimental data.
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NASA Astrophysics Data System (ADS)
Yang, Kangjian; Yang, Ping; Wang, Shuai; Dong, Lizhi; Xu, Bing
2018-05-01
We propose a method to identify tip-tilt disturbance model for Linear Quadratic Gaussian control. This identification method based on Levenberg-Marquardt method conducts with a little prior information and no auxiliary system and it is convenient to identify the tip-tilt disturbance model on-line for real-time control. This identification method makes it easy that Linear Quadratic Gaussian control runs efficiently in different adaptive optics systems for vibration mitigation. The validity of the Linear Quadratic Gaussian control associated with this tip-tilt disturbance model identification method is verified by experimental data, which is conducted in replay mode by simulation.
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Improved LTVMPC design for steering control of autonomous vehicle
NASA Astrophysics Data System (ADS)
Velhal, Shridhar; Thomas, Susy
2017-01-01
An improved linear time varying model predictive control for steering control of autonomous vehicle running on slippery road is presented. Control strategy is designed such that the vehicle will follow the predefined trajectory with highest possible entry speed. In linear time varying model predictive control, nonlinear vehicle model is successively linearized at each sampling instant. This linear time varying model is used to design MPC which will predict the future horizon. By incorporating predicted input horizon in each successive linearization the effectiveness of controller has been improved. The tracking performance using steering with front wheel and braking at four wheels are presented to illustrate the effectiveness of the proposed method.
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Employment of CB models for non-linear dynamic analysis
NASA Technical Reports Server (NTRS)
Klein, M. R. M.; Deloo, P.; Fournier-Sicre, A.
1990-01-01
The non-linear dynamic analysis of large structures is always very time, effort and CPU consuming. Whenever possible the reduction of the size of the mathematical model involved is of main importance to speed up the computational procedures. Such reduction can be performed for the part of the structure which perform linearly. Most of the time, the classical Guyan reduction process is used. For non-linear dynamic process where the non-linearity is present at interfaces between different structures, Craig-Bampton models can provide a very rich information, and allow easy selection of the relevant modes with respect to the phenomenon driving the non-linearity. The paper presents the employment of Craig-Bampton models combined with Newmark direct integration for solving non-linear friction problems appearing at the interface between the Hubble Space Telescope and its solar arrays during in-orbit maneuvers. Theory, implementation in the FEM code ASKA, and practical results are shown.
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NASA Technical Reports Server (NTRS)
Holdaway, Daniel; Kent, James
2015-01-01
The linearity of a selection of common advection schemes is tested and examined with a view to their use in the tangent linear and adjoint versions of an atmospheric general circulation model. The schemes are tested within a simple offline one-dimensional periodic domain as well as using a simplified and complete configuration of the linearised version of NASA's Goddard Earth Observing System version 5 (GEOS-5). All schemes which prevent the development of negative values and preserve the shape of the solution are confirmed to have nonlinear behaviour. The piecewise parabolic method (PPM) with certain flux limiters, including that used by default in GEOS-5, is found to support linear growth near the shocks. This property can cause the rapid development of unrealistically large perturbations within the tangent linear and adjoint models. It is shown that these schemes with flux limiters should not be used within the linearised version of a transport scheme. The results from tests using GEOS-5 show that the current default scheme (a version of PPM) is not suitable for the tangent linear and adjoint model, and that using a linear third-order scheme for the linearised model produces better behaviour. Using the third-order scheme for the linearised model improves the correlations between the linear and non-linear perturbation trajectories for cloud liquid water and cloud liquid ice in GEOS-5.
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NASA Astrophysics Data System (ADS)
Al-Mayah, Adil; Moseley, Joanne; Velec, Mike; Brock, Kristy
2011-08-01
Both accuracy and efficiency are critical for the implementation of biomechanical model-based deformable registration in clinical practice. The focus of this investigation is to evaluate the potential of improving the efficiency of the deformable image registration of the human lungs without loss of accuracy. Three-dimensional finite element models have been developed using image data of 14 lung cancer patients. Each model consists of two lungs, tumor and external body. Sliding of the lungs inside the chest cavity is modeled using a frictionless surface-based contact model. The effect of the type of element, finite deformation and elasticity on the accuracy and computing time is investigated. Linear and quadrilateral tetrahedral elements are used with linear and nonlinear geometric analysis. Two types of material properties are applied namely: elastic and hyperelastic. The accuracy of each of the four models is examined using a number of anatomical landmarks representing the vessels bifurcation points distributed across the lungs. The registration error is not significantly affected by the element type or linearity of analysis, with an average vector error of around 2.8 mm. The displacement differences between linear and nonlinear analysis methods are calculated for all lungs nodes and a maximum value of 3.6 mm is found in one of the nodes near the entrance of the bronchial tree into the lungs. The 95 percentile of displacement difference ranges between 0.4 and 0.8 mm. However, the time required for the analysis is reduced from 95 min in the quadratic elements nonlinear geometry model to 3.4 min in the linear element linear geometry model. Therefore using linear tetrahedral elements with linear elastic materials and linear geometry is preferable for modeling the breathing motion of lungs for image-guided radiotherapy applications.
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NASA Technical Reports Server (NTRS)
Stoliker, Patrick C.; Bosworth, John T.; Georgie, Jennifer
1997-01-01
The X-31A aircraft has a unique configuration that uses thrust-vector vanes and aerodynamic control effectors to provide an operating envelope to a maximum 70 deg angle of attack, an inherently nonlinear portion of the flight envelope. This report presents linearized versions of the X-31A longitudinal and lateral-directional control systems, with aerodynamic models sufficient to evaluate characteristics in the poststall envelope at 30 deg, 45 deg, and 60 deg angle of attack. The models are presented with detail sufficient to allow the reader to reproduce the linear results or perform independent control studies. Comparisons between the responses of the linear models and flight data are presented in the time and frequency domains to demonstrate the strengths and weaknesses of the ability to predict high-angle-of-attack flight dynamics using linear models. The X-31A six-degree-of-freedom simulation contains a program that calculates linear perturbation models throughout the X-31A flight envelope. The models include aerodynamics and flight control system dynamics that are used for stability, controllability, and handling qualities analysis. The models presented in this report demonstrate the ability to provide reasonable linear representations in the poststall flight regime.
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NASA Astrophysics Data System (ADS)
Manzoor, Ali; Rafique, Sajid; Usman Iftikhar, Muhammad; Mahmood Ul Hassan, Khalid; Nasir, Ali
2017-08-01
Piezoelectric vibration energy harvester (PVEH) consists of a cantilever bimorph with piezoelectric layers pasted on its top and bottom, which can harvest power from vibrations and feed to low power wireless sensor nodes through some power conditioning circuit. In this paper, a non-linear conditioning circuit, consisting of a full-bridge rectifier followed by a buck-boost converter, is employed to investigate the issues of electrical side of the energy harvesting system. An integrated mathematical model of complete electromechanical system has been developed. Previously, researchers have studied PVEH with sophisticated piezo-beam models but employed simplistic linear circuits, such as resistor, as electrical load. In contrast, other researchers have worked on more complex non-linear circuits but with over-simplified piezo-beam models. Such models neglect different aspects of the system which result from complex interactions of its electrical and mechanical subsystems. In this work, authors have integrated the distributed parameter-based model of piezo-beam presented in literature with a real world non-linear electrical load. Then, the developed integrated model is employed to analyse the stability of complete energy harvesting system. This work provides a more realistic and useful electromechanical model having realistic non-linear electrical load unlike the simplistic linear circuit elements employed by many researchers.
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An R2 statistic for fixed effects in the linear mixed model.
Edwards, Lloyd J; Muller, Keith E; Wolfinger, Russell D; Qaqish, Bahjat F; Schabenberger, Oliver
2008-12-20
Statisticians most often use the linear mixed model to analyze Gaussian longitudinal data. The value and familiarity of the R(2) statistic in the linear univariate model naturally creates great interest in extending it to the linear mixed model. We define and describe how to compute a model R(2) statistic for the linear mixed model by using only a single model. The proposed R(2) statistic measures multivariate association between the repeated outcomes and the fixed effects in the linear mixed model. The R(2) statistic arises as a 1-1 function of an appropriate F statistic for testing all fixed effects (except typically the intercept) in a full model. The statistic compares the full model with a null model with all fixed effects deleted (except typically the intercept) while retaining exactly the same covariance structure. Furthermore, the R(2) statistic leads immediately to a natural definition of a partial R(2) statistic. A mixed model in which ethnicity gives a very small p-value as a longitudinal predictor of blood pressure (BP) compellingly illustrates the value of the statistic. In sharp contrast to the extreme p-value, a very small R(2) , a measure of statistical and scientific importance, indicates that ethnicity has an almost negligible association with the repeated BP outcomes for the study.
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NASA Astrophysics Data System (ADS)
Morén, B.; Larsson, T.; Carlsson Tedgren, Å.
2018-03-01
High dose-rate brachytherapy is a method for cancer treatment where the radiation source is placed within the body, inside or close to a tumour. For dose planning, mathematical optimization techniques are being used in practice and the most common approach is to use a linear model which penalizes deviations from specified dose limits for the tumour and for nearby organs. This linear penalty model is easy to solve, but its weakness lies in the poor correlation of its objective value and the dose-volume objectives that are used clinically to evaluate dose distributions. Furthermore, the model contains parameters that have no clear clinical interpretation. Another approach for dose planning is to solve mixed-integer optimization models with explicit dose-volume constraints which include parameters that directly correspond to dose-volume objectives, and which are therefore tangible. The two mentioned models take the overall goals for dose planning into account in fundamentally different ways. We show that there is, however, a mathematical relationship between them by deriving a linear penalty model from a dose-volume model. This relationship has not been established before and improves the understanding of the linear penalty model. In particular, the parameters of the linear penalty model can be interpreted as dual variables in the dose-volume model.
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Kumar, P; Kumar, Dinesh; Rai, K N
2016-08-01
In this article, a non-linear dual-phase-lag (DPL) bio-heat transfer model based on temperature dependent metabolic heat generation rate is derived to analyze the heat transfer phenomena in living tissues during thermal ablation treatment. The numerical solution of the present non-linear problem has been done by finite element Runge-Kutta (4,5) method which combines the essence of Runge-Kutta (4,5) method together with finite difference scheme. Our study demonstrates that at the thermal ablation position temperature predicted by non-linear and linear DPL models show significant differences. A comparison has been made among non-linear DPL, thermal wave and Pennes model and it has been found that non-linear DPL and thermal wave bio-heat model show almost same nature whereas non-linear Pennes model shows significantly different temperature profile at the initial stage of thermal ablation treatment. The effect of Fourier number and Vernotte number (relaxation Fourier number) on temperature profile in presence and absence of externally applied heat source has been studied in detail and it has been observed that the presence of externally applied heat source term highly affects the efficiency of thermal treatment method. Copyright © 2016 Elsevier Ltd. All rights reserved.
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A Linearized Prognostic Cloud Scheme in NASAs Goddard Earth Observing System Data Assimilation Tools
NASA Technical Reports Server (NTRS)
Holdaway, Daniel; Errico, Ronald M.; Gelaro, Ronald; Kim, Jong G.; Mahajan, Rahul
2015-01-01
A linearized prognostic cloud scheme has been developed to accompany the linearized convection scheme recently implemented in NASA's Goddard Earth Observing System data assimilation tools. The linearization, developed from the nonlinear cloud scheme, treats cloud variables prognostically so they are subject to linearized advection, diffusion, generation, and evaporation. Four linearized cloud variables are modeled, the ice and water phases of clouds generated by large-scale condensation and, separately, by detraining convection. For each species the scheme models their sources, sublimation, evaporation, and autoconversion. Large-scale, anvil and convective species of precipitation are modeled and evaporated. The cloud scheme exhibits linearity and realistic perturbation growth, except around the generation of clouds through large-scale condensation. Discontinuities and steep gradients are widely used here and severe problems occur in the calculation of cloud fraction. For data assimilation applications this poor behavior is controlled by replacing this part of the scheme with a perturbation model. For observation impacts, where efficiency is less of a concern, a filtering is developed that examines the Jacobian. The replacement scheme is only invoked if Jacobian elements or eigenvalues violate a series of tuned constants. The linearized prognostic cloud scheme is tested by comparing the linear and nonlinear perturbation trajectories for 6-, 12-, and 24-h forecast times. The tangent linear model performs well and perturbations of clouds are well captured for the lead times of interest.
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Estimating linear temporal trends from aggregated environmental monitoring data
Erickson, Richard A.; Gray, Brian R.; Eager, Eric A.
2017-01-01
Trend estimates are often used as part of environmental monitoring programs. These trends inform managers (e.g., are desired species increasing or undesired species decreasing?). Data collected from environmental monitoring programs is often aggregated (i.e., averaged), which confounds sampling and process variation. State-space models allow sampling variation and process variations to be separated. We used simulated time-series to compare linear trend estimations from three state-space models, a simple linear regression model, and an auto-regressive model. We also compared the performance of these five models to estimate trends from a long term monitoring program. We specifically estimated trends for two species of fish and four species of aquatic vegetation from the Upper Mississippi River system. We found that the simple linear regression had the best performance of all the given models because it was best able to recover parameters and had consistent numerical convergence. Conversely, the simple linear regression did the worst job estimating populations in a given year. The state-space models did not estimate trends well, but estimated population sizes best when the models converged. We found that a simple linear regression performed better than more complex autoregression and state-space models when used to analyze aggregated environmental monitoring data.
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Estimation of group means when adjusting for covariates in generalized linear models.
Qu, Yongming; Luo, Junxiang
2015-01-01
Generalized linear models are commonly used to analyze categorical data such as binary, count, and ordinal outcomes. Adjusting for important prognostic factors or baseline covariates in generalized linear models may improve the estimation efficiency. The model-based mean for a treatment group produced by most software packages estimates the response at the mean covariate, not the mean response for this treatment group for the studied population. Although this is not an issue for linear models, the model-based group mean estimates in generalized linear models could be seriously biased for the true group means. We propose a new method to estimate the group mean consistently with the corresponding variance estimation. Simulation showed the proposed method produces an unbiased estimator for the group means and provided the correct coverage probability. The proposed method was applied to analyze hypoglycemia data from clinical trials in diabetes. Copyright © 2014 John Wiley & Sons, Ltd.
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Composite Linear Models | Division of Cancer Prevention
By Stuart G. Baker The composite linear models software is a matrix approach to compute maximum likelihood estimates and asymptotic standard errors for models for incomplete multinomial data. It implements the method described in Baker SG. Composite linear models for incomplete multinomial data. Statistics in Medicine 1994;13:609-622. The software includes a library of thirty
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ERIC Educational Resources Information Center
Rocconi, Louis M.
2011-01-01
Hierarchical linear models (HLM) solve the problems associated with the unit of analysis problem such as misestimated standard errors, heterogeneity of regression and aggregation bias by modeling all levels of interest simultaneously. Hierarchical linear modeling resolves the problem of misestimated standard errors by incorporating a unique random…
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Ladstätter, Felix; Garrosa, Eva; Moreno-Jiménez, Bernardo; Ponsoda, Vicente; Reales Aviles, José Manuel; Dai, Junming
2016-01-01
Artificial neural networks are sophisticated modelling and prediction tools capable of extracting complex, non-linear relationships between predictor (input) and predicted (output) variables. This study explores this capacity by modelling non-linearities in the hardiness-modulated burnout process with a neural network. Specifically, two multi-layer feed-forward artificial neural networks are concatenated in an attempt to model the composite non-linear burnout process. Sensitivity analysis, a Monte Carlo-based global simulation technique, is then utilised to examine the first-order effects of the predictor variables on the burnout sub-dimensions and consequences. Results show that (1) this concatenated artificial neural network approach is feasible to model the burnout process, (2) sensitivity analysis is a prolific method to study the relative importance of predictor variables and (3) the relationships among variables involved in the development of burnout and its consequences are to different degrees non-linear. Many relationships among variables (e.g., stressors and strains) are not linear, yet researchers use linear methods such as Pearson correlation or linear regression to analyse these relationships. Artificial neural network analysis is an innovative method to analyse non-linear relationships and in combination with sensitivity analysis superior to linear methods.
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Numerical solution of non-linear dual-phase-lag bioheat transfer equation within skin tissues.
Kumar, Dinesh; Kumar, P; Rai, K N
2017-11-01
This paper deals with numerical modeling and simulation of heat transfer in skin tissues using non-linear dual-phase-lag (DPL) bioheat transfer model under periodic heat flux boundary condition. The blood perfusion is assumed temperature-dependent which results in non-linear DPL bioheat transfer model in order to predict more accurate results. A numerical method of line which is based on finite difference and Runge-Kutta (4,5) schemes, is used to solve the present non-linear problem. Under specific case, the exact solution has been obtained and compared with the present numerical scheme, and we found that those are in good agreement. A comparison based on model selection criterion (AIC) has been made among non-linear DPL models when the variation of blood perfusion rate with temperature is of constant, linear and exponential type with the experimental data and it has been found that non-linear DPL model with exponential variation of blood perfusion rate is closest to the experimental data. In addition, it is found that due to absence of phase-lag phenomena in Pennes bioheat transfer model, it achieves steady state more quickly and always predict higher temperature than thermal and DPL non-linear models. The effect of coefficient of blood perfusion rate, dimensionless heating frequency and Kirchoff number on dimensionless temperature distribution has also been analyzed. The whole analysis is presented in dimensionless form. Copyright © 2017 Elsevier Inc. All rights reserved.
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Godin, Bruno; Mayer, Frédéric; Agneessens, Richard; Gerin, Patrick; Dardenne, Pierre; Delfosse, Philippe; Delcarte, Jérôme
2015-01-01
The reliability of different models to predict the biochemical methane potential (BMP) of various plant biomasses using a multispecies dataset was compared. The most reliable prediction models of the BMP were those based on the near infrared (NIR) spectrum compared to those based on the chemical composition. The NIR predictions of local (specific regression and non-linear) models were able to estimate quantitatively, rapidly, cheaply and easily the BMP. Such a model could be further used for biomethanation plant management and optimization. The predictions of non-linear models were more reliable compared to those of linear models. The presentation form (green-dried, silage-dried and silage-wet form) of biomasses to the NIR spectrometer did not influence the performances of the NIR prediction models. The accuracy of the BMP method should be improved to enhance further the BMP prediction models. Copyright © 2014 Elsevier Ltd. All rights reserved.
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Rajeswaran, Jeevanantham; Blackstone, Eugene H
2017-02-01
In medical sciences, we often encounter longitudinal temporal relationships that are non-linear in nature. The influence of risk factors may also change across longitudinal follow-up. A system of multiphase non-linear mixed effects model is presented to model temporal patterns of longitudinal continuous measurements, with temporal decomposition to identify the phases and risk factors within each phase. Application of this model is illustrated using spirometry data after lung transplantation using readily available statistical software. This application illustrates the usefulness of our flexible model when dealing with complex non-linear patterns and time-varying coefficients.
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NASA Astrophysics Data System (ADS)
Glane, Sebastian; Reich, Felix A.; Müller, Wolfgang H.
2017-11-01
This study is dedicated to continuum-scale material modeling of isotropic permanent magnets. An affine-linear extension to the commonly used ideal hard model for permanent magnets is proposed, motivated, and detailed. In order to demonstrate the differences between these models, bar and horseshoe magnets are considered. The structure of the boundary value problem for the magnetic field and related solution techniques are discussed. For the ideal model, closed-form analytical solutions were obtained for both geometries. Magnetic fields of the boundary value problems for both models and differently shaped magnets were computed numerically by using the boundary element method. The results show that the character of the magnetic field is strongly influenced by the model that is used. Furthermore, it can be observed that the shape of an affine-linear magnet influences the near-field significantly. Qualitative comparisons with experiments suggest that both the ideal and the affine-linear models are relevant in practice, depending on the magnetic material employed. Mathematically speaking, the ideal magnetic model is a special case of the affine-linear one. Therefore, in applications where knowledge of the near-field is important, the affine-linear model can yield more accurate results—depending on the magnetic material.
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Convex set and linear mixing model
NASA Technical Reports Server (NTRS)
Xu, P.; Greeley, R.
1993-01-01
A major goal of optical remote sensing is to determine surface compositions of the earth and other planetary objects. For assessment of composition, single pixels in multi-spectral images usually record a mixture of the signals from various materials within the corresponding surface area. In this report, we introduce a closed and bounded convex set as a mathematical model for linear mixing. This model has a clear geometric implication because the closed and bounded convex set is a natural generalization of a triangle in n-space. The endmembers are extreme points of the convex set. Every point in the convex closure of the endmembers is a linear mixture of those endmembers, which is exactly how linear mixing is defined. With this model, some general criteria for selecting endmembers could be described. This model can lead to a better understanding of linear mixing models.
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NASA Astrophysics Data System (ADS)
Förner, K.; Polifke, W.
2017-10-01
The nonlinear acoustic behavior of Helmholtz resonators is characterized by a data-based reduced-order model, which is obtained by a combination of high-resolution CFD simulation and system identification. It is shown that even in the nonlinear regime, a linear model is capable of describing the reflection behavior at a particular amplitude with quantitative accuracy. This observation motivates to choose a local-linear model structure for this study, which consists of a network of parallel linear submodels. A so-called fuzzy-neuron layer distributes the input signal over the linear submodels, depending on the root mean square of the particle velocity at the resonator surface. The resulting model structure is referred to as an local-linear neuro-fuzzy network. System identification techniques are used to estimate the free parameters of this model from training data. The training data are generated by CFD simulations of the resonator, with persistent acoustic excitation over a wide range of frequencies and sound pressure levels. The estimated nonlinear, reduced-order models show good agreement with CFD and experimental data over a wide range of amplitudes for several test cases.
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NASA Technical Reports Server (NTRS)
Melcher, Kevin J.
1997-01-01
The NASA Lewis Research Center is developing analytical methods and software tools to create a bridge between the controls and computational fluid dynamics (CFD) disciplines. Traditionally, control design engineers have used coarse nonlinear simulations to generate information for the design of new propulsion system controls. However, such traditional methods are not adequate for modeling the propulsion systems of complex, high-speed vehicles like the High Speed Civil Transport. To properly model the relevant flow physics of high-speed propulsion systems, one must use simulations based on CFD methods. Such CFD simulations have become useful tools for engineers that are designing propulsion system components. The analysis techniques and software being developed as part of this effort are an attempt to evolve CFD into a useful tool for control design as well. One major aspect of this research is the generation of linear models from steady-state CFD results. CFD simulations, often used during the design of high-speed inlets, yield high resolution operating point data. Under a NASA grant, the University of Akron has developed analytical techniques and software tools that use these data to generate linear models for control design. The resulting linear models have the same number of states as the original CFD simulation, so they are still very large and computationally cumbersome. Model reduction techniques have been successfully applied to reduce these large linear models by several orders of magnitude without significantly changing the dynamic response. The result is an accurate, easy to use, low-order linear model that takes less time to generate than those generated by traditional means. The development of methods for generating low-order linear models from steady-state CFD is most complete at the one-dimensional level, where software is available to generate models with different kinds of input and output variables. One-dimensional methods have been extended somewhat so that linear models can also be generated from two- and three-dimensional steady-state results. Standard techniques are adequate for reducing the order of one-dimensional CFD-based linear models. However, reduction of linear models based on two- and three-dimensional CFD results is complicated by very sparse, ill-conditioned matrices. Some novel approaches are being investigated to solve this problem.
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Predicting musically induced emotions from physiological inputs: linear and neural network models.
Russo, Frank A; Vempala, Naresh N; Sandstrom, Gillian M
2013-01-01
Listening to music often leads to physiological responses. Do these physiological responses contain sufficient information to infer emotion induced in the listener? The current study explores this question by attempting to predict judgments of "felt" emotion from physiological responses alone using linear and neural network models. We measured five channels of peripheral physiology from 20 participants-heart rate (HR), respiration, galvanic skin response, and activity in corrugator supercilii and zygomaticus major facial muscles. Using valence and arousal (VA) dimensions, participants rated their felt emotion after listening to each of 12 classical music excerpts. After extracting features from the five channels, we examined their correlation with VA ratings, and then performed multiple linear regression to see if a linear relationship between the physiological responses could account for the ratings. Although linear models predicted a significant amount of variance in arousal ratings, they were unable to do so with valence ratings. We then used a neural network to provide a non-linear account of the ratings. The network was trained on the mean ratings of eight of the 12 excerpts and tested on the remainder. Performance of the neural network confirms that physiological responses alone can be used to predict musically induced emotion. The non-linear model derived from the neural network was more accurate than linear models derived from multiple linear regression, particularly along the valence dimension. A secondary analysis allowed us to quantify the relative contributions of inputs to the non-linear model. The study represents a novel approach to understanding the complex relationship between physiological responses and musically induced emotion.
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NASA Astrophysics Data System (ADS)
Xu, Jiuping; Li, Jun
2002-09-01
In this paper a class of stochastic multiple-objective programming problems with one quadratic, several linear objective functions and linear constraints has been introduced. The former model is transformed into a deterministic multiple-objective nonlinear programming model by means of the introduction of random variables' expectation. The reference direction approach is used to deal with linear objectives and results in a linear parametric optimization formula with a single linear objective function. This objective function is combined with the quadratic function using the weighted sums. The quadratic problem is transformed into a linear (parametric) complementary problem, the basic formula for the proposed approach. The sufficient and necessary conditions for (properly, weakly) efficient solutions and some construction characteristics of (weakly) efficient solution sets are obtained. An interactive algorithm is proposed based on reference direction and weighted sums. Varying the parameter vector on the right-hand side of the model, the DM can freely search the efficient frontier with the model. An extended portfolio selection model is formed when liquidity is considered as another objective to be optimized besides expectation and risk. The interactive approach is illustrated with a practical example.
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Roberts, Steven; Martin, Michael A
2007-06-01
The majority of studies that have investigated the relationship between particulate matter (PM) air pollution and mortality have assumed a linear dose-response relationship and have used either a single-day's PM or a 2- or 3-day moving average of PM as the measure of PM exposure. Both of these modeling choices have come under scrutiny in the literature, the linear assumption because it does not allow for non-linearities in the dose-response relationship, and the use of the single- or multi-day moving average PM measure because it does not allow for differential PM-mortality effects spread over time. These two problems have been dealt with on a piecemeal basis with non-linear dose-response models used in some studies and distributed lag models (DLMs) used in others. In this paper, we propose a method for investigating the shape of the PM-mortality dose-response relationship that combines a non-linear dose-response model with a DLM. This combined model will be shown to produce satisfactory estimates of the PM-mortality dose-response relationship in situations where non-linear dose response models and DLMs alone do not; that is, the combined model did not systemically underestimate or overestimate the effect of PM on mortality. The combined model is applied to ten cities in the US and a pooled dose-response model formed. When fitted with a change-point value of 60 microg/m(3), the pooled model provides evidence for a positive association between PM and mortality. The combined model produced larger estimates for the effect of PM on mortality than when using a non-linear dose-response model or a DLM in isolation. For the combined model, the estimated percentage increase in mortality for PM concentrations of 25 and 75 microg/m(3) were 3.3% and 5.4%, respectively. In contrast, the corresponding values from a DLM used in isolation were 1.2% and 3.5%, respectively.
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A model of the extent and distribution of woody linear features in rural Great Britain.
Scholefield, Paul; Morton, Dan; Rowland, Clare; Henrys, Peter; Howard, David; Norton, Lisa
2016-12-01
Hedges and lines of trees (woody linear features) are important boundaries that connect and enclose habitats, buffer the effects of land management, and enhance biodiversity in increasingly impoverished landscapes. Despite their acknowledged importance in the wider countryside, they are usually not considered in models of landscape function due to their linear nature and the difficulties of acquiring relevant data about their character, extent, and location. We present a model which uses national datasets to describe the distribution of woody linear features along boundaries in Great Britain. The method can be applied for other boundary types and in other locations around the world across a range of spatial scales where different types of linear feature can be separated using characteristics such as height or width. Satellite-derived Land Cover Map 2007 (LCM2007) provided the spatial framework for locating linear features and was used to screen out areas unsuitable for their occurrence, that is, offshore, urban, and forest areas. Similarly, Ordnance Survey Land-Form PANORAMA®, a digital terrain model, was used to screen out where they do not occur. The presence of woody linear features on boundaries was modelled using attributes from a canopy height dataset obtained by subtracting a digital terrain map (DTM) from a digital surface model (DSM). The performance of the model was evaluated against existing woody linear feature data in Countryside Survey across a range of scales. The results indicate that, despite some underestimation, this simple approach may provide valuable information on the extents and locations of woody linear features in the countryside at both local and national scales.
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Item Purification in Differential Item Functioning Using Generalized Linear Mixed Models
ERIC Educational Resources Information Center
Liu, Qian
2011-01-01
For this dissertation, four item purification procedures were implemented onto the generalized linear mixed model for differential item functioning (DIF) analysis, and the performance of these item purification procedures was investigated through a series of simulations. Among the four procedures, forward and generalized linear mixed model (GLMM)…
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Howe, Laura D; Tilling, Kate; Matijasevich, Alicia; Petherick, Emily S; Santos, Ana Cristina; Fairley, Lesley; Wright, John; Santos, Iná S; Barros, Aluísio Jd; Martin, Richard M; Kramer, Michael S; Bogdanovich, Natalia; Matush, Lidia; Barros, Henrique; Lawlor, Debbie A
2016-10-01
Childhood growth is of interest in medical research concerned with determinants and consequences of variation from healthy growth and development. Linear spline multilevel modelling is a useful approach for deriving individual summary measures of growth, which overcomes several data issues (co-linearity of repeat measures, the requirement for all individuals to be measured at the same ages and bias due to missing data). Here, we outline the application of this methodology to model individual trajectories of length/height and weight, drawing on examples from five cohorts from different generations and different geographical regions with varying levels of economic development. We describe the unique features of the data within each cohort that have implications for the application of linear spline multilevel models, for example, differences in the density and inter-individual variation in measurement occasions, and multiple sources of measurement with varying measurement error. After providing example Stata syntax and a suggested workflow for the implementation of linear spline multilevel models, we conclude with a discussion of the advantages and disadvantages of the linear spline approach compared with other growth modelling methods such as fractional polynomials, more complex spline functions and other non-linear models. © The Author(s) 2013.
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Tilling, Kate; Matijasevich, Alicia; Petherick, Emily S; Santos, Ana Cristina; Fairley, Lesley; Wright, John; Santos, Iná S.; Barros, Aluísio JD; Martin, Richard M; Kramer, Michael S; Bogdanovich, Natalia; Matush, Lidia; Barros, Henrique; Lawlor, Debbie A
2013-01-01
Childhood growth is of interest in medical research concerned with determinants and consequences of variation from healthy growth and development. Linear spline multilevel modelling is a useful approach for deriving individual summary measures of growth, which overcomes several data issues (co-linearity of repeat measures, the requirement for all individuals to be measured at the same ages and bias due to missing data). Here, we outline the application of this methodology to model individual trajectories of length/height and weight, drawing on examples from five cohorts from different generations and different geographical regions with varying levels of economic development. We describe the unique features of the data within each cohort that have implications for the application of linear spline multilevel models, for example, differences in the density and inter-individual variation in measurement occasions, and multiple sources of measurement with varying measurement error. After providing example Stata syntax and a suggested workflow for the implementation of linear spline multilevel models, we conclude with a discussion of the advantages and disadvantages of the linear spline approach compared with other growth modelling methods such as fractional polynomials, more complex spline functions and other non-linear models. PMID:24108269
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Performance Metrics, Error Modeling, and Uncertainty Quantification
NASA Technical Reports Server (NTRS)
Tian, Yudong; Nearing, Grey S.; Peters-Lidard, Christa D.; Harrison, Kenneth W.; Tang, Ling
2016-01-01
A common set of statistical metrics has been used to summarize the performance of models or measurements- the most widely used ones being bias, mean square error, and linear correlation coefficient. They assume linear, additive, Gaussian errors, and they are interdependent, incomplete, and incapable of directly quantifying uncertainty. The authors demonstrate that these metrics can be directly derived from the parameters of the simple linear error model. Since a correct error model captures the full error information, it is argued that the specification of a parametric error model should be an alternative to the metrics-based approach. The error-modeling methodology is applicable to both linear and nonlinear errors, while the metrics are only meaningful for linear errors. In addition, the error model expresses the error structure more naturally, and directly quantifies uncertainty. This argument is further explained by highlighting the intrinsic connections between the performance metrics, the error model, and the joint distribution between the data and the reference.
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A new adaptive multiple modelling approach for non-linear and non-stationary systems
NASA Astrophysics Data System (ADS)
Chen, Hao; Gong, Yu; Hong, Xia
2016-07-01
This paper proposes a novel adaptive multiple modelling algorithm for non-linear and non-stationary systems. This simple modelling paradigm comprises K candidate sub-models which are all linear. With data available in an online fashion, the performance of all candidate sub-models are monitored based on the most recent data window, and M best sub-models are selected from the K candidates. The weight coefficients of the selected sub-model are adapted via the recursive least square (RLS) algorithm, while the coefficients of the remaining sub-models are unchanged. These M model predictions are then optimally combined to produce the multi-model output. We propose to minimise the mean square error based on a recent data window, and apply the sum to one constraint to the combination parameters, leading to a closed-form solution, so that maximal computational efficiency can be achieved. In addition, at each time step, the model prediction is chosen from either the resultant multiple model or the best sub-model, whichever is the best. Simulation results are given in comparison with some typical alternatives, including the linear RLS algorithm and a number of online non-linear approaches, in terms of modelling performance and time consumption.
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Australia's Next Top Fraction Model
ERIC Educational Resources Information Center
Gould, Peter
2013-01-01
Peter Gould suggests Australia's next top fraction model should be a linear model rather than an area model. He provides a convincing argument and gives examples of ways to introduce a linear model in primary classrooms.
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An Application to the Prediction of LOD Change Based on General Regression Neural Network
NASA Astrophysics Data System (ADS)
Zhang, X. H.; Wang, Q. J.; Zhu, J. J.; Zhang, H.
2011-07-01
Traditional prediction of the LOD (length of day) change was based on linear models, such as the least square model and the autoregressive technique, etc. Due to the complex non-linear features of the LOD variation, the performances of the linear model predictors are not fully satisfactory. This paper applies a non-linear neural network - general regression neural network (GRNN) model to forecast the LOD change, and the results are analyzed and compared with those obtained with the back propagation neural network and other models. The comparison shows that the performance of the GRNN model in the prediction of the LOD change is efficient and feasible.
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Non-linear Growth Models in Mplus and SAS
Grimm, Kevin J.; Ram, Nilam
2013-01-01
Non-linear growth curves or growth curves that follow a specified non-linear function in time enable researchers to model complex developmental patterns with parameters that are easily interpretable. In this paper we describe how a variety of sigmoid curves can be fit using the Mplus structural modeling program and the non-linear mixed-effects modeling procedure NLMIXED in SAS. Using longitudinal achievement data collected as part of a study examining the effects of preschool instruction on academic gain we illustrate the procedures for fitting growth models of logistic, Gompertz, and Richards functions. Brief notes regarding the practical benefits, limitations, and choices faced in the fitting and estimation of such models are included. PMID:23882134
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Comparison between Linear and Nonlinear Regression in a Laboratory Heat Transfer Experiment
ERIC Educational Resources Information Center
Gonçalves, Carine Messias; Schwaab, Marcio; Pinto, José Carlos
2013-01-01
In order to interpret laboratory experimental data, undergraduate students are used to perform linear regression through linearized versions of nonlinear models. However, the use of linearized models can lead to statistically biased parameter estimates. Even so, it is not an easy task to introduce nonlinear regression and show for the students…
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Application of General Regression Neural Network to the Prediction of LOD Change
NASA Astrophysics Data System (ADS)
Zhang, Xiao-Hong; Wang, Qi-Jie; Zhu, Jian-Jun; Zhang, Hao
2012-01-01
Traditional methods for predicting the change in length of day (LOD change) are mainly based on some linear models, such as the least square model and autoregression model, etc. However, the LOD change comprises complicated non-linear factors and the prediction effect of the linear models is always not so ideal. Thus, a kind of non-linear neural network — general regression neural network (GRNN) model is tried to make the prediction of the LOD change and the result is compared with the predicted results obtained by taking advantage of the BP (back propagation) neural network model and other models. The comparison result shows that the application of the GRNN to the prediction of the LOD change is highly effective and feasible.
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Computation of linear acceleration through an internal model in the macaque cerebellum
Laurens, Jean; Meng, Hui; Angelaki, Dora E.
2013-01-01
A combination of theory and behavioral findings has supported a role for internal models in the resolution of sensory ambiguities and sensorimotor processing. Although the cerebellum has been proposed as a candidate for implementation of internal models, concrete evidence from neural responses is lacking. Here we exploit un-natural motion stimuli, which induce incorrect self-motion perception and eye movements, to explore the neural correlates of an internal model proposed to compensate for Einstein’s equivalence principle and generate neural estimates of linear acceleration and gravity. We show that caudal cerebellar vermis Purkinje cells and cerebellar nuclei neurons selective for actual linear acceleration also encode erroneous linear acceleration, as expected from the internal model hypothesis, even when no actual linear acceleration occurs. These findings provide strong evidence that the cerebellum might be involved in the implementation of internal models that mimic physical principles to interpret sensory signals, as previously hypothesized by theorists. PMID:24077562
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Armey, Michael F; Crowther, Janis H
2008-02-01
Research has identified a significant increase in both the incidence and prevalence of non-suicidal self-injury (NSSI). The present study sought to test both linear and non-linear cusp catastrophe models by using aversive self-awareness, which was operationalized as a composite of aversive self-relevant affect and cognitions, and dissociation as predictors of NSSI. The cusp catastrophe model evidenced a better fit to the data, accounting for 6 times the variance (66%) of a linear model (9%-10%). These results support models of NSSI implicating emotion regulation deficits and experiential avoidance in the occurrence of NSSI and provide preliminary support for the use of cusp catastrophe models to study certain types of low base rate psychopathology such as NSSI. These findings suggest novel approaches to prevention and treatment of NSSI as well.
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Reed, Danielle R.; Mathew, Phoebe S.; Roberts, Kristi M.; Mansfield, Corrine J.
2015-01-01
Sweeteners are often added to liquid formulations of drugs but whether they merely make them better tasting or actually reduce the perception of bitterness remains unknown. In a group of children and adults, we determined whether adding sucrose to urea, caffeine, denatonium benzoate, propylthiouracil (PROP), and quinine would reduce their bitterness using a forced-choice method of paired comparisons. To better understand individual differences, adults also rated each solution using a more complex test (general Labeled Magnitude Scale [gLMS]) and were genotyped for the sweet taste receptor gene TAS1R3 and the bitter receptor TAS2R38. Sucrose suppressed the bitterness of each agent in children and adults. In adults, sucrose was effective in reducing the bitterness ratings from moderate to weak for all compounds tested, but those with the sensitive form of the sweet receptor reported greater reduction for caffeine and quinine. For PROP, sucrose was most effective for those who were genetically the most sensitive, although this did not attain statistical significance. Not only is the paired comparison method a valid tool to study how sucrose improves the taste of pediatric medicines among children but knowledge gleaned from basic research in bitter taste and how to alleviate it remains an important public health priority. PMID:25381313
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A Multiphase Non-Linear Mixed Effects Model: An Application to Spirometry after Lung Transplantation
Rajeswaran, Jeevanantham; Blackstone, Eugene H.
2014-01-01
In medical sciences, we often encounter longitudinal temporal relationships that are non-linear in nature. The influence of risk factors may also change across longitudinal follow-up. A system of multiphase non-linear mixed effects model is presented to model temporal patterns of longitudinal continuous measurements, with temporal decomposition to identify the phases and risk factors within each phase. Application of this model is illustrated using spirometry data after lung transplantation using readily available statistical software. This application illustrates the usefulness of our flexible model when dealing with complex non-linear patterns and time varying coefficients. PMID:24919830
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NASA Astrophysics Data System (ADS)
Fan, Zuhui
2000-01-01
The linear bias of the dark halos from a model under the Zeldovich approximation is derived and compared with the fitting formula of simulation results. While qualitatively similar to the Press-Schechter formula, this model gives a better description for the linear bias around the turnaround point. This advantage, however, may be compromised by the large uncertainty of the actual behavior of the linear bias near the turnaround point. For a broad class of structure formation models in the cold dark matter framework, a general relation exists between the number density and the linear bias of dark halos. This relation can be readily tested by numerical simulations. Thus, instead of laboriously checking these models one by one, numerical simulation studies can falsify a whole category of models. The general validity of this relation is important in identifying key physical processes responsible for the large-scale structure formation in the universe.
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Frequency Response of Synthetic Vocal Fold Models with Linear and Nonlinear Material Properties
ERIC Educational Resources Information Center
Shaw, Stephanie M.; Thomson, Scott L.; Dromey, Christopher; Smith, Simeon
2012-01-01
Purpose: The purpose of this study was to create synthetic vocal fold models with nonlinear stress-strain properties and to investigate the effect of linear versus nonlinear material properties on fundamental frequency (F[subscript 0]) during anterior-posterior stretching. Method: Three materially linear and 3 materially nonlinear models were…
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Smoothed Residual Plots for Generalized Linear Models. Technical Report #450.
ERIC Educational Resources Information Center
Brant, Rollin
Methods for examining the viability of assumptions underlying generalized linear models are considered. By appealing to the likelihood, a natural generalization of the raw residual plot for normal theory models is derived and is applied to investigating potential misspecification of the linear predictor. A smooth version of the plot is also…
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An Expert System for the Evaluation of Cost Models
1990-09-01
contrast to the condition of equal error variance, called homoscedasticity. (Reference: Applied Linear Regression Models by John Neter - page 423...normal. (Reference: Applied Linear Regression Models by John Neter - page 125) Click Here to continue -> Autocorrelation Click Here for the index - Index...over time. Error terms correlated over time are said to be autocorrelated or serially correlated. (REFERENCE: Applied Linear Regression Models by John
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Effect of non-linearity in predicting doppler waveforms through a novel model
Gayasen, Aman; Dua, Sunil Kumar; Sengupta, Amit; Nagchoudhuri, D
2003-01-01
Background In pregnancy, the uteroplacental vascular system develops de novo locally in utero and a systemic haemodynamic & bio-rheological alteration accompany it. Any abnormality in the non-linear vascular system is believed to trigger the onset of serious morbid conditions like pre-eclampsia and/or intrauterine growth restriction (IUGR). Exact Aetiopathogenesis is unknown. Advancement in the field of non-invasive doppler image analysis and simulation incorporating non-linearities may unfold the complexities associated with the inaccessible uteroplacental vessels. Earlier modeling approaches approximate it as a linear system. Method We proposed a novel electrical model for the uteroplacental system that uses MOSFETs as non-linear elements in place of traditional linear transmission line (TL) model. The model to simulate doppler FVW's was designed by including the inputs from our non-linear mathematical model. While using the MOSFETs as voltage-controlled switches, a fair degree of controlled-non-linearity has been introduced in the model. Comparative analysis was done between the simulated data and the actual doppler FVW's waveforms. Results & Discussion Normal pregnancy has been successfully modeled and the doppler output waveforms are simulated for different gestation time using the model. It is observed that the dicrotic notch disappears and the S/D ratio decreases as the pregnancy matures. Both these results are established clinical facts. Effects of blood density, viscosity and the arterial wall elasticity on the blood flow velocity profile were also studied. Spectral analysis on the output of the model (blood flow velocity) indicated that the Total Harmonic Distortion (THD) falls during the mid-gestation. Conclusion Total harmonic distortion (THD) is found to be informative in determining the Feto-maternal health. Effects of the blood density, the viscosity and the elasticity changes on the blood FVW are simulated. Future works are expected to concentrate mainly on improving the load with respect to varying non-linear parameters in the model. Heart rate variability, which accounts for the vascular tone, should also be included. We also expect the model to initiate extensive clinical or experimental studies in the near future. PMID:14561227
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A model for rotorcraft flying qualities studies
NASA Technical Reports Server (NTRS)
Mittal, Manoj; Costello, Mark F.
1993-01-01
This paper outlines the development of a mathematical model that is expected to be useful for rotorcraft flying qualities research. A computer model is presented that can be applied to a range of different rotorcraft configurations. The algorithm computes vehicle trim and a linear state-space model of the aircraft. The trim algorithm uses non linear optimization theory to solve the nonlinear algebraic trim equations. The linear aircraft equations consist of an airframe model and a flight control system dynamic model. The airframe model includes coupled rotor and fuselage rigid body dynamics and aerodynamics. The aerodynamic model for the rotors utilizes blade element theory and a three state dynamic inflow model. Aerodynamics of the fuselage and fuselage empennages are included. The linear state-space description for the flight control system is developed using standard block diagram data.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Abe, H.; Okuda, H.
We study linear and nonlinear properties of a new computer simulation model developed to study the propagation of electromagnetic waves in a dielectric medium in the linear and nonlinear regimes. The model is constructed by combining a microscopic model used in the semi-classical approximation for the dielectric media and the particle model developed for the plasma simulations. It is shown that the model may be useful for studying linear and nonlinear wave propagation in the dielectric media.
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The NASA High Speed ASE Project: Computational Analyses of a Low-Boom Supersonic Configuration
NASA Technical Reports Server (NTRS)
Silva, Walter A.; DeLaGarza, Antonio; Zink, Scott; Bounajem, Elias G.; Johnson, Christopher; Buonanno, Michael; Sanetrik, Mark D.; Yoo, Seung Y.; Kopasakis, George; Christhilf, David M.;
2014-01-01
A summary of NASA's High Speed Aeroservoelasticity (ASE) project is provided with a focus on a low-boom supersonic configuration developed by Lockheed-Martin and referred to as the N+2 configuration. The summary includes details of the computational models developed to date including a linear finite element model (FEM), linear unsteady aerodynamic models, structured and unstructured CFD grids, and discussion of the FEM development including sizing and structural constraints applied to the N+2 configuration. Linear results obtained to date include linear mode shapes and linear flutter boundaries. In addition to the tasks associated with the N+2 configuration, a summary of the work involving the development of AeroPropulsoServoElasticity (APSE) models is also discussed.
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Johnson, Brent A
2009-10-01
We consider estimation and variable selection in the partial linear model for censored data. The partial linear model for censored data is a direct extension of the accelerated failure time model, the latter of which is a very important alternative model to the proportional hazards model. We extend rank-based lasso-type estimators to a model that may contain nonlinear effects. Variable selection in such partial linear model has direct application to high-dimensional survival analyses that attempt to adjust for clinical predictors. In the microarray setting, previous methods can adjust for other clinical predictors by assuming that clinical and gene expression data enter the model linearly in the same fashion. Here, we select important variables after adjusting for prognostic clinical variables but the clinical effects are assumed nonlinear. Our estimator is based on stratification and can be extended naturally to account for multiple nonlinear effects. We illustrate the utility of our method through simulation studies and application to the Wisconsin prognostic breast cancer data set.
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Comparison of linear and nonlinear models for coherent hemodynamics spectroscopy (CHS)
NASA Astrophysics Data System (ADS)
Sassaroli, Angelo; Kainerstorfer, Jana; Fantini, Sergio
2015-03-01
A recently proposed linear time-invariant hemodynamic model for coherent hemodynamics spectroscopy1 (CHS) relates the tissue concentrations of oxy- and deoxy-hemoglobin (outputs of the system) to given dynamics of the tissue blood volume, blood flow and rate constant of oxygen diffusion (inputs of the system). This linear model was derived in the limit of "small" perturbations in blood flow velocity. We have extended this model to a more general model (which will be referred to as the nonlinear extension to the original model) that yields the time-dependent changes of oxy and deoxy-hemoglobin concentrations in response to arbitrary dynamic changes in capillary blood flow velocity. The nonlinear extension to the model relies on a general solution of the partial differential equation that governs the spatio-temporal behavior of oxygen saturation of hemoglobin in capillaries and venules on the basis of dynamic (or time resolved) blood transit time. We show preliminary results where the CHS spectra obtained from the linear and nonlinear models are compared to quantify the limits of applicability of the linear model.
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On the modelling of linear-assisted DC-DC voltage regulators for photovoltaic solar energy systems
NASA Astrophysics Data System (ADS)
Martínez-García, Herminio; García-Vílchez, Encarna
2017-11-01
This paper shows the modelling of linear-assisted or hybrid (linear & switching) DC/DC voltage regulators. In this kind of regulators, an auxiliary linear regulator is used, which objective is to cancel the ripple at the output voltage and provide fast responses for load variations. On the other hand, a switching DC/DC converter, connected in parallel with the linear regulator, allows to supply almost the whole output current demanded by the load. The objective of this topology is to take advantage of the suitable regulation characteristics that series linear voltage regulators have, but almost achieving the high efficiency that switching DC/DC converters provide. Linear-assisted DC/DC regulators are feedback systems with potential instability. Therefore, their modelling is mandatory in order to obtain design guidelines and assure stability of the implemented power supply system.
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Linear regression crash prediction models : issues and proposed solutions.
DOT National Transportation Integrated Search
2010-05-01
The paper develops a linear regression model approach that can be applied to : crash data to predict vehicle crashes. The proposed approach involves novice data aggregation : to satisfy linear regression assumptions; namely error structure normality ...
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Portfolio optimization by using linear programing models based on genetic algorithm
NASA Astrophysics Data System (ADS)
Sukono; Hidayat, Y.; Lesmana, E.; Putra, A. S.; Napitupulu, H.; Supian, S.
2018-01-01
In this paper, we discussed the investment portfolio optimization using linear programming model based on genetic algorithms. It is assumed that the portfolio risk is measured by absolute standard deviation, and each investor has a risk tolerance on the investment portfolio. To complete the investment portfolio optimization problem, the issue is arranged into a linear programming model. Furthermore, determination of the optimum solution for linear programming is done by using a genetic algorithm. As a numerical illustration, we analyze some of the stocks traded on the capital market in Indonesia. Based on the analysis, it is shown that the portfolio optimization performed by genetic algorithm approach produces more optimal efficient portfolio, compared to the portfolio optimization performed by a linear programming algorithm approach. Therefore, genetic algorithms can be considered as an alternative on determining the investment portfolio optimization, particularly using linear programming models.
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Design of Linear-Quadratic-Regulator for a CSTR process
NASA Astrophysics Data System (ADS)
Meghna, P. R.; Saranya, V.; Jaganatha Pandian, B.
2017-11-01
This paper aims at creating a Linear Quadratic Regulator (LQR) for a Continuous Stirred Tank Reactor (CSTR). A CSTR is a common process used in chemical industries. It is a highly non-linear system. Therefore, in order to create the gain feedback controller, the model is linearized. The controller is designed for the linearized model and the concentration and volume of the liquid in the reactor are kept at a constant value as required.
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Feedback linearization for control of air breathing engines
NASA Technical Reports Server (NTRS)
Phillips, Stephen; Mattern, Duane
1991-01-01
The method of feedback linearization for control of the nonlinear nozzle and compressor components of an air breathing engine is presented. This method overcomes the need for a large number of scheduling variables and operating points to accurately model highly nonlinear plants. Feedback linearization also results in linear closed loop system performance simplifying subsequent control design. Feedback linearization is used for the nonlinear partial engine model and performance is verified through simulation.
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Hao, Xu; Yujun, Sun; Xinjie, Wang; Jin, Wang; Yao, Fu
2015-01-01
A multiple linear model was developed for individual tree crown width of Cunninghamia lanceolata (Lamb.) Hook in Fujian province, southeast China. Data were obtained from 55 sample plots of pure China-fir plantation stands. An Ordinary Linear Least Squares (OLS) regression was used to establish the crown width model. To adjust for correlations between observations from the same sample plots, we developed one level linear mixed-effects (LME) models based on the multiple linear model, which take into account the random effects of plots. The best random effects combinations for the LME models were determined by the Akaike's information criterion, the Bayesian information criterion and the -2logarithm likelihood. Heteroscedasticity was reduced by three residual variance functions: the power function, the exponential function and the constant plus power function. The spatial correlation was modeled by three correlation structures: the first-order autoregressive structure [AR(1)], a combination of first-order autoregressive and moving average structures [ARMA(1,1)], and the compound symmetry structure (CS). Then, the LME model was compared to the multiple linear model using the absolute mean residual (AMR), the root mean square error (RMSE), and the adjusted coefficient of determination (adj-R2). For individual tree crown width models, the one level LME model showed the best performance. An independent dataset was used to test the performance of the models and to demonstrate the advantage of calibrating LME models.
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Recent Updates to the GEOS-5 Linear Model
NASA Technical Reports Server (NTRS)
Holdaway, Dan; Kim, Jong G.; Errico, Ron; Gelaro, Ronald; Mahajan, Rahul
2014-01-01
Global Modeling and Assimilation Office (GMAO) is close to having a working 4DVAR system and has developed a linearized version of GEOS-5.This talk outlines a series of improvements made to the linearized dynamics, physics and trajectory.Of particular interest is the development of linearized cloud microphysics, which provides the framework for 'all-sky' data assimilation.
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Generalized Multilevel Structural Equation Modeling
ERIC Educational Resources Information Center
Rabe-Hesketh, Sophia; Skrondal, Anders; Pickles, Andrew
2004-01-01
A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent…
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NASA Astrophysics Data System (ADS)
Fisher, Karl B.
1995-08-01
The relation between the galaxy correlation functions in real-space and redshift-space is derived in the linear regime by an appropriate averaging of the joint probability distribution of density and velocity. The derivation recovers the familiar linear theory result on large scales but has the advantage of clearly revealing the dependence of the redshift distortions on the underlying peculiar velocity field; streaming motions give rise to distortions of θ(Ω0.6/b) while variations in the anisotropic velocity dispersion yield terms of order θ(Ω1.2/b2). This probabilistic derivation of the redshift-space correlation function is similar in spirit to the derivation of the commonly used "streaming" model, in which the distortions are given by a convolution of the real-space correlation function with a velocity distribution function. The streaming model is often used to model the redshift-space correlation function on small, highly nonlinear, scales. There have been claims in the literature, however, that the streaming model is not valid in the linear regime. Our analysis confirms this claim, but we show that the streaming model can be made consistent with linear theory provided that the model for the streaming has the functional form predicted by linear theory and that the velocity distribution is chosen to be a Gaussian with the correct linear theory dispersion.
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A Method for Generating Reduced-Order Linear Models of Multidimensional Supersonic Inlets
NASA Technical Reports Server (NTRS)
Chicatelli, Amy; Hartley, Tom T.
1998-01-01
Simulation of high speed propulsion systems may be divided into two categories, nonlinear and linear. The nonlinear simulations are usually based on multidimensional computational fluid dynamics (CFD) methodologies and tend to provide high resolution results that show the fine detail of the flow. Consequently, these simulations are large, numerically intensive, and run much slower than real-time. ne linear simulations are usually based on large lumping techniques that are linearized about a steady-state operating condition. These simplistic models often run at or near real-time but do not always capture the detailed dynamics of the plant. Under a grant sponsored by the NASA Lewis Research Center, Cleveland, Ohio, a new method has been developed that can be used to generate improved linear models for control design from multidimensional steady-state CFD results. This CFD-based linear modeling technique provides a small perturbation model that can be used for control applications and real-time simulations. It is important to note the utility of the modeling procedure; all that is needed to obtain a linear model of the propulsion system is the geometry and steady-state operating conditions from a multidimensional CFD simulation or experiment. This research represents a beginning step in establishing a bridge between the controls discipline and the CFD discipline so that the control engineer is able to effectively use multidimensional CFD results in control system design and analysis.
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Linear and non-linear dynamic models of a geared rotor-bearing system
NASA Technical Reports Server (NTRS)
Kahraman, Ahmet; Singh, Rajendra
1990-01-01
A three degree of freedom non-linear model of a geared rotor-bearing system with gear backlash and radial clearances in rolling element bearings is proposed here. This reduced order model can be used to describe the transverse-torsional motion of the system. It is justified by comparing the eigen solutions yielded by corresponding linear model with the finite element method results. Nature of nonlinearities in bearings is examined and two approximate nonlinear stiffness functions are proposed. These approximate bearing models are verified by comparing their frequency responses with the results given by the exact form of nonlinearity. The proposed nonlinear dynamic model of the geared rotor-bearing system can be used to investigate the dynamic behavior and chaos.
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ERIC Educational Resources Information Center
Kane, Michael T.; Mroch, Andrew A.; Suh, Youngsuk; Ripkey, Douglas R.
2009-01-01
This paper analyzes five linear equating models for the "nonequivalent groups with anchor test" (NEAT) design with internal anchors (i.e., the anchor test is part of the full test). The analysis employs a two-dimensional framework. The first dimension contrasts two general approaches to developing the equating relationship. Under a "parameter…
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NASA Technical Reports Server (NTRS)
Merenyi, E.; Miller, J. S.; Singer, R. B.
1992-01-01
The linear mixing model approach was successfully applied to data sets of various natures. In these sets, the measured radiance could be assumed to be a linear combination of radiance contributions. The present work is an attempt to analyze a spectral image of Mars with linear mixing modeling.
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ERIC Educational Resources Information Center
Preacher, Kristopher J.; Curran, Patrick J.; Bauer, Daniel J.
2006-01-01
Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the…
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Some comparisons of complexity in dictionary-based and linear computational models.
Gnecco, Giorgio; Kůrková, Věra; Sanguineti, Marcello
2011-03-01
Neural networks provide a more flexible approximation of functions than traditional linear regression. In the latter, one can only adjust the coefficients in linear combinations of fixed sets of functions, such as orthogonal polynomials or Hermite functions, while for neural networks, one may also adjust the parameters of the functions which are being combined. However, some useful properties of linear approximators (such as uniqueness, homogeneity, and continuity of best approximation operators) are not satisfied by neural networks. Moreover, optimization of parameters in neural networks becomes more difficult than in linear regression. Experimental results suggest that these drawbacks of neural networks are offset by substantially lower model complexity, allowing accuracy of approximation even in high-dimensional cases. We give some theoretical results comparing requirements on model complexity for two types of approximators, the traditional linear ones and so called variable-basis types, which include neural networks, radial, and kernel models. We compare upper bounds on worst-case errors in variable-basis approximation with lower bounds on such errors for any linear approximator. Using methods from nonlinear approximation and integral representations tailored to computational units, we describe some cases where neural networks outperform any linear approximator. Copyright © 2010 Elsevier Ltd. All rights reserved.
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NASA Technical Reports Server (NTRS)
Kent, James; Holdaway, Daniel
2015-01-01
A number of geophysical applications require the use of the linearized version of the full model. One such example is in numerical weather prediction, where the tangent linear and adjoint versions of the atmospheric model are required for the 4DVAR inverse problem. The part of the model that represents the resolved scale processes of the atmosphere is known as the dynamical core. Advection, or transport, is performed by the dynamical core. It is a central process in many geophysical applications and is a process that often has a quasi-linear underlying behavior. However, over the decades since the advent of numerical modelling, significant effort has gone into developing many flavors of high-order, shape preserving, nonoscillatory, positive definite advection schemes. These schemes are excellent in terms of transporting the quantities of interest in the dynamical core, but they introduce nonlinearity through the use of nonlinear limiters. The linearity of the transport schemes used in Goddard Earth Observing System version 5 (GEOS-5), as well as a number of other schemes, is analyzed using a simple 1D setup. The linearized version of GEOS-5 is then tested using a linear third order scheme in the tangent linear version.
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Rothenberg, Stephen J; Rothenberg, Jesse C
2005-09-01
Statistical evaluation of the dose-response function in lead epidemiology is rarely attempted. Economic evaluation of health benefits of lead reduction usually assumes a linear dose-response function, regardless of the outcome measure used. We reanalyzed a previously published study, an international pooled data set combining data from seven prospective lead studies examining contemporaneous blood lead effect on IQ (intelligence quotient) of 7-year-old children (n = 1,333). We constructed alternative linear multiple regression models with linear blood lead terms (linear-linear dose response) and natural-log-transformed blood lead terms (log-linear dose response). We tested the two lead specifications for nonlinearity in the models, compared the two lead specifications for significantly better fit to the data, and examined the effects of possible residual confounding on the functional form of the dose-response relationship. We found that a log-linear lead-IQ relationship was a significantly better fit than was a linear-linear relationship for IQ (p = 0.009), with little evidence of residual confounding of included model variables. We substituted the log-linear lead-IQ effect in a previously published health benefits model and found that the economic savings due to U.S. population lead decrease between 1976 and 1999 (from 17.1 microg/dL to 2.0 microg/dL) was 2.2 times (319 billion dollars) that calculated using a linear-linear dose-response function (149 billion dollars). The Centers for Disease Control and Prevention action limit of 10 microg/dL for children fails to protect against most damage and economic cost attributable to lead exposure.
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Forcing Regression through a Given Point Using Any Familiar Computational Routine.
1983-03-01
a linear model , Y =a + OX + e ( Model I) then adopt the principle of least squares; and use sample data to estimate the unknown parameters, a and 8...has an expected value of zero indicates that the "average" response is considered linear . If c varies widely, Model I, though conceptually correct, may...relationship is linear from the maximum observed x to x - a, then Model II should be used. To pro- ceed with the customary evaluation of Model I would be
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Molenaar, Dylan; Tuerlinckx, Francis; van der Maas, Han L J
2015-01-01
A generalized linear modeling framework to the analysis of responses and response times is outlined. In this framework, referred to as bivariate generalized linear item response theory (B-GLIRT), separate generalized linear measurement models are specified for the responses and the response times that are subsequently linked by cross-relations. The cross-relations can take various forms. Here, we focus on cross-relations with a linear or interaction term for ability tests, and cross-relations with a curvilinear term for personality tests. In addition, we discuss how popular existing models from the psychometric literature are special cases in the B-GLIRT framework depending on restrictions in the cross-relation. This allows us to compare existing models conceptually and empirically. We discuss various extensions of the traditional models motivated by practical problems. We also illustrate the applicability of our approach using various real data examples, including data on personality and cognitive ability.
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Cost Estimation of Naval Ship Acquisition.
1983-12-01
one a 9-sub- system model , the other a single total cost model . The models were developed using the linear least squares regression tech- nique with...to Linear Statistical Models , McGraw-Hill, 1961. 11. Helmer, F. T., Bibliography on Pricing Methodology and Cost Estimating, Dept. of Economics and...SUPPI.EMSaTARY NOTES IS. KWRo" (Cowaft. en tever aide of ..aesep M’ Idab~t 6 Week ONNa.) Cost estimation; Acquisition; Parametric cost estimate; linear
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Non-linear models for the detection of impaired cerebral blood flow autoregulation.
Chacón, Max; Jara, José Luis; Miranda, Rodrigo; Katsogridakis, Emmanuel; Panerai, Ronney B
2018-01-01
The ability to discriminate between normal and impaired dynamic cerebral autoregulation (CA), based on measurements of spontaneous fluctuations in arterial blood pressure (BP) and cerebral blood flow (CBF), has considerable clinical relevance. We studied 45 normal subjects at rest and under hypercapnia induced by breathing a mixture of carbon dioxide and air. Non-linear models with BP as input and CBF velocity (CBFV) as output, were implemented with support vector machines (SVM) using separate recordings for learning and validation. Dynamic SVM implementations used either moving average or autoregressive structures. The efficiency of dynamic CA was estimated from the model's derived CBFV response to a step change in BP as an autoregulation index for both linear and non-linear models. Non-linear models with recurrences (autoregressive) showed the best results, with CA indexes of 5.9 ± 1.5 in normocapnia, and 2.5 ± 1.2 for hypercapnia with an area under the receiver-operator curve of 0.955. The high performance achieved by non-linear SVM models to detect deterioration of dynamic CA should encourage further assessment of its applicability to clinical conditions where CA might be impaired.
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Cook, James P; Mahajan, Anubha; Morris, Andrew P
2017-02-01
Linear mixed models are increasingly used for the analysis of genome-wide association studies (GWAS) of binary phenotypes because they can efficiently and robustly account for population stratification and relatedness through inclusion of random effects for a genetic relationship matrix. However, the utility of linear (mixed) models in the context of meta-analysis of GWAS of binary phenotypes has not been previously explored. In this investigation, we present simulations to compare the performance of linear and logistic regression models under alternative weighting schemes in a fixed-effects meta-analysis framework, considering designs that incorporate variable case-control imbalance, confounding factors and population stratification. Our results demonstrate that linear models can be used for meta-analysis of GWAS of binary phenotypes, without loss of power, even in the presence of extreme case-control imbalance, provided that one of the following schemes is used: (i) effective sample size weighting of Z-scores or (ii) inverse-variance weighting of allelic effect sizes after conversion onto the log-odds scale. Our conclusions thus provide essential recommendations for the development of robust protocols for meta-analysis of binary phenotypes with linear models.
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An approximate generalized linear model with random effects for informative missing data.
Follmann, D; Wu, M
1995-03-01
This paper develops a class of models to deal with missing data from longitudinal studies. We assume that separate models for the primary response and missingness (e.g., number of missed visits) are linked by a common random parameter. Such models have been developed in the econometrics (Heckman, 1979, Econometrica 47, 153-161) and biostatistics (Wu and Carroll, 1988, Biometrics 44, 175-188) literature for a Gaussian primary response. We allow the primary response, conditional on the random parameter, to follow a generalized linear model and approximate the generalized linear model by conditioning on the data that describes missingness. The resultant approximation is a mixed generalized linear model with possibly heterogeneous random effects. An example is given to illustrate the approximate approach, and simulations are performed to critique the adequacy of the approximation for repeated binary data.
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Butler, Samuel D; Nauyoks, Stephen E; Marciniak, Michael A
2015-06-01
Of the many classes of bidirectional reflectance distribution function (BRDF) models, two popular classes of models are the microfacet model and the linear systems diffraction model. The microfacet model has the benefit of speed and simplicity, as it uses geometric optics approximations, while linear systems theory uses a diffraction approach to compute the BRDF, at the expense of greater computational complexity. In this Letter, nongrazing BRDF measurements of rough and polished surface-reflecting materials at multiple incident angles are scaled by the microfacet cross section conversion term, but in the linear systems direction cosine space, resulting in great alignment of BRDF data at various incident angles in this space. This results in a predictive BRDF model for surface-reflecting materials at nongrazing angles, while avoiding some of the computational complexities in the linear systems diffraction model.
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Generation of linear dynamic models from a digital nonlinear simulation
NASA Technical Reports Server (NTRS)
Daniele, C. J.; Krosel, S. M.
1979-01-01
The results and methodology used to derive linear models from a nonlinear simulation are presented. It is shown that averaged positive and negative perturbations in the state variables can reduce numerical errors in finite difference, partial derivative approximations and, in the control inputs, can better approximate the system response in both directions about the operating point. Both explicit and implicit formulations are addressed. Linear models are derived for the F 100 engine, and comparisons of transients are made with the nonlinear simulation. The problem of startup transients in the nonlinear simulation in making these comparisons is addressed. Also, reduction of the linear models is investigated using the modal and normal techniques. Reduced-order models of the F 100 are derived and compared with the full-state models.
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NASA Technical Reports Server (NTRS)
Cho, Jeongho; Principe, Jose C.; Erdogmus, Deniz; Motter, Mark A.
2005-01-01
The next generation of aircraft will have dynamics that vary considerably over the operating regime. A single controller will have difficulty to meet the design specifications. In this paper, a SOM-based local linear modeling scheme of an unmanned aerial vehicle (UAV) is developed to design a set of inverse controllers. The SOM selects the operating regime depending only on the embedded output space information and avoids normalization of the input data. Each local linear model is associated with a linear controller, which is easy to design. Switching of the controllers is done synchronously with the active local linear model that tracks the different operating conditions. The proposed multiple modeling and control strategy has been successfully tested in a simulator that models the LoFLYTE UAV.
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Linear and non-linear perturbations in dark energy models
DOE Office of Scientific and Technical Information (OSTI.GOV)
Escamilla-Rivera, Celia; Casarini, Luciano; Fabris, Júlio C.
2016-11-01
In this work we discuss observational aspects of three time-dependent parameterisations of the dark energy equation of state w ( z ). In order to determine the dynamics associated with these models, we calculate their background evolution and perturbations in a scalar field representation. After performing a complete treatment of linear perturbations, we also show that the non-linear contribution of the selected w ( z ) parameterisations to the matter power spectra is almost the same for all scales, with no significant difference from the predictions of the standard ΛCDM model.
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Non-Linear Concentration-Response Relationships between Ambient Ozone and Daily Mortality.
Bae, Sanghyuk; Lim, Youn-Hee; Kashima, Saori; Yorifuji, Takashi; Honda, Yasushi; Kim, Ho; Hong, Yun-Chul
2015-01-01
Ambient ozone (O3) concentration has been reported to be significantly associated with mortality. However, linearity of the relationships and the presence of a threshold has been controversial. The aim of the present study was to examine the concentration-response relationship and threshold of the association between ambient O3 concentration and non-accidental mortality in 13 Japanese and Korean cities from 2000 to 2009. We selected Japanese and Korean cities which have population of over 1 million. We constructed Poisson regression models adjusting daily mean temperature, daily mean PM10, humidity, time trend, season, year, day of the week, holidays and yearly population. The association between O3 concentration and mortality was examined using linear, spline and linear-threshold models. The thresholds were estimated for each city, by constructing linear-threshold models. We also examined the city-combined association using a generalized additive mixed model. The mean O3 concentration did not differ greatly between Korea and Japan, which were 26.2 ppb and 24.2 ppb, respectively. Seven out of 13 cities showed better fits for the spline model compared with the linear model, supporting a non-linear relationships between O3 concentration and mortality. All of the 7 cities showed J or U shaped associations suggesting the existence of thresholds. The range of city-specific thresholds was from 11 to 34 ppb. The city-combined analysis also showed a non-linear association with a threshold around 30-40 ppb. We have observed non-linear concentration-response relationship with thresholds between daily mean ambient O3 concentration and daily number of non-accidental death in Japanese and Korean cities.
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NASA Astrophysics Data System (ADS)
Unger, Johannes; Hametner, Christoph; Jakubek, Stefan; Quasthoff, Marcus
2014-12-01
An accurate state of charge (SoC) estimation of a traction battery in hybrid electric non-road vehicles, which possess higher dynamics and power densities than on-road vehicles, requires a precise battery cell terminal voltage model. This paper presents a novel methodology for non-linear system identification of battery cells to obtain precise battery models. The methodology comprises the architecture of local model networks (LMN) and optimal model based design of experiments (DoE). Three main novelties are proposed: 1) Optimal model based DoE, which aims to high dynamically excite the battery cells at load ranges frequently used in operation. 2) The integration of corresponding inputs in the LMN to regard the non-linearities SoC, relaxation, hysteresis as well as temperature effects. 3) Enhancements to the local linear model tree (LOLIMOT) construction algorithm, to achieve a physical appropriate interpretation of the LMN. The framework is applicable for different battery cell chemistries and different temperatures, and is real time capable, which is shown on an industrial PC. The accuracy of the obtained non-linear battery model is demonstrated on cells with different chemistries and temperatures. The results show significant improvement due to optimal experiment design and integration of the battery non-linearities within the LMN structure.
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Phylogenetic mixtures and linear invariants for equal input models.
Casanellas, Marta; Steel, Mike
2017-04-01
The reconstruction of phylogenetic trees from molecular sequence data relies on modelling site substitutions by a Markov process, or a mixture of such processes. In general, allowing mixed processes can result in different tree topologies becoming indistinguishable from the data, even for infinitely long sequences. However, when the underlying Markov process supports linear phylogenetic invariants, then provided these are sufficiently informative, the identifiability of the tree topology can be restored. In this paper, we investigate a class of processes that support linear invariants once the stationary distribution is fixed, the 'equal input model'. This model generalizes the 'Felsenstein 1981' model (and thereby the Jukes-Cantor model) from four states to an arbitrary number of states (finite or infinite), and it can also be described by a 'random cluster' process. We describe the structure and dimension of the vector spaces of phylogenetic mixtures and of linear invariants for any fixed phylogenetic tree (and for all trees-the so called 'model invariants'), on any number n of leaves. We also provide a precise description of the space of mixtures and linear invariants for the special case of [Formula: see text] leaves. By combining techniques from discrete random processes and (multi-) linear algebra, our results build on a classic result that was first established by James Lake (Mol Biol Evol 4:167-191, 1987).
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Vajargah, Kianoush Fathi; Sadeghi-Bazargani, Homayoun; Mehdizadeh-Esfanjani, Robab; Savadi-Oskouei, Daryoush; Farhoudi, Mehdi
2012-01-01
The objective of the present study was to assess the comparable applicability of orthogonal projections to latent structures (OPLS) statistical model vs traditional linear regression in order to investigate the role of trans cranial doppler (TCD) sonography in predicting ischemic stroke prognosis. The study was conducted on 116 ischemic stroke patients admitted to a specialty neurology ward. The Unified Neurological Stroke Scale was used once for clinical evaluation on the first week of admission and again six months later. All data was primarily analyzed using simple linear regression and later considered for multivariate analysis using PLS/OPLS models through the SIMCA P+12 statistical software package. The linear regression analysis results used for the identification of TCD predictors of stroke prognosis were confirmed through the OPLS modeling technique. Moreover, in comparison to linear regression, the OPLS model appeared to have higher sensitivity in detecting the predictors of ischemic stroke prognosis and detected several more predictors. Applying the OPLS model made it possible to use both single TCD measures/indicators and arbitrarily dichotomized measures of TCD single vessel involvement as well as the overall TCD result. In conclusion, the authors recommend PLS/OPLS methods as complementary rather than alternative to the available classical regression models such as linear regression.
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ERIC Educational Resources Information Center
Armey, Michael F.; Crowther, Janis H.
2008-01-01
Research has identified a significant increase in both the incidence and prevalence of non-suicidal self-injury (NSSI). The present study sought to test both linear and non-linear cusp catastrophe models by using aversive self-awareness, which was operationalized as a composite of aversive self-relevant affect and cognitions, and dissociation as…
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Nonlinearity measure and internal model control based linearization in anti-windup design
DOE Office of Scientific and Technical Information (OSTI.GOV)
Perev, Kamen
2013-12-18
This paper considers the problem of internal model control based linearization in anti-windup design. The nonlinearity measure concept is used for quantifying the control system degree of nonlinearity. The linearizing effect of a modified internal model control structure is presented by comparing the nonlinearity measures of the open-loop and closed-loop systems. It is shown that the linearization properties are improved by increasing the control system local feedback gain. However, it is emphasized that at the same time the stability of the system deteriorates. The conflicting goals of stability and linearization are resolved by solving the design problem in different frequencymore » ranges.« less
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Perfect commuting-operator strategies for linear system games
NASA Astrophysics Data System (ADS)
Cleve, Richard; Liu, Li; Slofstra, William
2017-01-01
Linear system games are a generalization of Mermin's magic square game introduced by Cleve and Mittal. They show that perfect strategies for linear system games in the tensor-product model of entanglement correspond to finite-dimensional operator solutions of a certain set of non-commutative equations. We investigate linear system games in the commuting-operator model of entanglement, where Alice and Bob's measurement operators act on a joint Hilbert space, and Alice's operators must commute with Bob's operators. We show that perfect strategies in this model correspond to possibly infinite-dimensional operator solutions of the non-commutative equations. The proof is based around a finitely presented group associated with the linear system which arises from the non-commutative equations.
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Modelling and Predicting Backstroke Start Performance Using Non-Linear and Linear Models.
de Jesus, Karla; Ayala, Helon V H; de Jesus, Kelly; Coelho, Leandro Dos S; Medeiros, Alexandre I A; Abraldes, José A; Vaz, Mário A P; Fernandes, Ricardo J; Vilas-Boas, João Paulo
2018-03-01
Our aim was to compare non-linear and linear mathematical model responses for backstroke start performance prediction. Ten swimmers randomly completed eight 15 m backstroke starts with feet over the wedge, four with hands on the highest horizontal and four on the vertical handgrip. Swimmers were videotaped using a dual media camera set-up, with the starts being performed over an instrumented block with four force plates. Artificial neural networks were applied to predict 5 m start time using kinematic and kinetic variables and to determine the accuracy of the mean absolute percentage error. Artificial neural networks predicted start time more robustly than the linear model with respect to changing training to the validation dataset for the vertical handgrip (3.95 ± 1.67 vs. 5.92 ± 3.27%). Artificial neural networks obtained a smaller mean absolute percentage error than the linear model in the horizontal (0.43 ± 0.19 vs. 0.98 ± 0.19%) and vertical handgrip (0.45 ± 0.19 vs. 1.38 ± 0.30%) using all input data. The best artificial neural network validation revealed a smaller mean absolute error than the linear model for the horizontal (0.007 vs. 0.04 s) and vertical handgrip (0.01 vs. 0.03 s). Artificial neural networks should be used for backstroke 5 m start time prediction due to the quite small differences among the elite level performances.
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Scoring and staging systems using cox linear regression modeling and recursive partitioning.
Lee, J W; Um, S H; Lee, J B; Mun, J; Cho, H
2006-01-01
Scoring and staging systems are used to determine the order and class of data according to predictors. Systems used for medical data, such as the Child-Turcotte-Pugh scoring and staging systems for ordering and classifying patients with liver disease, are often derived strictly from physicians' experience and intuition. We construct objective and data-based scoring/staging systems using statistical methods. We consider Cox linear regression modeling and recursive partitioning techniques for censored survival data. In particular, to obtain a target number of stages we propose cross-validation and amalgamation algorithms. We also propose an algorithm for constructing scoring and staging systems by integrating local Cox linear regression models into recursive partitioning, so that we can retain the merits of both methods such as superior predictive accuracy, ease of use, and detection of interactions between predictors. The staging system construction algorithms are compared by cross-validation evaluation of real data. The data-based cross-validation comparison shows that Cox linear regression modeling is somewhat better than recursive partitioning when there are only continuous predictors, while recursive partitioning is better when there are significant categorical predictors. The proposed local Cox linear recursive partitioning has better predictive accuracy than Cox linear modeling and simple recursive partitioning. This study indicates that integrating local linear modeling into recursive partitioning can significantly improve prediction accuracy in constructing scoring and staging systems.
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Action Centered Contextual Bandits.
Greenewald, Kristjan; Tewari, Ambuj; Klasnja, Predrag; Murphy, Susan
2017-12-01
Contextual bandits have become popular as they offer a middle ground between very simple approaches based on multi-armed bandits and very complex approaches using the full power of reinforcement learning. They have demonstrated success in web applications and have a rich body of associated theoretical guarantees. Linear models are well understood theoretically and preferred by practitioners because they are not only easily interpretable but also simple to implement and debug. Furthermore, if the linear model is true, we get very strong performance guarantees. Unfortunately, in emerging applications in mobile health, the time-invariant linear model assumption is untenable. We provide an extension of the linear model for contextual bandits that has two parts: baseline reward and treatment effect. We allow the former to be complex but keep the latter simple. We argue that this model is plausible for mobile health applications. At the same time, it leads to algorithms with strong performance guarantees as in the linear model setting, while still allowing for complex nonlinear baseline modeling. Our theory is supported by experiments on data gathered in a recently concluded mobile health study.
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Wang, Zheng-Xin; Hao, Peng; Yao, Pei-Yi
2017-01-01
The non-linear relationship between provincial economic growth and carbon emissions is investigated by using panel smooth transition regression (PSTR) models. The research indicates that, on the condition of separately taking Gross Domestic Product per capita (GDPpc), energy structure (Es), and urbanisation level (Ul) as transition variables, three models all reject the null hypothesis of a linear relationship, i.e., a non-linear relationship exists. The results show that the three models all contain only one transition function but different numbers of location parameters. The model taking GDPpc as the transition variable has two location parameters, while the other two models separately considering Es and Ul as the transition variables both contain one location parameter. The three models applied in the study all favourably describe the non-linear relationship between economic growth and CO2 emissions in China. It also can be seen that the conversion rate of the influence of Ul on per capita CO2 emissions is significantly higher than those of GDPpc and Es on per capita CO2 emissions. PMID:29236083
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Fogolari, Federico; Corazza, Alessandra; Esposito, Gennaro
2015-04-05
The generalized Born model in the Onufriev, Bashford, and Case (Onufriev et al., Proteins: Struct Funct Genet 2004, 55, 383) implementation has emerged as one of the best compromises between accuracy and speed of computation. For simulations of nucleic acids, however, a number of issues should be addressed: (1) the generalized Born model is based on a linear model and the linearization of the reference Poisson-Boltmann equation may be questioned for highly charged systems as nucleic acids; (2) although much attention has been given to potentials, solvation forces could be much less sensitive to linearization than the potentials; and (3) the accuracy of the Onufriev-Bashford-Case (OBC) model for nucleic acids depends on fine tuning of parameters. Here, we show that the linearization of the Poisson Boltzmann equation has mild effects on computed forces, and that with optimal choice of the OBC model parameters, solvation forces, essential for molecular dynamics simulations, agree well with those computed using the reference Poisson-Boltzmann model. © 2015 Wiley Periodicals, Inc.
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Wang, Zheng-Xin; Hao, Peng; Yao, Pei-Yi
2017-12-13
The non-linear relationship between provincial economic growth and carbon emissions is investigated by using panel smooth transition regression (PSTR) models. The research indicates that, on the condition of separately taking Gross Domestic Product per capita (GDPpc), energy structure (Es), and urbanisation level (Ul) as transition variables, three models all reject the null hypothesis of a linear relationship, i.e., a non-linear relationship exists. The results show that the three models all contain only one transition function but different numbers of location parameters. The model taking GDPpc as the transition variable has two location parameters, while the other two models separately considering Es and Ul as the transition variables both contain one location parameter. The three models applied in the study all favourably describe the non-linear relationship between economic growth and CO₂ emissions in China. It also can be seen that the conversion rate of the influence of Ul on per capita CO₂ emissions is significantly higher than those of GDPpc and Es on per capita CO₂ emissions.
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A General Accelerated Degradation Model Based on the Wiener Process.
Liu, Le; Li, Xiaoyang; Sun, Fuqiang; Wang, Ning
2016-12-06
Accelerated degradation testing (ADT) is an efficient tool to conduct material service reliability and safety evaluations by analyzing performance degradation data. Traditional stochastic process models are mainly for linear or linearization degradation paths. However, those methods are not applicable for the situations where the degradation processes cannot be linearized. Hence, in this paper, a general ADT model based on the Wiener process is proposed to solve the problem for accelerated degradation data analysis. The general model can consider the unit-to-unit variation and temporal variation of the degradation process, and is suitable for both linear and nonlinear ADT analyses with single or multiple acceleration variables. The statistical inference is given to estimate the unknown parameters in both constant stress and step stress ADT. The simulation example and two real applications demonstrate that the proposed method can yield reliable lifetime evaluation results compared with the existing linear and time-scale transformation Wiener processes in both linear and nonlinear ADT analyses.
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A General Accelerated Degradation Model Based on the Wiener Process
Liu, Le; Li, Xiaoyang; Sun, Fuqiang; Wang, Ning
2016-01-01
Accelerated degradation testing (ADT) is an efficient tool to conduct material service reliability and safety evaluations by analyzing performance degradation data. Traditional stochastic process models are mainly for linear or linearization degradation paths. However, those methods are not applicable for the situations where the degradation processes cannot be linearized. Hence, in this paper, a general ADT model based on the Wiener process is proposed to solve the problem for accelerated degradation data analysis. The general model can consider the unit-to-unit variation and temporal variation of the degradation process, and is suitable for both linear and nonlinear ADT analyses with single or multiple acceleration variables. The statistical inference is given to estimate the unknown parameters in both constant stress and step stress ADT. The simulation example and two real applications demonstrate that the proposed method can yield reliable lifetime evaluation results compared with the existing linear and time-scale transformation Wiener processes in both linear and nonlinear ADT analyses. PMID:28774107
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Aircraft Airframe Cost Estimation Using a Random Coefficients Model
1979-12-01
approach will also be used here. 2 Model Formulation Several different types of equations could be used for the basic form of the CER, such as linear ...5) Marcotte developed several CER’s for fighter aircraft airframes using the log- linear model . A plot of the residuals from the CER for recurring...of the natural logarithm. Ordinary Least Squares The ordinary least squares procedure starts with the equation for the general linear model . The
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Kallehauge, Jesper F; Sourbron, Steven; Irving, Benjamin; Tanderup, Kari; Schnabel, Julia A; Chappell, Michael A
2017-06-01
Fitting tracer kinetic models using linear methods is much faster than using their nonlinear counterparts, although this comes often at the expense of reduced accuracy and precision. The aim of this study was to derive and compare the performance of the linear compartmental tissue uptake (CTU) model with its nonlinear version with respect to their percentage error and precision. The linear and nonlinear CTU models were initially compared using simulations with varying noise and temporal sampling. Subsequently, the clinical applicability of the linear model was demonstrated on 14 patients with locally advanced cervical cancer examined with dynamic contrast-enhanced magnetic resonance imaging. Simulations revealed equal percentage error and precision when noise was within clinical achievable ranges (contrast-to-noise ratio >10). The linear method was significantly faster than the nonlinear method, with a minimum speedup of around 230 across all tested sampling rates. Clinical analysis revealed that parameters estimated using the linear and nonlinear CTU model were highly correlated (ρ ≥ 0.95). The linear CTU model is computationally more efficient and more stable against temporal downsampling, whereas the nonlinear method is more robust to variations in noise. The two methods may be used interchangeably within clinical achievable ranges of temporal sampling and noise. Magn Reson Med 77:2414-2423, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.
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Modeling of salt and pH gradient elution in ion-exchange chromatography.
Schmidt, Michael; Hafner, Mathias; Frech, Christian
2014-01-01
The separation of proteins by internally and externally generated pH gradients in chromatofocusing on ion-exchange columns is a well-established analytical method with a large number of applications. In this work, a stoichiometric displacement model was used to describe the retention behavior of lysozyme on SP Sepharose FF and a monoclonal antibody on Fractogel SO3 (S) in linear salt and pH gradient elution. The pH dependence of the binding charge B in the linear gradient elution model is introduced using a protein net charge model, while the pH dependence of the equilibrium constant is based on a thermodynamic approach. The model parameter and pH dependences are calculated from linear salt gradient elutions at different pH values as well as from linear pH gradient elutions at different fixed salt concentrations. The application of the model for the well-characterized protein lysozyme resulted in almost identical model parameters based on either linear salt or pH gradient elution data. For the antibody, only the approach based on linear pH gradients is feasible because of the limited pH range useful for salt gradient elution. The application of the model for the separation of an acid variant of the antibody from the major monomeric form is discussed. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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Liu, X T; Ma, W F; Zeng, X F; Xie, C Y; Thacker, P A; Htoo, J K; Qiao, S Y
2015-10-01
Four 28-d experiments were conducted to determine the standardized ileal digestible (SID) valine (Val) to lysine (Lys) ratio required for 26- to 46- (Exp. 1), 49- to 70- (Exp. 2), 71- to 92- (Exp. 3), and 94- to 119-kg (Exp. 4) pigs fed low CP diets supplemented with crystalline AA. The first 3 experiments utilized 150 pigs (Duroc × Landrace × Large White), while Exp. 4 utilized 90 finishing pigs. Pigs in all 4 experiments were randomly allocated to 1 of 5 diets with 6 pens per treatment (3 pens of barrows and 3 pens of gilts) and 5 pigs per pen for the first 3 experiments and 3 pigs per pen for Exp. 4. Diets for all experiments were formulated to contain SID Val to Lys ratios of 0.55, 0.60, 0.65, 0.70, or 0.75. In Exp. 1 (26 to 46 kg), ADG increased (linear, = 0.039; quadratic, = 0.042) with an increasing dietary Val:Lys ratio. The SID Val:Lys ratio to maximize ADG was 0.62 using a linear broken-line model and 0.71 using a quadratic model. In Exp. 2 (49 to 70 kg), ADG increased (linear, = 0.021; quadratic, = 0.042) as the SID Val:Lys ratio increased. G:F improved (linear, = 0.039) and serum urea nitrogen (SUN) decreased (linear, = 0.021; quadratic, = 0.024) with an increased SID Val:Lys ratio. The SID Val:Lys ratios to maximize ADG as well as to minimize SUN levels were 0.67 and 0.65, respectively, using a linear broken-line model and 0.72 and 0.71, respectively, using a quadratic model. In Exp. 3 (71 to 92 kg), ADG increased (linear, = 0.007; quadratic, = 0.022) and SUN decreased (linear, = 0.011; quadratic, = 0.034) as the dietary SID Val:Lys ratio increased. The SID Val:Lys ratios to maximize ADG as well as to minimize SUN levels were 0.67 and 0.67, respectively, using a linear broken-line model and 0.72 and 0.74, respectively, using a quadratic model. In Exp. 4 (94 to 119 kg), ADG increased (linear, = 0.041) and G:F was improved (linear, = 0.004; quadratic, = 0.005) as the dietary SID Val:Lys ratio increased. The SID Val:Lys ratio to maximize G:F was 0.68 using a linear broken-line model and 0.72 using a quadratic model. Carcass traits and muscle quality were not influenced by SID Val:Lys ratio. In conclusion, the dietary SID Val:Lys ratios required for 26- to 46-, 49- to 70-, 71- to 92-, and 94- to 119-kg pigs were estimated to be 0.62, 0.66, 0.67, and 0.68, respectively, using a linear broken-line model and 0.71, 0.72, 0.73, and 0.72, respectively, using a quadratic model.
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NASA Astrophysics Data System (ADS)
Benettin, G.; Pasquali, S.; Ponno, A.
2018-05-01
FPU models, in dimension one, are perturbations either of the linear model or of the Toda model; perturbations of the linear model include the usual β -model, perturbations of Toda include the usual α +β model. In this paper we explore and compare two families, or hierarchies, of FPU models, closer and closer to either the linear or the Toda model, by computing numerically, for each model, the maximal Lyapunov exponent χ . More precisely, we consider statistically typical trajectories and study the asymptotics of χ for large N (the number of particles) and small ɛ (the specific energy E / N), and find, for all models, asymptotic power laws χ ˜eq Cɛ ^a, C and a depending on the model. The asymptotics turns out to be, in general, rather slow, and producing accurate results requires a great computational effort. We also revisit and extend the analytic computation of χ introduced by Casetti, Livi and Pettini, originally formulated for the β -model. With great evidence the theory extends successfully to all models of the linear hierarchy, but not to models close to Toda.
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2011-01-01
Background Design of newly engineered microbial strains for biotechnological purposes would greatly benefit from the development of realistic mathematical models for the processes to be optimized. Such models can then be analyzed and, with the development and application of appropriate optimization techniques, one could identify the modifications that need to be made to the organism in order to achieve the desired biotechnological goal. As appropriate models to perform such an analysis are necessarily non-linear and typically non-convex, finding their global optimum is a challenging task. Canonical modeling techniques, such as Generalized Mass Action (GMA) models based on the power-law formalism, offer a possible solution to this problem because they have a mathematical structure that enables the development of specific algorithms for global optimization. Results Based on the GMA canonical representation, we have developed in previous works a highly efficient optimization algorithm and a set of related strategies for understanding the evolution of adaptive responses in cellular metabolism. Here, we explore the possibility of recasting kinetic non-linear models into an equivalent GMA model, so that global optimization on the recast GMA model can be performed. With this technique, optimization is greatly facilitated and the results are transposable to the original non-linear problem. This procedure is straightforward for a particular class of non-linear models known as Saturable and Cooperative (SC) models that extend the power-law formalism to deal with saturation and cooperativity. Conclusions Our results show that recasting non-linear kinetic models into GMA models is indeed an appropriate strategy that helps overcoming some of the numerical difficulties that arise during the global optimization task. PMID:21867520
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Marrero-Ponce, Yovani; Medina-Marrero, Ricardo; Castillo-Garit, Juan A; Romero-Zaldivar, Vicente; Torrens, Francisco; Castro, Eduardo A
2005-04-15
A novel approach to bio-macromolecular design from a linear algebra point of view is introduced. A protein's total (whole protein) and local (one or more amino acid) linear indices are a new set of bio-macromolecular descriptors of relevance to protein QSAR/QSPR studies. These amino-acid level biochemical descriptors are based on the calculation of linear maps on Rn[f k(xmi):Rn-->Rn] in canonical basis. These bio-macromolecular indices are calculated from the kth power of the macromolecular pseudograph alpha-carbon atom adjacency matrix. Total linear indices are linear functional on Rn. That is, the kth total linear indices are linear maps from Rn to the scalar R[f k(xm):Rn-->R]. Thus, the kth total linear indices are calculated by summing the amino-acid linear indices of all amino acids in the protein molecule. A study of the protein stability effects for a complete set of alanine substitutions in the Arc repressor illustrates this approach. A quantitative model that discriminates near wild-type stability alanine mutants from the reduced-stability ones in a training series was obtained. This model permitted the correct classification of 97.56% (40/41) and 91.67% (11/12) of proteins in the training and test set, respectively. It shows a high Matthews correlation coefficient (MCC=0.952) for the training set and an MCC=0.837 for the external prediction set. Additionally, canonical regression analysis corroborated the statistical quality of the classification model (Rcanc=0.824). This analysis was also used to compute biological stability canonical scores for each Arc alanine mutant. On the other hand, the linear piecewise regression model compared favorably with respect to the linear regression one on predicting the melting temperature (tm) of the Arc alanine mutants. The linear model explains almost 81% of the variance of the experimental tm (R=0.90 and s=4.29) and the LOO press statistics evidenced its predictive ability (q2=0.72 and scv=4.79). Moreover, the TOMOCOMD-CAMPS method produced a linear piecewise regression (R=0.97) between protein backbone descriptors and tm values for alanine mutants of the Arc repressor. A break-point value of 51.87 degrees C characterized two mutant clusters and coincided perfectly with the experimental scale. For this reason, we can use the linear discriminant analysis and piecewise models in combination to classify and predict the stability of the mutant Arc homodimers. These models also permitted the interpretation of the driving forces of such folding process, indicating that topologic/topographic protein backbone interactions control the stability profile of wild-type Arc and its alanine mutants.
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Kumar, K Vasanth; Sivanesan, S
2006-08-25
Pseudo second order kinetic expressions of Ho, Sobkowsk and Czerwinski, Blanachard et al. and Ritchie were fitted to the experimental kinetic data of malachite green onto activated carbon by non-linear and linear method. Non-linear method was found to be a better way of obtaining the parameters involved in the second order rate kinetic expressions. Both linear and non-linear regression showed that the Sobkowsk and Czerwinski and Ritchie's pseudo second order model were the same. Non-linear regression analysis showed that both Blanachard et al. and Ho have similar ideas on the pseudo second order model but with different assumptions. The best fit of experimental data in Ho's pseudo second order expression by linear and non-linear regression method showed that Ho pseudo second order model was a better kinetic expression when compared to other pseudo second order kinetic expressions. The amount of dye adsorbed at equilibrium, q(e), was predicted from Ho pseudo second order expression and were fitted to the Langmuir, Freundlich and Redlich Peterson expressions by both linear and non-linear method to obtain the pseudo isotherms. The best fitting pseudo isotherm was found to be the Langmuir and Redlich Peterson isotherm. Redlich Peterson is a special case of Langmuir when the constant g equals unity.
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A simple method for identifying parameter correlations in partially observed linear dynamic models.
Li, Pu; Vu, Quoc Dong
2015-12-14
Parameter estimation represents one of the most significant challenges in systems biology. This is because biological models commonly contain a large number of parameters among which there may be functional interrelationships, thus leading to the problem of non-identifiability. Although identifiability analysis has been extensively studied by analytical as well as numerical approaches, systematic methods for remedying practically non-identifiable models have rarely been investigated. We propose a simple method for identifying pairwise correlations and higher order interrelationships of parameters in partially observed linear dynamic models. This is made by derivation of the output sensitivity matrix and analysis of the linear dependencies of its columns. Consequently, analytical relations between the identifiability of the model parameters and the initial conditions as well as the input functions can be achieved. In the case of structural non-identifiability, identifiable combinations can be obtained by solving the resulting homogenous linear equations. In the case of practical non-identifiability, experiment conditions (i.e. initial condition and constant control signals) can be provided which are necessary for remedying the non-identifiability and unique parameter estimation. It is noted that the approach does not consider noisy data. In this way, the practical non-identifiability issue, which is popular for linear biological models, can be remedied. Several linear compartment models including an insulin receptor dynamics model are taken to illustrate the application of the proposed approach. Both structural and practical identifiability of partially observed linear dynamic models can be clarified by the proposed method. The result of this method provides important information for experimental design to remedy the practical non-identifiability if applicable. The derivation of the method is straightforward and thus the algorithm can be easily implemented into a software packet.
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Nonlinear time series modeling and forecasting the seismic data of the Hindu Kush region
NASA Astrophysics Data System (ADS)
Khan, Muhammad Yousaf; Mittnik, Stefan
2018-01-01
In this study, we extended the application of linear and nonlinear time models in the field of earthquake seismology and examined the out-of-sample forecast accuracy of linear Autoregressive (AR), Autoregressive Conditional Duration (ACD), Self-Exciting Threshold Autoregressive (SETAR), Threshold Autoregressive (TAR), Logistic Smooth Transition Autoregressive (LSTAR), Additive Autoregressive (AAR), and Artificial Neural Network (ANN) models for seismic data of the Hindu Kush region. We also extended the previous studies by using Vector Autoregressive (VAR) and Threshold Vector Autoregressive (TVAR) models and compared their forecasting accuracy with linear AR model. Unlike previous studies that typically consider the threshold model specifications by using internal threshold variable, we specified these models with external transition variables and compared their out-of-sample forecasting performance with the linear benchmark AR model. The modeling results show that time series models used in the present study are capable of capturing the dynamic structure present in the seismic data. The point forecast results indicate that the AR model generally outperforms the nonlinear models. However, in some cases, threshold models with external threshold variables specification produce more accurate forecasts, indicating that specification of threshold time series models is of crucial importance. For raw seismic data, the ACD model does not show an improved out-of-sample forecasting performance over the linear AR model. The results indicate that the AR model is the best forecasting device to model and forecast the raw seismic data of the Hindu Kush region.
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Dossa, Gbadamassi G. O.; Paudel, Ekananda; Fujinuma, Junichi; Yu, Haiying; Chutipong, Wanlop; Zhang, Yuan; Paz, Sherryl; Harrison, Rhett D.
2013-01-01
Tropical volcanoes are an important but understudied ecosystem, and the relationships between plant species diversity and compositional change and elevation may differ from mountains created by uplift, because of their younger and more homogeneous soils. We sampled vegetation over an altitudinal gradient on Mt. Rinjani, Lombok, Indonesia. We modeled alpha- (plot) and beta- (among plot) diversity (Fisher's alpha), compositional change, and biomass against elevation and selected covariates. We also examined community phylogenetic structure across the elevational gradient. We recorded 902 trees and shrubs among 92 species, and 67 species of ground-cover plants. For understorey, subcanopy and canopy plants, an increase in elevation was associated with a decline in alpha-diversity, whereas data for ground-cover plants suggested a hump-shaped pattern. Elevation was consistently the most important factor in determining alpha-diversity for all components. The alpha-diversity of ground-cover vegetation was also negatively correlated with leaf area index, which suggests low light conditions in the understorey may limit diversity at lower elevations. Beta-diversity increased with elevation for ground-cover plants and declined at higher elevations for other components of the vegetation. However, statistical power was low and we could not resolve the relative importance to beta-diversity of different factors. Multivariate GLMs of variation in community composition among plots explained 67.05%, 27.63%, 18.24%, and 19.80% of the variation (deviance) for ground-cover, understorey, subcanopy and canopy plants, respectively, and demonstrated that elevation was a consistently important factor in determining community composition. Above-ground biomass showed no significant pattern with elevation and was also not significantly associated with alpha-diversity. At lower elevations communities had a random phylogenetic structure, but from 1600 m communities were phylogenetically clustered. This suggests a greater role of environmental filtering at higher elevations, and thus provides a possible explanation for the observed decline in diversity with elevation. PMID:23935842
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Kinjo, Ken; Uchibe, Eiji; Doya, Kenji
2013-01-01
Linearly solvable Markov Decision Process (LMDP) is a class of optimal control problem in which the Bellman's equation can be converted into a linear equation by an exponential transformation of the state value function (Todorov, 2009b). In an LMDP, the optimal value function and the corresponding control policy are obtained by solving an eigenvalue problem in a discrete state space or an eigenfunction problem in a continuous state using the knowledge of the system dynamics and the action, state, and terminal cost functions. In this study, we evaluate the effectiveness of the LMDP framework in real robot control, in which the dynamics of the body and the environment have to be learned from experience. We first perform a simulation study of a pole swing-up task to evaluate the effect of the accuracy of the learned dynamics model on the derived the action policy. The result shows that a crude linear approximation of the non-linear dynamics can still allow solution of the task, despite with a higher total cost. We then perform real robot experiments of a battery-catching task using our Spring Dog mobile robot platform. The state is given by the position and the size of a battery in its camera view and two neck joint angles. The action is the velocities of two wheels, while the neck joints were controlled by a visual servo controller. We test linear and bilinear dynamic models in tasks with quadratic and Guassian state cost functions. In the quadratic cost task, the LMDP controller derived from a learned linear dynamics model performed equivalently with the optimal linear quadratic regulator (LQR). In the non-quadratic task, the LMDP controller with a linear dynamics model showed the best performance. The results demonstrate the usefulness of the LMDP framework in real robot control even when simple linear models are used for dynamics learning.
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Linear and Nonlinear Thinking: A Multidimensional Model and Measure
ERIC Educational Resources Information Center
Groves, Kevin S.; Vance, Charles M.
2015-01-01
Building upon previously developed and more general dual-process models, this paper provides empirical support for a multidimensional thinking style construct comprised of linear thinking and multiple dimensions of nonlinear thinking. A self-report assessment instrument (Linear/Nonlinear Thinking Style Profile; LNTSP) is presented and…
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Women's Endorsement of Models of Sexual Response: Correlates and Predictors.
Nowosielski, Krzysztof; Wróbel, Beata; Kowalczyk, Robert
2016-02-01
Few studies have investigated endorsement of female sexual response models, and no single model has been accepted as a normative description of women's sexual response. The aim of the study was to establish how women from a population-based sample endorse current theoretical models of the female sexual response--the linear models and circular model (partial and composite Basson models)--as well as predictors of endorsement. Accordingly, 174 heterosexual women aged 18-55 years were included in a cross-sectional study: 74 women diagnosed with female sexual dysfunction (FSD) based on DSM-5 criteria and 100 non-dysfunctional women. The description of sexual response models was used to divide subjects into four subgroups: linear (Masters-Johnson and Kaplan models), circular (partial Basson model), mixed (linear and circular models in similar proportions, reflective of the composite Basson model), and a different model. Women were asked to choose which of the models best described their pattern of sexual response and how frequently they engaged in each model. Results showed that 28.7% of women endorsed the linear models, 19.5% the partial Basson model, 40.8% the composite Basson model, and 10.9% a different model. Women with FSD endorsed the partial Basson model and a different model more frequently than did non-dysfunctional controls. Individuals who were dissatisfied with a partner as a lover were more likely to endorse a different model. Based on the results, we concluded that the majority of women endorsed a mixed model combining the circular response with the possibility of an innate desire triggering a linear response. Further, relationship difficulties, not FSD, predicted model endorsement.
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A numerical study of linear and nonlinear kinematic models in fish swimming with the DSD/SST method
NASA Astrophysics Data System (ADS)
Tian, Fang-Bao
2015-03-01
Flow over two fish (modeled by two flexible plates) in tandem arrangement is investigated by solving the incompressible Navier-Stokes equations numerically with the DSD/SST method to understand the differences between the geometrically linear and nonlinear models. In the simulation, the motions of the plates are reconstructed from a vertically flowing soap film tunnel experiment with linear and nonlinear kinematic models. Based on the simulations, the drag, lift, power consumption, vorticity and pressure fields are discussed in detail. It is found that the linear and nonlinear models are able to reasonably predict the forces and power consumption of a single plate in flow. Moreover, if multiple plates are considered, these two models yield totally different results, which implies that the nonlinear model should be used. The results presented in this work provide a guideline for future studies in fish swimming.
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Gras, Laure-Lise; Mitton, David; Crevier-Denoix, Nathalie; Laporte, Sébastien
2012-01-01
Most recent finite element models that represent muscles are generic or subject-specific models that use complex, constitutive laws. Identification of the parameters of such complex, constitutive laws could be an important limit for subject-specific approaches. The aim of this study was to assess the possibility of modelling muscle behaviour in compression with a parametric model and a simple, constitutive law. A quasi-static compression test was performed on the muscles of dogs. A parametric finite element model was designed using a linear, elastic, constitutive law. A multi-variate analysis was performed to assess the effects of geometry on muscle response. An inverse method was used to define Young's modulus. The non-linear response of the muscles was obtained using a subject-specific geometry and a linear elastic law. Thus, a simple muscle model can be used to have a bio-faithful, biomechanical response.
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Informativeness of Wind Data in Linear Madden-Julian Oscillation Prediction
2016-08-15
Linear inverse models (LIMs) are used to explore predictability and information content of the Madden–Julian Oscillation (MJO). Hindcast skill for...mostly at the largest scales, adds 1–2 days of skill. Keywords: linear inverse modeling; Madden–Julian Oscillation; sub-seasonal prediction 1...tion that may reflect on the MJO’s incompletely under- stood dynamics. Cavanaugh et al. (2014, hereafter C14) explored the skill of linear inverse
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Narayanan, Neethu; Gupta, Suman; Gajbhiye, V T; Manjaiah, K M
2017-04-01
A carboxy methyl cellulose-nano organoclay (nano montmorillonite modified with 35-45 wt % dimethyl dialkyl (C 14 -C 18 ) amine (DMDA)) composite was prepared by solution intercalation method. The prepared composite was characterized by infrared spectroscopy (FTIR), X-Ray diffraction spectroscopy (XRD) and scanning electron microscopy (SEM). The composite was utilized for its pesticide sorption efficiency for atrazine, imidacloprid and thiamethoxam. The sorption data was fitted into Langmuir and Freundlich isotherms using linear and non linear methods. The linear regression method suggested best fitting of sorption data into Type II Langmuir and Freundlich isotherms. In order to avoid the bias resulting from linearization, seven different error parameters were also analyzed by non linear regression method. The non linear error analysis suggested that the sorption data fitted well into Langmuir model rather than in Freundlich model. The maximum sorption capacity, Q 0 (μg/g) was given by imidacloprid (2000) followed by thiamethoxam (1667) and atrazine (1429). The study suggests that the degree of determination of linear regression alone cannot be used for comparing the best fitting of Langmuir and Freundlich models and non-linear error analysis needs to be done to avoid inaccurate results. Copyright © 2017 Elsevier Ltd. All rights reserved.
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A Nonlinear Dynamic Model and Free Vibration Analysis of Deployable Mesh Reflectors
NASA Technical Reports Server (NTRS)
Shi, H.; Yang, B.; Thomson, M.; Fang, H.
2011-01-01
This paper presents a dynamic model of deployable mesh reflectors, in which geometric and material nonlinearities of such a space structure are fully described. Then, by linearization around an equilibrium configuration of the reflector structure, a linearized model is obtained. With this linearized model, the natural frequencies and mode shapes of a reflector can be computed. The nonlinear dynamic model of deployable mesh reflectors is verified by using commercial finite element software in numerical simulation. As shall be seen, the proposed nonlinear model is useful for shape (surface) control of deployable mesh reflectors under thermal loads.
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Model checking for linear temporal logic: An efficient implementation
NASA Technical Reports Server (NTRS)
Sherman, Rivi; Pnueli, Amir
1990-01-01
This report provides evidence to support the claim that model checking for linear temporal logic (LTL) is practically efficient. Two implementations of a linear temporal logic model checker is described. One is based on transforming the model checking problem into a satisfiability problem; the other checks an LTL formula for a finite model by computing the cross-product of the finite state transition graph of the program with a structure containing all possible models for the property. An experiment was done with a set of mutual exclusion algorithms and tested safety and liveness under fairness for these algorithms.
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Hou, Tingjun; Xu, Xiaojie
2002-12-01
In this study, the relationships between the brain-blood concentration ratio of 96 structurally diverse compounds with a large number of structurally derived descriptors were investigated. The linear models were based on molecular descriptors that can be calculated for any compound simply from a knowledge of its molecular structure. The linear correlation coefficients of the models were optimized by genetic algorithms (GAs), and the descriptors used in the linear models were automatically selected from 27 structurally derived descriptors. The GA optimizations resulted in a group of linear models with three or four molecular descriptors with good statistical significance. The change of descriptor use as the evolution proceeds demonstrates that the octane/water partition coefficient and the partial negative solvent-accessible surface area multiplied by the negative charge are crucial to brain-blood barrier permeability. Moreover, we found that the predictions using multiple QSPR models from GA optimization gave quite good results in spite of the diversity of structures, which was better than the predictions using the best single model. The predictions for the two external sets with 37 diverse compounds using multiple QSPR models indicate that the best linear models with four descriptors are sufficiently effective for predictive use. Considering the ease of computation of the descriptors, the linear models may be used as general utilities to screen the blood-brain barrier partitioning of drugs in a high-throughput fashion.
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Estimating the remaining useful life of bearings using a neuro-local linear estimator-based method.
Ahmad, Wasim; Ali Khan, Sheraz; Kim, Jong-Myon
2017-05-01
Estimating the remaining useful life (RUL) of a bearing is required for maintenance scheduling. While the degradation behavior of a bearing changes during its lifetime, it is usually assumed to follow a single model. In this letter, bearing degradation is modeled by a monotonically increasing function that is globally non-linear and locally linearized. The model is generated using historical data that is smoothed with a local linear estimator. A neural network learns this model and then predicts future levels of vibration acceleration to estimate the RUL of a bearing. The proposed method yields reasonably accurate estimates of the RUL of a bearing at different points during its operational life.
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Data mining for the analysis of hippocampal zones in Alzheimer's disease
NASA Astrophysics Data System (ADS)
Ovando Vázquez, Cesaré M.
2012-02-01
In this work, a methodology to classify people with Alzheimer's Disease (AD), Healthy Controls (HC) and people with Mild Cognitive Impairment (MCI) is presented. This methodology consists of an ensemble of Support Vector Machines (SVM) with the hippocampal boxes (HB) as input data, these hippocampal zones are taken from Magnetic Resonance (MRI) and Positron Emission Tomography (PET) images. Two ways of constructing this ensemble are presented, the first consists of linear SVM models and the second of non-linear SVM models. Results demonstrate that the linear models classify HBs more accurately than the non-linear models between HC and MCI and that there are no differences between HC and AD.
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The Use of Shrinkage Techniques in the Estimation of Attrition Rates for Large Scale Manpower Models
1988-07-27
auto regressive model combined with a linear program that solves for the coefficients using MAD. But this success has diminished with time (Rowe...8217Harrison-Stevens Forcasting and the Multiprocess Dy- namic Linear Model ", The American Statistician, v.40, pp. 12 9 - 1 3 5 . 1986. 8. Box, G. E. P. and...1950. 40. McCullagh, P. and Nelder, J., Generalized Linear Models , Chapman and Hall. 1983. 41. McKenzie, E. General Exponential Smoothing and the
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Free-piston engine linear generator for hybrid vehicles modeling study
NASA Astrophysics Data System (ADS)
Callahan, T. J.; Ingram, S. K.
1995-05-01
Development of a free piston engine linear generator was investigated for use as an auxiliary power unit for a hybrid electric vehicle. The main focus of the program was to develop an efficient linear generator concept to convert the piston motion directly into electrical power. Computer modeling techniques were used to evaluate five different designs for linear generators. These designs included permanent magnet generators, reluctance generators, linear DC generators, and two and three-coil induction generators. The efficiency of the linear generator was highly dependent on the design concept. The two-coil induction generator was determined to be the best design, with an efficiency of approximately 90 percent.
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Linear network representation of multistate models of transport.
Sandblom, J; Ring, A; Eisenman, G
1982-01-01
By introducing external driving forces in rate-theory models of transport we show how the Eyring rate equations can be transformed into Ohm's law with potentials that obey Kirchhoff's second law. From such a formalism the state diagram of a multioccupancy multicomponent system can be directly converted into linear network with resistors connecting nodal (branch) points and with capacitances connecting each nodal point with a reference point. The external forces appear as emf or current generators in the network. This theory allows the algebraic methods of linear network theory to be used in solving the flux equations for multistate models and is particularly useful for making proper simplifying approximation in models of complex membrane structure. Some general properties of linear network representation are also deduced. It is shown, for instance, that Maxwell's reciprocity relationships of linear networks lead directly to Onsager's relationships in the near equilibrium region. Finally, as an example of the procedure, the equivalent circuit method is used to solve the equations for a few transport models. PMID:7093425
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A linear model fails to predict orientation selectivity of cells in the cat visual cortex.
Volgushev, M; Vidyasagar, T R; Pei, X
1996-01-01
1. Postsynaptic potentials (PSPs) evoked by visual stimulation in simple cells in the cat visual cortex were recorded using in vivo whole-cell technique. Responses to small spots of light presented at different positions over the receptive field and responses to elongated bars of different orientations centred on the receptive field were recorded. 2. To test whether a linear model can account for orientation selectivity of cortical neurones, responses to elongated bars were compared with responses predicted by a linear model from the receptive field map obtained from flashing spots. 3. The linear model faithfully predicted the preferred orientation, but not the degree of orientation selectivity or the sharpness of orientation tuning. The ratio of optimal to non-optimal responses was always underestimated by the model. 4. Thus non-linear mechanisms, which can include suppression of non-optimal responses and/or amplification of optimal responses, are involved in the generation of orientation selectivity in the primary visual cortex. PMID:8930828
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Doubly robust estimation of generalized partial linear models for longitudinal data with dropouts.
Lin, Huiming; Fu, Bo; Qin, Guoyou; Zhu, Zhongyi
2017-12-01
We develop a doubly robust estimation of generalized partial linear models for longitudinal data with dropouts. Our method extends the highly efficient aggregate unbiased estimating function approach proposed in Qu et al. (2010) to a doubly robust one in the sense that under missing at random (MAR), our estimator is consistent when either the linear conditional mean condition is satisfied or a model for the dropout process is correctly specified. We begin with a generalized linear model for the marginal mean, and then move forward to a generalized partial linear model, allowing for nonparametric covariate effect by using the regression spline smoothing approximation. We establish the asymptotic theory for the proposed method and use simulation studies to compare its finite sample performance with that of Qu's method, the complete-case generalized estimating equation (GEE) and the inverse-probability weighted GEE. The proposed method is finally illustrated using data from a longitudinal cohort study. © 2017, The International Biometric Society.
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Linear approximations of nonlinear systems
NASA Technical Reports Server (NTRS)
Hunt, L. R.; Su, R.
1983-01-01
The development of a method for designing an automatic flight controller for short and vertical take off aircraft is discussed. This technique involves transformations of nonlinear systems to controllable linear systems and takes into account the nonlinearities of the aircraft. In general, the transformations cannot always be given in closed form. Using partial differential equations, an approximate linear system called the modified tangent model was introduced. A linear transformation of this tangent model to Brunovsky canonical form can be constructed, and from this the linear part (about a state space point x sub 0) of an exact transformation for the nonlinear system can be found. It is shown that a canonical expansion in Lie brackets about the point x sub 0 yields the same modified tangent model.
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Modification of 2-D Time-Domain Shallow Water Wave Equation using Asymptotic Expansion Method
NASA Astrophysics Data System (ADS)
Khairuman, Teuku; Nasruddin, MN; Tulus; Ramli, Marwan
2018-01-01
Generally, research on the tsunami wave propagation model can be conducted by using a linear model of shallow water theory, where a non-linear side on high order is ignored. In line with research on the investigation of the tsunami waves, the Boussinesq equation model underwent a change aimed to obtain an improved quality of the dispersion relation and non-linearity by increasing the order to be higher. To solve non-linear sides at high order is used a asymptotic expansion method. This method can be used to solve non linear partial differential equations. In the present work, we found that this method needs much computational time and memory with the increase of the number of elements.
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TENSOR DECOMPOSITIONS AND SPARSE LOG-LINEAR MODELS
Johndrow, James E.; Bhattacharya, Anirban; Dunson, David B.
2017-01-01
Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. We derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions. PMID:29332971
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Sexual Response Models: Toward a More Flexible Pattern of Women's Sexuality.
Ferenidou, Fotini; Kirana, Paraskevi-Sofia; Fokas, Konstantinos; Hatzichristou, Dimitrios; Athanasiadis, Loukas
2016-09-01
Recent research suggests that none of the current theoretical models can sufficiently describe women's sexual response, because several factors and situations can influence this. To explore individual variations of a sexual model that describes women's sexual responses and to assess the association of endorsement of that model with sexual dysfunctions and reasons to engage in sexual activity. A sample of 157 randomly selected hospital employees completed self-administered questionnaires. Two models were developed: one merged the Master and Johnson model with the Kaplan model (linear) and the other was the Basson model (circular). Sexual function was evaluated by the Female Sexual Function Index and the Brief Sexual Symptom Checklist for Women. The Reasons for Having Sex Questionnaire was administered to investigate the reasons for which women have sex. Women reported that their current sexual experiences were at times consistent with the linear and circular models (66.9%), only the linear model (27%), only the circular model (5.4%), and neither model (0.7%). When the groups were reconfigured to the group that endorsed more than 5 of 10 sexual experiences, 64.3% of women endorsed the linear model, 20.4% chose the linear and circular models, 14.6% chose the circular model, and 0.7% selected neither. The Female Sexual Function Index, demographic factors, having sex for insecurity reasons, and sexual satisfaction correlated with the endorsement of a sexual response model. When these factors were entered in a stepwise logistic regression analysis, only the Female Sexual Function Index and having sex for insecurity reasons maintained a significant association with the sexual response model. The present study emphasizes the heterogeneity of female sexuality, with most of the sample reporting alternating between the linear and circular models. Sexual dysfunctions and having sex for insecurity reasons were associated with the Basson model. Copyright © 2016 International Society for Sexual Medicine. Published by Elsevier Inc. All rights reserved.
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Frequency-domain full-waveform inversion with non-linear descent directions
NASA Astrophysics Data System (ADS)
Geng, Yu; Pan, Wenyong; Innanen, Kristopher A.
2018-05-01
Full-waveform inversion (FWI) is a highly non-linear inverse problem, normally solved iteratively, with each iteration involving an update constructed through linear operations on the residuals. Incorporating a flexible degree of non-linearity within each update may have important consequences for convergence rates, determination of low model wavenumbers and discrimination of parameters. We examine one approach for doing so, wherein higher order scattering terms are included within the sensitivity kernel during the construction of the descent direction, adjusting it away from that of the standard Gauss-Newton approach. These scattering terms are naturally admitted when we construct the sensitivity kernel by varying not the current but the to-be-updated model at each iteration. Linear and/or non-linear inverse scattering methodologies allow these additional sensitivity contributions to be computed from the current data residuals within any given update. We show that in the presence of pre-critical reflection data, the error in a second-order non-linear update to a background of s0 is, in our scheme, proportional to at most (Δs/s0)3 in the actual parameter jump Δs causing the reflection. In contrast, the error in a standard Gauss-Newton FWI update is proportional to (Δs/s0)2. For numerical implementation of more complex cases, we introduce a non-linear frequency-domain scheme, with an inner and an outer loop. A perturbation is determined from the data residuals within the inner loop, and a descent direction based on the resulting non-linear sensitivity kernel is computed in the outer loop. We examine the response of this non-linear FWI using acoustic single-parameter synthetics derived from the Marmousi model. The inverted results vary depending on data frequency ranges and initial models, but we conclude that the non-linear FWI has the capability to generate high-resolution model estimates in both shallow and deep regions, and to converge rapidly, relative to a benchmark FWI approach involving the standard gradient.
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ERIC Educational Resources Information Center
Kobrin, Jennifer L.; Sinharay, Sandip; Haberman, Shelby J.; Chajewski, Michael
2011-01-01
This study examined the adequacy of a multiple linear regression model for predicting first-year college grade point average (FYGPA) using SAT[R] scores and high school grade point average (HSGPA). A variety of techniques, both graphical and statistical, were used to examine if it is possible to improve on the linear regression model. The results…
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NASA Technical Reports Server (NTRS)
Gettman, Chang-Ching L.; Adams, Neil; Bedrossian, Nazareth; Valavani, Lena
1993-01-01
This paper demonstrates an approach to nonlinear control system design that uses linearization by state feedback to allow faster maneuvering of payloads by the Shuttle Remote Manipulator System (SRMS). A nonlinear feedback law is defined to cancel the nonlinear plant dynamics so that a linear controller can be designed for the SRMS. First a nonlinear design model was generated via SIMULINK. This design model included nonlinear arm dynamics derived from the Lagrangian approach, linearized servo model, and linearized gearbox model. The current SRMS position hold controller was implemented on this system. Next, a trajectory was defined using a rigid body kinematics SRMS tool, KRMS. The maneuver was simulated. Finally, higher bandwidth controllers were developed. Results of the new controllers were compared with the existing SRMS automatic control modes for the Space Station Freedom Mission Build 4 Payload extended on the SRMS.
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Model Capabilities | Regional Energy Deployment System Model | Energy
representation of those effects throughout the scenario. Because those effects are highly non-linear and other models, limited foresight, price penalties for rapid growth, and other non-linear effects
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A method for fitting regression splines with varying polynomial order in the linear mixed model.
Edwards, Lloyd J; Stewart, Paul W; MacDougall, James E; Helms, Ronald W
2006-02-15
The linear mixed model has become a widely used tool for longitudinal analysis of continuous variables. The use of regression splines in these models offers the analyst additional flexibility in the formulation of descriptive analyses, exploratory analyses and hypothesis-driven confirmatory analyses. We propose a method for fitting piecewise polynomial regression splines with varying polynomial order in the fixed effects and/or random effects of the linear mixed model. The polynomial segments are explicitly constrained by side conditions for continuity and some smoothness at the points where they join. By using a reparameterization of this explicitly constrained linear mixed model, an implicitly constrained linear mixed model is constructed that simplifies implementation of fixed-knot regression splines. The proposed approach is relatively simple, handles splines in one variable or multiple variables, and can be easily programmed using existing commercial software such as SAS or S-plus. The method is illustrated using two examples: an analysis of longitudinal viral load data from a study of subjects with acute HIV-1 infection and an analysis of 24-hour ambulatory blood pressure profiles.
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2013-01-01
Background This study aims to improve accuracy of Bioelectrical Impedance Analysis (BIA) prediction equations for estimating fat free mass (FFM) of the elderly by using non-linear Back Propagation Artificial Neural Network (BP-ANN) model and to compare the predictive accuracy with the linear regression model by using energy dual X-ray absorptiometry (DXA) as reference method. Methods A total of 88 Taiwanese elderly adults were recruited in this study as subjects. Linear regression equations and BP-ANN prediction equation were developed using impedances and other anthropometrics for predicting the reference FFM measured by DXA (FFMDXA) in 36 male and 26 female Taiwanese elderly adults. The FFM estimated by BIA prediction equations using traditional linear regression model (FFMLR) and BP-ANN model (FFMANN) were compared to the FFMDXA. The measuring results of an additional 26 elderly adults were used to validate than accuracy of the predictive models. Results The results showed the significant predictors were impedance, gender, age, height and weight in developed FFMLR linear model (LR) for predicting FFM (coefficient of determination, r2 = 0.940; standard error of estimate (SEE) = 2.729 kg; root mean square error (RMSE) = 2.571kg, P < 0.001). The above predictors were set as the variables of the input layer by using five neurons in the BP-ANN model (r2 = 0.987 with a SD = 1.192 kg and relatively lower RMSE = 1.183 kg), which had greater (improved) accuracy for estimating FFM when compared with linear model. The results showed a better agreement existed between FFMANN and FFMDXA than that between FFMLR and FFMDXA. Conclusion When compared the performance of developed prediction equations for estimating reference FFMDXA, the linear model has lower r2 with a larger SD in predictive results than that of BP-ANN model, which indicated ANN model is more suitable for estimating FFM. PMID:23388042
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Non-Gaussian lineshapes and dynamics of time-resolved linear and nonlinear (correlation) spectra.
Dinpajooh, Mohammadhasan; Matyushov, Dmitry V
2014-07-17
Signatures of nonlinear and non-Gaussian dynamics in time-resolved linear and nonlinear (correlation) 2D spectra are analyzed in a model considering a linear plus quadratic dependence of the spectroscopic transition frequency on a Gaussian nuclear coordinate of the thermal bath (quadratic coupling). This new model is contrasted to the commonly assumed linear dependence of the transition frequency on the medium nuclear coordinates (linear coupling). The linear coupling model predicts equality between the Stokes shift and equilibrium correlation functions of the transition frequency and time-independent spectral width. Both predictions are often violated, and we are asking here the question of whether a nonlinear solvent response and/or non-Gaussian dynamics are required to explain these observations. We find that correlation functions of spectroscopic observables calculated in the quadratic coupling model depend on the chromophore's electronic state and the spectral width gains time dependence, all in violation of the predictions of the linear coupling models. Lineshape functions of 2D spectra are derived assuming Ornstein-Uhlenbeck dynamics of the bath nuclear modes. The model predicts asymmetry of 2D correlation plots and bending of the center line. The latter is often used to extract two-point correlation functions from 2D spectra. The dynamics of the transition frequency are non-Gaussian. However, the effect of non-Gaussian dynamics is limited to the third-order (skewness) time correlation function, without affecting the time correlation functions of higher order. The theory is tested against molecular dynamics simulations of a model polar-polarizable chromophore dissolved in a force field water.
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Design of Linear Control System for Wind Turbine Blade Fatigue Testing
NASA Astrophysics Data System (ADS)
Toft, Anders; Roe-Poulsen, Bjarke; Christiansen, Rasmus; Knudsen, Torben
2016-09-01
This paper proposes a linear method for wind turbine blade fatigue testing at Siemens Wind Power. The setup consists of a blade, an actuator (motor and load mass) that acts on the blade with a sinusoidal moment, and a distribution of strain gauges to measure the blade flexure. Based on the frequency of the sinusoidal input, the blade will start oscillating with a given gain, hence the objective of the fatigue test is to make the blade oscillate with a controlled amplitude. The system currently in use is based on frequency control, which involves some non-linearities that make the system difficult to control. To make a linear controller, a different approach has been chosen, namely making a controller which is not regulating on the input frequency, but on the input amplitude. A non-linear mechanical model for the blade and the motor has been constructed. This model has been simplified based on the desired output, namely the amplitude of the blade. Furthermore, the model has been linearised to make it suitable for linear analysis and control design methods. The controller is designed based on a simplified and linearised model, and its gain parameter determined using pole placement. The model variants have been simulated in the MATLAB toolbox Simulink, which shows that the controller design based on the simple model performs adequately with the non-linear model. Moreover, the developed controller solves the robustness issue found in the existent solution and also reduces the needed energy for actuation as it always operates at the blade eigenfrequency.
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Abdelnour, Farras; Voss, Henning U.; Raj, Ashish
2014-01-01
The relationship between anatomic connectivity of large-scale brain networks and their functional connectivity is of immense importance and an area of active research. Previous attempts have required complex simulations which model the dynamics of each cortical region, and explore the coupling between regions as derived by anatomic connections. While much insight is gained from these non-linear simulations, they can be computationally taxing tools for predicting functional from anatomic connectivities. Little attention has been paid to linear models. Here we show that a properly designed linear model appears to be superior to previous non-linear approaches in capturing the brain’s long-range second order correlation structure that governs the relationship between anatomic and functional connectivities. We derive a linear network of brain dynamics based on graph diffusion, whereby the diffusing quantity undergoes a random walk on a graph. We test our model using subjects who underwent diffusion MRI and resting state fMRI. The network diffusion model applied to the structural networks largely predicts the correlation structures derived from their fMRI data, to a greater extent than other approaches. The utility of the proposed approach is that it can routinely be used to infer functional correlation from anatomic connectivity. And since it is linear, anatomic connectivity can also be inferred from functional data. The success of our model confirms the linearity of ensemble average signals in the brain, and implies that their long-range correlation structure may percolate within the brain via purely mechanistic processes enacted on its structural connectivity pathways. PMID:24384152
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Non-linear models for the detection of impaired cerebral blood flow autoregulation
Miranda, Rodrigo; Katsogridakis, Emmanuel
2018-01-01
The ability to discriminate between normal and impaired dynamic cerebral autoregulation (CA), based on measurements of spontaneous fluctuations in arterial blood pressure (BP) and cerebral blood flow (CBF), has considerable clinical relevance. We studied 45 normal subjects at rest and under hypercapnia induced by breathing a mixture of carbon dioxide and air. Non-linear models with BP as input and CBF velocity (CBFV) as output, were implemented with support vector machines (SVM) using separate recordings for learning and validation. Dynamic SVM implementations used either moving average or autoregressive structures. The efficiency of dynamic CA was estimated from the model’s derived CBFV response to a step change in BP as an autoregulation index for both linear and non-linear models. Non-linear models with recurrences (autoregressive) showed the best results, with CA indexes of 5.9 ± 1.5 in normocapnia, and 2.5 ± 1.2 for hypercapnia with an area under the receiver-operator curve of 0.955. The high performance achieved by non-linear SVM models to detect deterioration of dynamic CA should encourage further assessment of its applicability to clinical conditions where CA might be impaired. PMID:29381724
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Casellas, J; Bach, R
2012-06-01
Lambing interval is a relevant reproductive indicator for sheep populations under continuous mating systems, although there is a shortage of selection programs accounting for this trait in the sheep industry. Both the historical assumption of small genetic background and its unorthodox distribution pattern have limited its implementation as a breeding objective. In this manuscript, statistical performances of 3 alternative parametrizations [i.e., symmetric Gaussian mixed linear (GML) model, skew-Gaussian mixed linear (SGML) model, and piecewise Weibull proportional hazard (PWPH) model] have been compared to elucidate the preferred methodology to handle lambing interval data. More specifically, flock-by-flock analyses were performed on 31,986 lambing interval records (257.3 ± 0.2 d) from 6 purebred Ripollesa flocks. Model performances were compared in terms of deviance information criterion (DIC) and Bayes factor (BF). For all flocks, PWPH models were clearly preferred; they generated a reduction of 1,900 or more DIC units and provided BF estimates larger than 100 (i.e., PWPH models against linear models). These differences were reduced when comparing PWPH models with different number of change points for the baseline hazard function. In 4 flocks, only 2 change points were required to minimize the DIC, whereas 4 and 6 change points were needed for the 2 remaining flocks. These differences demonstrated a remarkable degree of heterogeneity across sheep flocks that must be properly accounted for in genetic evaluation models to avoid statistical biases and suboptimal genetic trends. Within this context, all 6 Ripollesa flocks revealed substantial genetic background for lambing interval with heritabilities ranging between 0.13 and 0.19. This study provides the first evidence of the suitability of PWPH models for lambing interval analysis, clearly discarding previous parametrizations focused on mixed linear models.
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Nonautonomous linear system of the terrestrial carbon cycle
NASA Astrophysics Data System (ADS)
Luo, Y.
2012-12-01
Carbon cycle has been studied by uses of observation through various networks, field and laboratory experiments, and simulation models. Much less has been done on theoretical thinking and analysis to understand fundament properties of carbon cycle and then guide observatory, experimental, and modeling research. This presentation is to explore what would be the theoretical properties of terrestrial carbon cycle and how those properties can be used to make observatory, experimental, and modeling research more effective. Thousands of published data sets from litter decomposition and soil incubation studies almost all indicate that decay processes of litter and soil organic carbon can be well described by first order differential equations with one or more pools. Carbon pool dynamics in plants and soil after disturbances (e.g., wildfire, clear-cut of forests, and plows of soil for cropping) and during natural recovery or ecosystem restoration also exhibit characteristics of first-order linear systems. Thus, numerous lines of empirical evidence indicate that the terrestrial carbon cycle can be adequately described as a nonautonomous linear system. The linearity reflects the nature of the carbon cycle that carbon, once fixed by photosynthesis, is linearly transferred among pools within an ecosystem. The linear carbon transfer, however, is modified by nonlinear functions of external forcing variables. In addition, photosynthetic carbon influx is also nonlinearly influenced by external variables. This nonautonomous linear system can be mathematically expressed by a first-order linear ordinary matrix equation. We have recently used this theoretical property of terrestrial carbon cycle to develop a semi-analytic solution of spinup. The new methods have been applied to five global land models, including NCAR's CLM and CABLE models and can computationally accelerate spinup by two orders of magnitude. We also use this theoretical property to develop an analytic framework to decompose modeled carbon cycle into a few traceable components so as to facilitate model intercompsirosn, benchmark analysis, and data assimilation of global land models.
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Modelling and Predicting Backstroke Start Performance Using Non-Linear and Linear Models
de Jesus, Karla; Ayala, Helon V. H.; de Jesus, Kelly; Coelho, Leandro dos S.; Medeiros, Alexandre I.A.; Abraldes, José A.; Vaz, Mário A.P.; Fernandes, Ricardo J.; Vilas-Boas, João Paulo
2018-01-01
Abstract Our aim was to compare non-linear and linear mathematical model responses for backstroke start performance prediction. Ten swimmers randomly completed eight 15 m backstroke starts with feet over the wedge, four with hands on the highest horizontal and four on the vertical handgrip. Swimmers were videotaped using a dual media camera set-up, with the starts being performed over an instrumented block with four force plates. Artificial neural networks were applied to predict 5 m start time using kinematic and kinetic variables and to determine the accuracy of the mean absolute percentage error. Artificial neural networks predicted start time more robustly than the linear model with respect to changing training to the validation dataset for the vertical handgrip (3.95 ± 1.67 vs. 5.92 ± 3.27%). Artificial neural networks obtained a smaller mean absolute percentage error than the linear model in the horizontal (0.43 ± 0.19 vs. 0.98 ± 0.19%) and vertical handgrip (0.45 ± 0.19 vs. 1.38 ± 0.30%) using all input data. The best artificial neural network validation revealed a smaller mean absolute error than the linear model for the horizontal (0.007 vs. 0.04 s) and vertical handgrip (0.01 vs. 0.03 s). Artificial neural networks should be used for backstroke 5 m start time prediction due to the quite small differences among the elite level performances. PMID:29599857
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Parameterized Linear Longitudinal Airship Model
NASA Technical Reports Server (NTRS)
Kulczycki, Eric; Elfes, Alberto; Bayard, David; Quadrelli, Marco; Johnson, Joseph
2010-01-01
A parameterized linear mathematical model of the longitudinal dynamics of an airship is undergoing development. This model is intended to be used in designing control systems for future airships that would operate in the atmospheres of Earth and remote planets. Heretofore, the development of linearized models of the longitudinal dynamics of airships has been costly in that it has been necessary to perform extensive flight testing and to use system-identification techniques to construct models that fit the flight-test data. The present model is a generic one that can be relatively easily specialized to approximate the dynamics of specific airships at specific operating points, without need for further system identification, and with significantly less flight testing. The approach taken in the present development is to merge the linearized dynamical equations of an airship with techniques for estimation of aircraft stability derivatives, and to thereby make it possible to construct a linearized dynamical model of the longitudinal dynamics of a specific airship from geometric and aerodynamic data pertaining to that airship. (It is also planned to develop a model of the lateral dynamics by use of the same methods.) All of the aerodynamic data needed to construct the model of a specific airship can be obtained from wind-tunnel testing and computational fluid dynamics
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Analysis of Nonlinear Dynamics in Linear Compressors Driven by Linear Motors
NASA Astrophysics Data System (ADS)
Chen, Liangyuan
2018-03-01
The analysis of dynamic characteristics of the mechatronics system is of great significance for the linear motor design and control. Steady-state nonlinear response characteristics of a linear compressor are investigated theoretically based on the linearized and nonlinear models. First, the influence factors considering the nonlinear gas force load were analyzed. Then, a simple linearized model was set up to analyze the influence on the stroke and resonance frequency. Finally, the nonlinear model was set up to analyze the effects of piston mass, spring stiffness, driving force as an example of design parameter variation. The simulating results show that the stroke can be obtained by adjusting the excitation amplitude, frequency and other adjustments, the equilibrium position can be adjusted by adjusting the DC input, and to make the more efficient operation, the operating frequency must always equal to the resonance frequency.
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Classical Testing in Functional Linear Models.
Kong, Dehan; Staicu, Ana-Maria; Maity, Arnab
2016-01-01
We extend four tests common in classical regression - Wald, score, likelihood ratio and F tests - to functional linear regression, for testing the null hypothesis, that there is no association between a scalar response and a functional covariate. Using functional principal component analysis, we re-express the functional linear model as a standard linear model, where the effect of the functional covariate can be approximated by a finite linear combination of the functional principal component scores. In this setting, we consider application of the four traditional tests. The proposed testing procedures are investigated theoretically for densely observed functional covariates when the number of principal components diverges. Using the theoretical distribution of the tests under the alternative hypothesis, we develop a procedure for sample size calculation in the context of functional linear regression. The four tests are further compared numerically for both densely and sparsely observed noisy functional data in simulation experiments and using two real data applications.
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Classical Testing in Functional Linear Models
Kong, Dehan; Staicu, Ana-Maria; Maity, Arnab
2016-01-01
We extend four tests common in classical regression - Wald, score, likelihood ratio and F tests - to functional linear regression, for testing the null hypothesis, that there is no association between a scalar response and a functional covariate. Using functional principal component analysis, we re-express the functional linear model as a standard linear model, where the effect of the functional covariate can be approximated by a finite linear combination of the functional principal component scores. In this setting, we consider application of the four traditional tests. The proposed testing procedures are investigated theoretically for densely observed functional covariates when the number of principal components diverges. Using the theoretical distribution of the tests under the alternative hypothesis, we develop a procedure for sample size calculation in the context of functional linear regression. The four tests are further compared numerically for both densely and sparsely observed noisy functional data in simulation experiments and using two real data applications. PMID:28955155
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Ion radial diffusion in an electrostatic impulse model for stormtime ring current formation
NASA Technical Reports Server (NTRS)
Chen, Margaret W.; Schulz, Michael; Lyons, Larry R.; Gorney, David J.
1992-01-01
Two refinements to the quasi-linear theory of ion radial diffusion are proposed and examined analytically with simulations of particle trajectories. The resonance-broadening correction by Dungey (1965) is applied to the quasi-linear diffusion theory by Faelthammar (1965) for an individual model storm. Quasi-linear theory is then applied to the mean diffusion coefficients resulting from simulations of particle trajectories in 20 model storms. The correction for drift-resonance broadening results in quasi-linear diffusion coefficients with discrepancies from the corresponding simulated values that are reduced by a factor of about 3. Further reductions in the discrepancies are noted following the averaging of the quasi-linear diffusion coefficients, the simulated coefficients, and the resonance-broadened coefficients for the 20 storms. Quasi-linear theory provides good descriptions of particle transport for a single storm but performs even better in conjunction with the present ensemble-averaging.
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Bounded Linear Stability Analysis - A Time Delay Margin Estimation Approach for Adaptive Control
NASA Technical Reports Server (NTRS)
Nguyen, Nhan T.; Ishihara, Abraham K.; Krishnakumar, Kalmanje Srinlvas; Bakhtiari-Nejad, Maryam
2009-01-01
This paper presents a method for estimating time delay margin for model-reference adaptive control of systems with almost linear structured uncertainty. The bounded linear stability analysis method seeks to represent the conventional model-reference adaptive law by a locally bounded linear approximation within a small time window using the comparison lemma. The locally bounded linear approximation of the combined adaptive system is cast in a form of an input-time-delay differential equation over a small time window. The time delay margin of this system represents a local stability measure and is computed analytically by a matrix measure method, which provides a simple analytical technique for estimating an upper bound of time delay margin. Based on simulation results for a scalar model-reference adaptive control system, both the bounded linear stability method and the matrix measure method are seen to provide a reasonably accurate and yet not too conservative time delay margin estimation.
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Assessing the performance of eight real-time updating models and procedures for the Brosna River
NASA Astrophysics Data System (ADS)
Goswami, M.; O'Connor, K. M.; Bhattarai, K. P.; Shamseldin, A. Y.
2005-10-01
The flow forecasting performance of eight updating models, incorporated in the Galway River Flow Modelling and Forecasting System (GFMFS), was assessed using daily data (rainfall, evaporation and discharge) of the Irish Brosna catchment (1207 km2), considering their one to six days lead-time discharge forecasts. The Perfect Forecast of Input over the Forecast Lead-time scenario was adopted, where required, in place of actual rainfall forecasts. The eight updating models were: (i) the standard linear Auto-Regressive (AR) model, applied to the forecast errors (residuals) of a simulation (non-updating) rainfall-runoff model; (ii) the Neural Network Updating (NNU) model, also using such residuals as input; (iii) the Linear Transfer Function (LTF) model, applied to the simulated and the recently observed discharges; (iv) the Non-linear Auto-Regressive eXogenous-Input Model (NARXM), also a neural network-type structure, but having wide options of using recently observed values of one or more of the three data series, together with non-updated simulated outflows, as inputs; (v) the Parametric Simple Linear Model (PSLM), of LTF-type, using recent rainfall and observed discharge data; (vi) the Parametric Linear perturbation Model (PLPM), also of LTF-type, using recent rainfall and observed discharge data, (vii) n-AR, an AR model applied to the observed discharge series only, as a naïve updating model; and (viii) n-NARXM, a naive form of the NARXM, using only the observed discharge data, excluding exogenous inputs. The five GFMFS simulation (non-updating) models used were the non-parametric and parametric forms of the Simple Linear Model and of the Linear Perturbation Model, the Linearly-Varying Gain Factor Model, the Artificial Neural Network Model, and the conceptual Soil Moisture Accounting and Routing (SMAR) model. As the SMAR model performance was found to be the best among these models, in terms of the Nash-Sutcliffe R2 value, both in calibration and in verification, the simulated outflows of this model only were selected for the subsequent exercise of producing updated discharge forecasts. All the eight forms of updating models for producing lead-time discharge forecasts were found to be capable of producing relatively good lead-1 (1-day ahead) forecasts, with R2 values almost 90% or above. However, for higher lead time forecasts, only three updating models, viz., NARXM, LTF, and NNU, were found to be suitable, with lead-6 values of R2 about 90% or higher. Graphical comparisons were made of the lead-time forecasts for the two largest floods, one in the calibration period and the other in the verification period.
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Available pressure amplitude of linear compressor based on phasor triangle model
NASA Astrophysics Data System (ADS)
Duan, C. X.; Jiang, X.; Zhi, X. Q.; You, X. K.; Qiu, L. M.
2017-12-01
The linear compressor for cryocoolers possess the advantages of long-life operation, high efficiency, low vibration and compact structure. It is significant to study the match mechanisms between the compressor and the cold finger, which determines the working efficiency of the cryocooler. However, the output characteristics of linear compressor are complicated since it is affected by many interacting parameters. The existing matching methods are simplified and mainly focus on the compressor efficiency and output acoustic power, while neglecting the important output parameter of pressure amplitude. In this study, a phasor triangle model basing on analyzing the forces of the piston is proposed. It can be used to predict not only the output acoustic power, the efficiency, but also the pressure amplitude of the linear compressor. Calculated results agree well with the measurement results of the experiment. By this phasor triangle model, the theoretical maximum output pressure amplitude of the linear compressor can be calculated simply based on a known charging pressure and operating frequency. Compared with the mechanical and electrical model of the linear compressor, the new model can provide an intuitionistic understanding on the match mechanism with faster computational process. The model can also explain the experimental phenomenon of the proportional relationship between the output pressure amplitude and the piston displacement in experiments. By further model analysis, such phenomenon is confirmed as an expression of the unmatched design of the compressor. The phasor triangle model may provide an alternative method for the compressor design and matching with the cold finger.
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Rigatos, Gerasimos G
2016-06-01
It is proven that the model of the p53-mdm2 protein synthesis loop is a differentially flat one and using a diffeomorphism (change of state variables) that is proposed by differential flatness theory it is shown that the protein synthesis model can be transformed into the canonical (Brunovsky) form. This enables the design of a feedback control law that maintains the concentration of the p53 protein at the desirable levels. To estimate the non-measurable elements of the state vector describing the p53-mdm2 system dynamics, the derivative-free non-linear Kalman filter is used. Moreover, to compensate for modelling uncertainties and external disturbances that affect the p53-mdm2 system, the derivative-free non-linear Kalman filter is re-designed as a disturbance observer. The derivative-free non-linear Kalman filter consists of the Kalman filter recursion applied on the linearised equivalent of the protein synthesis model together with an inverse transformation based on differential flatness theory that enables to retrieve estimates for the state variables of the initial non-linear model. The proposed non-linear feedback control and perturbations compensation method for the p53-mdm2 system can result in more efficient chemotherapy schemes where the infusion of medication will be better administered.
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NASA Astrophysics Data System (ADS)
Benhalouche, Fatima Zohra; Karoui, Moussa Sofiane; Deville, Yannick; Ouamri, Abdelaziz
2015-10-01
In this paper, a new Spectral-Unmixing-based approach, using Nonnegative Matrix Factorization (NMF), is proposed to locally multi-sharpen hyperspectral data by integrating a Digital Surface Model (DSM) obtained from LIDAR data. In this new approach, the nature of the local mixing model is detected by using the local variance of the object elevations. The hyper/multispectral images are explored using small zones. In each zone, the variance of the object elevations is calculated from the DSM data in this zone. This variance is compared to a threshold value and the adequate linear/linearquadratic spectral unmixing technique is used in the considered zone to independently unmix hyperspectral and multispectral data, using an adequate linear/linear-quadratic NMF-based approach. The obtained spectral and spatial information thus respectively extracted from the hyper/multispectral images are then recombined in the considered zone, according to the selected mixing model. Experiments based on synthetic hyper/multispectral data are carried out to evaluate the performance of the proposed multi-sharpening approach and literature linear/linear-quadratic approaches used on the whole hyper/multispectral data. In these experiments, real DSM data are used to generate synthetic data containing linear and linear-quadratic mixed pixel zones. The DSM data are also used for locally detecting the nature of the mixing model in the proposed approach. Globally, the proposed approach yields good spatial and spectral fidelities for the multi-sharpened data and significantly outperforms the used literature methods.
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Derivation and definition of a linear aircraft model
NASA Technical Reports Server (NTRS)
Duke, Eugene L.; Antoniewicz, Robert F.; Krambeer, Keith D.
1988-01-01
A linear aircraft model for a rigid aircraft of constant mass flying over a flat, nonrotating earth is derived and defined. The derivation makes no assumptions of reference trajectory or vehicle symmetry. The linear system equations are derived and evaluated along a general trajectory and include both aircraft dynamics and observation variables.
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Probing kinematics and fate of the Universe with linearly time-varying deceleration parameter
NASA Astrophysics Data System (ADS)
Akarsu, Özgür; Dereli, Tekin; Kumar, Suresh; Xu, Lixin
2014-02-01
The parametrizations q = q 0+ q 1 z and q = q 0+ q 1(1 - a/ a 0) (Chevallier-Polarski-Linder parametrization) of the deceleration parameter, which are linear in cosmic redshift z and scale factor a , have been frequently utilized in the literature to study the kinematics of the Universe. In this paper, we follow a strategy that leads to these two well-known parametrizations of the deceleration parameter as well as an additional new parametrization, q = q 0+ q 1(1 - t/ t 0), which is linear in cosmic time t. We study the features of this linearly time-varying deceleration parameter in contrast with the other two linear parametrizations. We investigate in detail the kinematics of the Universe by confronting the three models with the latest observational data. We further study the dynamics of the Universe by considering the linearly time-varying deceleration parameter model in comparison with the standard ΛCDM model. We also discuss the future of the Universe in the context of the models under consideration.
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Acceleration of the direct reconstruction of linear parametric images using nested algorithms.
Wang, Guobao; Qi, Jinyi
2010-03-07
Parametric imaging using dynamic positron emission tomography (PET) provides important information for biological research and clinical diagnosis. Indirect and direct methods have been developed for reconstructing linear parametric images from dynamic PET data. Indirect methods are relatively simple and easy to implement because the image reconstruction and kinetic modeling are performed in two separate steps. Direct methods estimate parametric images directly from raw PET data and are statistically more efficient. However, the convergence rate of direct algorithms can be slow due to the coupling between the reconstruction and kinetic modeling. Here we present two fast gradient-type algorithms for direct reconstruction of linear parametric images. The new algorithms decouple the reconstruction and linear parametric modeling at each iteration by employing the principle of optimization transfer. Convergence speed is accelerated by running more sub-iterations of linear parametric estimation because the computation cost of the linear parametric modeling is much less than that of the image reconstruction. Computer simulation studies demonstrated that the new algorithms converge much faster than the traditional expectation maximization (EM) and the preconditioned conjugate gradient algorithms for dynamic PET.
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Vanderick, S; Troch, T; Gillon, A; Glorieux, G; Gengler, N
2014-12-01
Calving ease scores from Holstein dairy cattle in the Walloon Region of Belgium were analysed using univariate linear and threshold animal models. Variance components and derived genetic parameters were estimated from a data set including 33,155 calving records. Included in the models were season, herd and sex of calf × age of dam classes × group of calvings interaction as fixed effects, herd × year of calving, maternal permanent environment and animal direct and maternal additive genetic as random effects. Models were fitted with the genetic correlation between direct and maternal additive genetic effects either estimated or constrained to zero. Direct heritability for calving ease was approximately 8% with linear models and approximately 12% with threshold models. Maternal heritabilities were approximately 2 and 4%, respectively. Genetic correlation between direct and maternal additive effects was found to be not significantly different from zero. Models were compared in terms of goodness of fit and predictive ability. Criteria of comparison such as mean squared error, correlation between observed and predicted calving ease scores as well as between estimated breeding values were estimated from 85,118 calving records. The results provided few differences between linear and threshold models even though correlations between estimated breeding values from subsets of data for sires with progeny from linear model were 17 and 23% greater for direct and maternal genetic effects, respectively, than from threshold model. For the purpose of genetic evaluation for calving ease in Walloon Holstein dairy cattle, the linear animal model without covariance between direct and maternal additive effects was found to be the best choice. © 2014 Blackwell Verlag GmbH.
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2015-07-15
Long-term effects on cancer survivors’ quality of life of physical training versus physical training combined with cognitive-behavioral therapy ...COMPARISON OF NEURAL NETWORK AND LINEAR REGRESSION MODELS IN STATISTICALLY PREDICTING MENTAL AND PHYSICAL HEALTH STATUS OF BREAST...34Comparison of Neural Network and Linear Regression Models in Statistically Predicting Mental and Physical Health Status of Breast Cancer Survivors
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Linear Reconstruction of Non-Stationary Image Ensembles Incorporating Blur and Noise Models
1998-03-01
for phase distortions due to noise which leads to less deblurring as noise increases [41]. In contrast, the vector Wiener filter incorporates some a...AFIT/DS/ENG/98- 06 Linear Reconstruction of Non-Stationary Image Ensembles Incorporating Blur and Noise Models DISSERTATION Stephen D. Ford Captain...Dissertation 4. TITLE AND SUBTITLE 5. FUNDING NUMBERS LINEAR RECONSTRUCTION OF NON-STATIONARY IMAGE ENSEMBLES INCORPORATING BLUR AND NOISE MODELS 6. AUTHOR(S
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1992-12-01
desirable. In this study, the proposed model consists of a thick-walled, highly deformable elastic tube in which the blood flow is described by linearized ...presented a mechanical model consisting of linearized Navier-Stokes and finite elasticity equations to predict blood pooling under acceleration stress... linear multielement model of the cardiovascular system which can calculate blood pressures and flows at any point in the cardio- vascular system. It
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Prakash, J; Srinivasan, K
2009-07-01
In this paper, the authors have represented the nonlinear system as a family of local linear state space models, local PID controllers have been designed on the basis of linear models, and the weighted sum of the output from the local PID controllers (Nonlinear PID controller) has been used to control the nonlinear process. Further, Nonlinear Model Predictive Controller using the family of local linear state space models (F-NMPC) has been developed. The effectiveness of the proposed control schemes has been demonstrated on a CSTR process, which exhibits dynamic nonlinearity.
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NASA Technical Reports Server (NTRS)
Dieudonne, J. E.
1978-01-01
A numerical technique was developed which generates linear perturbation models from nonlinear aircraft vehicle simulations. The technique is very general and can be applied to simulations of any system that is described by nonlinear differential equations. The computer program used to generate these models is discussed, with emphasis placed on generation of the Jacobian matrices, calculation of the coefficients needed for solving the perturbation model, and generation of the solution of the linear differential equations. An example application of the technique to a nonlinear model of the NASA terminal configured vehicle is included.
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Zhang, Xin; Liu, Pan; Chen, Yuguang; Bai, Lu; Wang, Wei
2014-01-01
The primary objective of this study was to identify whether the frequency of traffic conflicts at signalized intersections can be modeled. The opposing left-turn conflicts were selected for the development of conflict predictive models. Using data collected at 30 approaches at 20 signalized intersections, the underlying distributions of the conflicts under different traffic conditions were examined. Different conflict-predictive models were developed to relate the frequency of opposing left-turn conflicts to various explanatory variables. The models considered include a linear regression model, a negative binomial model, and separate models developed for four traffic scenarios. The prediction performance of different models was compared. The frequency of traffic conflicts follows a negative binominal distribution. The linear regression model is not appropriate for the conflict frequency data. In addition, drivers behaved differently under different traffic conditions. Accordingly, the effects of conflicting traffic volumes on conflict frequency vary across different traffic conditions. The occurrences of traffic conflicts at signalized intersections can be modeled using generalized linear regression models. The use of conflict predictive models has potential to expand the uses of surrogate safety measures in safety estimation and evaluation.
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Stirling System Modeling for Space Nuclear Power Systems
NASA Technical Reports Server (NTRS)
Lewandowski, Edward J.; Johnson, Paul K.
2008-01-01
A dynamic model of a high-power Stirling convertor has been developed for space nuclear power systems modeling. The model is based on the Component Test Power Convertor (CTPC), a 12.5-kWe free-piston Stirling convertor. The model includes the fluid heat source, the Stirling convertor, output power, and heat rejection. The Stirling convertor model includes the Stirling cycle thermodynamics, heat flow, mechanical mass-spring damper systems, and the linear alternator. The model was validated against test data. Both nonlinear and linear versions of the model were developed. The linear version algebraically couples two separate linear dynamic models; one model of the Stirling cycle and one model of the thermal system, through the pressure factors. Future possible uses of the Stirling system dynamic model are discussed. A pair of commercially available 1-kWe Stirling convertors is being purchased by NASA Glenn Research Center. The specifications of those convertors may eventually be incorporated into the dynamic model and analysis compared to the convertor test data. Subsequent potential testing could include integrating the convertors into a pumped liquid metal hot-end interface. This test would provide more data for comparison to the dynamic model analysis.
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Model Selection with the Linear Mixed Model for Longitudinal Data
ERIC Educational Resources Information Center
Ryoo, Ji Hoon
2011-01-01
Model building or model selection with linear mixed models (LMMs) is complicated by the presence of both fixed effects and random effects. The fixed effects structure and random effects structure are codependent, so selection of one influences the other. Most presentations of LMM in psychology and education are based on a multilevel or…
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On the Relation between the Linear Factor Model and the Latent Profile Model
ERIC Educational Resources Information Center
Halpin, Peter F.; Dolan, Conor V.; Grasman, Raoul P. P. P.; De Boeck, Paul
2011-01-01
The relationship between linear factor models and latent profile models is addressed within the context of maximum likelihood estimation based on the joint distribution of the manifest variables. Although the two models are well known to imply equivalent covariance decompositions, in general they do not yield equivalent estimates of the…
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A General Linear Model (GLM) was used to evaluate the deviation of predicted values from expected values for a complex environmental model. For this demonstration, we used the default level interface of the Regional Mercury Cycling Model (R-MCM) to simulate epilimnetic total mer...
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Modeling containment of large wildfires using generalized linear mixed-model analysis
Mark Finney; Isaac C. Grenfell; Charles W. McHugh
2009-01-01
Billions of dollars are spent annually in the United States to contain large wildland fires, but the factors contributing to suppression success remain poorly understood. We used a regression model (generalized linear mixed-model) to model containment probability of individual fires, assuming that containment was a repeated-measures problem (fixed effect) and...
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Correlation and simple linear regression.
Eberly, Lynn E
2007-01-01
This chapter highlights important steps in using correlation and simple linear regression to address scientific questions about the association of two continuous variables with each other. These steps include estimation and inference, assessing model fit, the connection between regression and ANOVA, and study design. Examples in microbiology are used throughout. This chapter provides a framework that is helpful in understanding more complex statistical techniques, such as multiple linear regression, linear mixed effects models, logistic regression, and proportional hazards regression.
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Procedures for generation and reduction of linear models of a turbofan engine
NASA Technical Reports Server (NTRS)
Seldner, K.; Cwynar, D. S.
1978-01-01
A real time hybrid simulation of the Pratt & Whitney F100-PW-F100 turbofan engine was used for linear-model generation. The linear models were used to analyze the effect of disturbances about an operating point on the dynamic performance of the engine. A procedure that disturbs, samples, and records the state and control variables was developed. For large systems, such as the F100 engine, the state vector is large and may contain high-frequency information not required for control. This, reducing the full-state to a reduced-order model may be a practicable approach to simplifying the control design. A reduction technique was developed to generate reduced-order models. Selected linear and nonlinear output responses to exhaust-nozzle area and main-burner fuel flow disturbances are presented for comparison.
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Scarneciu, Camelia C; Sangeorzan, Livia; Rus, Horatiu; Scarneciu, Vlad D; Varciu, Mihai S; Andreescu, Oana; Scarneciu, Ioan
2017-01-01
This study aimed at assessing the incidence of pulmonary hypertension (PH) at newly diagnosed hyperthyroid patients and at finding a simple model showing the complex functional relation between pulmonary hypertension in hyperthyroidism and the factors causing it. The 53 hyperthyroid patients (H-group) were evaluated mainly by using an echocardiographical method and compared with 35 euthyroid (E-group) and 25 healthy people (C-group). In order to identify the factors causing pulmonary hypertension the statistical method of comparing the values of arithmetical means is used. The functional relation between the two random variables (PAPs and each of the factors determining it within our research study) can be expressed by linear or non-linear function. By applying the linear regression method described by a first-degree equation the line of regression (linear model) has been determined; by applying the non-linear regression method described by a second degree equation, a parabola-type curve of regression (non-linear or polynomial model) has been determined. We made the comparison and the validation of these two models by calculating the determination coefficient (criterion 1), the comparison of residuals (criterion 2), application of AIC criterion (criterion 3) and use of F-test (criterion 4). From the H-group, 47% have pulmonary hypertension completely reversible when obtaining euthyroidism. The factors causing pulmonary hypertension were identified: previously known- level of free thyroxin, pulmonary vascular resistance, cardiac output; new factors identified in this study- pretreatment period, age, systolic blood pressure. According to the four criteria and to the clinical judgment, we consider that the polynomial model (graphically parabola- type) is better than the linear one. The better model showing the functional relation between the pulmonary hypertension in hyperthyroidism and the factors identified in this study is given by a polynomial equation of second degree where the parabola is its graphical representation.
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A Refined Model for Radar Homing Intercepts.
1983-10-27
Helge Toutenburq, Prior Information in Linear Models ,(Wiley, NY, 1982). 7. F. A. Graybill , Introduction to Matrices with Applications in StatisticF... linear target trajectory model z i = 0 + 1 r i + wi () where w i i=I,..., N is a sequence of uncorrelated zero-mean A noise, the general formula for...z i (i=l,..., N) at r. and a linear regression model 1 z i = a0 + a1 r i + w i =(Al) where wi is the corruption noise; the problem is to estimate a0
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Advanced statistics: linear regression, part I: simple linear regression.
Marill, Keith A
2004-01-01
Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.
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Normalization of cell responses in cat striate cortex
NASA Technical Reports Server (NTRS)
Heeger, D. J.
1992-01-01
Simple cells in the striate cortex have been depicted as half-wave-rectified linear operators. Complex cells have been depicted as energy mechanisms, constructed from the squared sum of the outputs of quadrature pairs of linear operators. However, the linear/energy model falls short of a complete explanation of striate cell responses. In this paper, a modified version of the linear/energy model is presented in which striate cells mutually inhibit one another, effectively normalizing their responses with respect to stimulus contrast. This paper reviews experimental measurements of striate cell responses, and shows that the new model explains a significantly larger body of physiological data.
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NASA Technical Reports Server (NTRS)
Gettman, Chang-Ching LO
1993-01-01
This thesis develops and demonstrates an approach to nonlinear control system design using linearization by state feedback. The design provides improved transient response behavior allowing faster maneuvering of payloads by the SRMS. Modeling uncertainty is accounted for by using a second feedback loop designed around the feedback linearized dynamics. A classical feedback loop is developed to provide the easy implementation required for the relatively small on board computers. Feedback linearization also allows the use of higher bandwidth model based compensation in the outer loop, since it helps maintain stability in the presence of the nonlinearities typically neglected in model based designs.
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Optimization Research of Generation Investment Based on Linear Programming Model
NASA Astrophysics Data System (ADS)
Wu, Juan; Ge, Xueqian
Linear programming is an important branch of operational research and it is a mathematical method to assist the people to carry out scientific management. GAMS is an advanced simulation and optimization modeling language and it will combine a large number of complex mathematical programming, such as linear programming LP, nonlinear programming NLP, MIP and other mixed-integer programming with the system simulation. In this paper, based on the linear programming model, the optimized investment decision-making of generation is simulated and analyzed. At last, the optimal installed capacity of power plants and the final total cost are got, which provides the rational decision-making basis for optimized investments.
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Optimal non-linear health insurance.
Blomqvist, A
1997-06-01
Most theoretical and empirical work on efficient health insurance has been based on models with linear insurance schedules (a constant co-insurance parameter). In this paper, dynamic optimization techniques are used to analyse the properties of optimal non-linear insurance schedules in a model similar to one originally considered by Spence and Zeckhauser (American Economic Review, 1971, 61, 380-387) and reminiscent of those that have been used in the literature on optimal income taxation. The results of a preliminary numerical example suggest that the welfare losses from the implicit subsidy to employer-financed health insurance under US tax law may be a good deal smaller than previously estimated using linear models.
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The linearized multistage model and the future of quantitative risk assessment.
Crump, K S
1996-10-01
The linearized multistage (LMS) model has for over 15 years been the default dose-response model used by the U.S. Environmental Protection Agency (USEPA) and other federal and state regulatory agencies in the United States for calculating quantitative estimates of low-dose carcinogenic risks from animal data. The LMS model is in essence a flexible statistical model that can describe both linear and non-linear dose-response patterns, and that produces an upper confidence bound on the linear low-dose slope of the dose-response curve. Unlike its namesake, the Armitage-Doll multistage model, the parameters of the LMS do not correspond to actual physiological phenomena. Thus the LMS is 'biological' only to the extent that the true biological dose response is linear at low dose and that low-dose slope is reflected in the experimental data. If the true dose response is non-linear the LMS upper bound may overestimate the true risk by many orders of magnitude. However, competing low-dose extrapolation models, including those derived from 'biologically-based models' that are capable of incorporating additional biological information, have not shown evidence to date of being able to produce quantitative estimates of low-dose risks that are any more accurate than those obtained from the LMS model. Further, even if these attempts were successful, the extent to which more accurate estimates of low-dose risks in a test animal species would translate into improved estimates of human risk is questionable. Thus, it does not appear possible at present to develop a quantitative approach that would be generally applicable and that would offer significant improvements upon the crude bounding estimates of the type provided by the LMS model. Draft USEPA guidelines for cancer risk assessment incorporate an approach similar to the LMS for carcinogens having a linear mode of action. However, under these guidelines quantitative estimates of low-dose risks would not be developed for carcinogens having a non-linear mode of action; instead dose-response modelling would be used in the experimental range to calculate an LED10* (a statistical lower bound on the dose corresponding to a 10% increase in risk), and safety factors would be applied to the LED10* to determine acceptable exposure levels for humans. This approach is very similar to the one presently used by USEPA for non-carcinogens. Rather than using one approach for carcinogens believed to have a linear mode of action and a different approach for all other health effects, it is suggested herein that it would be more appropriate to use an approach conceptually similar to the 'LED10*-safety factor' approach for all health effects, and not to routinely develop quantitative risk estimates from animal data.
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The development of global GRAPES 4DVAR
NASA Astrophysics Data System (ADS)
Liu, Yongzhu
2017-04-01
Four-dimensional variation data assimilation (4DVAR) has given a great contribution to the improvement of NWP system over the past twenty years. Therefore, our strategy is to develop an operational global 4D-Var system from the outset. The aim at the paper is to introduce the development of the global GRAPES four-dimensional variation data assimilation (4DVAR) using incremental analysis schemes and to presents results of a comparison between 4DVAR using 6-hour assimilation window and simplified physics during the minimization with three-dimensional variation data assimilation (3DVAR). The dynamical cores of the tangent-linear and adjoint models are developed directly based on the non-hydrostatic forecast model. In addition, the standard correctness checks have been performed. As well as the development adjoint codes, most of our work is focused on improving the computational efficiency since the bulk of the computational cost of 4D-Var is in the integration of the tangent-linear and adjoint models. In terms of tangent-linear model, the wall-clock time is reduced to about 1.2 times as much as one of nonlinear model through the optimizing of the software framework. The significant computational cost savings on adjoint model result from the removing the redundant recompilations of model trajectories. It is encouraging that the wall-clock time of adjoint model is less than 1.5 times as much as one of nonlinear model. The current difficulty is that the numerical scheme used within the linear model is based on strategically on the numeric of the corresponding nonlinear model. Further computational acceleration should be expected from the improvement on nonlinear numerical algorithm. A series of linearized physical parameterization schemes has been developed to improve the representation of perturbed fields in the linear model. It consists of horizontal and vertical diffusion, sub-grid scale orographic gravity wave drag, large-scale condensation and cumulus convection schemes. We also found the straightforward linearization based on the nonlinear physical scheme might lead to significant growing of spurious unstable perturbations. It is essential to simplify the linear physics with respect to the non-linear schemes. The improvement on the perturbed fields in the tangent-linear model is visible with the linear physics included, especially at the low level. GRAPES variation data assimilation system adopts the incremental approach. The work is ongoing to develop a pre-operational 4DVAR suite with 0.25° outer loop resolution and multiple outer-loops configurations. One 4DVAR analysis using 6-hour assimilation windows can be finished within 40-minutes when using the available conventional and satellite data. In summary, it was found that the analysis over the northern, southern hemispheres, tropical region and East Asian area of GRAPES 4DVAR performed better than GRAPES 3DVAR for one month experiments. Moreover, the forecast results show that northern and southern extra-tropical scores for GRAPES 4DVAR are already better than GRAPES 3DVAR, but the tropical performance needs further investigations. Therefore, the subsequent main improvements will aim to enhance its computational efficiency and accuracy in 2017. The global GRAPES 4DVAR is planned for operation in 2018.
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ERIC Educational Resources Information Center
Mandys, Frantisek; Dolan, Conor V.; Molenaar, Peter C. M.
1994-01-01
Studied the conditions under which the quasi-Markov simplex model fits a linear growth curve covariance structure and determined when the model is rejected. Presents a quasi-Markov simplex model with structured means and gives an example. (SLD)
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DOT National Transportation Integrated Search
1995-08-01
A linear model structure applicable to identification of the UH-60 flight : dynamics in hover and forward flight without rotor-state data is developed. The : structure of the model is determined through consideration of the important : dynamic modes ...
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Linear discrete systems with memory: a generalization of the Langmuir model
NASA Astrophysics Data System (ADS)
Băleanu, Dumitru; Nigmatullin, Raoul R.
2013-10-01
In this manuscript we analyzed a general solution of the linear nonlocal Langmuir model within time scale calculus. Several generalizations of the Langmuir model are presented together with their exact corresponding solutions. The physical meaning of the proposed models are investigated and their corresponding geometries are reported.
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Molenaar, Dylan; Bolsinova, Maria
2017-05-01
In generalized linear modelling of responses and response times, the observed response time variables are commonly transformed to make their distribution approximately normal. A normal distribution for the transformed response times is desirable as it justifies the linearity and homoscedasticity assumptions in the underlying linear model. Past research has, however, shown that the transformed response times are not always normal. Models have been developed to accommodate this violation. In the present study, we propose a modelling approach for responses and response times to test and model non-normality in the transformed response times. Most importantly, we distinguish between non-normality due to heteroscedastic residual variances, and non-normality due to a skewed speed factor. In a simulation study, we establish parameter recovery and the power to separate both effects. In addition, we apply the model to a real data set. © 2017 The Authors. British Journal of Mathematical and Statistical Psychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.
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Sensory processing and world modeling for an active ranging device
NASA Technical Reports Server (NTRS)
Hong, Tsai-Hong; Wu, Angela Y.
1991-01-01
In this project, we studied world modeling and sensory processing for laser range data. World Model data representation and operation were defined. Sensory processing algorithms for point processing and linear feature detection were designed and implemented. The interface between world modeling and sensory processing in the Servo and Primitive levels was investigated and implemented. In the primitive level, linear features detectors for edges were also implemented, analyzed and compared. The existing world model representations is surveyed. Also presented is the design and implementation of the Y-frame model, a hierarchical world model. The interfaces between the world model module and the sensory processing module are discussed as well as the linear feature detectors that were designed and implemented.
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Reduced-order model based feedback control of the modified Hasegawa-Wakatani model
DOE Office of Scientific and Technical Information (OSTI.GOV)
Goumiri, I. R.; Rowley, C. W.; Ma, Z.
2013-04-15
In this work, the development of model-based feedback control that stabilizes an unstable equilibrium is obtained for the Modified Hasegawa-Wakatani (MHW) equations, a classic model in plasma turbulence. First, a balanced truncation (a model reduction technique that has proven successful in flow control design problems) is applied to obtain a low dimensional model of the linearized MHW equation. Then, a model-based feedback controller is designed for the reduced order model using linear quadratic regulators. Finally, a linear quadratic Gaussian controller which is more resistant to disturbances is deduced. The controller is applied on the non-reduced, nonlinear MHW equations to stabilizemore » the equilibrium and suppress the transition to drift-wave induced turbulence.« less
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Wu, Liming; Wu, Huijun; Chen, Lina; Xie, Shanshan; Zang, Haoyu; Borriss, Rainer
2014-01-01
Harmful algal blooms, caused by massive and exceptional overgrowth of microalgae and cyanobacteria, are a serious environmental problem worldwide. In the present study, we looked for Bacillus strains with sufficiently strong anticyanobacterial activity to be used as biocontrol agents. Among 24 strains, Bacillus amyloliquefaciens FZB42 showed the strongest bactericidal activity against Microcystis aeruginosa, with a kill rate of 98.78%. The synthesis of the anticyanobacterial substance did not depend on Sfp, an enzyme that catalyzes a necessary processing step in the nonribosomal synthesis of lipopeptides and polyketides, but was associated with the aro gene cluster that is involved in the synthesis of the sfp-independent antibiotic bacilysin. Disruption of bacB, the gene in the cluster responsible for synthesizing bacilysin, or supplementation with the antagonist N-acetylglucosamine abolished the inhibitory effect, but this was restored when bacilysin synthesis was complemented. Bacilysin caused apparent changes in the algal cell wall and cell organelle membranes, and this resulted in cell lysis. Meanwhile, there was downregulated expression of glmS, psbA1, mcyB, and ftsZ—genes involved in peptidoglycan synthesis, photosynthesis, microcystin synthesis, and cell division, respectively. In addition, bacilysin suppressed the growth of other harmful algal species. In summary, bacilysin produced by B. amyloliquefaciens FZB42 has anticyanobacterial activity and thus could be developed as a biocontrol agent to mitigate the effects of harmful algal blooms. PMID:25261512
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Identification of functional interactome of a key cell division regulatory protein CedA of E.coli.
Sharma, Pankaj; Tomar, Anil Kumar; Kundu, Bishwajit
2018-01-01
Cell division is compromised in DnaAcos mutant Escherichia coli cells that results in filamentous cell morphology. This is countered by over-expression of CedA protein that induces cytokinesis and thus, regular cell morphology is regained; however via an unknown mechanism. To understand the process systematically, exact role of CedA should be deciphered. Protein interactions are crucial for functional organization of a cell and their identification helps in revealing exact function(s) of a protein and its binding partners. Thus, this study was intended to identify CedA binding proteins (CBPs) to gain more clues of CedA function. We isolated CBPs by pull down assay using purified recombinant CedA and identified nine CBPs by mass spectrometric analysis (MALDI-TOF MS and LC-MS/MS), viz. PDHA1, RL2, DNAK, LPP, RPOB, G6PD, GLMS, RL3 and YBCJ. Based on CBPs identified, we hypothesize that CedA plays a crucial and multifaceted role in cell cycle regulation and specific pathways in which CedA participates may include transcription and energy metabolism. However, further validation through in-vitro and in-vivo experiments is necessary. In conclusion, identification of CBPs may help us in deciphering mechanism of CedA mediated cell division during chromosomal DNA over-replication. Copyright © 2017 Elsevier B.V. All rights reserved.
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Small-Caliber Projectile Target Impact Angle Determined From Close Proximity Radiographs
2006-10-01
discrete motion data that can be numerically modeled using linear aerodynamic theory or 6-degrees-of- freedom equations of motion. The values of Fφ...Prediction Excel® Spreadsheet shown in figure 9. The Gamma at Impact Spreadsheet uses the linear aerodynamics model , equations 5 and 6, to calculate αT...trajectory angle error via consideration of the RMS fit errors of the actual firings. However, the linear aerodynamics model does not include this effect
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Linear Models for Systematics and Nuisances
NASA Astrophysics Data System (ADS)
Luger, Rodrigo; Foreman-Mackey, Daniel; Hogg, David W.
2017-12-01
The target of many astronomical studies is the recovery of tiny astrophysical signals living in a sea of uninteresting (but usually dominant) noise. In many contexts (i.e., stellar time-series, or high-contrast imaging, or stellar spectroscopy), there are structured components in this noise caused by systematic effects in the astronomical source, the atmosphere, the telescope, or the detector. More often than not, evaluation of the true physical model for these nuisances is computationally intractable and dependent on too many (unknown) parameters to allow rigorous probabilistic inference. Sometimes, housekeeping data---and often the science data themselves---can be used as predictors of the systematic noise. Linear combinations of simple functions of these predictors are often used as computationally tractable models that can capture the nuisances. These models can be used to fit and subtract systematics prior to investigation of the signals of interest, or they can be used in a simultaneous fit of the systematics and the signals. In this Note, we show that if a Gaussian prior is placed on the weights of the linear components, the weights can be marginalized out with an operation in pure linear algebra, which can (often) be made fast. We illustrate this model by demonstrating the applicability of a linear model for the non-linear systematics in K2 time-series data, where the dominant noise source for many stars is spacecraft motion and variability.
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Lattice Boltzmann methods for global linear instability analysis
NASA Astrophysics Data System (ADS)
Pérez, José Miguel; Aguilar, Alfonso; Theofilis, Vassilis
2017-12-01
Modal global linear instability analysis is performed using, for the first time ever, the lattice Boltzmann method (LBM) to analyze incompressible flows with two and three inhomogeneous spatial directions. Four linearization models have been implemented in order to recover the linearized Navier-Stokes equations in the incompressible limit. Two of those models employ the single relaxation time and have been proposed previously in the literature as linearization of the collision operator of the lattice Boltzmann equation. Two additional models are derived herein for the first time by linearizing the local equilibrium probability distribution function. Instability analysis results are obtained in three benchmark problems, two in closed geometries and one in open flow, namely the square and cubic lid-driven cavity flow and flow in the wake of the circular cylinder. Comparisons with results delivered by classic spectral element methods verify the accuracy of the proposed new methodologies and point potential limitations particular to the LBM approach. The known issue of appearance of numerical instabilities when the SRT model is used in direct numerical simulations employing the LBM is shown to be reflected in a spurious global eigenmode when the SRT model is used in the instability analysis. Although this mode is absent in the multiple relaxation times model, other spurious instabilities can also arise and are documented herein. Areas of potential improvements in order to make the proposed methodology competitive with established approaches for global instability analysis are discussed.
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Finite element modeling of concrete structures strengthened with FRP laminates
DOT National Transportation Integrated Search
2001-05-01
Linear and non-linear method models were developed for a reinforced concrete bridge that had been strengthened with fiber reinforced polymer (FRP) composites. ANSYS and SAP2000 modeling software were used; however, most of the development effort used...
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Parameter Recovery for the 1-P HGLLM with Non-Normally Distributed Level-3 Residuals
ERIC Educational Resources Information Center
Kara, Yusuf; Kamata, Akihito
2017-01-01
A multilevel Rasch model using a hierarchical generalized linear model is one approach to multilevel item response theory (IRT) modeling and is referred to as a one-parameter hierarchical generalized linear logistic model (1-P HGLLM). Although it has the flexibility to model nested structure of data with covariates, the model assumes the normality…
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Wear-caused deflection evolution of a slide rail, considering linear and non-linear wear models
NASA Astrophysics Data System (ADS)
Kim, Dongwook; Quagliato, Luca; Park, Donghwi; Murugesan, Mohanraj; Kim, Naksoo; Hong, Seokmoo
2017-05-01
The research presented in this paper details an experimental-numerical approach for the quantitative correlation between wear and end-point deflection in a slide rail. Focusing the attention on slide rail utilized in white-goods applications, the aim is to evaluate the number of cycles the slide rail can operate, under different load conditions, before it should be replaced due to unacceptable end-point deflection. In this paper, two formulations are utilized to describe the wear: Archard model for the linear wear and Lemaitre damage model for the nonlinear wear. The linear wear gradually reduces the surface of the slide rail whereas the nonlinear one accounts for the surface element deletion (i.e. due to pitting). To determine the constants to use in the wear models, simple tension test and sliding wear test, by utilizing a designed and developed experiment machine, have been carried out. A full slide rail model simulation has been implemented in ABAQUS including both linear and non-linear wear models and the results have been compared with those of the real rails under different load condition, provided by the rail manufacturer. The comparison between numerically estimated and real rail results proved the reliability of the developed numerical model, limiting the error in a ±10% range. The proposed approach allows predicting the displacement vs cycle curves, parametrized for different loads and, based on a chosen failure criterion, to predict the lifetime of the rail.
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Preprocessing Inconsistent Linear System for a Meaningful Least Squares Solution
NASA Technical Reports Server (NTRS)
Sen, Syamal K.; Shaykhian, Gholam Ali
2011-01-01
Mathematical models of many physical/statistical problems are systems of linear equations. Due to measurement and possible human errors/mistakes in modeling/data, as well as due to certain assumptions to reduce complexity, inconsistency (contradiction) is injected into the model, viz. the linear system. While any inconsistent system irrespective of the degree of inconsistency has always a least-squares solution, one needs to check whether an equation is too much inconsistent or, equivalently too much contradictory. Such an equation will affect/distort the least-squares solution to such an extent that renders it unacceptable/unfit to be used in a real-world application. We propose an algorithm which (i) prunes numerically redundant linear equations from the system as these do not add any new information to the model, (ii) detects contradictory linear equations along with their degree of contradiction (inconsistency index), (iii) removes those equations presumed to be too contradictory, and then (iv) obtain the minimum norm least-squares solution of the acceptably inconsistent reduced linear system. The algorithm presented in Matlab reduces the computational and storage complexities and also improves the accuracy of the solution. It also provides the necessary warning about the existence of too much contradiction in the model. In addition, we suggest a thorough relook into the mathematical modeling to determine the reason why unacceptable contradiction has occurred thus prompting us to make necessary corrections/modifications to the models - both mathematical and, if necessary, physical.
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Preprocessing in Matlab Inconsistent Linear System for a Meaningful Least Squares Solution
NASA Technical Reports Server (NTRS)
Sen, Symal K.; Shaykhian, Gholam Ali
2011-01-01
Mathematical models of many physical/statistical problems are systems of linear equations Due to measurement and possible human errors/mistakes in modeling/data, as well as due to certain assumptions to reduce complexity, inconsistency (contradiction) is injected into the model, viz. the linear system. While any inconsistent system irrespective of the degree of inconsistency has always a least-squares solution, one needs to check whether an equation is too much inconsistent or, equivalently too much contradictory. Such an equation will affect/distort the least-squares solution to such an extent that renders it unacceptable/unfit to be used in a real-world application. We propose an algorithm which (i) prunes numerically redundant linear equations from the system as these do not add any new information to the model, (ii) detects contradictory linear equations along with their degree of contradiction (inconsistency index), (iii) removes those equations presumed to be too contradictory, and then (iv) obtain the . minimum norm least-squares solution of the acceptably inconsistent reduced linear system. The algorithm presented in Matlab reduces the computational and storage complexities and also improves the accuracy of the solution. It also provides the necessary warning about the existence of too much contradiction in the model. In addition, we suggest a thorough relook into the mathematical modeling to determine the reason why unacceptable contradiction has occurred thus prompting us to make necessary corrections/modifications to the models - both mathematical and, if necessary, physical.
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ERIC Educational Resources Information Center
Dyehouse, Melissa; Bennett, Deborah; Harbor, Jon; Childress, Amy; Dark, Melissa
2009-01-01
Logic models are based on linear relationships between program resources, activities, and outcomes, and have been used widely to support both program development and evaluation. While useful in describing some programs, the linear nature of the logic model makes it difficult to capture the complex relationships within larger, multifaceted…
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Linear summation of outputs in a balanced network model of motor cortex.
Capaday, Charles; van Vreeswijk, Carl
2015-01-01
Given the non-linearities of the neural circuitry's elements, we would expect cortical circuits to respond non-linearly when activated. Surprisingly, when two points in the motor cortex are activated simultaneously, the EMG responses are the linear sum of the responses evoked by each of the points activated separately. Additionally, the corticospinal transfer function is close to linear, implying that the synaptic interactions in motor cortex must be effectively linear. To account for this, here we develop a model of motor cortex composed of multiple interconnected points, each comprised of reciprocally connected excitatory and inhibitory neurons. We show how non-linearities in neuronal transfer functions are eschewed by strong synaptic interactions within each point. Consequently, the simultaneous activation of multiple points results in a linear summation of their respective outputs. We also consider the effects of reduction of inhibition at a cortical point when one or more surrounding points are active. The network response in this condition is linear over an approximately two- to three-fold decrease of inhibitory feedback strength. This result supports the idea that focal disinhibition allows linear coupling of motor cortical points to generate movement related muscle activation patterns; albeit with a limitation on gain control. The model also explains why neural activity does not spread as far out as the axonal connectivity allows, whilst also explaining why distant cortical points can be, nonetheless, functionally coupled by focal disinhibition. Finally, we discuss the advantages that linear interactions at the cortical level afford to motor command synthesis.
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NASA Astrophysics Data System (ADS)
Amsallem, David; Tezaur, Radek; Farhat, Charbel
2016-12-01
A comprehensive approach for real-time computations using a database of parametric, linear, projection-based reduced-order models (ROMs) based on arbitrary underlying meshes is proposed. In the offline phase of this approach, the parameter space is sampled and linear ROMs defined by linear reduced operators are pre-computed at the sampled parameter points and stored. Then, these operators and associated ROMs are transformed into counterparts that satisfy a certain notion of consistency. In the online phase of this approach, a linear ROM is constructed in real-time at a queried but unsampled parameter point by interpolating the pre-computed linear reduced operators on matrix manifolds and therefore computing an interpolated linear ROM. The proposed overall model reduction framework is illustrated with two applications: a parametric inverse acoustic scattering problem associated with a mockup submarine, and a parametric flutter prediction problem associated with a wing-tank system. The second application is implemented on a mobile device, illustrating the capability of the proposed computational framework to operate in real-time.
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The Routine Fitting of Kinetic Data to Models
Berman, Mones; Shahn, Ezra; Weiss, Marjory F.
1962-01-01
A mathematical formalism is presented for use with digital computers to permit the routine fitting of data to physical and mathematical models. Given a set of data, the mathematical equations describing a model, initial conditions for an experiment, and initial estimates for the values of model parameters, the computer program automatically proceeds to obtain a least squares fit of the data by an iterative adjustment of the values of the parameters. When the experimental measures are linear combinations of functions, the linear coefficients for a least squares fit may also be calculated. The values of both the parameters of the model and the coefficients for the sum of functions may be unknown independent variables, unknown dependent variables, or known constants. In the case of dependence, only linear dependencies are provided for in routine use. The computer program includes a number of subroutines, each one of which performs a special task. This permits flexibility in choosing various types of solutions and procedures. One subroutine, for example, handles linear differential equations, another, special non-linear functions, etc. The use of analytic or numerical solutions of equations is possible. PMID:13867975
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NASA Technical Reports Server (NTRS)
1979-01-01
The computer program Linear SCIDNT which evaluates rotorcraft stability and control coefficients from flight or wind tunnel test data is described. It implements the maximum likelihood method to maximize the likelihood function of the parameters based on measured input/output time histories. Linear SCIDNT may be applied to systems modeled by linear constant-coefficient differential equations. This restriction in scope allows the application of several analytical results which simplify the computation and improve its efficiency over the general nonlinear case.
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ERIC Educational Resources Information Center
Nimon, Kim
2012-01-01
Using state achievement data that are openly accessible, this paper demonstrates the application of hierarchical linear modeling within the context of career technical education research. Three prominent approaches to analyzing clustered data (i.e., modeling aggregated data, modeling disaggregated data, modeling hierarchical data) are discussed…
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Ramo, Nicole L.; Puttlitz, Christian M.
2018-01-01
Compelling evidence that many biological soft tissues display both strain- and time-dependent behavior has led to the development of fully non-linear viscoelastic modeling techniques to represent the tissue’s mechanical response under dynamic conditions. Since the current stress state of a viscoelastic material is dependent on all previous loading events, numerical analyses are complicated by the requirement of computing and storing the stress at each step throughout the load history. This requirement quickly becomes computationally expensive, and in some cases intractable, for finite element models. Therefore, we have developed a strain-dependent numerical integration approach for capturing non-linear viscoelasticity that enables calculation of the current stress from a strain-dependent history state variable stored from the preceding time step only, which improves both fitting efficiency and computational tractability. This methodology was validated based on its ability to recover non-linear viscoelastic coefficients from simulated stress-relaxation (six strain levels) and dynamic cyclic (three frequencies) experimental stress-strain data. The model successfully fit each data set with average errors in recovered coefficients of 0.3% for stress-relaxation fits and 0.1% for cyclic. The results support the use of the presented methodology to develop linear or non-linear viscoelastic models from stress-relaxation or cyclic experimental data of biological soft tissues. PMID:29293558
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Senn, Stephen; Graf, Erika; Caputo, Angelika
2007-12-30
Stratifying and matching by the propensity score are increasingly popular approaches to deal with confounding in medical studies investigating effects of a treatment or exposure. A more traditional alternative technique is the direct adjustment for confounding in regression models. This paper discusses fundamental differences between the two approaches, with a focus on linear regression and propensity score stratification, and identifies points to be considered for an adequate comparison. The treatment estimators are examined for unbiasedness and efficiency. This is illustrated in an application to real data and supplemented by an investigation on properties of the estimators for a range of underlying linear models. We demonstrate that in specific circumstances the propensity score estimator is identical to the effect estimated from a full linear model, even if it is built on coarser covariate strata than the linear model. As a consequence the coarsening property of the propensity score-adjustment for a one-dimensional confounder instead of a high-dimensional covariate-may be viewed as a way to implement a pre-specified, richly parametrized linear model. We conclude that the propensity score estimator inherits the potential for overfitting and that care should be taken to restrict covariates to those relevant for outcome. Copyright (c) 2007 John Wiley & Sons, Ltd.
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Controlling Flexible Manipulators, an Experimental Investigation. Ph.D. Thesis
NASA Technical Reports Server (NTRS)
Hastings, Gordon Greene
1986-01-01
Lightweight, slender manipulators offer faster response and/or greater workspace range for the same size actuators than tradional manipulators. Lightweight construction of manipulator links results in increased structural flexibility. The increase flexibility must be considered in the design of control systems to properly account for the dynamic flexible vibrations and static deflections. Real time control of the flexible manipulator vibrations are experimentally investigated. Models intended for real-time control of distributed parameter system such as flexible manipulators rely on model approximation schemes. An linear model based on the application of Lagrangian dynamics to a rigid body mode and a series of separable flexible modes is examined with respect to model order requirements, and modal candidate selection. Balanced realizations are applied to the linear flexible model to obtain an estimate of appropriate order for a selected model. Describing the flexible deflections as a linear combination of modes results in measurements of beam state, which yield information about several modes. To realize the potential of linear systems theory, knowledge of each state must be available. State estimation is also accomplished by implementation of a Kalman Filter. State feedback control laws are implemented based upon linear quadratic regulator design.
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Non-linear modeling of RF in fusion grade plasmas
NASA Astrophysics Data System (ADS)
Austin, Travis; Smithe, David; Hakim, Ammar; Jenkins, Thomas
2011-10-01
We are seeking to model nonlinear effects, particularly parametric decay instability in the vicinity of the edge plasma and RF launchers, which is thought to be a potential parasitic loss mechanism. We will use time-domain approaches which treat the full spectrum of modes. Two approaches are being tested for feasibility, a non-linear delta-f particle approach, and a higher order many-fluid closure approach. Our particle approach builds on extensive previous work demonstrating the ability to model IBW waves (one of the PDI daughter waves) with a linear delta-f particle model. Here we report on the performance of such simulations when the linear constraint is relaxed, and in particular on the ability of the low-noise loading scheme, specially developed for RF and ion-time scale physics, to operate and maintain low noise in the non-linear regime. Similarly, a novel high-order closure of the fluid equations is necessary to model the IBW and higher harmonics. We will report on the benchmarking of the fluid closure, and its ability to model the anticipated pump and daughter waves in a PDI scenario. This research supported by US DOE Grant # DE-SC0006242.
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NASA Astrophysics Data System (ADS)
Zielnica, J.; Ziółkowski, A.; Cempel, C.
2003-03-01
Design and theoretical and experimental investigation of vibroisolation pads with non-linear static and dynamic responses is the objective of the paper. The analytical investigations are based on non-linear finite element analysis where the load-deflection response is traced against the shape and material properties of the analysed model of the vibroisolation pad. A new model of vibroisolation pad of antisymmetrical type was designed and analysed by the finite element method based on the second-order theory (large displacements and strains) with the assumption of material's non-linearities (Mooney-Rivlin model). Stability loss phenomenon was used in the design of the vibroisolators, and it was proved that it would be possible to design a model of vibroisolator in the form of a continuous pad with non-linear static and dynamic response, typical to vibroisolation purposes. The materials used for the vibroisolator are those of rubber, elastomers, and similar ones. The results of theoretical investigations were examined experimentally. A series of models made of soft rubber were designed for the test purposes. The experimental investigations of the vibroisolation models, under static and dynamic loads, confirmed the results of the FEM analysis.
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Log-normal frailty models fitted as Poisson generalized linear mixed models.
Hirsch, Katharina; Wienke, Andreas; Kuss, Oliver
2016-12-01
The equivalence of a survival model with a piecewise constant baseline hazard function and a Poisson regression model has been known since decades. As shown in recent studies, this equivalence carries over to clustered survival data: A frailty model with a log-normal frailty term can be interpreted and estimated as a generalized linear mixed model with a binary response, a Poisson likelihood, and a specific offset. Proceeding this way, statistical theory and software for generalized linear mixed models are readily available for fitting frailty models. This gain in flexibility comes at the small price of (1) having to fix the number of pieces for the baseline hazard in advance and (2) having to "explode" the data set by the number of pieces. In this paper we extend the simulations of former studies by using a more realistic baseline hazard (Gompertz) and by comparing the model under consideration with competing models. Furthermore, the SAS macro %PCFrailty is introduced to apply the Poisson generalized linear mixed approach to frailty models. The simulations show good results for the shared frailty model. Our new %PCFrailty macro provides proper estimates, especially in case of 4 events per piece. The suggested Poisson generalized linear mixed approach for log-normal frailty models based on the %PCFrailty macro provides several advantages in the analysis of clustered survival data with respect to more flexible modelling of fixed and random effects, exact (in the sense of non-approximate) maximum likelihood estimation, and standard errors and different types of confidence intervals for all variance parameters. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
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Valid statistical approaches for analyzing sholl data: Mixed effects versus simple linear models.
Wilson, Machelle D; Sethi, Sunjay; Lein, Pamela J; Keil, Kimberly P
2017-03-01
The Sholl technique is widely used to quantify dendritic morphology. Data from such studies, which typically sample multiple neurons per animal, are often analyzed using simple linear models. However, simple linear models fail to account for intra-class correlation that occurs with clustered data, which can lead to faulty inferences. Mixed effects models account for intra-class correlation that occurs with clustered data; thus, these models more accurately estimate the standard deviation of the parameter estimate, which produces more accurate p-values. While mixed models are not new, their use in neuroscience has lagged behind their use in other disciplines. A review of the published literature illustrates common mistakes in analyses of Sholl data. Analysis of Sholl data collected from Golgi-stained pyramidal neurons in the hippocampus of male and female mice using both simple linear and mixed effects models demonstrates that the p-values and standard deviations obtained using the simple linear models are biased downwards and lead to erroneous rejection of the null hypothesis in some analyses. The mixed effects approach more accurately models the true variability in the data set, which leads to correct inference. Mixed effects models avoid faulty inference in Sholl analysis of data sampled from multiple neurons per animal by accounting for intra-class correlation. Given the widespread practice in neuroscience of obtaining multiple measurements per subject, there is a critical need to apply mixed effects models more widely. Copyright © 2017 Elsevier B.V. All rights reserved.
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Nonlinear aeroservoelastic analysis of a controlled multiple-actuated-wing model with free-play
NASA Astrophysics Data System (ADS)
Huang, Rui; Hu, Haiyan; Zhao, Yonghui
2013-10-01
In this paper, the effects of structural nonlinearity due to free-play in both leading-edge and trailing-edge outboard control surfaces on the linear flutter control system are analyzed for an aeroelastic model of three-dimensional multiple-actuated-wing. The free-play nonlinearities in the control surfaces are modeled theoretically by using the fictitious mass approach. The nonlinear aeroelastic equations of the presented model can be divided into nine sub-linear modal-based aeroelastic equations according to the different combinations of deflections of the leading-edge and trailing-edge outboard control surfaces. The nonlinear aeroelastic responses can be computed based on these sub-linear aeroelastic systems. To demonstrate the effects of nonlinearity on the linear flutter control system, a single-input and single-output controller and a multi-input and multi-output controller are designed based on the unconstrained optimization techniques. The numerical results indicate that the free-play nonlinearity can lead to either limit cycle oscillations or divergent motions when the linear control system is implemented.
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Henry, B I; Langlands, T A M; Wearne, S L
2006-09-01
We have revisited the problem of anomalously diffusing species, modeled at the mesoscopic level using continuous time random walks, to include linear reaction dynamics. If a constant proportion of walkers are added or removed instantaneously at the start of each step then the long time asymptotic limit yields a fractional reaction-diffusion equation with a fractional order temporal derivative operating on both the standard diffusion term and a linear reaction kinetics term. If the walkers are added or removed at a constant per capita rate during the waiting time between steps then the long time asymptotic limit has a standard linear reaction kinetics term but a fractional order temporal derivative operating on a nonstandard diffusion term. Results from the above two models are compared with a phenomenological model with standard linear reaction kinetics and a fractional order temporal derivative operating on a standard diffusion term. We have also developed further extensions of the CTRW model to include more general reaction dynamics.
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A Hierarchical Linear Model for Estimating Gender-Based Earnings Differentials.
ERIC Educational Resources Information Center
Haberfield, Yitchak; Semyonov, Moshe; Addi, Audrey
1998-01-01
Estimates of gender earnings inequality in data from 116,431 Jewish workers were compared using a hierarchical linear model (HLM) and ordinary least squares model. The HLM allows estimation of the extent to which earnings inequality depends on occupational characteristics. (SK)
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DOT National Transportation Integrated Search
2001-05-01
Linear and non-linear finite element method models were developed for a reinforced concrete bridge that had been strengthened with fiber reinforced polymer composites. ANSYS and SAP2000 modeling software were used; however, most of the development ef...
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Cost drivers and resource allocation in military health care systems.
Fulton, Larry; Lasdon, Leon S; McDaniel, Reuben R
2007-03-01
This study illustrates the feasibility of incorporating technical efficiency considerations in the funding of military hospitals and identifies the primary drivers for hospital costs. Secondary data collected for 24 U.S.-based Army hospitals and medical centers for the years 2001 to 2003 are the basis for this analysis. Technical efficiency was measured by using data envelopment analysis; subsequently, efficiency estimates were included in logarithmic-linear cost models that specified cost as a function of volume, complexity, efficiency, time, and facility type. These logarithmic-linear models were compared against stochastic frontier analysis models. A parsimonious, three-variable, logarithmic-linear model composed of volume, complexity, and efficiency variables exhibited a strong linear relationship with observed costs (R(2) = 0.98). This model also proved reliable in forecasting (R(2) = 0.96). Based on our analysis, as much as $120 million might be reallocated to improve the United States-based Army hospital performance evaluated in this study.
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NASA Technical Reports Server (NTRS)
Wilson, R. B.; Bak, M. J.; Nakazawa, S.; Banerjee, P. K.
1984-01-01
A 3-D inelastic analysis methods program consists of a series of computer codes embodying a progression of mathematical models (mechanics of materials, special finite element, boundary element) for streamlined analysis of combustor liners, turbine blades, and turbine vanes. These models address the effects of high temperatures and thermal/mechanical loadings on the local (stress/strain) and global (dynamics, buckling) structural behavior of the three selected components. These models are used to solve 3-D inelastic problems using linear approximations in the sense that stresses/strains and temperatures in generic modeling regions are linear functions of the spatial coordinates, and solution increments for load, temperature and/or time are extrapolated linearly from previous information. Three linear formulation computer codes, referred to as MOMM (Mechanics of Materials Model), MHOST (MARC-Hot Section Technology), and BEST (Boundary Element Stress Technology), were developed and are described.
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NASA Astrophysics Data System (ADS)
Rong, Bao; Rui, Xiaoting; Lu, Kun; Tao, Ling; Wang, Guoping; Ni, Xiaojun
2018-05-01
In this paper, an efficient method of dynamics modeling and vibration control design of a linear hybrid multibody system (MS) is studied based on the transfer matrix method. The natural vibration characteristics of a linear hybrid MS are solved by using low-order transfer equations. Then, by constructing the brand-new body dynamics equation, augmented operator and augmented eigenvector, the orthogonality of augmented eigenvector of a linear hybrid MS is satisfied, and its state space model expressed in each independent model space is obtained easily. According to this dynamics model, a robust independent modal space-fuzzy controller is designed for vibration control of a general MS, and the genetic optimization of some critical control parameters of fuzzy tuners is also presented. Two illustrative examples are performed, which results show that this method is computationally efficient and with perfect control performance.
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Confirming the Lanchestrian linear-logarithmic model of attrition
DOE Office of Scientific and Technical Information (OSTI.GOV)
Hartley, D.S. III.
1990-12-01
This paper is the fourth in a series of reports on the breakthrough research in historical validation of attrition in conflict. Significant defense policy decisions, including weapons acquisition and arms reduction, are based in part on models of conflict. Most of these models are driven by their attrition algorithms, usually forms of the Lanchester square and linear laws. None of these algorithms have been validated. The results of this paper confirm the results of earlier papers, using a large database of historical results. The homogeneous linear-logarithmic Lanchestrian attrition model is validated to the extent possible with current initial and finalmore » force size data and is consistent with the Iwo Jima data. A particular differential linear-logarithmic model is described that fits the data very well. A version of Helmbold's victory predicting parameter is also confirmed, with an associated probability function. 37 refs., 73 figs., 68 tabs.« less
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Deformation-Aware Log-Linear Models
NASA Astrophysics Data System (ADS)
Gass, Tobias; Deselaers, Thomas; Ney, Hermann
In this paper, we present a novel deformation-aware discriminative model for handwritten digit recognition. Unlike previous approaches our model directly considers image deformations and allows discriminative training of all parameters, including those accounting for non-linear transformations of the image. This is achieved by extending a log-linear framework to incorporate a latent deformation variable. The resulting model has an order of magnitude less parameters than competing approaches to handling image deformations. We tune and evaluate our approach on the USPS task and show its generalization capabilities by applying the tuned model to the MNIST task. We gain interesting insights and achieve highly competitive results on both tasks.
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NASA Technical Reports Server (NTRS)
Sidik, S. M.
1972-01-01
A sequential adaptive experimental design procedure for a related problem is studied. It is assumed that a finite set of potential linear models relating certain controlled variables to an observed variable is postulated, and that exactly one of these models is correct. The problem is to sequentially design most informative experiments so that the correct model equation can be determined with as little experimentation as possible. Discussion includes: structure of the linear models; prerequisite distribution theory; entropy functions and the Kullback-Leibler information function; the sequential decision procedure; and computer simulation results. An example of application is given.
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Koopman Operator Framework for Time Series Modeling and Analysis
NASA Astrophysics Data System (ADS)
Surana, Amit
2018-01-01
We propose an interdisciplinary framework for time series classification, forecasting, and anomaly detection by combining concepts from Koopman operator theory, machine learning, and linear systems and control theory. At the core of this framework is nonlinear dynamic generative modeling of time series using the Koopman operator which is an infinite-dimensional but linear operator. Rather than working with the underlying nonlinear model, we propose two simpler linear representations or model forms based on Koopman spectral properties. We show that these model forms are invariants of the generative model and can be readily identified directly from data using techniques for computing Koopman spectral properties without requiring the explicit knowledge of the generative model. We also introduce different notions of distances on the space of such model forms which is essential for model comparison/clustering. We employ the space of Koopman model forms equipped with distance in conjunction with classical machine learning techniques to develop a framework for automatic feature generation for time series classification. The forecasting/anomaly detection framework is based on using Koopman model forms along with classical linear systems and control approaches. We demonstrate the proposed framework for human activity classification, and for time series forecasting/anomaly detection in power grid application.
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Can we detect a nonlinear response to temperature in European plant phenology?
NASA Astrophysics Data System (ADS)
Jochner, Susanne; Sparks, Tim H.; Laube, Julia; Menzel, Annette
2016-10-01
Over a large temperature range, the statistical association between spring phenology and temperature is often regarded and treated as a linear function. There are suggestions that a sigmoidal relationship with definite upper and lower limits to leaf unfolding and flowering onset dates might be more realistic. We utilised European plant phenological records provided by the European phenology database PEP725 and gridded monthly mean temperature data for 1951-2012 calculated from the ENSEMBLES data set E-OBS (version 7.0). We analysed 568,456 observations of ten spring flowering or leafing phenophases derived from 3657 stations in 22 European countries in order to detect possible nonlinear responses to temperature. Linear response rates averaged for all stations ranged between -7.7 (flowering of hazel) and -2.7 days °C-1 (leaf unfolding of beech and oak). A lower sensitivity at the cooler end of the temperature range was detected for most phenophases. However, a similar lower sensitivity at the warmer end was not that evident. For only ˜14 % of the station time series (where a comparison between linear and nonlinear model was possible), nonlinear models described the relationship significantly better than linear models. Although in most cases simple linear models might be still sufficient to predict future changes, this linear relationship between phenology and temperature might not be appropriate when incorporating phenological data of very cold (and possibly very warm) environments. For these cases, extrapolations on the basis of linear models would introduce uncertainty in expected ecosystem changes.
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Gain optimization with non-linear controls
NASA Technical Reports Server (NTRS)
Slater, G. L.; Kandadai, R. D.
1984-01-01
An algorithm has been developed for the analysis and design of controls for non-linear systems. The technical approach is to use statistical linearization to model the non-linear dynamics of a system by a quasi-Gaussian model. A covariance analysis is performed to determine the behavior of the dynamical system and a quadratic cost function. Expressions for the cost function and its derivatives are determined so that numerical optimization techniques can be applied to determine optimal feedback laws. The primary application for this paper is centered about the design of controls for nominally linear systems but where the controls are saturated or limited by fixed constraints. The analysis is general, however, and numerical computation requires only that the specific non-linearity be considered in the analysis.
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NASA Astrophysics Data System (ADS)
Bertolesi, Elisa; Milani, Gabriele; Poggi, Carlo
2016-12-01
Two FE modeling techniques are presented and critically discussed for the non-linear analysis of tuff masonry panels reinforced with FRCM and subjected to standard diagonal compression tests. The specimens, tested at the University of Naples (Italy), are unreinforced and FRCM retrofitted walls. The extensive characterization of the constituent materials allowed adopting here very sophisticated numerical modeling techniques. In particular, here the results obtained by means of a micro-modeling strategy and homogenization approach are compared. The first modeling technique is a tridimensional heterogeneous micro-modeling where constituent materials (bricks, joints, reinforcing mortar and reinforcing grid) are modeled separately. The second approach is based on a two-step homogenization procedure, previously developed by the authors, where the elementary cell is discretized by means of three-noded plane stress elements and non-linear interfaces. The non-linear structural analyses are performed replacing the homogenized orthotropic continuum with a rigid element and non-linear spring assemblage (RBSM). All the simulations here presented are performed using the commercial software Abaqus. Pros and cons of the two approaches are herein discussed with reference to their reliability in reproducing global force-displacement curves and crack patterns, as well as to the rather different computational effort required by the two strategies.
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A position-aware linear solid constitutive model for peridynamics
Mitchell, John A.; Silling, Stewart A.; Littlewood, David J.
2015-11-06
A position-aware linear solid (PALS) peridynamic constitutive model is proposed for isotropic elastic solids. The PALS model addresses problems that arise, in ordinary peridynamic material models such as the linear peridynamic solid (LPS), due to incomplete neighborhoods near the surface of a body. We improved model behavior in the vicinity of free surfaces through the application of two influence functions that correspond, respectively, to the volumetric and deviatoric parts of the deformation. Furthermore, the model is position-aware in that the influence functions vary over the body and reflect the proximity of each material point to free surfaces. Demonstration calculations onmore » simple benchmark problems show a sharp reduction in error relative to the LPS model.« less
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A position-aware linear solid constitutive model for peridynamics
DOE Office of Scientific and Technical Information (OSTI.GOV)
Mitchell, John A.; Silling, Stewart A.; Littlewood, David J.
A position-aware linear solid (PALS) peridynamic constitutive model is proposed for isotropic elastic solids. The PALS model addresses problems that arise, in ordinary peridynamic material models such as the linear peridynamic solid (LPS), due to incomplete neighborhoods near the surface of a body. We improved model behavior in the vicinity of free surfaces through the application of two influence functions that correspond, respectively, to the volumetric and deviatoric parts of the deformation. Furthermore, the model is position-aware in that the influence functions vary over the body and reflect the proximity of each material point to free surfaces. Demonstration calculations onmore » simple benchmark problems show a sharp reduction in error relative to the LPS model.« less
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Investigation of the flight mechanics simulation of a hovering helicopter
NASA Technical Reports Server (NTRS)
Chaimovich, M.; Rosen, A.; Rand, O.; Mansur, M. H.; Tischler, M. B.
1992-01-01
The flight mechanics simulation of a hovering helicopter is investigated by comparing the results of two different numerical models with flight test data for a hovering AH-64 Apache. The two models are the U.S. Army BEMAP and the Technion model. These nonlinear models are linearized by applying a numerical linearization procedure. The results of the linear models are compared with identification results in terms of eigenvalues, stability and control derivatives, and frequency responses. Detailed time histories of the responses of the complete nonlinear models, as a result of various pilots' inputs, are compared with flight test results. In addition the sensitivity of the models to various effects are also investigated. The results are discussed and problematic aspects of the simulation are identified.
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Reduced-Order Model Based Feedback Control For Modified Hasegawa-Wakatani Model
DOE Office of Scientific and Technical Information (OSTI.GOV)
Goumiri, I. R.; Rowley, C. W.; Ma, Z.
2013-01-28
In this work, the development of model-based feedback control that stabilizes an unstable equilibrium is obtained for the Modi ed Hasegawa-Wakatani (MHW) equations, a classic model in plasma turbulence. First, a balanced truncation (a model reduction technique that has proven successful in ow control design problems) is applied to obtain a low dimensional model of the linearized MHW equation. Then a modelbased feedback controller is designed for the reduced order model using linear quadratic regulators (LQR). Finally, a linear quadratic gaussian (LQG) controller, which is more resistant to disturbances is deduced. The controller is applied on the non-reduced, nonlinear MHWmore » equations to stabilize the equilibrium and suppress the transition to drift-wave induced turbulence.« less
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Jaffe, B.E.; Rubin, D.M.
1996-01-01
The time-dependent response of sediment suspension to flow velocity was explored by modeling field measurements collected in the surf zone during a large storm. Linear and nonlinear models were created and tested using flow velocity as input and suspended-sediment concentration as output. A sequence of past velocities (velocity history), as well as velocity from the same instant as the suspended-sediment concentration, was used as input; this velocity history length was allowed to vary. The models also allowed for a lag between input (instantaneous velocity or end of velocity sequence) and output (suspended-sediment concentration). Predictions of concentration from instantaneous velocity or instantaneous velocity raised to a power (up to 8) using linear models were poor (correlation coefficients between predicted and observed concentrations were less than 0.10). Allowing a lag between velocity and concentration improved linear models (correlation coefficient of 0.30), with optimum lag time increasing with elevation above the seabed (from 1.5 s at 13 cm to 8.5 s at 60 cm). These lags are largely due to the time for an observed flow event to effect the bed and mix sediment upward. Using a velocity history further improved linear models (correlation coefficient of 0.43). The best linear model used 12.5 s of velocity history (approximately one wave period) to predict concentration. Nonlinear models gave better predictions than linear models, and, as with linear models, nonlinear models using a velocity history performed better than models using only instantaneous velocity as input. Including a lag time between the velocity and concentration also improved the predictions. The best model (correlation coefficient of 0.58) used 3 s (approximately a quarter wave period) of the cross-shore velocity squared, starting at 4.5 s before the observed concentration, to predict concentration. Using a velocity history increases the performance of the models by specifying a more complete description of the dynamical forcing of the flow (including accelerations and wave phase and shape) responsible for sediment suspension. Incorporating such a velocity history and a lag time into the formulation of the forcing for time-dependent models for sediment suspension in the surf zone will greatly increase our ability to predict suspended-sediment transport.
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Clark, M.P.; Rupp, D.E.; Woods, R.A.; Tromp-van, Meerveld; Peters, N.E.; Freer, J.E.
2009-01-01
The purpose of this paper is to identify simple connections between observations of hydrological processes at the hillslope scale and observations of the response of watersheds following rainfall, with a view to building a parsimonious model of catchment processes. The focus is on the well-studied Panola Mountain Research Watershed (PMRW), Georgia, USA. Recession analysis of discharge Q shows that while the relationship between dQ/dt and Q is approximately consistent with a linear reservoir for the hillslope, there is a deviation from linearity that becomes progressively larger with increasing spatial scale. To account for these scale differences conceptual models of streamflow recession are defined at both the hillslope scale and the watershed scale, and an assessment made as to whether models at the hillslope scale can be aggregated to be consistent with models at the watershed scale. Results from this study show that a model with parallel linear reservoirs provides the most plausible explanation (of those tested) for both the linear hillslope response to rainfall and non-linear recession behaviour observed at the watershed outlet. In this model each linear reservoir is associated with a landscape type. The parallel reservoir model is consistent with both geochemical analyses of hydrological flow paths and water balance estimates of bedrock recharge. Overall, this study demonstrates that standard approaches of using recession analysis to identify the functional form of storage-discharge relationships identify model structures that are inconsistent with field evidence, and that recession analysis at multiple spatial scales can provide useful insights into catchment behaviour. Copyright ?? 2008 John Wiley & Sons, Ltd.
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Interpreting linear support vector machine models with heat map molecule coloring
2011-01-01
Background Model-based virtual screening plays an important role in the early drug discovery stage. The outcomes of high-throughput screenings are a valuable source for machine learning algorithms to infer such models. Besides a strong performance, the interpretability of a machine learning model is a desired property to guide the optimization of a compound in later drug discovery stages. Linear support vector machines showed to have a convincing performance on large-scale data sets. The goal of this study is to present a heat map molecule coloring technique to interpret linear support vector machine models. Based on the weights of a linear model, the visualization approach colors each atom and bond of a compound according to its importance for activity. Results We evaluated our approach on a toxicity data set, a chromosome aberration data set, and the maximum unbiased validation data sets. The experiments show that our method sensibly visualizes structure-property and structure-activity relationships of a linear support vector machine model. The coloring of ligands in the binding pocket of several crystal structures of a maximum unbiased validation data set target indicates that our approach assists to determine the correct ligand orientation in the binding pocket. Additionally, the heat map coloring enables the identification of substructures important for the binding of an inhibitor. Conclusions In combination with heat map coloring, linear support vector machine models can help to guide the modification of a compound in later stages of drug discovery. Particularly substructures identified as important by our method might be a starting point for optimization of a lead compound. The heat map coloring should be considered as complementary to structure based modeling approaches. As such, it helps to get a better understanding of the binding mode of an inhibitor. PMID:21439031
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Anderson, Emma L; Tilling, Kate; Fraser, Abigail; Macdonald-Wallis, Corrie; Emmett, Pauline; Cribb, Victoria; Northstone, Kate; Lawlor, Debbie A; Howe, Laura D
2013-07-01
Methods for the assessment of changes in dietary intake across the life course are underdeveloped. We demonstrate the use of linear-spline multilevel models to summarize energy-intake trajectories through childhood and adolescence and their application as exposures, outcomes, or mediators. The Avon Longitudinal Study of Parents and Children assessed children's dietary intake several times between ages 3 and 13 years, using both food frequency questionnaires (FFQs) and 3-day food diaries. We estimated energy-intake trajectories for 12,032 children using linear-spline multilevel models. We then assessed the associations of these trajectories with maternal body mass index (BMI), and later offspring BMI, and also their role in mediating the relation between maternal and offspring BMIs. Models estimated average and individual energy intake at 3 years, and linear changes in energy intake from age 3 to 7 years and from age 7 to 13 years. By including the exposure (in this example, maternal BMI) in the multilevel model, we were able to estimate the average energy-intake trajectories across levels of the exposure. When energy-intake trajectories are the exposure for a later outcome (in this case offspring BMI) or a mediator (between maternal and offspring BMI), results were similar, whether using a two-step process (exporting individual-level intercepts and slopes from multilevel models and using these in linear regression/path analysis), or a single-step process (multivariate multilevel models). Trajectories were similar when FFQs and food diaries were assessed either separately, or when combined into one model. Linear-spline multilevel models provide useful summaries of trajectories of dietary intake that can be used as an exposure, outcome, or mediator.
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Zörnig, Peter
2015-08-01
We present integer programming models for some variants of the farthest string problem. The number of variables and constraints is substantially less than that of the integer linear programming models known in the literature. Moreover, the solution of the linear programming-relaxation contains only a small proportion of noninteger values, which considerably simplifies the rounding process. Numerical tests have shown excellent results, especially when a small set of long sequences is given.
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Reliability Analysis of the Gradual Degradation of Semiconductor Devices.
1983-07-20
under the heading of linear models or linear statistical models . 3 ,4 We have not used this material in this report. Assuming catastrophic failure when...assuming a catastrophic model . In this treatment we first modify our system loss formula and then proceed to the actual analysis. II. ANALYSIS OF...Failure Time 1 Ti Ti 2 T2 T2 n Tn n and are easily analyzed by simple linear regression. Since we have assumed a log normal/Arrhenius activation
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Modelling and Inverse-Modelling: Experiences with O.D.E. Linear Systems in Engineering Courses
ERIC Educational Resources Information Center
Martinez-Luaces, Victor
2009-01-01
In engineering careers courses, differential equations are widely used to solve problems concerned with modelling. In particular, ordinary differential equations (O.D.E.) linear systems appear regularly in Chemical Engineering, Food Technology Engineering and Environmental Engineering courses, due to the usefulness in modelling chemical kinetics,…
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A Linear Variable-[theta] Model for Measuring Individual Differences in Response Precision
ERIC Educational Resources Information Center
Ferrando, Pere J.
2011-01-01
Models for measuring individual response precision have been proposed for binary and graded responses. However, more continuous formats are quite common in personality measurement and are usually analyzed with the linear factor analysis model. This study extends the general Gaussian person-fluctuation model to the continuous-response case and…
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NASA Astrophysics Data System (ADS)
Mahaboob, B.; Venkateswarlu, B.; Sankar, J. Ravi; Balasiddamuni, P.
2017-11-01
This paper uses matrix calculus techniques to obtain Nonlinear Least Squares Estimator (NLSE), Maximum Likelihood Estimator (MLE) and Linear Pseudo model for nonlinear regression model. David Pollard and Peter Radchenko [1] explained analytic techniques to compute the NLSE. However the present research paper introduces an innovative method to compute the NLSE using principles in multivariate calculus. This study is concerned with very new optimization techniques used to compute MLE and NLSE. Anh [2] derived NLSE and MLE of a heteroscedatistic regression model. Lemcoff [3] discussed a procedure to get linear pseudo model for nonlinear regression model. In this research article a new technique is developed to get the linear pseudo model for nonlinear regression model using multivariate calculus. The linear pseudo model of Edmond Malinvaud [4] has been explained in a very different way in this paper. David Pollard et.al used empirical process techniques to study the asymptotic of the LSE (Least-squares estimation) for the fitting of nonlinear regression function in 2006. In Jae Myung [13] provided a go conceptual for Maximum likelihood estimation in his work “Tutorial on maximum likelihood estimation
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Right-Sizing Statistical Models for Longitudinal Data
Wood, Phillip K.; Steinley, Douglas; Jackson, Kristina M.
2015-01-01
Arguments are proposed that researchers using longitudinal data should consider more and less complex statistical model alternatives to their initially chosen techniques in an effort to “right-size” the model to the data at hand. Such model comparisons may alert researchers who use poorly fitting overly parsimonious models to more complex better fitting alternatives, and, alternatively, may identify more parsimonious alternatives to overly complex (and perhaps empirically under-identified and/or less powerful) statistical models. A general framework is proposed for considering (often nested) relationships between a variety of psychometric and growth curve models. A three-step approach is proposed in which models are evaluated based on the number and patterning of variance components prior to selection of better-fitting growth models that explain both mean and variation/covariation patterns. The orthogonal, free-curve slope-intercept (FCSI) growth model is considered as a general model which includes, as special cases, many models including the Factor Mean model (FM, McArdle & Epstein, 1987), McDonald's (1967) linearly constrained factor model, Hierarchical Linear Models (HLM), Repeated Measures MANOVA, and the Linear Slope Intercept (LinearSI) Growth Model. The FCSI model, in turn, is nested within the Tuckerized factor model. The approach is illustrated by comparing alternative models in a longitudinal study of children's vocabulary and by comparison of several candidate parametric growth and chronometric models in a Monte Carlo study. PMID:26237507
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Right-sizing statistical models for longitudinal data.
Wood, Phillip K; Steinley, Douglas; Jackson, Kristina M
2015-12-01
Arguments are proposed that researchers using longitudinal data should consider more and less complex statistical model alternatives to their initially chosen techniques in an effort to "right-size" the model to the data at hand. Such model comparisons may alert researchers who use poorly fitting, overly parsimonious models to more complex, better-fitting alternatives and, alternatively, may identify more parsimonious alternatives to overly complex (and perhaps empirically underidentified and/or less powerful) statistical models. A general framework is proposed for considering (often nested) relationships between a variety of psychometric and growth curve models. A 3-step approach is proposed in which models are evaluated based on the number and patterning of variance components prior to selection of better-fitting growth models that explain both mean and variation-covariation patterns. The orthogonal free curve slope intercept (FCSI) growth model is considered a general model that includes, as special cases, many models, including the factor mean (FM) model (McArdle & Epstein, 1987), McDonald's (1967) linearly constrained factor model, hierarchical linear models (HLMs), repeated-measures multivariate analysis of variance (MANOVA), and the linear slope intercept (linearSI) growth model. The FCSI model, in turn, is nested within the Tuckerized factor model. The approach is illustrated by comparing alternative models in a longitudinal study of children's vocabulary and by comparing several candidate parametric growth and chronometric models in a Monte Carlo study. (c) 2015 APA, all rights reserved).
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Evaluation of confidence intervals for a steady-state leaky aquifer model
Christensen, S.; Cooley, R.L.
1999-01-01
The fact that dependent variables of groundwater models are generally nonlinear functions of model parameters is shown to be a potentially significant factor in calculating accurate confidence intervals for both model parameters and functions of the parameters, such as the values of dependent variables calculated by the model. The Lagrangian method of Vecchia and Cooley [Vecchia, A.V. and Cooley, R.L., Water Resources Research, 1987, 23(7), 1237-1250] was used to calculate nonlinear Scheffe-type confidence intervals for the parameters and the simulated heads of a steady-state groundwater flow model covering 450 km2 of a leaky aquifer. The nonlinear confidence intervals are compared to corresponding linear intervals. As suggested by the significant nonlinearity of the regression model, linear confidence intervals are often not accurate. The commonly made assumption that widths of linear confidence intervals always underestimate the actual (nonlinear) widths was not correct. Results show that nonlinear effects can cause the nonlinear intervals to be asymmetric and either larger or smaller than the linear approximations. Prior information on transmissivities helps reduce the size of the confidence intervals, with the most notable effects occurring for the parameters on which there is prior information and for head values in parameter zones for which there is prior information on the parameters.The fact that dependent variables of groundwater models are generally nonlinear functions of model parameters is shown to be a potentially significant factor in calculating accurate confidence intervals for both model parameters and functions of the parameters, such as the values of dependent variables calculated by the model. The Lagrangian method of Vecchia and Cooley was used to calculate nonlinear Scheffe-type confidence intervals for the parameters and the simulated heads of a steady-state groundwater flow model covering 450 km2 of a leaky aquifer. The nonlinear confidence intervals are compared to corresponding linear intervals. As suggested by the significant nonlinearity of the regression model, linear confidence intervals are often not accurate. The commonly made assumption that widths of linear confidence intervals always underestimate the actual (nonlinear) widths was not correct. Results show that nonlinear effects can cause the nonlinear intervals to be asymmetric and either larger or smaller than the linear approximations. Prior information on transmissivities helps reduce the size of the confidence intervals, with the most notable effects occurring for the parameters on which there is prior information and for head values in parameter zones for which there is prior information on the parameters.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Spears, Robert Edward; Coleman, Justin Leigh
2015-08-01
Seismic analysis of nuclear structures is routinely performed using guidance provided in “Seismic Analysis of Safety-Related Nuclear Structures and Commentary (ASCE 4, 1998).” This document, which is currently under revision, provides detailed guidance on linear seismic soil-structure-interaction (SSI) analysis of nuclear structures. To accommodate the linear analysis, soil material properties are typically developed as shear modulus and damping ratio versus cyclic shear strain amplitude. A new Appendix in ASCE 4-2014 (draft) is being added to provide guidance for nonlinear time domain SSI analysis. To accommodate the nonlinear analysis, a more appropriate form of the soil material properties includes shear stressmore » and energy absorbed per cycle versus shear strain. Ideally, nonlinear soil model material properties would be established with soil testing appropriate for the nonlinear constitutive model being used. However, much of the soil testing done for SSI analysis is performed for use with linear analysis techniques. Consequently, a method is described in this paper that uses soil test data intended for linear analysis to develop nonlinear soil material properties. To produce nonlinear material properties that are equivalent to the linear material properties, the linear and nonlinear model hysteresis loops are considered. For equivalent material properties, the shear stress at peak shear strain and energy absorbed per cycle should match when comparing the linear and nonlinear model hysteresis loops. Consequently, nonlinear material properties are selected based on these criteria.« less
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Modeling turbidity and flow at daily steps in karst using ARIMA/ARFIMA-GARCH error models
NASA Astrophysics Data System (ADS)
Massei, N.
2013-12-01
Hydrological and physico-chemical variations recorded at karst springs usually reflect highly non-linear processes and the corresponding time series are then very often also highly non-linear. Among others, turbidity, as an important parameter regarding water quality and management, is a very complex response of karst systems to rain events, involving direct transfer of particles from point-source recharge as well as resuspension of particles previously deposited and stored within the system. For those reasons, turbidity modeling has not been well taken in karst hydrological models so far. Most of the time, the modeling approaches would involve stochastic linear models such ARIMA-type models and their derivatives (ARMA, ARMAX, ARIMAX, ARFIMA...). Yet, linear models usually fail to represent well the whole (stochastic) process variability, and their residuals still contain useful information that can be used to either understand the whole variability or to enhance short-term predictability and forecasting. Model residuals are actually not i.i.d., which can be identified by the fact that squared residuals still present clear and significant serial correlation. Indeed, high (low) amplitudes are followed in time by high (low) amplitudes, which can be seen on residuals time series as periods of time during which amplitudes are higher (lower) then the mean amplitude. This is known as the ARCH effet (AutoRegressive Conditional Heteroskedasticity), and the corresponding non-linear process affecting residuals of a linear model can be modeled using ARCH or generalized ARCH (GARCH) non-linear modeling, which approaches are very well known in econometrics. Here we investigated the capability of ARIMA-GARCH error models to represent a ~20-yr daily turbidity time series recorded at a karst spring used for water supply of the city of Le Havre (Upper Normandy, France). ARIMA and ARFIMA models were used to represent the mean behavior of the time series and the residuals clearly appeared to present a pronounced ARCH effect, as confirmed by Ljung-Box and McLeod-Li tests. We then identified and fitted GARCH models to the residuals of ARIMA and ARFIMA models in order to model the conditional variance and volatility of the turbidity time series. The results eventually showed that serial correlation was succesfully removed in the last standardized residuals of the GARCH model, and hence that the ARIMA-GARCH error model appeared consistent for modeling such time series. The approach finally improved short-term (e.g a few steps-ahead) turbidity forecasting.
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A comparison of washout filters using a human dynamic orientation model. M.S. Thesis
NASA Technical Reports Server (NTRS)
Riedel, S. A.
1977-01-01
The Ormsby model of human dynamic orientation, a discrete time computer program, was used to provide a vestibular explanation for observed differences between two washout schemes. These washout schemes, a linear washout and a nonlinear washout, were subjectively evaluated. It was found that the linear washout presented false rate cues, causing pilots to rate the simulation fidelity of the linear scheme much lower than the nonlinear scheme. By inputting these motion histories into the Ormsby model, it was shown that the linear filter causes discontinuities in the pilot's perceived angular velocity, resulting in the sensation of an anomalous rate cue. This phenomenon does not occur with the use of the nonlinear filter.
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Simulating the dynamics of linear forests in great plains agroecosystems under changing climates
Qinfeng Guo; J. Brandle; Michele Schoeneberger; D. Buettner
2004-01-01
Most forest growth models are not suitable for the highly fragmented, linear (or linearly shaped) forests in the Great Plains agroecosystems (e.g., windbreaks, riparian forest buffers), where such forests are a minor but ecologically important component of the land mosaics. This study used SEEI)SCAPE, a recently modified gap model designed for cultivated land mosaics...
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Coarse-grained description of cosmic structure from Szekeres models
DOE Office of Scientific and Technical Information (OSTI.GOV)
Sussman, Roberto A.; Gaspar, I. Delgado; Hidalgo, Juan Carlos, E-mail: sussman@nucleares.unam.mx, E-mail: ismael.delgadog@uaem.edu.mx, E-mail: hidalgo@fis.unam.mx
2016-03-01
We show that the full dynamical freedom of the well known Szekeres models allows for the description of elaborated 3-dimensional networks of cold dark matter structures (over-densities and/or density voids) undergoing ''pancake'' collapse. By reducing Einstein's field equations to a set of evolution equations, which themselves reduce in the linear limit to evolution equations for linear perturbations, we determine the dynamics of such structures, with the spatial comoving location of each structure uniquely specified by standard early Universe initial conditions. By means of a representative example we examine in detail the density contrast, the Hubble flow and peculiar velocities ofmore » structures that evolved, from linear initial data at the last scattering surface, to fully non-linear 10–20 Mpc scale configurations today. To motivate further research, we provide a qualitative discussion on the connection of Szekeres models with linear perturbations and the pancake collapse of the Zeldovich approximation. This type of structure modelling provides a coarse grained—but fully relativistic non-linear and non-perturbative —description of evolving large scale cosmic structures before their virialisation, and as such it has an enormous potential for applications in cosmological research.« less
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Non-Linear Finite Element Modeling of THUNDER Piezoelectric Actuators
NASA Technical Reports Server (NTRS)
Taleghani, Barmac K.; Campbell, Joel F.
1999-01-01
A NASTRAN non-linear finite element model has been developed for predicting the dome heights of THUNDER (THin Layer UNimorph Ferroelectric DrivER) piezoelectric actuators. To analytically validate the finite element model, a comparison was made with a non-linear plate solution using Von Karmen's approximation. A 500 volt input was used to examine the actuator deformation. The NASTRAN finite element model was also compared with experimental results. Four groups of specimens were fabricated and tested. Four different input voltages, which included 120, 160, 200, and 240 Vp-p with a 0 volts offset, were used for this comparison.
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Analysis of an inventory model for both linearly decreasing demand and holding cost
NASA Astrophysics Data System (ADS)
Malik, A. K.; Singh, Parth Raj; Tomar, Ajay; Kumar, Satish; Yadav, S. K.
2016-03-01
This study proposes the analysis of an inventory model for linearly decreasing demand and holding cost for non-instantaneous deteriorating items. The inventory model focuses on commodities having linearly decreasing demand without shortages. The holding cost doesn't remain uniform with time due to any form of variation in the time value of money. Here we consider that the holding cost decreases with respect to time. The optimal time interval for the total profit and the optimal order quantity are determined. The developed inventory model is pointed up through a numerical example. It also includes the sensitivity analysis.
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Multivariate Strategies in Functional Magnetic Resonance Imaging
ERIC Educational Resources Information Center
Hansen, Lars Kai
2007-01-01
We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a "mind reading" predictive multivariate fMRI model.
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MULTIVARIATE LINEAR MIXED MODELS FOR MULTIPLE OUTCOMES. (R824757)
We propose a multivariate linear mixed (MLMM) for the analysis of multiple outcomes, which generalizes the latent variable model of Sammel and Ryan. The proposed model assumes a flexible correlation structure among the multiple outcomes, and allows a global test of the impact of ...
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A green vehicle routing problem with customer satisfaction criteria
NASA Astrophysics Data System (ADS)
Afshar-Bakeshloo, M.; Mehrabi, A.; Safari, H.; Maleki, M.; Jolai, F.
2016-12-01
This paper develops an MILP model, named Satisfactory-Green Vehicle Routing Problem. It consists of routing a heterogeneous fleet of vehicles in order to serve a set of customers within predefined time windows. In this model in addition to the traditional objective of the VRP, both the pollution and customers' satisfaction have been taken into account. Meanwhile, the introduced model prepares an effective dashboard for decision-makers that determines appropriate routes, the best mixed fleet, speed and idle time of vehicles. Additionally, some new factors evaluate the greening of each decision based on three criteria. This model applies piecewise linear functions (PLFs) to linearize a nonlinear fuzzy interval for incorporating customers' satisfaction into other linear objectives. We have presented a mixed integer linear programming formulation for the S-GVRP. This model enriches managerial insights by providing trade-offs between customers' satisfaction, total costs and emission levels. Finally, we have provided a numerical study for showing the applicability of the model.
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NASA Astrophysics Data System (ADS)
Samhouri, M.; Al-Ghandoor, A.; Fouad, R. H.
2009-08-01
In this study two techniques, for modeling electricity consumption of the Jordanian industrial sector, are presented: (i) multivariate linear regression and (ii) neuro-fuzzy models. Electricity consumption is modeled as function of different variables such as number of establishments, number of employees, electricity tariff, prevailing fuel prices, production outputs, capacity utilizations, and structural effects. It was found that industrial production and capacity utilization are the most important variables that have significant effect on future electrical power demand. The results showed that both the multivariate linear regression and neuro-fuzzy models are generally comparable and can be used adequately to simulate industrial electricity consumption. However, comparison that is based on the square root average squared error of data suggests that the neuro-fuzzy model performs slightly better for future prediction of electricity consumption than the multivariate linear regression model. Such results are in full agreement with similar work, using different methods, for other countries.
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Size effects in non-linear heat conduction with flux-limited behaviors
NASA Astrophysics Data System (ADS)
Li, Shu-Nan; Cao, Bing-Yang
2017-11-01
Size effects are discussed for several non-linear heat conduction models with flux-limited behaviors, including the phonon hydrodynamic, Lagrange multiplier, hierarchy moment, nonlinear phonon hydrodynamic, tempered diffusion, thermon gas and generalized nonlinear models. For the phonon hydrodynamic, Lagrange multiplier and tempered diffusion models, heat flux will not exist in problems with sufficiently small scale. The existence of heat flux needs the sizes of heat conduction larger than their corresponding critical sizes, which are determined by the physical properties and boundary temperatures. The critical sizes can be regarded as the theoretical limits of the applicable ranges for these non-linear heat conduction models with flux-limited behaviors. For sufficiently small scale heat conduction, the phonon hydrodynamic and Lagrange multiplier models can also predict the theoretical possibility of violating the second law and multiplicity. Comparisons are also made between these non-Fourier models and non-linear Fourier heat conduction in the type of fast diffusion, which can also predict flux-limited behaviors.
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A penalized framework for distributed lag non-linear models.
Gasparrini, Antonio; Scheipl, Fabian; Armstrong, Ben; Kenward, Michael G
2017-09-01
Distributed lag non-linear models (DLNMs) are a modelling tool for describing potentially non-linear and delayed dependencies. Here, we illustrate an extension of the DLNM framework through the use of penalized splines within generalized additive models (GAM). This extension offers built-in model selection procedures and the possibility of accommodating assumptions on the shape of the lag structure through specific penalties. In addition, this framework includes, as special cases, simpler models previously proposed for linear relationships (DLMs). Alternative versions of penalized DLNMs are compared with each other and with the standard unpenalized version in a simulation study. Results show that this penalized extension to the DLNM class provides greater flexibility and improved inferential properties. The framework exploits recent theoretical developments of GAMs and is implemented using efficient routines within freely available software. Real-data applications are illustrated through two reproducible examples in time series and survival analysis. © 2017 The Authors Biometrics published by Wiley Periodicals, Inc. on behalf of International Biometric Society.
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Takagi-Sugeno-Kang fuzzy models of the rainfall-runoff transformation
NASA Astrophysics Data System (ADS)
Jacquin, A. P.; Shamseldin, A. Y.
2009-04-01
Fuzzy inference systems, or fuzzy models, are non-linear models that describe the relation between the inputs and the output of a real system using a set of fuzzy IF-THEN rules. This study deals with the application of Takagi-Sugeno-Kang type fuzzy models to the development of rainfall-runoff models operating on a daily basis, using a system based approach. The models proposed are classified in two types, each intended to account for different kinds of dominant non-linear effects in the rainfall-runoff relationship. Fuzzy models type 1 are intended to incorporate the effect of changes in the prevailing soil moisture content, while fuzzy models type 2 address the phenomenon of seasonality. Each model type consists of five fuzzy models of increasing complexity; the most complex fuzzy model of each model type includes all the model components found in the remaining fuzzy models of the respective type. The models developed are applied to data of six catchments from different geographical locations and sizes. Model performance is evaluated in terms of two measures of goodness of fit, namely the Nash-Sutcliffe criterion and the index of volumetric fit. The results of the fuzzy models are compared with those of the Simple Linear Model, the Linear Perturbation Model and the Nearest Neighbour Linear Perturbation Model, which use similar input information. Overall, the results of this study indicate that Takagi-Sugeno-Kang fuzzy models are a suitable alternative for modelling the rainfall-runoff relationship. However, it is also observed that increasing the complexity of the model structure does not necessarily produce an improvement in the performance of the fuzzy models. The relative importance of the different model components in determining the model performance is evaluated through sensitivity analysis of the model parameters in the accompanying study presented in this meeting. Acknowledgements: We would like to express our gratitude to Prof. Kieran M. O'Connor from the National University of Ireland, Galway, for providing the data used in this study.
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ERIC Educational Resources Information Center
Deboeck, Pascal R.; Boker, Steven M.; Bergeman, C. S.
2008-01-01
Among the many methods available for modeling intraindividual time series, differential equation modeling has several advantages that make it promising for applications to psychological data. One interesting differential equation model is that of the damped linear oscillator (DLO), which can be used to model variables that have a tendency to…
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The Dangers of Estimating V˙O2max Using Linear, Nonexercise Prediction Models.
Nevill, Alan M; Cooke, Carlton B
2017-05-01
This study aimed to compare the accuracy and goodness of fit of two competing models (linear vs allometric) when estimating V˙O2max (mL·kg·min) using nonexercise prediction models. The two competing models were fitted to the V˙O2max (mL·kg·min) data taken from two previously published studies. Study 1 (the Allied Dunbar National Fitness Survey) recruited 1732 randomly selected healthy participants, 16 yr and older, from 30 English parliamentary constituencies. Estimates of V˙O2max were obtained using a progressive incremental test on a motorized treadmill. In study 2, maximal oxygen uptake was measured directly during a fatigue limited treadmill test in older men (n = 152) and women (n = 146) 55 to 86 yr old. In both studies, the quality of fit associated with estimating V˙O2max (mL·kg·min) was superior using allometric rather than linear (additive) models based on all criteria (R, maximum log-likelihood, and Akaike information criteria). Results suggest that linear models will systematically overestimate V˙O2max for participants in their 20s and underestimate V˙O2max for participants in their 60s and older. The residuals saved from the linear models were neither normally distributed nor independent of the predicted values nor age. This will probably explain the absence of a key quadratic age term in the linear models, crucially identified using allometric models. Not only does the curvilinear age decline within an exponential function follow a more realistic age decline (the right-hand side of a bell-shaped curve), but the allometric models identified either a stature-to-body mass ratio (study 1) or a fat-free mass-to-body mass ratio (study 2), both associated with leanness when estimating V˙O2max. Adopting allometric models will provide more accurate predictions of V˙O2max (mL·kg·min) using plausible, biologically sound, and interpretable models.
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A note on probabilistic models over strings: the linear algebra approach.
Bouchard-Côté, Alexandre
2013-12-01
Probabilistic models over strings have played a key role in developing methods that take into consideration indels as phylogenetically informative events. There is an extensive literature on using automata and transducers on phylogenies to do inference on these probabilistic models, in which an important theoretical question is the complexity of computing the normalization of a class of string-valued graphical models. This question has been investigated using tools from combinatorics, dynamic programming, and graph theory, and has practical applications in Bayesian phylogenetics. In this work, we revisit this theoretical question from a different point of view, based on linear algebra. The main contribution is a set of results based on this linear algebra view that facilitate the analysis and design of inference algorithms on string-valued graphical models. As an illustration, we use this method to give a new elementary proof of a known result on the complexity of inference on the "TKF91" model, a well-known probabilistic model over strings. Compared to previous work, our proving method is easier to extend to other models, since it relies on a novel weak condition, triangular transducers, which is easy to establish in practice. The linear algebra view provides a concise way of describing transducer algorithms and their compositions, opens the possibility of transferring fast linear algebra libraries (for example, based on GPUs), as well as low rank matrix approximation methods, to string-valued inference problems.
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Ahmadpanah, J; Ghavi Hossein-Zadeh, N; Shadparvar, A A; Pakdel, A
2017-02-01
1. The objectives of the current study were to investigate the effect of incidence rate (5%, 10%, 20%, 30% and 50%) of ascites syndrome on the expression of genetic characteristics for body weight at 5 weeks of age (BW5) and AS and to compare different methods of genetic parameter estimation for these traits. 2. Based on stochastic simulation, a population with discrete generations was created in which random mating was used for 10 generations. Two methods of restricted maximum likelihood and Bayesian approach via Gibbs sampling were used for the estimation of genetic parameters. A bivariate model including maternal effects was used. The root mean square error for direct heritabilities was also calculated. 3. The results showed that when incidence rates of ascites increased from 5% to 30%, the heritability of AS increased from 0.013 and 0.005 to 0.110 and 0.162 for linear and threshold models, respectively. 4. Maternal effects were significant for both BW5 and AS. Genetic correlations were decreased by increasing incidence rates of ascites in the population from 0.678 and 0.587 at 5% level of ascites to 0.393 and -0.260 at 50% occurrence for linear and threshold models, respectively. 5. The RMSE of direct heritability from true values for BW5 was greater based on a linear-threshold model compared with the linear model of analysis (0.0092 vs. 0.0015). The RMSE of direct heritability from true values for AS was greater based on a linear-linear model (1.21 vs. 1.14). 6. In order to rank birds for ascites incidence, it is recommended to use a threshold model because it resulted in higher heritability estimates compared with the linear model and that BW5 could be one of the main components of selection goals.
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Hemanth, M; Deoli, Shilpi; Raghuveer, H P; Rani, M S; Hegde, Chatura; Vedavathi, B
2015-09-01
Simulation of periodontal ligament (PDL) using non-linear finite element method (FEM) analysis gives better insight into understanding of the biology of tooth movement. The stresses in the PDL were evaluated for intrusion and lingual root torque using non-linear properties. A three-dimensional (3D) FEM model of the maxillary incisors was generated using Solidworks modeling software. Stresses in the PDL were evaluated for intrusive and lingual root torque movements by 3D FEM using ANSYS software. These stresses were compared with linear and non-linear analyses. For intrusive and lingual root torque movements, distribution of stress over the PDL was within the range of optimal stress value as proposed by Lee, but was exceeding the force system given by Proffit as optimum forces for orthodontic tooth movement with linear properties. When same force load was applied in non-linear analysis, stresses were more compared to linear analysis and were beyond the optimal stress range as proposed by Lee for both intrusive and lingual root torque. To get the same stress as linear analysis, iterations were done using non-linear properties and the force level was reduced. This shows that the force level required for non-linear analysis is lesser than that of linear analysis.
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Grajeda, Laura M; Ivanescu, Andrada; Saito, Mayuko; Crainiceanu, Ciprian; Jaganath, Devan; Gilman, Robert H; Crabtree, Jean E; Kelleher, Dermott; Cabrera, Lilia; Cama, Vitaliano; Checkley, William
2016-01-01
Childhood growth is a cornerstone of pediatric research. Statistical models need to consider individual trajectories to adequately describe growth outcomes. Specifically, well-defined longitudinal models are essential to characterize both population and subject-specific growth. Linear mixed-effect models with cubic regression splines can account for the nonlinearity of growth curves and provide reasonable estimators of population and subject-specific growth, velocity and acceleration. We provide a stepwise approach that builds from simple to complex models, and account for the intrinsic complexity of the data. We start with standard cubic splines regression models and build up to a model that includes subject-specific random intercepts and slopes and residual autocorrelation. We then compared cubic regression splines vis-à-vis linear piecewise splines, and with varying number of knots and positions. Statistical code is provided to ensure reproducibility and improve dissemination of methods. Models are applied to longitudinal height measurements in a cohort of 215 Peruvian children followed from birth until their fourth year of life. Unexplained variability, as measured by the variance of the regression model, was reduced from 7.34 when using ordinary least squares to 0.81 (p < 0.001) when using a linear mixed-effect models with random slopes and a first order continuous autoregressive error term. There was substantial heterogeneity in both the intercept (p < 0.001) and slopes (p < 0.001) of the individual growth trajectories. We also identified important serial correlation within the structure of the data (ρ = 0.66; 95 % CI 0.64 to 0.68; p < 0.001), which we modeled with a first order continuous autoregressive error term as evidenced by the variogram of the residuals and by a lack of association among residuals. The final model provides a parametric linear regression equation for both estimation and prediction of population- and individual-level growth in height. We show that cubic regression splines are superior to linear regression splines for the case of a small number of knots in both estimation and prediction with the full linear mixed effect model (AIC 19,352 vs. 19,598, respectively). While the regression parameters are more complex to interpret in the former, we argue that inference for any problem depends more on the estimated curve or differences in curves rather than the coefficients. Moreover, use of cubic regression splines provides biological meaningful growth velocity and acceleration curves despite increased complexity in coefficient interpretation. Through this stepwise approach, we provide a set of tools to model longitudinal childhood data for non-statisticians using linear mixed-effect models.
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Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data
Zhao, Xin; Cheung, Leo Wang-Kit
2007-01-01
Background Designing appropriate machine learning methods for identifying genes that have a significant discriminating power for disease outcomes has become more and more important for our understanding of diseases at genomic level. Although many machine learning methods have been developed and applied to the area of microarray gene expression data analysis, the majority of them are based on linear models, which however are not necessarily appropriate for the underlying connection between the target disease and its associated explanatory genes. Linear model based methods usually also bring in false positive significant features more easily. Furthermore, linear model based algorithms often involve calculating the inverse of a matrix that is possibly singular when the number of potentially important genes is relatively large. This leads to problems of numerical instability. To overcome these limitations, a few non-linear methods have recently been introduced to the area. Many of the existing non-linear methods have a couple of critical problems, the model selection problem and the model parameter tuning problem, that remain unsolved or even untouched. In general, a unified framework that allows model parameters of both linear and non-linear models to be easily tuned is always preferred in real-world applications. Kernel-induced learning methods form a class of approaches that show promising potentials to achieve this goal. Results A hierarchical statistical model named kernel-imbedded Gaussian process (KIGP) is developed under a unified Bayesian framework for binary disease classification problems using microarray gene expression data. In particular, based on a probit regression setting, an adaptive algorithm with a cascading structure is designed to find the appropriate kernel, to discover the potentially significant genes, and to make the optimal class prediction accordingly. A Gibbs sampler is built as the core of the algorithm to make Bayesian inferences. Simulation studies showed that, even without any knowledge of the underlying generative model, the KIGP performed very close to the theoretical Bayesian bound not only in the case with a linear Bayesian classifier but also in the case with a very non-linear Bayesian classifier. This sheds light on its broader usability to microarray data analysis problems, especially to those that linear methods work awkwardly. The KIGP was also applied to four published microarray datasets, and the results showed that the KIGP performed better than or at least as well as any of the referred state-of-the-art methods did in all of these cases. Conclusion Mathematically built on the kernel-induced feature space concept under a Bayesian framework, the KIGP method presented in this paper provides a unified machine learning approach to explore both the linear and the possibly non-linear underlying relationship between the target features of a given binary disease classification problem and the related explanatory gene expression data. More importantly, it incorporates the model parameter tuning into the framework. The model selection problem is addressed in the form of selecting a proper kernel type. The KIGP method also gives Bayesian probabilistic predictions for disease classification. These properties and features are beneficial to most real-world applications. The algorithm is naturally robust in numerical computation. The simulation studies and the published data studies demonstrated that the proposed KIGP performs satisfactorily and consistently. PMID:17328811
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Linear summation of outputs in a balanced network model of motor cortex
Capaday, Charles; van Vreeswijk, Carl
2015-01-01
Given the non-linearities of the neural circuitry's elements, we would expect cortical circuits to respond non-linearly when activated. Surprisingly, when two points in the motor cortex are activated simultaneously, the EMG responses are the linear sum of the responses evoked by each of the points activated separately. Additionally, the corticospinal transfer function is close to linear, implying that the synaptic interactions in motor cortex must be effectively linear. To account for this, here we develop a model of motor cortex composed of multiple interconnected points, each comprised of reciprocally connected excitatory and inhibitory neurons. We show how non-linearities in neuronal transfer functions are eschewed by strong synaptic interactions within each point. Consequently, the simultaneous activation of multiple points results in a linear summation of their respective outputs. We also consider the effects of reduction of inhibition at a cortical point when one or more surrounding points are active. The network response in this condition is linear over an approximately two- to three-fold decrease of inhibitory feedback strength. This result supports the idea that focal disinhibition allows linear coupling of motor cortical points to generate movement related muscle activation patterns; albeit with a limitation on gain control. The model also explains why neural activity does not spread as far out as the axonal connectivity allows, whilst also explaining why distant cortical points can be, nonetheless, functionally coupled by focal disinhibition. Finally, we discuss the advantages that linear interactions at the cortical level afford to motor command synthesis. PMID:26097452
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A Linear Electromagnetic Piston Pump
NASA Astrophysics Data System (ADS)
Hogan, Paul H.
Advancements in mobile hydraulics for human-scale applications have increased demand for a compact hydraulic power supply. Conventional designs couple a rotating electric motor to a hydraulic pump, which increases the package volume and requires several energy conversions. This thesis investigates the use of a free piston as the moving element in a linear motor to eliminate multiple energy conversions and decrease the overall package volume. A coupled model used a quasi-static magnetic equivalent circuit to calculate the motor inductance and the electromagnetic force acting on the piston. The force was an input to a time domain model to evaluate the mechanical and pressure dynamics. The magnetic circuit model was validated with finite element analysis and an experimental prototype linear motor. The coupled model was optimized using a multi-objective genetic algorithm to explore the parameter space and maximize power density and efficiency. An experimental prototype linear pump coupled pistons to an off-the-shelf linear motor to validate the mechanical and pressure dynamics models. The magnetic circuit force calculation agreed within 3% of finite element analysis, and within 8% of experimental data from the unoptimized prototype linear motor. The optimized motor geometry also had good agreement with FEA; at zero piston displacement, the magnetic circuit calculates optimized motor force within 10% of FEA in less than 1/1000 the computational time. This makes it well suited to genetic optimization algorithms. The mechanical model agrees very well with the experimental piston pump position data when tuned for additional unmodeled mechanical friction. Optimized results suggest that an improvement of 400% of the state of the art power density is attainable with as high as 85% net efficiency. This demonstrates that a linear electromagnetic piston pump has potential to serve as a more compact and efficient supply of fluid power for the human scale.
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Voit, E O; Knapp, R G
1997-08-15
The linear-logistic regression model and Cox's proportional hazard model are widely used in epidemiology. Their successful application leaves no doubt that they are accurate reflections of observed disease processes and their associated risks or incidence rates. In spite of their prominence, it is not a priori evident why these models work. This article presents a derivation of the two models from the framework of canonical modeling. It begins with a general description of the dynamics between risk sources and disease development, formulates this description in the canonical representation of an S-system, and shows how the linear-logistic model and Cox's proportional hazard model follow naturally from this representation. The article interprets the model parameters in terms of epidemiological concepts as well as in terms of general systems theory and explains the assumptions and limitations generally accepted in the application of these epidemiological models.
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Yadav, Manuj; Cabrera, Densil; Kenny, Dianna T
2015-09-01
Messa di voce (MDV) is a singing exercise that involves sustaining a single pitch with a linear change in loudness from silence to maximum intensity (the crescendo part) and back to silence again (the decrescendo part), with time symmetry between the two parts. Previous studies have used the sound pressure level (SPL, in decibels) of a singer's voice to measure loudness, so as to assess the linearity of each part-an approach that has limitations due to loudness and SPL not being linearly related. This article studies the loudness envelope shapes of MDVs, comparing the SPL approach with approaches that are more closely related to human loudness perception. The MDVs were performed by a cohort of tertiary singing students, recorded six times (once per semester) over a period of 3 years. The loudness envelopes were derived for a typical audience listening position, and for listening to one's own singing, using three models: SPL, Stevens' power law-based model, and a computational loudness model. The effects on the envelope shape due to room acoustics (an important effect) and vibrato (minimal effect) were also considered. The results showed that the SPL model yielded a lower proportion of linear crescendi and decrescendi, compared with other models. The Stevens' power law-based model provided results similar to the more complicated computational loudness model. Longitudinally, there was no consistent trend in the shape of the MDV loudness envelope for the cohort although there were some individual singers who exhibited improvements in linearity. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.
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NASA Astrophysics Data System (ADS)
Fang, Wei; Huang, Shengzhi; Huang, Qiang; Huang, Guohe; Meng, Erhao; Luan, Jinkai
2018-06-01
In this study, reference evapotranspiration (ET0) forecasting models are developed for the least economically developed regions subject to meteorological data scarcity. Firstly, the partial mutual information (PMI) capable of capturing the linear and nonlinear dependence is investigated regarding its utility to identify relevant predictors and exclude those that are redundant through the comparison with partial linear correlation. An efficient input selection technique is crucial for decreasing model data requirements. Then, the interconnection between global climate indices and regional ET0 is identified. Relevant climatic indices are introduced as additional predictors to comprise information regarding ET0, which ought to be provided by meteorological data unavailable. The case study in the Jing River and Beiluo River basins, China, reveals that PMI outperforms the partial linear correlation in excluding the redundant information, favouring the yield of smaller predictor sets. The teleconnection analysis identifies the correlation between Nino 1 + 2 and regional ET0, indicating influences of ENSO events on the evapotranspiration process in the study area. Furthermore, introducing Nino 1 + 2 as predictors helps to yield more accurate ET0 forecasts. A model performance comparison also shows that non-linear stochastic models (SVR or RF with input selection through PMI) do not always outperform linear models (MLR with inputs screen by linear correlation). However, the former can offer quite comparable performance depending on smaller predictor sets. Therefore, efforts such as screening model inputs through PMI and incorporating global climatic indices interconnected with ET0 can benefit the development of ET0 forecasting models suitable for data-scarce regions.
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Quantitative model of diffuse speckle contrast analysis for flow measurement.
Liu, Jialin; Zhang, Hongchao; Lu, Jian; Ni, Xiaowu; Shen, Zhonghua
2017-07-01
Diffuse speckle contrast analysis (DSCA) is a noninvasive optical technique capable of monitoring deep tissue blood flow. However, a detailed study of the speckle contrast model for DSCA has yet to be presented. We deduced the theoretical relationship between speckle contrast and exposure time and further simplified it to a linear approximation model. The feasibility of this linear model was validated by the liquid phantoms which demonstrated that the slope of this linear approximation was able to rapidly determine the Brownian diffusion coefficient of the turbid media at multiple distances using multiexposure speckle imaging. Furthermore, we have theoretically quantified the influence of optical property on the measurements of the Brownian diffusion coefficient which was a consequence of the fact that the slope of this linear approximation was demonstrated to be equal to the inverse of correlation time of the speckle.
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Linear Power-Flow Models in Multiphase Distribution Networks: Preprint
DOE Office of Scientific and Technical Information (OSTI.GOV)
Bernstein, Andrey; Dall'Anese, Emiliano
This paper considers multiphase unbalanced distribution systems and develops approximate power-flow models where bus-voltages, line-currents, and powers at the point of common coupling are linearly related to the nodal net power injections. The linearization approach is grounded on a fixed-point interpretation of the AC power-flow equations, and it is applicable to distribution systems featuring (i) wye connections; (ii) ungrounded delta connections; (iii) a combination of wye-connected and delta-connected sources/loads; and, (iv) a combination of line-to-line and line-to-grounded-neutral devices at the secondary of distribution transformers. The proposed linear models can facilitate the development of computationally-affordable optimization and control applications -- frommore » advanced distribution management systems settings to online and distributed optimization routines. Performance of the proposed models is evaluated on different test feeders.« less
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Shek, Daniel T L; Ma, Cecilia M S
2011-01-05
Although different methods are available for the analyses of longitudinal data, analyses based on generalized linear models (GLM) are criticized as violating the assumption of independence of observations. Alternatively, linear mixed models (LMM) are commonly used to understand changes in human behavior over time. In this paper, the basic concepts surrounding LMM (or hierarchical linear models) are outlined. Although SPSS is a statistical analyses package commonly used by researchers, documentation on LMM procedures in SPSS is not thorough or user friendly. With reference to this limitation, the related procedures for performing analyses based on LMM in SPSS are described. To demonstrate the application of LMM analyses in SPSS, findings based on six waves of data collected in the Project P.A.T.H.S. (Positive Adolescent Training through Holistic Social Programmes) in Hong Kong are presented.
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Longitudinal Data Analyses Using Linear Mixed Models in SPSS: Concepts, Procedures and Illustrations
Shek, Daniel T. L.; Ma, Cecilia M. S.
2011-01-01
Although different methods are available for the analyses of longitudinal data, analyses based on generalized linear models (GLM) are criticized as violating the assumption of independence of observations. Alternatively, linear mixed models (LMM) are commonly used to understand changes in human behavior over time. In this paper, the basic concepts surrounding LMM (or hierarchical linear models) are outlined. Although SPSS is a statistical analyses package commonly used by researchers, documentation on LMM procedures in SPSS is not thorough or user friendly. With reference to this limitation, the related procedures for performing analyses based on LMM in SPSS are described. To demonstrate the application of LMM analyses in SPSS, findings based on six waves of data collected in the Project P.A.T.H.S. (Positive Adolescent Training through Holistic Social Programmes) in Hong Kong are presented. PMID:21218263
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Establishing a conceptual framework for handoffs using communication theory.
Mohorek, Matthew; Webb, Travis P
2015-01-01
A significant consequence of the 2003 Accreditation Council for Graduate Medical Education duty hour restrictions has been the dramatic increase in patient care handoffs. Ineffective handoffs have been identified as the third most common cause of medical error. However, research into health care handoffs lacks a unifying foundational structure. We sought to identify a conceptual framework that could be used to critically analyze handoffs. A scholarly review focusing on communication theory as a possible conceptual framework for handoffs was conducted. A PubMed search of published handoff research was also performed, and the literature was analyzed and matched to the most relevant theory for health care handoff models. The Shannon-Weaver Linear Model of Communication was identified as the most appropriate conceptual framework for health care handoffs. The Linear Model describes communication as a linear process. A source encodes a message into a signal, the signal is sent through a channel, and the signal is decoded back into a message at the destination, all in the presence of internal and external noise. The Linear Model identifies 3 separate instances in handoff communication where error occurs: the transmitter (message encoding), channel, and receiver (signal decoding). The Linear Model of Communication is a suitable conceptual framework for handoff research and provides a structured approach for describing handoff variables. We propose the Linear Model should be used as a foundation for further research into interventions to improve health care handoffs. Copyright © 2015 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.
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Mathematical Modelling in Engineering: A Proposal to Introduce Linear Algebra Concepts
ERIC Educational Resources Information Center
Cárcamo Bahamonde, Andrea; Gómez Urgelles, Joan; Fortuny Aymemí, Josep
2016-01-01
The modern dynamic world requires that basic science courses for engineering, including linear algebra, emphasise the development of mathematical abilities primarily associated with modelling and interpreting, which are not exclusively calculus abilities. Considering this, an instructional design was created based on mathematical modelling and…
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Genetic parameters for racing records in trotters using linear and generalized linear models.
Suontama, M; van der Werf, J H J; Juga, J; Ojala, M
2012-09-01
Heritability and repeatability and genetic and phenotypic correlations were estimated for trotting race records with linear and generalized linear models using 510,519 records on 17,792 Finnhorses and 513,161 records on 25,536 Standardbred trotters. Heritability and repeatability were estimated for single racing time and earnings traits with linear models, and logarithmic scale was used for racing time and fourth-root scale for earnings to correct for nonnormality. Generalized linear models with a gamma distribution were applied for single racing time and with a multinomial distribution for single earnings traits. In addition, genetic parameters for annual earnings were estimated with linear models on the observed and fourth-root scales. Racing success traits of single placings, winnings, breaking stride, and disqualifications were analyzed using generalized linear models with a binomial distribution. Estimates of heritability were greatest for racing time, which ranged from 0.32 to 0.34. Estimates of heritability were low for single earnings with all distributions, ranging from 0.01 to 0.09. Annual earnings were closer to normal distribution than single earnings. Heritability estimates were moderate for annual earnings on the fourth-root scale, 0.19 for Finnhorses and 0.27 for Standardbred trotters. Heritability estimates for binomial racing success variables ranged from 0.04 to 0.12, being greatest for winnings and least for breaking stride. Genetic correlations among racing traits were high, whereas phenotypic correlations were mainly low to moderate, except correlations between racing time and earnings were high. On the basis of a moderate heritability and moderate to high repeatability for racing time and annual earnings, selection of horses for these traits is effective when based on a few repeated records. Because of high genetic correlations, direct selection for racing time and annual earnings would also result in good genetic response in racing success.
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MO-F-16A-02: Simulation of a Medical Linear Accelerator for Teaching Purposes
DOE Office of Scientific and Technical Information (OSTI.GOV)
Carlone, M; Lamey, M; Anderson, R
Purpose: Detailed functioning of linear accelerator physics is well known. Less well developed is the basic understanding of how the adjustment of the linear accelerator's electrical components affects the resulting radiation beam. Other than the text by Karzmark, there is very little literature devoted to the practical understanding of linear accelerator functionality targeted at the radiotherapy clinic level. The purpose of this work is to describe a simulation environment for medical linear accelerators with the purpose of teaching linear accelerator physics. Methods: Varian type lineacs were simulated. Klystron saturation and peak output were modelled analytically. The energy gain of anmore » electron beam was modelled using load line expressions. The bending magnet was assumed to be a perfect solenoid whose pass through energy varied linearly with solenoid current. The dose rate calculated at depth in water was assumed to be a simple function of the target's beam current. The flattening filter was modelled as an attenuator with conical shape, and the time-averaged dose rate at a depth in water was determined by calculating kerma. Results: Fifteen analytical models were combined into a single model called SIMAC. Performance was verified systematically by adjusting typical linac control parameters. Increasing klystron pulse voltage increased dose rate to a peak, which then decreased as the beam energy was further increased due to the fixed pass through energy of the bending magnet. Increasing accelerator beam current leads to a higher dose per pulse. However, the energy of the electron beam decreases due to beam loading and so the dose rate eventually maximizes and the decreases as beam current was further increased. Conclusion: SIMAC can realistically simulate the functionality of a linear accelerator. It is expected to have value as a teaching tool for both medical physicists and linear accelerator service personnel.« less
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Ding, Junjie; Wang, Yi; Lin, Weiwei; Wang, Changlian; Zhao, Limei; Li, Xingang; Zhao, Zhigang; Miao, Liyan; Jiao, Zheng
2015-03-01
Valproic acid (VPA) follows a non-linear pharmacokinetic profile in terms of protein-binding saturation. The total daily dose regarding VPA clearance is a simple power function, which may partially explain the non-linearity of the pharmacokinetic profile; however, it may be confounded by the therapeutic drug monitoring effect. The aim of this study was to develop a population pharmacokinetic model for VPA based on protein-binding saturation in pediatric patients with epilepsy. A total of 1,107 VPA serum trough concentrations at steady state were collected from 902 epileptic pediatric patients aged from 3 weeks to 14 years at three hospitals. The population pharmacokinetic model was developed using NONMEM(®) software. The ability of three candidate models (the simple power exponent model, the dose-dependent maximum effect [DDE] model, and the protein-binding model) to describe the non-linear pharmacokinetic profile of VPA was investigated, and potential covariates were screened using a stepwise approach. Bootstrap, normalized prediction distribution errors and external evaluations from two independent studies were performed to determine the stability and predictive performance of the candidate models. The age-dependent exponent model described the effects of body weight and age on the clearance well. Co-medication with carbamazepine was identified as a significant covariate. The DDE model best fitted the aim of this study, although there were no obvious differences in the predictive performances. The condition number was less than 500, and the precision of the parameter estimates was less than 30 %, indicating stability and validity of the final model. The DDE model successfully described the non-linear pharmacokinetics of VPA. Furthermore, the proposed population pharmacokinetic model of VPA can be used to design rational dosage regimens to achieve desirable serum concentrations.
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A Vernacular for Linear Latent Growth Models
ERIC Educational Resources Information Center
Hancock, Gregory R.; Choi, Jaehwa
2006-01-01
In its most basic form, latent growth modeling (latent curve analysis) allows an assessment of individuals' change in a measured variable X over time. For simple linear models, as with other growth models, parameter estimates associated with the a construct (amount of X at a chosen temporal reference point) and b construct (growth in X per unit…