Thermal signature identification system (TheSIS): a spread spectrum temperature cycling method

NASA Astrophysics Data System (ADS)

Merritt, Scott

2015-03-01

NASA GSFC's Thermal Signature Identification System (TheSIS) 1) measures the high order dynamic responses of optoelectronic components to direct sequence spread-spectrum temperature cycling, 2) estimates the parameters of multiple autoregressive moving average (ARMA) or other models the of the responses, 3) and selects the most appropriate model using the Akaike Information Criterion (AIC). Using the AIC-tested model and parameter vectors from TheSIS, one can 1) select high-performing components on a multivariate basis, i.e., with multivariate Figures of Merit (FOMs), 2) detect subtle reversible shifts in performance, and 3) investigate irreversible changes in component or subsystem performance, e.g. aging. We show examples of the TheSIS methodology for passive and active components and systems, e.g. fiber Bragg gratings (FBGs) and DFB lasers with coupled temperature control loops, respectively.

Congruence analysis of geodetic networks - hypothesis tests versus model selection by information criteria

NASA Astrophysics Data System (ADS)

Lehmann, Rüdiger; Lösler, Michael

2017-12-01

Geodetic deformation analysis can be interpreted as a model selection problem. The null model indicates that no deformation has occurred. It is opposed to a number of alternative models, which stipulate different deformation patterns. A common way to select the right model is the usage of a statistical hypothesis test. However, since we have to test a series of deformation patterns, this must be a multiple test. As an alternative solution for the test problem, we propose the p-value approach. Another approach arises from information theory. Here, the Akaike information criterion (AIC) or some alternative is used to select an appropriate model for a given set of observations. Both approaches are discussed and applied to two test scenarios: A synthetic levelling network and the Delft test data set. It is demonstrated that they work but behave differently, sometimes even producing different results. Hypothesis tests are well-established in geodesy, but may suffer from an unfavourable choice of the decision error rates. The multiple test also suffers from statistical dependencies between the test statistics, which are neglected. Both problems are overcome by applying information criterions like AIC.

Adaptive interference cancel filter for evoked potential using high-order cumulants.

PubMed

Lin, Bor-Shyh; Lin, Bor-Shing; Chong, Fok-Ching; Lai, Feipei

2004-01-01

This paper is to present evoked potential (EP) processing using adaptive interference cancel (AIC) filter with second and high order cumulants. In conventional ensemble averaging method, people have to conduct repetitively experiments to record the required data. Recently, the use of AIC structure with second statistics in processing EP has proved more efficiency than traditional averaging method, but it is sensitive to both of the reference signal statistics and the choice of step size. Thus, we proposed higher order statistics-based AIC method to improve these disadvantages. This study was experimented in somatosensory EP corrupted with EEG. Gradient type algorithm is used in AIC method. Comparisons with AIC filter on second, third, fourth order statistics are also presented in this paper. We observed that AIC filter with third order statistics has better convergent performance for EP processing and is not sensitive to the selection of step size and reference input.

The role of multicollinearity in landslide susceptibility assessment by means of Binary Logistic Regression: comparison between VIF and AIC stepwise selection

NASA Astrophysics Data System (ADS)

Cama, Mariaelena; Cristi Nicu, Ionut; Conoscenti, Christian; Quénéhervé, Geraldine; Maerker, Michael

2016-04-01

Landslide susceptibility can be defined as the likelihood of a landslide occurring in a given area on the basis of local terrain conditions. In the last decades many research focused on its evaluation by means of stochastic approaches under the assumption that 'the past is the key to the future' which means that if a model is able to reproduce a known landslide spatial distribution, it will be able to predict the future locations of new (i.e. unknown) slope failures. Among the various stochastic approaches, Binary Logistic Regression (BLR) is one of the most used because it calculates the susceptibility in probabilistic terms and its results are easily interpretable from a geomorphological point of view. However, very often not much importance is given to multicollinearity assessment whose effect is that the coefficient estimates are unstable, with opposite sign and therefore difficult to interpret. Therefore, it should be evaluated every time in order to make a model whose results are geomorphologically correct. In this study the effects of multicollinearity in the predictive performance and robustness of landslide susceptibility models are analyzed. In particular, the multicollinearity is estimated by means of Variation Inflation Index (VIF) which is also used as selection criterion for the independent variables (VIF Stepwise Selection) and compared to the more commonly used AIC Stepwise Selection. The robustness of the results is evaluated through 100 replicates of the dataset. The study area selected to perform this analysis is the Moldavian Plateau where landslides are among the most frequent geomorphological processes. This area has an increasing trend of urbanization and a very high potential regarding the cultural heritage, being the place of discovery of the largest settlement belonging to the Cucuteni Culture from Eastern Europe (that led to the development of the great complex Cucuteni-Tripyllia). Therefore, identifying the areas susceptible to landslides may lead to a better understanding and mitigation for government, local authorities and stakeholders to plan the economic activities, minimize the damages costs, environmental and cultural heritage protection. The results show that although the VIF Stepwise selection allows a more stable selection of the controlling factors, the AIC Stepwise selection produces better predictive performance. Moreover, when working with replicates the effect of multicollinearity are statistically reduced by the application of the AIC stepwise selection and the results are easily interpretable in geomorphologic terms.

Efficient Variable Selection Method for Exposure Variables on Binary Data

NASA Astrophysics Data System (ADS)

Ohno, Manabu; Tarumi, Tomoyuki

In this paper, we propose a new variable selection method for "robust" exposure variables. We define "robust" as property that the same variable can select among original data and perturbed data. There are few studies of effective for the selection method. The problem that selects exposure variables is almost the same as a problem that extracts correlation rules without robustness. [Brin 97] is suggested that correlation rules are possible to extract efficiently using chi-squared statistic of contingency table having monotone property on binary data. But the chi-squared value does not have monotone property, so it's is easy to judge the method to be not independent with an increase in the dimension though the variable set is completely independent, and the method is not usable in variable selection for robust exposure variables. We assume anti-monotone property for independent variables to select robust independent variables and use the apriori algorithm for it. The apriori algorithm is one of the algorithms which find association rules from the market basket data. The algorithm use anti-monotone property on the support which is defined by association rules. But independent property does not completely have anti-monotone property on the AIC of independent probability model, but the tendency to have anti-monotone property is strong. Therefore, selected variables with anti-monotone property on the AIC have robustness. Our method judges whether a certain variable is exposure variable for the independent variable using previous comparison of the AIC. Our numerical experiments show that our method can select robust exposure variables efficiently and precisely.

Anterior Insular Cortex and Emotional Awareness

PubMed Central

Gu, Xiaosi; Hof, Patrick R.; Friston, Karl J.; Fan, Jin

2014-01-01

This paper reviews the foundation for a role of the human anterior insular cortex (AIC) in emotional awareness, defined as the conscious experience of emotions. We first introduce the neuroanatomical features of AIC and existing findings on emotional awareness. Using empathy, the awareness and understanding of other people’s emotional states, as a test case, we then present evidence to demonstrate: 1) AIC and anterior cingulate cortex (ACC) are commonly coactivated as revealed by a meta-analysis, 2) AIC is functionally dissociable from ACC, 3) AIC integrates stimulus-driven and top-down information, and 4) AIC is necessary for emotional awareness. We propose a model in which AIC serves two major functions: integrating bottom-up interoceptive signals with top-down predictions to generate a current awareness state and providing descending predictions to visceral systems that provide a point of reference for autonomic reflexes. We argue that AIC is critical and necessary for emotional awareness. PMID:23749500

AIC Computations Using Navier-Stokes Equations on Single Image Supercomputers For Design Optimization

NASA Technical Reports Server (NTRS)

Guruswamy, Guru

2004-01-01

A procedure to accurately generate AIC using the Navier-Stokes solver including grid deformation is presented. Preliminary results show good comparisons between experiment and computed flutter boundaries for a rectangular wing. A full wing body configuration of an orbital space plane is selected for demonstration on a large number of processors. In the final paper the AIC of full wing body configuration will be computed. The scalability of the procedure on supercomputer will be demonstrated.

The cross-validated AUC for MCP-logistic regression with high-dimensional data.

PubMed

Jiang, Dingfeng; Huang, Jian; Zhang, Ying

2013-10-01

We propose a cross-validated area under the receiving operator characteristic (ROC) curve (CV-AUC) criterion for tuning parameter selection for penalized methods in sparse, high-dimensional logistic regression models. We use this criterion in combination with the minimax concave penalty (MCP) method for variable selection. The CV-AUC criterion is specifically designed for optimizing the classification performance for binary outcome data. To implement the proposed approach, we derive an efficient coordinate descent algorithm to compute the MCP-logistic regression solution surface. Simulation studies are conducted to evaluate the finite sample performance of the proposed method and its comparison with the existing methods including the Akaike information criterion (AIC), Bayesian information criterion (BIC) or Extended BIC (EBIC). The model selected based on the CV-AUC criterion tends to have a larger predictive AUC and smaller classification error than those with tuning parameters selected using the AIC, BIC or EBIC. We illustrate the application of the MCP-logistic regression with the CV-AUC criterion on three microarray datasets from the studies that attempt to identify genes related to cancers. Our simulation studies and data examples demonstrate that the CV-AUC is an attractive method for tuning parameter selection for penalized methods in high-dimensional logistic regression models.

Evaluation of two outlier-detection-based methods for detecting tissue-selective genes from microarray data.

PubMed

Kadota, Koji; Konishi, Tomokazu; Shimizu, Kentaro

2007-05-01

Large-scale expression profiling using DNA microarrays enables identification of tissue-selective genes for which expression is considerably higher and/or lower in some tissues than in others. Among numerous possible methods, only two outlier-detection-based methods (an AIC-based method and Sprent's non-parametric method) can treat equally various types of selective patterns, but they produce substantially different results. We investigated the performance of these two methods for different parameter settings and for a reduced number of samples. We focused on their ability to detect selective expression patterns robustly. We applied them to public microarray data collected from 36 normal human tissue samples and analyzed the effects of both changing the parameter settings and reducing the number of samples. The AIC-based method was more robust in both cases. The findings confirm that the use of the AIC-based method in the recently proposed ROKU method for detecting tissue-selective expression patterns is correct and that Sprent's method is not suitable for ROKU.

Performance of soil particle-size distribution models for describing deposited soils adjacent to constructed dams in the China Loess Plateau

NASA Astrophysics Data System (ADS)

Zhao, Pei; Shao, Ming-an; Horton, Robert

2011-02-01

Soil particle-size distributions (PSD) have been used to estimate soil hydraulic properties. Various parametric PSD models have been proposed to describe the soil PSD from sparse experimental data. It is important to determine which PSD model best represents specific soils. Fourteen PSD models were examined in order to determine the best model for representing the deposited soils adjacent to dams in the China Loess Plateau; these were: Skaggs (S-1, S-2, and S-3), fractal (FR), Jaky (J), Lima and Silva (LS), Morgan (M), Gompertz (G), logarithm (L), exponential (E), log-exponential (LE), Weibull (W), van Genuchten type (VG) as well as Fredlund (F) models. Four-hundred and eighty samples were obtained from soils deposited in the Liudaogou catchment. The coefficient of determination (R 2), the Akaike's information criterion (AIC), and the modified AIC (mAIC) were used. Based upon R 2 and AIC, the three- and four-parameter models were both good at describing the PSDs of deposited soils, and the LE, FR, and E models were the poorest. However, the mAIC in conjunction with R 2 and AIC results indicated that the W model was optimum for describing PSD of the deposited soils for emphasizing the effect of parameter number. This analysis was also helpful for finding out which model is the best one. Our results are applicable to the China Loess Plateau.

Empirical extensions of the lasso penalty to reduce the false discovery rate in high-dimensional Cox regression models.

PubMed

Ternès, Nils; Rotolo, Federico; Michiels, Stefan

2016-07-10

Correct selection of prognostic biomarkers among multiple candidates is becoming increasingly challenging as the dimensionality of biological data becomes higher. Therefore, minimizing the false discovery rate (FDR) is of primary importance, while a low false negative rate (FNR) is a complementary measure. The lasso is a popular selection method in Cox regression, but its results depend heavily on the penalty parameter λ. Usually, λ is chosen using maximum cross-validated log-likelihood (max-cvl). However, this method has often a very high FDR. We review methods for a more conservative choice of λ. We propose an empirical extension of the cvl by adding a penalization term, which trades off between the goodness-of-fit and the parsimony of the model, leading to the selection of fewer biomarkers and, as we show, to the reduction of the FDR without large increase in FNR. We conducted a simulation study considering null and moderately sparse alternative scenarios and compared our approach with the standard lasso and 10 other competitors: Akaike information criterion (AIC), corrected AIC, Bayesian information criterion (BIC), extended BIC, Hannan and Quinn information criterion (HQIC), risk information criterion (RIC), one-standard-error rule, adaptive lasso, stability selection, and percentile lasso. Our extension achieved the best compromise across all the scenarios between a reduction of the FDR and a limited raise of the FNR, followed by the AIC, the RIC, and the adaptive lasso, which performed well in some settings. We illustrate the methods using gene expression data of 523 breast cancer patients. In conclusion, we propose to apply our extension to the lasso whenever a stringent FDR with a limited FNR is targeted. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

A new methodology based on sensitivity analysis to simplify the recalibration of functional-structural plant models in new conditions.

PubMed

Mathieu, Amélie; Vidal, Tiphaine; Jullien, Alexandra; Wu, QiongLi; Chambon, Camille; Bayol, Benoit; Cournède, Paul-Henry

2018-06-19

Functional-structural plant models (FSPMs) describe explicitly the interactions between plants and their environment at organ to plant scale. However, the high level of description of the structure or model mechanisms makes this type of model very complex and hard to calibrate. A two-step methodology to facilitate the calibration process is proposed here. First, a global sensitivity analysis method was applied to the calibration loss function. It provided first-order and total-order sensitivity indexes that allow parameters to be ranked by importance in order to select the most influential ones. Second, the Akaike information criterion (AIC) was used to quantify the model's quality of fit after calibration with different combinations of selected parameters. The model with the lowest AIC gives the best combination of parameters to select. This methodology was validated by calibrating the model on an independent data set (same cultivar, another year) with the parameters selected in the second step. All the parameters were set to their nominal value; only the most influential ones were re-estimated. Sensitivity analysis applied to the calibration loss function is a relevant method to underline the most significant parameters in the estimation process. For the studied winter oilseed rape model, 11 out of 26 estimated parameters were selected. Then, the model could be recalibrated for a different data set by re-estimating only three parameters selected with the model selection method. Fitting only a small number of parameters dramatically increases the efficiency of recalibration, increases the robustness of the model and helps identify the principal sources of variation in varying environmental conditions. This innovative method still needs to be more widely validated but already gives interesting avenues to improve the calibration of FSPMs.

Diffusion weighted imaging in patients with rectal cancer: Comparison between Gaussian and non-Gaussian models

PubMed Central

Marias, Kostas; Lambregts, Doenja M. J.; Nikiforaki, Katerina; van Heeswijk, Miriam M.; Bakers, Frans C. H.; Beets-Tan, Regina G. H.

2017-01-01

Purpose The purpose of this study was to compare the performance of four diffusion models, including mono and bi-exponential both Gaussian and non-Gaussian models, in diffusion weighted imaging of rectal cancer. Material and methods Nineteen patients with rectal adenocarcinoma underwent MRI examination of the rectum before chemoradiation therapy including a 7 b-value diffusion sequence (0, 25, 50, 100, 500, 1000 and 2000 s/mm2) at a 1.5T scanner. Four different diffusion models including mono- and bi-exponential Gaussian (MG and BG) and non-Gaussian (MNG and BNG) were applied on whole tumor volumes of interest. Two different statistical criteria were recruited to assess their fitting performance, including the adjusted-R2 and Root Mean Square Error (RMSE). To decide which model better characterizes rectal cancer, model selection was relied on Akaike Information Criteria (AIC) and F-ratio. Results All candidate models achieved a good fitting performance with the two most complex models, the BG and the BNG, exhibiting the best fitting performance. However, both criteria for model selection indicated that the MG model performed better than any other model. In particular, using AIC Weights and F-ratio, the pixel-based analysis demonstrated that tumor areas better described by the simplest MG model in an average area of 53% and 33%, respectively. Non-Gaussian behavior was illustrated in an average area of 37% according to the F-ratio, and 7% using AIC Weights. However, the distributions of the pixels best fitted by each of the four models suggest that MG failed to perform better than any other model in all patients, and the overall tumor area. Conclusion No single diffusion model evaluated herein could accurately describe rectal tumours. These findings probably can be explained on the basis of increased tumour heterogeneity, where areas with high vascularity could be fitted better with bi-exponential models, and areas with necrosis would mostly follow mono-exponential behavior. PMID:28863161

Diffusion weighted imaging in patients with rectal cancer: Comparison between Gaussian and non-Gaussian models.

PubMed

Manikis, Georgios C; Marias, Kostas; Lambregts, Doenja M J; Nikiforaki, Katerina; van Heeswijk, Miriam M; Bakers, Frans C H; Beets-Tan, Regina G H; Papanikolaou, Nikolaos

2017-01-01

The purpose of this study was to compare the performance of four diffusion models, including mono and bi-exponential both Gaussian and non-Gaussian models, in diffusion weighted imaging of rectal cancer. Nineteen patients with rectal adenocarcinoma underwent MRI examination of the rectum before chemoradiation therapy including a 7 b-value diffusion sequence (0, 25, 50, 100, 500, 1000 and 2000 s/mm2) at a 1.5T scanner. Four different diffusion models including mono- and bi-exponential Gaussian (MG and BG) and non-Gaussian (MNG and BNG) were applied on whole tumor volumes of interest. Two different statistical criteria were recruited to assess their fitting performance, including the adjusted-R2 and Root Mean Square Error (RMSE). To decide which model better characterizes rectal cancer, model selection was relied on Akaike Information Criteria (AIC) and F-ratio. All candidate models achieved a good fitting performance with the two most complex models, the BG and the BNG, exhibiting the best fitting performance. However, both criteria for model selection indicated that the MG model performed better than any other model. In particular, using AIC Weights and F-ratio, the pixel-based analysis demonstrated that tumor areas better described by the simplest MG model in an average area of 53% and 33%, respectively. Non-Gaussian behavior was illustrated in an average area of 37% according to the F-ratio, and 7% using AIC Weights. However, the distributions of the pixels best fitted by each of the four models suggest that MG failed to perform better than any other model in all patients, and the overall tumor area. No single diffusion model evaluated herein could accurately describe rectal tumours. These findings probably can be explained on the basis of increased tumour heterogeneity, where areas with high vascularity could be fitted better with bi-exponential models, and areas with necrosis would mostly follow mono-exponential behavior.

The Role of the Anterior Insula in Adolescent Decision Making

PubMed Central

Smith, Ashley R.; Steinberg, Laurence; Chein, Jason

2017-01-01

Much recent research on adolescent decision making has sought to characterize the neurobiological mechanisms that underlie the proclivity of adolescents to engage in risky behavior. One class of influential neurodevelopmental models focuses on the asynchronous development of neural systems, particularly those responsible for self-regulation and reward seeking. While this work has largely focused on the development of prefrontal (self-regulation) and striatal (reward processing) circuitry, the present article explores the significance of a different region, the anterior insular cortex (AIC), in adolescent decision making. Although the AIC is known for its role as a cognitive-emotional hub, and is included in some models of adult self-regulation and reward seeking, the importance of the AIC and its maturation in adolescent risk taking has not been extensively explored. In this article we discuss evidence on AIC development, and consider how age-related differences in AIC engagement may contribute to heightened risk taking during adolescence. Based on this review, we propose a model in which the engagement of adolescents in risk taking may be linked in part to the maturation of the AIC and its connectivity to the broader brain networks in which it participates. PMID:24853135

Prediction of thoracic injury severity in frontal impacts by selected anatomical morphomic variables through model-averaged logistic regression approach.

PubMed

Zhang, Peng; Parenteau, Chantal; Wang, Lu; Holcombe, Sven; Kohoyda-Inglis, Carla; Sullivan, June; Wang, Stewart

2013-11-01

This study resulted in a model-averaging methodology that predicts crash injury risk using vehicle, demographic, and morphomic variables and assesses the importance of individual predictors. The effectiveness of this methodology was illustrated through analysis of occupant chest injuries in frontal vehicle crashes. The crash data were obtained from the International Center for Automotive Medicine (ICAM) database for calendar year 1996 to 2012. The morphomic data are quantitative measurements of variations in human body 3-dimensional anatomy. Morphomics are obtained from imaging records. In this study, morphomics were obtained from chest, abdomen, and spine CT using novel patented algorithms. A NASS-trained crash investigator with over thirty years of experience collected the in-depth crash data. There were 226 cases available with occupants involved in frontal crashes and morphomic measurements. Only cases with complete recorded data were retained for statistical analysis. Logistic regression models were fitted using all possible configurations of vehicle, demographic, and morphomic variables. Different models were ranked by the Akaike Information Criteria (AIC). An averaged logistic regression model approach was used due to the limited sample size relative to the number of variables. This approach is helpful when addressing variable selection, building prediction models, and assessing the importance of individual variables. The final predictive results were developed using this approach, based on the top 100 models in the AIC ranking. Model-averaging minimized model uncertainty, decreased the overall prediction variance, and provided an approach to evaluating the importance of individual variables. There were 17 variables investigated: four vehicle, four demographic, and nine morphomic. More than 130,000 logistic models were investigated in total. The models were characterized into four scenarios to assess individual variable contribution to injury risk. Scenario 1 used vehicle variables; Scenario 2, vehicle and demographic variables; Scenario 3, vehicle and morphomic variables; and Scenario 4 used all variables. AIC was used to rank the models and to address over-fitting. In each scenario, the results based on the top three models and the averages of the top 100 models were presented. The AIC and the area under the receiver operating characteristic curve (AUC) were reported in each model. The models were re-fitted after removing each variable one at a time. The increases of AIC and the decreases of AUC were then assessed to measure the contribution and importance of the individual variables in each model. The importance of the individual variables was also determined by their weighted frequencies of appearance in the top 100 selected models. Overall, the AUC was 0.58 in Scenario 1, 0.78 in Scenario 2, 0.76 in Scenario 3 and 0.82 in Scenario 4. The results showed that morphomic variables are as accurate at predicting injury risk as demographic variables. The results of this study emphasize the importance of including morphomic variables when assessing injury risk. The results also highlight the need for morphomic data in the development of human mathematical models when assessing restraint performance in frontal crashes, since morphomic variables are more "tangible" measurements compared to demographic variables such as age and gender. Copyright © 2013 Elsevier Ltd. All rights reserved.

An automated process for building reliable and optimal in vitro/in vivo correlation models based on Monte Carlo simulations.

PubMed

Sutton, Steven C; Hu, Mingxiu

2006-05-05

Many mathematical models have been proposed for establishing an in vitro/in vivo correlation (IVIVC). The traditional IVIVC model building process consists of 5 steps: deconvolution, model fitting, convolution, prediction error evaluation, and cross-validation. This is a time-consuming process and typically a few models at most are tested for any given data set. The objectives of this work were to (1) propose a statistical tool to screen models for further development of an IVIVC, (2) evaluate the performance of each model under different circumstances, and (3) investigate the effectiveness of common statistical model selection criteria for choosing IVIVC models. A computer program was developed to explore which model(s) would be most likely to work well with a random variation from the original formulation. The process used Monte Carlo simulation techniques to build IVIVC models. Data-based model selection criteria (Akaike Information Criteria [AIC], R2) and the probability of passing the Food and Drug Administration "prediction error" requirement was calculated. To illustrate this approach, several real data sets representing a broad range of release profiles are used to illustrate the process and to demonstrate the advantages of this automated process over the traditional approach. The Hixson-Crowell and Weibull models were often preferred over the linear. When evaluating whether a Level A IVIVC model was possible, the model selection criteria AIC generally selected the best model. We believe that the approach we proposed may be a rapid tool to determine which IVIVC model (if any) is the most applicable.

Comparing simple respiration models for eddy flux and dynamic chamber data

Treesearch

Andrew D. Richardson; Bobby H. Braswell; David Y. Hollinger; Prabir Burman; Eric A. Davidson; Robert S. Evans; Lawrence B. Flanagan; J. William Munger; Kathleen Savage; Shawn P. Urbanski; Steven C. Wofsy

2006-01-01

Selection of an appropriate model for respiration (R) is important for accurate gap-filling of CO2 flux data, and for partitioning measurements of net ecosystem exchange (NEE) to respiration and gross ecosystem exchange (GEE). Using cross-validation methods and a version of Akaike's Information Criterion (AIC), we evaluate a wide range of...

Evaluation of Two Outlier-Detection-Based Methods for Detecting Tissue-Selective Genes from Microarray Data

PubMed Central

Kadota, Koji; Konishi, Tomokazu; Shimizu, Kentaro

2007-01-01

Large-scale expression profiling using DNA microarrays enables identification of tissue-selective genes for which expression is considerably higher and/or lower in some tissues than in others. Among numerous possible methods, only two outlier-detection-based methods (an AIC-based method and Sprent’s non-parametric method) can treat equally various types of selective patterns, but they produce substantially different results. We investigated the performance of these two methods for different parameter settings and for a reduced number of samples. We focused on their ability to detect selective expression patterns robustly. We applied them to public microarray data collected from 36 normal human tissue samples and analyzed the effects of both changing the parameter settings and reducing the number of samples. The AIC-based method was more robust in both cases. The findings confirm that the use of the AIC-based method in the recently proposed ROKU method for detecting tissue-selective expression patterns is correct and that Sprent’s method is not suitable for ROKU. PMID:19936074

Spotted Towhee population dynamics in a riparian restoration context

Treesearch

Stacy L. Small; Frank R., III Thompson; Geoffery R. Geupel; John Faaborg

2007-01-01

We investigated factors at multiple scales that might influence nest predation risk for Spotted Towhees (Pipilo maculates) along the Sacramento River, California, within the context of large-scale riparian habitat restoration. We used the logistic-exposure method and Akaike's information criterion (AIC) for model selection to compare predator...

Bayes factors and multimodel inference

USGS Publications Warehouse

Link, W.A.; Barker, R.J.; Thomson, David L.; Cooch, Evan G.; Conroy, Michael J.

2009-01-01

Multimodel inference has two main themes: model selection, and model averaging. Model averaging is a means of making inference conditional on a model set, rather than on a selected model, allowing formal recognition of the uncertainty associated with model choice. The Bayesian paradigm provides a natural framework for model averaging, and provides a context for evaluation of the commonly used AIC weights. We review Bayesian multimodel inference, noting the importance of Bayes factors. Noting the sensitivity of Bayes factors to the choice of priors on parameters, we define and propose nonpreferential priors as offering a reasonable standard for objective multimodel inference.

Enhancing micro-seismic P-phase arrival picking: EMD-cosine function-based denoising with an application to the AIC picker

NASA Astrophysics Data System (ADS)

Shang, Xueyi; Li, Xibing; Morales-Esteban, A.; Dong, Longjun

2018-03-01

Micro-seismic P-phase arrival picking is an elementary step into seismic event location, source mechanism analysis, and seismic tomography. However, a micro-seismic signal is often mixed with high frequency noises and power frequency noises (50 Hz), which could considerably reduce P-phase picking accuracy. To solve this problem, an Empirical Mode Decomposition (EMD)-cosine function denoising-based Akaike Information Criterion (AIC) picker (ECD-AIC picker) is proposed for picking the P-phase arrival time. Unlike traditional low pass filters which are ineffective when seismic data and noise bandwidths overlap, the EMD adaptively separates the seismic data and the noise into different Intrinsic Mode Functions (IMFs). Furthermore, the EMD-cosine function-based denoising retains the P-phase arrival amplitude and phase spectrum more reliably than any traditional low pass filter. The ECD-AIC picker was tested on 1938 sets of micro-seismic waveforms randomly selected from the Institute of Mine Seismology (IMS) database of the Chinese Yongshaba mine. The results have shown that the EMD-cosine function denoising can effectively estimate high frequency and power frequency noises and can be easily adapted to perform on signals with different shapes and forms. Qualitative and quantitative comparisons show that the combined ECD-AIC picker provides better picking results than both the ED-AIC picker and the AIC picker, and the comparisons also show more reliable source localization results when the ECD-AIC picker is applied, thus showing the potential of this combined P-phase picking technique.

The Development of the Extended Adolescent Injury Checklist (E-AIC): A Measure for Injury Prevention Program Evaluation

ERIC Educational Resources Information Center

Chapman, Rebekah; Buckley, Lisa; Sheehan, Mary

2011-01-01

The Extended Adolescent Injury Checklist (E-AIC), a self-report measure of injury based on the model of the Adolescent Injury Checklist (AIC), was developed for use in the evaluation of school-based interventions. The three stages of this development involved focus groups with adolescents and consultations with medical staff, pilot testing of the…

Extended AIC model based on high order moments and its application in the financial market

NASA Astrophysics Data System (ADS)

Mao, Xuegeng; Shang, Pengjian

2018-07-01

In this paper, an extended method of traditional Akaike Information Criteria(AIC) is proposed to detect the volatility of time series by combining it with higher order moments, such as skewness and kurtosis. Since measures considering higher order moments are powerful in many aspects, the properties of asymmetry and flatness can be observed. Furthermore, in order to reduce the effect of noise and other incoherent features, we combine the extended AIC algorithm with multiscale wavelet analysis, in which the newly extended AIC algorithm is applied to wavelet coefficients at several scales and the time series are reconstructed by wavelet transform. After that, we create AIC planes to derive the relationship among AIC values using variance, skewness and kurtosis respectively. When we test this technique on the financial market, the aim is to analyze the trend and volatility of the closing price of stock indices and classify them. And we also adapt multiscale analysis to measure complexity of time series over a range of scales. Empirical results show that the singularity of time series in stock market can be detected via extended AIC algorithm.

Genome-wide heterogeneity of nucleotide substitution model fit.

PubMed

Arbiza, Leonardo; Patricio, Mateus; Dopazo, Hernán; Posada, David

2011-01-01

At a genomic scale, the patterns that have shaped molecular evolution are believed to be largely heterogeneous. Consequently, comparative analyses should use appropriate probabilistic substitution models that capture the main features under which different genomic regions have evolved. While efforts have concentrated in the development and understanding of model selection techniques, no descriptions of overall relative substitution model fit at the genome level have been reported. Here, we provide a characterization of best-fit substitution models across three genomic data sets including coding regions from mammals, vertebrates, and Drosophila (24,000 alignments). According to the Akaike Information Criterion (AIC), 82 of 88 models considered were selected as best-fit models at least in one occasion, although with very different frequencies. Most parameter estimates also varied broadly among genes. Patterns found for vertebrates and Drosophila were quite similar and often more complex than those found in mammals. Phylogenetic trees derived from models in the 95% confidence interval set showed much less variance and were significantly closer to the tree estimated under the best-fit model than trees derived from models outside this interval. Although alternative criteria selected simpler models than the AIC, they suggested similar patterns. All together our results show that at a genomic scale, different gene alignments for the same set of taxa are best explained by a large variety of different substitution models and that model choice has implications on different parameter estimates including the inferred phylogenetic trees. After taking into account the differences related to sample size, our results suggest a noticeable diversity in the underlying evolutionary process. All together, we conclude that the use of model selection techniques is important to obtain consistent phylogenetic estimates from real data at a genomic scale.

A study on the use and modeling of geographical information system for combating forest crimes: an assessment of crimes in the eastern Mediterranean forests.

PubMed

Pak, Mehmet; Gülci, Sercan; Okumuş, Arif

2018-01-06

This study focuses on the geo-statistical assessment of spatial estimation models in forest crimes. Used widely in the assessment of crime and crime-dependent variables, geographic information system (GIS) helps the detection of forest crimes in rural regions. In this study, forest crimes (forest encroachment, illegal use, illegal timber logging, etc.) are assessed holistically and modeling was performed with ten different independent variables in GIS environment. The research areas are three Forest Enterprise Chiefs (Baskonus, Cinarpinar, and Hartlap) affiliated to Kahramanmaras Forest Regional Directorate in Kahramanmaras. An estimation model was designed using ordinary least squares (OLS) and geographically weighted regression (GWR) methods, which are often used in spatial association. Three different models were proposed in order to increase the accuracy of the estimation model. The use of variables with a variance inflation factor (VIF) value of lower than 7.5 in Model I and lower than 4 in Model II and dependent variables with significant robust probability values in Model III are associated with forest crimes. Afterwards, the model with the lowest corrected Akaike Information Criterion (AIC c ), and the highest R 2 value was selected as the comparison criterion. Consequently, Model III proved to be more accurate compared to other models. For Model III, while AIC c was 328,491 and R 2 was 0.634 for OLS-3 model, AIC c was 318,489 and R 2 was 0.741 for GWR-3 model. In this respect, the uses of GIS for combating forest crimes provide different scenarios and tangible information that will help take political and strategic measures.

Analysis of significant factors for dengue fever incidence prediction.

PubMed

Siriyasatien, Padet; Phumee, Atchara; Ongruk, Phatsavee; Jampachaisri, Katechan; Kesorn, Kraisak

2016-04-16

Many popular dengue forecasting techniques have been used by several researchers to extrapolate dengue incidence rates, including the K-H model, support vector machines (SVM), and artificial neural networks (ANN). The time series analysis methodology, particularly ARIMA and SARIMA, has been increasingly applied to the field of epidemiological research for dengue fever, dengue hemorrhagic fever, and other infectious diseases. The main drawback of these methods is that they do not consider other variables that are associated with the dependent variable. Additionally, new factors correlated to the disease are needed to enhance the prediction accuracy of the model when it is applied to areas of similar climates, where weather factors such as temperature, total rainfall, and humidity are not substantially different. Such drawbacks may consequently lower the predictive power for the outbreak. The predictive power of the forecasting model-assessed by Akaike's information criterion (AIC), Bayesian information criterion (BIC), and the mean absolute percentage error (MAPE)-is improved by including the new parameters for dengue outbreak prediction. This study's selected model outperforms all three other competing models with the lowest AIC, the lowest BIC, and a small MAPE value. The exclusive use of climate factors from similar locations decreases a model's prediction power. The multivariate Poisson regression, however, effectively forecasts even when climate variables are slightly different. Female mosquitoes and seasons were strongly correlated with dengue cases. Therefore, the dengue incidence trends provided by this model will assist the optimization of dengue prevention. The present work demonstrates the important roles of female mosquito infection rates from the previous season and climate factors (represented as seasons) in dengue outbreaks. Incorporating these two factors in the model significantly improves the predictive power of dengue hemorrhagic fever forecasting models, as confirmed by AIC, BIC, and MAPE.

Model selection bias and Freedman's paradox

USGS Publications Warehouse

Lukacs, P.M.; Burnham, K.P.; Anderson, D.R.

2010-01-01

In situations where limited knowledge of a system exists and the ratio of data points to variables is small, variable selection methods can often be misleading. Freedman (Am Stat 37:152-155, 1983) demonstrated how common it is to select completely unrelated variables as highly "significant" when the number of data points is similar in magnitude to the number of variables. A new type of model averaging estimator based on model selection with Akaike's AIC is used with linear regression to investigate the problems of likely inclusion of spurious effects and model selection bias, the bias introduced while using the data to select a single seemingly "best" model from a (often large) set of models employing many predictor variables. The new model averaging estimator helps reduce these problems and provides confidence interval coverage at the nominal level while traditional stepwise selection has poor inferential properties. ?? The Institute of Statistical Mathematics, Tokyo 2009.

Double point source W-phase inversion: Real-time implementation and automated model selection

USGS Publications Warehouse

Nealy, Jennifer; Hayes, Gavin

2015-01-01

Rapid and accurate characterization of an earthquake source is an extremely important and ever evolving field of research. Within this field, source inversion of the W-phase has recently been shown to be an effective technique, which can be efficiently implemented in real-time. An extension to the W-phase source inversion is presented in which two point sources are derived to better characterize complex earthquakes. A single source inversion followed by a double point source inversion with centroid locations fixed at the single source solution location can be efficiently run as part of earthquake monitoring network operational procedures. In order to determine the most appropriate solution, i.e., whether an earthquake is most appropriately described by a single source or a double source, an Akaike information criterion (AIC) test is performed. Analyses of all earthquakes of magnitude 7.5 and greater occurring since January 2000 were performed with extended analyses of the September 29, 2009 magnitude 8.1 Samoa earthquake and the April 19, 2014 magnitude 7.5 Papua New Guinea earthquake. The AIC test is shown to be able to accurately select the most appropriate model and the selected W-phase inversion is shown to yield reliable solutions that match published analyses of the same events.

How Well Can We Detect Lineage-Specific Diversification-Rate Shifts? A Simulation Study of Sequential AIC Methods.

PubMed

May, Michael R; Moore, Brian R

2016-11-01

Evolutionary biologists have long been fascinated by the extreme differences in species numbers across branches of the Tree of Life. This has motivated the development of statistical methods for detecting shifts in the rate of lineage diversification across the branches of phylogenic trees. One of the most frequently used methods, MEDUSA, explores a set of diversification-rate models, where each model assigns branches of the phylogeny to a set of diversification-rate categories. Each model is first fit to the data, and the Akaike information criterion (AIC) is then used to identify the optimal diversification model. Surprisingly, the statistical behavior of this popular method is uncharacterized, which is a concern in light of: (1) the poor performance of the AIC as a means of choosing among models in other phylogenetic contexts; (2) the ad hoc algorithm used to visit diversification models, and; (3) errors that we reveal in the likelihood function used to fit diversification models to the phylogenetic data. Here, we perform an extensive simulation study demonstrating that MEDUSA (1) has a high false-discovery rate (on average, spurious diversification-rate shifts are identified [Formula: see text] of the time), and (2) provides biased estimates of diversification-rate parameters. Understanding the statistical behavior of MEDUSA is critical both to empirical researchers-in order to clarify whether these methods can make reliable inferences from empirical datasets-and to theoretical biologists-in order to clarify the specific problems that need to be solved in order to develop more reliable approaches for detecting shifts in the rate of lineage diversification. [Akaike information criterion; extinction; lineage-specific diversification rates; phylogenetic model selection; speciation.]. © The Author(s) 2016. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.

How Well Can We Detect Lineage-Specific Diversification-Rate Shifts? A Simulation Study of Sequential AIC Methods

PubMed Central

May, Michael R.; Moore, Brian R.

2016-01-01

Evolutionary biologists have long been fascinated by the extreme differences in species numbers across branches of the Tree of Life. This has motivated the development of statistical methods for detecting shifts in the rate of lineage diversification across the branches of phylogenic trees. One of the most frequently used methods, MEDUSA, explores a set of diversification-rate models, where each model assigns branches of the phylogeny to a set of diversification-rate categories. Each model is first fit to the data, and the Akaike information criterion (AIC) is then used to identify the optimal diversification model. Surprisingly, the statistical behavior of this popular method is uncharacterized, which is a concern in light of: (1) the poor performance of the AIC as a means of choosing among models in other phylogenetic contexts; (2) the ad hoc algorithm used to visit diversification models, and; (3) errors that we reveal in the likelihood function used to fit diversification models to the phylogenetic data. Here, we perform an extensive simulation study demonstrating that MEDUSA (1) has a high false-discovery rate (on average, spurious diversification-rate shifts are identified ≈30% of the time), and (2) provides biased estimates of diversification-rate parameters. Understanding the statistical behavior of MEDUSA is critical both to empirical researchers—in order to clarify whether these methods can make reliable inferences from empirical datasets—and to theoretical biologists—in order to clarify the specific problems that need to be solved in order to develop more reliable approaches for detecting shifts in the rate of lineage diversification. [Akaike information criterion; extinction; lineage-specific diversification rates; phylogenetic model selection; speciation.] PMID:27037081

Methods for selecting fixed-effect models for heterogeneous codon evolution, with comments on their application to gene and genome data.

PubMed

Bao, Le; Gu, Hong; Dunn, Katherine A; Bielawski, Joseph P

2007-02-08

Models of codon evolution have proven useful for investigating the strength and direction of natural selection. In some cases, a priori biological knowledge has been used successfully to model heterogeneous evolutionary dynamics among codon sites. These are called fixed-effect models, and they require that all codon sites are assigned to one of several partitions which are permitted to have independent parameters for selection pressure, evolutionary rate, transition to transversion ratio or codon frequencies. For single gene analysis, partitions might be defined according to protein tertiary structure, and for multiple gene analysis partitions might be defined according to a gene's functional category. Given a set of related fixed-effect models, the task of selecting the model that best fits the data is not trivial. In this study, we implement a set of fixed-effect codon models which allow for different levels of heterogeneity among partitions in the substitution process. We describe strategies for selecting among these models by a backward elimination procedure, Akaike information criterion (AIC) or a corrected Akaike information criterion (AICc). We evaluate the performance of these model selection methods via a simulation study, and make several recommendations for real data analysis. Our simulation study indicates that the backward elimination procedure can provide a reliable method for model selection in this setting. We also demonstrate the utility of these models by application to a single-gene dataset partitioned according to tertiary structure (abalone sperm lysin), and a multi-gene dataset partitioned according to the functional category of the gene (flagellar-related proteins of Listeria). Fixed-effect models have advantages and disadvantages. Fixed-effect models are desirable when data partitions are known to exhibit significant heterogeneity or when a statistical test of such heterogeneity is desired. They have the disadvantage of requiring a priori knowledge for partitioning sites. We recommend: (i) selection of models by using backward elimination rather than AIC or AICc, (ii) use a stringent cut-off, e.g., p = 0.0001, and (iii) conduct sensitivity analysis of results. With thoughtful application, fixed-effect codon models should provide a useful tool for large scale multi-gene analyses.

Selective anti-herpesvirus agents.

PubMed

De Clercq, Erik

2013-01-23

This review article focuses on the anti-herpesvirus agents effective against herpes simplex virus, varicella-zoster virus and cytomegalovirus, which have either been licensed for clinical use (idoxuridine, trifluridine, brivudin, acyclovir, valaciclovir, valganciclovir, famciclovir and foscarnet) or are under clinical development (CMX001 [the hexadecyloxypropyl prodrug of cidofovir], the helicase-primase inhibitor BAY 57-1293 [now referred to as AIC316], FV-100 [the valine ester of Cf 1743] and the terminase inhibitor letermovir [AIC246]).

Use of generalised additive models to categorise continuous variables in clinical prediction

PubMed Central

2013-01-01

Background In medical practice many, essentially continuous, clinical parameters tend to be categorised by physicians for ease of decision-making. Indeed, categorisation is a common practice both in medical research and in the development of clinical prediction rules, particularly where the ensuing models are to be applied in daily clinical practice to support clinicians in the decision-making process. Since the number of categories into which a continuous predictor must be categorised depends partly on the relationship between the predictor and the outcome, the need for more than two categories must be borne in mind. Methods We propose a categorisation methodology for clinical-prediction models, using Generalised Additive Models (GAMs) with P-spline smoothers to determine the relationship between the continuous predictor and the outcome. The proposed method consists of creating at least one average-risk category along with high- and low-risk categories based on the GAM smooth function. We applied this methodology to a prospective cohort of patients with exacerbated chronic obstructive pulmonary disease. The predictors selected were respiratory rate and partial pressure of carbon dioxide in the blood (PCO2), and the response variable was poor evolution. An additive logistic regression model was used to show the relationship between the covariates and the dichotomous response variable. The proposed categorisation was compared to the continuous predictor as the best option, using the AIC and AUC evaluation parameters. The sample was divided into a derivation (60%) and validation (40%) samples. The first was used to obtain the cut points while the second was used to validate the proposed methodology. Results The three-category proposal for the respiratory rate was ≤ 20;(20,24];> 24, for which the following values were obtained: AIC=314.5 and AUC=0.638. The respective values for the continuous predictor were AIC=317.1 and AUC=0.634, with no statistically significant differences being found between the two AUCs (p =0.079). The four-category proposal for PCO2 was ≤ 43;(43,52];(52,65];> 65, for which the following values were obtained: AIC=258.1 and AUC=0.81. No statistically significant differences were found between the AUC of the four-category option and that of the continuous predictor, which yielded an AIC of 250.3 and an AUC of 0.825 (p =0.115). Conclusions Our proposed method provides clinicians with the number and location of cut points for categorising variables, and performs as successfully as the original continuous predictor when it comes to developing clinical prediction rules. PMID:23802742

Selecting a distributional assumption for modelling relative densities of benthic macroinvertebrates

USGS Publications Warehouse

Gray, B.R.

2005-01-01

The selection of a distributional assumption suitable for modelling macroinvertebrate density data is typically challenging. Macroinvertebrate data often exhibit substantially larger variances than expected under a standard count assumption, that of the Poisson distribution. Such overdispersion may derive from multiple sources, including heterogeneity of habitat (historically and spatially), differing life histories for organisms collected within a single collection in space and time, and autocorrelation. Taken to extreme, heterogeneity of habitat may be argued to explain the frequent large proportions of zero observations in macroinvertebrate data. Sampling locations may consist of habitats defined qualitatively as either suitable or unsuitable. The former category may yield random or stochastic zeroes and the latter structural zeroes. Heterogeneity among counts may be accommodated by treating the count mean itself as a random variable, while extra zeroes may be accommodated using zero-modified count assumptions, including zero-inflated and two-stage (or hurdle) approaches. These and linear assumptions (following log- and square root-transformations) were evaluated using 9 years of mayfly density data from a 52 km, ninth-order reach of the Upper Mississippi River (n = 959). The data exhibited substantial overdispersion relative to that expected under a Poisson assumption (i.e. variance:mean ratio = 23 ??? 1), and 43% of the sampling locations yielded zero mayflies. Based on the Akaike Information Criterion (AIC), count models were improved most by treating the count mean as a random variable (via a Poisson-gamma distributional assumption) and secondarily by zero modification (i.e. improvements in AIC values = 9184 units and 47-48 units, respectively). Zeroes were underestimated by the Poisson, log-transform and square root-transform models, slightly by the standard negative binomial model but not by the zero-modified models (61%, 24%, 32%, 7%, and 0%, respectively). However, the zero-modified Poisson models underestimated small counts (1 ??? y ??? 4) and overestimated intermediate counts (7 ??? y ??? 23). Counts greater than zero were estimated well by zero-modified negative binomial models, while counts greater than one were also estimated well by the standard negative binomial model. Based on AIC and percent zero estimation criteria, the two-stage and zero-inflated models performed similarly. The above inferences were largely confirmed when the models were used to predict values from a separate, evaluation data set (n = 110). An exception was that, using the evaluation data set, the standard negative binomial model appeared superior to its zero-modified counterparts using the AIC (but not percent zero criteria). This and other evidence suggest that a negative binomial distributional assumption should be routinely considered when modelling benthic macroinvertebrate data from low flow environments. Whether negative binomial models should themselves be routinely examined for extra zeroes requires, from a statistical perspective, more investigation. However, this question may best be answered by ecological arguments that may be specific to the sampled species and locations. ?? 2004 Elsevier B.V. All rights reserved.

Simultaneous optimization of limited sampling points for pharmacokinetic analysis of amrubicin and amrubicinol in cancer patients.

PubMed

Makino, Yoshinori; Watanabe, Michiko; Makihara, Reiko Ando; Nokihara, Hiroshi; Yamamoto, Noboru; Ohe, Yuichiro; Sugiyama, Erika; Sato, Hitoshi; Hayashi, Yoshikazu

2016-09-01

Limited sampling points for both amrubicin (AMR) and its active metabolite amrubicinol (AMR-OH) were simultaneously optimized using Akaike's information criterion (AIC) calculated by pharmacokinetic modeling. In this pharmacokinetic study, 40 mg/m(2) of AMR was administered as a 5-min infusion on three consecutive days to 21 Japanese lung cancer patients. Blood samples were taken at 0, 0.08, 0.25, 0.5, 1, 2, 4, 8 and 24 h after drug infusion, and AMR and AMR-OH concentrations in plasma were quantitated using a high-performance liquid chromatography. The pharmacokinetic profile of AMR was characterized using a three-compartment model and that of AMR-OH using a one-compartment model following a first-order absorption process. These pharmacokinetic profiles were then integrated into one pharmacokinetic model for simultaneous fitting of AMR and AMR-OH. After fitting to the pharmacokinetic model, 65 combinations of four sampling points from the concentration profiles were evaluated for their AICs. Stepwise regression analysis was applied to select the sampling points for AMR and AMR-OH to predict the area under the concentration-time curves (AUCs) at best. Of the three combinations that yielded favorable AIC values, 0.25, 2, 4 and 8 h yielded the best AUC prediction for both AMR (R(2) = 0.977) and AMR-OH (R(2) = 0.886). The prediction error for AUC was less than 15%. The optimal limited sampling points of AMR and AMR-OH after AMR infusion were found to be 0.25, 2, 4 and 8 h, enabling less frequent blood sampling in further expanded pharmacokinetic studies for both AMR and AMR-OH. © 2016 John Wiley & Sons Australia, Ltd.

Predictability of Seasonal Rainfall over the Greater Horn of Africa

NASA Astrophysics Data System (ADS)

Ngaina, J. N.

2016-12-01

The El Nino-Southern Oscillation (ENSO) is a primary mode of climate variability in the Greater of Africa (GHA). The expected impacts of climate variability and change on water, agriculture, and food resources in GHA underscore the importance of reliable and accurate seasonal climate predictions. The study evaluated different model selection criteria which included the Coefficient of determination (R2), Akaike's Information Criterion (AIC), Bayesian Information Criterion (BIC), and the Fisher information approximation (FIA). A forecast scheme based on the optimal model was developed to predict the October-November-December (OND) and March-April-May (MAM) rainfall. The predictability of GHA rainfall based on ENSO was quantified based on composite analysis, correlations and contingency tables. A test for field-significance considering the properties of finiteness and interdependence of the spatial grid was applied to avoid correlations by chance. The study identified FIA as the optimal model selection criterion. However, complex model selection criteria (FIA followed by BIC) performed better compared to simple approach (R2 and AIC). Notably, operational seasonal rainfall predictions over the GHA makes of simple model selection procedures e.g. R2. Rainfall is modestly predictable based on ENSO during OND and MAM seasons. El Nino typically leads to wetter conditions during OND and drier conditions during MAM. The correlations of ENSO indices with rainfall are statistically significant for OND and MAM seasons. Analysis based on contingency tables shows higher predictability of OND rainfall with the use of ENSO indices derived from the Pacific and Indian Oceans sea surfaces showing significant improvement during OND season. The predictability based on ENSO for OND rainfall is robust on a decadal scale compared to MAM. An ENSO-based scheme based on an optimal model selection criterion can thus provide skillful rainfall predictions over GHA. This study concludes that the negative phase of ENSO (La Niña) leads to dry conditions while the positive phase of ENSO (El Niño) anticipates enhanced wet conditions

Antibiotic-Induced Depletion of Anti-inflammatory Clostridia Is Associated with the Development of Graft-versus-Host Disease in Pediatric Stem Cell Transplantation Patients.

PubMed

Simms-Waldrip, Tiffany R; Sunkersett, Gauri; Coughlin, Laura A; Savani, Milan R; Arana, Carlos; Kim, Jiwoong; Kim, Minsoo; Zhan, Xiaowei; Greenberg, David E; Xie, Yang; Davies, Stella M; Koh, Andrew Y

2017-05-01

Adult stem cell transplantation (SCT) patients with graft-versus-host-disease (GVHD) exhibit significant disruptions in gut microbial communities. These changes are associated with higher overall mortality and appear to be driven by specific antibiotic therapies. It is unclear whether pediatric SCT patients who develop GVHD exhibit similar antibiotic-induced gut microbiota community changes. Here, we show that pediatric SCT patients (from Children's Medical Center Dallas, n = 8, and Cincinnati Children's Hospital, n = 7) who developed GVHD showed a significant decline, up to 10-log fold, in gut anti-inflammatory Clostridia (AIC) compared with those without GVHD. In fact, the development of GVHD is significantly associated with this AIC decline and with cumulative antibiotic exposure, particularly antibiotics effective against anaerobic bacteria (P = .003, Firth logistic regression analysis). Using metagenomic shotgun sequencing analysis, we were able to identify specific commensal bacterial species, including AIC, that were significantly depleted in GVHD patients. We then used a preclinical GVHD model to verify our clinical observations. Clindamycin depleted AIC and exacerbated GVHD in mice, whereas oral AIC supplementation increased gut AIC levels and mitigated GVHD in mice. Together, these data suggest that an antibiotic-induced AIC depletion in the gut microbiota is associated with the development of GVHD in pediatric SCT patients. Copyright © 2017 The American Society for Blood and Marrow Transplantation. Published by Elsevier Inc. All rights reserved.

A Matched Cohort Study of Patients With End-Stage Heart Failure from Anthracycline-Induced Cardiomyopathy Requiring Advanced Cardiac Support.

PubMed

Thomas, Garry R; McDonald, Michael A; Day, Jennifer; Ross, Heather J; Delgado, Diego H; Billia, Filio; Butany, Jagdish W; Rao, Vivek; Amir, Eitan; Bedard, Philippe L; Thavendiranathan, Paaladinesh

2016-11-15

Anthracycline-induced cardiomyopathy (AIC) may progress to end-stage heart failure requiring mechanical circulatory support or orthotopic heart transplantation (OHT). Previous studies have described important clinical differences between AIC and nonischemic cardiomyopathy (NIC) cohorts requiring these advanced interventions. Therefore, we sought to extend this literature by comparing echocardiographic parameters, treatment strategies, and the prognosis between matched patients from these cohorts. This is a retrospective matched cohort study. All patients who received a ventricular assist device or OHT at a large Canadian center were reviewed (n = 421; 1988 to 2015) and subjects with clinical and pathologic evidence of AIC were included (n = 17, 4.0%). A comparison cohort with idiopathic NIC from the same database, matched 3:1 for age, gender, ethnicity, and year of heart failure onset was selected. The Mann-Whitney rank-sum and Fisher's exact tests were used for comparisons. Patients with AIC were predominantly women (70.6%) with heart failure diagnosed at age 40.2 ± 15.8 and 8.3 ± 8.9 years after anthracycline treatment. Compared with NIC, no differences were seen in co-morbidities, echocardiographic measures, the proportion of patients receiving a defibrillator, ventricular assist device, or OHT, the incidence of graft failure, and all-cause mortality. In contrast to other studies, AIC was not associated with a higher incidence of right ventricular dysfunction. A greater proportion of patients with AIC developed cancer (recurrence or new primary) post-OHT (21.4% vs 2.3%, p = 0.042). In conclusion, we demonstrate that when matched cohorts of patients with end-stage heart failure secondary to AIC and idiopathic NIC are compared, they are similar with respect to co-morbidities, degree of ventricular dysfunction, and advanced therapeutics used. The prognosis with OHT is also similar. Copyright © 2016 Elsevier Inc. All rights reserved.

Truth, models, model sets, AIC, and multimodel inference: a Bayesian perspective

USGS Publications Warehouse

Barker, Richard J.; Link, William A.

2015-01-01

Statistical inference begins with viewing data as realizations of stochastic processes. Mathematical models provide partial descriptions of these processes; inference is the process of using the data to obtain a more complete description of the stochastic processes. Wildlife and ecological scientists have become increasingly concerned with the conditional nature of model-based inference: what if the model is wrong? Over the last 2 decades, Akaike's Information Criterion (AIC) has been widely and increasingly used in wildlife statistics for 2 related purposes, first for model choice and second to quantify model uncertainty. We argue that for the second of these purposes, the Bayesian paradigm provides the natural framework for describing uncertainty associated with model choice and provides the most easily communicated basis for model weighting. Moreover, Bayesian arguments provide the sole justification for interpreting model weights (including AIC weights) as coherent (mathematically self consistent) model probabilities. This interpretation requires treating the model as an exact description of the data-generating mechanism. We discuss the implications of this assumption, and conclude that more emphasis is needed on model checking to provide confidence in the quality of inference.

[Effect of stock abundance and environmental factors on the recruitment success of small yellow croaker in the East China Sea].

PubMed

Liu, Zun-lei; Yuan, Xing-wei; Yang, Lin-lin; Yan, Li-ping; Zhang, Hui; Cheng, Jia-hua

2015-02-01

Multiple hypotheses are available to explain recruitment rate. Model selection methods can be used to identify the best model that supports a particular hypothesis. However, using a single model for estimating recruitment success is often inadequate for overexploited population because of high model uncertainty. In this study, stock-recruitment data of small yellow croaker in the East China Sea collected from fishery dependent and independent surveys between 1992 and 2012 were used to examine density-dependent effects on recruitment success. Model selection methods based on frequentist (AIC, maximum adjusted R2 and P-values) and Bayesian (Bayesian model averaging, BMA) methods were applied to identify the relationship between recruitment and environment conditions. Interannual variability of the East China Sea environment was indicated by sea surface temperature ( SST) , meridional wind stress (MWS), zonal wind stress (ZWS), sea surface pressure (SPP) and runoff of Changjiang River ( RCR). Mean absolute error, mean squared predictive error and continuous ranked probability score were calculated to evaluate the predictive performance of recruitment success. The results showed that models structures were not consistent based on three kinds of model selection methods, predictive variables of models were spawning abundance and MWS by AIC, spawning abundance by P-values, spawning abundance, MWS and RCR by maximum adjusted R2. The recruitment success decreased linearly with stock abundance (P < 0.01), suggesting overcompensation effect in the recruitment success might be due to cannibalism or food competition. Meridional wind intensity showed marginally significant and positive effects on the recruitment success (P = 0.06), while runoff of Changjiang River showed a marginally negative effect (P = 0.07). Based on mean absolute error and continuous ranked probability score, predictive error associated with models obtained from BMA was the smallest amongst different approaches, while that from models selected based on the P-value of the independent variables was the highest. However, mean squared predictive error from models selected based on the maximum adjusted R2 was highest. We found that BMA method could improve the prediction of recruitment success, derive more accurate prediction interval and quantitatively evaluate model uncertainty.

α-Intercalated cells defend the urinary system from bacterial infection.

PubMed

Paragas, Neal; Kulkarni, Ritwij; Werth, Max; Schmidt-Ott, Kai M; Forster, Catherine; Deng, Rong; Zhang, Qingyin; Singer, Eugenia; Klose, Alexander D; Shen, Tian Huai; Francis, Kevin P; Ray, Sunetra; Vijayakumar, Soundarapandian; Seward, Samuel; Bovino, Mary E; Xu, Katherine; Takabe, Yared; Amaral, Fábio E; Mohan, Sumit; Wax, Rebecca; Corbin, Kaitlyn; Sanna-Cherchi, Simone; Mori, Kiyoshi; Johnson, Lynne; Nickolas, Thomas; D'Agati, Vivette; Lin, Chyuan-Sheng; Qiu, Andong; Al-Awqati, Qais; Ratner, Adam J; Barasch, Jonathan

2014-07-01

α-Intercalated cells (A-ICs) within the collecting duct of the kidney are critical for acid-base homeostasis. Here, we have shown that A-ICs also serve as both sentinels and effectors in the defense against urinary infections. In a murine urinary tract infection model, A-ICs bound uropathogenic E. coli and responded by acidifying the urine and secreting the bacteriostatic protein lipocalin 2 (LCN2; also known as NGAL). A-IC-dependent LCN2 secretion required TLR4, as mice expressing an LPS-insensitive form of TLR4 expressed reduced levels of LCN2. The presence of LCN2 in urine was both necessary and sufficient to control the urinary tract infection through iron sequestration, even in the harsh condition of urine acidification. In mice lacking A-ICs, both urinary LCN2 and urinary acidification were reduced, and consequently bacterial clearance was limited. Together these results indicate that A-ICs, which are known to regulate acid-base metabolism, are also critical for urinary defense against pathogenic bacteria. They respond to both cystitis and pyelonephritis by delivering bacteriostatic chemical agents to the lower urinary system.

α–Intercalated cells defend the urinary system from bacterial infection

PubMed Central

Paragas, Neal; Kulkarni, Ritwij; Werth, Max; Schmidt-Ott, Kai M.; Forster, Catherine; Deng, Rong; Zhang, Qingyin; Singer, Eugenia; Klose, Alexander D.; Shen, Tian Huai; Francis, Kevin P.; Ray, Sunetra; Vijayakumar, Soundarapandian; Seward, Samuel; Bovino, Mary E.; Xu, Katherine; Takabe, Yared; Amaral, Fábio E.; Mohan, Sumit; Wax, Rebecca; Corbin, Kaitlyn; Sanna-Cherchi, Simone; Mori, Kiyoshi; Johnson, Lynne; Nickolas, Thomas; D’Agati, Vivette; Lin, Chyuan-Sheng; Qiu, Andong; Al-Awqati, Qais; Ratner, Adam J.; Barasch, Jonathan

2014-01-01

α–Intercalated cells (A-ICs) within the collecting duct of the kidney are critical for acid-base homeostasis. Here, we have shown that A-ICs also serve as both sentinels and effectors in the defense against urinary infections. In a murine urinary tract infection model, A-ICs bound uropathogenic E. coli and responded by acidifying the urine and secreting the bacteriostatic protein lipocalin 2 (LCN2; also known as NGAL). A-IC–dependent LCN2 secretion required TLR4, as mice expressing an LPS-insensitive form of TLR4 expressed reduced levels of LCN2. The presence of LCN2 in urine was both necessary and sufficient to control the urinary tract infection through iron sequestration, even in the harsh condition of urine acidification. In mice lacking A-ICs, both urinary LCN2 and urinary acidification were reduced, and consequently bacterial clearance was limited. Together these results indicate that A-ICs, which are known to regulate acid-base metabolism, are also critical for urinary defense against pathogenic bacteria. They respond to both cystitis and pyelonephritis by delivering bacteriostatic chemical agents to the lower urinary system. PMID:24937428

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

PubMed

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

2018-03-27

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

Linear and curvilinear correlations of brain gray matter volume and density with age using voxel-based morphometry with the Akaike information criterion in 291 healthy children.

PubMed

Taki, Yasuyuki; Hashizume, Hiroshi; Thyreau, Benjamin; Sassa, Yuko; Takeuchi, Hikaru; Wu, Kai; Kotozaki, Yuka; Nouchi, Rui; Asano, Michiko; Asano, Kohei; Fukuda, Hiroshi; Kawashima, Ryuta

2013-08-01

We examined linear and curvilinear correlations of gray matter volume and density in cortical and subcortical gray matter with age using magnetic resonance images (MRI) in a large number of healthy children. We applied voxel-based morphometry (VBM) and region-of-interest (ROI) analyses with the Akaike information criterion (AIC), which was used to determine the best-fit model by selecting which predictor terms should be included. We collected data on brain structural MRI in 291 healthy children aged 5-18 years. Structural MRI data were segmented and normalized using a custom template by applying the diffeomorphic anatomical registration using exponentiated lie algebra (DARTEL) procedure. Next, we analyzed the correlations of gray matter volume and density with age in VBM with AIC by estimating linear, quadratic, and cubic polynomial functions. Several regions such as the prefrontal cortex, the precentral gyrus, and cerebellum showed significant linear or curvilinear correlations between gray matter volume and age on an increasing trajectory, and between gray matter density and age on a decreasing trajectory in VBM and ROI analyses with AIC. Because the trajectory of gray matter volume and density with age suggests the progress of brain maturation, our results may contribute to clarifying brain maturation in healthy children from the viewpoint of brain structure. Copyright © 2012 Wiley Periodicals, Inc.

Incidence and description of autoimmune cytopenias during treatment with ibrutinib for chronic lymphocytic leukemia.

PubMed

Rogers, K A; Ruppert, A S; Bingman, A; Andritsos, L A; Awan, F T; Blum, K A; Flynn, J M; Jaglowski, S M; Lozanski, G; Maddocks, K J; Byrd, J C; Woyach, J A; Jones, J A

2016-02-01

Chronic lymphocytic leukemia (CLL) is frequently complicated by secondary autoimmune cytopenias (AICs). Ibrutinib is an irreversible inhibitor of Bruton's tyrosine kinase approved for the treatment of relapsed CLL and CLL with del(17p). The effect of ibrutinib treatment on the incidence of AIC is currently unknown. We reviewed medical records of 301 patients treated with ibrutinib, as participants in therapeutic clinical trials at The Ohio State University Comprehensive Cancer Center between July 2010 and July 2014. Subjects were reviewed with respect to past history of AIC, and treatment-emergent AIC cases were identified. Before starting ibrutinib treatment, 26% of patients had experienced AIC. Information was available for a total of 468 patient-years of ibrutinib exposure, during which there were six cases of treatment-emergent AIC. This corresponds to an estimated incidence rate of 13 episodes for every 1000 patient-years of ibrutinib treatment. We further identified 22 patients receiving therapy for AIC at the time ibrutinib was started. Of these 22 patients, 19 were able to discontinue AIC therapy. We found that ibrutinib treatment is associated with a low rate of treatment-emergent AIC. Patients with an existing AIC have been successfully treated with ibrutinib and subsequently discontinued AIC therapy.

Distribution pattern of public transport passenger in Yogyakarta, Indonesia

NASA Astrophysics Data System (ADS)

Narendra, Alfa; Malkhamah, Siti; Sopha, Bertha Maya

2018-03-01

The arrival and departure distribution pattern of Trans Jogja bus passenger is one of the fundamental model for simulation. The purpose of this paper is to build models of passengers flows. This research used passengers data from January to May 2014. There is no policy that change the operation system affecting the nature of this pattern nowadays. The roads, buses, land uses, schedule, and people are relatively still the same. The data then categorized based on the direction, days, and location. Moreover, each category was fitted into some well-known discrete distributions. Those distributions are compared based on its AIC value and BIC. The chosen distribution model has the smallest AIC and BIC value and the negative binomial distribution found has the smallest AIC and BIC value. Probability mass function (PMF) plots of those models were compared to draw generic model from each categorical negative binomial distribution models. The value of accepted generic negative binomial distribution is 0.7064 and 1.4504 of mu. The minimum and maximum passenger vector value of distribution are is 0 and 41.

RADIO TRANSIENTS FROM ACCRETION-INDUCED COLLAPSE OF WHITE DWARFS

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

Moriya, Takashi J., E-mail: takashi.moriya@nao.ac.jp

2016-10-20

We investigate observational properties of accretion-induced collapse (AIC) of white dwarfs (WDs) in radio frequencies. If AIC is triggered by accretion from a companion star, a dense circumstellar medium can be formed around the progenitor system. Then, the ejecta from AIC collide with the dense circumstellar medium, creating a strong shock. The strong shock can produce synchrotron emission that can be observed in radio frequencies. Even if AIC occurs as a result of WD mergers, we argue that AIC may cause fast radio bursts (FRBs) if a certain condition is satisfied. If AIC forms neutron stars (NSs) that are somore » massive that rotation is required to support themselves (i.e., supramassive NSs), the supramassive NSs may immediately lose their rotational energy by the r-mode instability and collapse to black holes. If the collapsing supramassive NSs are strongly magnetized, they may emit FRBs, as previously proposed. The AIC radio transients from single-degenerate systems may be detected in future radio transient surveys like the Very Large Array Sky Survey or the Square Kilometer Array transient survey. Because AIC has been proposed as a source of gravitational waves (GWs), GWs from AIC may be accompanied by radio-bright transients that can be used to confirm the AIC origin of observed GWs.« less

Bayesian Decision Tree for the Classification of the Mode of Motion in Single-Molecule Trajectories

PubMed Central

Türkcan, Silvan; Masson, Jean-Baptiste

2013-01-01

Membrane proteins move in heterogeneous environments with spatially (sometimes temporally) varying friction and with biochemical interactions with various partners. It is important to reliably distinguish different modes of motion to improve our knowledge of the membrane architecture and to understand the nature of interactions between membrane proteins and their environments. Here, we present an analysis technique for single molecule tracking (SMT) trajectories that can determine the preferred model of motion that best matches observed trajectories. The method is based on Bayesian inference to calculate the posteriori probability of an observed trajectory according to a certain model. Information theory criteria, such as the Bayesian information criterion (BIC), the Akaike information criterion (AIC), and modified AIC (AICc), are used to select the preferred model. The considered group of models includes free Brownian motion, and confined motion in 2nd or 4th order potentials. We determine the best information criteria for classifying trajectories. We tested its limits through simulations matching large sets of experimental conditions and we built a decision tree. This decision tree first uses the BIC to distinguish between free Brownian motion and confined motion. In a second step, it classifies the confining potential further using the AIC. We apply the method to experimental Clostridium Perfingens -toxin (CPT) receptor trajectories to show that these receptors are confined by a spring-like potential. An adaptation of this technique was applied on a sliding window in the temporal dimension along the trajectory. We applied this adaptation to experimental CPT trajectories that lose confinement due to disaggregation of confining domains. This new technique adds another dimension to the discussion of SMT data. The mode of motion of a receptor might hold more biologically relevant information than the diffusion coefficient or domain size and may be a better tool to classify and compare different SMT experiments. PMID:24376584

Selecting among competing models of electro-optic, infrared camera system range performance

USGS Publications Warehouse

Nichols, Jonathan M.; Hines, James E.; Nichols, James D.

2013-01-01

Range performance is often the key requirement around which electro-optical and infrared camera systems are designed. This work presents an objective framework for evaluating competing range performance models. Model selection based on the Akaike’s Information Criterion (AIC) is presented for the type of data collected during a typical human observer and target identification experiment. These methods are then demonstrated on observer responses to both visible and infrared imagery in which one of three maritime targets was placed at various ranges. We compare the performance of a number of different models, including those appearing previously in the literature. We conclude that our model-based approach offers substantial improvements over the traditional approach to inference, including increased precision and the ability to make predictions for some distances other than the specific set for which experimental trials were conducted.

Melatonin treatment further improves adipose-derived mesenchymal stem cell therapy for acute interstitial cystitis in rat.

PubMed

Chen, Yen-Ta; Chiang, Hsin-Ju; Chen, Chih-Hung; Sung, Pei-Hsun; Lee, Fan-Yen; Tsai, Tzu-Hsien; Chang, Chia-Lo; Chen, Hong-Hwa; Sun, Cheuk-Kwan; Leu, Steve; Chang, Hsueh-Wen; Yang, Chih-Chao; Yip, Hon-Kan

2014-10-01

This study tests the hypothesis that combined melatonin and adipose-derived mesenchymal stem cell (ADMSC, 1.2 × 10(6) given intravenously) treatment offer superior protection against cyclophosphamide (CYP 150 mg/kg)-induced acute interstitial cystitis (AIC) in rats. Male adult Sprague-Dawley rats were treated as follows: sham controls, AIC alone, AIC + melatonin, AIC + ADMSC, and AIC + melatonin +ADMSC. When melatonin was used, it was given as follows: 20 mg/kg at 30 min after CYP and 50 mg/kg at 6 and 18 hr after CYP. Twenty-four-hour urine volume, urine albumin level, and severity of hematuria were highest in AIC rats and lowest in the controls; likewise urine volume was higher in AIC + melatonin rats than in AIC + ADMSC and AIC + melatonin + ADMSC treated rats; in all cases, P < 0.001. The numbers of CD14+, CD74+, CD68+, MIP+, Cox-2+, substance P+, cells and protein expression of IL-6, IL-12, RANTES, TNF-α, NF-κB, MMP-9, iNOS (i.e. inflammatory biomarkers), glycosaminoglycan level, expression of oxidized protein, and protein expression of reactive oxygen species (NOX-1, NOX-2, NOX-4) in the bladder tissue exhibited an identical pattern compared with that of hematuria among the five groups (all P < 0.0001). The integrity of epithelial layer and area of collagen deposition displayed an opposite pattern compared to that of hematuria among all groups (P < 0.0001). The cellular expressions of antioxidants (GR, GPx, HO-1, NQO 1) showed a significant progressive increase form controls to AIC + melatonin + ADMSC (all P < 0.0001). Combined regimen of melatonin and ADMSC was superior to either alone in protecting against CYP-induced AIC. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

EEG sleep in Cushing's disease and Cushing's syndrome: comparison with patients with major depressive disorder.

PubMed

Shipley, J E; Schteingart, D E; Tandon, R; Pande, A C; Grunhaus, L; Haskett, R F; Starkman, M N

1992-07-15

Because patients with Cushing' syndrome (CS) and Major depressive disorder (MDD) share features of hypercortisolism and the depressive syndrome, we compared electro-encephalographic (EEG) sleep in patients with pituitary-ACTH-dependent Cushing's syndrome (Cushing's disease, CD), patients with ACTH-independent Cushing's syndrome (AICS), patients with major depressive disorder (MDD), and normal subjects. There were substantial similarities in the abnormal polysomnography profiles of patients with CD, AICS, and MDD. All three patient groups demonstrated poorer sleep continuity, shortened rapid eye movement (REM) latency, and increased first REM period density compared with normal subjects. In addition, AICS patients and MDD patients had elevated REM activity and density. These findings are discussed in terms of models of pathophysiology that relate abnormalities in sleep, mood, and hypothalamic-pituitary-adrenal function.

Water availability determines the richness and density of fig trees within Brazilian semideciduous forest landscapes

NASA Astrophysics Data System (ADS)

Coelho, Luís Francisco Mello; Ribeiro, Milton Cezar; Pereira, Rodrigo Augusto Santinelo

2014-05-01

The success of fig trees in tropical ecosystems is evidenced by the great diversity (+750 species) and wide geographic distribution of the genus. We assessed the contribution of environmental variables on the species richness and density of fig trees in fragments of seasonal semideciduous forest (SSF) in Brazil. We assessed 20 forest fragments in three regions in Sao Paulo State, Brazil. Fig tree richness and density was estimated in rectangular plots, comprising 31.4 ha sampled. Both richness and fig tree density were linearly modeled as function of variables representing (1) fragment metrics, (2) forest structure, and (3) landscape metrics expressing water drainage in the fragments. Model selection was performed by comparing the AIC values (Akaike Information Criterion) and the relative weight of each model (wAIC). Both species richness and fig tree density were better explained by the water availability in the fragment (meter of streams/ha): wAICrichness = 0.45, wAICdensity = 0.96. The remaining variables related to anthropic perturbation and forest structure were of little weight in the models. The rainfall seasonality in SSF seems to select for both establishment strategies and morphological adaptations in the hemiepiphytic fig tree species. In the studied SSF, hemiepiphytes established at lower heights in their host trees than reported for fig trees in evergreen rainforests. Some hemiepiphytic fig species evolved superficial roots extending up to 100 m from their trunks, resulting in hectare-scale root zones that allow them to efficiently forage water and soil nutrients. The community of fig trees was robust to variation in forest structure and conservation level of SSF fragments, making this group of plants an important element for the functioning of seasonal tropical forests.

Effects of floods on fish assemblages in an intermittent prairie stream

USGS Publications Warehouse

Franssen, N.R.; Gido, K.B.; Guy, C.S.; Tripe, J.A.; Shrank, S.J.; Strakosh, T.R.; Bertrand, K.N.; Franssen, C.M.; Pitts, K.L.; Paukert, C.P.

2006-01-01

1. Floods are major disturbances to stream ecosystems that can kill or displace organisms and modify habitats. Many studies have reported changes in fish assemblages after a single flood, but few studies have evaluated the importance of timing and intensity of floods on long-term fish assemblage dynamics. 2. We used a 10-year dataset to evaluate the effects of floods on fishes in Kings Creek, an intermittent prairie stream in north-eastern, Kansas, U.S.A. Samples were collected seasonally at two perennial headwater sites (1995-2005) and one perennial downstream flowing site (1997-2005) allowing us to evaluate the effects of floods at different locations within a watershed. In addition, four surveys during 2003 and 2004 sampled 3-5 km of stream between the long-term study sites to evaluate the use of intermittent reaches of this stream. 3. Because of higher discharge and bed scouring at the downstream site, we predicted that the fish assemblage would have lowered species richness and abundance following floods. In contrast, we expected increased species richness and abundance at headwater sites because floods increase stream connectivity and create the potential for colonisation from downstream reaches. 4. Akaike Information Criteria (AIC) was used to select among candidate regression models that predicted species richness and abundance based on Julian date, time since floods, season and physical habitat at each site. At the downstream site, AIC weightings suggested Julian date was the best predictor of fish assemblage structure, but no model explained >16% of the variation in species richness or community structure. Variation explained by Julian date was primarily attributed to a long-term pattern of declining abundance of common species. At the headwater sites, there was not a single candidate model selected to predict total species abundance and assemblage structure. AIC weightings suggested variation in assemblage structure was associated with either Julian date or local habitat characteristics. 5. Fishes rapidly colonised isolated or dry habitats following floods. This was evidenced by the occurrence of fishes in intermittent reaches and the positive association between maximum daily discharge and colonisation events at both headwater sites. 6. Our study suggests floods allow dispersal into intermittent habitats with little or no downstream displacement of fishes. Movement of fishes among habitats during flooding highlights the importance of maintaining connectivity of stream networks of low to medium order prairie streams. ?? 2006 The Authors.

Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention.

PubMed

Attallah, Omneya; Karthikesalingam, Alan; Holt, Peter J E; Thompson, Matthew M; Sayers, Rob; Bown, Matthew J; Choke, Eddie C; Ma, Xianghong

2017-08-03

Feature selection (FS) process is essential in the medical area as it reduces the effort and time needed for physicians to measure unnecessary features. Choosing useful variables is a difficult task with the presence of censoring which is the unique characteristic in survival analysis. Most survival FS methods depend on Cox's proportional hazard model; however, machine learning techniques (MLT) are preferred but not commonly used due to censoring. Techniques that have been proposed to adopt MLT to perform FS with survival data cannot be used with the high level of censoring. The researcher's previous publications proposed a technique to deal with the high level of censoring. It also used existing FS techniques to reduce dataset dimension. However, in this paper a new FS technique was proposed and combined with feature transformation and the proposed uncensoring approaches to select a reduced set of features and produce a stable predictive model. In this paper, a FS technique based on artificial neural network (ANN) MLT is proposed to deal with highly censored Endovascular Aortic Repair (EVAR). Survival data EVAR datasets were collected during 2004 to 2010 from two vascular centers in order to produce a final stable model. They contain almost 91% of censored patients. The proposed approach used a wrapper FS method with ANN to select a reduced subset of features that predict the risk of EVAR re-intervention after 5 years to patients from two different centers located in the United Kingdom, to allow it to be potentially applied to cross-centers predictions. The proposed model is compared with the two popular FS techniques; Akaike and Bayesian information criteria (AIC, BIC) that are used with Cox's model. The final model outperforms other methods in distinguishing the high and low risk groups; as they both have concordance index and estimated AUC better than the Cox's model based on AIC, BIC, Lasso, and SCAD approaches. These models have p-values lower than 0.05, meaning that patients with different risk groups can be separated significantly and those who would need re-intervention can be correctly predicted. The proposed approach will save time and effort made by physicians to collect unnecessary variables. The final reduced model was able to predict the long-term risk of aortic complications after EVAR. This predictive model can help clinicians decide patients' future observation plan.

Accounting for and predicting the influence of spatial autocorrelation in water quality modeling

NASA Astrophysics Data System (ADS)

Miralha, L.; Kim, D.

2017-12-01

Although many studies have attempted to investigate the spatial trends of water quality, more attention is yet to be paid to the consequences of considering and ignoring the spatial autocorrelation (SAC) that exists in water quality parameters. Several studies have mentioned the importance of accounting for SAC in water quality modeling, as well as the differences in outcomes between models that account for and ignore SAC. However, the capacity to predict the magnitude of such differences is still ambiguous. In this study, we hypothesized that SAC inherently possessed by a response variable (i.e., water quality parameter) influences the outcomes of spatial modeling. We evaluated whether the level of inherent SAC is associated with changes in R-Squared, Akaike Information Criterion (AIC), and residual SAC (rSAC), after accounting for SAC during modeling procedure. The main objective was to analyze if water quality parameters with higher Moran's I values (inherent SAC measure) undergo a greater increase in R² and a greater reduction in both AIC and rSAC. We compared a non-spatial model (OLS) to two spatial regression approaches (spatial lag and error models). Predictor variables were the principal components of topographic (elevation and slope), land cover, and hydrological soil group variables. We acquired these data from federal online sources (e.g. USGS). Ten watersheds were selected, each in a different state of the USA. Results revealed that water quality parameters with higher inherent SAC showed substantial increase in R² and decrease in rSAC after performing spatial regressions. However, AIC values did not show significant changes. Overall, the higher the level of inherent SAC in water quality variables, the greater improvement of model performance. This indicates a linear and direct relationship between the spatial model outcomes (R² and rSAC) and the degree of SAC in each water quality variable. Therefore, our study suggests that the inherent level of SAC in response variables can predict improvements in models even before performing spatial regression approaches. We also recognize the constraints of this research and suggest that further studies focus on better ways of defining spatial neighborhoods, considering the differences among stations set in tributaries near to each other and in upstream areas.

ULF waves and plasma stability in different regions of the magnetosheath

NASA Astrophysics Data System (ADS)

Soucek, Jan; Escoubet, C. Philippe; Grison, Benjamin

2016-04-01

We present a statistical study of the occurrence and properties of ultra low frequency waves in the magnetosheath and interpret the results in terms of the competition of mirror and Alfvén-ion-cyclotron (AIC) instabilities. Both mirror and AIC waves are generated in high beta plasma of the magnetosheath when ion temperature anisotropy exceeds the threshold of the respective instabilities. These waves are frequently observed in the terrestrial and planetary magnetosheaths, but their distribution within the magnetosheath is inhomogeneous and their character varies as a function of location, local and upstream plasma parameters. We studied the spatial distribution of the two wave modes in the magnetosheath together with the local plasma parameters important for the stability of ULF waves. This analysis was performed on a dataset of all magnetosheath crossings observed by Cluster spacecraft over two years. For each observation we used bow shock, magnetopause and magnetosheath flow models to identify the relative position of the spacecraft with respect to magnetosheath boundaries and local properties of the upstream shock crossing. A strong dependence of parameters characterizing plasma stability and mirror/AIC wave occurrence on upstream ΘBn and MA is identified. The occurrence of mirror and AIC modes was compared against the respective instability thresholds and it was observed that AIC waves occurred nearly exclusively under mirror stable conditions. This is interpreted in terms of the different character of non-linear saturation of the two modes.

Mission science value-cost savings from the Advanced Imaging Communication System (AICS)

NASA Technical Reports Server (NTRS)

Rice, R. F.

1984-01-01

An Advanced Imaging Communication System (AICS) was proposed in the mid-1970s as an alternative to the Voyager data/communication system architecture. The AICS achieved virtually error free communication with little loss in the downlink data rate by concatenating a powerful Reed-Solomon block code with the Voyager convolutionally coded, Viterbi decoded downlink channel. The clean channel allowed AICS sophisticated adaptive data compression techniques. Both Voyager and the Galileo mission have implemented AICS components, and the concatenated channel itself is heading for international standardization. An analysis that assigns a dollar value/cost savings to AICS mission performance gains is presented. A conservative value or savings of $3 million for Voyager, $4.5 million for Galileo, and as much as $7 to 9.5 million per mission for future projects such as the proposed Mariner Mar 2 series is shown.

Deposition and characterization of silicon thin-films by aluminum-induced crystallization

NASA Astrophysics Data System (ADS)

Ebil, Ozgenc

Polycrystalline silicon (poly-Si) as a thin-film solar cell material could have major advantages compared to non-silicon thin-film technologies. In theory, thin-film poly-Si may retain the performance and stability of c-Si while taking advantage of established manufacturing techniques. However, poly-Si films deposited onto foreign substrates at low temperatures typically have an average grain size of 10--50 nm. Such a grain structure presents a potential problem for device performance since it introduces an excessive number of grain boundaries which, if left unpassivated, lead to poor solar cell properties. Therefore, for optimum device performance, the grain size of the poly-Si film should be at least comparable to the thickness of the films. For this project, the objectives were the deposition of poly-Si thin-films with 2--5 mum grain size on glass substrates using in-situ and conventional aluminum-induced crystallization (AIC) and the development of a model for AIC process. In-situ AIC experiments were performed using Hot-Wire Chemical Vapor Deposition (HWCVD) both above and below the eutectic temperature (577°C) of Si-Al binary system. Conventional AIC experiments were performed using a-Si layers deposited on aluminum coated glass substrates by Electron-beam deposition, Plasma Enhanced Chemical Vapor Deposition (PECVD) and HWCVD. Continuous poly-Si films with an average grain size of 10 mum on glass substrates were achieved by both in-situ and conventional aluminum-induced crystallization of Si below eutectic temperature. The grain size was determined by three factors; the grain structure of Al layer, the nature of the interfacial oxide, and crystallization temperature. The interface oxide was found to be crucial for AIC process but not necessary for crystallization itself. The characterization of interfacial oxide layer formed on Al films revealed a bilayer structure containing Al2O3 and Al(OH)3 . The effective activation energy for AIC process was determined to be 0.9 eV and depended on the nature of the interfacial oxide layer. Poly-Si layers prepared by AIC technique can be used as seed layers for epitaxial growth of bulk Si layer or as back contacts in c-Si based solar cells.

Magnetosheath plasma stability and ULF wave occurrence as a function of location in the magnetosheath and upstream bow shock parameters

NASA Astrophysics Data System (ADS)

Soucek, Jan; Escoubet, C. Philippe; Grison, Benjamin

2015-04-01

We present the results of a statistical study of the distribution of mirror and Alfvén-ion cyclotron (AIC) waves in the magnetosheath together with plasma parameters important for the stability of ULF waves, specifically ion temperature anisotropy and ion beta. Magnetosheath crossings registered by Cluster spacecraft over the course of 2 years served as a basis for the statistics. For each observation we used bow shock, magnetopause, and magnetosheath flow models to identify the relative position of the spacecraft with respect to magnetosheath boundaries and local properties of the upstream shock crossing. A strong dependence of both plasma parameters and mirror/AIC wave occurrence on upstream ΘBn and MA is identified. We analyzed a joint dependence of the same parameters on ΘBn and fractional distance between shock and magnetopause, zenith angle, and length of the flow line. Finally, the occurrence of mirror and AIC modes was compared against the respective instability thresholds. We noted that AIC waves occurred nearly exclusively under mirror stable conditions. This is interpreted in terms of different characters of nonlinear saturation of the two modes.

Bivariate copula in fitting rainfall data

NASA Astrophysics Data System (ADS)

Yee, Kong Ching; Suhaila, Jamaludin; Yusof, Fadhilah; Mean, Foo Hui

2014-07-01

The usage of copula to determine the joint distribution between two variables is widely used in various areas. The joint distribution of rainfall characteristic obtained using the copula model is more ideal than the standard bivariate modelling where copula is belief to have overcome some limitation. Six copula models will be applied to obtain the most suitable bivariate distribution between two rain gauge stations. The copula models are Ali-Mikhail-Haq (AMH), Clayton, Frank, Galambos, Gumbel-Hoogaurd (GH) and Plackett. The rainfall data used in the study is selected from rain gauge stations which are located in the southern part of Peninsular Malaysia, during the period from 1980 to 2011. The goodness-of-fit test in this study is based on the Akaike information criterion (AIC).

Anterior insula coordinates hierarchical processing of tactile mismatch responses

PubMed Central

Allen, Micah; Fardo, Francesca; Dietz, Martin J.; Hillebrandt, Hauke; Friston, Karl J.; Rees, Geraint; Roepstorff, Andreas

2016-01-01

The body underlies our sense of self, emotion, and agency. Signals arising from the skin convey warmth, social touch, and the physical characteristics of external stimuli. Surprising or unexpected tactile sensations can herald events of motivational salience, including imminent threats (e.g., an insect bite) and hedonic rewards (e.g., a caressing touch). Awareness of such events is thought to depend upon the hierarchical integration of body-related mismatch responses by the anterior insula. To investigate this possibility, we measured brain activity using functional magnetic resonance imaging, while healthy participants performed a roving tactile oddball task. Mass-univariate analysis demonstrated robust activations in limbic, somatosensory, and prefrontal cortical areas previously implicated in tactile deviancy, body awareness, and cognitive control. Dynamic Causal Modelling revealed that unexpected stimuli increased the strength of forward connections along a caudal to rostral hierarchy—projecting from thalamic and somatosensory regions towards insula, cingulate and prefrontal cortices. Within this ascending flow of sensory information, the AIC was the only region to show increased backwards connectivity to the somatosensory cortex, augmenting a reciprocal exchange of neuronal signals. Further, participants who rated stimulus changes as easier to detect showed stronger modulation of descending PFC to AIC connections by deviance. These results suggest that the AIC coordinates hierarchical processing of tactile prediction error. They are interpreted in support of an embodied predictive coding model where AIC mediated body awareness is involved in anchoring a global neuronal workspace. PMID:26584870

The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models

PubMed Central

Mahara, Gehendra; Wang, Chao; Yang, Kun; Chen, Sipeng; Guo, Jin; Gao, Qi; Wang, Wei; Wang, Quanyi; Guo, Xiuhua

2016-01-01

(1) Background: Evidence regarding scarlet fever and its relationship with meteorological, including air pollution factors, is not very available. This study aimed to examine the relationship between ambient air pollutants and meteorological factors with scarlet fever occurrence in Beijing, China. (2) Methods: A retrospective ecological study was carried out to distinguish the epidemic characteristics of scarlet fever incidence in Beijing districts from 2013 to 2014. Daily incidence and corresponding air pollutant and meteorological data were used to develop the model. Global Moran’s I statistic and Anselin’s local Moran’s I (LISA) were applied to detect the spatial autocorrelation (spatial dependency) and clusters of scarlet fever incidence. The spatial lag model (SLM) and spatial error model (SEM) including ordinary least squares (OLS) models were then applied to probe the association between scarlet fever incidence and meteorological including air pollution factors. (3) Results: Among the 5491 cases, more than half (62%) were male, and more than one-third (37.8%) were female, with the annual average incidence rate 14.64 per 100,000 population. Spatial autocorrelation analysis exhibited the existence of spatial dependence; therefore, we applied spatial regression models. After comparing the values of R-square, log-likelihood and the Akaike information criterion (AIC) among the three models, the OLS model (R2 = 0.0741, log likelihood = −1819.69, AIC = 3665.38), SLM (R2 = 0.0786, log likelihood = −1819.04, AIC = 3665.08) and SEM (R2 = 0.0743, log likelihood = −1819.67, AIC = 3665.36), identified that the spatial lag model (SLM) was best for model fit for the regression model. There was a positive significant association between nitrogen oxide (p = 0.027), rainfall (p = 0.036) and sunshine hour (p = 0.048), while the relative humidity (p = 0.034) had an adverse association with scarlet fever incidence in SLM. (4) Conclusions: Our findings indicated that meteorological, as well as air pollutant factors may increase the incidence of scarlet fever; these findings may help to guide scarlet fever control programs and targeting the intervention. PMID:27827946

The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models.

PubMed

Mahara, Gehendra; Wang, Chao; Yang, Kun; Chen, Sipeng; Guo, Jin; Gao, Qi; Wang, Wei; Wang, Quanyi; Guo, Xiuhua

2016-11-04

(1) Background: Evidence regarding scarlet fever and its relationship with meteorological, including air pollution factors, is not very available. This study aimed to examine the relationship between ambient air pollutants and meteorological factors with scarlet fever occurrence in Beijing, China. (2) Methods: A retrospective ecological study was carried out to distinguish the epidemic characteristics of scarlet fever incidence in Beijing districts from 2013 to 2014. Daily incidence and corresponding air pollutant and meteorological data were used to develop the model. Global Moran's I statistic and Anselin's local Moran's I (LISA) were applied to detect the spatial autocorrelation (spatial dependency) and clusters of scarlet fever incidence. The spatial lag model (SLM) and spatial error model (SEM) including ordinary least squares (OLS) models were then applied to probe the association between scarlet fever incidence and meteorological including air pollution factors. (3) Results: Among the 5491 cases, more than half (62%) were male, and more than one-third (37.8%) were female, with the annual average incidence rate 14.64 per 100,000 population. Spatial autocorrelation analysis exhibited the existence of spatial dependence; therefore, we applied spatial regression models. After comparing the values of R-square, log-likelihood and the Akaike information criterion (AIC) among the three models, the OLS model (R² = 0.0741, log likelihood = -1819.69, AIC = 3665.38), SLM (R² = 0.0786, log likelihood = -1819.04, AIC = 3665.08) and SEM (R² = 0.0743, log likelihood = -1819.67, AIC = 3665.36), identified that the spatial lag model (SLM) was best for model fit for the regression model. There was a positive significant association between nitrogen oxide ( p = 0.027), rainfall ( p = 0.036) and sunshine hour ( p = 0.048), while the relative humidity ( p = 0.034) had an adverse association with scarlet fever incidence in SLM. (4) Conclusions: Our findings indicated that meteorological, as well as air pollutant factors may increase the incidence of scarlet fever; these findings may help to guide scarlet fever control programs and targeting the intervention.

Markov Mixed Effects Modeling Using Electronic Adherence Monitoring Records Identifies Influential Covariates to HIV Preexposure Prophylaxis.

PubMed

Madrasi, Kumpal; Chaturvedula, Ayyappa; Haberer, Jessica E; Sale, Mark; Fossler, Michael J; Bangsberg, David; Baeten, Jared M; Celum, Connie; Hendrix, Craig W

2017-05-01

Adherence is a major factor in the effectiveness of preexposure prophylaxis (PrEP) for HIV prevention. Modeling patterns of adherence helps to identify influential covariates of different types of adherence as well as to enable clinical trial simulation so that appropriate interventions can be developed. We developed a Markov mixed-effects model to understand the covariates influencing adherence patterns to daily oral PrEP. Electronic adherence records (date and time of medication bottle cap opening) from the Partners PrEP ancillary adherence study with a total of 1147 subjects were used. This study included once-daily dosing regimens of placebo, oral tenofovir disoproxil fumarate (TDF), and TDF in combination with emtricitabine (FTC), administered to HIV-uninfected members of serodiscordant couples. One-coin and first- to third-order Markov models were fit to the data using NONMEM ® 7.2. Model selection criteria included objective function value (OFV), Akaike information criterion (AIC), visual predictive checks, and posterior predictive checks. Covariates were included based on forward addition (α = 0.05) and backward elimination (α = 0.001). Markov models better described the data than 1-coin models. A third-order Markov model gave the lowest OFV and AIC, but the simpler first-order model was used for covariate model building because no additional benefit on prediction of target measures was observed for higher-order models. Female sex and older age had a positive impact on adherence, whereas Sundays, sexual abstinence, and sex with a partner other than the study partner had a negative impact on adherence. Our findings suggest adherence interventions should consider the role of these factors. © 2016, The American College of Clinical Pharmacology.

Parameter Estimation and Model Selection for Indoor Environments Based on Sparse Observations

NASA Astrophysics Data System (ADS)

Dehbi, Y.; Loch-Dehbi, S.; Plümer, L.

2017-09-01

This paper presents a novel method for the parameter estimation and model selection for the reconstruction of indoor environments based on sparse observations. While most approaches for the reconstruction of indoor models rely on dense observations, we predict scenes of the interior with high accuracy in the absence of indoor measurements. We use a model-based top-down approach and incorporate strong but profound prior knowledge. The latter includes probability density functions for model parameters and sparse observations such as room areas and the building footprint. The floorplan model is characterized by linear and bi-linear relations with discrete and continuous parameters. We focus on the stochastic estimation of model parameters based on a topological model derived by combinatorial reasoning in a first step. A Gauss-Markov model is applied for estimation and simulation of the model parameters. Symmetries are represented and exploited during the estimation process. Background knowledge as well as observations are incorporated in a maximum likelihood estimation and model selection is performed with AIC/BIC. The likelihood is also used for the detection and correction of potential errors in the topological model. Estimation results are presented and discussed.

Evaluation of some random effects methodology applicable to bird ringing data

USGS Publications Warehouse

Burnham, K.P.; White, Gary C.

2002-01-01

Existing models for ring recovery and recapture data analysis treat temporal variations in annual survival probability (S) as fixed effects. Often there is no explainable structure to the temporal variation in S1,..., Sk; random effects can then be a useful model: Si = E(S) + ??i. Here, the temporal variation in survival probability is treated as random with average value E(??2) = ??2. This random effects model can now be fit in program MARK. Resultant inferences include point and interval estimation for process variation, ??2, estimation of E(S) and var (E??(S)) where the latter includes a component for ??2 as well as the traditional component for v??ar(S??\\S??). Furthermore, the random effects model leads to shrinkage estimates, Si, as improved (in mean square error) estimators of Si compared to the MLE, S??i, from the unrestricted time-effects model. Appropriate confidence intervals based on the Si are also provided. In addition, AIC has been generalized to random effects models. This paper presents results of a Monte Carlo evaluation of inference performance under the simple random effects model. Examined by simulation, under the simple one group Cormack-Jolly-Seber (CJS) model, are issues such as bias of ??s2, confidence interval coverage on ??2, coverage and mean square error comparisons for inference about Si based on shrinkage versus maximum likelihood estimators, and performance of AIC model selection over three models: Si ??? S (no effects), Si = E(S) + ??i (random effects), and S1,..., Sk (fixed effects). For the cases simulated, the random effects methods performed well and were uniformly better than fixed effects MLE for the Si.

The anterior insula bidirectionally modulates cost-benefit decision-making on a rodent gambling task.

PubMed

Daniel, M L; Cocker, P J; Lacoste, J; Mar, A C; Houeto, J L; Belin-Rauscent, A; Belin, D

2017-11-01

Deficits in cost-benefit decision-making, as assessed in the Iowa Gambling Task (IGT), are commonly observed in neuropsychiatric disorders such as addiction. There is considerable variation in the maximization of rewards on such tasks, both in the general population and in rodent models, suggesting individual differences in decision-making may represent a key endophenotype for vulnerability to neuropsychiatric disorders. Increasing evidence suggests that the insular cortex, which is involved in interoception and emotional processes in humans, may be a key neural locus in the control of decision-making processes. However, the extent to which the insula contributes to individual differences in cost-benefit decision-making remains unknown. Using male Sprague Dawley rats, we first assessed individual differences in the performance over the course of a single session on a rodent analogue of the IGT (rGT). Rats were matched for their ability to maximize reward and received bilateral excitotoxic or sham lesions of the anterior insula cortex (AIC). Animals were subsequently challenged on a second rGT session with altered contingencies. Finally, animals were also assessed for instrumental conditioning and reversal learning. AIC lesions produced bidirectional alterations on rGT performance; rats that had performed optimally prior to surgery subsequently showed impairments, and animals that had performed poorly showed improvements in comparison with sham-operated controls. These bidirectional effects were not attributable to alterations in behavioural flexibility or in motivation. These data suggest that the recruitment of the AIC during decision-making may be state-dependent and help guide response selection towards subjectively favourable options. © 2017 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Double-input compartmental modeling and spectral analysis for the quantification of positron emission tomography data in oncology

NASA Astrophysics Data System (ADS)

Tomasi, G.; Kimberley, S.; Rosso, L.; Aboagye, E.; Turkheimer, F.

2012-04-01

In positron emission tomography (PET) studies involving organs different from the brain, ignoring the metabolite contribution to the tissue time-activity curves (TAC), as in the standard single-input (SI) models, may compromise the accuracy of the estimated parameters. We employed here double-input (DI) compartmental modeling (CM), previously used for [11C]thymidine, and a novel DI spectral analysis (SA) approach on the tracers 5-[18F]fluorouracil (5-[18F]FU) and [18F]fluorothymidine ([18F]FLT). CM and SA were performed initially with a SI approach using the parent plasma TAC as an input function. These methods were then employed using a DI approach with the metabolite plasma TAC as an additional input function. Regions of interest (ROIs) corresponding to healthy liver, kidneys and liver metastases for 5-[18F]FU and to tumor, vertebra and liver for [18F]FLT were analyzed. For 5-[18F]FU, the improvement of the fit quality with the DI approaches was remarkable; in CM, the Akaike information criterion (AIC) always selected the DI over the SI model. Volume of distribution estimates obtained with DI CM and DI SA were in excellent agreement, for both parent 5-[18F]FU (R2 = 0.91) and metabolite [18F]FBAL (R2 = 0.99). For [18F]FLT, the DI methods provided notable improvements but less substantial than for 5-[18F]FU due to the lower rate of metabolism of [18F]FLT. On the basis of the AIC values, agreement between [18F]FLT Ki estimated with the SI and DI models was good (R2 = 0.75) for the ROIs where the metabolite contribution was negligible, indicating that the additional input did not bias the parent tracer only-related estimates. When the AIC suggested a substantial contribution of the metabolite [18F]FLT-glucuronide, on the other hand, the change in the parent tracer only-related parameters was significant (R2 = 0.33 for Ki). Our results indicated that improvements of DI over SI approaches can range from moderate to substantial and are more significant for tracers with a high rate of metabolism. Furthermore, they showed that SA is suitable for DI modeling and can be used effectively in the analysis of PET data.

Modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: prediction accuracy implications

PubMed Central

Bouvet, J-M; Makouanzi, G; Cros, D; Vigneron, Ph

2016-01-01

Hybrids are broadly used in plant breeding and accurate estimation of variance components is crucial for optimizing genetic gain. Genome-wide information may be used to explore models designed to assess the extent of additive and non-additive variance and test their prediction accuracy for the genomic selection. Ten linear mixed models, involving pedigree- and marker-based relationship matrices among parents, were developed to estimate additive (A), dominance (D) and epistatic (AA, AD and DD) effects. Five complementary models, involving the gametic phase to estimate marker-based relationships among hybrid progenies, were developed to assess the same effects. The models were compared using tree height and 3303 single-nucleotide polymorphism markers from 1130 cloned individuals obtained via controlled crosses of 13 Eucalyptus urophylla females with 9 Eucalyptus grandis males. Akaike information criterion (AIC), variance ratios, asymptotic correlation matrices of estimates, goodness-of-fit, prediction accuracy and mean square error (MSE) were used for the comparisons. The variance components and variance ratios differed according to the model. Models with a parent marker-based relationship matrix performed better than those that were pedigree-based, that is, an absence of singularities, lower AIC, higher goodness-of-fit and accuracy and smaller MSE. However, AD and DD variances were estimated with high s.es. Using the same criteria, progeny gametic phase-based models performed better in fitting the observations and predicting genetic values. However, DD variance could not be separated from the dominance variance and null estimates were obtained for AA and AD effects. This study highlighted the advantages of progeny models using genome-wide information. PMID:26328760

Amylose-potassium oleate inclusion complex in plain set-style yogurt.

PubMed

Singh, Mukti; Byars, Jeffrey A; Kenar, James A

2014-05-01

Health and wellness aspirations of U.S. consumers continue to drive the demand for lower fat from inherently beneficial foods such as yogurt. Removing fat from yogurt negatively affects the gel strength, texture, syneresis, and storage of yogurt. Amylose-potassium oleate inclusion complexes (AIC) were used to replace skim milk solids to improve the quality of nonfat yogurt. The effect of AIC on fermentation of yogurt mix and strength of yogurt gel was studied and compared to full-fat samples. Texture, storage modulus, and syneresis of yogurt were observed over 4 weeks of storage at 4 °C. Yogurt mixes having the skim milk solids partially replaced by AIC fermented at a similar rate as yogurt samples with no milk solids replaced and full-fat milk. Initial viscosity was higher for yogurt mixes with AIC. The presence of 3% AIC strengthened the yogurt gel as indicated by texture and rheology measurements. Yogurt samples with 3% AIC maintained the gel strength during storage and resulted in low syneresis after storage for 4 wk. © 2014 Institute of Food Technologists®

Evaluation of portfolio credit risk based on survival analysis for progressive censored data

NASA Astrophysics Data System (ADS)

Jaber, Jamil J.; Ismail, Noriszura; Ramli, Siti Norafidah Mohd

2017-04-01

In credit risk management, the Basel committee provides a choice of three approaches to the financial institutions for calculating the required capital: the standardized approach, the Internal Ratings-Based (IRB) approach, and the Advanced IRB approach. The IRB approach is usually preferred compared to the standard approach due to its higher accuracy and lower capital charges. This paper use several parametric models (Exponential, log-normal, Gamma, Weibull, Log-logistic, Gompertz) to evaluate the credit risk of the corporate portfolio in the Jordanian banks based on the monthly sample collected from January 2010 to December 2015. The best model is selected using several goodness-of-fit criteria (MSE, AIC, BIC). The results indicate that the Gompertz distribution is the best model parametric model for the data.

Combining Frequency Doubling Technology Perimetry and Scanning Laser Polarimetry for Glaucoma Detection.

PubMed

Mwanza, Jean-Claude; Warren, Joshua L; Hochberg, Jessica T; Budenz, Donald L; Chang, Robert T; Ramulu, Pradeep Y

2015-01-01

To determine the ability of frequency doubling technology (FDT) and scanning laser polarimetry with variable corneal compensation (GDx-VCC) to detect glaucoma when used individually and in combination. One hundred ten normal and 114 glaucomatous subjects were tested with FDT C-20-5 screening protocol and the GDx-VCC. The discriminating ability was tested for each device individually and for both devices combined using GDx-NFI, GDx-TSNIT, number of missed points of FDT, and normal or abnormal FDT. Measures of discrimination included sensitivity, specificity, area under the curve (AUC), Akaike's information criterion (AIC), and prediction confidence interval lengths. For detecting glaucoma regardless of severity, the multivariable model resulting from the combination of GDx-TSNIT, number of abnormal points on FDT (NAP-FDT), and the interaction GDx-TSNIT×NAP-FDT (AIC: 88.28, AUC: 0.959, sensitivity: 94.6%, specificity: 89.5%) outperformed the best single-variable model provided by GDx-NFI (AIC: 120.88, AUC: 0.914, sensitivity: 87.8%, specificity: 84.2%). The multivariable model combining GDx-TSNIT, NAP-FDT, and interaction GDx-TSNIT×NAP-FDT consistently provided better discriminating abilities for detecting early, moderate, and severe glaucoma than the best single-variable models. The multivariable model including GDx-TSNIT, NAP-FDT, and the interaction GDx-TSNIT×NAP-FDT provides the best glaucoma prediction compared with all other multivariable and univariable models. Combining the FDT C-20-5 screening protocol and GDx-VCC improves glaucoma detection compared with using GDx or FDT alone.

Accurate identification of microseismic P- and S-phase arrivals using the multi-step AIC algorithm

NASA Astrophysics Data System (ADS)

Zhu, Mengbo; Wang, Liguan; Liu, Xiaoming; Zhao, Jiaxuan; Peng, Ping'an

2018-03-01

Identification of P- and S-phase arrivals is the primary work in microseismic monitoring. In this study, a new multi-step AIC algorithm is proposed. This algorithm consists of P- and S-phase arrival pickers (P-picker and S-picker). The P-picker contains three steps: in step 1, a preliminary P-phase arrival window is determined by the waveform peak. Then a preliminary P-pick is identified using the AIC algorithm. Finally, the P-phase arrival window is narrowed based on the above P-pick. Thus the P-phase arrival can be identified accurately by using the AIC algorithm again. The S-picker contains five steps: in step 1, a narrow S-phase arrival window is determined based on the P-pick and the AIC curve of amplitude biquadratic time-series. In step 2, the S-picker automatically judges whether the S-phase arrival is clear to identify. In step 3 and 4, the AIC extreme points are extracted, and the relationship between the local minimum and the S-phase arrival is researched. In step 5, the S-phase arrival is picked based on the maximum probability criterion. To evaluate of the proposed algorithm, a P- and S-picks classification criterion is also established based on a source location numerical simulation. The field data tests show a considerable improvement of the multi-step AIC algorithm in comparison with the manual picks and the original AIC algorithm. Furthermore, the technique is independent of the kind of SNR. Even in the poor-quality signal group which the SNRs are below 5, the effective picking rates (the corresponding location error is <15 m) of P- and S-phase arrivals are still up to 80.9% and 76.4% respectively.

Identifying the most appropriate age threshold for TNM stage grouping of well-differentiated thyroid cancer.

PubMed

Hendrickson-Rebizant, J; Sigvaldason, H; Nason, R W; Pathak, K A

2015-08-01

Age is integrated in most risk stratification systems for well-differentiated thyroid cancer (WDTC). The most appropriate age threshold for stage grouping of WDTC is debatable. The objective of this study was to evaluate the best age threshold for stage grouping by comparing multivariable models designed to evaluate the independent impact of various prognostic factors, including age based stage grouping, on the disease specific survival (DSS) of our population-based cohort. Data from population-based thyroid cancer cohort of 2125 consecutive WDTC, diagnosed during 1970-2010, with a median follow-up of 11.5 years, was used to calculate DSS using the Kaplan Meier method. Multivariable analysis with Cox proportional hazard model was used to assess independent impact of different prognostic factors on DSS. The Akaike information criterion (AIC), a measure of statistical model fit, was used to identify the most appropriate age threshold model. Delta AIC, Akaike weight, and evidence ratios were calculated to compare the relative strength of different models. The mean age of the patients was 47.3 years. DSS of the cohort was 95.6% and 92.8% at 10 and 20 years respectively. A threshold of 55 years, with the lowest AIC, was identified as the best model. Akaike weight indicated an 85% chance that this age threshold is the best among the compared models, and is 16.8 times more likely to be the best model as compared to a threshold of 45 years. The age threshold of 55 years was found to be the best for TNM stage grouping. Copyright © 2015 Elsevier Ltd. All rights reserved.

Exploring Several Methods of Groundwater Model Selection

NASA Astrophysics Data System (ADS)

Samani, Saeideh; Ye, Ming; Asghari Moghaddam, Asghar

2017-04-01

Selecting reliable models for simulating groundwater flow and solute transport is essential to groundwater resources management and protection. This work is to explore several model selection methods for avoiding over-complex and/or over-parameterized groundwater models. We consider six groundwater flow models with different numbers (6, 10, 10, 13, 13 and 15) of model parameters. These models represent alternative geological interpretations, recharge estimates, and boundary conditions at a study site in Iran. The models were developed with Model Muse, and calibrated against observations of hydraulic head using UCODE. Model selection was conducted by using the following four approaches: (1) Rank the models using their root mean square error (RMSE) obtained after UCODE-based model calibration, (2) Calculate model probability using GLUE method, (3) Evaluate model probability using model selection criteria (AIC, AICc, BIC, and KIC), and (4) Evaluate model weights using the Fuzzy Multi-Criteria-Decision-Making (MCDM) approach. MCDM is based on the fuzzy analytical hierarchy process (AHP) and fuzzy technique for order performance, which is to identify the ideal solution by a gradual expansion from the local to the global scale of model parameters. The KIC and MCDM methods are superior to other methods, as they consider not only the fit between observed and simulated data and the number of parameter, but also uncertainty in model parameters. Considering these factors can prevent from occurring over-complexity and over-parameterization, when selecting the appropriate groundwater flow models. These methods selected, as the best model, one with average complexity (10 parameters) and the best parameter estimation (model 3).

Structural brain correlates of executive engagement in working memory: children's inter-individual differences are reflected in the anterior insular cortex.

PubMed

Rossi, Sandrine; Lubin, Amélie; Simon, Grégory; Lanoë, Céline; Poirel, Nicolas; Cachia, Arnaud; Pineau, Arlette; Houdé, Olivier

2013-06-01

Although the development of executive functions has been extensively investigated at a neurofunctional level, studies of the structural relationships between executive functions and brain anatomy are still scarce. Based on our previous meta-analysis of functional neuroimaging studies examining executive functions in children (Houdé, Rossi, Lubin, and Joliot, (2010). Developmental Science, 13, 876-885), we investigated six a priori regions of interest: the left anterior insular cortex (AIC), the left and the right supplementary motor areas, the right middle and superior frontal gyri, and the left precentral gyrus. Structural magnetic resonance imaging scans were acquired from 22 to 10-year-old children. Local gray matter volumes, assessed automatically using a standard voxel-based morphometry approach, were correlated with executive and storage working memory capacities evaluated using backward and forward digit span tasks, respectively. We found an association between smaller gray matter volume--i.e., an index of neural maturation--in the left AIC and high backward memory span while gray matter volumes in the a priori selected regions of interest were not linked with forward memory span. These results were corroborated by a whole-brain a priori free analysis that revealed a significant negative correlation in the frontal and prefrontal regions, including the left AIC, with the backward memory span, and in the right inferior parietal lobe, with the forward memory span. Taken together, these results suggest a distinct and specific association between regional gray matter volume and the executive component vs. the storage component of working memory. Moreover, they support a key role for the AIC in the executive network of children. Copyright © 2013 Elsevier Ltd. All rights reserved.

Assessing Local Model Adequacy in Bayesian Hierarchical Models Using the Partitioned Deviance Information Criterion

PubMed Central

Wheeler, David C.; Hickson, DeMarc A.; Waller, Lance A.

2010-01-01

Many diagnostic tools and goodness-of-fit measures, such as the Akaike information criterion (AIC) and the Bayesian deviance information criterion (DIC), are available to evaluate the overall adequacy of linear regression models. In addition, visually assessing adequacy in models has become an essential part of any regression analysis. In this paper, we focus on a spatial consideration of the local DIC measure for model selection and goodness-of-fit evaluation. We use a partitioning of the DIC into the local DIC, leverage, and deviance residuals to assess local model fit and influence for both individual observations and groups of observations in a Bayesian framework. We use visualization of the local DIC and differences in local DIC between models to assist in model selection and to visualize the global and local impacts of adding covariates or model parameters. We demonstrate the utility of the local DIC in assessing model adequacy using HIV prevalence data from pregnant women in the Butare province of Rwanda during 1989-1993 using a range of linear model specifications, from global effects only to spatially varying coefficient models, and a set of covariates related to sexual behavior. Results of applying the diagnostic visualization approach include more refined model selection and greater understanding of the models as applied to the data. PMID:21243121

Modeling cumulative dose and exposure duration provided insights regarding the associations between benzodiazepines and injuries.

PubMed

Abrahamowicz, Michal; Bartlett, Gillian; Tamblyn, Robyn; du Berger, Roxane

2006-04-01

Accurate assessment of medication impact requires modeling cumulative effects of exposure duration and dose; however, postmarketing studies usually represent medication exposure by baseline or current use only. We propose new methods for modeling various aspects of medication use history and employment of them to assess the adverse effects of selected benzodiazepines. Time-dependent measures of cumulative dose or duration of use, with weighting of past exposures by recency, were proposed. These measures were then included in alternative versions of the multivariable Cox model to analyze the risk of fall related injuries among the elderly new users of three benzodiazepines (nitrazepam, temazepam, and flurazepam) in Quebec. Akaike's information criterion (AIC) was used to select the most predictive model for a given benzodiazepine. The best-fitting model included a combination of cumulative duration and current dose for temazepam, and cumulative dose for flurazepam and nitrazepam, with different weighting functions. The window of clinically relevant exposure was shorter for flurazepam than for the two other products. Careful modeling of the medication exposure history may enhance our understanding of the mechanisms underlying their adverse effects.

Thermal Signature Identification System (TheSIS)

NASA Technical Reports Server (NTRS)

Merritt, Scott; Bean, Brian

2015-01-01

We characterize both nonlinear and high order linear responses of fiber-optic and optoelectronic components using spread spectrum temperature cycling methods. This Thermal Signature Identification System (TheSIS) provides much more detail than conventional narrowband or quasi-static temperature profiling methods. This detail allows us to match components more thoroughly, detect subtle reversible shifts in performance, and investigate the cause of instabilities or irreversible changes. In particular, we create parameterized models of athermal fiber Bragg gratings (FBGs), delay line interferometers (DLIs), and distributed feedback (DFB) lasers, then subject the alternative models to selection via the Akaike Information Criterion (AIC). Detailed pairing of components, e.g. FBGs, is accomplished by means of weighted distance metrics or norms, rather than on the basis of a single parameter, such as center wavelength.

Combining Frequency Doubling Technology Perimetry and Scanning Laser Polarimetry for Glaucoma Detection

PubMed Central

Mwanza, Jean-Claude; Warren, Joshua L.; Hochberg, Jessica T.; Budenz, Donald L.; Chang, Robert T.; Ramulu, Pradeep Y.

2014-01-01

Purpose To determine the ability of frequency doubling technology (FDT) and scanning laser polarimetry with variable corneal compensation (GDx-VCC) to detect glaucoma when used individually and in combination. Methods One hundred and ten normal and 114 glaucomatous subjects were tested with FDT C-20-5 screening protocol and the GDx-VCC. The discriminating ability was tested for each device individually and for both devices combined using GDx-NFI, GDx-TSNIT, number of missed points of FDT, and normal or abnormal FDT. Measures of discrimination included sensitivity, specificity, area under the curve (AUC), Akaike’s information criterion (AIC), and prediction confidence interval lengths (PIL). Results For detecting glaucoma regardless of severity, the multivariable model resulting from the combination of GDX-TSNIT, number of abnormal points on FDT (NAP-FDT), and the interaction GDx-TSNIT * NAP-FDT (AIC: 88.28, AUC: 0.959, sensitivity: 94.6%, specificity: 89.5%) outperformed the best single variable model provided by GDx-NFI (AIC: 120.88, AUC: 0.914, sensitivity: 87.8%, specificity: 84.2%). The multivariable model combining GDx-TSNIT, NAPFDT, and interaction GDx-TSNIT*NAP-FDT consistently provided better discriminating abilities for detecting early, moderate and severe glaucoma than the best single variable models. Conclusions The multivariable model including GDx-TSNIT, NAP-FDT, and the interaction GDX-TSNIT * NAP-FDT provides the best glaucoma prediction compared to all other multivariable and univariable models. Combining the FDT C-20-5 screening protocol and GDx-VCC improves glaucoma detection compared to using GDx or FDT alone. PMID:24777046

From "AICE-ing" the Test to Earning the Degree: Enrollment and Graduation Patterns among Students with the Cambridge Advanced International Certificate of Education (AICE) Diploma

ERIC Educational Resources Information Center

Rodeiro, Carmen Vidal; Crawford, Cara; Shaw, Stuart

2017-01-01

A key issue for admissions teams is to distinguish which students of those who apply are truly able and sufficiently committed to complete a degree. One signal of a student's ability to achieve college-level academic requirements is participation in high school acceleration programs such as Advanced Placement, International Baccalaureate or…

INCIDENCE OF ABNORMAL POSITRON EMISSION TOMOGRAPHY IN PATIENTS WITH UNEXPLAINED CARDIOMYOPATHY AND VENTRICULAR ARRHYTHMIAS

PubMed Central

Tung, Roderick; Bauer, Brenton; Schelbert, Heinrich; Lynch, Joseph; Auerbach, Martin; Gupta, Pawan; Schiepers, Christiaan; Chan, Samantha; Ferris, Julie; Barrio, Martin; Ajijola, Olujimi; Bradfield, Jason; Shivkumar, Kalyanam

2015-01-01

Background The incidence of myocardial inflammation in patients with unexplained cardiomyopathy referred for ventricular arrhythmias (VA) is unknown. Objective To report fasting PET scan findings in consecutive patients referred with unexplained cardiomyopathy and VA. Methods 18-FDG PET/CT scans with a >16 hour fasting protocol were prospectively ordered for patients referred for VA and unexplained cardiomyopathy (EF<55%). Patients with focal myocardial FDG uptake were labeled as arrhythmogenic inflammatory cardiomyopathy (AIC) and classified into four groups based on the presence of lymph node uptake (AIC+) and perfusion abnormalities (early vs late stage). Results Over a 3-year period, 103 PET scan were performed with 49% (AIC+=17, AIC=33) exhibiting focal FDG uptake. The mean age was 52±12 years with an EF of 36±16%. Patients with AIC were more likely to have a history of pacemaker (32% vs 6%, p=0.002) compared to those with normal PET. When biopsy was performed, histologic diagnosis revealed non-granulomatous inflammation in 6 patients and sarcoidosis in 18 patients. 90% of patients with AIC/AIC+ were prescribed immunosuppressive therapy and 58% underwent ablation. Correlation between areas of perfusion abnormalities and FDG uptake with electro-anatomic mapping was observed in 79% patients and MRI findings matched in only 33%. Conclusions Nearly 50% of patients referred with unexplained cardiomyopathy and VA demonstrate ongoing focal myocardial inflammation on FDG PET. These data suggests that a significant proportion of patients labeled “idiopathic” may have occult arrhythmogenic inflammatory cardiomyopathy, which may benefit from early detection and immunosuppressive medical therapy. PMID:26272522

A K-BKZ Formulation for Soft-Tissue Viscoelasticity

NASA Technical Reports Server (NTRS)

Freed, Alan D.; Diethelm, Kai

2005-01-01

A viscoelastic model of the K-BKZ (Kaye 1962; Bernstein et al. 1963) type is developed for isotropic biological tissues, and applied to the fat pad of the human heel. To facilitate this pursuit, a class of elastic solids is introduced through a novel strain-energy function whose elements possess strong ellipticity, and therefore lead to stable material models. The standard fractional-order viscoelastic (FOV) solid is used to arrive at the overall elastic/viscoelastic structure of the model, while the elastic potential via the K-BKZ hypothesis is used to arrive at the tensorial structure of the model. Candidate sets of functions are proposed for the elastic and viscoelastic material functions present in the model, including a regularized fractional derivative that was determined to be the best. The Akaike information criterion (AIC) is advocated for performing multi-model inference, enabling an objective selection of the best material function from within a candidate set.

Does the choice of nucleotide substitution models matter topologically?

PubMed

Hoff, Michael; Orf, Stefan; Riehm, Benedikt; Darriba, Diego; Stamatakis, Alexandros

2016-03-24

In the context of a master level programming practical at the computer science department of the Karlsruhe Institute of Technology, we developed and make available an open-source code for testing all 203 possible nucleotide substitution models in the Maximum Likelihood (ML) setting under the common Akaike, corrected Akaike, and Bayesian information criteria. We address the question if model selection matters topologically, that is, if conducting ML inferences under the optimal, instead of a standard General Time Reversible model, yields different tree topologies. We also assess, to which degree models selected and trees inferred under the three standard criteria (AIC, AICc, BIC) differ. Finally, we assess if the definition of the sample size (#sites versus #sites × #taxa) yields different models and, as a consequence, different tree topologies. We find that, all three factors (by order of impact: nucleotide model selection, information criterion used, sample size definition) can yield topologically substantially different final tree topologies (topological difference exceeding 10 %) for approximately 5 % of the tree inferences conducted on the 39 empirical datasets used in our study. We find that, using the best-fit nucleotide substitution model may change the final ML tree topology compared to an inference under a default GTR model. The effect is less pronounced when comparing distinct information criteria. Nonetheless, in some cases we did obtain substantial topological differences.

Double-input compartmental modeling and spectral analysis for the quantification of positron emission tomography data in oncology.

PubMed

Tomasi, G; Kimberley, S; Rosso, L; Aboagye, E; Turkheimer, F

2012-04-07

In positron emission tomography (PET) studies involving organs different from the brain, ignoring the metabolite contribution to the tissue time-activity curves (TAC), as in the standard single-input (SI) models, may compromise the accuracy of the estimated parameters. We employed here double-input (DI) compartmental modeling (CM), previously used for [¹¹C]thymidine, and a novel DI spectral analysis (SA) approach on the tracers 5-[¹⁸F]fluorouracil (5-[¹⁸F]FU) and [¹⁸F]fluorothymidine ([¹⁸F]FLT). CM and SA were performed initially with a SI approach using the parent plasma TAC as an input function. These methods were then employed using a DI approach with the metabolite plasma TAC as an additional input function. Regions of interest (ROIs) corresponding to healthy liver, kidneys and liver metastases for 5-[¹⁸F]FU and to tumor, vertebra and liver for [¹⁸F]FLT were analyzed. For 5-[¹⁸F]FU, the improvement of the fit quality with the DI approaches was remarkable; in CM, the Akaike information criterion (AIC) always selected the DI over the SI model. Volume of distribution estimates obtained with DI CM and DI SA were in excellent agreement, for both parent 5-[¹⁸F]FU (R(2) = 0.91) and metabolite [¹⁸F]FBAL (R(2) = 0.99). For [¹⁸F]FLT, the DI methods provided notable improvements but less substantial than for 5-[¹⁸F]FU due to the lower rate of metabolism of [¹⁸F]FLT. On the basis of the AIC values, agreement between [¹⁸F]FLT K(i) estimated with the SI and DI models was good (R² = 0.75) for the ROIs where the metabolite contribution was negligible, indicating that the additional input did not bias the parent tracer only-related estimates. When the AIC suggested a substantial contribution of the metabolite [¹⁸F]FLT-glucuronide, on the other hand, the change in the parent tracer only-related parameters was significant (R² = 0.33 for K(i)). Our results indicated that improvements of DI over SI approaches can range from moderate to substantial and are more significant for tracers with a high rate of metabolism. Furthermore, they showed that SA is suitable for DI modeling and can be used effectively in the analysis of PET data.

Robust automatic P-phase picking: an on-line implementation in the analysis of broadband seismogram recordings

NASA Astrophysics Data System (ADS)

Sleeman, Reinoud; van Eck, Torild

1999-06-01

The onset of a seismic signal is determined through joint AR modeling of the noise and the seismic signal, and the application of the Akaike Information Criterion (AIC) using the onset time as parameter. This so-called AR-AIC phase picker has been tested successfully and implemented on the Z-component of the broadband station HGN to provide automatic P-phase picks for a rapid warning system. The AR-AIC picker is shown to provide accurate and robust automatic picks on a large experimental database. Out of 1109 P-phase onsets with signal-to-noise ratio (SNR) above 1 from local, regional and teleseismic earthquakes, our implementation detects 71% and gives a mean difference with manual picks of 0.1 s. An optimal version of the well-established picker of Baer and Kradolfer [Baer, M., Kradolfer, U., An automatic phase picker for local and teleseismic events, Bull. Seism. Soc. Am. 77 (1987) 1437-1445] detects less than 41% and gives a mean difference with manual picks of 0.3 s using the same dataset.

Evaluation of Model Fit in Cognitive Diagnosis Models

ERIC Educational Resources Information Center

Hu, Jinxiang; Miller, M. David; Huggins-Manley, Anne Corinne; Chen, Yi-Hsin

2016-01-01

Cognitive diagnosis models (CDMs) estimate student ability profiles using latent attributes. Model fit to the data needs to be ascertained in order to determine whether inferences from CDMs are valid. This study investigated the usefulness of some popular model fit statistics to detect CDM fit including relative fit indices (AIC, BIC, and CAIC),…

A DFT Study of Pyrrole-Isoxazole Derivatives as Chemosensors for Fluoride Anion

PubMed Central

Jin, Ruifa; Sun, Weidong; Tang, Shanshan

2012-01-01

The interactions between chemosensors, 3-amino-5-(4,5,6,7-tetrahydro-1H-indol-2-yl)isoxazole-4-carboxamide (AIC) derivatives, and different anions (F− Cl−, Br−, AcO−, and H2PO4−) have been theoretically investigated using DFT approaches. It turned out that the unique selectivity of AIC derivatives for F− is ascribed to their ability of deprotonating the host sensors. Frontier molecular orbital (FMO) analyses have shown that the vertical electronic transitions of absorption and emission for the sensing signals are characterized as intramolecular charge transfer (ICT). The study of substituent effects suggests that all the substituted derivatives are expected to be promising candidates for fluoride chemosensors both in UV-vis and fluorescence spectra except for derivative with benzo[d]thieno[3,2-b]thiophene fragment that can serve as ratiometric fluorescent fluoride chemosensor only. PMID:23109833

Using generalized linear models to estimate selectivity from short-term recoveries of tagged red drum Sciaenops ocellatus: Effects of gear, fate, and regulation period

USGS Publications Warehouse

Bacheler, N.M.; Hightower, J.E.; Burdick, S.M.; Paramore, L.M.; Buckel, J.A.; Pollock, K.H.

2010-01-01

Estimating the selectivity patterns of various fishing gears is a critical component of fisheries stock assessment due to the difficulty in obtaining representative samples from most gears. We used short-term recoveries (n = 3587) of tagged red drum Sciaenops ocellatus to directly estimate age- and length-based selectivity patterns using generalized linear models. The most parsimonious models were selected using AIC, and standard deviations were estimated using simulations. Selectivity of red drum was dependent upon the regulation period in which the fish was caught, the gear used to catch the fish (i.e., hook-and-line, gill nets, pound nets), and the fate of the fish upon recovery (i.e., harvested or released); models including all first-order interactions between main effects outperformed models without interactions. Selectivity of harvested fish was generally dome-shaped and shifted toward larger, older fish in response to regulation changes. Selectivity of caught-and-released red drum was highest on the youngest and smallest fish in the early and middle regulation periods, but increased on larger, legal-sized fish in the late regulation period. These results suggest that catch-and-release mortality has consistently been high for small, young red drum, but has recently become more common in larger, older fish. This method of estimating selectivity from short-term tag recoveries is valuable because it is simpler than full tag-return models, and may be more robust because yearly fishing and natural mortality rates do not need to be modeled and estimated. ?? 2009 Elsevier B.V.

Using generalized linear models to estimate selectivity from short-term recoveries of tagged red drum Sciaenops ocellatus: Effects of gear, fate, and regulation period

USGS Publications Warehouse

Burdick, Summer M.; Hightower, Joseph E.; Bacheler, Nathan M.; Paramore, Lee M.; Buckel, Jeffrey A.; Pollock, Kenneth H.

2010-01-01

Estimating the selectivity patterns of various fishing gears is a critical component of fisheries stock assessment due to the difficulty in obtaining representative samples from most gears. We used short-term recoveries (n = 3587) of tagged red drum Sciaenops ocellatus to directly estimate age- and length-based selectivity patterns using generalized linear models. The most parsimonious models were selected using AIC, and standard deviations were estimated using simulations. Selectivity of red drum was dependent upon the regulation period in which the fish was caught, the gear used to catch the fish (i.e., hook-and-line, gill nets, pound nets), and the fate of the fish upon recovery (i.e., harvested or released); models including all first-order interactions between main effects outperformed models without interactions. Selectivity of harvested fish was generally dome-shaped and shifted toward larger, older fish in response to regulation changes. Selectivity of caught-and-released red drum was highest on the youngest and smallest fish in the early and middle regulation periods, but increased on larger, legal-sized fish in the late regulation period. These results suggest that catch-and-release mortality has consistently been high for small, young red drum, but has recently become more common in larger, older fish. This method of estimating selectivity from short-term tag recoveries is valuable because it is simpler than full tag-return models, and may be more robust because yearly fishing and natural mortality rates do not need to be modeled and estimated.

Quality-of-life outcomes in patients with gynecologic cancer referred to integrative oncology treatment during chemotherapy.

PubMed

Ben-Arye, Eran; Samuels, Noah; Schiff, Elad; Raz, Orit Gressel; Sharabi, Ilanit Shalom; Lavie, Ofer

2015-12-01

Integrative oncology incorporates complementary medicine (CM) therapies in patients with cancer. We explored the impact of an integrative oncology therapeutic regimen on quality-of-life (QOL) outcomes in women with gynecological cancer undergoing chemotherapy. A prospective preference study examined patients referred by oncology health care practitioners (HCPs) to an integrative physician (IP) consultation and CM treatments. QOL and chemotherapy-related toxicities were evaluated using the Edmonton Symptom Assessment Scale (ESAS) and Measure Yourself Concerns and Wellbeing (MYCAW) questionnaire, at baseline and at a 6-12-week follow-up assessment. Adherence to the integrative care (AIC) program was defined as ≥ 4 CM treatments, with ≤ 30 days between each session. Of 128 patients referred by their HCP, 102 underwent IP consultation and subsequent CM treatments. The main concerns expressed by patients were fatigue (79.8%), gastrointestinal symptoms (64.6%), pain and neuropathy (54.5 %), and emotional distress (45.5%). Patients in both AIC (n = 68) and non-AIC (n = 28) groups shared similar demographic, treatment, and cancer-related characteristics. ESAS fatigue scores improved by a mean of 1.97 points in the AIC group on a scale of 0-10 and worsened by a mean of 0.27 points in the non-AIC group (p = 0.033). In the AIC group, MYCAW scores improved significantly (p < 0.0001) for each of the leading concerns as well as for well-being, a finding which was not apparent in the non-AIC group. An IP-guided CM treatment regimen provided to patients with gynecological cancer during chemotherapy may reduce cancer-related fatigue and improve other QOL outcomes.

Anti-intercellular substance antibody log titres are correlated with serum concentrations of interleukin-6, interleukin-15 and tumor necrosis factor-alpha in patients with Pemphigus vulgaris relationships with peripheral blood neutrophil counts, disease severity and duration and patients' age.

PubMed

Ameglio, F; D'Auria, L; Cordiali-Fei, P; Trento, E; D'Agosto, G; Mastroianni, A; Giannetti, A; Giacalone, B

1999-01-01

Pemphigus vulgaris is a rare dermatosis of autoimmune origin, characterized by autoantibodies directed against intercellular substance (AICS) and presenting with intra-epidermal blisters and/or erosions of the skin and mucous membranes. The aim of this paper is to analyze the relationships between serum AICS titers (after log transformation) and: patients' age, disease duration and disease activity; serum cytokine (IL-6, IL-7, IL-15 and TNF-alpha) concentrations and peripheral blood cell counts (namely neutrophils, lymphocytes and natural killer cells). Fifteen consecutive subjects affected with PV were enrolled. Diagnosis was supported by histological examination as well as by direct and indirect immunofluorescence tests. Cytokine determinations were made by means of commercially available ELISA kits. This study shows for the first time that AICS titers have a significant correlation with age of PV patients (R=0.57, p=0.031) and with the disease duration (R=0.73, p=0.002). A correlation between blood neutrophils count and log (AICS) titres was observed (R=0.6, p=0.021). Furthermore, significant correlations were observed between log (AICS) titres and serum IL-15 (R=0.54, p=0.048), serum IL-6 (R=0.53, p=0.05) or serum TNF-alpha concentrations (R=0.53, p=0.05). These data, taken together, show that there are several connections between the log (AICS) titres, some proinflammatory cytokines, peripheral blood neutrophil counts and the numbers of individuals' lesions, suggesting a relationship between AICS production and lesion development.

Repeated forced swim stress enhances CFA-evoked thermal hyperalgesia and affects the expressions of pCREB and c-Fos in the insular cortex.

PubMed

Imbe, H; Kimura, A; Donishi, T; Kaneoke, Y

2014-02-14

Stress affects brain activity and promotes long-term changes in multiple neural systems. Exposure to stressors causes substantial effects on the perception and response to pain. In several animal models, chronic stress produces lasting hyperalgesia. The insular (IC) and anterior cingulate cortices (ACC) are the regions exhibiting most reliable pain-related activity. And the IC and ACC play an important role in pain modulation via the descending pain modulatory system. In the present study we examined the expression of phospho-cAMP response element-binding protein (pCREB) and c-Fos in the IC and ACC after forced swim stress (FS) and complete Freund's adjuvant (CFA) injection to clarify changes in the cerebral cortices that affect the activity of the descending pain modulatory system in the rats with stress-induced hyperalgesia. FS (day 1, 10min; days 2-3, 20min) induced an increase in the expression of pCREB and c-Fos in the anterior IC (AIC). CFA injection into the hindpaw after the FS shows significantly enhanced thermal hyperalgesia and induced a decrease in the expression of c-Fos in the AIC and the posterior IC (PIC). Quantitative image analysis showed that the numbers of c-Fos-immunoreactive neurons in the left AIC and PIC were significantly lower in the FS+CFA group (L AIC, 95.9±6.8; L PIC, 181.9±23.1) than those in the naive group (L AIC, 151.1±19.3, p<0.05; L PIC, 274.2±37.3, p<0.05). These findings suggest a neuroplastic change in the IC after FS, which may be involved in the enhancement of CFA-induced thermal hyperalgesia through dysfunction of the descending pain modulatory system. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

77 FR 59618 - Medicare Program; Medicare Appeals; Adjustment to the Amount in Controversy Threshold Amounts for...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-09-28

...This notice announces the annual adjustment in the amount in controversy (AIC) threshold amounts for Administrative Law Judge (ALJ) hearings and judicial review under the Medicare appeals process. The adjustment to the AIC threshold amounts will be effective for requests for ALJ hearings and judicial review filed on or after January 1, 2013. The calendar year 2013 AIC threshold amounts are $140 for ALJ hearings and $1,400 for judicial review.

78 FR 59702 - Medicare Program; Medicare Appeals: Adjustment to the Amount in Controversy Threshold Amounts for...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-27

...This notice announces the annual adjustment in the amount in controversy (AIC) threshold amounts for Administrative Law Judge (ALJ) hearings and judicial review under the Medicare appeals process. The adjustment to the AIC threshold amounts will be effective for requests for ALJ hearings and judicial review filed on or after January 1, 2014. The calendar year 2014 AIC threshold amounts are $140 for ALJ hearings and $1,430 for judicial review.

76 FR 59138 - Medicare Program; Medicare Appeals; Adjustment to the Amount in Controversy Threshold Amounts for...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-09-23

...This notice announces the annual adjustment in the amount in controversy (AIC) threshold amounts for Administrative Law Judge (ALJ) hearings and judicial review under the Medicare appeals process. The adjustment to the AIC threshold amounts will be effective for requests for ALJ hearings and judicial review filed on or after January 1, 2012. The calendar year 2012 AIC threshold amounts are $130 for ALJ hearings and $1,350 for judicial review.

75 FR 58407 - Medicare Program; Medicare Appeals; Adjustment to the Amount in Controversy Threshold Amounts for...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-09-24

...This notice announces the annual adjustment in the amount in controversy (AIC) threshold amounts for Administrative Law Judge (ALJ) hearings and judicial review under the Medicare appeals process. The adjustment to the AIC threshold amounts will be effective for requests for ALJ hearings and judicial review filed on or after January 1, 2011. The 2011 AIC threshold amounts are $130 for ALJ hearings and $1,300 for judicial review.

Dental caries clusters among adolescents.

PubMed

Warren, John J; Van Buren, John M; Levy, Steven M; Marshall, Teresa A; Cavanaugh, Joseph E; Curtis, Alexandra M; Kolker, Justine L; Weber-Gasparoni, Karin

2017-12-01

There have been very few longitudinal studies of dental caries in adolescents, and little study of the caries risk factors in this age group. The purpose of this study was to describe different caries trajectories and associated risk factors among members of the Iowa Fluoride Study (IFS) cohort. The IFS recruited a birth cohort from 1992 to 1995, and has gathered dietary, fluoride and behavioural data at least twice yearly since recruitment. Examinations for dental caries were completed when participants were ages 5, 9, 13 and 17 years. For this study, only participants with decayed and filled surface (DFS) caries data at ages 9, 13 and 17 were included (N=396). The individual DFS counts at age 13 and the DFS increment from 13 to 17 were used to identify distinct caries trajectories using Ward's hierarchical clustering algorithm. A number of multinomial logistic regression models were developed to predict trajectory membership, using longitudinal dietary, fluoride and demographic/behavioural data from 9 to 17 years. Model selection was based on the akaike information criterion (AIC). Several different trajectory schemes were considered, and a three-trajectory scheme-no DFS at age 17 (n=142), low DFS (n=145) and high DFS (n=109)-was chosen to balance sample sizes and interpretability. The model selection process resulted in use of an arithmetic average for dietary variables across the period from 9 to 17 years. The multinomial logistic regression model with the best fit included the variables maternal education level, 100% juice consumption, brushing frequency and sex. Other favoured models also included water and milk consumption and home water fluoride concentration. The high caries cluster was most consistently associated with lower maternal education level, lower 100% juice consumption, lower brushing frequency and being female. The use of a clustering algorithm and use of Akaike's Information Criterion (AIC) to determine the best representation of the data were useful means in presenting longitudinal caries data. Findings suggest that high caries incidence in adolescence is associated with lower maternal educational level, less frequent tooth brushing, lower 100% juice consumption and being female. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

APIC: Absolute Position Interfero Coronagraph for direct exoplanet detection: first laboratory results

NASA Astrophysics Data System (ADS)

Allouche, Fatmé; Glindemann, Andreas; Aristidi, Eric; Vakili, Farrokh

2010-07-01

For the detection and direct imaging of exoplanets, when the intensity ratio between a star and its orbiting planet can largely exceed 106, coronagraphic methods are mandatory. In 1996, a concept of achromatic interferocoronagraph (AIC) was presented by J. Gay and Y. Rabbia for the detection of very faint stellar companions, such as exoplanets. In an earlier paper, we presented a modified version of the AIC permitting to determine the relative position of these faint companions with respect to the parent star, a problem unsolved in the original design of the AIC. Our modification lied in the use of cylindrical lens doublets as field rotator. By placing two of them in one arm of the interferometric set-up of AIC, we destroyed the axis of symmetry induced by the AIC's original design. Our theoretical study, along with the numerical computations, presented then, and the preliminary test bench results aiming at validating the cylindrical lens doublet field rotation capability, presented in this paper, show that the axis of symmetry is destroyed when one of the cylindrical doublets is rotated around the optic axis.

A Bayesian random effects discrete-choice model for resource selection: Population-level selection inference

USGS Publications Warehouse

Thomas, D.L.; Johnson, D.; Griffith, B.

2006-01-01

Modeling the probability of use of land units characterized by discrete and continuous measures, we present a Bayesian random-effects model to assess resource selection. This model provides simultaneous estimation of both individual- and population-level selection. Deviance information criterion (DIC), a Bayesian alternative to AIC that is sample-size specific, is used for model selection. Aerial radiolocation data from 76 adult female caribou (Rangifer tarandus) and calf pairs during 1 year on an Arctic coastal plain calving ground were used to illustrate models and assess population-level selection of landscape attributes, as well as individual heterogeneity of selection. Landscape attributes included elevation, NDVI (a measure of forage greenness), and land cover-type classification. Results from the first of a 2-stage model-selection procedure indicated that there is substantial heterogeneity among cow-calf pairs with respect to selection of the landscape attributes. In the second stage, selection of models with heterogeneity included indicated that at the population-level, NDVI and land cover class were significant attributes for selection of different landscapes by pairs on the calving ground. Population-level selection coefficients indicate that the pairs generally select landscapes with higher levels of NDVI, but the relationship is quadratic. The highest rate of selection occurs at values of NDVI less than the maximum observed. Results for land cover-class selections coefficients indicate that wet sedge, moist sedge, herbaceous tussock tundra, and shrub tussock tundra are selected at approximately the same rate, while alpine and sparsely vegetated landscapes are selected at a lower rate. Furthermore, the variability in selection by individual caribou for moist sedge and sparsely vegetated landscapes is large relative to the variability in selection of other land cover types. The example analysis illustrates that, while sometimes computationally intense, a Bayesian hierarchical discrete-choice model for resource selection can provide managers with 2 components of population-level inference: average population selection and variability of selection. Both components are necessary to make sound management decisions based on animal selection.

Comparison of Regression Methods to Compute Atmospheric Pressure and Earth Tidal Coefficients in Water Level Associated with Wenchuan Earthquake of 12 May 2008

NASA Astrophysics Data System (ADS)

He, Anhua; Singh, Ramesh P.; Sun, Zhaohua; Ye, Qing; Zhao, Gang

2016-07-01

The earth tide, atmospheric pressure, precipitation and earthquake fluctuations, especially earthquake greatly impacts water well levels, thus anomalous co-seismic changes in ground water levels have been observed. In this paper, we have used four different models, simple linear regression (SLR), multiple linear regression (MLR), principal component analysis (PCA) and partial least squares (PLS) to compute the atmospheric pressure and earth tidal effects on water level. Furthermore, we have used the Akaike information criterion (AIC) to study the performance of various models. Based on the lowest AIC and sum of squares for error values, the best estimate of the effects of atmospheric pressure and earth tide on water level is found using the MLR model. However, MLR model does not provide multicollinearity between inputs, as a result the atmospheric pressure and earth tidal response coefficients fail to reflect the mechanisms associated with the groundwater level fluctuations. On the premise of solving serious multicollinearity of inputs, PLS model shows the minimum AIC value. The atmospheric pressure and earth tidal response coefficients show close response with the observation using PLS model. The atmospheric pressure and the earth tidal response coefficients are found to be sensitive to the stress-strain state using the observed data for the period 1 April-8 June 2008 of Chuan 03# well. The transient enhancement of porosity of rock mass around Chuan 03# well associated with the Wenchuan earthquake (Mw = 7.9 of 12 May 2008) that has taken its original pre-seismic level after 13 days indicates that the co-seismic sharp rise of water well could be induced by static stress change, rather than development of new fractures.

Trading Habitat Patches for the Red Cockaded Woodpecker: Incorporating the Role of Landscape Structure and Uncertainty in Decision Making

DTIC Science & Technology

2007-06-11

to 35 cm dbh 2b. basal area of pines 25.4 to 35 cm dbh between 0 and 9.2 m2/ha PS1, PS4 , PS12 Small Pines No. pine stems/ha < 25.4 cm dbh...1Model A p-value Adj R2 AIC Fit = 2.50 + 0.0079 PS1 – 0.036 PM1 – 0.0102 PS4 – 0.019 HS12 0.063 0.033 426 Parameter p-value t-value Intercept...0.0001 8.23 PS1 0.125 1.54 PM1 0.038 -2.10 PS4 0.107 -1.62 HS12 0.063 -1.88 Model A – Most Parsimonious p-value Adj R2 AIC Fit = 2.02 – 0.03 PM1

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

The Autonomic Intelligent Cyber Sensor (AICS) provides cyber security and industrial network state awareness for Ethernet based control network implementations. The AICS utilizes collaborative mechanisms based on Autonomic Research and a Service Oriented Architecture (SOA) to: 1) identify anomalous network traffic; 2) discover network entity information; 3) deploy deceptive virtual hosts; and 4) implement self-configuring modules. AICS achieves these goals by dynamically reacting to the industrial human-digital ecosystem in which it resides. Information is transported internally and externally on a standards based, flexible two-level communication structure.

Choice Behavior Guided by Learned, But Not Innate, Taste Aversion Recruits the Orbitofrontal Cortex.

PubMed

Ramírez-Lugo, Leticia; Peñas-Rincón, Ana; Ángeles-Durán, Sandybel; Sotres-Bayon, Francisco

2016-10-12

The ability to select an appropriate behavioral response guided by previous emotional experiences is critical for survival. Although much is known about brain mechanisms underlying emotional associations, little is known about how these associations guide behavior when several choices are available. To address this, we performed local pharmacological inactivations of several cortical regions before retrieval of an aversive memory in choice-based versus no-choice-based conditioned taste aversion (CTA) tasks in rats. Interestingly, we found that inactivation of the orbitofrontal cortex (OFC), but not the dorsal or ventral medial prefrontal cortices, blocked retrieval of choice CTA. However, OFC inactivation left retrieval of no-choice CTA intact, suggesting its role in guiding choice, but not in retrieval of CTA memory. Consistently, OFC activity increased in the choice condition compared with no-choice, as measured with c-Fos immunolabeling. Notably, OFC inactivation did not affect choice behavior when it was guided by innate taste aversion. Consistent with an anterior insular cortex (AIC) involvement in storing taste memories, we found that AIC inactivation impaired retrieval of both choice and no-choice CTA. Therefore, this study provides evidence for OFC's role in guiding choice behavior and shows that this is dissociable from AIC-dependent taste aversion memory. Together, our results suggest that OFC is required and recruited to guide choice selection between options of taste associations relayed from AIC. Survival and mental health depend on being able to choose stimuli not associated with danger. This is particularly important when danger is associated with stimuli that we ingest. Although much is known about the brain mechanisms that underlie associations with dangerous taste stimuli, very little is known about how these stored emotional associations guide behavior when it involves choice. By combining pharmacological and immunohistochemistry tools with taste-guided tasks, our study provides evidence for the key role of orbitofrontal cortex activity in choice behavior and shows that this is dissociable from the adjacent insular cortex-dependent taste aversion memory. Understanding the brain mechanisms that underlie the impact that emotional associations have on survival choice behaviors may lead to better treatments for mental disorders characterized by emotional decision-making deficits. Copyright © 2016 the authors 0270-6474/16/3610574-10$15.00/0.

Spatial Distribution and Conservation of Speckled Hind and Warsaw Grouper in the Atlantic Ocean off the Southeastern U.S.

PubMed Central

Farmer, Nicholas A.; Karnauskas, Mandy

2013-01-01

There is broad interest in the development of efficient marine protected areas (MPAs) to reduce bycatch and end overfishing of speckled hind (Epinephelus drummondhayi) and warsaw grouper (Hyporthodus nigritus) in the Atlantic Ocean off the southeastern U.S. We assimilated decades of data from many fishery-dependent, fishery-independent, and anecdotal sources to describe the spatial distribution of these data limited stocks. A spatial classification model was developed to categorize depth-grids based on the distribution of speckled hind and warsaw grouper point observations and identified benthic habitats. Logistic regression analysis was used to develop a quantitative model to predict the spatial distribution of speckled hind and warsaw grouper as a function of depth, latitude, and habitat. Models, controlling for sampling gear effects, were selected based on AIC and 10-fold cross validation. The best-fitting model for warsaw grouper included latitude and depth to explain 10.8% of the variability in probability of detection, with a false prediction rate of 28–33%. The best-fitting model for speckled hind, per cross-validation, included latitude and depth to explain 36.8% of the variability in probability of detection, with a false prediction rate of 25–27%. The best-fitting speckled hind model, per AIC, also included habitat, but had false prediction rates up to 36%. Speckled hind and warsaw grouper habitats followed a shelf-edge hardbottom ridge from North Carolina to southeast Florida, with speckled hind more common to the north and warsaw grouper more common to the south. The proportion of habitat classifications and model-estimated stock contained within established and proposed MPAs was computed. Existing MPAs covered 10% of probable shelf-edge habitats for speckled hind and warsaw grouper, protecting 3–8% of speckled hind and 8% of warsaw grouper stocks. Proposed MPAs could add 24% more probable shelf-edge habitat, and protect an additional 14–29% of speckled hind and 20% of warsaw grouper stocks. PMID:24260126

Landscape risk factors for Lyme disease in the eastern broadleaf forest province of the Hudson River valley and the effect of explanatory data classification resolution.

PubMed

Messier, Kyle P; Jackson, Laura E; White, Jennifer L; Hilborn, Elizabeth D

2015-01-01

This study assessed how landcover classification affects associations between landscape characteristics and Lyme disease rate. Landscape variables were derived from the National Land Cover Database (NLCD), including native classes (e.g., deciduous forest, developed low intensity) and aggregate classes (e.g., forest, developed). Percent of each landcover type, median income, and centroid coordinates were calculated by census tract. Regression results from individual and aggregate variable models were compared with the dispersion parameter-based R(2) (Rα(2)) and AIC. The maximum Rα(2) was 0.82 and 0.83 for the best aggregate and individual model, respectively. The AICs for the best models differed by less than 0.5%. The aggregate model variables included forest, developed, agriculture, agriculture-squared, y-coordinate, y-coordinate-squared, income and income-squared. The individual model variables included deciduous forest, deciduous forest-squared, developed low intensity, pasture, y-coordinate, y-coordinate-squared, income, and income-squared. Results indicate that regional landscape models for Lyme disease rate are robust to NLCD landcover classification resolution. Published by Elsevier Ltd.

Two-protein signature of novel serological markers apolipoprotein-A2 and serum amyloid alpha predicts prognosis in patients with metastatic renal cell cancer and improves the currently used prognostic survival models.

PubMed

Vermaat, J S; van der Tweel, I; Mehra, N; Sleijfer, S; Haanen, J B; Roodhart, J M; Engwegen, J Y; Korse, C M; Langenberg, M H; Kruit, W; Groenewegen, G; Giles, R H; Schellens, J H; Beijnen, J H; Voest, E E

2010-07-01

In metastatic renal cell cancer (mRCC), the Memorial Sloan-Kettering Cancer Center (MSKCC) risk model is widely used for clinical trial design and patient management. To improve prognostication, we applied proteomics to identify novel serological proteins associated with overall survival (OS). Sera from 114 mRCC patients were screened by surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF MS). Identified proteins were related to OS. Three proteins were subsequently validated with enzyme-linked immunosorbent assays and immunoturbidimetry. Prognostic models were statistically bootstrapped to correct for overestimation. SELDI-TOF MS detected 10 proteins associated with OS. Of these, apolipoprotein A2 (ApoA2), serum amyloid alpha (SAA) and transthyretin were validated for their association with OS (P = 5.5 x 10(-9), P = 1.1 x 10(-7) and P = 0.0004, respectively). Combining ApoA2 and SAA yielded a prognostic two-protein signature [Akaike's Information Criteria (AIC) = 732, P = 5.2 x 10(-7)]. Including previously identified prognostic factors, multivariable Cox regression analysis revealed ApoA2, SAA, lactate dehydrogenase, performance status and number of metastasis sites as independent factors for survival. Using these five factors, categorization of patients into three risk groups generated a novel protein-based model predicting patient prognosis (AIC = 713, P = 4.3 x 10(-11)) more robustly than the MSKCC model (AIC = 729, P = 1.3 x 10(-7)). Applying this protein-based model instead of the MSKCC model would have changed the risk group in 38% of the patients. Proteomics and subsequent validation yielded two novel prognostic markers and survival models which improved prediction of OS in mRCC patients over commonly used risk models. Implementation of these models has the potential to improve current risk stratification, although prospective validation will still be necessary.

Autonomous intelligent cars: proof that the EPSRC Principles are future-proof

NASA Astrophysics Data System (ADS)

de Cock Buning, Madeleine; de Bruin, Roeland

2017-07-01

Principle 2 of the EPSRC's principles of robotics (AISB workshop on Principles of Robotics, 2016) proves to be future proof when applied to the current state of the art of law and technology surrounding autonomous intelligent cars (AICs). Humans, not AICS, are responsible agents. AICs should be designed; operated as far as is practicable to comply with existing laws and fundamental rights and freedoms, including privacy by design. It will show that some legal questions arising from autonomous intelligent driving technology can be answered by the technology itself.

Robust geographically weighted regression of modeling the Air Polluter Standard Index (APSI)

NASA Astrophysics Data System (ADS)

Warsito, Budi; Yasin, Hasbi; Ispriyanti, Dwi; Hoyyi, Abdul

2018-05-01

The Geographically Weighted Regression (GWR) model has been widely applied to many practical fields for exploring spatial heterogenity of a regression model. However, this method is inherently not robust to outliers. Outliers commonly exist in data sets and may lead to a distorted estimate of the underlying regression model. One of solution to handle the outliers in the regression model is to use the robust models. So this model was called Robust Geographically Weighted Regression (RGWR). This research aims to aid the government in the policy making process related to air pollution mitigation by developing a standard index model for air polluter (Air Polluter Standard Index - APSI) based on the RGWR approach. In this research, we also consider seven variables that are directly related to the air pollution level, which are the traffic velocity, the population density, the business center aspect, the air humidity, the wind velocity, the air temperature, and the area size of the urban forest. The best model is determined by the smallest AIC value. There are significance differences between Regression and RGWR in this case, but Basic GWR using the Gaussian kernel is the best model to modeling APSI because it has smallest AIC.

Development of a coupled diffusion denuder system combined with gas chromatography/mass spectrometry for the separation and quantification of molecular iodine and the activated iodine compounds iodine monochloride and hypoiodous acid in the marine atmosphere.

PubMed

Huang, Ru-Jin; Hoffmann, Thorsten

2009-03-01

This study concerns the development of a coupled diffusion denuder system capable of separating and quantifying gaseous molecular iodine (I(2)) and two other highly reactive iodine species, ICl and HOI, which are collectively named activated iodine compounds (AIC). Both I(2) and AIC are key species in the atmospheric chemistry of iodine. 1,3,5-Trimethoxybenzene (1,3,5-TMB)- and alpha-cyclodextrin/(129)I(-) (alpha-CD/(129)I(-))-coated denuders proved to be suitable for the collection of gaseous AIC and I(2), respectively. The experimental collection efficiencies for AIC (tested as ICl) and I(2) agreed well with the theoretical values for gas flow rates in the range between 300 and 1800 mL min(-1). The coupled denuder system (1,3,5-TMB-coated denuder as front-denuder coupled upstream of an alpha-CD/(129)I(-)-coated denuder) was applied successfully to separate test gas mixtures of ICl and I(2) at various mixing ratios in the laboratory. The operation of both denuder systems was demonstrated to be independent of relative humidity (0-100%) and storage period (at least 2 weeks prior to and after sampling). Detection limits were achieved at sub-parts-per-trillion-by-volume (sub-pptv) level. The presented method provides a reliable and practical approach for the speciation of gaseous iodine compounds. In addition, we report for the first time ambient air measurements of AIC mixing ratios, carried out at the atmospheric research station in Mace Head, Ireland. A maximum concentration of AIC of 30.2 pptv was observed for nighttime measurements and 6.0 pptv for daytime measurements. A similar diurnal pattern was found for I(2) with an average concentration level of 23.2 pptv during daytime and 85.1 pptv during nighttime, indicating a strong correlation with AIC.

APIC. Absolute Position Interfero-Coronagraph for direct exoplanet detection

NASA Astrophysics Data System (ADS)

Allouche, F.; Glindemann, A.; Aristidi, E.; Vakili, F.

2009-06-01

Context: For detecting and directly imaging exoplanets, coronagraphic methods are mandatory when the intensity ratio between a star and its orbiting planet can be as large as 10^6. In 1996, a concept of an achromatic interfero-coronagraph (AIC) was presented for detecting very faint stellar companions, such as exoplanets. Aims: We present a modified version of the AIC not only permitting these faint companions to be detected but also their relative position to be determined with respect to the parent star, a problem that was not solved in the original design of the AIC. Methods: In our modified design, two cylindrical lens doublets were used to remove the 180° ambiguity introduced by the AIC's original design. Results: Our theoretical study and the numerical computations show that the axis of symmetry is destroyed when one of the cylindrical doublets is rotated around the optical axis.

Identifying the neural substrates of intrinsic motivation during task performance.

PubMed

Lee, Woogul; Reeve, Johnmarshall

2017-10-01

Intrinsic motivation is the inherent tendency to seek out novelty and challenge, to explore and investigate, and to stretch and extend one's capacities. When people imagine performing intrinsically motivating tasks, they show heightened anterior insular cortex (AIC) activity. To fully explain the neural system of intrinsic motivation, however, requires assessing neural activity while people actually perform intrinsically motivating tasks (i.e., while answering curiosity-inducing questions or solving competence-enabling anagrams). Using event-related functional magnetic resonance imaging, we found that the neural system of intrinsic motivation involves not only AIC activity, but also striatum activity and, further, AIC-striatum functional interactions. These findings suggest that subjective feelings of intrinsic satisfaction (associated with AIC activations), reward processing (associated with striatum activations), and their interactions underlie the actual experience of intrinsic motivation. These neural findings are consistent with the conceptualization of intrinsic motivation as the pursuit and satisfaction of subjective feelings (interest and enjoyment) as intrinsic rewards.

Spatial Distribution of Black Bear Incident Reports in Michigan

PubMed Central

McFadden-Hiller, Jamie E.; Beyer, Dean E.; Belant, Jerrold L.

2016-01-01

Interactions between humans and carnivores have existed for centuries due to competition for food and space. American black bears are increasing in abundance and populations are expanding geographically in many portions of its range, including areas that are also increasing in human density, often resulting in associated increases in human-bear conflict (hereafter, bear incidents). We used public reports of bear incidents in Michigan, USA, from 2003–2011 to assess the relative contributions of ecological and anthropogenic variables in explaining the spatial distribution of bear incidents and estimated the potential risk of bear incidents. We used weighted Normalized Difference Vegetation Index mean as an index of primary productivity, region (i.e., Upper Peninsula or Lower Peninsula), primary and secondary road densities, and percentage land cover type within 6.5-km2 circular buffers around bear incidents and random points. We developed 22 a priori models and used generalized linear models and Akaike’s Information Criterion (AIC) to rank models. The global model was the best compromise between model complexity and model fit (w = 0.99), with a ΔAIC 8.99 units from the second best performing model. We found that as deciduous forest cover increased, the probability of bear incident occurrence increased. Among the measured anthropogenic variables, cultivated crops and primary roads were the most important in our AIC-best model and were both positively related to the probability of bear incident occurrence. The spatial distribution of relative bear incident risk varied markedly throughout Michigan. Forest cover fragmented with agriculture and other anthropogenic activities presents an environment that likely facilitates bear incidents. Our map can help wildlife managers identify areas of bear incident occurrence, which in turn can be used to help develop strategies aimed at reducing incidents. Researchers and wildlife managers can use similar mapping techniques to assess locations of specific conflict types or to address human impacts on endangered species. PMID:27119344

Spatial Distribution of Black Bear Incident Reports in Michigan.

PubMed

McFadden-Hiller, Jamie E; Beyer, Dean E; Belant, Jerrold L

2016-01-01

Interactions between humans and carnivores have existed for centuries due to competition for food and space. American black bears are increasing in abundance and populations are expanding geographically in many portions of its range, including areas that are also increasing in human density, often resulting in associated increases in human-bear conflict (hereafter, bear incidents). We used public reports of bear incidents in Michigan, USA, from 2003-2011 to assess the relative contributions of ecological and anthropogenic variables in explaining the spatial distribution of bear incidents and estimated the potential risk of bear incidents. We used weighted Normalized Difference Vegetation Index mean as an index of primary productivity, region (i.e., Upper Peninsula or Lower Peninsula), primary and secondary road densities, and percentage land cover type within 6.5-km2 circular buffers around bear incidents and random points. We developed 22 a priori models and used generalized linear models and Akaike's Information Criterion (AIC) to rank models. The global model was the best compromise between model complexity and model fit (w = 0.99), with a ΔAIC 8.99 units from the second best performing model. We found that as deciduous forest cover increased, the probability of bear incident occurrence increased. Among the measured anthropogenic variables, cultivated crops and primary roads were the most important in our AIC-best model and were both positively related to the probability of bear incident occurrence. The spatial distribution of relative bear incident risk varied markedly throughout Michigan. Forest cover fragmented with agriculture and other anthropogenic activities presents an environment that likely facilitates bear incidents. Our map can help wildlife managers identify areas of bear incident occurrence, which in turn can be used to help develop strategies aimed at reducing incidents. Researchers and wildlife managers can use similar mapping techniques to assess locations of specific conflict types or to address human impacts on endangered species.

Soil temperature synchronisation improves estimation of daily variation of ecosystem respiration in Sphagnum peatlands

NASA Astrophysics Data System (ADS)

D'Angelo, Benoît; Gogo, Sébastien; Le Moing, Franck; Jégou, Fabrice; Guimbaud, Christophe; Laggoun, Fatima

2015-04-01

Ecosystem respiration (ER) is a key process in the global C cycle and thus, plays an important role in the climate regulation. Peatlands contain a third of the world soil C in spite of their relatively low global area (3% of land area). Although these ecosystems represent potentially a significant source of C under global change, they are still not taken into account accordingly in global climatic models. Therefore, ER variations have to be accounted for, especially by estimating its dependence to temperature.s The relationship between ER and temperature often relies only on one soil temperature depth and the latter is generally taken in the first 10 centimetres. Previous studies showed that the temperature dependence of ER depends on the depth at which the temperature is recorded. The depth selection for temperature measurement is thus a predominant issue. A way to deal with this is to analyse the time-delay between ER and temperature. The aim of this work is to assess whether using synchronised data in models leads to a better ER daily variation estimation than using non-synchronised data. ER measurements were undertaken in 2013 in 4 Sphagnum peatlands across France: La Guette (N 47°19'44', E 2°17'04', 154m) in July, Landemarais (N 48°26'30', E -1°10'54', 145m) in August, Frasne (N 46°49'35', E 6°10'20', 836m) in September, and Bernadouze (N 42°48'09', E 1°25'24', 1500m) in October. A closed method chamber was used to measure ER hourly during 72 hours in each of the 4 replicates installed in each site. Average ER ranged from 1.75 μmol m-2 s-1 to 6.13 μmol m-2 s-1. A weather station was used to record meteorological data and soil temperature profiles (5, 10, 20 and 30 cm). Synchronised data were determined for each depth by selecting the time-delay leading to the best correlation between ER and soil temperature. The data were used to simulate ER according to commonly used equations: linear, exponential with Q10, Arrhenius, Lloyd and Taylor. Models comparison was performed using RMSE (goodness-of-fit) and AIC (goodness-of-fit and model complexity) as indicators to assess their relative quality. Both indicators showed a wide variation between sites. However, for each site differences between synchronised and non-synchronised data were larger than the differences between models equations. According to the AIC, models using synchronised data produced better ER estimations than models using non-synchronised data, at all depth. RMSE support this result for all sites for superficial peat layer. In some locations, mainly Frasne, synchronised data at 5 cm depth provide better estimation than air temperature, i.e. 25.0 vs. 26.4 for RMSE and 337.1 vs. 379.8 for AIC, respectively. The equation of the most appropriate model varies between sites, but the differences between them are small. At a daily scale, data synchronisation in Sphagnum peatlands improves ER estimation regardless of the model used. Moreover, to estimate ER flux, the use of synchronised data at 5 cm depth seems the most adequate method.

Developing approaches for linear mixed modeling in landscape genetics through landscape-directed dispersal simulations

USGS Publications Warehouse

Row, Jeffrey R.; Knick, Steven T.; Oyler-McCance, Sara J.; Lougheed, Stephen C.; Fedy, Bradley C.

2017-01-01

Dispersal can impact population dynamics and geographic variation, and thus, genetic approaches that can establish which landscape factors influence population connectivity have ecological and evolutionary importance. Mixed models that account for the error structure of pairwise datasets are increasingly used to compare models relating genetic differentiation to pairwise measures of landscape resistance. A model selection framework based on information criteria metrics or explained variance may help disentangle the ecological and landscape factors influencing genetic structure, yet there are currently no consensus for the best protocols. Here, we develop landscape-directed simulations and test a series of replicates that emulate independent empirical datasets of two species with different life history characteristics (greater sage-grouse; eastern foxsnake). We determined that in our simulated scenarios, AIC and BIC were the best model selection indices and that marginal R2 values were biased toward more complex models. The model coefficients for landscape variables generally reflected the underlying dispersal model with confidence intervals that did not overlap with zero across the entire model set. When we controlled for geographic distance, variables not in the underlying dispersal models (i.e., nontrue) typically overlapped zero. Our study helps establish methods for using linear mixed models to identify the features underlying patterns of dispersal across a variety of landscapes.

Causal network in a deafferented non-human primate brain.

PubMed

Balasubramanian, Karthikeyan; Takahashi, Kazutaka; Hatsopoulos, Nicholas G

2015-01-01

De-afferented/efferented neural ensembles can undergo causal changes when interfaced to neuroprosthetic devices. These changes occur via recruitment or isolation of neurons, alterations in functional connectivity within the ensemble and/or changes in the role of neurons, i.e., excitatory/inhibitory. In this work, emergence of a causal network and changes in the dynamics are demonstrated for a deafferented brain region exposed to BMI (brain-machine interface) learning. The BMI was controlling a robot for reach-and-grasp behavior. And, the motor cortical regions used for the BMI were deafferented due to chronic amputation, and ensembles of neurons were decoded for velocity control of the multi-DOF robot. A generalized linear model-framework based Granger causality (GLM-GC) technique was used in estimating the ensemble connectivity. Model selection was based on the AIC (Akaike Information Criterion).

Identification and synthetic modeling of factors affecting American black duck populations

USGS Publications Warehouse

Conroy, Michael J.; Miller, Mark W.; Hines, James E.

2002-01-01

We reviewed the literature on factors potentially affecting the population status of American black ducks (Anas rupribes). Our review suggests that there is some support for the influence of 4 major, continental-scope factors in limiting or regulating black duck populations: 1) loss in the quantity or quality of breeding habitats; 2) loss in the quantity or quality of wintering habitats; 3) harvest, and 4) interactions (competition, hybridization) with mallards (Anas platyrhychos) during the breeding and/or wintering periods. These factors were used as the basis of an annual life cycle model in which reproduction rates and survival rates were modeled as functions of the above factors, with parameters of the model describing the strength of these relationships. Variation in the model parameter values allows for consideration of scientific uncertainty as to the degree each of these factors may be contributing to declines in black duck populations, and thus allows for the investigation of the possible effects of management (e.g., habitat improvement, harvest reductions) under different assumptions. We then used available, historical data on black duck populations (abundance, annual reproduction rates, and survival rates) and possible driving factors (trends in breeding and wintering habitats, harvest rates, and abundance of mallards) to estimate model parameters. Our estimated reproduction submodel included parameters describing negative density feedback of black ducks, positive influence of breeding habitat, and negative influence of mallard densities; our survival submodel included terms for positive influence of winter habitat on reproduction rates, and negative influences of black duck density (i.e., compensation to harvest mortality). Individual models within each group (reproduction, survival) involved various combinations of these factors, and each was given an information theoretic weight for use in subsequent prediction. The reproduction model with highest AIC weight (0.70) predicted black duck age ratios increasing as a function of decreasing mallard abundance and increasing acreage of breeding habitat; all models considered involved negative density dependence for black ducks. The survival model with highest AIC weight (0.51) predicted nonharvest survival increasing as a function of increasing acreage of wintering habitat and decreasing harvest rates (additive mortality); models involving compensatory mortality effects received ≈0.12 total weight, vs. 0.88 for additive models. We used the combined model, together with our historical data set, to perform a series of 1-year population forecasts, similar to those that might be performed under adaptive management. Initial model forecasts over-predicted observed breeding populations by ≈25%. Least-squares calibration reduced the bias to ≈0.5% under prediction. After calibration, model-averaged predictions over the 16 alternative models (4 reproduction × 4 survival, weighted by AIC model weights) explained 67% of the variation in annual breeding population abundance for black ducks, suggesting that it might have utility as a predictive tool in adaptive management. We investigated the effects of statistical uncertainty in parameter values on predicted population growth rates for the combined annual model, via sensitivity analyses. Parameter sensitivity varied in relation to the parameter values over the estimated confidence intervals, and in relation to harvest rates and mallard abundance. Forecasts of black duck abundance were extremely sensitive to variation in parameter values for the coefficients for breeding and wintering habitat effects. Model-averaged forecasts of black duck abundance were also sensitive to changes in harvest rate and mallard abundance, with rapid declines in black duck abundance predicted for a range of harvest rates and mallard abundance higher than current levels of either factor, but easily envisaged, particularly given current rates of growth for mallard populations. Because of concerns about sensitivity to habitat coefficients, and particularly in light of deficiencies in the historical data used to estimate these parameters, we developed a simplified model that excludes habitat effects. We also developed alternative models involving a calibration adjustment for reproduction rates, survival rates, or neither. Calibration of survival rates performed best (AIC weight 0.59, % BIAS = -0.280, R2=0.679), with reproduction calibration somewhat inferior (AIC weight 0.41, % BIAS = -0.267, R2=0.672); models without calibration received virtually no AIC weight and were discarded. We recommend that the simplified model set (4 biological models × 2 alternative calibration factors) be retained as the best working set of alternative models for research and management. Finally, we provide some preliminary guidance for the development of adaptive harvest management for black ducks, using our working set of models.

Bayesian model selection: Evidence estimation based on DREAM simulation and bridge sampling

NASA Astrophysics Data System (ADS)

Volpi, Elena; Schoups, Gerrit; Firmani, Giovanni; Vrugt, Jasper A.

2017-04-01

Bayesian inference has found widespread application in Earth and Environmental Systems Modeling, providing an effective tool for prediction, data assimilation, parameter estimation, uncertainty analysis and hypothesis testing. Under multiple competing hypotheses, the Bayesian approach also provides an attractive alternative to traditional information criteria (e.g. AIC, BIC) for model selection. The key variable for Bayesian model selection is the evidence (or marginal likelihood) that is the normalizing constant in the denominator of Bayes theorem; while it is fundamental for model selection, the evidence is not required for Bayesian inference. It is computed for each hypothesis (model) by averaging the likelihood function over the prior parameter distribution, rather than maximizing it as by information criteria; the larger a model evidence the more support it receives among a collection of hypothesis as the simulated values assign relatively high probability density to the observed data. Hence, the evidence naturally acts as an Occam's razor, preferring simpler and more constrained models against the selection of over-fitted ones by information criteria that incorporate only the likelihood maximum. Since it is not particularly easy to estimate the evidence in practice, Bayesian model selection via the marginal likelihood has not yet found mainstream use. We illustrate here the properties of a new estimator of the Bayesian model evidence, which provides robust and unbiased estimates of the marginal likelihood; the method is coined Gaussian Mixture Importance Sampling (GMIS). GMIS uses multidimensional numerical integration of the posterior parameter distribution via bridge sampling (a generalization of importance sampling) of a mixture distribution fitted to samples of the posterior distribution derived from the DREAM algorithm (Vrugt et al., 2008; 2009). Some illustrative examples are presented to show the robustness and superiority of the GMIS estimator with respect to other commonly used approaches in the literature.

An Interoceptive Predictive Coding Model of Conscious Presence

PubMed Central

Seth, Anil K.; Suzuki, Keisuke; Critchley, Hugo D.

2011-01-01

We describe a theoretical model of the neurocognitive mechanisms underlying conscious presence and its disturbances. The model is based on interoceptive prediction error and is informed by predictive models of agency, general models of hierarchical predictive coding and dopaminergic signaling in cortex, the role of the anterior insular cortex (AIC) in interoception and emotion, and cognitive neuroscience evidence from studies of virtual reality and of psychiatric disorders of presence, specifically depersonalization/derealization disorder. The model associates presence with successful suppression by top-down predictions of informative interoceptive signals evoked by autonomic control signals and, indirectly, by visceral responses to afferent sensory signals. The model connects presence to agency by allowing that predicted interoceptive signals will depend on whether afferent sensory signals are determined, by a parallel predictive-coding mechanism, to be self-generated or externally caused. Anatomically, we identify the AIC as the likely locus of key neural comparator mechanisms. Our model integrates a broad range of previously disparate evidence, makes predictions for conjoint manipulations of agency and presence, offers a new view of emotion as interoceptive inference, and represents a step toward a mechanistic account of a fundamental phenomenological property of consciousness. PMID:22291673

Model selection on solid ground: Rigorous comparison of nine ways to evaluate Bayesian model evidence

PubMed Central

Schöniger, Anneli; Wöhling, Thomas; Samaniego, Luis; Nowak, Wolfgang

2014-01-01

Bayesian model selection or averaging objectively ranks a number of plausible, competing conceptual models based on Bayes' theorem. It implicitly performs an optimal trade-off between performance in fitting available data and minimum model complexity. The procedure requires determining Bayesian model evidence (BME), which is the likelihood of the observed data integrated over each model's parameter space. The computation of this integral is highly challenging because it is as high-dimensional as the number of model parameters. Three classes of techniques to compute BME are available, each with its own challenges and limitations: (1) Exact and fast analytical solutions are limited by strong assumptions. (2) Numerical evaluation quickly becomes unfeasible for expensive models. (3) Approximations known as information criteria (ICs) such as the AIC, BIC, or KIC (Akaike, Bayesian, or Kashyap information criterion, respectively) yield contradicting results with regard to model ranking. Our study features a theory-based intercomparison of these techniques. We further assess their accuracy in a simplistic synthetic example where for some scenarios an exact analytical solution exists. In more challenging scenarios, we use a brute-force Monte Carlo integration method as reference. We continue this analysis with a real-world application of hydrological model selection. This is a first-time benchmarking of the various methods for BME evaluation against true solutions. Results show that BME values from ICs are often heavily biased and that the choice of approximation method substantially influences the accuracy of model ranking. For reliable model selection, bias-free numerical methods should be preferred over ICs whenever computationally feasible. PMID:25745272

Development of three-dimensional lung multicellular spheroids in air- and liquid-interface culture for the evaluation of anticancer therapeutics.

PubMed

Meenach, Samantha A; Tsoras, Alexandra N; McGarry, Ronald C; Mansour, Heidi M; Hilt, J Zach; Anderson, Kimberly W

2016-04-01

Three-dimensional (3D) lung multicellular spheroids (MCS) in liquid-covered culture (LCC) and air-interface culture (AIC) conditions have both been developed for the evaluation of aerosol anticancer therapeutics in solution and aerosols, respectively. The MCS were formed by seeding lung cancer cells on top of collagen where they formed spheroids due to the prevalence of cell-to-cell interactions. LCC MCS were exposed to paclitaxel (PTX) in media whereas AIC MCS were exposed to dry powder PEGylated phospholipid aerosol microparticles containing paclitaxel. The difference in viability for 2D versus 3D culture for both LCC and AIC was evaluated along with the effects of the particles on lung epithelium via transepithelial electrical resistance (TEER) measurements. For LCC and AIC conditions, the 3D spheroids were more resistant to treatment with higher IC50 values for A549 and H358 cell lines. TEER results initially indicated a decrease in resistance upon drug or particle exposure, however, these values increased over the course of several days indicating the ability of the cells to recover. Overall, these studies offer a comprehensive in vitro evaluation of aerosol particles used in the treatment of lung cancer while introducing a new method for culturing lung cancer MCS in both LCC and AIC conditions.

Ifenprodil infusion in agranular insular cortex alters social behavior and vocalizations in rats exposed to moderate levels of ethanol during prenatal development

PubMed Central

Bird, Clark W.; Barto, Daniel; Magcalas, Christy M.; Rodriguez, Carlos I.; Donaldson, Tia; Davies, Suzy; Savage, Daniel D.; Hamilton, Derek A.

2016-01-01

Moderate exposure to alcohol during development leads to subtle neurobiological and behavioral effects classified under the umbrella term fetal alcohol spectrum disorders (FASDs). Alterations in social behaviors are a frequently observed consequence of maternal drinking, as children with FASDs display inappropriate aggressive behaviors and altered responses to social cues. Rodent models of FASDs mimic the behavioral alterations seen in humans, with rats exposed to ethanol during development displaying increased aggressive behaviors, decreased social investigation, and altered play behavior. Work from our laboratory has observed increased wrestling behavior in adult male rats following prenatal alcohol exposure (PAE), and increased expression of GluN2B-containing NMDA receptors in the agranular insular cortex (AIC). This study was undertaken to determine if ifenprodil, a GluN2B preferring negative allosteric modulator, has a significant effect on social behaviors in PAE rats. Using a voluntary ethanol exposure paradigm, rat dams were allowed to drink a saccharin-sweetened solution of either 0% or 5% ethanol throughout gestation. Offspring at 6–8 months of age were implanted with cannulae into AIC. Animals were isolated for 24 hours before ifenprodil or vehicle was infused into AIC, and after 15 minutes they were recorded in a social interaction chamber. Ifenprodil treatment altered aspects of wrestling, social investigatory behaviors, and ultrasonic vocalizations in rats exposed to ethanol during development that were not observed in control animals. These data indicate that GluN2B-containing NMDA receptors in AIC play a role in social behaviors and may underlie alterations in behavior and vocalizations observed in PAE animals. PMID:27888019

Endometrial cancer risk prediction including serum-based biomarkers: results from the EPIC cohort.

PubMed

Fortner, Renée T; Hüsing, Anika; Kühn, Tilman; Konar, Meric; Overvad, Kim; Tjønneland, Anne; Hansen, Louise; Boutron-Ruault, Marie-Christine; Severi, Gianluca; Fournier, Agnès; Boeing, Heiner; Trichopoulou, Antonia; Benetou, Vasiliki; Orfanos, Philippos; Masala, Giovanna; Agnoli, Claudia; Mattiello, Amalia; Tumino, Rosario; Sacerdote, Carlotta; Bueno-de-Mesquita, H B As; Peeters, Petra H M; Weiderpass, Elisabete; Gram, Inger T; Gavrilyuk, Oxana; Quirós, J Ramón; Maria Huerta, José; Ardanaz, Eva; Larrañaga, Nerea; Lujan-Barroso, Leila; Sánchez-Cantalejo, Emilio; Butt, Salma Tunå; Borgquist, Signe; Idahl, Annika; Lundin, Eva; Khaw, Kay-Tee; Allen, Naomi E; Rinaldi, Sabina; Dossus, Laure; Gunter, Marc; Merritt, Melissa A; Tzoulaki, Ioanna; Riboli, Elio; Kaaks, Rudolf

2017-03-15

Endometrial cancer risk prediction models including lifestyle, anthropometric and reproductive factors have limited discrimination. Adding biomarker data to these models may improve predictive capacity; to our knowledge, this has not been investigated for endometrial cancer. Using a nested case-control study within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, we investigated the improvement in discrimination gained by adding serum biomarker concentrations to risk estimates derived from an existing risk prediction model based on epidemiologic factors. Serum concentrations of sex steroid hormones, metabolic markers, growth factors, adipokines and cytokines were evaluated in a step-wise backward selection process; biomarkers were retained at p < 0.157 indicating improvement in the Akaike information criterion (AIC). Improvement in discrimination was assessed using the C-statistic for all biomarkers alone, and change in C-statistic from addition of biomarkers to preexisting absolute risk estimates. We used internal validation with bootstrapping (1000-fold) to adjust for over-fitting. Adiponectin, estrone, interleukin-1 receptor antagonist, tumor necrosis factor-alpha and triglycerides were selected into the model. After accounting for over-fitting, discrimination was improved by 2.0 percentage points when all evaluated biomarkers were included and 1.7 percentage points in the model including the selected biomarkers. Models including etiologic markers on independent pathways and genetic markers may further improve discrimination. © 2016 UICC.

X-inactivation patterns in Aicardi syndrome

USDA-ARS?s Scientific Manuscript database

Aicardi syndrome (AIC) is a severe sporadic neurodevelopmental disorder, characterized by a classic triad of agenesis of the corpus callosum, chorioretinal lacunae, and infantile spasms. Because nearly all affected individuals are female and the few known males with AIC have a 47,XXY karyotype, it i...

A new approach to correct the QT interval for changes in heart rate using a nonparametric regression model in beagle dogs.

PubMed

Watanabe, Hiroyuki; Miyazaki, Hiroyasu

2006-01-01

Over- and/or under-correction of QT intervals for changes in heart rate may lead to misleading conclusions and/or masking the potential of a drug to prolong the QT interval. This study examines a nonparametric regression model (Loess Smoother) to adjust the QT interval for differences in heart rate, with an improved fitness over a wide range of heart rates. 240 sets of (QT, RR) observations collected from each of 8 conscious and non-treated beagle dogs were used as the materials for investigation. The fitness of the nonparametric regression model to the QT-RR relationship was compared with four models (individual linear regression, common linear regression, and Bazett's and Fridericia's correlation models) with reference to Akaike's Information Criterion (AIC). Residuals were visually assessed. The bias-corrected AIC of the nonparametric regression model was the best of the models examined in this study. Although the parametric models did not fit, the nonparametric regression model improved the fitting at both fast and slow heart rates. The nonparametric regression model is the more flexible method compared with the parametric method. The mathematical fit for linear regression models was unsatisfactory at both fast and slow heart rates, while the nonparametric regression model showed significant improvement at all heart rates in beagle dogs.

An Investigation of State-Space Model Fidelity for SSME Data

NASA Technical Reports Server (NTRS)

Martin, Rodney Alexander

2008-01-01

In previous studies, a variety of unsupervised anomaly detection techniques for anomaly detection were applied to SSME (Space Shuttle Main Engine) data. The observed results indicated that the identification of certain anomalies were specific to the algorithmic method under consideration. This is the reason why one of the follow-on goals of these previous investigations was to build an architecture to support the best capabilities of all algorithms. We appeal to that goal here by investigating a cascade, serial architecture for the best performing and most suitable candidates from previous studies. As a precursor to a formal ROC (Receiver Operating Characteristic) curve analysis for validation of resulting anomaly detection algorithms, our primary focus here is to investigate the model fidelity as measured by variants of the AIC (Akaike Information Criterion) for state-space based models. We show that placing constraints on a state-space model during or after the training of the model introduces a modest level of suboptimality. Furthermore, we compare the fidelity of all candidate models including those embodying the cascade, serial architecture. We make recommendations on the most suitable candidates for application to subsequent anomaly detection studies as measured by AIC-based criteria.

Crucial nesting habitat for gunnison sage-grouse: A spatially explicit hierarchical approach

USGS Publications Warehouse

Aldridge, Cameron L.; Saher, D.J.; Childers, T.M.; Stahlnecker, K.E.; Bowen, Z.H.

2012-01-01

Gunnison sage-grouse (Centrocercus minimus) is a species of special concern and is currently considered a candidate species under Endangered Species Act. Careful management is therefore required to ensure that suitable habitat is maintained, particularly because much of the species' current distribution is faced with exurban development pressures. We assessed hierarchical nest site selection patterns of Gunnison sage-grouse inhabiting the western portion of the Gunnison Basin, Colorado, USA, at multiple spatial scales, using logistic regression-based resource selection functions. Models were selected using Akaike Information Criterion corrected for small sample sizes (AIC c) and predictive surfaces were generated using model averaged relative probabilities. Landscape-scale factors that had the most influence on nest site selection included the proportion of sagebrush cover >5%, mean productivity, and density of 2 wheel-drive roads. The landscape-scale predictive surface captured 97% of known Gunnison sage-grouse nests within the top 5 of 10 prediction bins, implicating 57% of the basin as crucial nesting habitat. Crucial habitat identified by the landscape model was used to define the extent for patch-scale modeling efforts. Patch-scale variables that had the greatest influence on nest site selection were the proportion of big sagebrush cover >10%, distance to residential development, distance to high volume paved roads, and mean productivity. This model accurately predicted independent nest locations. The unique hierarchical structure of our models more accurately captures the nested nature of habitat selection, and allowed for increased discrimination within larger landscapes of suitable habitat. We extrapolated the landscape-scale model to the entire Gunnison Basin because of conservation concerns for this species. We believe this predictive surface is a valuable tool which can be incorporated into land use and conservation planning as well the assessment of future land-use scenarios. ?? 2011 The Wildlife Society.

Computational and human observer image quality evaluation of low dose, knowledge-based CT iterative reconstruction

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

Eck, Brendan L.; Fahmi, Rachid; Miao, Jun

2015-10-15

Purpose: Aims in this study are to (1) develop a computational model observer which reliably tracks the detectability of human observers in low dose computed tomography (CT) images reconstructed with knowledge-based iterative reconstruction (IMR™, Philips Healthcare) and filtered back projection (FBP) across a range of independent variables, (2) use the model to evaluate detectability trends across reconstructions and make predictions of human observer detectability, and (3) perform human observer studies based on model predictions to demonstrate applications of the model in CT imaging. Methods: Detectability (d′) was evaluated in phantom studies across a range of conditions. Images were generated usingmore » a numerical CT simulator. Trained observers performed 4-alternative forced choice (4-AFC) experiments across dose (1.3, 2.7, 4.0 mGy), pin size (4, 6, 8 mm), contrast (0.3%, 0.5%, 1.0%), and reconstruction (FBP, IMR), at fixed display window. A five-channel Laguerre–Gauss channelized Hotelling observer (CHO) was developed with internal noise added to the decision variable and/or to channel outputs, creating six different internal noise models. Semianalytic internal noise computation was tested against Monte Carlo and used to accelerate internal noise parameter optimization. Model parameters were estimated from all experiments at once using maximum likelihood on the probability correct, P{sub C}. Akaike information criterion (AIC) was used to compare models of different orders. The best model was selected according to AIC and used to predict detectability in blended FBP-IMR images, analyze trends in IMR detectability improvements, and predict dose savings with IMR. Predicted dose savings were compared against 4-AFC study results using physical CT phantom images. Results: Detection in IMR was greater than FBP in all tested conditions. The CHO with internal noise proportional to channel output standard deviations, Model-k4, showed the best trade-off between fit and model complexity according to AIC{sub c}. With parameters fixed, the model reasonably predicted detectability of human observers in blended FBP-IMR images. Semianalytic internal noise computation gave results equivalent to Monte Carlo, greatly speeding parameter estimation. Using Model-k4, the authors found an average detectability improvement of 2.7 ± 0.4 times that of FBP. IMR showed greater improvements in detectability with larger signals and relatively consistent improvements across signal contrast and x-ray dose. In the phantom tested, Model-k4 predicted an 82% dose reduction compared to FBP, verified with physical CT scans at 80% reduced dose. Conclusions: IMR improves detectability over FBP and may enable significant dose reductions. A channelized Hotelling observer with internal noise proportional to channel output standard deviation agreed well with human observers across a wide range of variables, even across reconstructions with drastically different image characteristics. Utility of the model observer was demonstrated by predicting the effect of image processing (blending), analyzing detectability improvements with IMR across dose, size, and contrast, and in guiding real CT scan dose reduction experiments. Such a model observer can be applied in optimizing parameters in advanced iterative reconstruction algorithms as well as guiding dose reduction protocols in physical CT experiments.« less

Characterizing the relationship between temperature and mortality in tropical and subtropical cities: a distributed lag non-linear model analysis in Hue, Viet Nam, 2009-2013.

PubMed

Dang, Tran Ngoc; Seposo, Xerxes T; Duc, Nguyen Huu Chau; Thang, Tran Binh; An, Do Dang; Hang, Lai Thi Minh; Long, Tran Thanh; Loan, Bui Thi Hong; Honda, Yasushi

2016-01-01

The relationship between temperature and mortality has been found to be U-, V-, or J-shaped in developed temperate countries; however, in developing tropical/subtropical cities, it remains unclear. Our goal was to investigate the relationship between temperature and mortality in Hue, a subtropical city in Viet Nam. We collected daily mortality data from the Vietnamese A6 mortality reporting system for 6,214 deceased persons between 2009 and 2013. A distributed lag non-linear model was used to examine the temperature effects on all-cause and cause-specific mortality by assuming negative binomial distribution for count data. We developed an objective-oriented model selection with four steps following the Akaike information criterion (AIC) rule (i.e. a smaller AIC value indicates a better model). High temperature-related mortality was more strongly associated with short lags, whereas low temperature-related mortality was more strongly associated with long lags. The low temperatures increased risk in all-category mortality compared to high temperatures. We observed elevated temperature-mortality risk in vulnerable groups: elderly people (high temperature effect, relative risk [RR]=1.42, 95% confidence interval [CI]=1.11-1.83; low temperature effect, RR=2.0, 95% CI=1.13-3.52), females (low temperature effect, RR=2.19, 95% CI=1.14-4.21), people with respiratory disease (high temperature effect, RR=2.45, 95% CI=0.91-6.63), and those with cardiovascular disease (high temperature effect, RR=1.6, 95% CI=1.15-2.22; low temperature effect, RR=1.99, 95% CI=0.92-4.28). In Hue, the temperature significantly increased the risk of mortality, especially in vulnerable groups (i.e. elderly, female, people with respiratory and cardiovascular diseases). These findings may provide a foundation for developing adequate policies to address the effects of temperature on health in Hue City.

Characterizing the relationship between temperature and mortality in tropical and subtropical cities: a distributed lag non-linear model analysis in Hue, Viet Nam, 2009–2013

PubMed Central

Dang, Tran Ngoc; Seposo, Xerxes T.; Duc, Nguyen Huu Chau; Thang, Tran Binh; An, Do Dang; Hang, Lai Thi Minh; Long, Tran Thanh; Loan, Bui Thi Hong; Honda, Yasushi

2016-01-01

Background The relationship between temperature and mortality has been found to be U-, V-, or J-shaped in developed temperate countries; however, in developing tropical/subtropical cities, it remains unclear. Objectives Our goal was to investigate the relationship between temperature and mortality in Hue, a subtropical city in Viet Nam. Design We collected daily mortality data from the Vietnamese A6 mortality reporting system for 6,214 deceased persons between 2009 and 2013. A distributed lag non-linear model was used to examine the temperature effects on all-cause and cause-specific mortality by assuming negative binomial distribution for count data. We developed an objective-oriented model selection with four steps following the Akaike information criterion (AIC) rule (i.e. a smaller AIC value indicates a better model). Results High temperature-related mortality was more strongly associated with short lags, whereas low temperature-related mortality was more strongly associated with long lags. The low temperatures increased risk in all-category mortality compared to high temperatures. We observed elevated temperature-mortality risk in vulnerable groups: elderly people (high temperature effect, relative risk [RR]=1.42, 95% confidence interval [CI]=1.11–1.83; low temperature effect, RR=2.0, 95% CI=1.13–3.52), females (low temperature effect, RR=2.19, 95% CI=1.14–4.21), people with respiratory disease (high temperature effect, RR=2.45, 95% CI=0.91–6.63), and those with cardiovascular disease (high temperature effect, RR=1.6, 95% CI=1.15–2.22; low temperature effect, RR=1.99, 95% CI=0.92–4.28). Conclusions In Hue, the temperature significantly increased the risk of mortality, especially in vulnerable groups (i.e. elderly, female, people with respiratory and cardiovascular diseases). These findings may provide a foundation for developing adequate policies to address the effects of temperature on health in Hue City. PMID:26781954

Amylose-potassium oleate inclusion complex in plain set-style yogurt

USDA-ARS?s Scientific Manuscript database

Amylose-potassium oleate inclusion complex (AIC) were used to replace skim milk solids in yogurt. The effect of AIC on yogurt fermentation and small amplitude oscillatory shear flow measurements of storage and loss moduli were studied and compared to full fat samples. Texture, storage modulus, and s...

Modeling chlorophyll-a and dissolved oxygen concentration in tropical floodplain lakes (Paraná River, Brazil).

PubMed

Rocha, R R A; Thomaz, S M; Carvalho, P; Gomes, L C

2009-06-01

The need for prediction is widely recognized in limnology. In this study, data from 25 lakes of the Upper Paraná River floodplain were used to build models to predict chlorophyll-a and dissolved oxygen concentrations. Akaike's information criterion (AIC) was used as a criterion for model selection. Models were validated with independent data obtained in the same lakes in 2001. Predictor variables that significantly explained chlorophyll-a concentration were pH, electrical conductivity, total seston (positive correlation) and nitrate (negative correlation). This model explained 52% of chlorophyll variability. Variables that significantly explained dissolved oxygen concentration were pH, lake area and nitrate (all positive correlations); water temperature and electrical conductivity were negatively correlated with oxygen. This model explained 54% of oxygen variability. Validation with independent data showed that both models had the potential to predict algal biomass and dissolved oxygen concentration in these lakes. These findings suggest that multiple regression models are valuable and practical tools for understanding the dynamics of ecosystems and that predictive limnology may still be considered a powerful approach in aquatic ecology.

Proxies for soil organic carbon derived from remote sensing

NASA Astrophysics Data System (ADS)

Rasel, S. M. M.; Groen, T. A.; Hussin, Y. A.; Diti, I. J.

2017-07-01

The possibility of carbon storage in soils is of interest because compared to vegetation it contains more carbon. Estimation of soil carbon through remote sensing based techniques can be a cost effective approach, but is limited by available methods. This study aims to develop a model based on remotely sensed variables (elevation, forest type and above ground biomass) to estimate soil carbon stocks. Field observations on soil organic carbon, species composition, and above ground biomass were recorded in the subtropical forest of Chitwan, Nepal. These variables were also estimated using LiDAR data and a WorldView 2 image. Above ground biomass was estimated from the LiDAR image using a novel approach where the image was segmented to identify individual trees, and for these trees estimates of DBH and Height were made. Based on AIC (Akaike Information Criterion) a regression model with above ground biomass derived from LiDAR data, and forest type derived from WorldView 2 imagery was selected to estimate soil organic carbon (SOC) stocks. The selected model had a coefficient of determination (R2) of 0.69. This shows the scope of estimating SOC with remote sensing derived variables in sub-tropical forests.

The Harvard Medical School Academic Innovations Collaborative: transforming primary care practice and education.

PubMed

Bitton, Asaf; Ellner, Andrew; Pabo, Erika; Stout, Somava; Sugarman, Jonathan R; Sevin, Cory; Goodell, Kristen; Bassett, Jill S; Phillips, Russell S

2014-09-01

Academic medical centers (AMCs) need new approaches to delivering higher-quality care at lower costs, and engaging trainees in the work of high-functioning primary care practices. In 2012, the Harvard Medical School Center for Primary Care, in partnership with with local AMCs, established an Academic Innovations Collaborative (AIC) with the goal of transforming primary care education and practice. This novel two-year learning collaborative consisted of hospital- and community-based primary care teaching practices, committed to building highly functional teams, managing populations, and engaging patients. The AIC built on models developed by Qualis Health and the Institute for Healthcare Improvement, optimized for the local AMC context. Foundational elements included leadership engagement and development, application of rapid-cycle process improvement, and the creation of teams to care for defined patient populations. Nineteen practices across six AMCs participated, with nearly 260,000 patients and 450 resident learners. The collaborative offered three 1.5-day learning sessions each year featuring shared learning, practice coaches, and improvement measures, along with monthly data reporting, webinars, and site visits. Validated self-reports by transformation teams showed that practices made substantial improvement across all areas of change. Important factors for success included leadership development, practice-level resources, and engaging patients and trainees. The AIC model shows promise as a path for AMCs to catalyze health system transformation through primary care improvement. In addition to further evaluating the impact of practice transformation, expansion will require support from AMCs and payers, and the application of similar approaches on a broader scale.

Are Skills the Answer? The Political Economy of Skill Creation in Advanced Industrial Countries.

ERIC Educational Resources Information Center

Crouch, Colin; Finegold, David; Sako, Mari

This book analyzes vocational education and training (VET) systems in seven advanced industrial countries (AICs) to determine institutional arrangements for skills creation most promising in attaining the learning society. The AICs are France, Germany, Italy, Japan, United Kingdom, United States, and Sweden. Chapter 1 discusses special problems of…

Trading Habitat Patches for the Red Cockaded Woodpecker: Incorporating the Role of Landscape Structure and Uncertainty in Decision Making

DTIC Science & Technology

2007-06-11

basal area of pines 25.4 to 35 cm dbh between 0 and 9.2 m2/ha PS1, PS4 , PS12 Small Pines No. pine stems/ha < 25.4 cm dbh NA HL1, HL4...value Adj R2 AIC Fit = 2.50 + 0.0079 PS1 – 0.036 PM1 – 0.0102 PS4 – 0.019 HS12 0.063 0.033 426 Parameter p-value t-value Intercept ɘ.0001 8.23 PS1...0.125 1.54 PM1 0.038 -2.10 PS4 0.107 -1.62 HS12 0.063 -1.88 Model A – Most Parsimonious p-value Adj R2 AIC Fit = 2.02 – 0.03 PM1 0.053 0.018 427

The development of an airborne instrumentation computer system for flight test

NASA Technical Reports Server (NTRS)

Bever, G. A.

1984-01-01

Instrumentation interfacing frequently requires the linking of intelligent systems together, as well as requiring the link itself to be intelligent. The airborne instrumentation computer system (AICS) was developed to address this requirement. Its small size, approximately 254 by 133 by 140 mm (10 by 51/4 by 51/2 in), standard bus, and modular board configuration give it the ability to solve instrumentation interfacing and computation problems without forcing a redesign of the entire unit. This system has been used on the F-15 aircraft digital electronic engine control (DEEC) and its follow on engine model derivative (EMD) project and in an OV-1C Mohawk aircraft stall speed warning system. The AICS is presently undergoing configuration for use on an F-104 pace aircraft and on the advanced fighter technology integration (AFTI) F-111 aircraft.

Assessment and Selection of Competing Models for Zero-Inflated Microbiome Data

PubMed Central

Xu, Lizhen; Paterson, Andrew D.; Turpin, Williams; Xu, Wei

2015-01-01

Typical data in a microbiome study consist of the operational taxonomic unit (OTU) counts that have the characteristic of excess zeros, which are often ignored by investigators. In this paper, we compare the performance of different competing methods to model data with zero inflated features through extensive simulations and application to a microbiome study. These methods include standard parametric and non-parametric models, hurdle models, and zero inflated models. We examine varying degrees of zero inflation, with or without dispersion in the count component, as well as different magnitude and direction of the covariate effect on structural zeros and the count components. We focus on the assessment of type I error, power to detect the overall covariate effect, measures of model fit, and bias and effectiveness of parameter estimations. We also evaluate the abilities of model selection strategies using Akaike information criterion (AIC) or Vuong test to identify the correct model. The simulation studies show that hurdle and zero inflated models have well controlled type I errors, higher power, better goodness of fit measures, and are more accurate and efficient in the parameter estimation. Besides that, the hurdle models have similar goodness of fit and parameter estimation for the count component as their corresponding zero inflated models. However, the estimation and interpretation of the parameters for the zero components differs, and hurdle models are more stable when structural zeros are absent. We then discuss the model selection strategy for zero inflated data and implement it in a gut microbiome study of > 400 independent subjects. PMID:26148172

Assessment and Selection of Competing Models for Zero-Inflated Microbiome Data.

PubMed

Xu, Lizhen; Paterson, Andrew D; Turpin, Williams; Xu, Wei

2015-01-01

Typical data in a microbiome study consist of the operational taxonomic unit (OTU) counts that have the characteristic of excess zeros, which are often ignored by investigators. In this paper, we compare the performance of different competing methods to model data with zero inflated features through extensive simulations and application to a microbiome study. These methods include standard parametric and non-parametric models, hurdle models, and zero inflated models. We examine varying degrees of zero inflation, with or without dispersion in the count component, as well as different magnitude and direction of the covariate effect on structural zeros and the count components. We focus on the assessment of type I error, power to detect the overall covariate effect, measures of model fit, and bias and effectiveness of parameter estimations. We also evaluate the abilities of model selection strategies using Akaike information criterion (AIC) or Vuong test to identify the correct model. The simulation studies show that hurdle and zero inflated models have well controlled type I errors, higher power, better goodness of fit measures, and are more accurate and efficient in the parameter estimation. Besides that, the hurdle models have similar goodness of fit and parameter estimation for the count component as their corresponding zero inflated models. However, the estimation and interpretation of the parameters for the zero components differs, and hurdle models are more stable when structural zeros are absent. We then discuss the model selection strategy for zero inflated data and implement it in a gut microbiome study of > 400 independent subjects.

Selection for the best ETS (error, trend, seasonal) model to forecast weather in the Aceh Besar District

NASA Astrophysics Data System (ADS)

Amora Jofipasi, Chesilia; Miftahuddin; Hizir

2018-05-01

Weather is a phenomenon that occurs in certain areas that indicate a change in natural activity. Weather can be predicted using data in previous periods over a period. The purpose of this study is to get the best ETS model to predict the weather in Aceh Besar. The ETS model is a time series univariate forecasting method; its use focuses on trend and seasonal components. The data used are air temperature, dew point, sea level pressure, station pressure, visibility, wind speed, and sea surface temperature from January 2006 to December 2016. Based on AIC, AICc and BIC the smallest values obtained the conclusion that the ETS (M, N, A) is used to predict air temperature, and sea surface temperature, ETS (A, N, A) is used to predict dew point, sea level pressure and station pressure, ETS (A, A, N) is used to predict visibility, and ETS (A, N, N) is used to predict wind speed.

Discrimination of numerical proportions: A comparison of binomial and Gaussian models.

PubMed

Raidvee, Aire; Lember, Jüri; Allik, Jüri

2017-01-01

Observers discriminated the numerical proportion of two sets of elements (N = 9, 13, 33, and 65) that differed either by color or orientation. According to the standard Thurstonian approach, the accuracy of proportion discrimination is determined by irreducible noise in the nervous system that stochastically transforms the number of presented visual elements onto a continuum of psychological states representing numerosity. As an alternative to this customary approach, we propose a Thurstonian-binomial model, which assumes discrete perceptual states, each of which is associated with a certain visual element. It is shown that the probability β with which each visual element can be noticed and registered by the perceptual system can explain data of numerical proportion discrimination at least as well as the continuous Thurstonian-Gaussian model, and better, if the greater parsimony of the Thurstonian-binomial model is taken into account using AIC model selection. We conclude that Gaussian and binomial models represent two different fundamental principles-internal noise vs. using only a fraction of available information-which are both plausible descriptions of visual perception.

Model averaging and muddled multimodel inferences.

PubMed

Cade, Brian S

2015-09-01

Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the t statistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse (Centrocercus urophasianus) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources.

Model averaging and muddled multimodel inferences

USGS Publications Warehouse

Cade, Brian S.

2015-01-01

Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the tstatistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse (Centrocercus urophasianus) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources.

Is First-Order Vector Autoregressive Model Optimal for fMRI Data?

PubMed

Ting, Chee-Ming; Seghouane, Abd-Krim; Khalid, Muhammad Usman; Salleh, Sh-Hussain

2015-09-01

We consider the problem of selecting the optimal orders of vector autoregressive (VAR) models for fMRI data. Many previous studies used model order of one and ignored that it may vary considerably across data sets depending on different data dimensions, subjects, tasks, and experimental designs. In addition, the classical information criteria (IC) used (e.g., the Akaike IC (AIC)) are biased and inappropriate for the high-dimensional fMRI data typically with a small sample size. We examine the mixed results on the optimal VAR orders for fMRI, especially the validity of the order-one hypothesis, by a comprehensive evaluation using different model selection criteria over three typical data types--a resting state, an event-related design, and a block design data set--with varying time series dimensions obtained from distinct functional brain networks. We use a more balanced criterion, Kullback's IC (KIC) based on Kullback's symmetric divergence combining two directed divergences. We also consider the bias-corrected versions (AICc and KICc) to improve VAR model selection in small samples. Simulation results show better small-sample selection performance of the proposed criteria over the classical ones. Both bias-corrected ICs provide more accurate and consistent model order choices than their biased counterparts, which suffer from overfitting, with KICc performing the best. Results on real data show that orders greater than one were selected by all criteria across all data sets for the small to moderate dimensions, particularly from small, specific networks such as the resting-state default mode network and the task-related motor networks, whereas low orders close to one but not necessarily one were chosen for the large dimensions of full-brain networks.

Nested Sampling for Bayesian Model Comparison in the Context of Salmonella Disease Dynamics

PubMed Central

Dybowski, Richard; McKinley, Trevelyan J.; Mastroeni, Pietro; Restif, Olivier

2013-01-01

Understanding the mechanisms underlying the observed dynamics of complex biological systems requires the statistical assessment and comparison of multiple alternative models. Although this has traditionally been done using maximum likelihood-based methods such as Akaike's Information Criterion (AIC), Bayesian methods have gained in popularity because they provide more informative output in the form of posterior probability distributions. However, comparison between multiple models in a Bayesian framework is made difficult by the computational cost of numerical integration over large parameter spaces. A new, efficient method for the computation of posterior probabilities has recently been proposed and applied to complex problems from the physical sciences. Here we demonstrate how nested sampling can be used for inference and model comparison in biological sciences. We present a reanalysis of data from experimental infection of mice with Salmonella enterica showing the distribution of bacteria in liver cells. In addition to confirming the main finding of the original analysis, which relied on AIC, our approach provides: (a) integration across the parameter space, (b) estimation of the posterior parameter distributions (with visualisations of parameter correlations), and (c) estimation of the posterior predictive distributions for goodness-of-fit assessments of the models. The goodness-of-fit results suggest that alternative mechanistic models and a relaxation of the quasi-stationary assumption should be considered. PMID:24376528

Ultraviolet and Optical Line Profile Variations in the Spectrum of epsilon Persei

DTIC Science & Technology

1999-11-01

hollow cathode tube via two optical Ðbers that place the comparison spec- trum above and below the stellar spectra on each exposure. An additional...of adding a new sinus- oid can be determined by the size of the decrease between AIC(N) and AIC(N ] 1). In practice, however, statistical criteria are

Principles and Practices of Biblical Leadership: An Undergraduate Course for American Indian College

ERIC Educational Resources Information Center

Clouse, Ronald J.

2017-01-01

In recent years, numerous pastors, in many cases alumni of American Indian College (AIC), have observed that graduates from Assemblies of God (AG) educational institutions, whether native or non-native, lack leadership skills necessary for an efficacious post-college profession. In order to address this dearth of leadership among AIC alumni and…

Higher value films prepared from poly(vinyl alcohol) and amylose-fatty acid derivatives inclusion complexes

USDA-ARS?s Scientific Manuscript database

Water soluble amylose fatty acid and fatty ammonium salt inclusion complexes (AIC) were prepared by jet cooked high amylose corn starch with water soluble salts of long chain fatty acids or fatty amines. The formation of AIC was confirmed by X-ray diffraction of freeze-dried samples. After dissoluti...

Multiple-trait structured antedependence model to study the relationship between litter size and birth weight in pigs and rabbits.

PubMed

David, Ingrid; Garreau, Hervé; Balmisse, Elodie; Billon, Yvon; Canario, Laurianne

2017-01-20

Some genetic studies need to take into account correlations between traits that are repeatedly measured over time. Multiple-trait random regression models are commonly used to analyze repeated traits but suffer from several major drawbacks. In the present study, we developed a multiple-trait extension of the structured antedependence model (SAD) to overcome this issue and validated its usefulness by modeling the association between litter size (LS) and average birth weight (ABW) over parities in pigs and rabbits. The single-trait SAD model assumes that a random effect at time [Formula: see text] can be explained by the previous values of the random effect (i.e. at previous times). The proposed multiple-trait extension of the SAD model consists in adding a cross-antedependence parameter to the single-trait SAD model. This model can be easily fitted using ASReml and the OWN Fortran program that we have developed. In comparison with the random regression model, we used our multiple-trait SAD model to analyze the LS and ABW of 4345 litters from 1817 Large White sows and 8706 litters from 2286 L-1777 does over a maximum of five successive parities. For both species, the multiple-trait SAD fitted the data better than the random regression model. The difference between AIC of the two models (AIC_random regression-AIC_SAD) were equal to 7 and 227 for pigs and rabbits, respectively. A similar pattern of heritability and correlation estimates was obtained for both species. Heritabilities were lower for LS (ranging from 0.09 to 0.29) than for ABW (ranging from 0.23 to 0.39). The general trend was a decrease of the genetic correlation for a given trait between more distant parities. Estimates of genetic correlations between LS and ABW were negative and ranged from -0.03 to -0.52 across parities. No correlation was observed between the permanent environmental effects, except between the permanent environmental effects of LS and ABW of the same parity, for which the estimate of the correlation was strongly negative (ranging from -0.57 to -0.67). We demonstrated that application of our multiple-trait SAD model is feasible for studying several traits with repeated measurements and showed that it provided a better fit to the data than the random regression model.

Multimodel predictive system for carbon dioxide solubility in saline formation waters.

PubMed

Wang, Zan; Small, Mitchell J; Karamalidis, Athanasios K

2013-02-05

The prediction of carbon dioxide solubility in brine at conditions relevant to carbon sequestration (i.e., high temperature, pressure, and salt concentration (T-P-X)) is crucial when this technology is applied. Eleven mathematical models for predicting CO(2) solubility in brine are compared and considered for inclusion in a multimodel predictive system. Model goodness of fit is evaluated over the temperature range 304-433 K, pressure range 74-500 bar, and salt concentration range 0-7 m (NaCl equivalent), using 173 published CO(2) solubility measurements, particularly selected for those conditions. The performance of each model is assessed using various statistical methods, including the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). Different models emerge as best fits for different subranges of the input conditions. A classification tree is generated using machine learning methods to predict the best-performing model under different T-P-X subranges, allowing development of a multimodel predictive system (MMoPS) that selects and applies the model expected to yield the most accurate CO(2) solubility prediction. Statistical analysis of the MMoPS predictions, including a stratified 5-fold cross validation, shows that MMoPS outperforms each individual model and increases the overall accuracy of CO(2) solubility prediction across the range of T-P-X conditions likely to be encountered in carbon sequestration applications.

Multi-point measurement using two-channel reflectometer with antenna switching for study of high-frequency fluctuations in GAMMA 10

NASA Astrophysics Data System (ADS)

Ikezoe, R.; Ichimura, M.; Okada, T.; Itagaki, J.; Hirata, M.; Sumida, S.; Jang, S.; Izumi, K.; Tanaka, A.; Yoshikawa, M.; Kohagura, J.; Sakamoto, M.; Nakashima, Y.

2017-03-01

A two-channel microwave reflectometer system with fast microwave antenna switching capability was developed and applied to the GAMMA 10 tandem mirror device to study high-frequency small-amplitude fluctuations in a hot mirror plasma. The fast switching of the antennas is controlled using PIN diode switches, which offers the significant advantage of reducing the number of high-cost microwave components and digitizers with high bandwidths and large memory that are required to measure the spatiotemporal behavior of the high-frequency fluctuations. The use of two channels rather than one adds the important function of a simultaneous two-point measurement in either the radial direction or the direction of the antenna array to measure the phase profile of the fluctuations along with the normal amplitude profile. The density fluctuations measured using this system clearly showed the high-frequency coherent fluctuations that are associated with Alfvén-ion-cyclotron (AIC) waves in GAMMA 10. A correlation analysis applied to simultaneously measured density fluctuations showed that the phase component that was included in a reflected microwave provided both high coherence and a clear phase difference for the AIC waves, while the amplitude component showed neither significant coherence nor clear phase difference. The axial phase differences of the AIC waves measured inside the hot plasma confirmed the formation of a standing wave structure. The axial variation of the radial profiles was evaluated and a clear difference was found among the AIC waves for the first time, which would be a key to clarify the unknown boundary conditions of the AIC waves.

Statistical methods of fracture characterization using acoustic borehole televiewer log interpretation

NASA Astrophysics Data System (ADS)

Massiot, Cécile; Townend, John; Nicol, Andrew; McNamara, David D.

2017-08-01

Acoustic borehole televiewer (BHTV) logs provide measurements of fracture attributes (orientations, thickness, and spacing) at depth. Orientation, censoring, and truncation sampling biases similar to those described for one-dimensional outcrop scanlines, and other logging or drilling artifacts specific to BHTV logs, can affect the interpretation of fracture attributes from BHTV logs. K-means, fuzzy K-means, and agglomerative clustering methods provide transparent means of separating fracture groups on the basis of their orientation. Fracture spacing is calculated for each of these fracture sets. Maximum likelihood estimation using truncated distributions permits the fitting of several probability distributions to the fracture attribute data sets within truncation limits, which can then be extrapolated over the entire range where they naturally occur. Akaike Information Criterion (AIC) and Schwartz Bayesian Criterion (SBC) statistical information criteria rank the distributions by how well they fit the data. We demonstrate these attribute analysis methods with a data set derived from three BHTV logs acquired from the high-temperature Rotokawa geothermal field, New Zealand. Varying BHTV log quality reduces the number of input data points, but careful selection of the quality levels where fractures are deemed fully sampled increases the reliability of the analysis. Spacing data analysis comprising up to 300 data points and spanning three orders of magnitude can be approximated similarly well (similar AIC rankings) with several distributions. Several clustering configurations and probability distributions can often characterize the data at similar levels of statistical criteria. Thus, several scenarios should be considered when using BHTV log data to constrain numerical fracture models.

Prediction of Vigilant Attention and Cognitive Performance Using Self-Reported Alertness, Circadian Phase, Hours since Awakening, and Accumulated Sleep Loss

PubMed Central

Bermudez, Eduardo B.; Klerman, Elizabeth B.; Czeisler, Charles A.; Cohen, Daniel A.; Wyatt, James K.; Phillips, Andrew J. K.

2016-01-01

Sleep restriction causes impaired cognitive performance that can result in adverse consequences in many occupational settings. Individuals may rely on self-perceived alertness to decide if they are able to adequately perform a task. It is therefore important to determine the relationship between an individual’s self-assessed alertness and their objective performance, and how this relationship depends on circadian phase, hours since awakening, and cumulative lost hours of sleep. Healthy young adults (aged 18–34) completed an inpatient schedule that included forced desynchrony of sleep/wake and circadian rhythms with twelve 42.85-hour “days” and either a 1:2 (n = 8) or 1:3.3 (n = 9) ratio of sleep-opportunity:enforced-wakefulness. We investigated whether subjective alertness (visual analog scale), circadian phase (melatonin), hours since awakening, and cumulative sleep loss could predict objective performance on the Psychomotor Vigilance Task (PVT), an Addition/Calculation Test (ADD) and the Digit Symbol Substitution Test (DSST). Mathematical models that allowed nonlinear interactions between explanatory variables were evaluated using the Akaike Information Criterion (AIC). Subjective alertness was the single best predictor of PVT, ADD, and DSST performance. Subjective alertness alone, however, was not an accurate predictor of PVT performance. The best AIC scores for PVT and DSST were achieved when all explanatory variables were included in the model. The best AIC score for ADD was achieved with circadian phase and subjective alertness variables. We conclude that subjective alertness alone is a weak predictor of objective vigilant or cognitive performance. Predictions can, however, be improved by knowing an individual’s circadian phase, current wake duration, and cumulative sleep loss. PMID:27019198

Wetland and microhabitat use by nesting four-toed salamanders in Maine

USGS Publications Warehouse

Chalmers, R.J.; Loftin, C.S.

2006-01-01

Little is known of Four-Toed Salamander (Hemidactylium scutatum) habitat use, despite the species' extensive range and elevated conservation status. We investigated species-habitat relationships that predict H. scutatum nesting presence in Maine at wetland and microhabitat scales by comparing microhabitats with and without nests. We created logistic regression models, selected models with AIC, and evaluated models with reserve data. Wetlands with nests were best predicted by shoreline microhabitat of Sphagnum spp., wood substrate, water flow, blue-joint reed grass (Calamagrostis canadensis), meadowsweet (Spiraea alba), steeplebush (Spiraea tomentosa), sensitive fern (Onoclea sensibilis), and absence of sheep laurel (Kalmia angustifolia) or deciduous forest canopy. Within occupied wetlands, shoreline microhabitat where nests occurred was best distinguished from available, unoccupied shoreline microhabitat by steeper shore, greater near-shore and basin water depth, deeper nesting vegetation, presence of moss spp. and winterberry (Ilex verticillata), and a negative association with S. alba, leatherleaf (Chamaedaphne calyculata), and K. angustifolia. These models of wetland and microhabitat use by H. scutatum may assist ecologists and managers in detecting and conserving this species. Copyright 2006 Society for the Study of Amphibians and Reptiles.

Modeling Dark Energy Through AN Ising Fluid with Network Interactions

NASA Astrophysics Data System (ADS)

Luongo, Orlando; Tommasini, Damiano

2014-12-01

We show that the dark energy (DE) effects can be modeled by using an Ising perfect fluid with network interactions, whose low redshift equation of state (EoS), i.e. ω0, becomes ω0 = -1 as in the ΛCDM model. In our picture, DE is characterized by a barotropic fluid on a lattice in the equilibrium configuration. Thus, mimicking the spin interaction by replacing the spin variable with an occupational number, the pressure naturally becomes negative. We find that the corresponding EoS mimics the effects of a variable DE term, whose limiting case reduces to the cosmological constant Λ. This permits us to avoid the introduction of a vacuum energy as DE source by hand, alleviating the coincidence and fine tuning problems. We find fairly good cosmological constraints, by performing three tests with supernovae Ia (SNeIa), baryonic acoustic oscillation (BAO) and cosmic microwave background (CMB) measurements. Finally, we perform the Akaike information criterion (AIC) and Bayesian information criterion (BIC) selection criteria, showing that our model is statistically favored with respect to the Chevallier-Polarsky-Linder (CPL) parametrization.

Voltage dependence of the rat chorda tympani response to Na+ salts: implications for the functional organization of taste receptor cells.

PubMed

Ye, Q; Heck, G L; DeSimone, J A

1993-07-01

1. Voltage-clamp and current-clamp data were obtained from a circumscribed region of the anterior rat lingual epithelium while simultaneously monitoring the afferent, stimulus-evoked, neural response from the same receptive field. 2. Chorda tympani (CT) responses at constant Na(+)-salt concentration were enhanced by submucosa negative voltage clamp and suppressed by positive voltage clamp. The complete CT response profile, including the time course of adaptation, was not uniquely determined by NaCl concentration alone. The response could be reproduced at different NaCl concentrations by applying a compensating voltage. 3. The form of the concentration and voltage dependence of the CT response indicates that the complete stimulus energy is the Na+ electrochemical potential difference across receptor cell apical membranes, and not Na+ concentration alone. This is the underlying principal behind the equivalence of chemical and electric taste for Na+ salts. 4. CT responses to sodium gluconate (25 and 200 mM) and 25 mM NaCl produced amiloride-insensitive components (AIC) of low magnitude. NaCl at 200 mM produced a significantly larger AIC. The AIC was voltage-clamp independent. The relative magnitude of the AIC was positively correlated with the transepithelial conductance of each salt. This suggests that the large AIC for 200 mM NaCl results from its relatively high permeability through the paracellular pathway. 5. Analysis of the CT response under voltage clamp revealed two anion effects on Na(+)-salt taste, both of which act through the paracellular shunt. 1) Anions modify the transepithelial potential (TP) across tight junctions and thereby modulate the cell receptor potential. This anion effect can be eliminated by voltage clamping the TP. 2) Sufficiently mobile anions facilitate electroneutral diffusion of Na+ salts through tight junctions. This effect is observed especially when Cl- is the anion and when the stimulus concentration favors NaCl influx, allowing Na+ to stimulate receptor cells from the submucosal side. Because the submucosal intercellular spaces are nearly isopotential regions, this effect is insensitive to voltage clamp of the TP. The large AIC associated with this anion effect is due to the low permeability of amiloride.

A water quality index model using stepwise regression and neural networks models for the Piabanha River basin in Rio de Janeiro, Brazil

NASA Astrophysics Data System (ADS)

Villas Boas, M. D.; Olivera, F.; Azevedo, J. S.

2013-12-01

The evaluation of water quality through 'indexes' is widely used in environmental sciences. There are a number of methods available for calculating water quality indexes (WQI), usually based on site-specific parameters. In Brazil, WQI were initially used in the 1970s and were adapted from the methodology developed in association with the National Science Foundation (Brown et al, 1970). Specifically, the WQI 'IQA/SCQA', developed by the Institute of Water Management of Minas Gerais (IGAM), is estimated based on nine parameters: Temperature Range, Biochemical Oxygen Demand, Fecal Coliforms, Nitrate, Phosphate, Turbidity, Dissolved Oxygen, pH and Electrical Conductivity. The goal of this study was to develop a model for calculating the IQA/SCQA, for the Piabanha River basin in the State of Rio de Janeiro (Brazil), using only the parameters measurable by a Multiparameter Water Quality Sonde (MWQS) available in the study area. These parameters are: Dissolved Oxygen, pH and Electrical Conductivity. The use of this model will allow to further the water quality monitoring network in the basin, without requiring significant increases of resources. The water quality measurement with MWQS is less expensive than the laboratory analysis required for the other parameters. The water quality data used in the study were obtained by the Geological Survey of Brazil in partnership with other public institutions (i.e. universities and environmental institutes) as part of the project "Integrated Studies in Experimental and Representative Watersheds". Two models were developed to correlate the values of the three measured parameters and the IQA/SCQA values calculated based on all nine parameters. The results were evaluated according to the following validation statistics: coefficient of determination (R2), Root Mean Square Error (RMSE), Akaike information criterion (AIC) and Final Prediction Error (FPE). The first model was a linear stepwise regression between three independent variables (input) and one dependent variable (output) to establish an equation relating input to output. This model produced the following statistics: R2 = 0.85, RMSE = 6.19, AIC =0.65 and FPE = 1.93. The second model was a Feedforward Neural Network with one tan-sigmoid hidden layer (4 neurons) and one linear output layer. The neural network was trained based on a backpropagation algorithm using the input as predictors and the output as target. The following statistics were found: R2 = 0.95, RMSE = 4.86, AIC= 0.33 and FPE = 1.39. The second model produced a better fit than the first one, having a greater R2 and smaller RMSE, AIC and FPE. The best performance of the second method can be attributed to the fact that the water quality parameters often exhibit nonlinear behaviors and neural networks are capable of representing nonlinear relationship efficiently, while the regression is limited to linear relationships. References: Brown, R.M., McLelland, N.I., Deininger, R.A., Tozer, R.G.1970. A Water Quality Index-Do we dare? Water & Sewage Works, October: 339-343.

An Improved P-Phase Arrival Picking Method S/L-K-A with an Application to the Yongshaba Mine in China

NASA Astrophysics Data System (ADS)

Shang, Xueyi; Li, Xibing; Morales-Esteban, A.; Dong, Longjun

2018-02-01

Automatic microseismic P-phase arrival picking is paramount for microseismic event identification, event location and source mechanism analysis. The commonly used STA/LTA picker, PAI-K picker, AIC picker and three proposed pickers have been applied to determine the P-phase arrivals of 580 microseismic signals (the sampling frequency is 6000 Hz). These have been obtained from the Institute of Mine Seismology (IMS) acquisition system of the Yongshaba mine in China. Then, the six above-mentioned pickers have been compared in their picking accuracy, typical waveforms, signal-to-noise ratio (SNR) adaptabilities and quantitative evaluation. The results have shown that: (1) the triggered STA/LTA picker has a good picking stability but a low picking accuracy. While the PAI-K and the AIC pickers have a higher picking accuracy but a poorer picking stability compared with the triggered STA/LTA picker. Moreover, the AIC picker usually has a better picking result than the PAI-K picker; (2) the S/L-K-A picker significantly improves the STA/LTA, the PAI-K and the S/L + PAI-K pickers. Moreover, it obviously improves the AIC and the S/L + AIC pickers' large picking error (> 30 ms) signals; (3) the picking error ratios of the S/L-K-A picker within 10, 20 and 30 ms achieve 92.76, 95.86 and 97.41%, respectively. The S/L-K-A picker enhances the picking adaptability to different waveforms and SNRs. In conclusion, the S/L-K-A picker provides a new method for automatic microseismic P-phase arrival picking with a high accuracy and a good stability.

Infection control and prevention practices implemented to reduce transmission risk of Middle East respiratory syndrome-coronavirus in a tertiary care institution in Saudi Arabia.

PubMed

Butt, Taimur S; Koutlakis-Barron, Irene; AlJumaah, Suliman; AlThawadi, Sahar; AlMofada, Saleh

2016-05-01

Transmission of Middle East respiratory syndrome-coronavirus (MERS-CoV) among health care workers (HCWs) and patients has been documented with mortality rate approximating 36%. We propose advanced infection control measures (A-IC) used in conjunction with basic infection control measures (B-IC) help reduce pathogen transmission. B-IC include standard and transmission-based precautions. A-IC are initiatives implemented within our center to enhance effectiveness of B-IC. Study effectiveness of combining B-IC and A-IC to prevent transmission of MERS-CoV to HCWs. A retrospective observational study was undertaken. A-IC measures include administrative support with daily rounds; infection control risk assessment; timely screening, isolation, and specimen analysis; collaboration; epidemic planning; stockpiling; implementation of contingency plans; full personal protective equipment use for advanced airway management; use of a real-time electronic isolation flagging system; infection prevention and control team on-call protocols; pretransfer MERS-CoV testing; and education. A total of 874 real-time polymerase chain reaction MERS-CoV tests were performed during the period beginning July 1, 2013, and ending January 31, 2015. Six hundred ninety-four non-HCWs were tested, of these 16 tested positive for MERS-CoV and their infection was community acquired. Sixty-nine percent of the confirmed MERS-CoV-positive cases were men, with an average age of 56 years (range, 19-84 years). Of the total tested for MERS-CoV, 180 individuals were HCWs with zero positivity. Adhering to a combination of B-IC and A-IC reduces the risk of MERS-CoV transmission to HCWs. Copyright © 2016 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

Perioperative dynamics and significance of plasma-free amino acid profiles in colorectal cancer.

PubMed

Katayama, Kayoko; Higuchi, Akio; Yamamoto, Hiroshi; Ikeda, Atsuko; Kikuchi, Shinya; Shiozawa, Manabu

2018-02-21

For early detection of cancer, we have previously developed the AminoIndex Cancer Screening (AICS) system, which quantifies 6 plasma-free amino acids (PFAAs) in blood samples. Herein, we examined the usefulness of the AICS in patients with colorectal cancer (CRC) by comparing the preoperative and postoperative PFAA profiles. Our study cohort consisted of 62 patients who had undergone curative resection for CRC at our cancer center, with no recurrence at the time of the study. Blood samples were collected from fasted patients within 1 week before the resection and at 0.5-6.5 years post-resection. Following plasmapheresis, the PFAA levels were measured via liquid chromatography/mass spectrometry, and the AICS values were computed (the higher the value, the greater the probability of cancer). Risk was calculated from the AICS value and ranked as A, B, or C, with rank C representing the highest risk. All patients in our study were rank B + C. The postoperative AICS value was lower than the preoperative value in 57 of the 62 patients; the rank was also lower postoperatively (49 patients, p < 0.001). The decline in both was stage-independent, even occurring in patients with right-sided tumors or poorly differentiated adenocarcinomas. For comparative purposes, the levels of 2 tumor markers (carbohydrate antigen 19-9 and carcinoembryonic antigen) were also examined; these were within the reference ranges in 70-80% of patients preoperatively and in 80-90% postoperatively. We suggest that tumor-bearing conditions alter the PFAA profiles, which may be used to predict prognosis and monitor for recurrence in CRC patients after tumor resection. This trial has been retrospectively registered at UMIN-CTR R000028005 , Oct 06, 2016.

The evaluation of different forest structural indices to predict the stand aboveground biomass of even-aged Scotch pine (Pinus sylvestris L.) forests in Kunduz, Northern Turkey.

PubMed

Ercanli, İlker; Kahriman, Aydın

2015-03-01

We assessed the effect of stand structural diversity, including the Shannon, improved Shannon, Simpson, McIntosh, Margelef, and Berger-Parker indices, on stand aboveground biomass (AGB) and developed statistical prediction models for the stand AGB values, including stand structural diversity indices and some stand attributes. The AGB prediction model, including only stand attributes, accounted for 85 % of the total variance in AGB (R (2)) with an Akaike's information criterion (AIC) of 807.2407, Bayesian information criterion (BIC) of 809.5397, Schwarz Bayesian criterion (SBC) of 818.0426, and root mean square error (RMSE) of 38.529 Mg. After inclusion of the stand structural diversity into the model structure, considerable improvement was observed in statistical accuracy, including 97.5 % of the total variance in AGB, with an AIC of 614.1819, BIC of 617.1242, SBC of 633.0853, and RMSE of 15.8153 Mg. The predictive fitting results indicate that some indices describing the stand structural diversity can be employed as significant independent variables to predict the AGB production of the Scotch pine stand. Further, including the stand diversity indices in the AGB prediction model with the stand attributes provided important predictive contributions in estimating the total variance in AGB.

Wave and particle evolution downstream of quasi-perpendicular shocks

NASA Technical Reports Server (NTRS)

Mckean, M. E.; Omidi, N.; Krauss-Varban, D.; Karimabadi, H.

1995-01-01

Distributions of ions heated in quasi-perpendicular bow shocks have large perpendicular temperature anisotropies that provide free energy for the growth of Alfven ion cyclotron (AIC) and mirror waves. These modes are often obsreved in the Earth's magnetosheath. Using two-dimensional hybrid simulations, we show that these waves are produced near the shock front and convected downstream rather than being produced locally downstream. The wave activity reduces the proton anisotropy to magnetosheath levels within a few tens of gyroradii of the shock but takes significantly longer to reduce the anisotropy of He(++) ions. The waves are primarily driven by proton anisotropy and the dynamics of the helium ions is controlled by the proton waves. Downstream of high Mach number shocks, mirror waves compete effectively with AIC waves. Downstream of low Mach number shocks, AIC waves dominate.

AIC-based diffraction stacking for local earthquake locations at the Sumatran Fault (Indonesia)

NASA Astrophysics Data System (ADS)

Hendriyana, Andri; Bauer, Klaus; Muksin, Umar; Weber, Michael

2018-05-01

We present a new workflow for the localization of seismic events which is based on a diffraction stacking approach. In order to address the effects from complex source radiation patterns, we suggest to compute diffraction stacking from a characteristic function (CF) instead of stacking the original waveform data. A new CF, which is called in the following mAIC (modified from Akaike Information Criterion) is proposed. We demonstrate that both P- and S-wave onsets can be detected accurately. To avoid cross-talk between P and S waves due to inaccurate velocity models, we separate the P and S waves from the mAIC function by making use of polarization attributes. Then, the final image function is represented by the largest eigenvalue as a result of the covariance analysis between P- and S-image functions. Results from synthetic experiments show that the proposed diffraction stacking provides reliable results. The workflow of the diffraction stacking method was finally applied to local earthquake data from Sumatra, Indonesia. Recordings from a temporary network of 42 stations deployed for nine months around the Tarutung pull-apart basin were analysed. The seismic event locations resulting from the diffraction stacking method align along a segment of the Sumatran Fault. A more complex distribution of seismicity is imaged within and around the Tarutung basin. Two lineaments striking N-S were found in the centre of the Tarutung basin which support independent results from structural geology.

Testing and selection of cosmological models with (1+z){sup 6} corrections

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

Szydlowski, Marek; Marc Kac Complex Systems Research Centre, Jagiellonian University, ul. Reymonta 4, 30-059 Cracow; Godlowski, Wlodzimierz

2008-02-15

In the paper we check whether the contribution of (-)(1+z){sup 6} type in the Friedmann equation can be tested. We consider some astronomical tests to constrain the density parameters in such models. We describe different interpretations of such an additional term: geometric effects of loop quantum cosmology, effects of braneworld cosmological models, nonstandard cosmological models in metric-affine gravity, and models with spinning fluid. Kinematical (or geometrical) tests based on null geodesics are insufficient to separate individual matter components when they behave like perfect fluid and scale in the same way. Still, it is possible to measure their overall effect. Wemore » use recent measurements of the coordinate distances from the Fanaroff-Riley type IIb radio galaxy data, supernovae type Ia data, baryon oscillation peak and cosmic microwave background radiation observations to obtain stronger bounds for the contribution of the type considered. We demonstrate that, while {rho}{sup 2} corrections are very small, they can be tested by astronomical observations--at least in principle. Bayesian criteria of model selection (the Bayesian factor, AIC, and BIC) are used to check if additional parameters are detectable in the present epoch. As it turns out, the {lambda}CDM model is favored over the bouncing model driven by loop quantum effects. Or, in other words, the bounds obtained from cosmography are very weak, and from the point of view of the present data this model is indistinguishable from the {lambda}CDM one.« less

18F-FLT uptake kinetics in head and neck squamous cell carcinoma: a PET imaging study.

PubMed

Liu, Dan; Chalkidou, Anastasia; Landau, David B; Marsden, Paul K; Fenwick, John D

2014-04-01

To analyze the kinetics of 3(')-deoxy-3(')-[F-18]-fluorothymidine (18F-FLT) uptake by head and neck squamous cell carcinomas and involved nodes imaged using positron emission tomography (PET). Two- and three-tissue compartment models were fitted to 12 tumor time-activity-curves (TACs) obtained for 6 structures (tumors or involved nodes) imaged in ten dynamic PET studies of 1 h duration, carried out for five patients. The ability of the models to describe the data was assessed using a runs test, the Akaike information criterion (AIC) and leave-one-out cross-validation. To generate parametric maps the models were also fitted to TACs of individual voxels. Correlations between maps of different parameters were characterized using Pearson'sr coefficient; in particular the phosphorylation rate-constants k3-2tiss and k5 of the two- and three-tissue models were studied alongside the flux parameters KFLT- 2tiss and KFLT of these models, and standardized uptake values (SUV). A methodology based on expectation-maximization clustering and the Bayesian information criterion ("EM-BIC clustering") was used to distil the information from noisy parametric images. Fits of two-tissue models 2C3K and 2C4K and three-tissue models 3C5K and 3C6K comprising three, four, five, and six rate-constants, respectively, pass the runs test for 4, 8, 10, and 11 of 12 tumor TACs. The three-tissue models have lower AIC and cross-validation scores for nine of the 12 tumors. Overall the 3C6K model has the lowest AIC and cross-validation scores and its fitted parameter values are of the same orders of magnitude as literature estimates. Maps of KFLT and KFLT- 2tiss are strongly correlated (r = 0.85) and also correlate closely with SUV maps (r = 0.72 for KFLT- 2tiss, 0.64 for KFLT). Phosphorylation rate-constant maps are moderately correlated with flux maps (r = 0.48 for k3-2tiss vs KFLT- 2tiss and r = 0.68 for k5 vs KFLT); however, neither phosphorylation rate-constant correlates significantly with SUV. EM-BIC clustering reduces the parametric maps to a small number of levels--on average 5.8, 3.5, 3.4, and 1.4 for KFLT- 2tiss, KFLT, k3-2tiss, and k5. This large simplification is potentially useful for radiotherapy dose-painting, but demonstrates the high noise in some maps. Statistical simulations show that voxel level noise degrades TACs generated from the 3C6K model sufficiently that the average AIC score, parameter bias, and total uncertainty of 2C4K model fits are similar to those of 3C6K fits, whereas at the whole tumor level the scores are lower for 3C6K fits. For the patients studied here, whole tumor FLT uptake time-courses are represented better overall by a three-tissue than by a two-tissue model. EM-BIC clustering simplifies noisy parametric maps, providing the best description of the underlying information they contain and is potentially useful for radiotherapy dose-painting. However, the clustering highlights the large degree of noise present in maps of the phosphorylation rate-constantsk5 and k3-2tiss, which are conceptually tightly linked to cellular proliferation. Methods must be found to make these maps more robust-either by constraining other model parameters or modifying dynamic imaging protocols. © 2014 American Association of Physicists in Medicine.

Validated hydrophilic interaction LC-MS/MS method for simultaneous quantification of dacarbazine and 5-amino-4-imidazole-carboxamide in human plasma.

PubMed

Liu, Yanhong; Zhang, Weihua; Yang, Yuhui

2008-10-19

A hydrophilic interaction high performance liquid chromatography-tandem mass spectrometric method has been developed and validated for simultaneous quantification of dacarbazine (DTIC) and its terminal metabolite, 5-amino-4-imidazole-carboxamide (AIC) in human plasma. The plasma samples are first extracted by a C8+SCX mixed-mode 96-well plate to extend the extraction stability of DTIC and AIC. The extracted residues are further cleaned by a primary and secondary amine (PSA) adsorbent for minimization of matrix effect. Analyses are done on an Amide-80 HPLC column coupled to a tandem mass spectrometer fitted with an atmospheric pressure turbo ion spray ionization interface in the positive-ion mode. Both DTIC and AIC have reproducible retention times on the Amide-80 HPLC column. This type of column not only has an excellent column life (over 4000 injections), but also has zero carryover effect. The injection volume should be limited at 10 microL or less to avoid the peak splitting. The validated concentration ranges are from 0.5 to 500 ng/mL for DTIC and from 2.0 to 500 ng/mL for AIC. The validated method has been successfully applied to determine the pharmacokinetic profiles for human patients receiving DTIC infusions.

The Hyper-Envelope Modeling Interface (HEMI): A Novel Approach Illustrated Through Predicting Tamarisk (Tamarix spp.) Habitat in the Western USA

USGS Publications Warehouse

Graham, Jim; Young, Nick; Jarnevich, Catherine S.; Newman, Greg; Evangelista, Paul; Stohlgren, Thomas J.

2013-01-01

Habitat suitability maps are commonly created by modeling a species’ environmental niche from occurrences and environmental characteristics. Here, we introduce the hyper-envelope modeling interface (HEMI), providing a new method for creating habitat suitability models using Bezier surfaces to model a species niche in environmental space. HEMI allows modeled surfaces to be visualized and edited in environmental space based on expert knowledge and does not require absence points for model development. The modeled surfaces require relatively few parameters compared to similar modeling approaches and may produce models that better match ecological niche theory. As a case study, we modeled the invasive species tamarisk (Tamarix spp.) in the western USA. We compare results from HEMI with those from existing similar modeling approaches (including BioClim, BioMapper, and Maxent). We used synthetic surfaces to create visualizations of the various models in environmental space and used modified area under the curve (AUC) statistic and akaike information criterion (AIC) as measures of model performance. We show that HEMI produced slightly better AUC values, except for Maxent and better AIC values overall. HEMI created a model with only ten parameters while Maxent produced a model with over 100 and BioClim used only eight. Additionally, HEMI allowed visualization and editing of the model in environmental space to develop alternative potential habitat scenarios. The use of Bezier surfaces can provide simple models that match our expectations of biological niche models and, at least in some cases, out-perform more complex approaches.

Hydraulic Model Study of Port Huron Ice Control Structure,

DTIC Science & Technology

1982-11-01

thickness for Lake Huron, Alpena , M ichigan, data...measurements was Alpena , Michigan. The following table summarizes these monthly values in terms of degree days. The solid ice sheet thickness for a...ice thickness for Lake Huron, Alpena , Michigan, data. Freezing degree days Cumulative Ice thickness CDays FDys , ’C Day) E CF Day) () (ft) Jan 277

Comparison of the performance of the CMS Hierarchical Condition Category (CMS-HCC) risk adjuster with the Charlson and Elixhauser comorbidity measures in predicting mortality.

PubMed

Li, Pengxiang; Kim, Michelle M; Doshi, Jalpa A

2010-08-20

The Centers for Medicare and Medicaid Services (CMS) has implemented the CMS-Hierarchical Condition Category (CMS-HCC) model to risk adjust Medicare capitation payments. This study intends to assess the performance of the CMS-HCC risk adjustment method and to compare it to the Charlson and Elixhauser comorbidity measures in predicting in-hospital and six-month mortality in Medicare beneficiaries. The study used the 2005-2006 Chronic Condition Data Warehouse (CCW) 5% Medicare files. The primary study sample included all community-dwelling fee-for-service Medicare beneficiaries with a hospital admission between January 1st, 2006 and June 30th, 2006. Additionally, four disease-specific samples consisting of subgroups of patients with principal diagnoses of congestive heart failure (CHF), stroke, diabetes mellitus (DM), and acute myocardial infarction (AMI) were also selected. Four analytic files were generated for each sample by extracting inpatient and/or outpatient claims for each patient. Logistic regressions were used to compare the methods. Model performance was assessed using the c-statistic, the Akaike's information criterion (AIC), the Bayesian information criterion (BIC) and their 95% confidence intervals estimated using bootstrapping. The CMS-HCC had statistically significant higher c-statistic and lower AIC and BIC values than the Charlson and Elixhauser methods in predicting in-hospital and six-month mortality across all samples in analytic files that included claims from the index hospitalization. Exclusion of claims for the index hospitalization generally led to drops in model performance across all methods with the highest drops for the CMS-HCC method. However, the CMS-HCC still performed as well or better than the other two methods. The CMS-HCC method demonstrated better performance relative to the Charlson and Elixhauser methods in predicting in-hospital and six-month mortality. The CMS-HCC model is preferred over the Charlson and Elixhauser methods if information about the patient's diagnoses prior to the index hospitalization is available and used to code the risk adjusters. However, caution should be exercised in studies evaluating inpatient processes of care and where data on pre-index admission diagnoses are unavailable.

A Comparison of Dose-Response Models for the Parotid Gland in a Large Group of Head-and-Neck Cancer Patients

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

Houweling, Antonetta C., E-mail: A.Houweling@umcutrecht.n; Philippens, Marielle E.P.; Dijkema, Tim

2010-03-15

Purpose: The dose-response relationship of the parotid gland has been described most frequently using the Lyman-Kutcher-Burman model. However, various other normal tissue complication probability (NTCP) models exist. We evaluated in a large group of patients the value of six NTCP models that describe the parotid gland dose response 1 year after radiotherapy. Methods and Materials: A total of 347 patients with head-and-neck tumors were included in this prospective parotid gland dose-response study. The patients were treated with either conventional radiotherapy or intensity-modulated radiotherapy. Dose-volume histograms for the parotid glands were derived from three-dimensional dose calculations using computed tomography scans. Stimulatedmore » salivary flow rates were measured before and 1 year after radiotherapy. A threshold of 25% of the pretreatment flow rate was used to define a complication. The evaluated models included the Lyman-Kutcher-Burman model, the mean dose model, the relative seriality model, the critical volume model, the parallel functional subunit model, and the dose-threshold model. The goodness of fit (GOF) was determined by the deviance and a Monte Carlo hypothesis test. Ranking of the models was based on Akaike's information criterion (AIC). Results: None of the models was rejected based on the evaluation of the GOF. The mean dose model was ranked as the best model based on the AIC. The TD{sub 50} in these models was approximately 39 Gy. Conclusions: The mean dose model was preferred for describing the dose-response relationship of the parotid gland.« less

How good is crude MDL for solving the bias-variance dilemma? An empirical investigation based on Bayesian networks.

PubMed

Cruz-Ramírez, Nicandro; Acosta-Mesa, Héctor Gabriel; Mezura-Montes, Efrén; Guerra-Hernández, Alejandro; Hoyos-Rivera, Guillermo de Jesús; Barrientos-Martínez, Rocío Erandi; Gutiérrez-Fragoso, Karina; Nava-Fernández, Luis Alonso; González-Gaspar, Patricia; Novoa-del-Toro, Elva María; Aguilera-Rueda, Vicente Josué; Ameca-Alducin, María Yaneli

2014-01-01

The bias-variance dilemma is a well-known and important problem in Machine Learning. It basically relates the generalization capability (goodness of fit) of a learning method to its corresponding complexity. When we have enough data at hand, it is possible to use these data in such a way so as to minimize overfitting (the risk of selecting a complex model that generalizes poorly). Unfortunately, there are many situations where we simply do not have this required amount of data. Thus, we need to find methods capable of efficiently exploiting the available data while avoiding overfitting. Different metrics have been proposed to achieve this goal: the Minimum Description Length principle (MDL), Akaike's Information Criterion (AIC) and Bayesian Information Criterion (BIC), among others. In this paper, we focus on crude MDL and empirically evaluate its performance in selecting models with a good balance between goodness of fit and complexity: the so-called bias-variance dilemma, decomposition or tradeoff. Although the graphical interaction between these dimensions (bias and variance) is ubiquitous in the Machine Learning literature, few works present experimental evidence to recover such interaction. In our experiments, we argue that the resulting graphs allow us to gain insights that are difficult to unveil otherwise: that crude MDL naturally selects balanced models in terms of bias-variance, which not necessarily need be the gold-standard ones. We carry out these experiments using a specific model: a Bayesian network. In spite of these motivating results, we also should not overlook three other components that may significantly affect the final model selection: the search procedure, the noise rate and the sample size.

How Good Is Crude MDL for Solving the Bias-Variance Dilemma? An Empirical Investigation Based on Bayesian Networks

PubMed Central

Cruz-Ramírez, Nicandro; Acosta-Mesa, Héctor Gabriel; Mezura-Montes, Efrén; Guerra-Hernández, Alejandro; Hoyos-Rivera, Guillermo de Jesús; Barrientos-Martínez, Rocío Erandi; Gutiérrez-Fragoso, Karina; Nava-Fernández, Luis Alonso; González-Gaspar, Patricia; Novoa-del-Toro, Elva María; Aguilera-Rueda, Vicente Josué; Ameca-Alducin, María Yaneli

2014-01-01

The bias-variance dilemma is a well-known and important problem in Machine Learning. It basically relates the generalization capability (goodness of fit) of a learning method to its corresponding complexity. When we have enough data at hand, it is possible to use these data in such a way so as to minimize overfitting (the risk of selecting a complex model that generalizes poorly). Unfortunately, there are many situations where we simply do not have this required amount of data. Thus, we need to find methods capable of efficiently exploiting the available data while avoiding overfitting. Different metrics have been proposed to achieve this goal: the Minimum Description Length principle (MDL), Akaike’s Information Criterion (AIC) and Bayesian Information Criterion (BIC), among others. In this paper, we focus on crude MDL and empirically evaluate its performance in selecting models with a good balance between goodness of fit and complexity: the so-called bias-variance dilemma, decomposition or tradeoff. Although the graphical interaction between these dimensions (bias and variance) is ubiquitous in the Machine Learning literature, few works present experimental evidence to recover such interaction. In our experiments, we argue that the resulting graphs allow us to gain insights that are difficult to unveil otherwise: that crude MDL naturally selects balanced models in terms of bias-variance, which not necessarily need be the gold-standard ones. We carry out these experiments using a specific model: a Bayesian network. In spite of these motivating results, we also should not overlook three other components that may significantly affect the final model selection: the search procedure, the noise rate and the sample size. PMID:24671204

Assessing Greater Sage-Grouse Selection of Brood-Rearing Habitat Using Remotely-Sensed Imagery: Can Readily Available High-Resolution Imagery Be Used to Identify Brood-Rearing Habitat Across a Broad Landscape?

PubMed

Westover, Matthew; Baxter, Jared; Baxter, Rick; Day, Casey; Jensen, Ryan; Petersen, Steve; Larsen, Randy

2016-01-01

Greater sage-grouse populations have decreased steadily since European settlement in western North America. Reduced availability of brood-rearing habitat has been identified as a limiting factor for many populations. We used radio-telemetry to acquire locations of sage-grouse broods from 1998 to 2012 in Strawberry Valley, Utah. Using these locations and remotely-sensed NAIP (National Agricultural Imagery Program) imagery, we 1) determined which characteristics of brood-rearing habitat could be used in widely available, high resolution imagery 2) assessed the spatial extent at which sage-grouse selected brood-rearing habitat, and 3) created a predictive habitat model to identify areas of preferred brood-rearing habitat. We used AIC model selection to evaluate support for a list of variables derived from remotely-sensed imagery. We examined the relationship of these explanatory variables at three spatial extents (45, 200, and 795 meter radii). Our top model included 10 variables (percent shrub, percent grass, percent tree, percent paved road, percent riparian, meters of sage/tree edge, meters of riparian/tree edge, distance to tree, distance to transmission lines, and distance to permanent structures). Variables from each spatial extent were represented in our top model with the majority being associated with the larger (795 meter) spatial extent. When applied to our study area, our top model predicted 75% of naïve brood locations suggesting reasonable success using this method and widely available NAIP imagery. We encourage application of our methodology to other sage-grouse populations and species of conservation concern.

End-to-end imaging information rate advantages of various alternative communication systems

NASA Technical Reports Server (NTRS)

Rice, R. F.

1982-01-01

The efficiency of various deep space communication systems which are required to transmit both imaging and a typically error sensitive class of data called general science and engineering (gse) are compared. The approach jointly treats the imaging and gse transmission problems, allowing comparisons of systems which include various channel coding and data compression alternatives. Actual system comparisons include an advanced imaging communication system (AICS) which exhibits the rather significant advantages of sophisticated data compression coupled with powerful yet practical channel coding. For example, under certain conditions the improved AICS efficiency could provide as much as two orders of magnitude increase in imaging information rate compared to a single channel uncoded, uncompressed system while maintaining the same gse data rate in both systems. Additional details describing AICS compression and coding concepts as well as efforts to apply them are provided in support of the system analysis.

Fabrication of Si(111) crystalline thin film on graphene by aluminum-induced crystallization

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

Høiaas, I. M.; Kim, D. C., E-mail: dc.kim@crayonano.com, E-mail: helge.weman@ntnu.no; Weman, H., E-mail: dc.kim@crayonano.com, E-mail: helge.weman@ntnu.no

2016-04-18

We report the fabrication of a Si(111) crystalline thin film on graphene by the aluminum-induced crystallization (AIC) process. The AIC process of Si(111) on graphene is shown to be enhanced compared to that on an amorphous SiO{sub 2} substrate, resulting in a more homogeneous Si(111) thin film structure as revealed by X-ray diffraction and atomic force microscopy measurements. Raman measurements confirm that the graphene is intact throughout the process, retaining its characteristic phonon spectrum without any appearance of the D peak. A red-shift of Raman peaks, which is more pronounced for the 2D peak, is observed in graphene after themore » crystallization process. It is found to correlate with the red-shift of the Si Raman peak, suggesting an epitaxial relationship between graphene and the adsorbed AIC Si(111) film with both the graphene and Si under tensile strain.« less

Functional response models to estimate feeding rates of wading birds

USGS Publications Warehouse

Collazo, J.A.; Gilliam, J.F.; Miranda-Castro, L.

2010-01-01

Forager (predator) abundance may mediate feeding rates in wading birds. Yet, when modeled, feeding rates are typically derived from the purely prey-dependent Holling Type II (HoII) functional response model. Estimates of feeding rates are necessary to evaluate wading bird foraging strategies and their role in food webs; thus, models that incorporate predator dependence warrant consideration. Here, data collected in a mangrove swamp in Puerto Rico in 1994 were reanalyzed, reporting feeding rates for mixed-species flocks after comparing fits of the HoII model, as used in the original work, to the Beddington-DeAngelis (BD) and Crowley-Martin (CM) predator-dependent models. Model CM received most support (AIC c wi = 0.44), but models BD and HoII were plausible alternatives (AIC c ??? 2). Results suggested that feeding rates were constrained by predator abundance. Reductions in rates were attributed to interference, which was consistent with the independently observed increase in aggression as flock size increased (P < 0.05). Substantial discrepancies between the CM and HoII models were possible depending on flock sizes used to model feeding rates. However, inferences derived from the HoII model, as used in the original work, were sound. While Holling's Type II and other purely prey-dependent models have fostered advances in wading bird foraging ecology, evaluating models that incorporate predator dependence could lead to a more adequate description of data and processes of interest. The mechanistic bases used to derive models used here lead to biologically interpretable results and advance understanding of wading bird foraging ecology.

MMA, A Computer Code for Multi-Model Analysis

USGS Publications Warehouse

Poeter, Eileen P.; Hill, Mary C.

2007-01-01

This report documents the Multi-Model Analysis (MMA) computer code. MMA can be used to evaluate results from alternative models of a single system using the same set of observations for all models. As long as the observations, the observation weighting, and system being represented are the same, the models can differ in nearly any way imaginable. For example, they may include different processes, different simulation software, different temporal definitions (for example, steady-state and transient models could be considered), and so on. The multiple models need to be calibrated by nonlinear regression. Calibration of the individual models needs to be completed before application of MMA. MMA can be used to rank models and calculate posterior model probabilities. These can be used to (1) determine the relative importance of the characteristics embodied in the alternative models, (2) calculate model-averaged parameter estimates and predictions, and (3) quantify the uncertainty of parameter estimates and predictions in a way that integrates the variations represented by the alternative models. There is a lack of consensus on what model analysis methods are best, so MMA provides four default methods. Two are based on Kullback-Leibler information, and use the AIC (Akaike Information Criterion) or AICc (second-order-bias-corrected AIC) model discrimination criteria. The other two default methods are the BIC (Bayesian Information Criterion) and the KIC (Kashyap Information Criterion) model discrimination criteria. Use of the KIC criterion is equivalent to using the maximum-likelihood Bayesian model averaging (MLBMA) method. AIC, AICc, and BIC can be derived from Frequentist or Bayesian arguments. The default methods based on Kullback-Leibler information have a number of theoretical advantages, including that they tend to favor more complicated models as more data become available than do the other methods, which makes sense in many situations. Many applications of MMA will be well served by the default methods provided. To use the default methods, the only required input for MMA is a list of directories where the files for the alternate models are located. Evaluation and development of model-analysis methods are active areas of research. To facilitate exploration and innovation, MMA allows the user broad discretion to define alternatives to the default procedures. For example, MMA allows the user to (a) rank models based on model criteria defined using a wide range of provided and user-defined statistics in addition to the default AIC, AICc, BIC, and KIC criteria, (b) create their own criteria using model measures available from the code, and (c) define how each model criterion is used to calculate related posterior model probabilities. The default model criteria rate models are based on model fit to observations, the number of observations and estimated parameters, and, for KIC, the Fisher information matrix. In addition, MMA allows the analysis to include an evaluation of estimated parameter values. This is accomplished by allowing the user to define unreasonable estimated parameter values or relative estimated parameter values. An example of the latter is that it may be expected that one parameter value will be less than another, as might be the case if two parameters represented the hydraulic conductivity of distinct materials such as fine and coarse sand. Models with parameter values that violate the user-defined conditions are excluded from further consideration by MMA. Ground-water models are used as examples in this report, but MMA can be used to evaluate any set of models for which the required files have been produced. MMA needs to read files from a separate directory for each alternative model considered. The needed files are produced when using the Sensitivity-Analysis or Parameter-Estimation mode of UCODE_2005, or, possibly, the equivalent capability of another program. MMA is constructed using

Mixture of autoregressive modeling orders and its implication on single trial EEG classification

PubMed Central

Atyabi, Adham; Shic, Frederick; Naples, Adam

2016-01-01

Autoregressive (AR) models are of commonly utilized feature types in Electroencephalogram (EEG) studies due to offering better resolution, smoother spectra and being applicable to short segments of data. Identifying correct AR’s modeling order is an open challenge. Lower model orders poorly represent the signal while higher orders increase noise. Conventional methods for estimating modeling order includes Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Final Prediction Error (FPE). This article assesses the hypothesis that appropriate mixture of multiple AR orders is likely to better represent the true signal compared to any single order. Better spectral representation of underlying EEG patterns can increase utility of AR features in Brain Computer Interface (BCI) systems by increasing timely & correctly responsiveness of such systems to operator’s thoughts. Two mechanisms of Evolutionary-based fusion and Ensemble-based mixture are utilized for identifying such appropriate mixture of modeling orders. The classification performance of the resultant AR-mixtures are assessed against several conventional methods utilized by the community including 1) A well-known set of commonly used orders suggested by the literature, 2) conventional order estimation approaches (e.g., AIC, BIC and FPE), 3) blind mixture of AR features originated from a range of well-known orders. Five datasets from BCI competition III that contain 2, 3 and 4 motor imagery tasks are considered for the assessment. The results indicate superiority of Ensemble-based modeling order mixture and evolutionary-based order fusion methods within all datasets. PMID:28740331

Short communication: Antimicrobial efficacy of intramammary treatment with a novel biphenomycin compound against Staphylococcus aureus, Streptococcus uberis, and Escherichia coli-induced mouse mastitis.

PubMed

Demon, Dieter; Breyne, Koen; Schiffer, Guido; Meyer, Evelyne

2013-01-01

Bovine mastitis undermines udder health, jeopardizes milk production, and entails prohibitive costs, estimated at $2 billion per year in the dairy industry of the United States. Despite intensive research, the dairy industry has not managed to eradicate the 3 major bovine mastitis-inducing pathogens: Staphylococcus aureus, Streptococcus uberis, and Escherichia coli. In this study, the antimicrobial efficacy of a newly formulated biphenomycin compound (AIC102827) was assessed against intramammary Staph. aureus, Strep. uberis, and E. coli infections, using an experimental mouse mastitis model. Based on its effective and protective doses, AIC102827 applied into the mammary gland was most efficient to treat Staph. aureus, but also adequately reduced growth of Strep. uberis or E. coli, indicating its potential as a broad-spectrum candidate to treat staphylococcal, streptococcal, and coliform mastitis in dairy cattle. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Remote-Sensing Procedures for Detecting and Monitoring Various Activities Regulated by the Mobile District.

DTIC Science & Technology

1980-04-01

0.8-1.1 8 10.4-12.6 (Landsat 3 only) Note: Bands 1, 2, and 3 do not appear in this listing since they were assigned to the three detectors of the Return...may be reduced. To obtain satisfactory photographs, it is necessary to compensate for this loss by increasing the exposure time (i.e., de - creasing...1.00 for first -- 2. Select photo tndenrs frua the AICS publications de - arh 0.10 fcribed bove that r11 provide the desired coverage -cof the seen u

Evaluation of an activated carbon packed bed for the adsorption of phenols from petroleum refinery wastewater.

PubMed

El-Naas, Muftah H; Alhaija, Manal A; Al-Zuhair, Sulaiman

2017-03-01

The performance of an adsorption column packed with granular activated carbon was evaluated for the removal of phenols from refinery wastewater. The effects of phenol feed concentration (80-182 mg/l), feed flow rate (5-20 ml/min), and activated carbon packing mass (5-15 g) on the breakthrough characteristics of the adsorption system were determined. The continuous adsorption process was simulated using batch data and the parameters for a new empirical model were determined. Different dynamic models such as Adams-Bohart, Wolborsko, Thomas, and Yoon-Nelson models were also fitted to the experimental data for the sake of comparison. The empirical, Yoon-Nelson and Thomas models showed a high degree of fitting at different operation conditions, with the empirical model giving the best fit based on the Akaike information criterion (AIC). At an initial phenol concentration of 175 mg/l, packing mass of 10 g, a flow rate of 10 ml/min and a temperature of 25 °C, the SSE of the new empirical and Thomas models were identical (248.35) and very close to that of the Yoon-Nelson model (259.49). The values were significantly lower than that of the Adams-Bohart model, which was determined to be 19,358.48. The superiority of the new empirical model and the Thomas model was also confirmed from the values of the R 2 and AIC, which were 0.99 and 38.3, respectively, compared to 0.92 and 86.2 for Adams-Bohart model.

Influence of Porous Spherical-Shaped Hydroxyapatite on Mechanical Strength and Bioactive Function of Conventional Glass Ionomer Cement.

PubMed

Chiu, Szu-Yu; Shinonaga, Yukari; Abe, Yoko; Harada, Kyoko; Arita, Kenji

2017-01-03

Glass-ionomer-cement (GIC) is helpful in Minimal Intervention Dentistry because it releases fluoride ions and is highly biocompatible. The aim of this study is to investigate the mechanisms by which hydroxyapatite (HAp) improves the mechanical strength and bioactive functioning of GIC when these materials are combined to make apatite ionomer cement (AIC). A conventional GIC powder was mixed with porous, spherical-HAp particles (HApS), crystalline HAp (HAp200) or one of two types of cellulose. The micro-compressive strengths of the additive particles were measured, and various specimens were evaluated with regard to their compressive strengths (CS), fluoride release concentrations (fluoride electrode) and multi-element release concentrations. The AIC was found to release higher concentrations of fluoride (1.2 times) and strontium ions (1.5 times) compared to the control GIC. It was detected the more release of calcium originated from HApS than HAp200 in AIC. The CS of the AIC incorporating an optimum level of HAp was also significantly higher than that of the GIC. These results suggest that adding HAp can increase the release concentration of ions required for remineralization while maintaining the CS of the GIC. This effect does not result from a physical phenomenon, but rather from chemical reactions between the HAp and polyacrylic acid of GIC.

Influence of Porous Spherical-Shaped Hydroxyapatite on Mechanical Strength and Bioactive Function of Conventional Glass Ionomer Cement

PubMed Central

Chiu, Szu-Yu; Shinonaga, Yukari; Abe, Yoko; Harada, Kyoko; Arita, Kenji

2017-01-01

Glass-ionomer-cement (GIC) is helpful in Minimal Intervention Dentistry because it releases fluoride ions and is highly biocompatible. The aim of this study is to investigate the mechanisms by which hydroxyapatite (HAp) improves the mechanical strength and bioactive functioning of GIC when these materials are combined to make apatite ionomer cement (AIC). A conventional GIC powder was mixed with porous, spherical-HAp particles (HApS), crystalline HAp (HAp200) or one of two types of cellulose. The micro-compressive strengths of the additive particles were measured, and various specimens were evaluated with regard to their compressive strengths (CS), fluoride release concentrations (fluoride electrode) and multi-element release concentrations. The AIC was found to release higher concentrations of fluoride (1.2 times) and strontium ions (1.5 times) compared to the control GIC. It was detected the more release of calcium originated from HApS than HAp200 in AIC. The CS of the AIC incorporating an optimum level of HAp was also significantly higher than that of the GIC. These results suggest that adding HAp can increase the release concentration of ions required for remineralization while maintaining the CS of the GIC. This effect does not result from a physical phenomenon, but rather from chemical reactions between the HAp and polyacrylic acid of GIC. PMID:28772386

Wave excitation by nonlinear coupling among shear Alfvén waves in a mirror-confined plasma

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

Ikezoe, R., E-mail: ikezoe@prc.tsukuba.ac.jp; Ichimura, M.; Okada, T.

2015-09-15

A shear Alfvén wave at slightly below the ion-cyclotron frequency overcomes the ion-cyclotron damping and grows because of the strong anisotropy of the ion temperature in the magnetic mirror configuration, and is called the Alfvén ion-cyclotron (AIC) wave. Density fluctuations caused by the AIC waves and the ion-cyclotron range of frequencies (ICRF) waves used for ion heating have been detected using a reflectometer in a wide radial region of the GAMMA 10 tandem mirror plasma. Various wave-wave couplings are clearly observed in the density fluctuations in the interior of the plasma, but these couplings are not so clear in themore » magnetic fluctuations at the plasma edge when measured using a pick-up coil. A radial dependence of the nonlinearity is found, particularly in waves with the difference frequencies of the AIC waves; bispectral analysis shows that such wave-wave coupling is significant near the core, but is not so evident at the periphery. In contrast, nonlinear coupling with the low-frequency background turbulence is quite distinct at the periphery. Nonlinear coupling associated with the AIC waves may play a significant role in the beta- and anisotropy-limits of a mirror-confined plasma through decay of the ICRF heating power and degradation of the plasma confinement by nonlinearly generated waves.« less

Gray matter volume of the anterior insular cortex and social networking.

PubMed

Spagna, Alfredo; Dufford, Alexander J; Wu, Qiong; Wu, Tingting; Zheng, Weihao; Coons, Edgar E; Hof, Patrick R; Hu, Bin; Wu, Yanhong; Fan, Jin

2018-05-01

In human life, social context requires the engagement in complex interactions among individuals as the dynamics of social networks. The evolution of the brain as the neurological basis of the mind must be crucial in supporting social networking. Although the relationship between social networking and the amygdala, a small but core region for emotion processing, has been reported, other structures supporting sophisticated social interactions must be involved and need to be identified. In this study, we examined the relationship between morphology of the anterior insular cortex (AIC), a structure involved in basic and high-level cognition, and social networking. Two independent cohorts of individuals (New York group n = 50, Beijing group n = 100) were recruited. Structural magnetic resonance images were acquired and the social network index (SNI), a composite measure summarizing an individual's network diversity, size, and complexity, was measured. The association between morphological features of the AIC, in addition to amygdala, and the SNI was examined. Positive correlations between the measures of the volume as well as sulcal depth of the AIC and the SNI were found in both groups, while a significant positive correlation between the volume of the amygdala and the SNI was only found in the New York group. The converging results from the two groups suggest that the AIC supports network-level social interactions. © 2018 Wiley Periodicals, Inc.

Recent research on anidolic daylighting systems: highly reflective coating materials and chronobiological properties

NASA Astrophysics Data System (ADS)

Linhart, Friedrich; Wittkopf, Stephen K.; Münch, Mirjam; Scartezzini, Jean-Louis

2009-08-01

Making daylight more available in buildings is highly desirable for reasons of energy efficiency, visual comfort, occupant well-being and health. The Anidolic Integrated Ceiling (AIC) is a highly efficient daylighting system, designed to gather and redirect daylight from the outside of a building into its interior with minimal losses. The reflective coating materials used within AICs have a major impact on the optical efficiency of such systems. The first part of our article presents a new computer model of an AIC consisting of more than 30 distinct components. We discuss on which of them the use of expensive, highly reflective coatings makes the most sense. We conclude that coating the component "Anidolic element 1" is always a good choice and that considerable financial savings can be obtained by following an appropriate optimization sequence.The second part of our article discusses chronobiological properties of Anidolic Daylighting Systems (ADS). We recorded daytime irradiance values for several weeks from March to May 2009 in an experimental office setup in our laboratory using a portable digital spectroradiometer. Our results showed to which extent different sky conditions influenced daylight exposure of office workers in an ADS-equipped office room. We conclude that for the tested ADS-equipped office room, daylight supply can be considered largely sufficient during long periods on most working days. However, complementary artificial lighting with blue-enriched polychromatic fluorescent tubes might be useful on days with predominantly overcast skies as well as before 09:00 and after 16:30 on all days.

Analysis of survival in breast cancer patients by using different parametric models

NASA Astrophysics Data System (ADS)

Enera Amran, Syahila; Asrul Afendi Abdullah, M.; Kek, Sie Long; Afiqah Muhamad Jamil, Siti

2017-09-01

In biomedical applications or clinical trials, right censoring was often arising when studying the time to event data. In this case, some individuals are still alive at the end of the study or lost to follow up at a certain time. It is an important issue to handle the censoring data in order to prevent any bias information in the analysis. Therefore, this study was carried out to analyze the right censoring data with three different parametric models; exponential model, Weibull model and log-logistic models. Data of breast cancer patients from Hospital Sultan Ismail, Johor Bahru from 30 December 2008 until 15 February 2017 was used in this study to illustrate the right censoring data. Besides, the covariates included in this study are the time of breast cancer infection patients survive t, age of each patients X1 and treatment given to the patients X2 . In order to determine the best parametric models in analysing survival of breast cancer patients, the performance of each model was compare based on Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and log-likelihood value using statistical software R. When analysing the breast cancer data, all three distributions were shown consistency of data with the line graph of cumulative hazard function resembles a straight line going through the origin. As the result, log-logistic model was the best fitted parametric model compared with exponential and Weibull model since it has the smallest value in AIC and BIC, also the biggest value in log-likelihood.

Adaptive landscape and functional diversity of Neotropical cichlids: implications for the ecology and evolution of Cichlinae (Cichlidae; Cichliformes).

PubMed

Arbour, J H; López-Fernández, H

2014-11-01

Morphological, lineage and ecological diversity can vary substantially even among closely related lineages. Factors that influence morphological diversification, especially in functionally relevant traits, can help to explain the modern distribution of disparity across phylogenies and communities. Multivariate axes of feeding functional morphology from 75 species of Neotropical cichlid and a stepwise-AIC algorithm were used to estimate the adaptive landscape of functional morphospace in Cichlinae. Adaptive landscape complexity and convergence, as well as the functional diversity of Cichlinae, were compared with expectations under null evolutionary models. Neotropical cichlid feeding function varied primarily between traits associated with ram feeding vs. suction feeding/biting and secondarily with oral jaw muscle size and pharyngeal crushing capacity. The number of changes in selective regimes and the amount of convergence between lineages was higher than expected under a null model of evolution, but convergence was not higher than expected under a similarly complex adaptive landscape. Functional disparity was compatible with an adaptive landscape model, whereas the distribution of evolutionary change through morphospace corresponded with a process of evolution towards a single adaptive peak. The continentally distributed Neotropical cichlids have evolved relatively rapidly towards a number of adaptive peaks in functional trait space. Selection in Cichlinae functional morphospace is more complex than expected under null evolutionary models. The complexity of selective constraints in feeding morphology has likely been a significant contributor to the diversity of feeding ecology in this clade. © 2014 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2014 European Society For Evolutionary Biology.

Effects of error covariance structure on estimation of model averaging weights and predictive performance

USGS Publications Warehouse

Lu, Dan; Ye, Ming; Meyer, Philip D.; Curtis, Gary P.; Shi, Xiaoqing; Niu, Xu-Feng; Yabusaki, Steve B.

2013-01-01

When conducting model averaging for assessing groundwater conceptual model uncertainty, the averaging weights are often evaluated using model selection criteria such as AIC, AICc, BIC, and KIC (Akaike Information Criterion, Corrected Akaike Information Criterion, Bayesian Information Criterion, and Kashyap Information Criterion, respectively). However, this method often leads to an unrealistic situation in which the best model receives overwhelmingly large averaging weight (close to 100%), which cannot be justified by available data and knowledge. It was found in this study that this problem was caused by using the covariance matrix, CE, of measurement errors for estimating the negative log likelihood function common to all the model selection criteria. This problem can be resolved by using the covariance matrix, Cek, of total errors (including model errors and measurement errors) to account for the correlation between the total errors. An iterative two-stage method was developed in the context of maximum likelihood inverse modeling to iteratively infer the unknown Cek from the residuals during model calibration. The inferred Cek was then used in the evaluation of model selection criteria and model averaging weights. While this method was limited to serial data using time series techniques in this study, it can be extended to spatial data using geostatistical techniques. The method was first evaluated in a synthetic study and then applied to an experimental study, in which alternative surface complexation models were developed to simulate column experiments of uranium reactive transport. It was found that the total errors of the alternative models were temporally correlated due to the model errors. The iterative two-stage method using Cekresolved the problem that the best model receives 100% model averaging weight, and the resulting model averaging weights were supported by the calibration results and physical understanding of the alternative models. Using Cek obtained from the iterative two-stage method also improved predictive performance of the individual models and model averaging in both synthetic and experimental studies.

Influence of Connectivity, Wild Prey and Disturbance on Occupancy of Tigers in the Human-Dominated Western Terai Arc Landscape

PubMed Central

Harihar, Abishek; Pandav, Bivash

2012-01-01

Occupying only 7% of their historical range and confined to forested habitats interspersed in a matrix of human dominated landscapes, tigers (Panthera tigris) typify the problems faced by most large carnivores worldwide. With heads of governments of tiger range countries pledging to reverse the extinction process and setting a goal of doubling wild tiger numbers by 2022, achieving this target would require identifying existing breeding cores, potential breeding habitats and opportunities for dispersal. The Terai Arc Landscape (TAL) represents one region which has recently witnessed recovery of tiger populations following conservation efforts. In this study, we develop a spatially explicit tiger occupancy model with survey data from 2009–10 based on a priori knowledge of tiger biology and specific issues plaguing the western TAL (6,979 km2), which occurs in two disjunct units (Tiger Habitat Blocks; THBs). Although the overall occupancy of tigers was 0.588 (SE 0.071), our results clearly indicate that loss in functionality of a regional corridor has resulted in tigers now occupying 17.58% of the available habitat in THB I in comparison to 88.5% in THB II. The current patterns of occupancy were best explained by models incorporating the interactive effect of habitat blocks (AIC w = 0.883) on wild prey availability (AIC w = 0.742) and anthropogenic disturbances (AIC w = 0.143). Our analysis has helped identify areas of high tiger occupancy both within and outside existing protected areas, which highlights the need for a unified control of the landscape under a single conservation unit with the primary focus of managing tigers and associated wildlife. Finally, in the light of global conservation targets and recent legislations in India, our study assumes significance as we identify opportunities to secure (e.g. THB II) and increase (e.g. THB I) tiger populations in the landscape. PMID:22792220

Influence of connectivity, wild prey and disturbance on occupancy of tigers in the human-dominated western Terai Arc Landscape.

PubMed

Harihar, Abishek; Pandav, Bivash

2012-01-01

Occupying only 7% of their historical range and confined to forested habitats interspersed in a matrix of human dominated landscapes, tigers (Panthera tigris) typify the problems faced by most large carnivores worldwide. With heads of governments of tiger range countries pledging to reverse the extinction process and setting a goal of doubling wild tiger numbers by 2022, achieving this target would require identifying existing breeding cores, potential breeding habitats and opportunities for dispersal. The Terai Arc Landscape (TAL) represents one region which has recently witnessed recovery of tiger populations following conservation efforts. In this study, we develop a spatially explicit tiger occupancy model with survey data from 2009-10 based on a priori knowledge of tiger biology and specific issues plaguing the western TAL (6,979 km(2)), which occurs in two disjunct units (Tiger Habitat Blocks; THBs). Although the overall occupancy of tigers was 0.588 (SE 0.071), our results clearly indicate that loss in functionality of a regional corridor has resulted in tigers now occupying 17.58% of the available habitat in THB I in comparison to 88.5% in THB II. The current patterns of occupancy were best explained by models incorporating the interactive effect of habitat blocks (AIC w = 0.883) on wild prey availability (AIC w = 0.742) and anthropogenic disturbances (AIC w = 0.143). Our analysis has helped identify areas of high tiger occupancy both within and outside existing protected areas, which highlights the need for a unified control of the landscape under a single conservation unit with the primary focus of managing tigers and associated wildlife. Finally, in the light of global conservation targets and recent legislations in India, our study assumes significance as we identify opportunities to secure (e.g. THB II) and increase (e.g. THB I) tiger populations in the landscape.

Stochastic approaches for time series forecasting of boron: a case study of Western Turkey.

PubMed

Durdu, Omer Faruk

2010-10-01

In the present study, a seasonal and non-seasonal prediction of boron concentrations time series data for the period of 1996-2004 from Büyük Menderes river in western Turkey are addressed by means of linear stochastic models. The methodology presented here is to develop adequate linear stochastic models known as autoregressive integrated moving average (ARIMA) and multiplicative seasonal autoregressive integrated moving average (SARIMA) to predict boron content in the Büyük Menderes catchment. Initially, the Box-Whisker plots and Kendall's tau test are used to identify the trends during the study period. The measurements locations do not show significant overall trend in boron concentrations, though marginal increasing and decreasing trends are observed for certain periods at some locations. ARIMA modeling approach involves the following three steps: model identification, parameter estimation, and diagnostic checking. In the model identification step, considering the autocorrelation function (ACF) and partial autocorrelation function (PACF) results of boron data series, different ARIMA models are identified. The model gives the minimum Akaike information criterion (AIC) is selected as the best-fit model. The parameter estimation step indicates that the estimated model parameters are significantly different from zero. The diagnostic check step is applied to the residuals of the selected ARIMA models and the results indicate that the residuals are independent, normally distributed, and homoscadastic. For the model validation purposes, the predicted results using the best ARIMA models are compared to the observed data. The predicted data show reasonably good agreement with the actual data. The comparison of the mean and variance of 3-year (2002-2004) observed data vs predicted data from the selected best models show that the boron model from ARIMA modeling approaches could be used in a safe manner since the predicted values from these models preserve the basic statistics of observed data in terms of mean. The ARIMA modeling approach is recommended for predicting boron concentration series of a river.

Comparing of Cox model and parametric models in analysis of effective factors on event time of neuropathy in patients with type 2 diabetes.

PubMed

Kargarian-Marvasti, Sadegh; Rimaz, Shahnaz; Abolghasemi, Jamileh; Heydari, Iraj

2017-01-01

Cox proportional hazard model is the most common method for analyzing the effects of several variables on survival time. However, under certain circumstances, parametric models give more precise estimates to analyze survival data than Cox. The purpose of this study was to investigate the comparative performance of Cox and parametric models in a survival analysis of factors affecting the event time of neuropathy in patients with type 2 diabetes. This study included 371 patients with type 2 diabetes without neuropathy who were registered at Fereydunshahr diabetes clinic. Subjects were followed up for the development of neuropathy between 2006 to March 2016. To investigate the factors influencing the event time of neuropathy, significant variables in univariate model ( P < 0.20) were entered into the multivariate Cox and parametric models ( P < 0.05). In addition, Akaike information criterion (AIC) and area under ROC curves were used to evaluate the relative goodness of fitted model and the efficiency of each procedure, respectively. Statistical computing was performed using R software version 3.2.3 (UNIX platforms, Windows and MacOS). Using Kaplan-Meier, survival time of neuropathy was computed 76.6 ± 5 months after initial diagnosis of diabetes. After multivariate analysis of Cox and parametric models, ethnicity, high-density lipoprotein and family history of diabetes were identified as predictors of event time of neuropathy ( P < 0.05). According to AIC, "log-normal" model with the lowest Akaike's was the best-fitted model among Cox and parametric models. According to the results of comparison of survival receiver operating characteristics curves, log-normal model was considered as the most efficient and fitted model.

Evaluation of viral load thresholds for predicting new WHO Stage 3 and 4 events in HIV-infected children receiving highly active antiretroviral therapy

PubMed Central

Siberry, George K; Harris, D. Robert; Oliveira, Ricardo Hugo; Krauss, Margot R.; Hofer, Cristina B.; Tiraboschi, Adriana Aparecida; Marques, Heloisa; Succi, Regina C.; Abreu, Thalita; Negra, Marinella Della; Mofenson, Lynne M.; Hazra, Rohan

2012-01-01

Background This study evaluated a wide range of viral load (VL) thresholds to identify a cut-point that best predicts new clinical events in children on stable highly-active antiretroviral therapy (HAART). Methods Cox proportional hazards modeling was used to assess the adjusted risk of World Health Organization stage 3 or 4 clinical events (WHO events) as a function of time-varying CD4, VL, and hemoglobin values in a cohort study of Latin American children on HAART ≥ 6 months. Models were fit using different VL cut-points between 400 and 50,000 copies/mL, with model fit evaluated on the basis of the minimum Akaike Information Criterion (AIC) value, a standard model fit statistic. Results Models were based on 67 subjects with WHO events out of 550 subjects on study. The VL cutpoints of > 2600 copies/mL and > 32,000 copies/mL corresponded to the lowest AIC values and were associated with the highest hazard ratios [2.0 (p = 0.015) and 2.1 (p = 0.0058), respectively] for WHO events. Conclusions In HIV-infected Latin American children on stable HAART, two distinct VL thresholds (> 2,600 copies/mL and > 32,000 copies/mL) were identified for predicting children at significantly increased risk of HIV-related clinical illness, after accounting for CD4 level, hemoglobin level, and other significant factors. PMID:22343177

Seasonality in trauma admissions - Are daylight and weather variables better predictors than general cyclic effects?

PubMed

Røislien, Jo; Søvik, Signe; Eken, Torsten

2018-01-01

Trauma is a leading global cause of death, and predicting the burden of trauma admissions is vital for good planning of trauma care. Seasonality in trauma admissions has been found in several studies. Seasonal fluctuations in daylight hours, temperature and weather affect social and cultural practices but also individual neuroendocrine rhythms that may ultimately modify behaviour and potentially predispose to trauma. The aim of the present study was to explore to what extent the observed seasonality in daily trauma admissions could be explained by changes in daylight and weather variables throughout the year. Retrospective registry study on trauma admissions in the 10-year period 2001-2010 at Oslo University Hospital, Ullevål, Norway, where the amount of daylight varies from less than 6 hours to almost 19 hours per day throughout the year. Daily number of admissions was analysed by fitting non-linear Poisson time series regression models, simultaneously adjusting for several layers of temporal patterns, including a non-linear long-term trend and both seasonal and weekly cyclic effects. Five daylight and weather variables were explored, including hours of daylight and amount of precipitation. Models were compared using Akaike's Information Criterion (AIC). A regression model including daylight and weather variables significantly outperformed a traditional seasonality model in terms of AIC. A cyclic week effect was significant in all models. Daylight and weather variables are better predictors of seasonality in daily trauma admissions than mere information on day-of-year.

The Impact of Activity Interventions on the Well-Being of Older Adults in Continuing Care Communities

PubMed Central

Winstead, Vicki; Yost, Elizabeth A.; Cotten, Shelia R.; Berkowsky, Ronald W.; Anderson, William A.

2017-01-01

As the U.S. population ages, interventions are needed to ensure quality of life continues as boomers enter assisted and independent living communities (AICs). These transitions can significantly affect quality of life. Activity and continuity theories maintain that participation in discretionary/informal activities is crucial for psychosocial health and well-being (aspects of quality of life). This study evaluates the impacts of participation in discretionary activities on life satisfaction, social isolation, and loneliness, using data from a longitudinal study of older adults in AICs. Older adults who participated in 8 weeks of discretionary activities reported greater life satisfaction and lower levels of social isolation compared with non-participants. Forming alliances and group identities is the key for building new relationships and maintaining relationships in the community. Determining the impact participation in activities has on residents is vital to being able to help develop a more comprehensive understanding of how quality of life can be maintained in AICs. PMID:24942970

Application of Approximate Unsteady Aerodynamics for Flutter Analysis

NASA Technical Reports Server (NTRS)

Pak, Chan-gi; Li, Wesley W.

2010-01-01

A technique for approximating the modal aerodynamic influence coefficient (AIC) matrices by using basis functions has been developed. A process for using the resulting approximated modal AIC matrix in aeroelastic analysis has also been developed. The method requires the unsteady aerodynamics in frequency domain, and this methodology can be applied to the unsteady subsonic, transonic, and supersonic aerodynamics. The flutter solution can be found by the classic methods, such as rational function approximation, k, p-k, p, root locus et cetera. The unsteady aeroelastic analysis using unsteady subsonic aerodynamic approximation is demonstrated herein. The technique presented is shown to offer consistent flutter speed prediction on an aerostructures test wing (ATW) 2 and a hybrid wing body (HWB) type of vehicle configuration with negligible loss in precision. This method computes AICs that are functions of the changing parameters being studied and are generated within minutes of CPU time instead of hours. These results may have practical application in parametric flutter analyses as well as more efficient multidisciplinary design and optimization studies.

Injuries across adolescence: an investigation using the extended adolescent injury checklist (E-AIC).

PubMed

Chapman, Rebekah; Buckley, Lisa; Sheehan, Mary

2011-08-01

Injuries are the leading cause of death among adolescents. The current research examined a measure of adolescent injury in terms of whether it encompasses the diverse injury experiences of Australian adolescents, including high-risk and normative adolescents, and thus determine its utility as a tool for health promotion research. Grade 9 students from two Brisbane high schools (n=202, aged 13-14 years) and adolescents recruited from the Emergency Department waiting rooms of four Brisbane hospitals (n=98, aged 16-18 years) completed the Extended Adolescent Injury Checklist (E-AIC). The most common cause of injury among adolescents was a sports activity, followed by fights for all participants except school-based males, who experienced more bicycle injuries. Alcohol use was most frequently reported in association with interpersonal violence injuries. A broad variety of injuries, occurring in context of multiple risk as well as normative behaviours, were reported by adolescents in both school and ED settings, and were captured by the E-AIC.

A Novel Statistical Analysis and Interpretation of Flow Cytometry Data

DTIC Science & Technology

2013-07-05

ing parameters of the mathematical model are determined by using least squares to fit the data in Figures 1 and 2 (see Section 4). The second method is...variance (for all k and j). Then the AIC, which is the expected value of the relative Kullback – Leibler distance for a given model [16], is Kn = n log ( J...emphasized that the fit of the model is quite good for both donors and cell types. As such, we proceed to analyse the dynamic responsiveness of the

A Novel Statistical Analysis and Interpretation of Flow Cytometry Data

DTIC Science & Technology

2013-03-31

the resulting residuals appear random. In the work that follows, I∗ = 200. The values of B and b̂j are known from the experiment. Notice that the...conjunction with the model parameter vector in a two- stage process. Unfortunately two- stage estimation may cause some parameters of the mathematical model to...information theoretic criteria such as Akaike’s Information Criterion (AIC). From (4.3), it follows that the scaled residuals rjk = λjI[n̂](tj , zk; ~q

Effect of correlation on covariate selection in linear and nonlinear mixed effect models.

PubMed

Bonate, Peter L

2017-01-01

The effect of correlation among covariates on covariate selection was examined with linear and nonlinear mixed effect models. Demographic covariates were extracted from the National Health and Nutrition Examination Survey III database. Concentration-time profiles were Monte Carlo simulated where only one covariate affected apparent oral clearance (CL/F). A series of univariate covariate population pharmacokinetic models was fit to the data and compared with the reduced model without covariate. The "best" covariate was identified using either the likelihood ratio test statistic or AIC. Weight and body surface area (calculated using Gehan and George equation, 1970) were highly correlated (r = 0.98). Body surface area was often selected as a better covariate than weight, sometimes as high as 1 in 5 times, when weight was the covariate used in the data generating mechanism. In a second simulation, parent drug concentration and three metabolites were simulated from a thorough QT study and used as covariates in a series of univariate linear mixed effects models of ddQTc interval prolongation. The covariate with the largest significant LRT statistic was deemed the "best" predictor. When the metabolite was formation-rate limited and only parent concentrations affected ddQTc intervals the metabolite was chosen as a better predictor as often as 1 in 5 times depending on the slope of the relationship between parent concentrations and ddQTc intervals. A correlated covariate can be chosen as being a better predictor than another covariate in a linear or nonlinear population analysis by sheer correlation These results explain why for the same drug different covariates may be identified in different analyses. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Disability weights for infectious diseases in four European countries: comparison between countries and across respondent characteristics

PubMed Central

Maertens de Noordhout, Charline; Devleesschauwer, Brecht; Salomon, Joshua A; Turner, Heather; Cassini, Alessandro; Colzani, Edoardo; Speybroeck, Niko; Polinder, Suzanne; Kretzschmar, Mirjam E; Havelaar, Arie H; Haagsma, Juanita A

2018-01-01

Abstract Background In 2015, new disability weights (DWs) for infectious diseases were constructed based on data from four European countries. In this paper, we evaluated if country, age, sex, disease experience status, income and educational levels have an impact on these DWs. Methods We analyzed paired comparison responses of the European DW study by participants’ characteristics with separate probit regression models. To evaluate the effect of participants’ characteristics, we performed correlation analyses between countries and within country by respondent characteristics and constructed seven probit regression models, including a null model and six models containing participants’ characteristics. We compared these seven models using Akaike Information Criterion (AIC). Results According to AIC, the probit model including country as covariate was the best model. We found a lower correlation of the probit coefficients between countries and income levels (range rs: 0.97–0.99, P < 0.01) than between age groups (range rs: 0.98–0.99, P < 0.01), educational level (range rs: 0.98–0.99, P < 0.01), sex (rs = 0.99, P < 0.01) and disease status (rs = 0.99, P < 0.01). Within country the lowest correlations of the probit coefficients were between low and high income level (range rs = 0.89–0.94, P < 0.01). Conclusions We observed variations in health valuation across countries and within country between income levels. These observations should be further explored in a systematic way, also in non-European countries. We recommend future researches studying the effect of other characteristics of respondents on health assessment. PMID:29020343

Estimation of renal allograft half-life: fact or fiction?

PubMed

Azancot, M Antonieta; Cantarell, Carme; Perelló, Manel; Torres, Irina B; Serón, Daniel; Seron, Daniel; Moreso, Francesc; Arias, Manuel; Campistol, Josep M; Curto, Jordi; Hernandez, Domingo; Morales, José M; Sanchez-Fructuoso, Ana; Abraira, Victor

2011-09-01

Renal allograft half-life time (t½) is the most straightforward representation of long-term graft survival. Since some statistical models overestimate this parameter, we compare different approaches to evaluate t½. Patients with a 1-year functioning graft transplanted in Spain during 1990, 1994, 1998 and 2002 were included. Exponential, Weibull, gamma, lognormal and log-logistic models censoring the last year of follow-up were evaluated. The goodness of fit of these models was evaluated according to the Cox-Snell residuals and the Akaike's information criterion (AIC) was employed to compare these models. We included 4842 patients. Real t½ in 1990 was 14.2 years. Median t½ (95% confidence interval) in 1990 and 2002 was 15.8 (14.2-17.5) versus 52.6 (35.6-69.5) according to the exponential model (P < 0.001). No differences between 1990 and 2002 were observed when t½ was estimated with the other models. In 1990 and 2002, t½ was 14.0 (13.1-15.0) versus 18.0 (13.7-22.4) according to Weibull, 15.5 (13.9-17.1) versus 19.1 (15.6-22.6) according to gamma, 14.4 (13.3-15.6) versus 18.3 (14.2-22.3) according to the log-logistic and 15.2 (13.8-16.6) versus 18.8 (15.3-22.3) according to the lognormal models. The AIC confirmed that the exponential model had the lowest goodness of fit, while the other models yielded a similar result. The exponential model overestimates t½, especially in cohorts of patients with a short follow-up, while any of the other studied models allow a better estimation even in cohorts with short follow-up.

Movement ecology: size-specific behavioral response of an invasive snail to food availability.

PubMed

Snider, Sunny B; Gilliam, James F

2008-07-01

Immigration, emigration, migration, and redistribution describe processes that involve movement of individuals. These movements are an essential part of contemporary ecological models, and understanding how movement is affected by biotic and abiotic factors is important for effectively modeling ecological processes that depend on movement. We asked how phenotypic heterogeneity (body size) and environmental heterogeneity (food resource level) affect the movement behavior of an aquatic snail (Tarebia granifera), and whether including these phenotypic and environmental effects improves advection-diffusion models of movement. We postulated various elaborations of the basic advection diffusion model as a priori working hypotheses. To test our hypotheses we measured individual snail movements in experimental streams at high- and low-food resource treatments. Using these experimental movement data, we examined the dependency of model selection on resource level and body size using Akaike's Information Criterion (AIC). At low resources, large individuals moved faster than small individuals, producing a platykurtic movement distribution; including size dependency in the model improved model performance. In stark contrast, at high resources, individuals moved upstream together as a wave, and body size differences largely disappeared. The model selection exercise indicated that population heterogeneity is best described by the advection component of movement for this species, because the top-ranked model included size dependency in advection, but not diffusion. Also, all probable models included resource dependency. Thus population and environmental heterogeneities both influence individual movement behaviors and the population-level distribution kernels, and their interaction may drive variation in movement behaviors in terms of both advection rates and diffusion rates. A behaviorally informed modeling framework will integrate the sentient response of individuals in terms of movement and enhance our ability to accurately model ecological processes that depend on animal movement.

Weather variability and the incidence of cryptosporidiosis: comparison of time series poisson regression and SARIMA models.

PubMed

Hu, Wenbiao; Tong, Shilu; Mengersen, Kerrie; Connell, Des

2007-09-01

Few studies have examined the relationship between weather variables and cryptosporidiosis in Australia. This paper examines the potential impact of weather variability on the transmission of cryptosporidiosis and explores the possibility of developing an empirical forecast system. Data on weather variables, notified cryptosporidiosis cases, and population size in Brisbane were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics for the period of January 1, 1996-December 31, 2004, respectively. Time series Poisson regression and seasonal auto-regression integrated moving average (SARIMA) models were performed to examine the potential impact of weather variability on the transmission of cryptosporidiosis. Both the time series Poisson regression and SARIMA models show that seasonal and monthly maximum temperature at a prior moving average of 1 and 3 months were significantly associated with cryptosporidiosis disease. It suggests that there may be 50 more cases a year for an increase of 1 degrees C maximum temperature on average in Brisbane. Model assessments indicated that the SARIMA model had better predictive ability than the Poisson regression model (SARIMA: root mean square error (RMSE): 0.40, Akaike information criterion (AIC): -12.53; Poisson regression: RMSE: 0.54, AIC: -2.84). Furthermore, the analysis of residuals shows that the time series Poisson regression appeared to violate a modeling assumption, in that residual autocorrelation persisted. The results of this study suggest that weather variability (particularly maximum temperature) may have played a significant role in the transmission of cryptosporidiosis. A SARIMA model may be a better predictive model than a Poisson regression model in the assessment of the relationship between weather variability and the incidence of cryptosporidiosis.

Does transport time help explain the high trauma mortality rates in rural areas? New and traditional predictors assessed by new and traditional statistical methods

PubMed Central

Røislien, Jo; Lossius, Hans Morten; Kristiansen, Thomas

2015-01-01

Background Trauma is a leading global cause of death. Trauma mortality rates are higher in rural areas, constituting a challenge for quality and equality in trauma care. The aim of the study was to explore population density and transport time to hospital care as possible predictors of geographical differences in mortality rates, and to what extent choice of statistical method might affect the analytical results and accompanying clinical conclusions. Methods Using data from the Norwegian Cause of Death registry, deaths from external causes 1998–2007 were analysed. Norway consists of 434 municipalities, and municipality population density and travel time to hospital care were entered as predictors of municipality mortality rates in univariate and multiple regression models of increasing model complexity. We fitted linear regression models with continuous and categorised predictors, as well as piecewise linear and generalised additive models (GAMs). Models were compared using Akaike's information criterion (AIC). Results Population density was an independent predictor of trauma mortality rates, while the contribution of transport time to hospital care was highly dependent on choice of statistical model. A multiple GAM or piecewise linear model was superior, and similar, in terms of AIC. However, while transport time was statistically significant in multiple models with piecewise linear or categorised predictors, it was not in GAM or standard linear regression. Conclusions Population density is an independent predictor of trauma mortality rates. The added explanatory value of transport time to hospital care is marginal and model-dependent, highlighting the importance of exploring several statistical models when studying complex associations in observational data. PMID:25972600

Defense Planning and Programming Categories: A Special Tool for Special Needs. Volume 3. Appendix E. Proposed Expanded DPPC Structure

DTIC Science & Technology

1990-04-01

SURVEILLANCE & WARNING SYTEMS A2C COMMAND & CONTROL ACTIVITIES A2D SPACE ACTIVITIES (STRATEGIC CONTROL & SURV) A2E STRAT CONTROL & SURV: COMMUNICATIONS A2F...STRATEGIC AIR DEFENSE 0501802A NIKE-AJAX (ARNS) (H) AID STRATEGIC AIR DEFENSE AIC SPACE DEFENSE OI02115N F-6 Squadrons (H) AIC SPACE DEFENSE 0102215N ABM ...WARNING SYTEMS 0102310F NCHC - TW/AA Systems A2B SURVEILLANCE & WARNIIIG SYTEMS 0102311F NCMC - Space Defense Systems A21 SURVEILLANCE & WARNING SYTEMS

Slowdowns in diversification rates from real phylogenies may not be real.

PubMed

Cusimano, Natalie; Renner, Susanne S

2010-07-01

Studies of diversification patterns often find a slowing in lineage accumulation toward the present. This seemingly pervasive pattern of rate downturns has been taken as evidence for adaptive radiations, density-dependent regulation, and metacommunity species interactions. The significance of rate downturns is evaluated with statistical tests (the gamma statistic and Monte Carlo constant rates (MCCR) test; birth-death likelihood models and Akaike Information Criterion [AIC] scores) that rely on null distributions, which assume that the included species are a random sample of the entire clade. Sampling in real phylogenies, however, often is nonrandom because systematists try to include early-diverging species or representatives of previous intrataxon classifications. We studied the effects of biased sampling, structured sampling, and random sampling by experimentally pruning simulated trees (60 and 150 species) as well as a completely sampled empirical tree (58 species) and then applying the gamma statistic/MCCR test and birth-death likelihood models/AIC scores to assess rate changes. For trees with random species sampling, the true model (i.e., the one fitting the complete phylogenies) could be inferred in most cases. Oversampling deep nodes, however, strongly biases inferences toward downturns, with simulations of structured and biased sampling suggesting that this occurs when sampling percentages drop below 80%. The magnitude of the effect and the sensitivity of diversification rate models is such that a useful rule of thumb may be not to infer rate downturns from real trees unless they have >80% species sampling.

{sup 18}F-FLT uptake kinetics in head and neck squamous cell carcinoma: A PET imaging study

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

Liu, Dan, E-mail: dan.liu@oncology.ox.ac.uk; Fenwick, John D.; Chalkidou, Anastasia

2014-04-15

Purpose: To analyze the kinetics of 3{sup ′}-deoxy-3{sup ′}-[F-18]-fluorothymidine (18F-FLT) uptake by head and neck squamous cell carcinomas and involved nodes imaged using positron emission tomography (PET). Methods: Two- and three-tissue compartment models were fitted to 12 tumor time-activity-curves (TACs) obtained for 6 structures (tumors or involved nodes) imaged in ten dynamic PET studies of 1 h duration, carried out for five patients. The ability of the models to describe the data was assessed using a runs test, the Akaike information criterion (AIC) and leave-one-out cross-validation. To generate parametric maps the models were also fitted to TACs of individual voxels.more » Correlations between maps of different parameters were characterized using Pearson'sr coefficient; in particular the phosphorylation rate-constants k{sub 3-2tiss} and k{sub 5} of the two- and three-tissue models were studied alongside the flux parameters K{sub FLT-2tiss} and K{sub FLT} of these models, and standardized uptake values (SUV). A methodology based on expectation-maximization clustering and the Bayesian information criterion (“EM-BIC clustering”) was used to distil the information from noisy parametric images. Results: Fits of two-tissue models 2C3K and 2C4K and three-tissue models 3C5K and 3C6K comprising three, four, five, and six rate-constants, respectively, pass the runs test for 4, 8, 10, and 11 of 12 tumor TACs. The three-tissue models have lower AIC and cross-validation scores for nine of the 12 tumors. Overall the 3C6K model has the lowest AIC and cross-validation scores and its fitted parameter values are of the same orders of magnitude as literature estimates. Maps ofK{sub FLT} and K{sub FLT-2tiss} are strongly correlated (r = 0.85) and also correlate closely with SUV maps (r = 0.72 for K{sub FLT-2tiss}, 0.64 for K{sub FLT}). Phosphorylation rate-constant maps are moderately correlated with flux maps (r = 0.48 for k{sub 3-2tiss} vs K{sub FLT-2tiss} and r = 0.68 for k{sub 5} vs K{sub FLT}); however, neither phosphorylation rate-constant correlates significantly with SUV. EM-BIC clustering reduces the parametric maps to a small number of levels—on average 5.8, 3.5, 3.4, and 1.4 for K{sub FLT-2tiss}, K{sub FLT}, k{sub 3-2tiss}, and k{sub 5.} This large simplification is potentially useful for radiotherapy dose-painting, but demonstrates the high noise in some maps. Statistical simulations show that voxel level noise degrades TACs generated from the 3C6K model sufficiently that the average AIC score, parameter bias, and total uncertainty of 2C4K model fits are similar to those of 3C6K fits, whereas at the whole tumor level the scores are lower for 3C6K fits. Conclusions: For the patients studied here, whole tumor FLT uptake time-courses are represented better overall by a three-tissue than by a two-tissue model. EM-BIC clustering simplifies noisy parametric maps, providing the best description of the underlying information they contain and is potentially useful for radiotherapy dose-painting. However, the clustering highlights the large degree of noise present in maps of the phosphorylation rate-constantsk{sub 5} and k{sub 3-2tiss}, which are conceptually tightly linked to cellular proliferation. Methods must be found to make these maps more robust—either by constraining other model parameters or modifying dynamic imaging protocols.« less

Where to deliver baits for deworming urban red foxes for Echinococcus multilocularis control: new protocol for micro-habitat modeling of fox denning requirements.

PubMed

Ikeda, Takako; Yoshimura, Masashi; Onoyama, Keiichi; Oku, Yuzaburo; Nonaka, Nariaki; Katakura, Ken

2014-08-06

Deworming wild foxes by baiting with the anthelmintic praziquantel is being established as a preventive technique against environmental contamination with Echinococcus multilocularis eggs. Improvement of the cost-benefit performance of baiting treatment is required urgently to raise and maintain the efficacy of deworming. We established a spatial model of den site selection by urban red foxes, the definitive host, to specify the optimal micro-habitats for delivering baits in a new modeling approach modified for urban fox populations. The model was established for two cities (Obihiro and Sapporo) in Hokkaido, Japan, in which a sylvatic cycle of E. multilocularis is maintained. The two cities have different degrees of urbanization. The modeling process was designed to detect the best combination of key environmental factors and spatial scale that foxes pay attention to most (here named 'heeding range') when they select den sites. All possible models were generated using logistic regression analysis, with "presence" or "absence" of fox den as the objective variable, and nine landscape categories customized for urban environments as predictor variables to detect the best subset of predictors. This procedure was conducted for each of ten sizes of concentric circles from dens and control points to detect the best circle size. Out of all models generated, the most parsimonious model was selected using Akaike's Information Criterion (AIC) inspection. Our models suggest that fox dens in Obihiro are located at the center of a circle with 500 m radius including low percentages of wide roads, narrow roads, and occupied buildings, but high percentages of green covered areas; the dens in Sapporo within 300 m radius with low percentages of wide roads, occupied buildings, but high percentages of riverbeds and green covered areas. The variation of the models suggests the necessity of accumulating models for various types of cities in order to reveal the patterns of the model. Our denning models indicating suitable sites for delivering baits will improve the cost-benefit performance of the campaign. Our modeling protocol is suitable for the urban landscapes, and for extracting the heeding range when they select the den sites.

Probability density function characterization for aggregated large-scale wind power based on Weibull mixtures

DOE PAGES

Gomez-Lazaro, Emilio; Bueso, Maria C.; Kessler, Mathieu; ...

2016-02-02

Here, the Weibull probability distribution has been widely applied to characterize wind speeds for wind energy resources. Wind power generation modeling is different, however, due in particular to power curve limitations, wind turbine control methods, and transmission system operation requirements. These differences are even greater for aggregated wind power generation in power systems with high wind penetration. Consequently, models based on one-Weibull component can provide poor characterizations for aggregated wind power generation. With this aim, the present paper focuses on discussing Weibull mixtures to characterize the probability density function (PDF) for aggregated wind power generation. PDFs of wind power datamore » are firstly classified attending to hourly and seasonal patterns. The selection of the number of components in the mixture is analyzed through two well-known different criteria: the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Finally, the optimal number of Weibull components for maximum likelihood is explored for the defined patterns, including the estimated weight, scale, and shape parameters. Results show that multi-Weibull models are more suitable to characterize aggregated wind power data due to the impact of distributed generation, variety of wind speed values and wind power curtailment.« less

A Bayesian method for assessing multiscalespecies-habitat relationships

USGS Publications Warehouse

Stuber, Erica F.; Gruber, Lutz F.; Fontaine, Joseph J.

2017-01-01

ContextScientists face several theoretical and methodological challenges in appropriately describing fundamental wildlife-habitat relationships in models. The spatial scales of habitat relationships are often unknown, and are expected to follow a multi-scale hierarchy. Typical frequentist or information theoretic approaches often suffer under collinearity in multi-scale studies, fail to converge when models are complex or represent an intractable computational burden when candidate model sets are large.ObjectivesOur objective was to implement an automated, Bayesian method for inference on the spatial scales of habitat variables that best predict animal abundance.MethodsWe introduce Bayesian latent indicator scale selection (BLISS), a Bayesian method to select spatial scales of predictors using latent scale indicator variables that are estimated with reversible-jump Markov chain Monte Carlo sampling. BLISS does not suffer from collinearity, and substantially reduces computation time of studies. We present a simulation study to validate our method and apply our method to a case-study of land cover predictors for ring-necked pheasant (Phasianus colchicus) abundance in Nebraska, USA.ResultsOur method returns accurate descriptions of the explanatory power of multiple spatial scales, and unbiased and precise parameter estimates under commonly encountered data limitations including spatial scale autocorrelation, effect size, and sample size. BLISS outperforms commonly used model selection methods including stepwise and AIC, and reduces runtime by 90%.ConclusionsGiven the pervasiveness of scale-dependency in ecology, and the implications of mismatches between the scales of analyses and ecological processes, identifying the spatial scales over which species are integrating habitat information is an important step in understanding species-habitat relationships. BLISS is a widely applicable method for identifying important spatial scales, propagating scale uncertainty, and testing hypotheses of scaling relationships.

The Brain Basis for Misophonia.

PubMed

Kumar, Sukhbinder; Tansley-Hancock, Olana; Sedley, William; Winston, Joel S; Callaghan, Martina F; Allen, Micah; Cope, Thomas E; Gander, Phillip E; Bamiou, Doris-Eva; Griffiths, Timothy D

2017-02-20

Misophonia is an affective sound-processing disorder characterized by the experience of strong negative emotions (anger and anxiety) in response to everyday sounds, such as those generated by other people eating, drinking, chewing, and breathing [1-8]. The commonplace nature of these sounds (often referred to as "trigger sounds") makes misophonia a devastating disorder for sufferers and their families, and yet nothing is known about the underlying mechanism. Using functional and structural MRI coupled with physiological measurements, we demonstrate that misophonic subjects show specific trigger-sound-related responses in brain and body. Specifically, fMRI showed that in misophonic subjects, trigger sounds elicit greatly exaggerated blood-oxygen-level-dependent (BOLD) responses in the anterior insular cortex (AIC), a core hub of the "salience network" that is critical for perception of interoceptive signals and emotion processing. Trigger sounds in misophonics were associated with abnormal functional connectivity between AIC and a network of regions responsible for the processing and regulation of emotions, including ventromedial prefrontal cortex (vmPFC), posteromedial cortex (PMC), hippocampus, and amygdala. Trigger sounds elicited heightened heart rate (HR) and galvanic skin response (GSR) in misophonic subjects, which were mediated by AIC activity. Questionnaire analysis showed that misophonic subjects perceived their bodies differently: they scored higher on interoceptive sensibility than controls, consistent with abnormal functioning of AIC. Finally, brain structural measurements implied greater myelination within vmPFC in misophonic individuals. Overall, our results show that misophonia is a disorder in which abnormal salience is attributed to particular sounds based on the abnormal activation and functional connectivity of AIC. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Experience-dependent modulation of feedback integration during singing: role of the right anterior insula.

PubMed

Kleber, Boris; Zeitouni, Anthony G; Friberg, Anders; Zatorre, Robert J

2013-04-03

Somatosensation plays an important role in the motor control of vocal functions, yet its neural correlate and relation to vocal learning is not well understood. We used fMRI in 17 trained singers and 12 nonsingers to study the effects of vocal-fold anesthesia on the vocal-motor singing network as a function of singing expertise. Tasks required participants to sing musical target intervals under normal conditions and after anesthesia. At the behavioral level, anesthesia altered pitch accuracy in both groups, but singers were less affected than nonsingers, indicating an experience-dependent effect of the intervention. At the neural level, this difference was accompanied by distinct patterns of decreased activation in singers (cortical and subcortical sensory and motor areas) and nonsingers (subcortical motor areas only) respectively, suggesting that anesthesia affected the higher-level voluntary (explicit) motor and sensorimotor integration network more in experienced singers, and the lower-level (implicit) subcortical motor loops in nonsingers. The right anterior insular cortex (AIC) was identified as the principal area dissociating the effect of expertise as a function of anesthesia by three separate sources of evidence. First, it responded differently to anesthesia in singers (decreased activation) and nonsingers (increased activation). Second, functional connectivity between AIC and bilateral A1, M1, and S1 was reduced in singers but augmented in nonsingers. Third, increased BOLD activity in right AIC in singers was correlated with larger pitch deviation under anesthesia. We conclude that the right AIC and sensory-motor areas play a role in experience-dependent modulation of feedback integration for vocal motor control during singing.

Time series ARIMA models for daily price of palm oil

NASA Astrophysics Data System (ADS)

Ariff, Noratiqah Mohd; Zamhawari, Nor Hashimah; Bakar, Mohd Aftar Abu

2015-02-01

Palm oil is deemed as one of the most important commodity that forms the economic backbone of Malaysia. Modeling and forecasting the daily price of palm oil is of great interest for Malaysia's economic growth. In this study, time series ARIMA models are used to fit the daily price of palm oil. The Akaike Infromation Criterion (AIC), Akaike Infromation Criterion with a correction for finite sample sizes (AICc) and Bayesian Information Criterion (BIC) are used to compare between different ARIMA models being considered. It is found that ARIMA(1,2,1) model is suitable for daily price of crude palm oil in Malaysia for the year 2010 to 2012.

Attitudes Towards and Limitations to ICT Use in Assisted and Independent Living Communities: Findings from a Specially-Designed Technological Intervention

PubMed Central

Berkowsky, Ronald W.; Cotten, Shelia R.; Yost, Elizabeth A.; Winstead, Vicki P.

2012-01-01

While much literature has been devoted to theoretical explanations of the learning processes of older adults and to the methods of teaching best utilized in older populations, less has focused on the education of older adults who reside in assisted and independent living communities (AICs), especially with regards to information and communication technology (ICT) education. The purpose of this study is to determine whether participants’ attitudes and views towards computers and the Internet are affected as a result of participating in an eight-week training program designed to enhance computer and Internet use among older adults in such communities. Specifically, we examine if ICT education specially designed for AIC residents results in more positive attitudes towards ICTs and a perceived decrease in factors that may limit or prevent computer and Internet use. We discuss the implications of these results for enhancing the quality of life for older adults in AICs and make recommendations for those seeking to decrease digital inequality among older adults in these communities through their own ICT classes. PMID:24244065

Evaluation of the best fit distribution for partial duration series of daily rainfall in Madinah, western Saudi Arabia

NASA Astrophysics Data System (ADS)

Alahmadi, F.; Rahman, N. A.; Abdulrazzak, M.

2014-09-01

Rainfall frequency analysis is an essential tool for the design of water related infrastructure. It can be used to predict future flood magnitudes for a given magnitude and frequency of extreme rainfall events. This study analyses the application of rainfall partial duration series (PDS) in the vast growing urban Madinah city located in the western part of Saudi Arabia. Different statistical distributions were applied (i.e. Normal, Log Normal, Extreme Value type I, Generalized Extreme Value, Pearson Type III, Log Pearson Type III) and their distribution parameters were estimated using L-moments methods. Also, different selection criteria models are applied, e.g. Akaike Information Criterion (AIC), Corrected Akaike Information Criterion (AICc), Bayesian Information Criterion (BIC) and Anderson-Darling Criterion (ADC). The analysis indicated the advantage of Generalized Extreme Value as the best fit statistical distribution for Madinah partial duration daily rainfall series. The outcome of such an evaluation can contribute toward better design criteria for flood management, especially flood protection measures.

External prognostic validations and comparisons of age- and gender-adjusted exercise capacity predictions.

PubMed

Kim, Esther S H; Ishwaran, Hemant; Blackstone, Eugene; Lauer, Michael S

2007-11-06

The purpose of this study was to externally validate the prognostic value of age- and gender-based nomograms and categorical definitions of impaired exercise capacity (EC). Exercise capacity predicts death, but its use in routine clinical practice is hampered by its close correlation with age and gender. For a median of 5 years, we followed 22,275 patients without known heart disease who underwent symptom-limited stress testing. Models for predicted or impaired EC were identified by literature search. Gender-specific multivariable proportional hazards models were constructed. Four methods were used to assess validity: Akaike Information Criterion (AIC), right-censored c-index in 100 out-of-bootstrap samples, the Nagelkerke Index R2, and calculation of calibration error in 100 bootstrap samples. There were 646 and 430 deaths in 13,098 men and 9,177 women, respectively. Of the 7 models tested in men, a model based on a Veterans Affairs cohort (predicted metabolic equivalents [METs] = 18 - [0.15 x age]) had the highest AIC and R2. In women, a model based on the St. James Take Heart Project (predicted METs = 14.7 - [0.13 x age]) performed best. Categorical definitions of fitness performed less well. Even after accounting for age and gender, there was still an important interaction with age, whereby predicted EC was a weaker predictor in older subjects (p for interaction <0.001 in men and 0.003 in women). Several methods describe EC accounting for age and gender-related differences, but their ability to predict mortality differ. Simple cutoff values fail to fully describe EC's strong predictive value.

Periodicity analysis of tourist arrivals to Banda Aceh using smoothing SARIMA approach

NASA Astrophysics Data System (ADS)

Miftahuddin, Helida, Desri; Sofyan, Hizir

2017-11-01

Forecasting the number of tourist arrivals who enters a region is needed for tourism businesses, economic and industrial policies, so that the statistical modeling needs to be conducted. Banda Aceh is the capital of Aceh province more economic activity is driven by the services sector, one of which is the tourism sector. Therefore, the prediction of the number of tourist arrivals is needed to develop further policies. The identification results indicate that the data arrival of foreign tourists to Banda Aceh to contain the trend and seasonal nature. Allegedly, the number of arrivals is influenced by external factors, such as economics, politics, and the holiday season caused the structural break in the data. Trend patterns are detected by using polynomial regression with quadratic and cubic approaches, while seasonal is detected by a periodic regression polynomial with quadratic and cubic approach. To model the data that has seasonal effects, one of the statistical methods that can be used is SARIMA (Seasonal Autoregressive Integrated Moving Average). The results showed that the smoothing, a method to detect the trend pattern is cubic polynomial regression approach, with the modified model and the multiplicative periodicity of 12 months. The AIC value obtained was 70.52. While the method for detecting the seasonal pattern is a periodic regression polynomial cubic approach, with the modified model and the multiplicative periodicity of 12 months. The AIC value obtained was 73.37. Furthermore, the best model to predict the number of foreign tourist arrivals to Banda Aceh in 2017 to 2018 is SARIMA (0,1,1)(1,1,0) with MAPE is 26%.

Modelling road accidents: An approach using structural time series

NASA Astrophysics Data System (ADS)

Junus, Noor Wahida Md; Ismail, Mohd Tahir

2014-09-01

In this paper, the trend of road accidents in Malaysia for the years 2001 until 2012 was modelled using a structural time series approach. The structural time series model was identified using a stepwise method, and the residuals for each model were tested. The best-fitted model was chosen based on the smallest Akaike Information Criterion (AIC) and prediction error variance. In order to check the quality of the model, a data validation procedure was performed by predicting the monthly number of road accidents for the year 2012. Results indicate that the best specification of the structural time series model to represent road accidents is the local level with a seasonal model.

Reproductive maturation and senescence in the female brown bear

USGS Publications Warehouse

Schwartz, Charles C.; Keating, Kim A.; Reynolds III, Harry V.; Barnes, Victor G.; Sellers, Richard A.; Swenson, J.E.; Miller, Sterling D.; McLellan, B.N.; Keay, Jeffrey A.; McCann, Robert; Gibeau, Michael; Wakkinen, Wayne F.; Mace, Richard D.; Kasworm, Wayne; Smith, Rodger; Herrero, Steven

2003-01-01

Changes in age-specific reproductive rates can have important implications for managing populations, but the number of female brown (grizzly) bears (Ursus arctos) observed in any one study is usually inadequate to quantify such patterns, especially for older females and in hunted areas. We examined patterns of reproductive maturation and senescence in female brown bears by combining data from 20 study areas from Sweden, Alaska, Canada, and the continental United States. We assessed reproductive performance based on 4,726 radiocollared years for free-ranging female brown bears (age 3); 482 of these were for bears 20 years of age. We modeled age-specific probability of litter production using extreme value distributions to describe probabilities for young- and old-age classes, and a power distribution function to describe probabilities for prime-aged animals. We then fit 4 models to pooled observations from our 20 study areas. We used Akaike’s Information Criterion (AIC) to select the best model. Inflection points suggest that major shifts in litter production occur at 4–5 and 28–29 years of age. The estimated model asymptote (0.332, 95% CI ¼ 0.319–0.344) was consistent with the expected reproductive cycle of a cub litter every 3 years (0.333). We discuss assumptions and biases in data collection relative to the shape of the model curve. Our results conform to senescence theory and suggest that female age structure in contemporary brown bear populations is considerably younger than would be expected in the absence of modern man. This implies that selective pressures today differ from those that influenced brown bear evolution.

Mapping risk for nest predation on a barrier island

USGS Publications Warehouse

Hackney, Amanda D.; Baldwin, Robert F.; Jodice, Patrick G.R.

2013-01-01

Barrier islands and coastal beach systems provide nesting habitat for marine and estuarine turtles. Densely settled coastal areas may subsidize nest predators. Our purpose was to inform conservation by providing a greater understanding of habitat-based risk factors for nest predation, for an estuarine turtle. We expected that habitat conditions at predated nests would differ from random locations at two spatial extents. We developed and validated an island-wide model for the distribution of predated Diamondback terrapin nests using locations of 198 predated nests collected during exhaustive searches at Fisherman Island National Wildlife Refuge, USA. We used aerial photographs to identify all areas of possible nesting habitat and searched each and surrounding environments for nests, collecting location and random-point microhabitat data. We built models for the probability of finding a predated nest using an equal number of random points and validated them with a reserve set (N = 67). Five variables in 9 a priori models were used and the best selected model (AIC weight 0.98) reflected positive associations with sand patches near marshes and roadways. Model validation had an average capture rate of predated nests of 84.14 % (26.17–97.38 %, Q1 77.53 %, median 88.07 %, Q3 95.08 %). Microhabitat selection results suggest that nests placed at the edges of sand patches adjacent to upland shrub/forest and marsh systems are vulnerable to predation. Forests and marshes provide cover and alternative resources for predators and roadways provide access; a suggestion is to focus nest protection efforts on the edges of dunes, near dense vegetation and roads.

Using electroretinograms and multi-model inference to identify spectral classes of photoreceptors and relative opsin expression levels.

PubMed

Lessios, Nicolas

2017-01-01

Understanding how individual photoreceptor cells factor in the spectral sensitivity of a visual system is essential to explain how they contribute to the visual ecology of the animal in question. Existing methods that model the absorption of visual pigments use templates which correspond closely to data from thin cross-sections of photoreceptor cells. However, few modeling approaches use a single framework to incorporate physical parameters of real photoreceptors, which can be fused, and can form vertical tiers. Akaike's information criterion (AIC c ) was used here to select absorptance models of multiple classes of photoreceptor cells that maximize information, given visual system spectral sensitivity data obtained using extracellular electroretinograms and structural parameters obtained by histological methods. This framework was first used to select among alternative hypotheses of photoreceptor number. It identified spectral classes from a range of dark-adapted visual systems which have between one and four spectral photoreceptor classes. These were the velvet worm, Principapillatus hitoyensis , the branchiopod water flea, Daphnia magna , normal humans, and humans with enhanced S-cone syndrome, a condition in which S-cone frequency is increased due to mutations in a transcription factor that controls photoreceptor expression. Data from the Asian swallowtail, Papilio xuthus , which has at least five main spectral photoreceptor classes in its compound eyes, were included to illustrate potential effects of model over-simplification on multi-model inference. The multi-model framework was then used with parameters of spectral photoreceptor classes and the structural photoreceptor array kept constant. The goal was to map relative opsin expression to visual pigment concentration. It identified relative opsin expression differences for two populations of the bluefin killifish, Lucania goodei . The modeling approach presented here will be useful in selecting the most likely alternative hypotheses of opsin-based spectral photoreceptor classes, using relative opsin expression and extracellular electroretinography.

RADARSAT: The Antarctic Mapping Project

NASA Technical Reports Server (NTRS)

Jezek, Kenneth C.; Lindstrom, E. (Technical Monitor)

2002-01-01

The first Antarctic Imaging Campaign (AIC) occurred during the period September 9, 1997 through October 20, 1997. The AIC utilized the unique attributes of the Canadian RADARSAT-1 to acquire the first, high-resolution, synthetic aperture imagery covering the entire Antarctic Continent. Although the primary goal of the mission was the acquisition of image data, the nearly flawless execution of the mission enabled additional collections of exact repeat orbit data. These data, covering an extensive portion of the interior Antarctic, potentially are suitable for interferometric analysis of topography and surface velocity. This document summarizes the Project through completion with delivery of products to the NASA DAACs.

Pacemaker mediated tachycardia as a complication of the autointrinsic conduction search function.

PubMed

Dennis, Malcolm J; Sparks, Paul B

2004-06-01

The autointrinsic conduction search (AICS) option, featured on some DDD pacemakers, performs periodic assessments of atrioventricular (AV) conduction capability during a single beat AV delay extension. Demonstration of ventricular conduction during the prolonged AV delay, permits ongoing AV delay extension if the patient's intrinsic conduction is preferred to ventricular pacing. A case is presented where the wide separation of atrial and ventricular pacing during the conduction search permitted retrograde ventriculoatrial conduction, precipitating pacemaker mediated tachycardia (PMT) on seven occasions in one patient. Two onset patterns are reported, both attributable to the AICS option. Recommendations for prevention strategies are made.

Possibility of modifying the growth trajectory in Raeini Cashmere goat.

PubMed

Ghiasi, Heydar; Mokhtari, M S

2018-03-27

The objective of this study was to investigate the possibility of modifying the growth trajectory in Raeini Cashmere goat breed. In total, 13,193 records on live body weight collected from 4788 Raeini Cashmere goats were used. According to Akanke's information criterion (AIC), the sing-trait random regression model included fourth-order Legendre polynomial for direct and maternal genetic effect; maternal and individual permanent environmental effect was the best model for estimating (co)variance components. The matrices of eigenvectors for (co)variances between random regression coefficients of direct additive genetic were used to calculate eigenfunctions, and different eigenvector indices were also constructed. The obtained results showed that the first eigenvalue explained 79.90% of total genetic variance. Therefore, changing the body weights applying the first eigenfunction will be obtained rapidly. Selection based on the first eigenvector will cause favorable positive genetic gains for all body weight considered from birth to 12 months of age. For modifying the growth trajectory in Raeini Cashmere goat, the selection should be based on the second eigenfunction. The second eigenvalue accounted for 14.41% of total genetic variance for body weights that is low in comparison with genetic variance explained by the first eigenvalue. The complex patterns of genetic change in growth trajectory observed under the third and fourth eigenfunction and low amount of genetic variance explained by the third and fourth eigenvalues.

Dynamic PET of human liver inflammation: impact of kinetic modeling with optimization-derived dual-blood input function.

PubMed

Wang, Guobao; Corwin, Michael T; Olson, Kristin A; Badawi, Ramsey D; Sarkar, Souvik

2018-05-30

The hallmark of nonalcoholic steatohepatitis is hepatocellular inflammation and injury in the setting of hepatic steatosis. Recent work has indicated that dynamic 18F-FDG PET with kinetic modeling has the potential to assess hepatic inflammation noninvasively, while static FDG-PET did not show a promise. Because the liver has dual blood supplies, kinetic modeling of dynamic liver PET data is challenging in human studies. The objective of this study is to evaluate and identify a dual-input kinetic modeling approach for dynamic FDG-PET of human liver inflammation. Fourteen human patients with nonalcoholic fatty liver disease were included in the study. Each patient underwent one-hour dynamic FDG-PET/CT scan and had liver biopsy within six weeks. Three models were tested for kinetic analysis: traditional two-tissue compartmental model with an image-derived single-blood input function (SBIF), model with population-based dual-blood input function (DBIF), and modified model with optimization-derived DBIF through a joint estimation framework. The three models were compared using Akaike information criterion (AIC), F test and histopathologic inflammation reference. The results showed that the optimization-derived DBIF model improved the fitting of liver time activity curves and achieved lower AIC values and higher F values than the SBIF and population-based DBIF models in all patients. The optimization-derived model significantly increased FDG K1 estimates by 101% and 27% as compared with traditional SBIF and population-based DBIF. K1 by the optimization-derived model was significantly associated with histopathologic grades of liver inflammation while the other two models did not provide a statistical significance. In conclusion, modeling of DBIF is critical for kinetic analysis of dynamic liver FDG-PET data in human studies. The optimization-derived DBIF model is more appropriate than SBIF and population-based DBIF for dynamic FDG-PET of liver inflammation. © 2018 Institute of Physics and Engineering in Medicine.

GABA-Mediated Inactivation of Medial Prefrontal and Agranular Insular Cortex in the Rat: Contrasting Effects on Hunger- and Palatability-Driven Feeding

PubMed Central

Baldo, Brian A; Spencer, Robert C; Sadeghian, Ken; Mena, Jesus D

2016-01-01

A microanalysis of hunger-driven and palatability-driven feeding was carried out after muscimol-mediated inactivation of two frontal regions in rats, the agranular/dysgranular insular cortex (AIC) and the ventromedial prefrontal cortex (vmPFC). Food and water intake, feeding microstructure, and general motor activity were measured under two motivational conditions: food-deprived rats given standard chow or ad libitum-fed rats given a palatable chocolate shake. Muscimol infusions into the AIC diminished intake, total feeding duration, and average feeding bout duration for the palatable-food condition only but failed to alter exploratory-like behavior (ambulation or rearing). In contrast, intra-vmPFC muscimol infusions did not alter the overall intake of chow or chocolate shake. However, these infusions markedly increased mean feeding bout duration for both food types and produced a modest but significant reduction of exploratory-like behavior. The lengthening of feeding-bout duration and reduction in rearing were mimicked by intra-vmPFC blockade of AMPA-type but not NMDA-type glutamate receptors. Neither water consumption nor the microstructure of water drinking was affected by inactivation of either site. These results indicate a regional heterogeneity in frontal control of feeding behavior. Neural processing in AIC supports palatability-driven feeding but is not necessary for intake of a standard food under a food-restriction condition, whereas ventromedial prefrontal cortex, and AMPA signaling therein, modulates the duration of individual feeding bouts regardless of motivational context. Results are discussed in the context of regionally heterogeneous frontal modulation of two distinct components of feeding behavior: reward valuation based upon taste perception (AIC) vs switching between ingestive and non-ingestive (eg, exploratory-like) behavioral repertoires (vmPFC). PMID:26202102

GABA-Mediated Inactivation of Medial Prefrontal and Agranular Insular Cortex in the Rat: Contrasting Effects on Hunger- and Palatability-Driven Feeding.

PubMed

Baldo, Brian A; Spencer, Robert C; Sadeghian, Ken; Mena, Jesus D

2016-03-01

A microanalysis of hunger-driven and palatability-driven feeding was carried out after muscimol-mediated inactivation of two frontal regions in rats, the agranular/dysgranular insular cortex (AIC) and the ventromedial prefrontal cortex (vmPFC). Food and water intake, feeding microstructure, and general motor activity were measured under two motivational conditions: food-deprived rats given standard chow or ad libitum-fed rats given a palatable chocolate shake. Muscimol infusions into the AIC diminished intake, total feeding duration, and average feeding bout duration for the palatable-food condition only but failed to alter exploratory-like behavior (ambulation or rearing). In contrast, intra-vmPFC muscimol infusions did not alter the overall intake of chow or chocolate shake. However, these infusions markedly increased mean feeding bout duration for both food types and produced a modest but significant reduction of exploratory-like behavior. The lengthening of feeding-bout duration and reduction in rearing were mimicked by intra-vmPFC blockade of AMPA-type but not NMDA-type glutamate receptors. Neither water consumption nor the microstructure of water drinking was affected by inactivation of either site. These results indicate a regional heterogeneity in frontal control of feeding behavior. Neural processing in AIC supports palatability-driven feeding but is not necessary for intake of a standard food under a food-restriction condition, whereas ventromedial prefrontal cortex, and AMPA signaling therein, modulates the duration of individual feeding bouts regardless of motivational context. Results are discussed in the context of regionally heterogeneous frontal modulation of two distinct components of feeding behavior: reward valuation based upon taste perception (AIC) vs switching between ingestive and non-ingestive (eg, exploratory-like) behavioral repertoires (vmPFC).

Rationale and Design of the SENECA (StEm cell iNjECtion in cAncer survivors) Trial.

PubMed

Bolli, Roberto; Hare, Joshua M; Henry, Timothy D; Lenneman, Carrie G; March, Keith L; Miller, Kathy; Pepine, Carl J; Perin, Emerson C; Traverse, Jay H; Willerson, James T; Yang, Phillip C; Gee, Adrian P; Lima, João A; Moyé, Lem; Vojvodic, Rachel W; Sayre, Shelly L; Bettencourt, Judy; Cohen, Michelle; Ebert, Ray F; Simari, Robert D

2018-07-01

SENECA (StEm cell iNjECtion in cAncer survivors) is a phase I, randomized, double-blind, placebo-controlled study to evaluate the safety and feasibility of delivering allogeneic mesenchymal stromal cells (allo-MSCs) transendocardially in subjects with anthracycline-induced cardiomyopathy (AIC). AIC is an incurable and often fatal syndrome, with a prognosis worse than that of ischemic or nonischemic cardiomyopathy. Recently, cell therapy with MSCs has emerged as a promising new approach to repair damaged myocardium. The study population is 36 cancer survivors with a diagnosis of AIC, left ventricular (LV) ejection fraction ≤40%, and symptoms of heart failure (NYHA class II-III) on optimally-tolerated medical therapy. Subjects must be clinically free of cancer for at least two years with a ≤ 30% estimated five-year risk of recurrence. The first six subjects participated in an open-label, lead-in phase and received 100 million allo-MSCs; the remaining 30 will be randomized 1:1 to receive allo-MSCs or vehicle via 20 transendocardial injections. Efficacy measures (obtained at baseline, 6 months, and 12 months) include MRI evaluation of LV function, LV volumes, fibrosis, and scar burden; assessment of exercise tolerance (six-minute walk test) and quality of life (Minnesota Living with Heart Failure Questionnaire); clinical outcomes (MACE and cumulative days alive and out of hospital); and biomarkers of heart failure (NT-proBNP). This is the first clinical trial using direct cardiac injection of cells for the treatment of AIC. If administration of allo-MSCs is found feasible and safe, SENECA will pave the way for larger phase II/III studies with therapeutic efficacy as the primary outcome. Copyright © 2018. Published by Elsevier Inc.

Seasonality and Trend Forecasting of Tuberculosis Prevalence Data in Eastern Cape, South Africa, Using a Hybrid Model.

PubMed

Azeez, Adeboye; Obaromi, Davies; Odeyemi, Akinwumi; Ndege, James; Muntabayi, Ruffin

2016-07-26

Tuberculosis (TB) is a deadly infectious disease caused by Mycobacteria tuberculosis. Tuberculosis as a chronic and highly infectious disease is prevalent in almost every part of the globe. More than 95% of TB mortality occurs in low/middle income countries. In 2014, approximately 10 million people were diagnosed with active TB and two million died from the disease. In this study, our aim is to compare the predictive powers of the seasonal autoregressive integrated moving average (SARIMA) and neural network auto-regression (SARIMA-NNAR) models of TB incidence and analyse its seasonality in South Africa. TB incidence cases data from January 2010 to December 2015 were extracted from the Eastern Cape Health facility report of the electronic Tuberculosis Register (ERT.Net). A SARIMA model and a combined model of SARIMA model and a neural network auto-regression (SARIMA-NNAR) model were used in analysing and predicting the TB data from 2010 to 2015. Simulation performance parameters of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean percent error (MPE), mean absolute scaled error (MASE) and mean absolute percentage error (MAPE) were applied to assess the better performance of prediction between the models. Though practically, both models could predict TB incidence, the combined model displayed better performance. For the combined model, the Akaike information criterion (AIC), second-order AIC (AICc) and Bayesian information criterion (BIC) are 288.56, 308.31 and 299.09 respectively, which were lower than the SARIMA model with corresponding values of 329.02, 327.20 and 341.99, respectively. The seasonality trend of TB incidence was forecast to have a slightly increased seasonal TB incidence trend from the SARIMA-NNAR model compared to the single model. The combined model indicated a better TB incidence forecasting with a lower AICc. The model also indicates the need for resolute intervention to reduce infectious disease transmission with co-infection with HIV and other concomitant diseases, and also at festival peak periods.

Age and growth of the round stingray Urotrygon rogersi, a particularly fast-growing and short-lived elasmobranch.

PubMed

Mejía-Falla, Paola A; Cortés, Enric; Navia, Andrés F; Zapata, Fernando A

2014-01-01

We examined the age and growth of Urotrygon rogersi on the Colombian coast of the Eastern Tropical Pacific Ocean by directly estimating age using vertebral centra. We verified annual deposition of growth increments with marginal increment analysis. Eight growth curves were fitted to four data sets defined on the basis of the reproductive cycle (unadjusted or adjusted for age at first band) and size variables (disc width or total length). Model performance was evaluated using Akaike's Information Criterion (AIC), AIC weights and multi-model inference criteria. A two-phase growth function with adjusted age provided the best description of growth for females (based on five parameters, DW∞  =  20.1 cm, k  =  0.22 yr⁻¹) and males (based on four and five parameters, DW(∞)  =  15.5 cm, k  =  0.65 yr⁻¹). Median maturity of female and male U. rogersi is reached very fast (mean ± SE  =  1.0 ± 0.1 year). This is the first age and growth study for a species of the genus Urotrygon and results indicate that U. rogersi attains a smaller maximum size and has a shorter lifespan and lower median age at maturity than species of closely related genera. These life history traits are in contrast with those typically reported for other elasmobranchs.

Mathematical Abstraction: Constructing Concept of Parallel Coordinates

NASA Astrophysics Data System (ADS)

Nurhasanah, F.; Kusumah, Y. S.; Sabandar, J.; Suryadi, D.

2017-09-01

Mathematical abstraction is an important process in teaching and learning mathematics so pre-service mathematics teachers need to understand and experience this process. One of the theoretical-methodological frameworks for studying this process is Abstraction in Context (AiC). Based on this framework, abstraction process comprises of observable epistemic actions, Recognition, Building-With, Construction, and Consolidation called as RBC + C model. This study investigates and analyzes how pre-service mathematics teachers constructed and consolidated concept of Parallel Coordinates in a group discussion. It uses AiC framework for analyzing mathematical abstraction of a group of pre-service teachers consisted of four students in learning Parallel Coordinates concepts. The data were collected through video recording, students’ worksheet, test, and field notes. The result shows that the students’ prior knowledge related to concept of the Cartesian coordinate has significant role in the process of constructing Parallel Coordinates concept as a new knowledge. The consolidation process is influenced by the social interaction between group members. The abstraction process taken place in this group were dominated by empirical abstraction that emphasizes on the aspect of identifying characteristic of manipulated or imagined object during the process of recognizing and building-with.

Interaction between the macrophage system and IgA immune complexes in IgA nephropathy.

PubMed

Roccatello, D; Coppo, R; Basolo, B; Martina, G; Rollino, C; Cordonnier, D; Busquet, G; Picciotto, G; Sena, L M; Piccoli, G

1983-01-01

In nine patients with IgA nephropathy, the function of the mononuclear phagocyte system was assessed by measuring in vivo clearance of anti-D coated red blood cells (RBC) and in vitro phagocytosis of sensitised RBC by monocytes. A strict correlation was found between in vivo macrophage function and in vitro monocyte phagocytosis. Statistical correlations were also found between in vivo clearance values and IgAIC and C3d values. A defective macrophage and monocyte function affects patients with major signs of clinical activity, highest IgAIC values, signs of complement activation and the most unfavourable clinical course.

Does Trabecular Bone Score (TBS) improve the predictive ability of FRAX® for major osteoporotic fractures according to the Japanese Population-Based Osteoporosis (JPOS) cohort study?

PubMed

Tamaki, Junko; Iki, Masayuki; Sato, Yuho; Winzenrieth, Renaud; Kajita, Etsuko; Kagamimori, Sadanobu

2018-02-21

This study examined whether bone microarchitecture determined by Trabecular Bone Score (TBS) is associated with the risk of major osteoporotic fractures independent of FRAX ® in Japanese women. Participants included 1541 women aged ≥ 40 at baseline. Major osteoporotic fractures during a 10-year follow-up period were documented by the Japanese Population-based Osteoporosis Cohort Study. TBS and areal bone mineral density (aBMD) were calculated for the same spinal regions at baseline. To compare the predictive ability of FRAX ® model when used alone versus in combination with TBS, Akaike information criterion (AIC), the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were calculated. We identified 67 events of major osteoporotic fractures. The skeletal sites of the first fracture event were as follows: hip (11), vertebrae (13), radius (42), and humerus (1). The model incorporating FRAX ® [1.35 (95% CI 1.09-1.67) for 1 standard deviation (SD) increase] with TBS [1.46 (95% CI 1.08-1.98) for 1 SD decrease] demonstrated better fit compared to a model consisting of FRAX alone (AIC 528.6 vs 532.7). NRI values for classification accuracy showed significant improvements in the FRAX ® and TBS model, as compared to FRAX ® alone [0.299 (95% CI 0.056-0.541)]. However, there were no significant differences in AUC or IDI between these models. The TBS score is associated with a risk of major osteoporotic fracture independent of FRAX ® score obtained with or without BMD values among Japanese women during a 10-year follow-up period.

Can an electronic glycaemic notebook associated with an insulin calculator improve HbA1c in diabetic patients on a multiple insulin injections regimen? A 26-week observational real-life study.

PubMed

Oriot, Philippe; Ponchon, Michel; Hermans, Michel P

2016-02-01

Automated insulin calculators (AICs) with carbohydrate counting (CHC) have been shown to be effective in improving glycated haemoglobin (HbA1c) levels. By contrast, use of AICs without CHC, with predetermined prandial insulin doses modified according to a correction factor and modulated as a function of glycaemia, has not yet been investigated. This comparative, retrospective, observational and non-randomized study took place over a 6-month period of routine clinical practice. It evaluated the use of Free-style InsuLinx® and Free-style Neo® Abbott Diabetes Care (AIC) in easy mode (no CHC). All patients performed a basal-prandial insulin dosing schedule, and were not educated as to how to determine carbohydrate intake. Changes in HbA1c and capillary blood glucose levels, insulin therapy, frequency of blood glucose tests and body weight were analyzed 6 months prior to inclusion (T-6), at the time of inclusion (T0) and 6 months later (T+6). From T-6 to T0 (period A), patients used a standard blood glucose meter and adjusted their insulin doses themselves, and from T0 to T+6 (period B), each patient was provided with an AIC on easy mode function. Of the 230 patients, 221 were retained at the end of the study (126 type 1 diabetes mellitus (T1DM) and 95 type 2 diabetes mellitus (T2DM)). At T-6, average (±standard error of mean) HbA1c level was 8.3 ± 0.1%; T1DM: 8.5 ± 0.1% and T2DM: 8.0 ± 0.1%, respectively. At T0, the average HbA1c level was 8.4 ± 0.1% (p = 0.02); T1DM: 8.5 ± 0.1% (ns) and T2DM: 8.2 ± 0.1% (p = 0.004). At T+6, with AIC in easy mode, average HbA1c level decreased significantly to 7.7 ± 0.1% (p < 0.0001); T1DM: 8.0 ± 0.1% (p < 0.0001) and T2DM: 7.5 ± 0.1% (p < 0.0001). At T+6, in all diabetics, blood glucose monitoring frequency increased by 0.4/day (p < 0.0001). Insulin correction amounted to 14% of changes in predetermined prandial insulin doses. Routine clinical use of an AIC without CHC improved self-management of blood glucose and on average, decreased HbA1c levels by 0.52% in T1DM and 0.80% in T2DM after 6 months.

An Integrated Model of Emotional Problems, Beta Power of Electroencephalography, and Low Frequency of Heart Rate Variability after Childhood Trauma in a Non-Clinical Sample: A Path Analysis Study.

PubMed

Jin, Min Jin; Kim, Ji Sun; Kim, Sungkean; Hyun, Myoung Ho; Lee, Seung-Hwan

2017-01-01

Childhood trauma is known to be related to emotional problems, quantitative electroencephalography (EEG) indices, and heart rate variability (HRV) indices in adulthood, whereas directions among these factors have not been reported yet. This study aimed to evaluate pathway models in young and healthy adults: (1) one with physiological factors first and emotional problems later in adulthood as results of childhood trauma and (2) one with emotional problems first and physiological factors later. A total of 103 non-clinical volunteers were included. Self-reported psychological scales, including the Childhood Trauma Questionnaire (CTQ), State-Trait Anxiety Inventory, Beck Depression Inventory, and Affective Lability Scale were administered. For physiological evaluation, EEG record was performed during resting eyes closed condition in addition to the resting-state HRV, and the quantitative power analyses of eight EEG bands and three HRV components were calculated in the frequency domain. After a normality test, Pearson's correlation analysis to make path models and path analyses to examine them were conducted. The CTQ score was significantly correlated with depression, state and trait anxiety, affective lability, and HRV low-frequency (LF) power. LF power was associated with beta2 (18-22 Hz) power that was related to affective lability. Affective lability was associated with state anxiety, trait anxiety, and depression. Based on the correlation and the hypothesis, two models were composed: a model with pathways from CTQ score to affective lability, and a model with pathways from CTQ score to LF power. The second model showed significantly better fit than the first model (AIC model1  = 63.403 > AIC model2  = 46.003), which revealed that child trauma could affect emotion, and then physiology. The specific directions of relationships among emotions, the EEG, and HRV in adulthood after childhood trauma was discussed.

Pharmacokinetic Modeling of Intranasal Scopolamine in Plasma Saliva and Urine

NASA Technical Reports Server (NTRS)

Wu, L.; Chow, D. S. L.; Tam, V.; Putcha, L.

2014-01-01

An intranasal gel formulation of scopolamine (INSCOP) was developed for the treatment of Space Motion Sickness. The bioavailability and pharmacokinetics (PK) were evaluated under the Food and Drug Administration guidelines for clinical trials for an Investigative New Drug (IND). The aim of this project was to develop a PK model that can predict the relationship between plasma, saliva and urinary scopolamine concentrations using data collected from the IND clinical trial with INSCOP. METHODS: Twelve healthy human subjects were administered three dose levels (0.1, 0.2 and 0.4 mg) of INSCOP. Serial blood, saliva and urine samples were collected between 5 min to 24 h after dosing and scopolamine concentrations measured by using a validated LC-MS-MS assay. Pharmacokinetic Compartmental models, using actual dosing and sampling times, were built using Phoenix (version 1.2). Model discrimination was performed, by minimizing the Akaike Information Criteria (AIC), maximizing the coefficient of determination (r²) and by comparison of the quality of fit plots. RESULTS: The best structural model to describe scopolamine disposition after INSCOP administration (minimal AIC =907.2) consisted of one compartment for plasma, saliva and urine respectively that were inter-connected with different rate constants. The estimated values of PK parameters were compiled in Table 1. The model fitting exercises revealed a nonlinear PK for scopolamine between plasma and saliva compartments for K21, Vmax and Km. CONCLUSION: PK model for INSCOP was developed and for the first time it satisfactorily predicted the PK of scopolamine in plasma, saliva and urine after INSCOP administration. Using non-linear PK yielded the best structural model to describe scopolamine disposition between plasma and saliva compartments, and inclusion of non-linear PK resulted in a significant improved model fitting. The model can be utilized to predict scopolamine plasma concentration using saliva and/or urine data that allows non-invasive assessment of pharmacotherapeutics of scopolamine in space and other remote environments without requiring blood sampling.

Ensemble classification of individual Pinus crowns from multispectral satellite imagery and airborne LiDAR

NASA Astrophysics Data System (ADS)

Kukunda, Collins B.; Duque-Lazo, Joaquín; González-Ferreiro, Eduardo; Thaden, Hauke; Kleinn, Christoph

2018-03-01

Distinguishing tree species is relevant in many contexts of remote sensing assisted forest inventory. Accurate tree species maps support management and conservation planning, pest and disease control and biomass estimation. This study evaluated the performance of applying ensemble techniques with the goal of automatically distinguishing Pinus sylvestris L. and Pinus uncinata Mill. Ex Mirb within a 1.3 km2 mountainous area in Barcelonnette (France). Three modelling schemes were examined, based on: (1) high-density LiDAR data (160 returns m-2), (2) Worldview-2 multispectral imagery, and (3) Worldview-2 and LiDAR in combination. Variables related to the crown structure and height of individual trees were extracted from the normalized LiDAR point cloud at individual-tree level, after performing individual tree crown (ITC) delineation. Vegetation indices and the Haralick texture indices were derived from Worldview-2 images and served as independent spectral variables. Selection of the best predictor subset was done after a comparison of three variable selection procedures: (1) Random Forests with cross validation (AUCRFcv), (2) Akaike Information Criterion (AIC) and (3) Bayesian Information Criterion (BIC). To classify the species, 9 regression techniques were combined using ensemble models. Predictions were evaluated using cross validation and an independent dataset. Integration of datasets and models improved individual tree species classification (True Skills Statistic, TSS; from 0.67 to 0.81) over individual techniques and maintained strong predictive power (Relative Operating Characteristic, ROC = 0.91). Assemblage of regression models and integration of the datasets provided more reliable species distribution maps and associated tree-scale mapping uncertainties. Our study highlights the potential of model and data assemblage at improving species classifications needed in present-day forest planning and management.

Adaptive Resource Utilization Prediction System for Infrastructure as a Service Cloud.

PubMed

Zia Ullah, Qazi; Hassan, Shahzad; Khan, Gul Muhammad

2017-01-01

Infrastructure as a Service (IaaS) cloud provides resources as a service from a pool of compute, network, and storage resources. Cloud providers can manage their resource usage by knowing future usage demand from the current and past usage patterns of resources. Resource usage prediction is of great importance for dynamic scaling of cloud resources to achieve efficiency in terms of cost and energy consumption while keeping quality of service. The purpose of this paper is to present a real-time resource usage prediction system. The system takes real-time utilization of resources and feeds utilization values into several buffers based on the type of resources and time span size. Buffers are read by R language based statistical system. These buffers' data are checked to determine whether their data follows Gaussian distribution or not. In case of following Gaussian distribution, Autoregressive Integrated Moving Average (ARIMA) is applied; otherwise Autoregressive Neural Network (AR-NN) is applied. In ARIMA process, a model is selected based on minimum Akaike Information Criterion (AIC) values. Similarly, in AR-NN process, a network with the lowest Network Information Criterion (NIC) value is selected. We have evaluated our system with real traces of CPU utilization of an IaaS cloud of one hundred and twenty servers.

Adaptive Resource Utilization Prediction System for Infrastructure as a Service Cloud

PubMed Central

Hassan, Shahzad; Khan, Gul Muhammad

2017-01-01

Infrastructure as a Service (IaaS) cloud provides resources as a service from a pool of compute, network, and storage resources. Cloud providers can manage their resource usage by knowing future usage demand from the current and past usage patterns of resources. Resource usage prediction is of great importance for dynamic scaling of cloud resources to achieve efficiency in terms of cost and energy consumption while keeping quality of service. The purpose of this paper is to present a real-time resource usage prediction system. The system takes real-time utilization of resources and feeds utilization values into several buffers based on the type of resources and time span size. Buffers are read by R language based statistical system. These buffers' data are checked to determine whether their data follows Gaussian distribution or not. In case of following Gaussian distribution, Autoregressive Integrated Moving Average (ARIMA) is applied; otherwise Autoregressive Neural Network (AR-NN) is applied. In ARIMA process, a model is selected based on minimum Akaike Information Criterion (AIC) values. Similarly, in AR-NN process, a network with the lowest Network Information Criterion (NIC) value is selected. We have evaluated our system with real traces of CPU utilization of an IaaS cloud of one hundred and twenty servers. PMID:28811819

Change in BMI accurately predicted by social exposure to acquaintances.

PubMed

Oloritun, Rahman O; Ouarda, Taha B M J; Moturu, Sai; Madan, Anmol; Pentland, Alex Sandy; Khayal, Inas

2013-01-01

Research has mostly focused on obesity and not on processes of BMI change more generally, although these may be key factors that lead to obesity. Studies have suggested that obesity is affected by social ties. However these studies used survey based data collection techniques that may be biased toward select only close friends and relatives. In this study, mobile phone sensing techniques were used to routinely capture social interaction data in an undergraduate dorm. By automating the capture of social interaction data, the limitations of self-reported social exposure data are avoided. This study attempts to understand and develop a model that best describes the change in BMI using social interaction data. We evaluated a cohort of 42 college students in a co-located university dorm, automatically captured via mobile phones and survey based health-related information. We determined the most predictive variables for change in BMI using the least absolute shrinkage and selection operator (LASSO) method. The selected variables, with gender, healthy diet category, and ability to manage stress, were used to build multiple linear regression models that estimate the effect of exposure and individual factors on change in BMI. We identified the best model using Akaike Information Criterion (AIC) and R(2). This study found a model that explains 68% (p<0.0001) of the variation in change in BMI. The model combined social interaction data, especially from acquaintances, and personal health-related information to explain change in BMI. This is the first study taking into account both interactions with different levels of social interaction and personal health-related information. Social interactions with acquaintances accounted for more than half the variation in change in BMI. This suggests the importance of not only individual health information but also the significance of social interactions with people we are exposed to, even people we may not consider as close friends.

Numerical solution of non-linear dual-phase-lag bioheat transfer equation within skin tissues.

PubMed

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.

Azathioprine and 6-mercaptopurine (6-MP) suppress the human mixed lymphocyte reaction (MLR) by different mechanisms.

PubMed Central

Al-Safi, S A; Maddocks, J L

1984-01-01

6-MP inhibitory effects on the MLR were reversed by AIC (46%), adenine (32%), hypoxanthine (89%), adenosine (86%) and inosine (93%). AIC, adenine, hypoxanthine and inosine had no effect on azathioprine inhibition of the MLR. Adenosine at 10 microM caused 29% reversal and had no effect at 100-400 microM on azathioprine inhibition of the MLR. Reversal of 6-MP suppression of the MLR was decreased with the delay of adenosine addition. Guanine, xanthine and guanosine caused no reversal of 6-MP or azathioprine inhibitory effects on the MLR. These results show that azathioprine and 6-MP suppress the MLR by different mechanisms. PMID:6232936

Large forest patches promote breeding success of a terrestrial mammal in urban landscapes.

PubMed

Soga, Masashi; Koike, Shinsuke

2013-01-01

Despite a marked increase in the focus toward biodiversity conservation in fragmented landscapes, studies that confirm species breeding success are scarce and limited. In this paper, we asked whether local (area of forest patches) and landscape (amount of suitable habitat surrounding of focal patches) factors affect the breeding success of raccoon dogs (Nyctereutes procyonoides) in Tokyo, Central Japan. The breeding success of raccoon dogs is easy to judge as adults travel with pups during the breeding season. We selected 21 forest patches (3.3-797.8 ha) as study sites. In each forest patch, we used infra-red-triggered cameras for a total of 60 camera days per site. We inspected each photo to determine whether it was of an adult or a pup. Although we found adult raccoon dogs in all 21 forest patches, pups were found only in 13 patches. To estimate probability of occurrence and detection for raccoon in 21 forest fragments, we used single season site occupancy models in PRESENCE program. Model selection based on AIC and model averaging showed that the occupancy probability of pups was positively affected by patch area. This result suggests that large forests improve breeding success of raccoon dogs. A major reason for the low habitat value of small, isolated patches may be the low availability of food sources and the high risk of being killed on the roads in such areas. Understanding the effects of local and landscape parameters on species breeding success may help us to devise and implement effective long-term conservation and management plans.

Cox Regression Models with Functional Covariates for Survival Data.

PubMed

Gellar, Jonathan E; Colantuoni, Elizabeth; Needham, Dale M; Crainiceanu, Ciprian M

2015-06-01

We extend the Cox proportional hazards model to cases when the exposure is a densely sampled functional process, measured at baseline. The fundamental idea is to combine penalized signal regression with methods developed for mixed effects proportional hazards models. The model is fit by maximizing the penalized partial likelihood, with smoothing parameters estimated by a likelihood-based criterion such as AIC or EPIC. The model may be extended to allow for multiple functional predictors, time varying coefficients, and missing or unequally-spaced data. Methods were inspired by and applied to a study of the association between time to death after hospital discharge and daily measures of disease severity collected in the intensive care unit, among survivors of acute respiratory distress syndrome.

The Linear Predictability of Sea Level: A Benchmark

NASA Astrophysics Data System (ADS)

Sonnewald, M.; Wunsch, C.; Heimbach, P.

2016-12-01

A benchmark of linear predictive skill of global sea level is presented, complimenting more complicated model studies of future predictive skill. Sea level is of great socioeconomic interest, as most of the worlds population live by the sea. Currently, the spread in model projections suggests poor predictive skill outside the seasonal cycle. We use 20 years of data from the ECCOv4 state estimate (1992-2012), assessing the variance attributable to the seasons and the linear predictability potential of the deseasoned component of sea level. The Northern Hemisphere has large regions where the seasons make up >90% of the variance, particularly in the western boundary current regions and zonal bands along the equator. The deaseasoned sea level is more dominant in the Southern Hemisphere, particularly in the Southern Ocean. We treat the deseasoned sea level as a weakly stationary random process, whose predictability is given by the covariance structure. Fitting an ARMA(n,m) model, we choose the order using the Akaike and Bayesian Information Criteria (AIC and BIC). The AIC is more appropriate, with generally higher orders chosen and offering slightly more predictive accuracy. Monthly detrended data shows skill generally of the order of a few months, with isolated regions of twelve months or more. With the trend, the predictive skill increases, particularly in the South Pacific. We assess the annually averaged data, although our time-series is too short to assess the variability. There is some predictive skill, which is enhanced if the trend is not removed. A major caveat of our approach is that we test and train our model on the same dataset due to the short duration of available data.

[Application of ARIMA model on prediction of malaria incidence].

PubMed

Jing, Xia; Hua-Xun, Zhang; Wen, Lin; Su-Jian, Pei; Ling-Cong, Sun; Xiao-Rong, Dong; Mu-Min, Cao; Dong-Ni, Wu; Shunxiang, Cai

2016-01-29

To predict the incidence of local malaria of Hubei Province applying the Autoregressive Integrated Moving Average model (ARIMA). SPSS 13.0 software was applied to construct the ARIMA model based on the monthly local malaria incidence in Hubei Province from 2004 to 2009. The local malaria incidence data of 2010 were used for model validation and evaluation. The model of ARIMA (1, 1, 1) (1, 1, 0) 12 was tested as relatively the best optimal with the AIC of 76.085 and SBC of 84.395. All the actual incidence data were in the range of 95% CI of predicted value of the model. The prediction effect of the model was acceptable. The ARIMA model could effectively fit and predict the incidence of local malaria of Hubei Province.

Forecasting the number of zoonotic cutaneous leishmaniasis cases in south of Fars province, Iran using seasonal ARIMA time series method.

PubMed

Sharafi, Mehdi; Ghaem, Haleh; Tabatabaee, Hamid Reza; Faramarzi, Hossein

2017-01-01

To predict the trend of cutaneous leishmaniasis and assess the relationship between the disease trend and weather variables in south of Fars province using Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The trend of cutaneous leishmaniasis was predicted using Mini tab software and SARIMA model. Besides, information about the disease and weather conditions was collected monthly based on time series design during January 2010 to March 2016. Moreover, various SARIMA models were assessed and the best one was selected. Then, the model's fitness was evaluated based on normality of the residuals' distribution, correspondence between the fitted and real amounts, and calculation of Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC). The study results indicated that SARIMA model (4,1,4)(0,1,0) (12) in general and SARIMA model (4,1,4)(0,1,1) (12) in below and above 15 years age groups could appropriately predict the disease trend in the study area. Moreover, temperature with a three-month delay (lag3) increased the disease trend, rainfall with a four-month delay (lag4) decreased the disease trend, and rainfall with a nine-month delay (lag9) increased the disease trend. Based on the results, leishmaniasis follows a descending trend in the study area in case drought condition continues, SARIMA models can suitably measure the disease trend, and the disease follows a seasonal trend. Copyright © 2017 Hainan Medical University. Production and hosting by Elsevier B.V. All rights reserved.

Analysis of time series for postal shipments in Regional VII East Java Indonesia

NASA Astrophysics Data System (ADS)

Kusrini, DE; Ulama, B. S. S.; Aridinanti, L.

2018-03-01

The change of number delivery goods through PT. Pos Regional VII East Java Indonesia indicates that the trend of increasing and decreasing the delivery of documents and non-documents in PT. Pos Regional VII East Java Indonesia is strongly influenced by conditions outside of PT. Pos Regional VII East Java Indonesia so that the prediction the number of document and non-documents requires a model that can accommodate it. Based on the time series plot monthly data fluctuations occur from 2013-2016 then the model is done using ARIMA or seasonal ARIMA and selected the best model based on the smallest AIC value. The results of data analysis about the number of shipments on each product sent through the Sub-Regional Postal Office VII East Java indicates that there are 5 post offices of 26 post offices entering the territory. The largest number of shipments is available on the PPB (Paket Pos Biasa is regular package shipment/non-document ) and SKH (Surat Kilat Khusus is Special Express Mail/document) products. The time series model generated is largely a Random walk model meaning that the number of shipment in the future is influenced by random effects that are difficult to predict. Some are AR and MA models, except for Express shipment products with Malang post office destination which has seasonal ARIMA model on lag 6 and 12. This means that the number of items in the following month is affected by the number of items in the previous 6 months.

Pharmacokinetics of dacarbazine (DTIC) in pregnancy.

PubMed

Kantrowitz-Gordon, Ira; Hays, Karen; Kayode, Olumide; Kumar, Aditya R; Kaplan, Henry G; Reid, Joel M; Safgren, Stephanie L; Ames, Matthew M; Easterling, Thomas R; Hebert, Mary F

2018-03-01

The purpose of this report is to describe, for the first time, the pharmacokinetics of dacarbazine (DTIC) and its metabolites [5-[3-methyl-triazen-1-yl]-imidazole-4-carboxamide (MTIC), 5-[3-hydroxymethyl-3-methyl-triazen-1-yl]-imidazole-4-carboxamide (HMMTIC) and 5-aminoimidazole-4-carboxamide (AIC)] during pregnancy (n = 2) and postpartum (n = 1). Non-compartmental DTIC, MTIC, HMMTIC, and AIC pharmacokinetics (PK) were estimated in one case at 29 week gestation and 18 day postpartum and a second case at 32 week gestation, in women receiving DTIC in combination with doxorubicin, bleomycin, and vinblastine for treatment of Hodgkin's lymphoma. Drug concentrations were measured by HPLC. In the subject who completed both pregnancy and postpartum study days, DTIC area under the concentration-time curve (AUC) was 27% higher and metabolite AUCs were lower by 27% for HMMTIC, 38% for MTIC, and 83% of AIC during pregnancy compared to postpartum. At 7 and 9 year follow-up, both subjects were in remission of their Hodgkin's lymphoma. Based on these two case reports, pregnancy appears to decrease the metabolism of the pro-drug dacarbazine, likely through inhibition of CYP1A2 activity. Lower concentrations of active metabolites and decreased efficacy may result, although both these subjects experienced long-term remission of their Hodgkin's lymphoma.

FADO: a statistical method to detect favored or avoided distances between occurrences of motifs using the Hawkes' model.

PubMed

Gusto, Gaelle; Schbath, Sophie

2005-01-01

We propose an original statistical method to estimate how the occurrences of a given process along a genome, genes or motifs for instance, may be influenced by the occurrences of a second process. More precisely, the aim is to detect avoided and/or favored distances between two motifs, for instance, suggesting possible interactions at a molecular level. For this, we consider occurrences along the genome as point processes and we use the so-called Hawkes' model. In such model, the intensity at position t depends linearly on the distances to past occurrences of both processes via two unknown profile functions to estimate. We perform a non parametric estimation of both profiles by using B-spline decompositions and a constrained maximum likelihood method. Finally, we use the AIC criterion for the model selection. Simulations show the excellent behavior of our estimation procedure. We then apply it to study (i) the dependence between gene occurrences along the E. coli genome and the occurrences of a motif known to be part of the major promoter for this bacterium, and (ii) the dependence between the yeast S. cerevisiae genes and the occurrences of putative polyadenylation signals. The results are coherent with known biological properties or previous predictions, meaning this method can be of great interest for functional motif detection, or to improve knowledge of some biological mechanisms.

Extracorporeal shock wave therapy ameliorates cyclophosphamide-induced rat acute interstitial cystitis though inhibiting inflammation and oxidative stress-in vitro and in vivo experiment studies

PubMed Central

Chen, Yen-Ta; Yang, Chih-Chao; Sun, Cheuk-Kwan; Chiang, Hsin-Ju; Chen, Yi-Ling; Sung, Pei-Hsun; Zhen, Yen-Yi; Huang, Tein-Hung; Chang, Chia-Lo; Chen, Hong-Hwa; Chang, Hsueh-Wen; Yip, Hon-Kan

2014-01-01

Background: We investigated whether extracorporeal shock wave (ECSW) therapy can attenuate cyclophosphamide (CYP)-induced acute interstitial cystitis (AIC) in rats. Methods and Results: Eighteen male-adult Sprague-Dawley rats were equally divided into group 1 (sham control), group 2 (AIC induced by 150 mg/kg CYP by intra-peritoneal injection) and group 3 (AIC + ECSW 200 impulses at 0.11 mJ/mm2 to the urinary bladder at 3 and 24 h after CYP treatment). Smooth-muscle cells co-culture with menadione (25 µM) with and without ECSW treatment was performed. Western-blot results demonstrated that ECSW significant attenuated oxidative stress and inflammatory reactions in this in-vitro studies (all p < 0.001). 24-hour urine amount and microscopic findings of red-blood-cell count (i.e., hematuria) were higher in group 2 than in groups 1 and 3, and significantly higher in group 3 than in group 1 (all p < 0.001). The urine levels of albumin and interleukin-6 showed an identical pattern of hematuria among all three groups (all p < 0.001). The cellular and mRNA expressions of macrophage migration inhibitory factor (MIF)+, CD74+, CD68+, substance p+, and Cox-2+ cells in the bladder tissue exhibited an identical pattern of hematuria among all groups (all p < 0.0001). The integrity of epithelial layer and collagen-deposition area as stained by Sirius red displayed an opposite pattern of hematuria among the three groups (p < 0.0001). The protein expression of IL-12, iNOS, TNF-α, NF-κB, MMP-9, NOX-1, NOX-2, RANTES, and Oxyblot displayed an identical pattern of hematuria among all groups (all p < 0.01). Conclusion: ECSW therapy markedly attenuated CYP-induced AIC through inhibitions of the inflammation and oxidative stress. PMID:25628776

Autoimmune conditions are associated with perioperative thrombotic complications in liver transplant recipients: A UNOS database analysis.

PubMed

Bezinover, Dmitri; Iskandarani, Khaled; Chinchilli, Vernon; McQuillan, Patrick; Saner, Fuat; Kadry, Zakiyah; Riley, Thomas R; Janicki, Piotr K

2016-05-21

End stage liver disease (ESLD) is associated with significant thrombotic complications. In this study, we attempted to determine if patients with ESLD, due to oncologic or autoimmune diseases, are susceptible to thrombosis to a greater extent than patients with ESLD due to other causes. In this retrospective study, we analyzed the UNOS database to determine the incidence of thrombotic complications in orthotopic liver transplant (OLT) recipients with autoimmune and oncologic conditions. Between 2000 and 2012, 65,646 OLTs were performed. We found 4,247 cases of preoperative portal vein thrombosis (PVT) and 1,233 cases of postoperative vascular thrombosis (VT) leading to graft failure. Statistical evaluation demonstrated that patients with either hepatocellular carcinoma (HCC) or autoimmune hepatitis (AIC) had a higher incidence of PVT (p = 0.05 and 0.03 respectively). Patients with primary biliary cirrhosis (PBC), primary sclerosing cholangitis (PSC) and AIC had a higher incidence of postoperative VT associated with graft failure (p < 0.0001, p < 0.0001, p = 0.05 respectively). Patients with preoperative PVT had a higher incidence of postoperative VT (p < 0.0001). Multivariable logistic regression demonstrated that patients with AIC, and BMI ≥40, having had a transjugular intrahepatic portosystemic shunt, and those with diabetes mellitus were more likely to have preoperative PVT: odds ratio (OR)(1.36, 1.19, 1.78, 1.22 respectively). Patients with PSC, PBC, AIC, BMI ≤18, or with a preoperative PVT were more likely to have a postoperative VT: OR (1.93, 2.09, 1.64, 1.60, and 2.01, respectively). Despite the limited number of variables available in the UNOS database potentially related to thrombotic complications, this analysis demonstrates a clear association between autoimmune causes of ESLD and perioperative thrombotic complications. Perioperative management of patients at risk should include strategies to reduce the potential for these complications.

A radiographic study of the mandibular third molar root development in different ethnic groups.

PubMed

Liversidge, H M; Peariasamy, K; Folayan, M O; Adeniyi, A O; Ngom, P I; Mikami, Y; Shimada, Y; Kuroe, K; Tvete, I F; Kvaal, S I

2017-12-01

The nature of differences in the timing of tooth formation between ethnic groups is important when estimating age. To calculate age of transition of the mandibular third (M3) molar tooth stages from archived dental radiographs from sub-Saharan Africa, Malaysia, Japan and two groups from London UK (Whites and Bangladeshi). The number of radiographs was 4555 (2028 males, 2527 females) with an age range 10-25 years. The left M3 was staged into Moorrees stages. A probit model was fitted to calculate mean ages for transitions between stages for males and females and each ethnic group separately. The estimated age distributions given each M3 stage was calculated. To assess differences in timing of M3 between ethnic groups, three models were proposed: a separate model for each ethnic group, a joint model and a third model combining some aspects across groups. The best model fit was tested using Bayesian and Akaikes information criteria (BIC and AIC) and log likelihood ratio test. Differences in mean ages of M3 root stages were found between ethnic groups, however all groups showed large standard deviation values. The AIC and log likelihood ratio test indicated that a separate model for each ethnic group was best. Small differences were also noted between timing of M3 between males and females, with the exception of the Malaysian group. These findings suggests that features of a reference data set (wide age range and uniform age distribution) and a Bayesian statistical approach are more important than population specific convenience samples to estimate age of an individual using M3. Some group differences were evident in M3 timing, however, this has some impact on the confidence interval of estimated age in females and little impact in males because of the large variation in age.

What's in a Day? A Guide to Decomposing the Variance in Intensive Longitudinal Data

PubMed Central

de Haan-Rietdijk, Silvia; Kuppens, Peter; Hamaker, Ellen L.

2016-01-01

In recent years there has been a growing interest in the use of intensive longitudinal research designs to study within-person processes. Examples are studies that use experience sampling data and autoregressive modeling to investigate emotion dynamics and between-person differences therein. Such designs often involve multiple measurements per day and multiple days per person, and it is not clear how this nesting of the data should be accounted for: That is, should such data be considered as two-level data (which is common practice at this point), with occasions nested in persons, or as three-level data with beeps nested in days which are nested in persons. We show that a significance test of the day-level variance in an empty three-level model is not reliable when there is autocorrelation. Furthermore, we show that misspecifying the number of levels can lead to spurious or misleading findings, such as inflated variance or autoregression estimates. Throughout the paper we present instructions and R code for the implementation of the proposed models, which includes a novel three-level AR(1) model that estimates moment-to-moment inertia and day-to-day inertia. Based on our simulations we recommend model selection using autoregressive multilevel models in combination with the AIC. We illustrate this method using empirical emotion data from two independent samples, and discuss the implications and the relevance of the existence of a day level for the field. PMID:27378986

What's in a Day? A Guide to Decomposing the Variance in Intensive Longitudinal Data.

PubMed

de Haan-Rietdijk, Silvia; Kuppens, Peter; Hamaker, Ellen L

2016-01-01

In recent years there has been a growing interest in the use of intensive longitudinal research designs to study within-person processes. Examples are studies that use experience sampling data and autoregressive modeling to investigate emotion dynamics and between-person differences therein. Such designs often involve multiple measurements per day and multiple days per person, and it is not clear how this nesting of the data should be accounted for: That is, should such data be considered as two-level data (which is common practice at this point), with occasions nested in persons, or as three-level data with beeps nested in days which are nested in persons. We show that a significance test of the day-level variance in an empty three-level model is not reliable when there is autocorrelation. Furthermore, we show that misspecifying the number of levels can lead to spurious or misleading findings, such as inflated variance or autoregression estimates. Throughout the paper we present instructions and R code for the implementation of the proposed models, which includes a novel three-level AR(1) model that estimates moment-to-moment inertia and day-to-day inertia. Based on our simulations we recommend model selection using autoregressive multilevel models in combination with the AIC. We illustrate this method using empirical emotion data from two independent samples, and discuss the implications and the relevance of the existence of a day level for the field.

Structural properties of a-Si films and their effect on aluminum induced crystallization

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

Tankut, Aydin; Ozkol, Engin; Karaman, Mehmet

2015-10-15

In this paper, we report the influence of the structural properties of amorphous silicon (a-Si) on its subsequent crystallization behavior via the aluminum induced crystallization (AIC) method. Two distinct a-Si deposition techniques, electron beam evaporation and plasma enhanced chemical vapor deposition (PECVD), are compared for their effect on the overall AIC kinetics as well as the properties of the final poly-crystalline (poly-Si) silicon film. Raman and FTIR spectroscopy results indicate that the PECVD grown a-Si films has higher intermediate-range order, which is enhanced for increased hydrogen dilution during deposition. With increasing intermediate-range order of the a-Si, the rate of AICmore » is diminished, leading larger poly-Si grain size.« less

Lipid correction model of carbon stable isotopes for a cosmopolitan predator, spiny dogfish Squalus acanthias.

PubMed

Reum, J C P

2011-12-01

Three lipid correction models were evaluated for liver and white dorsal muscle from Squalus acanthias. For muscle, all three models performed well, based on the Akaike Information Criterion value corrected for small sample sizes (AIC(c) ), and predicted similar lipid corrections to δ(13) C that were up to 2.8 ‰ higher than those predicted using previously published models based on multispecies data. For liver, which possessed higher bulk C:N values compared to that of white muscle, all three models performed poorly and lipid-corrected δ(13) C values were best approximated by simply adding 5.74 ‰ to bulk δ(13) C values. © 2011 The Author. Journal of Fish Biology © 2011 The Fisheries Society of the British Isles.

Racial and ethnic differences in psychotropic medication use among community-dwelling persons with dementia in the United States.

PubMed

Grace, Elsie L; Allen, Rebecca S; Ivey, Keisha; Knapp, Shannon M; Burgio, Louis D

2018-04-01

Little is known about the patterns of psychotropic medication use in community-dwelling minority persons with dementia (PWD). The purpose of this study was to investigate racial/ethnic differences in psychotropic medication use across a diverse population of community-dwelling PWD and to examine the extent to which caregiver characteristics influence this use. Data were drawn from the baseline assessment of the Resources for Enhancing Alzheimer's Caregiver Health II trial. Generalized linear models were used to identify racial/ethnic differences in psychotropic medication use. Akaike Information Criterion (AIC) model selection was used to evaluate possible explanations for observed differences across racial/ethnic group. Differences in anxiolytic and antipsychotic medication use were observed across racial/ethnic groups; however, race/ethnicity alone was not sufficient to explain those differences. Perceptions of caregiving and caregiver socioeconomic status were important predictors of anxiolytic use while PWD characteristics, including cognitive impairment, functional impairment, problem behavior frequency, pain, relationship to the caregiver, sex, and age were important for antipsychotic use. Racial/ethnic differences in psychotropic medication use among community-dwelling PWD cannot be explained by race/ethnicity alone. The importance of caregiver characteristics in predicting anxiolytic medication use suggest that interventions aimed at caregivers may hold promise as an effective alternative to pharmacotherapy.

Arrival-time picking method based on approximate negentropy for microseismic data

NASA Astrophysics Data System (ADS)

Li, Yue; Ni, Zhuo; Tian, Yanan

2018-05-01

Accurate and dependable picking of the first arrival time for microseismic data is an important part in microseismic monitoring, which directly affects analysis results of post-processing. This paper presents a new method based on approximate negentropy (AN) theory for microseismic arrival time picking in condition of much lower signal-to-noise ratio (SNR). According to the differences in information characteristics between microseismic data and random noise, an appropriate approximation of negentropy function is selected to minimize the effect of SNR. At the same time, a weighted function of the differences between maximum and minimum value of AN spectrum curve is designed to obtain a proper threshold function. In this way, the region of signal and noise is distinguished to pick the first arrival time accurately. To demonstrate the effectiveness of AN method, we make many experiments on a series of synthetic data with different SNR from -1 dB to -12 dB and compare it with previously published Akaike information criterion (AIC) and short/long time average ratio (STA/LTA) methods. Experimental results indicate that these three methods can achieve well picking effect when SNR is from -1 dB to -8 dB. However, when SNR is as low as -8 dB to -12 dB, the proposed AN method yields more accurate and stable picking result than AIC and STA/LTA methods. Furthermore, the application results of real three-component microseismic data also show that the new method is superior to the other two methods in accuracy and stability.

Spatial hydrological drought characteristics in Karkheh River basin, southwest Iran using copulas

NASA Astrophysics Data System (ADS)

Dodangeh, Esmaeel; Shahedi, Kaka; Shiau, Jenq-Tzong; MirAkbari, Maryam

2017-08-01

Investigation on drought characteristics such as severity, duration, and frequency is crucial for water resources planning and management in a river basin. While the methodology for multivariate drought frequency analysis is well established by applying the copulas, the estimation on the associated parameters by various parameter estimation methods and the effects on the obtained results have not yet been investigated. This research aims at conducting a comparative analysis between the maximum likelihood parametric and non-parametric method of the Kendall τ estimation method for copulas parameter estimation. The methods were employed to study joint severity-duration probability and recurrence intervals in Karkheh River basin (southwest Iran) which is facing severe water-deficit problems. Daily streamflow data at three hydrological gauging stations (Tang Sazbon, Huleilan and Polchehr) near the Karkheh dam were used to draw flow duration curves (FDC) of these three stations. The Q_{75} index extracted from the FDC were set as threshold level to abstract drought characteristics such as drought duration and severity on the basis of the run theory. Drought duration and severity were separately modeled using the univariate probabilistic distributions and gamma-GEV, LN2-exponential, and LN2-gamma were selected as the best paired drought severity-duration inputs for copulas according to the Akaike Information Criteria (AIC), Kolmogorov-Smirnov and chi-square tests. Archimedean Clayton, Frank, and extreme value Gumbel copulas were employed to construct joint cumulative distribution functions (JCDF) of droughts for each station. Frank copula at Tang Sazbon and Gumbel at Huleilan and Polchehr stations were identified as the best copulas based on the performance evaluation criteria including AIC, BIC, log-likelihood and root mean square error (RMSE) values. Based on the RMSE values, nonparametric Kendall-τ is preferred to the parametric maximum likelihood estimation method. The results showed greater drought return periods by the parametric ML method in comparison to the nonparametric Kendall τ estimation method. The results also showed that stations located in tributaries (Huleilan and Polchehr) have close return periods, while the station along the main river (Tang Sazbon) has the smaller return periods for the drought events with identical drought duration and severity.

Quantum lattice model solver HΦ

NASA Astrophysics Data System (ADS)

Kawamura, Mitsuaki; Yoshimi, Kazuyoshi; Misawa, Takahiro; Yamaji, Youhei; Todo, Synge; Kawashima, Naoki

2017-08-01

HΦ [aitch-phi ] is a program package based on the Lanczos-type eigenvalue solution applicable to a broad range of quantum lattice models, i.e., arbitrary quantum lattice models with two-body interactions, including the Heisenberg model, the Kitaev model, the Hubbard model and the Kondo-lattice model. While it works well on PCs and PC-clusters, HΦ also runs efficiently on massively parallel computers, which considerably extends the tractable range of the system size. In addition, unlike most existing packages, HΦ supports finite-temperature calculations through the method of thermal pure quantum (TPQ) states. In this paper, we explain theoretical background and user-interface of HΦ. We also show the benchmark results of HΦ on supercomputers such as the K computer at RIKEN Advanced Institute for Computational Science (AICS) and SGI ICE XA (Sekirei) at the Institute for the Solid State Physics (ISSP).

INSTABILITIES DRIVEN BY THE DRIFT AND TEMPERATURE ANISOTROPY OF ALPHA PARTICLES IN THE SOLAR WIND

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

Verscharen, Daniel; Bourouaine, Sofiane; Chandran, Benjamin D. G., E-mail: daniel.verscharen@unh.edu, E-mail: s.bourouaine@unh.edu, E-mail: benjamin.chandran@unh.edu

2013-08-20

We investigate the conditions under which parallel-propagating Alfven/ion-cyclotron (A/IC) waves and fast-magnetosonic/whistler (FM/W) waves are driven unstable by the differential flow and temperature anisotropy of alpha particles in the solar wind. We focus on the limit in which w{sub Parallel-To {alpha}} {approx}> 0.25v{sub A}, where w{sub Parallel-To {alpha}} is the parallel alpha-particle thermal speed and v{sub A} is the Alfven speed. We derive analytic expressions for the instability thresholds of these waves, which show, e.g., how the minimum unstable alpha-particle beam speed depends upon w{sub Parallel-To {alpha}}/v{sub A}, the degree of alpha-particle temperature anisotropy, and the alpha-to-proton temperature ratio. Wemore » validate our analytical results using numerical solutions to the full hot-plasma dispersion relation. Consistent with previous work, we find that temperature anisotropy allows A/IC waves and FM/W waves to become unstable at significantly lower values of the alpha-particle beam speed U{sub {alpha}} than in the isotropic-temperature case. Likewise, differential flow lowers the minimum temperature anisotropy needed to excite A/IC or FM/W waves relative to the case in which U{sub {alpha}} = 0. We discuss the relevance of our results to alpha particles in the solar wind near 1 AU.« less

[Establishing and applying of autoregressive integrated moving average model to predict the incidence rate of dysentery in Shanghai].

PubMed

Li, Jian; Wu, Huan-Yu; Li, Yan-Ting; Jin, Hui-Ming; Gu, Bao-Ke; Yuan, Zheng-An

2010-01-01

To explore the feasibility of establishing and applying of autoregressive integrated moving average (ARIMA) model to predict the incidence rate of dysentery in Shanghai, so as to provide the theoretical basis for prevention and control of dysentery. ARIMA model was established based on the monthly incidence rate of dysentery of Shanghai from 1990 to 2007. The parameters of model were estimated through unconditional least squares method, the structure was determined according to criteria of residual un-correlation and conclusion, and the model goodness-of-fit was determined through Akaike information criterion (AIC) and Schwarz Bayesian criterion (SBC). The constructed optimal model was applied to predict the incidence rate of dysentery of Shanghai in 2008 and evaluate the validity of model through comparing the difference of predicted incidence rate and actual one. The incidence rate of dysentery in 2010 was predicted by ARIMA model based on the incidence rate from January 1990 to June 2009. The model ARIMA (1, 1, 1) (0, 1, 2)(12) had a good fitness to the incidence rate with both autoregressive coefficient (AR1 = 0.443) during the past time series, moving average coefficient (MA1 = 0.806) and seasonal moving average coefficient (SMA1 = 0.543, SMA2 = 0.321) being statistically significant (P < 0.01). AIC and SBC were 2.878 and 16.131 respectively and predicting error was white noise. The mathematic function was (1-0.443B) (1-B) (1-B(12))Z(t) = (1-0.806B) (1-0.543B(12)) (1-0.321B(2) x 12) micro(t). The predicted incidence rate in 2008 was consistent with the actual one, with the relative error of 6.78%. The predicted incidence rate of dysentery in 2010 based on the incidence rate from January 1990 to June 2009 would be 9.390 per 100 thousand. ARIMA model can be used to fit the changes of incidence rate of dysentery and to forecast the future incidence rate in Shanghai. It is a predicted model of high precision for short-time forecast.

Climate-related variation in plant peak biomass and growth phenology across Pacific Northwest tidal marshes

NASA Astrophysics Data System (ADS)

Buffington, Kevin J.; Dugger, Bruce D.; Thorne, Karen M.

2018-03-01

The interannual variability of tidal marsh plant phenology is largely unknown and may have important ecological consequences. Marsh plants are critical to the biogeomorphic feedback processes that build estuarine soils, maintain marsh elevation relative to sea level, and sequester carbon. We calculated Tasseled Cap Greenness, a metric of plant biomass, using remotely sensed data available in the Landsat archive to assess how recent climate variation has affected biomass production and plant phenology across three maritime tidal marshes in the Pacific Northwest of the United States. First, we used clipped vegetation plots at one of our sites to confirm that tasseled cap greenness provided a useful measure of aboveground biomass (r2 = 0.72). We then used multiple measures of biomass each growing season over 20-25 years per study site and developed models to test how peak biomass and the date of peak biomass varied with 94 climate and sea-level metrics using generalized linear models and Akaike Information Criterion (AIC) model selection. Peak biomass was positively related to total annual precipitation, while the best predictor for date of peak biomass was average growing season temperature, with the peak 7.2 days earlier per degree C. Our study provides insight into how plants in maritime tidal marshes respond to interannual climate variation and demonstrates the utility of time-series remote sensing data to assess ecological responses to climate stressors.

Potential pitfalls of reconstructing deep time evolutionary history with only extant data, a case study using the canidae (mammalia, carnivora).

PubMed

Finarelli, John A; Goswami, Anjali

2013-12-01

Reconstructing evolutionary patterns and their underlying processes is a central goal in biology. Yet many analyses of deep evolutionary histories assume that data from the fossil record is too incomplete to include, and rely solely on databases of extant taxa. Excluding fossil taxa assumes that character state distributions across living taxa are faithful representations of a clade's entire evolutionary history. Many factors can make this assumption problematic. Fossil taxa do not simply lead-up to extant taxa; they represent now-extinct lineages that can substantially impact interpretations of character evolution for extant groups. Here, we analyze body mass data for extant and fossil canids (dogs, foxes, and relatives) for changes in mean and variance through time. AIC-based model selection recovered distinct models for each of eight canid subgroups. We compared model fit of parameter estimates for (1) extant data alone and (2) extant and fossil data, demonstrating that the latter performs significantly better. Moreover, extant-only analyses result in unrealistically low estimates of ancestral mass. Although fossil data are not always available, reconstructions of deep-time organismal evolution in the absence of deep-time data can be highly inaccurate, and we argue that every effort should be made to include fossil data in macroevolutionary studies. © 2013 The Authors. Evolution published by Wiley Periodicals, Inc. on behalf of The Society for the Study of Evolution.

The cyclophosphamide equivalent dose as an approach for quantifying alkylating agent exposure: a report from the Childhood Cancer Survivor Study.

PubMed

Green, Daniel M; Nolan, Vikki G; Goodman, Pamela J; Whitton, John A; Srivastava, DeoKumar; Leisenring, Wendy M; Neglia, Joseph P; Sklar, Charles A; Kaste, Sue C; Hudson, Melissa M; Diller, Lisa R; Stovall, Marilyn; Donaldson, Sarah S; Robison, Leslie L

2014-01-01

Estimation of the risk of adverse long-term outcomes such as second malignant neoplasms and infertility often requires reproducible quantification of exposures. The method for quantification should be easily utilized and valid across different study populations. The widely used Alkylating Agent Dose (AAD) score is derived from the drug dose distribution of the study population and thus cannot be used for comparisons across populations as each will have a unique distribution of drug doses. We compared the performance of the Cyclophosphamide Equivalent Dose (CED), a unit for quantifying alkylating agent exposure independent of study population, to the AAD. Comparisons included associations from three Childhood Cancer Survivor Study (CCSS) outcome analyses, receiver operator characteristic (ROC) curves and goodness of fit based on the Akaike's Information Criterion (AIC). The CED and AAD performed essentially identically in analyses of risk for pregnancy among the partners of male CCSS participants, risk for adverse dental outcomes among all CCSS participants and risk for premature menopause among female CCSS participants, based on similar associations, lack of statistically significant differences between the areas under the ROC curves and similar model fit values for the AIC between models including the two measures of exposure. The CED is easily calculated, facilitating its use for patient counseling. It is independent of the drug dose distribution of a particular patient population, a characteristic that will allow direct comparisons of outcomes among epidemiological cohorts. We recommend the use of the CED in future research assessing cumulative alkylating agent exposure. © 2013 Wiley Periodicals, Inc.

Short-term forecasting of meteorological time series using Nonparametric Functional Data Analysis (NPFDA)

NASA Astrophysics Data System (ADS)

Curceac, S.; Ternynck, C.; Ouarda, T.

2015-12-01

Over the past decades, a substantial amount of research has been conducted to model and forecast climatic variables. In this study, Nonparametric Functional Data Analysis (NPFDA) methods are applied to forecast air temperature and wind speed time series in Abu Dhabi, UAE. The dataset consists of hourly measurements recorded for a period of 29 years, 1982-2010. The novelty of the Functional Data Analysis approach is in expressing the data as curves. In the present work, the focus is on daily forecasting and the functional observations (curves) express the daily measurements of the above mentioned variables. We apply a non-linear regression model with a functional non-parametric kernel estimator. The computation of the estimator is performed using an asymmetrical quadratic kernel function for local weighting based on the bandwidth obtained by a cross validation procedure. The proximities between functional objects are calculated by families of semi-metrics based on derivatives and Functional Principal Component Analysis (FPCA). Additionally, functional conditional mode and functional conditional median estimators are applied and the advantages of combining their results are analysed. A different approach employs a SARIMA model selected according to the minimum Akaike (AIC) and Bayessian (BIC) Information Criteria and based on the residuals of the model. The performance of the models is assessed by calculating error indices such as the root mean square error (RMSE), relative RMSE, BIAS and relative BIAS. The results indicate that the NPFDA models provide more accurate forecasts than the SARIMA models. Key words: Nonparametric functional data analysis, SARIMA, time series forecast, air temperature, wind speed

Presence and Potential Distribution of Aedes albopictus and Aedes japonicus japonicus (Diptera: Culicidae) in Slovenia.

PubMed

Kalan, Katja; Ivovic, Vladimir; Glasnovic, Peter; Buzan, Elena

2017-11-07

In Slovenia, two invasive mosquito species are present, Aedes albopictus (Skuse, 1895) (Diptera: Culicidae) and Aedes japonicus (Theobald, 1901) (Diptera: Culicidae). In this study, we examined their actual distribution and suitable habitats for new colonizations. Data from survey of species presence in 2013 and 2015, bioclimatic variables and altitude were used for the construction of predictive maps. We produced various models in Maxent software and tested two bioclimatic variable sets, WorldClim and CHELSA. For the variable selection of A. albopictus modeling we used statistical and expert knowledge-based approach, whereas for A. j. japonicus we used only a statistically based approach. The best performing models for both species were chosen according to AIC score-based evaluation. In 2 yr of sampling, A. albopictus was largely confined to the western half of Slovenia, whereas A. j. japonicus spread significantly and can be considered as an established species in a large part of the country. Comparison of models with WorldClim and CHELSA variables for both species showed models with CHELSA variables as a better tool for prediction. Finally, we validated the models performance in predicting distribution of species according to collected field data. Our study confirms that both species are co-occurring and are sympatric in a large part of the country area. The tested models could be used for future prevention of invasive mosquitoes spreading in other countries with similar bioclimatic conditions. © The Authors 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Neighborhood and habitat effects on vital rates: expansion of the Barred Owl in the Oregon Coast Ranges

USGS Publications Warehouse

Yackulic, Charles B.; Reid, Janice; Davis, Raymond; Hines, James E.; Nichols, James D.; Forsman, Eric

2012-01-01

In this paper, we modify dynamic occupancy models developed for detection-nondetection data to allow for the dependence of local vital rates on neighborhood occupancy, where neighborhood is defined very flexibly. Such dependence of occupancy dynamics on the status of a relevant neighborhood is pervasive, yet frequently ignored. Our framework permits joint inference about the importance of neighborhood effects and habitat covariates in determining colonization and extinction rates. Our specific motivation is the recent expansion of the Barred Owl (Strix varia) in western Oregon, USA, over the period 1990-2010. Because the focal period was one of dramatic range expansion and local population increase, the use of models that incorporate regional occupancy (sources of colonists) as determinants of dynamic rate parameters is especially appropriate. We began our analysis of 21 years of Barred Owl presence/nondetection data in the Tyee Density Study Area (TDSA) by testing a suite of six models that varied only in the covariates included in the modeling of detection probability. We then tested whether models that used regional occupancy as a covariate for colonization and extinction outperformed models with constant or year-specific colonization or extinction rates. Finally we tested whether habitat covariates improved the AIC of our models, focusing on which habitat covariates performed best, and whether the signs of habitat effects are consistent with a priori hypotheses. We conclude that all covariates used to model detection probability lead to improved AIC, that regional occupancy influences colonization and extinction rates, and that habitat plays an important role in determining extinction and colonization rates. As occupancy increases from low levels toward equilibrium, colonization increases and extinction decreases, presumably because there are more and more dispersing juveniles. While both rates are affected, colonization increases more than extinction decreases. Colonization is higher and extinction is lower in survey polygons with more riparian forest. The effects of riparian forest on extinction rates are greater than on colonization rates. Model results have implications for management of the invading Barred Owl, both through habitat alteration and removal.

Particle-size distribution models for the conversion of Chinese data to FAO/USDA system.

PubMed

Shangguan, Wei; Dai, YongJiu; García-Gutiérrez, Carlos; Yuan, Hua

2014-01-01

We investigated eleven particle-size distribution (PSD) models to determine the appropriate models for describing the PSDs of 16349 Chinese soil samples. These data are based on three soil texture classification schemes, including one ISSS (International Society of Soil Science) scheme with four data points and two Katschinski's schemes with five and six data points, respectively. The adjusted coefficient of determination r (2), Akaike's information criterion (AIC), and geometric mean error ratio (GMER) were used to evaluate the model performance. The soil data were converted to the USDA (United States Department of Agriculture) standard using PSD models and the fractal concept. The performance of PSD models was affected by soil texture and classification of fraction schemes. The performance of PSD models also varied with clay content of soils. The Anderson, Fredlund, modified logistic growth, Skaggs, and Weilbull models were the best.

Disclosure of HIV serostatus to male partner and use of modern contraceptives among women receiving HIV care services in Kampala, Uganda.

PubMed

Zalwango, Vivian W; Tweheyo, Raymond; Makumbi, Fredrick

2013-11-01

To investigate whether disclosure of HIV status is associated with use of modern contraceptives (MCs) among women attending HIV care services at an AIDS Information Center (AIC) in an urban setting in Uganda. In a cross-sectional study between March and April 2010, HIV-positive married women aged 15-49years who had received their HIV-positive serostatus results at least 4weeks previously were interviewed at the AIC, Kampala, Uganda. Female use of MCs was compared by HIV disclosure to male marital partners. Log-binomial regression models were used to obtain crude and adjusted prevalence risk ratios (PRRs) and corresponding 95% confidence intervals (CIs). Nearly three-quarters (72.6%) of the women had disclosed their HIV-positive status to their partner. Overall, use of MCs was reported by 41.0% of the participants. Use of only 1 MC method was similar between those disclosing (81.1%) and those not disclosing (84.3%), but use of dual methods tended to be higher among disclosers (14.4%) than among non-disclosers (10.8%). In adjusted analyses, MC use was 41.0% lower among disclosers than among non-disclosers (adjusted PRR, 0.59; 95% CI, 0.46-0.77). HIV serostatus disclosure was associated with lower use of MCs among HIV-positive women in Kampala, Uganda. © 2013.

The role of the dorsal anterior insula in sexual risk: Evidence from an erotic Go/NoGo task and real-world risk-taking.

PubMed

Xue, Feng; Droutman, Vita; Barkley-Levenson, Emily E; Smith, Benjamin J; Xue, Gui; Miller, Lynn C; Bechara, Antoine; Lu, Zhong-Lin; Read, Stephen J

2018-04-01

The insula plays an important role in response inhibition. Most relevant here, it has been proposed that the dorsal anterior insular cortex (dAIC) plays a central role in a salience network that is responsible for switching between the default mode network and the executive control network. However, the insula's role in sexually motivated response inhibition has not yet been studied. In this study, eighty-five 18- to 30-year-old sexually active men who have sex with men (MSM) performed an erotic Go/NoGo task while in an MRI scanner. Participants' real-world sexual risk-taking (frequency of condomless anal intercourse over the past 90 days) was then correlated with their neural activity during the task. We found greater activity in bilateral anterior insular cortex (both dorsal and ventral) on contrasts with stronger motivational information (attractive naked male pictures versus pictures of clothed, middle-aged females) and on contrasts requiring greater response inhibition (NoGo versus Go). We also found that activity in the right dAIC was negatively correlated with participants' real-world sexual risk-taking. Our results confirmed the involvement of the insular cortex in motivated response inhibition. Especially, the decreased right dAIC activity may reduce the likelihood that the executive control network will come online when individuals are faced with situations requiring inhibitory control and thus lead them to make more risky choices. © 2018 Wiley Periodicals, Inc.

Distortion of time interval reproduction in an epileptic patient with a focal lesion in the right anterior insular/inferior frontal cortices.

PubMed

Monfort, Vincent; Pfeuty, Micha; Klein, Madelyne; Collé, Steffie; Brissart, Hélène; Jonas, Jacques; Maillard, Louis

2014-11-01

This case report on an epileptic patient suffering from a focal lesion at the junction of the right anterior insular cortex (AIC) and the adjacent inferior frontal cortex (IFC) provides the first evidence that damage to this brain region impairs temporal performance in a visual time reproduction task in which participants had to reproduce the presentation duration (3, 5 and 7s) of emotionally-neutral and -negative pictures. Strikingly, as compared to a group of healthy subjects, the AIC/IFC case considerably overestimated reproduction times despite normal variability. The effect was obtained in all duration and emotion conditions. Such a distortion in time reproduction was not observed in four other epileptic patients without insular or inferior frontal damage. Importantly, the absolute extent of temporal over-reproduction increased in proportion to the magnitude of the target durations, which concurs with the scalar property of interval timing, and points to an impairment of time-specific rather than of non temporal (such as motor) mechanisms. Our data suggest that the disability in temporal reproduction of the AIC/IFC case would result from a distorted memory representation of the encoded duration, occurring during the process of storage and/or of recovery from memory and leading to a deviation of the temporal judgment during the reproduction task. These findings support the recent proposal that the anterior insular/inferior frontal cortices would be involved in time interval representation. Copyright © 2014 Elsevier Ltd. All rights reserved.

Wave and ion evolution downstream of quasi-perpendicular bow shocks

NASA Technical Reports Server (NTRS)

Mckean, M. E.; Omidi, N.; Krauss-Varban, D.

1995-01-01

Distribution functions of ions heated in quasi-perpendicular bow shocks have a large perpendicular temperature anisotropy that provides free energy for the growth of Alfven ion cyclotron (AIC) waves and mirror waves. Both types of waves have been observed in the Earth's magnetosheath downstream of quasi-perpendicular shocks. We use a two-dimensional hybrid simulations to give a self-consistent description of the evolution of the wave spectra downstream of quasi-perpendicular shocks. Both mirror and AIC waves are identified in the simulated magnetosheath. They are generated at or near the shock front and convected away from it by the sheath plasma. Near the shock, the waves have a broad spectrum, but downstream of the shock, shorter-wavelength modes are heavily damped and only longer-wavelength modes persist. The characteristics of these surviving modes can be predicted with reasonable accuracy by linear kinetic theory appropriate for downstream conditions. We also follow the evolution of the ion distribution function. The shocked ions that provide the free energy for wave growth have a two-component distribution function. The halo is initially gyrophase-bunched and extremely anisotropic. Within a relatively short distance downstream of the shock (of the order of 10 ion inertial lengths), wave-particle interactions remove these features from the halo and reduce the anisotropy of the distribution to near-threshold levels for the mirror and AIC instabilities. A similar evolution has been observed for ions at the Earth's bow shock.

Comparing the impact of personal and parental risk factors, and parental lifespan on all-cause mortality and cardiovascular disease: findings from the Midspan Family cohort study.

PubMed

Hart, Carole; McCartney, Gerry; Gruer, Laurence; Watt, Graham

2015-10-01

We aimed to identify which personal and parental factors best explained all-cause mortality and cardiovascular disease (CVD). In 1996, data were collected on 2338 adult offspring of the participants in the 1972-1976 Renfrew and Paisley prospective cohort study. Recorded risk factors were assigned to 5 groups: mid-life biological and behavioural (BB), mid-life socioeconomic, parental BB, early-life socioeconomic and parental lifespan. Participants were followed up for mortality and hospital admissions to the end of 2011. Cox proportional hazards models were used to analyse how well each group explained all-cause mortality or CVD. Akaike's Information Criterion (AIC), a measure of goodness-of-fit, identified the most important groups. For all-cause mortality (1997 participants with complete data, 111 deaths), decreases in AIC from the null model (adjusting for age and sex) to models including mid-life BB, mid-life socioeconomic, parental BB, early-life socioeconomic and parental lifespan were 55.8, 21.6, 10.3, 7.3 and 5.9, respectively. For the CVD models (1736 participants, 276 with CVD), decreases were 37.8, 3.7, 6.7, 17.3 and 0.4. Mid-life BB factors were the most important for both all-cause mortality and CVD; mid-life socioeconomic factors were important for all-cause mortality, and early-life socioeconomic factors were important for CVD. Parental lifespan was the weakest factor. As mid-life BB risk factors best explained all-cause mortality and CVD, continued action to reduce these is warranted. Targeting adverse socioeconomic factors in mid-life and early life may contribute to reducing all-cause mortality and CVD risk, respectively. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Canada lynx Lynx canadensis habitat and forest succession in northern Maine, USA

USGS Publications Warehouse

Hoving, C.L.; Harrison, D.J.; Krohn, W.B.; Jakubas, W.J.; McCollough, M.A.

2004-01-01

The contiguous United States population of Canada lynx Lynx canadensis was listed as threatened in 2000. The long-term viability of lynx populations at the southern edge of their geographic range has been hypothesized to be dependent on old growth forests; however, lynx are a specialist predator on snowshoe hare Lepus americanus, a species associated with early-successional forests. To quantify the effects of succession and forest management on landscape-scale (100 km2) patterns of habitat occupancy by lynx, we compared landscape attributes in northern Maine, USA, where lynx had been detected on snow track surveys to landscape attributes where surveys had been conducted, but lynx tracks had not been detected. Models were constructed a priori and compared using logistic regression and Akaike's Information Criterion (AIC), which quantitatively balances data fit and parsimony. In the models with the lowest (i.e. best) AIC, lynx were more likely to occur in landscapes with much regenerating forest, and less likely to occur in landscapes with much recent clearcut, partial harvest and forested wetland. Lynx were not associated positively or negatively with mature coniferous forest. A probabilistic map of the model indicated a patchy distribution of lynx habitat in northern Maine. According to an additional survey of the study area for lynx tracks during the winter of 2003, the model correctly classified 63.5% of the lynx occurrences and absences. Lynx were more closely associated with young forests than mature forests; however, old-growth forests were functionally absent from the landscape. Lynx habitat could be reduced in northern Maine, given recent trends in forest management practices. Harvest strategies have shifted from clearcutting to partial harvesting. If this trend continues, future landscapes will shift away from extensive regenerating forests and toward landscapes dominated by pole-sized and larger stands. Because Maine presently supports the only verified populations of this federally threatened species in the eastern United States, changes in forest management practices could affect recovery efforts throughout that region.

Bayesian analysis of CCDM models

NASA Astrophysics Data System (ADS)

Jesus, J. F.; Valentim, R.; Andrade-Oliveira, F.

2017-09-01

Creation of Cold Dark Matter (CCDM), in the context of Einstein Field Equations, produces a negative pressure term which can be used to explain the accelerated expansion of the Universe. In this work we tested six different spatially flat models for matter creation using statistical criteria, in light of SNe Ia data: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Bayesian Evidence (BE). These criteria allow to compare models considering goodness of fit and number of free parameters, penalizing excess of complexity. We find that JO model is slightly favoured over LJO/ΛCDM model, however, neither of these, nor Γ = 3αH0 model can be discarded from the current analysis. Three other scenarios are discarded either because poor fitting or because of the excess of free parameters. A method of increasing Bayesian evidence through reparameterization in order to reducing parameter degeneracy is also developed.

Controls of channel morphology and sediment concentration on flow resistance in a large sand-bed river: A case study of the lower Yellow River

NASA Astrophysics Data System (ADS)

Ma, Yuanxu; Huang, He Qing

2016-07-01

Accurate estimation of flow resistance is crucial for flood routing, flow discharge and velocity estimation, and engineering design. Various empirical and semiempirical flow resistance models have been developed during the past century; however, a universal flow resistance model for varying types of rivers has remained difficult to be achieved to date. In this study, hydrometric data sets from six stations in the lower Yellow River during 1958-1959 are used to calibrate three empirical flow resistance models (Eqs. (5)-(7)) and evaluate their predictability. A group of statistical measures have been used to evaluate the goodness of fit of these models, including root mean square error (RMSE), coefficient of determination (CD), the Nash coefficient (NA), mean relative error (MRE), mean symmetry error (MSE), percentage of data with a relative error ≤ 50% and 25% (P50, P25), and percentage of data with overestimated error (POE). Three model selection criterions are also employed to assess the model predictability: Akaike information criterion (AIC), Bayesian information criterion (BIC), and a modified model selection criterion (MSC). The results show that mean flow depth (d) and water surface slope (S) can only explain a small proportion of variance in flow resistance. When channel width (w) and suspended sediment concentration (SSC) are involved, the new model (7) achieves a better performance than the previous ones. The MRE of model (7) is generally < 20%, which is apparently better than that reported by previous studies. This model is validated using the data sets from the corresponding stations during 1965-1966, and the results show larger uncertainties than the calibrating model. This probably resulted from the temporal shift of dominant controls caused by channel change resulting from varying flow regime. With the advancements of earth observation techniques, information about channel width, mean flow depth, and suspended sediment concentration can be effectively extracted from multisource satellite images. We expect that the empirical methods developed in this study can be used as an effective surrogate in estimation of flow resistance in the large sand-bed rivers like the lower Yellow River.

Potential Functional Byproducts from Guava Purée Processing.

PubMed

Lim, Si Yi; Tham, Paik Yean; Lim, Hilary Yi Ler; Heng, Wooi Shin; Chang, Ying Ping

2018-05-10

The valorization of guava waste requires compositional and functional studies. We tested three byproducts of guava purée processing, namely refiner, siever, and decanter. We analyzed the chemical composition and quantified the prebiotic activity score and selected carbohydrates; we also determined the water holding (WHC), oil holding (OHC), cation exchange capacities, bile acid binding, and glucose dialysis retardation (GDR) of the solid fraction and the antioxidative and α-amylase inhibitory capacities (AIC) of the ethanolic extract. Refiner contained 7.7% lipid, 7.08% protein and a relatively high phytate content; it had a high prebiotic activity score and possessed the highest binding capacity with deoxycholic acid. Siever contained high levels of low molecular weight carbohydrates and total tannin but relatively low crude fiber and cellulose contents. It had the highest binding with chenodeoxycholic acid (74.8%), and exhibited the highest 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging capacity. Decanter was rich in cellulose and had a high prebiotic activity score. The WHC and OHC values of decanter were within a narrow range and also exhibited the highest binding with cholic acid (86.6%), and the highest values of GDR and AIC. The refiner waste could be included in animal feed but requires further processing to reduce the high phytate levels. All three guava byproducts had the potential to be a source of antioxidant dietary fiber (DF), a finding that warrants further in vivo study. To differing extents, the guava byproducts exhibited useful physicochemical binding properties and so possessed the potential for health-promoting activity. These byproducts could also be upgraded to other marketable products so the manufacturers of processed guava might be able to develop their businesses sustainably by making better use of them. © 2018 Institute of Food Technologists®.

Very late antigen-5 facilitates stromal progenitor cell differentiation into myofibroblast.

PubMed

Sen, Namita; Weingarten, Mark; Peter, Yakov

2014-11-01

Fibrotic disease is associated with abrogated stromal cell proliferation and activity. The precise identity of the cells that drive fibrosis remains obscure, in part because of a lack of information on their lineage development. To investigate the role of an early stromal progenitor cell (SPC) on the fibrotic process, we selected for, and monitored the stages of, fibroblast development from a previously reported free-floating anchorage-independent cell (AIC) progenitor population. Our findings demonstrate that organotypic pulmonary, cardiac, and renal fibroblast commitment follows a two-step process of attachment and remodeling in culture. Cell differentiation was confirmed by the inability of SPCs to revert to the free-floating state and functional mesenchymal stem/stromal cell (MSC) differentiation into osteoblast, adipocyte, chondrocyte, and fibroblastic lineages. The myofibroblastic phenotype was reflected by actin stress-fiber formation, α-smooth muscle production, and a greater than threefold increase in proliferative activity compared with that of the progenitors. SPC-derived pulmonary myofibroblasts demonstrated a more than 300-fold increase in fibronectin-1 (Fn1), collagen, type 1, α1, integrin α-5 (Itga5), and integrin β-1 (Itgb1) transcript levels. Very late antigen-5 (ITGA5/ITGB1) protein cluster formations were also prevalent on the differentiated cells. Normalized SPC-derived myofibroblast expression patterns reflected those of primary cultured lung myofibroblasts. Intratracheal implantation of pulmonary AICs into recipient mouse lungs resulted in donor cell FN1 production and evidence of epithelial derivation. SPC derivation into stromal tissue in vitro and in vivo and the observation that MSC and fibroblast lineages share a common ancestor could potentially lead to personalized antifibrotic therapies. ©AlphaMed Press.

Landscape conditions predisposing grizzly bears to conflicts on private agricultural lands in the western USA

USGS Publications Warehouse

Wilson, S.M.; Madel, M.J.; Mattson, D.J.; Graham, J.M.; Merrill, T.

2006-01-01

We used multiple logistic regression to model how different landscape conditions contributed to the probability of human-grizzly bear conflicts on private agricultural ranch lands. We used locations of livestock pastures, traditional livestock carcass disposal areas (boneyards), beehives, and wetland-riparian associated vegetation to model the locations of 178 reported human-grizzly bear conflicts along the Rocky Mountain East Front, Montana, USA during 1986-2001. We surveyed 61 livestock producers in the upper Teton watershed of north-central Montana, to collect spatial and temporal data on livestock pastures, boneyards, and beehives for the same period, accounting for changes in livestock and boneyard management and beehive location and protection, for each season. We used 2032 random points to represent the null hypothesis of random location relative to potential explanatory landscape features, and used Akaike's Information Criteria (AIC/AICC) and Hosmer-Lemeshow goodness-of-fit statistics for model selection. We used a resulting "best" model to map contours of predicted probabilities of conflict, and used this map for verification with an independent dataset of conflicts to provide additional insights regarding the nature of conflicts. The presence of riparian vegetation and distances to spring, summer, and fall sheep or cattle pastures, calving and sheep lambing areas, unmanaged boneyards, and fenced and unfenced beehives were all associated with the likelihood of human-grizzly bear conflicts. Our model suggests that collections of attractants concentrated in high quality bear habitat largely explain broad patterns of human-grizzly bear conflicts on private agricultural land in our study area. ?? 2005 Elsevier Ltd. All rights reserved.

Comparing a medical records-based and a claims-based index for measuring comorbidity in patients with lung or colon cancer.

PubMed

Kehl, Kenneth L; Lamont, Elizabeth B; McNeil, Barbara J; Bozeman, Samuel R; Kelley, Michael J; Keating, Nancy L

2015-05-01

Ascertaining comorbid conditions in cancer patients is important for research and clinical quality measurement, and is particularly important for understanding care and outcomes for older patients and those with multi-morbidity. We compared the medical records-based ACE-27 index and the claims-based Charlson index in predicting receipt of therapy and survival for lung and colon cancer patients. We calculated the Charlson index using administrative data and the ACE-27 score using medical records for Veterans Affairs patients diagnosed with stage I/II non-small cell lung or stage III colon cancer from January 2003 to December 2004. We compared the proportion of patients identified by each index as having any comorbidity. We used multivariable logistic regression to ascertain the predictive power of each index regarding delivery of guideline-recommended therapies and two-year survival, comparing the c-statistic and the Akaike information criterion (AIC). Overall, 97.2% of lung and 90.9% of colon cancer patients had any comorbidity according to the ACE-27 index, versus 59.5% and 49.7%, respectively, according to the Charlson. Multivariable models including the ACE-27 index outperformed Charlson-based models when assessing receipt of guideline-recommended therapies, with higher c-statistics and lower AICs. Neither index was clearly superior in prediction of two-year survival. The ACE-27 index measured using medical records captured more comorbidity and outperformed the Charlson index measured using administrative data for predicting receipt of guideline-recommended therapies, demonstrating the potential value of more detailed comorbidity data. However, the two indices had relatively similar performance when predicting survival. Copyright © 2015 Elsevier Inc. All rights reserved.

Effects of human recreation on the incubation behavior of American Oystercatchers

USGS Publications Warehouse

McGowan, C.P.; Simons, T.R.

2006-01-01

Human recreational disturbance and its effects on wildlife demographics and behavior is an increasingly important area of research. We monitored the nesting success of American Oystercatchers (Haematopus palliatus) in coastal North Carolina in 2002 and 2003. We also used video monitoring at nests to measure the response of incubating birds to human recreation. We counted the number of trips per hour made by adult birds to and from the nest, and we calculated the percent time that adults spent incubating. We asked whether human recreational activities (truck, all-terrain vehicle [ATV], and pedestrian traffic) were correlated with parental behavioral patterns. Eleven a priori models of nest survival and behavioral covariates were evaluated using Akaike's Information Criterion (AIC) to see whether incubation behavior influenced nest survival. Factors associated with birds leaving their nests (n = 548) included ATV traffic (25%), truck traffic (17%), pedestrian traffic (4%), aggression with neighboring oystercatchers or paired birds exchanging incubation duties (26%), airplane traffic (1%) and unknown factors (29%). ATV traffic was positively associated with the rate of trips to and away from the nest (??1 = 0.749, P < 0.001) and negatively correlated with percent time spent incubating (??1 = -0.037, P = 0.025). Other forms of human recreation apparently had little effect on incubation behaviors. Nest survival models incorporating the frequency of trips by adults to and from the nest, and the percentage of time adults spent incubating, were somewhat supported in the AIC analyses. A low frequency of trips to and from the nest and, counter to expectations, low percent time spent incubating were associated with higher daily nest survival rates. These data suggest that changes in incubation behavior might be one mechanism by which human recreation affects the reproductive success of American Oystercatchers.

Limb muscle sound speed estimation by ultrasound computed tomography excluding receivers in bone shadow

NASA Astrophysics Data System (ADS)

Qu, Xiaolei; Azuma, Takashi; Lin, Hongxiang; Takeuchi, Hideki; Itani, Kazunori; Tamano, Satoshi; Takagi, Shu; Sakuma, Ichiro

2017-03-01

Sarcopenia is the degenerative loss of skeletal muscle ability associated with aging. One reason is the increasing of adipose ratio of muscle, which can be estimated by the speed of sound (SOS), since SOSs of muscle and adipose are different (about 7%). For SOS imaging, the conventional bent-ray method iteratively finds ray paths and corrects SOS along them by travel-time. However, the iteration is difficult to converge for soft tissue with bone inside, because of large speed variation. In this study, the bent-ray method is modified to produce SOS images for limb muscle with bone inside. The modified method includes three steps. First, travel-time is picked up by a proposed Akaike Information Criterion (AIC) with energy term (AICE) method. The energy term is employed for detecting and abandoning the transmissive wave through bone (low energy wave). It results in failed reconstruction for bone, but makes iteration convergence and gives correct SOS for skeletal muscle. Second, ray paths are traced using Fermat's principle. Finally, simultaneous algebraic reconstruction technique (SART) is employed to correct SOS along ray paths, but excluding paths with low energy wave which may pass through bone. The simulation evaluation was implemented by k-wave toolbox using a model of upper arm. As the result, SOS of muscle was 1572.0+/-7.3 m/s, closing to 1567.0 m/s in the model. For vivo evaluation, a ring transducer prototype was employed to scan the cross sections of lower arm and leg of a healthy volunteer. And the skeletal muscle SOSs were 1564.0+/-14.8 m/s and 1564.1±18.0 m/s, respectively.

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

Jesus, J.F.; Valentim, R.; Andrade-Oliveira, F., E-mail: jfjesus@itapeva.unesp.br, E-mail: valentim.rodolfo@unifesp.br, E-mail: felipe.oliveira@port.ac.uk

Creation of Cold Dark Matter (CCDM), in the context of Einstein Field Equations, produces a negative pressure term which can be used to explain the accelerated expansion of the Universe. In this work we tested six different spatially flat models for matter creation using statistical criteria, in light of SNe Ia data: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Bayesian Evidence (BE). These criteria allow to compare models considering goodness of fit and number of free parameters, penalizing excess of complexity. We find that JO model is slightly favoured over LJO/ΛCDM model, however, neither of these, nor Γmore » = 3α H {sub 0} model can be discarded from the current analysis. Three other scenarios are discarded either because poor fitting or because of the excess of free parameters. A method of increasing Bayesian evidence through reparameterization in order to reducing parameter degeneracy is also developed.« less

Proportional exponentiated link transformed hazards (ELTH) models for discrete time survival data with application

PubMed Central

Joeng, Hee-Koung; Chen, Ming-Hui; Kang, Sangwook

2015-01-01

Discrete survival data are routinely encountered in many fields of study including behavior science, economics, epidemiology, medicine, and social science. In this paper, we develop a class of proportional exponentiated link transformed hazards (ELTH) models. We carry out a detailed examination of the role of links in fitting discrete survival data and estimating regression coefficients. Several interesting results are established regarding the choice of links and baseline hazards. We also characterize the conditions for improper survival functions and the conditions for existence of the maximum likelihood estimates under the proposed ELTH models. An extensive simulation study is conducted to examine the empirical performance of the parameter estimates under the Cox proportional hazards model by treating discrete survival times as continuous survival times, and the model comparison criteria, AIC and BIC, in determining links and baseline hazards. A SEER breast cancer dataset is analyzed in details to further demonstrate the proposed methodology. PMID:25772374

Do classic blood biomarkers of JSLE identify active lupus nephritis? Evidence from the UK JSLE Cohort Study.

PubMed

Smith, E M D; Jorgensen, A L; Beresford, M W

2017-10-01

Background Lupus nephritis (LN) affects up to 80% of juvenile-onset systemic lupus erythematosus (JSLE) patients. The value of commonly available biomarkers, such as anti-dsDNA antibodies, complement (C3/C4), ESR and full blood count parameters in the identification of active LN remains uncertain. Methods Participants from the UK JSLE Cohort Study, aged <16 years at diagnosis, were categorized as having active or inactive LN according to the renal domain of the British Isles Lupus Assessment Group score. Classic biomarkers: anti-dsDNA, C3, C4, ESR, CRP, haemoglobin, total white cells, neutrophils, lymphocytes, platelets and immunoglobulins were assessed for their ability to identify active LN using binary logistic regression modeling, with stepAIC function applied to select a final model. Receiver-operating curve analysis was used to assess diagnostic accuracy. Results A total of 370 patients were recruited; 191 (52%) had active LN and 179 (48%) had inactive LN. Binary logistic regression modeling demonstrated a combination of ESR, C3, white cell count, neutrophils, lymphocytes and IgG to be best for the identification of active LN (area under the curve 0.724). Conclusions At best, combining common classic blood biomarkers of lupus activity using multivariate analysis provides a 'fair' ability to identify active LN. Urine biomarkers were not included in these analyses. These results add to the concern that classic blood biomarkers are limited in monitoring discrete JSLE manifestations such as LN.

Spatial heterogeneity in fishing creates de facto refugia for endangered Celtic Sea elasmobranchs.

PubMed

Shephard, Samuel; Gerritsen, Hans; Kaiser, Michel J; Reid, David G

2012-01-01

The life history characteristics of some elasmobranchs make them particularly vulnerable to fishing mortality; about a third of all species are listed by the IUCN as Threatened or Near Threatened. Marine Protected Areas (MPAs) have been suggested as a tool for conservation of elasmobranchs, but they are likely to be effective only if such populations respond to fishing impacts at spatial-scales corresponding to MPA size. Using the example of the Celtic Sea, we modelled elasmobranch biomass (kg h(-1)) in fisheries-independent survey hauls as a function of environmental variables and 'local' (within 20 km radius) fishing effort (h y(-1)) recorded from Vessel Monitoring Systems data. Model selection using AIC suggested strongest support for linear mixed effects models in which the variables (i) fishing effort, (ii) geographic location and (iii) demersal fish assemblage had approximately equal importance in explaining elasmobranch biomass. In the eastern Celtic Sea, sampling sites that occurred in the lowest 10% of the observed fishing effort range recorded 10 species of elasmobranch including the critically endangered Dipturus spp. The most intensely fished 10% of sites had only three elasmobranch species, with two IUCN listed as Least Concern. Our results suggest that stable spatial heterogeneity in fishing effort creates de facto refugia for elasmobranchs in the Celtic Sea. However, changes in the present fisheries management regime could impair the refuge effect by changing fisher's behaviour and displacing effort into these areas.

Spatial Heterogeneity in Fishing Creates de facto Refugia for Endangered Celtic Sea Elasmobranchs

PubMed Central

Shephard, Samuel; Gerritsen, Hans; Kaiser, Michel J.; Reid, David G.

2012-01-01

The life history characteristics of some elasmobranchs make them particularly vulnerable to fishing mortality; about a third of all species are listed by the IUCN as Threatened or Near Threatened. Marine Protected Areas (MPAs) have been suggested as a tool for conservation of elasmobranchs, but they are likely to be effective only if such populations respond to fishing impacts at spatial-scales corresponding to MPA size. Using the example of the Celtic Sea, we modelled elasmobranch biomass (kg h−1) in fisheries-independent survey hauls as a function of environmental variables and ‘local’ (within 20 km radius) fishing effort (h y−1) recorded from Vessel Monitoring Systems data. Model selection using AIC suggested strongest support for linear mixed effects models in which the variables (i) fishing effort, (ii) geographic location and (iii) demersal fish assemblage had approximately equal importance in explaining elasmobranch biomass. In the eastern Celtic Sea, sampling sites that occurred in the lowest 10% of the observed fishing effort range recorded 10 species of elasmobranch including the critically endangered Dipturus spp. The most intensely fished 10% of sites had only three elasmobranch species, with two IUCN listed as Least Concern. Our results suggest that stable spatial heterogeneity in fishing effort creates de facto refugia for elasmobranchs in the Celtic Sea. However, changes in the present fisheries management regime could impair the refuge effect by changing fisher's behaviour and displacing effort into these areas. PMID:23166635

Estimation of submarine mass failure probability from a sequence of deposits with age dates

USGS Publications Warehouse

Geist, Eric L.; Chaytor, Jason D.; Parsons, Thomas E.; ten Brink, Uri S.

2013-01-01

The empirical probability of submarine mass failure is quantified from a sequence of dated mass-transport deposits. Several different techniques are described to estimate the parameters for a suite of candidate probability models. The techniques, previously developed for analyzing paleoseismic data, include maximum likelihood and Type II (Bayesian) maximum likelihood methods derived from renewal process theory and Monte Carlo methods. The estimated mean return time from these methods, unlike estimates from a simple arithmetic mean of the center age dates and standard likelihood methods, includes the effects of age-dating uncertainty and of open time intervals before the first and after the last event. The likelihood techniques are evaluated using Akaike’s Information Criterion (AIC) and Akaike’s Bayesian Information Criterion (ABIC) to select the optimal model. The techniques are applied to mass transport deposits recorded in two Integrated Ocean Drilling Program (IODP) drill sites located in the Ursa Basin, northern Gulf of Mexico. Dates of the deposits were constrained by regional bio- and magnetostratigraphy from a previous study. Results of the analysis indicate that submarine mass failures in this location occur primarily according to a Poisson process in which failures are independent and return times follow an exponential distribution. However, some of the model results suggest that submarine mass failures may occur quasiperiodically at one of the sites (U1324). The suite of techniques described in this study provides quantitative probability estimates of submarine mass failure occurrence, for any number of deposits and age uncertainty distributions.

Climate-related variation in plant peak biomass and growth phenology across Pacific Northwest tidal marshes

USGS Publications Warehouse

Buffington, Kevin J.; Dugger, Bruce D.; Thorne, Karen M.

2018-01-01

The interannual variability of tidal marsh plant phenology is largely unknown and may have important ecological consequences. Marsh plants are critical to the biogeomorphic feedback processes that build estuarine soils, maintain marsh elevation relative to sea level, and sequester carbon. We calculated Tasseled Cap Greenness, a metric of plant biomass, using remotely sensed data available in the Landsat archive to assess how recent climate variation has affected biomass production and plant phenology across three maritime tidal marshes in the Pacific Northwest of the United States. First, we used clipped vegetation plots at one of our sites to confirm that tasseled cap greenness provided a useful measure of aboveground biomass (r2 = 0.72). We then used multiple measures of biomass each growing season over 20–25 years per study site and developed models to test how peak biomass and the date of peak biomass varied with 94 climate and sea-level metrics using generalized linear models and Akaike Information Criterion (AIC) model selection. Peak biomass was positively related to total annual precipitation, while the best predictor for date of peak biomass was average growing season temperature, with the peak 7.2 days earlier per degree C. Our study provides insight into how plants in maritime tidal marshes respond to interannual climate variation and demonstrates the utility of time-series remote sensing data to assess ecological responses to climate stressors.

Evolution of Post-accretion-induced Collapse Binaries: The Effect of Evaporation

NASA Astrophysics Data System (ADS)

Liu, Wei-Min; Li, Xiang-Dong

2017-12-01

Accretion-induced collapse (AIC) is widely accepted to be one of the formation channels for millisecond pulsars (MSPs). Since the MSPs have high spin-down luminosities, they can immediately start to evaporate their companion stars after birth. In this paper, we present a detailed investigation on the evolution of the post-AIC binaries, taking into account the effect of evaporation both before and during the Roche-lobe overflow process. We discuss the possible influence of the input parameters including the evaporation efficiency, the initial spin period, and the initial surface magnetic field of the newborn neutron star. We compare the calculated results with the traditional low-mass X-ray binary evolution and suggest that they may reproduce at least part of the observed redbacks and black widows in the companion mass–orbital period plane depending on the mechanisms of angular momentum loss associated with evaporation.

Research on unsteady transonic flow theory

NASA Technical Reports Server (NTRS)

Revell, J. D.

1973-01-01

A two-dimensional theory is considered for the unsteady flow disturbances caused by aeroelastic deformations of a thick wing at high subsonic freestream Mach numbers, having a single, internally embedded supercritical (locally supersonic) steady flow region adjacent to the low pressure side of the wing. The theory develops a matrix of unsteady aerodynamic influence coefficients (AICs) suitable as a strip theory for aeroelastic analysis of large aspect ratio thick wings of moderate sweep, typical of a wide class of current and future aircraft. The theory derives the linearized unsteady flow solutions separately for both the subcritical and supercritical regions. These solutions are coupled together to give the requisite (wing pressure-downwash) AICs by the intermediate step of defining flow disturbances on the sonic line, and at the shock wave; these intermediate quantities are then algebraically eliminated by expressing them in terms of the wing surface downwash.

Lee-Carter state space modeling: Application to the Malaysia mortality data

NASA Astrophysics Data System (ADS)

Zakiyatussariroh, W. H. Wan; Said, Z. Mohammad; Norazan, M. R.

2014-06-01

This article presents an approach that formalizes the Lee-Carter (LC) model as a state space model. Maximum likelihood through Expectation-Maximum (EM) algorithm was used to estimate the model. The methodology is applied to Malaysia's total population mortality data. Malaysia's mortality data was modeled based on age specific death rates (ASDR) data from 1971-2009. The fitted ASDR are compared to the actual observed values. However, results from the comparison of the fitted and actual values between LC-SS model and the original LC model shows that the fitted values from the LC-SS model and original LC model are quite close. In addition, there is not much difference between the value of root mean squared error (RMSE) and Akaike information criteria (AIC) from both models. The LC-SS model estimated for this study can be extended for forecasting ASDR in Malaysia. Then, accuracy of the LC-SS compared to the original LC can be further examined by verifying the forecasting power using out-of-sample comparison.

Expectation-maximization algorithms for learning a finite mixture of univariate survival time distributions from partially specified class values

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

Lee, Youngrok

2013-05-15

Heterogeneity exists on a data set when samples from di erent classes are merged into the data set. Finite mixture models can be used to represent a survival time distribution on heterogeneous patient group by the proportions of each class and by the survival time distribution within each class as well. The heterogeneous data set cannot be explicitly decomposed to homogeneous subgroups unless all the samples are precisely labeled by their origin classes; such impossibility of decomposition is a barrier to overcome for estimating nite mixture models. The expectation-maximization (EM) algorithm has been used to obtain maximum likelihood estimates ofmore » nite mixture models by soft-decomposition of heterogeneous samples without labels for a subset or the entire set of data. In medical surveillance databases we can find partially labeled data, that is, while not completely unlabeled there is only imprecise information about class values. In this study we propose new EM algorithms that take advantages of using such partial labels, and thus incorporate more information than traditional EM algorithms. We particularly propose four variants of the EM algorithm named EM-OCML, EM-PCML, EM-HCML and EM-CPCML, each of which assumes a specific mechanism of missing class values. We conducted a simulation study on exponential survival trees with five classes and showed that the advantages of incorporating substantial amount of partially labeled data can be highly signi cant. We also showed model selection based on AIC values fairly works to select the best proposed algorithm on each specific data set. A case study on a real-world data set of gastric cancer provided by Surveillance, Epidemiology and End Results (SEER) program showed a superiority of EM-CPCML to not only the other proposed EM algorithms but also conventional supervised, unsupervised and semi-supervised learning algorithms.« less

Punishing Unfairness: Rewarding or the Organization of a Reactively Aggressive Response?

PubMed Central

White, Stuart F.; Brislin, Sarah J.; Sinclair, Stephen; Blair, James R.

2014-01-01

Objectives The neural correlates of human cooperative behavior remain poorly understood. Previous work has suggested that increases in striatal activation while punishing unfair offers represents reward signaling. However, other regions are also implicated when punishing others, for example dorsomedial frontal cortex (dmFC), anterior insula cortex (AIC), and periaqueductal gray (PAG). Moreover, the response of other regions implicated in signaling reward, for example ventromedial prefrontal cortex (vmPFC) or posterior cingulate cortex (PCC), has not been systematically examined. Experimental Design Functional magnetic resonance imaging utilizing parametric modulation was conducted on 21 healthy adults participating in a social exchange paradigm. Principal Observations Participants showed significant positive modulation of activity as a function of delivered punishment in caudate, dmFC, AIC, and PAG; that is, higher punishments by participants of unsatisfactory offers were associated with increasing activity within these regions. However, participants showed significant negative modulation of activity as a function of delivered punishment in vmPFC and PCC; increases in punishment level by participants were associated with decreases in activity within these regions. Conclusions The current data question whether caudate activity when punishing unfair offers should be considered to indicate the reward value of this punishment. Instead, this activity, in conjunction with activity within dmFC, AIC, and PAG, may represent the organization of an untypical, punishing response that represents a reactive aggressive response to provocation. Notably, an inverse, regulatory relationship between vmPFC and PAG activity has been previously implicated in the context of another stimulus for reactive aggression; looming threat (Mobbs et al. [2007]: Science 317:1079–1083). PMID:23868733

Cortico-subcortical activation patterns for itch and pain imagery.

PubMed

Mochizuki, Hideki; Baumgärtner, Ulf; Kamping, Sandra; Ruttorf, Michaela; Schad, Lothar R; Flor, Herta; Kakigi, Ryusuke; Treede, Rolf-Detlef

2013-10-01

The imagery of itch and pain evokes emotional responses and covert motor responses (scratching to itch and withdrawal to pain). This suggests some similarity in cerebral mechanisms. However, itch is more socially contagious than pain, as evidenced by the fact that scratching behaviors can be easily initiated by watching itch-inducing situations, whereas withdrawal is less easily initiated by watching painful situations. Thus, we assumed that the cerebral mechanisms of itch imagery partly differ from those of pain imagery in particular with respect to motor regions. We addressed this issue in 18 healthy subjects using functional magnetic resonance imaging. The subjects were instructed to imagine itch and pain sensations in their own bodies while viewing pictures depicting stimuli associated with these sensations. Itch and pain imagery activated the anterior insular cortex (aIC) and motor-related regions such as supplementary motor area, basal ganglia, thalamus, and cerebellum. Activity in these regions was not significantly different between itch and pain imagery. However, functional connectivity between motor-related regions and the aIC showed marked differences between itch and pain imagery. Connectivity with the aIC was stronger in the primary motor and premotor cortices during pain imagery and stronger in the globus pallidus during itch imagery. These findings indicate that brain regions associated with imagery of itch are the same as those involved in imagery of pain, but their functional networks differ. These differences in brain networks may explain why motor responses to itch are more socially contagious than those related to pain. Copyright © 2013 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.

Comparison of the 7(th) and proposed 8(th) editions of the AJCC/UICC TNM staging system for non-small cell lung cancer undergoing radical surgery.

PubMed

Jin, Ying; Chen, Ming; Yu, Xinmin

2016-09-19

The present study aims to compare the 7(th) and the proposed 8(th) edition of the AJCC/UICC TNM staging system for NSCLC in a cohort of patients from a single institution. A total of 408 patients with NSCLC who underwent radical surgery were analyzed retrospectively. Survivals were analyzed using the Kaplan -Meier method and were compared using the log-rank test. Multivariate analysis was performed by the Cox proportional hazard model. The Akaike information criterion (AIC) and C-index were applied to compare the two prognostic systems with different numbers of stages. The 7(th) AJCC T categories, the proposed 8(th) AJCC T categories, N categories, visceral pleural invasion, and vessel invasion were found to have statistically significant associations with disease-free survival (DFS) on univariate analysis. In the 7(th) edition staging system as well as in the proposed 8(th) edition, T categories, N categories, and pleural invasion were independent factors for DFS on multivariate analysis. The AIC value was smaller for the 8(th) edition compared to the 7(th) edition staging system. The C-index value was larger for the 8(th) edition compared to the 7(th) edition staging system. Based on the data from our single center, the proposed 8(th) AJCC T classification seems to be superior to the 7(th) AJCC T classification in terms of DFS for patients with NSCLC underwent radical surgery.

Modeling Tetanus Neonatorum case using the regression of negative binomial and zero-inflated negative binomial

NASA Astrophysics Data System (ADS)

Amaliana, Luthfatul; Sa'adah, Umu; Wayan Surya Wardhani, Ni

2017-12-01

Tetanus Neonatorum is an infectious disease that can be prevented by immunization. The number of Tetanus Neonatorum cases in East Java Province is the highest in Indonesia until 2015. Tetanus Neonatorum data contain over dispersion and big enough proportion of zero-inflation. Negative Binomial (NB) regression is an alternative method when over dispersion happens in Poisson regression. However, the data containing over dispersion and zero-inflation are more appropriately analyzed by using Zero-Inflated Negative Binomial (ZINB) regression. The purpose of this study are: (1) to model Tetanus Neonatorum cases in East Java Province with 71.05 percent proportion of zero-inflation by using NB and ZINB regression, (2) to obtain the best model. The result of this study indicates that ZINB is better than NB regression with smaller AIC.

Kinetics of Methane Production from Swine Manure and Buffalo Manure.

PubMed

Sun, Chen; Cao, Weixing; Liu, Ronghou

2015-10-01

The degradation kinetics of swine and buffalo manure for methane production was investigated. Six kinetic models were employed to describe the corresponding experimental data. These models were evaluated by two statistical measurements, which were root mean square prediction error (RMSPE) and Akaike's information criterion (AIC). The results showed that the logistic and Fitzhugh models could predict the experimental data very well for the digestion of swine and buffalo manure, respectively. The predicted methane yield potential for swine and buffalo manure was 487.9 and 340.4 mL CH4/g volatile solid (VS), respectively, which was close to experimental values, when the digestion temperature was 36 ± 1 °C in the biochemical methane potential assays. Besides, the rate constant revealed that swine manure had a much faster methane production rate than buffalo manure.

Application of Reduced Order Transonic Aerodynamic Influence Coefficient Matrix for Design Optimization

NASA Technical Reports Server (NTRS)

Pak, Chan-gi; Li, Wesley W.

2009-01-01

Supporting the Aeronautics Research Mission Directorate guidelines, the National Aeronautics and Space Administration [NASA] Dryden Flight Research Center is developing a multidisciplinary design, analysis, and optimization [MDAO] tool. This tool will leverage existing tools and practices, and allow the easy integration and adoption of new state-of-the-art software. Today s modern aircraft designs in transonic speed are a challenging task due to the computation time required for the unsteady aeroelastic analysis using a Computational Fluid Dynamics [CFD] code. Design approaches in this speed regime are mainly based on the manual trial and error. Because of the time required for unsteady CFD computations in time-domain, this will considerably slow down the whole design process. These analyses are usually performed repeatedly to optimize the final design. As a result, there is considerable motivation to be able to perform aeroelastic calculations more quickly and inexpensively. This paper will describe the development of unsteady transonic aeroelastic design methodology for design optimization using reduced modeling method and unsteady aerodynamic approximation. The method requires the unsteady transonic aerodynamics be represented in the frequency or Laplace domain. Dynamically linear assumption is used for creating Aerodynamic Influence Coefficient [AIC] matrices in transonic speed regime. Unsteady CFD computations are needed for the important columns of an AIC matrix which corresponded to the primary modes for the flutter. Order reduction techniques, such as Guyan reduction and improved reduction system, are used to reduce the size of problem transonic flutter can be found by the classic methods, such as Rational function approximation, p-k, p, root-locus etc. Such a methodology could be incorporated into MDAO tool for design optimization at a reasonable computational cost. The proposed technique is verified using the Aerostructures Test Wing 2 actually designed, built, and tested at NASA Dryden Flight Research Center. The results from the full order model and the approximate reduced order model are analyzed and compared.

Predicting crappie recruitment in Ohio reservoirs with spawning stock size, larval density, and chlorophyll concentrations

USGS Publications Warehouse

Bunnell, David B.; Hale, R. Scott; Vanni, Michael J.; Stein, Roy A.

2006-01-01

Stock-recruit models typically use only spawning stock size as a predictor of recruitment to a fishery. In this paper, however, we used spawning stock size as well as larval density and key environmental variables to predict recruitment of white crappies Pomoxis annularis and black crappies P. nigromaculatus, a genus notorious for variable recruitment. We sampled adults and recruits from 11 Ohio reservoirs and larvae from 9 reservoirs during 1998-2001. We sampled chlorophyll as an index of reservoir productivity and obtained daily estimates of water elevation to determine the impact of hydrology on recruitment. Akaike's information criterion (AIC) revealed that Ricker and Beverton-Holt stock-recruit models that included chlorophyll best explained the variation in larval density and age-2 recruits. Specifically, spawning stock catch per effort (CPE) and chlorophyll explained 63-64% of the variation in larval density. In turn, larval density and chlorophyll explained 43-49% of the variation in age-2 recruit CPE. Finally, spawning stock CPE and chlorophyll were the best predictors of recruit CPE (i.e., 74-86%). Although larval density and recruitment increased with chlorophyll, neither was related to seasonal water elevation. Also, the AIC generally did not distinguish between Ricker and Beverton-Holt models. From these relationships, we concluded that crappie recruitment can be limited by spawning stock CPE and larval production when spawning stock sizes are low (i.e., CPE , 5 crappies/net-night). At higher levels of spawning stock sizes, spawning stock CPE and recruitment were less clearly related. To predict recruitment in Ohio reservoirs, managers should assess spawning stock CPE with trap nets and estimate chlorophyll concentrations. To increase crappie recruitment in reservoirs where recruitment is consistently poor, managers should use regulations to increase spawning stock size, which, in turn, should increase larval production and recruits to the fishery.

Socioeconomic status and geographical factors associated with active listing in primary care: a cross-sectional population study accounting for multimorbidity, age, sex and primary care

PubMed Central

Ranstad, Karin; Midlöv, Patrik; Halling, Anders

2017-01-01

Background Socioeconomic status and geographical factors are associated with health and use of healthcare. Well-performing primary care contributes to better health and more adequate healthcare. In a primary care system based on patient’s choice of practice, this choice (listing) is a key to understand the system. Objective To explore the relationship between population and practices in a primary care system based on listing. Methods Cross-sectional population-based study. Logistic regressions of the associations between active listing in primary care, income, education, distances to healthcare and geographical location, adjusting for multimorbidity, age, sex and type of primary care practice. Setting and subjects Population over 15 years (n=123 168) in a Swedish county, Blekinge (151 731 inhabitants), in year 2007, actively or passively listed in primary care. The proportion of actively listed was 68%. Main outcome measure Actively listed in primary care on 31 December 2007. Results Highest ORs for active listing in the model including all factors according to income had quartile two and three with OR 0.70 (95% CI 0.69 to 0.70), and those according to education less than 9 years of education had OR 0.70 (95% CI 0.68 to 0.70). Best odds for geographical factors in the same model had municipality C with OR 0.85 (95% CI 0.85 to 0.86) for active listing. Akaike’s Information Criterion (AIC) was 124 801 for a model including municipality, multimorbidity, age, sex and type of practice and including all factors gave AIC 123 934. Conclusions Higher income, shorter education, shorter distance to primary care or longer distance to hospital is associated with active listing in primary care. Multimorbidity, age, geographical location and type of primary care practice are more important to active listing in primary care than socioeconomic status and distance to healthcare. PMID:28601827

MMI: Multimodel inference or models with management implications?

USGS Publications Warehouse

Fieberg, J.; Johnson, Douglas H.

2015-01-01

We consider a variety of regression modeling strategies for analyzing observational data associated with typical wildlife studies, including all subsets and stepwise regression, a single full model, and Akaike's Information Criterion (AIC)-based multimodel inference. Although there are advantages and disadvantages to each approach, we suggest that there is no unique best way to analyze data. Further, we argue that, although multimodel inference can be useful in natural resource management, the importance of considering causality and accurately estimating effect sizes is greater than simply considering a variety of models. Determining causation is far more valuable than simply indicating how the response variable and explanatory variables covaried within a data set, especially when the data set did not arise from a controlled experiment. Understanding the causal mechanism will provide much better predictions beyond the range of data observed. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

Fitting NTCP models to bladder doses and acute urinary symptoms during post-prostatectomy radiotherapy.

PubMed

Mavroidis, Panayiotis; Pearlstein, Kevin A; Dooley, John; Sun, Jasmine; Saripalli, Srinivas; Das, Shiva K; Wang, Andrew Z; Chen, Ronald C

2018-02-02

To estimate the radiobiological parameters of three popular normal tissue complication probability (NTCP) models, which describe the dose-response relations of bladder regarding different acute urinary symptoms during post-prostatectomy radiotherapy (RT). To evaluate the goodness-of-fit and the correlation of those models with those symptoms. Ninety-three consecutive patients treated from 2010 to 2015 with post-prostatectomy image-guided intensity modulated radiotherapy (IMRT) were included in this study. Patient-reported urinary symptoms were collected pre-RT and weekly during treatment using the validated Prostate Cancer Symptom Indices (PCSI). The assessed symptoms were flow, dysuria, urgency, incontinence, frequency and nocturia using a Likert scale of 1 to 4 or 5. For this analysis, an increase by ≥2 levels in a symptom at any time during treatment compared to baseline was considered clinically significant. The dose volume histograms of the bladder were calculated. The Lyman-Kutcher-Burman (LKB), Relative Seriality (RS) and Logit NTCP models were used to fit the clinical data. The fitting of the different models was assessed through the area under the receiver operating characteristic curve (AUC), Akaike information criterion (AIC) and Odds Ratio methods. For the symptoms of urinary urgency, leakage, frequency and nocturia, the derived LKB model parameters were: 1) D 50  = 64.2Gy, m = 0.50, n = 1.0; 2) D 50  = 95.0Gy, m = 0.45, n = 0.50; 3) D 50  = 83.1Gy, m = 0.56, n = 1.00; and 4) D 50  = 85.4Gy, m = 0.60, n = 1.00, respectively. The AUC values for those symptoms were 0.66, 0.58, 0.64 and 0.64, respectively. The differences in AIC between the different models were less than 2 and ranged within 0.1 and 1.3. Different dose metrics were correlated with the symptoms of urgency, incontinence, frequency and nocturia. The symptoms of urinary flow and dysuria were poorly associated with dose. The values of the parameters of three NTCP models were determined for bladder regarding four acute urinary symptoms. All the models could fit the clinical data equally well. The NTCP predictions of urgency showed the best correlation with the patient reported outcomes.

Assessment of Tandem Measurements of pH and Total Gut Transit Time in Healthy Volunteers.

PubMed

Mikolajczyk, Adam E; Watson, Sydeaka; Surma, Bonnie L; Rubin, David T

2015-07-09

The variation of luminal pH and transit time in an individual is unknown, yet is necessary to interpret single measurements. This study aimed to assess the intrasubject variability of gut pH and transit time in healthy volunteers using SmartPill devices (Covidien, Minneapolis, MN). Each subject (n=10) ingested two SmartPill devices separated by 24 h. Mean pH values were calculated for 30 min after gastric emptying (AGE), before the ileocecal (BIC) valve, after the ileocecal (AIC) valve, and before body exit (BBE). Intrasubject variability was determined by comparing mean values from both ingestions for an individual subject using standard deviations, 95% limits of agreement, and Bland-Altman plots. Tandem device ingestion occurred without complication. The median (full range) intrasubject standard deviations for pH were 0.02 (0.0002-0.2048) for AGE, 0.06 (0.0002-0.3445) for BIC, 0.14 (0.0018-0.3042) for AIC, and 0.08 (0.0098-0.5202) for BBE. There was a significant change in pH for AIC (mean difference: -0.45±0.31, P=0.0015) observed across all subjects. The mean coefficients of variation for transit time were 12.0±7.4% and 25.8±15.8% for small and large bowels, respectively (P=0.01). This study demonstrates the safety and feasibility of tandem gut transit and pH assessments using the SmartPill device. In healthy individuals and over 24 h, the gut pH profile does not markedly fluctuate in a given region with more variation seen in the colon compared with the small bowel, which has important implications for future physiology and drug delivery studies.

The Analysis of AGEs and ALEs by Mass Spectrometry: What does the Future Hold?

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

Metz, Thomas O.

2009-09-15

In 1912, Louis-Camille Maillard described a reaction between amino acids and reducing sugars that produced a discolored (brown) reaction mixture in the presence of heat [1]. This complex network of reactions between reducing sugars and free amine groups on amino acids or proteins came to be known as the Maillard reaction and was the domain of food chemists for the next 50 years. Work in the 1960s began a very exciting era in the field. A few years earlier, several groups [2-5] reported on the heterogeneity of normal human adult hemoglobin (HbA) as determined chromatographically, and Allen et al weremore » the first to use cation-exchange chromatography to separate a previously observed fast moving component (HbAI) into three fractions that they termed AIa, AIb, and AIc. An increase in the fast moving HbAI of four diabetic patients was subsequently reported by Huisman and Dozy in 1962 [6], and the link between diabetes and increased HbAI was later strengthened by Rahbar’s observation of increased HbAI – the majority of which is HbAIc – in 47 cases of diabetes [7]. Bookchin and Gallop determined that HbAIc consisted of a hexose bound to both β-chains [8], and Bunn and colleagues subsequently proposed that glucose binds to the N-terminal amine groups of the β-chain valine residues in the form of a Schiff base, which then rearranges to form an Amadori compound [9]. Thus, while Maillard chemistry was known to occur during the heating and processing of food, the identification of Amadori-modified hemoglobin proved that it also occurred in vivo (after all, as John Baynes likes to point out, humans are essentially low temperature ovens with long cooking cycles!).« less

Basis Function Approximation of Transonic Aerodynamic Influence Coefficient Matrix

NASA Technical Reports Server (NTRS)

Li, Wesley Waisang; Pak, Chan-Gi

2010-01-01

A technique for approximating the modal aerodynamic influence coefficients [AIC] matrices by using basis functions has been developed and validated. An application of the resulting approximated modal AIC matrix for a flutter analysis in transonic speed regime has been demonstrated. This methodology can be applied to the unsteady subsonic, transonic and supersonic aerodynamics. The method requires the unsteady aerodynamics in frequency-domain. The flutter solution can be found by the classic methods, such as rational function approximation, k, p-k, p, root-locus et cetera. The unsteady aeroelastic analysis for design optimization using unsteady transonic aerodynamic approximation is being demonstrated using the ZAERO(TradeMark) flutter solver (ZONA Technology Incorporated, Scottsdale, Arizona). The technique presented has been shown to offer consistent flutter speed prediction on an aerostructures test wing [ATW] 2 configuration with negligible loss in precision in transonic speed regime. These results may have practical significance in the analysis of aircraft aeroelastic calculation and could lead to a more efficient design optimization cycle

[Identification of components of metabolic syndrome in Mexican patients hospitalized for acute ischemic coronary syndrome: a tool for prevention].

PubMed

Cárdenas-Villarreal, V M; Vargas-Estrada, M; Hernández González, M A; Flores-Peña, Y; Cerda-Flores, R M

2012-01-01

To determine the prevalence of metabolic syndrome (MS) and its risk factors in patients with Acute Ischemic Coronary Syndrome (AICS) in a tertiary hospital. A total of 65 patients admitted to Cardiac Intensive Care Unit with myocardial infarction or unstable angina participated. MS was diagnosed in accordance to the Adult Treatment Panel III (ATPIII) criteria. The total prevalence of MS was 84.6% (95% CI: 75.6 to 93.6). MS was more frequent in women, persons with obesity according to the body mass index (BMI), family background diabetes, and dyslipidemia. Phenotype predictors of MS were: BMI (OR=2.12, 95% CI: 1.24, 3.17) and familiar history of dyslipidemia (OR=0.026, 95% CI: 0.003, 0.587). The prevalence of MS with AICS is higher than that reported in other populations. This fact is alarming if this risk is maintained in the Mexican population. Copyright © 2011 Elsevier España, S.L. y SEEIUC. All rights reserved.

Successful management of multilineage autoimmune cytopenia complicated with severe infection and deep vein thrombosis in a patient with Hodgkin lymphoma post-autologous hematopoietic stem cell transplantation.

PubMed

Hsu, Wan-Yi; Chiou, Shyh-Shin; Liao, Yu-Mei; Shu, Hsiu-Lan; Zeng, Yu-Sheng; Wong, Cheong-Chew; Lin, Pei-Chin

2016-02-01

Autoimmune cytopenia (AIHA, AITP or AIN) were uncommon paraneoplastic manifestations of HL and have been recognized in patients after HSCT with dismal outcome. We presented a case of 16-yr-old male with Hodgkin's lymphoma who developed severe AIC involving all three cell lineages after autologus bone marrow transplantation. No disease relapse was noted. Treatments with steroid, IVIG and immunosuppresants were in vain and the disease course was complicated with sepsis and deep vein thrombosis. Rituximab was administered along with broad-spectrum antibiotics and low-molecular weight heparin. The condition became stable and pancytopenia recovered after four doses of rituximab treatment. Severe multi-lineage AIC post HSCT is usually refractory to first-line treatment and difficult to manage. Second-line treatment, such as rituximab, and dedicated care for pancytopenia-induced or treatment-related complications may provide a better outcome. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Comparison of alternatives to amplitude thresholding for onset detection of acoustic emission signals

NASA Astrophysics Data System (ADS)

Bai, F.; Gagar, D.; Foote, P.; Zhao, Y.

2017-02-01

Acoustic Emission (AE) monitoring can be used to detect the presence of damage as well as determine its location in Structural Health Monitoring (SHM) applications. Information on the time difference of the signal generated by the damage event arriving at different sensors in an array is essential in performing localisation. Currently, this is determined using a fixed threshold which is particularly prone to errors when not set to optimal values. This paper presents three new methods for determining the onset of AE signals without the need for a predetermined threshold. The performance of the techniques is evaluated using AE signals generated during fatigue crack growth and compared to the established Akaike Information Criterion (AIC) and fixed threshold methods. It was found that the 1D location accuracy of the new methods was within the range of < 1 - 7.1 % of the monitored region compared to 2.7% for the AIC method and a range of 1.8-9.4% for the conventional Fixed Threshold method at different threshold levels.

Do climate variables and human density affect Achatina fulica (Bowditch) (Gastropoda: Pulmonata) shell length, total weight and condition factor?

PubMed

Albuquerque, F S; Peso-Aguiar, M C; Assunção-Albuquerque, M J T; Gálvez, L

2009-08-01

The length-weight relationship and condition factor have been broadly investigated in snails to obtain the index of physical condition of populations and evaluate habitat quality. Herein, our goal was to describe the best predictors that explain Achatina fulica biometrical parameters and well being in a recently introduced population. From November 2001 to November 2002, monthly snail samples were collected in Lauro de Freitas City, Bahia, Brazil. Shell length and total weight were measured in the laboratory and the potential curve and condition factor were calculated. Five environmental variables were considered: temperature range, mean temperature, humidity, precipitation and human density. Multiple regressions were used to generate models including multiple predictors, via model selection approach, and then ranked with AIC criteria. Partial regressions were used to obtain the separated coefficients of determination of climate and human density models. A total of 1.460 individuals were collected, presenting a shell length range between 4.8 to 102.5 mm (mean: 42.18 mm). The relationship between total length and total weight revealed that Achatina fulica presented a negative allometric growth. Simple regression indicated that humidity has a significant influence on A. fulica total length and weight. Temperature range was the main variable that influenced the condition factor. Multiple regressions showed that climatic and human variables explain a small proportion of the variance in shell length and total weight, but may explain up to 55.7% of the condition factor variance. Consequently, we believe that the well being and biometric parameters of A. fulica can be influenced by climatic and human density factors.

Modelling dengue fever risk in the State of Yucatan, Mexico using regional-scale satellite-derived sea surface temperature.

PubMed

Laureano-Rosario, Abdiel E; Garcia-Rejon, Julian E; Gomez-Carro, Salvador; Farfan-Ale, Jose A; Muller-Karger, Frank E

2017-08-01

Accurately predicting vector-borne diseases, such as dengue fever, is essential for communities worldwide. Changes in environmental parameters such as precipitation, air temperature, and humidity are known to influence dengue fever dynamics. Furthermore, previous studies have shown how oceanographic variables, such as El Niño Southern Oscillation (ENSO)-related sea surface temperature from the Pacific Ocean, influences dengue fever in the Americas. However, literature is lacking on the use of regional-scale satellite-derived sea surface temperature (SST) to assess its relationship with dengue fever in coastal areas. Data on confirmed dengue cases, demographics, precipitation, and air temperature were collected. Incidence of weekly dengue cases was examined. Stepwise multiple regression analyses (AIC model selection) were used to assess which environmental variables best explained increased dengue incidence rates. SST, minimum air temperature, precipitation, and humidity substantially explained 42% of the observed variation (r 2 =0.42). Infectious diseases are characterized by the influence of past cases on current cases and results show that previous dengue cases alone explained 89% of the variation. Ordinary least-squares analyses showed a positive trend of 0.20±0.03°C in SST from 2006 to 2015. An important element of this study is to help develop strategic recommendations for public health officials in Mexico by providing a simple early warning capability for dengue incidence. Copyright © 2017 Elsevier B.V. All rights reserved.

Demand for pneumococcal vaccination under subsidy program for the elderly in Japan.

PubMed

Kondo, Masahide; Yamamura, Mariko; Hoshi, Shu-Ling; Okubo, Ichiro

2012-09-12

Vaccination programs often organize subsidies and public relations in order to obtain high uptake rates and coverage. However, effects of subsidies and public relations have not been studied well in the literature. In this study, the demand function of pneumococcal vaccination among the elderly in Japan is estimated, incorporating effects of public relations and subsidy. Using a data from a questionnaire survey sent to municipalities, the varying and constant elasticity models were applied to estimate the demand function. The response variable is the uptake rate. Explanatory variables are: subsidy supported shot price, operating years of the program, target population size for vaccination, shot location intensity, income and various public relations tools. The best model is selected by c-AIC, and varying and constant price elasticities are calculated from estimation results. The vaccine uptake rate and the shot price have a negative relation. From the results of varying price elasticity, the demand for vaccination is elastic at municipalities with a shot price higher than 3,708 JPY (35.7 USD). Effects of public relations on the uptake rate are not found. It can be suggested that municipalities with a shot price higher than 3,708 JPY (35.7 USD) could subsidize more and reduce price to increase the demand for vaccination. Effects of public relations are not confirmed in this study, probably due to measurement errors of variables used for public relations, and studies at micro level exploring individual's response to public relations would be required.

A testable model of earthquake probability based on changes in mean event size

NASA Astrophysics Data System (ADS)

Imoto, Masajiro

2003-02-01

We studied changes in mean event size using data on microearthquakes obtained from a local network in Kanto, central Japan, from a viewpoint that a mean event size tends to increase as the critical point is approached. A parameter describing changes was defined using a simple weighting average procedure. In order to obtain the distribution of the parameter in the background, we surveyed values of the parameter from 1982 to 1999 in a 160 × 160 × 80 km volume. The 16 events of M5.5 or larger in this volume were selected as target events. The conditional distribution of the parameter was estimated from the 16 values, each of which referred to the value immediately prior to each target event. The distribution of the background becomes a function of symmetry, the center of which corresponds to no change in b value. In contrast, the conditional distribution exhibits an asymmetric feature, which tends to decrease the b value. The difference in the distributions between the two groups was significant and provided us a hazard function for estimating earthquake probabilities. Comparing the hazard function with a Poisson process, we obtained an Akaike Information Criterion (AIC) reduction of 24. This reduction agreed closely with the probability gains of a retrospective study in a range of 2-4. A successful example of the proposed model can be seen in the earthquake of 3 June 2000, which is the only event during the period of prospective testing.

Testing the Distance-Duality Relation in the Rh = ct Universe

NASA Astrophysics Data System (ADS)

Hu, J.; Wang, F. Y.

2018-04-01

In this paper, we test the cosmic distance duality (CDD) relation using the luminosity distances from joint light-curve analysis (JLA) type Ia supernovae (SNe Ia) sample and angular diameter distance sample from galaxy clusters. The Rh = ct and ΛCDM models are considered. In order to compare the two models, we constrain the CCD relation and the SNe Ia light-curve parameters simultaneously. Considering the effects of Hubble constant, we find that η ≡ DA(1 + z)2/DL = 1 is valid at the 2σ confidence level in both models with H0 = 67.8 ± 0.9 km/s/Mpc. However, the CDD relation is valid at 3σ confidence level with H0 = 73.45 ± 1.66 km/s/Mpc. Using the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), we find that the ΛCDM model is very strongly preferred over the Rh = ct model with these data sets for the CDD relation test.

Lifetime assessment by intermittent inspection under the mixture Weibull power law model with application to XLPE cables.

PubMed

Hirose, H

1997-01-01

This paper proposes a new treatment for electrical insulation degradation. Some types of insulation which have been used under various circumstances are considered to degrade at various rates in accordance with their stress circumstances. The cross-linked polyethylene (XLPE) insulated cables inspected by major Japanese electric companies clearly indicate such phenomena. By assuming that the inspected specimen is sampled from one of the clustered groups, a mixed degradation model can be constructed. Since the degradation of the insulation under common circumstances is considered to follow a Weibull distribution, a mixture model and a Weibull power law can be combined. This is called The mixture Weibull power law model. By using the maximum likelihood estimation for the newly proposed model to Japanese 22 and 33 kV insulation class cables, they are clustered into a certain number of groups by using the AIC and the generalized likelihood ratio test method. The reliability of the cables at specified years are assessed.

New observational constraints on f ( R ) gravity from cosmic chronometers

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

Nunes, Rafael C.; Pan, Supriya; Saridakis, Emmanuel N.

We use the recently released cosmic chronometer data and the latest measured value of the local Hubble parameter, combined with the latest joint light curves of Supernovae Type Ia, and Baryon Acoustic Oscillation distance measurements, in order to impose constraints on the viable and most used f ( R ) gravity models. We consider four f ( R ) models, namely the Hu-Sawicki, the Starobinsky, the Tsujikawa, and the exponential one, and we parametrize them introducing a distortion parameter b that quantifies the deviation from ΛCDM cosmology. Our analysis reveals that a small but non-zero deviation from ΛCDM cosmology ismore » slightly favored, with the corresponding fittings exhibiting very efficient AIC and BIC Information Criteria values. Clearly, f ( R ) gravity is consistent with observations, and it can serve as a candidate for modified gravity.« less

Scaling cosmology with variable dark-energy equation of state

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

Castro, David R.; Velten, Hermano; Zimdahl, Winfried, E-mail: drodriguez-ufes@hotmail.com, E-mail: velten@physik.uni-bielefeld.de, E-mail: winfried.zimdahl@pq.cnpq.br

2012-06-01

Interactions between dark matter and dark energy which result in a power-law behavior (with respect to the cosmic scale factor) of the ratio between the energy densities of the dark components (thus generalizing the ΛCDM model) have been considered as an attempt to alleviate the cosmic coincidence problem phenomenologically. We generalize this approach by allowing for a variable equation of state for the dark energy within the CPL-parametrization. Based on analytic solutions for the Hubble rate and using the Constitution and Union2 SNIa sets, we present a statistical analysis and classify different interacting and non-interacting models according to the Akaikemore » (AIC) and the Bayesian (BIC) information criteria. We do not find noticeable evidence for an alleviation of the coincidence problem with the mentioned type of interaction.« less

Estimating distribution and connectivity of recolonizing American marten in the northeastern United States using expert elicitation techniques

USGS Publications Warehouse

Aylward, C.M.; Murdoch, J.D.; Donovan, Therese M.; Kilpatrick, C.W.; Bernier, C.; Katz, J.

2018-01-01

The American marten Martes americana is a species of conservation concern in the northeastern United States due to widespread declines from over‐harvesting and habitat loss. Little information exists on current marten distribution and how landscape characteristics shape patterns of occupancy across the region, which could help develop effective recovery strategies. The rarity of marten and lack of historical distribution records are also problematic for region‐wide conservation planning. Expert opinion can provide a source of information for estimating species–landscape relationships and is especially useful when empirical data are sparse. We created a survey to elicit expert opinion and build a model that describes marten occupancy in the northeastern United States as a function of landscape conditions. We elicited opinions from 18 marten experts that included wildlife managers, trappers and researchers. Each expert estimated occupancy probability at 30 sites in their geographic region of expertise. We, then, fit the response data with a set of 58 models that incorporated the effects of covariates related to forest characteristics, climate, anthropogenic impacts and competition at two spatial scales (1.5 and 5 km radii), and used model selection techniques to determine the best model in the set. Three top models had strong empirical support, which we model averaged based on AIC weights. The final model included effects of five covariates at the 5‐km scale: percent canopy cover (positive), percent spruce‐fir land cover (positive), winter temperature (negative), elevation (positive) and road density (negative). A receiver operating characteristic curve indicated that the model performed well based on recent occurrence records. We mapped distribution across the region and used circuit theory to estimate movement corridors between isolated core populations. The results demonstrate the effectiveness of expert‐opinion data at modeling occupancy for rare species and provide tools for planning marten recovery in the northeastern United States.

Three draft genomes of Vibrio coralliilyticus strains isolated from bivalve hatcheries

USDA-ARS?s Scientific Manuscript database

Reported here are the draft genomes of three Vibrio coralliilyticus isolates RE87, AIC-7, and 080116A. Each strain was isolated in association with diseased oyster larvae in commercial aquaculture systems. These draft genomes will be useful for further studies in understanding the genomic features...

Development of a New Measurement Tool for Individualism and Collectivism

ERIC Educational Resources Information Center

Shulruf, Boaz; Hattie, John; Dixon, Robyn

2007-01-01

A new measurement tool for individualism and collectivism has been developed to address critical methodological issues in this field of social psychology. This new measure, the Auckland Individualism and Collectivism Scale (AICS), defines three dimensions of individualism: (a) responsibility (acknowledging one's responsibility for one's actions),…

Design and Development of the Aircraft Instrument Comprehension Program.

ERIC Educational Resources Information Center

Higgins, Norman C.

The Aircraft Instrument Comprehension (AIC) Program is a self-instructional program designed to teach undergraduate student pilots to read instruments that indicate the position of the aircraft in flight, based on sequential instructional stages of information, prompted practice, and unprompted practice. The program includes a 36-item multiple…

Coral reef disturbance and recovery dynamics differ across gradients of localized stressors in the Mariana Islands.

PubMed

Houk, Peter; Benavente, David; Iguel, John; Johnson, Steven; Okano, Ryan

2014-01-01

The individual contribution of natural disturbances, localized stressors, and environmental regimes upon longer-term reef dynamics remains poorly resolved for many locales despite its significance for management. This study examined coral reefs in the Commonwealth of the Northern Mariana Islands across a 12-year period that included elevated Crown-of-Thorns Starfish densities (COTS) and tropical storms that were drivers of spatially-inconsistent disturbance and recovery patterns. At the island scale, disturbance impacts were highest on Saipan with reduced fish sizes, grazing urchins, and water quality, despite having a more favorable geological foundation for coral growth compared with Rota. However, individual drivers of reef dynamics were better quantified through site-level investigations that built upon island generalizations. While COTS densities were the strongest predictors of coral decline as expected, interactive terms that included wave exposure and size of the overall fish assemblages improved models (R2 and AIC values). Both wave exposure and fish size diminished disturbance impacts and had negative associations with COTS. However, contrasting findings emerged when examining net ecological change across the 12-year period. Wave exposure had a ubiquitous, positive influence upon the net change in favorable benthic substrates (i.e. corals and other heavily calcifying substrates, R2 = 0.17 for all reeftypes grouped), yet including interactive terms for herbivore size and grazing urchin densities, as well as stratifying by major reeftypes, substantially improved models (R2 = 0.21 to 0.89, lower AIC scores). Net changes in coral assemblages (i.e., coral ordination scores) were more sensitive to herbivore size or the water quality proxy acting independently (R2 = 0.28 to 0.44). We conclude that COTS densities were the strongest drivers of coral decline, however, net ecological change was most influenced by localized stressors, especially herbivore sizes and grazing urchin densities. Interestingly, fish size, rather than biomass, was consistently a better predictor, supporting allometric, size-and-function relationships of fish assemblages. Management implications are discussed.

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

PubMed

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

2016-01-01

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

Aging, Maturation and Growth of Sauropodomorph Dinosaurs as Deduced from Growth Curves Using Long Bone Histological Data: An Assessment of Methodological Constraints and Solutions

PubMed Central

Griebeler, Eva Maria; Klein, Nicole; Sander, P. Martin

2013-01-01

Information on aging, maturation, and growth is important for understanding life histories of organisms. In extinct dinosaurs, such information can be derived from the histological growth record preserved in the mid-shaft cortex of long bones. Here, we construct growth models to estimate ages at death, ages at sexual maturity, ages at which individuals were fully-grown, and maximum growth rates from the growth record preserved in long bones of six sauropod dinosaur individuals (one indeterminate mamenchisaurid, two Apatosaurus sp., two indeterminate diplodocids, and one Camarasaurus sp.) and one basal sauropodomorph dinosaur individual (Plateosaurus engelhardti). Using these estimates, we establish allometries between body mass and each of these traits and compare these to extant taxa. Growth models considered for each dinosaur individual were the von Bertalanffy model, the Gompertz model, and the logistic model (LGM), all of which have inherently fixed inflection points, and the Chapman-Richards model in which the point is not fixed. We use the arithmetic mean of the age at the inflection point and of the age at which 90% of asymptotic mass is reached to assess respectively the age at sexual maturity or the age at onset of reproduction, because unambiguous indicators of maturity in Sauropodomorpha are lacking. According to an AIC-based model selection process, the LGM was the best model for our sauropodomorph sample. Allometries established are consistent with literature data on other Sauropodomorpha. All Sauropodomorpha reached full size within a time span similar to scaled-up modern mammalian megaherbivores and had similar maximum growth rates to scaled-up modern megaherbivores and ratites, but growth rates of Sauropodomorpha were lower than of an average mammal. Sauropodomorph ages at death probably were lower than that of average scaled-up ratites and megaherbivores. Sauropodomorpha were older at maturation than scaled-up ratites and average mammals, but younger than scaled-up megaherbivores. PMID:23840575

Aging, Maturation and Growth of Sauropodomorph Dinosaurs as Deduced from Growth Curves Using Long Bone Histological Data: An Assessment of Methodological Constraints and Solutions.

PubMed

Griebeler, Eva Maria; Klein, Nicole; Sander, P Martin

2013-01-01

Information on aging, maturation, and growth is important for understanding life histories of organisms. In extinct dinosaurs, such information can be derived from the histological growth record preserved in the mid-shaft cortex of long bones. Here, we construct growth models to estimate ages at death, ages at sexual maturity, ages at which individuals were fully-grown, and maximum growth rates from the growth record preserved in long bones of six sauropod dinosaur individuals (one indeterminate mamenchisaurid, two Apatosaurus sp., two indeterminate diplodocids, and one Camarasaurus sp.) and one basal sauropodomorph dinosaur individual (Plateosaurus engelhardti). Using these estimates, we establish allometries between body mass and each of these traits and compare these to extant taxa. Growth models considered for each dinosaur individual were the von Bertalanffy model, the Gompertz model, and the logistic model (LGM), all of which have inherently fixed inflection points, and the Chapman-Richards model in which the point is not fixed. We use the arithmetic mean of the age at the inflection point and of the age at which 90% of asymptotic mass is reached to assess respectively the age at sexual maturity or the age at onset of reproduction, because unambiguous indicators of maturity in Sauropodomorpha are lacking. According to an AIC-based model selection process, the LGM was the best model for our sauropodomorph sample. Allometries established are consistent with literature data on other Sauropodomorpha. All Sauropodomorpha reached full size within a time span similar to scaled-up modern mammalian megaherbivores and had similar maximum growth rates to scaled-up modern megaherbivores and ratites, but growth rates of Sauropodomorpha were lower than of an average mammal. Sauropodomorph ages at death probably were lower than that of average scaled-up ratites and megaherbivores. Sauropodomorpha were older at maturation than scaled-up ratites and average mammals, but younger than scaled-up megaherbivores.

Within-Site Variation in Feather Stable Hydrogen Isotope (δ2Hf) Values of Boreal Songbirds: Implications for Assignment to Molt Origin.

PubMed

Nordell, Cameron J; Haché, Samuel; Bayne, Erin M; Sólymos, Péter; Foster, Kenneth R; Godwin, Christine M; Krikun, Richard; Pyle, Peter; Hobson, Keith A

2016-01-01

Understanding bird migration and dispersal is important to inform full life-cycle conservation planning. Stable hydrogen isotope ratios from feathers (δ2Hf) can be linked to amount-weighted long-term, growing season precipitation δ2H (δ2Hp) surfaces to create δ2Hf isoscapes for assignment to molt origin. However, transfer functions linking δ2Hp with δ2Hf are influenced by physiological and environmental processes. A better understanding of the causes and consequences of variation in δ2Hf values among individuals and species will improve the predictive ability of geographic assignment tests. We tested for effects of species, land cover, forage substrate, nest substrate, diet composition, body mass, sex, and phylogenetic relatedness on δ2Hf from individuals at least two years old of 21 songbird species captured during the same breeding season at a site in northeastern Alberta, Canada. For four species, we also tested for a year × species interaction effect on δ2Hf. A model including species as single predictor received the most support (AIC weight = 0.74) in explaining variation in δ2Hf. A species-specific variance parameter was part of all best-ranked models, suggesting variation in δ2Hf was not consistent among species. The second best-ranked model included a forage substrate × diet interaction term (AIC weight = 0.16). There was a significant year × species interaction effect on δ2Hf suggesting that interspecific differences in δ2Hf can differ among years. Our results suggest that within- and among-year interspecific variation in δ2Hf is the most important source of variance typically not being explicitly quantified in geographic assignment tests using non-specific transfer functions to convert δ2Hp into δ2Hf. However, this source of variation is consistent with the range of variation from the transfer functions most commonly being propagated in assignment tests of geographic origins for passerines breeding in North America.

Socioeconomic status and geographical factors associated with active listing in primary care: a cross-sectional population study accounting for multimorbidity, age, sex and primary care.

PubMed

Ranstad, Karin; Midlöv, Patrik; Halling, Anders

2017-06-09

Socioeconomic status and geographical factors are associated with health and use of healthcare. Well-performing primary care contributes to better health and more adequate healthcare. In a primary care system based on patient's choice of practice, this choice (listing) is a key to understand the system. To explore the relationship between population and practices in a primary care system based on listing. Cross-sectional population-based study. Logistic regressions of the associations between active listing in primary care, income, education, distances to healthcare and geographical location, adjusting for multimorbidity, age, sex and type of primary care practice. Population over 15 years (n=123 168) in a Swedish county, Blekinge (151 731 inhabitants), in year 2007, actively or passively listed in primary care. The proportion of actively listed was 68%. Actively listed in primary care on 31 December 2007. Highest ORs for active listing in the model including all factors according to income had quartile two and three with OR 0.70 (95% CI 0.69 to 0.70), and those according to education less than 9 years of education had OR 0.70 (95% CI 0.68 to 0.70). Best odds for geographical factors in the same model had municipality C with OR 0.85 (95% CI 0.85 to 0.86) for active listing. Akaike's Information Criterion (AIC) was 124 801 for a model including municipality, multimorbidity, age, sex and type of practice and including all factors gave AIC 123 934. Higher income, shorter education, shorter distance to primary care or longer distance to hospital is associated with active listing in primary care.Multimorbidity, age, geographical location and type of primary care practice are more important to active listing in primary care than socioeconomic status and distance to healthcare. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Using weighted power mean for equivalent square estimation.

PubMed

Zhou, Sumin; Wu, Qiuwen; Li, Xiaobo; Ma, Rongtao; Zheng, Dandan; Wang, Shuo; Zhang, Mutian; Li, Sicong; Lei, Yu; Fan, Qiyong; Hyun, Megan; Diener, Tyler; Enke, Charles

2017-11-01

Equivalent Square (ES) enables the calculation of many radiation quantities for rectangular treatment fields, based only on measurements from square fields. While it is widely applied in radiotherapy, its accuracy, especially for extremely elongated fields, still leaves room for improvement. In this study, we introduce a novel explicit ES formula based on Weighted Power Mean (WPM) function and compare its performance with the Sterling formula and Vadash/Bjärngard's formula. The proposed WPM formula is ESWPMa,b=waα+1-wbα1/α for a rectangular photon field with sides a and b. The formula performance was evaluated by three methods: standard deviation of model fitting residual error, maximum relative model prediction error, and model's Akaike Information Criterion (AIC). Testing datasets included the ES table from British Journal of Radiology (BJR), photon output factors (S cp ) from the Varian TrueBeam Representative Beam Data (Med Phys. 2012;39:6981-7018), and published S cp data for Varian TrueBeam Edge (J Appl Clin Med Phys. 2015;16:125-148). For the BJR dataset, the best-fit parameter value α = -1.25 achieved a 20% reduction in standard deviation in ES estimation residual error compared with the two established formulae. For the two Varian datasets, employing WPM reduced the maximum relative error from 3.5% (Sterling) or 2% (Vadash/Bjärngard) to 0.7% for open field sizes ranging from 3 cm to 40 cm, and the reduction was even more prominent for 1 cm field sizes on Edge (J Appl Clin Med Phys. 2015;16:125-148). The AIC value of the WPM formula was consistently lower than its counterparts from the traditional formulae on photon output factors, most prominent on very elongated small fields. The WPM formula outperformed the traditional formulae on three testing datasets. With increasing utilization of very elongated, small rectangular fields in modern radiotherapy, improved photon output factor estimation is expected by adopting the WPM formula in treatment planning and secondary MU check. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Clinicopathological predictors for progression of chronic kidney disease in nephrosclerosis: a biopsy-based cohort study.

PubMed

Yamanouchi, Masayuki; Hoshino, Junichi; Ubara, Yoshifumi; Takaichi, Kenmei; Kinowaki, Keiichi; Fujii, Takeshi; Ohashi, Kenichi; Mise, Koki; Toyama, Tadashi; Hara, Akinori; Shimizu, Miho; Furuichi, Kengo; Wada, Takashi

2018-05-19

Biopsy-based studies on nephrosclerosis are lacking and the clinicopathological predictors for progression of chronic kidney disease (CKD) are not well established. We retrospectively assessed 401 patients with biopsy-proven nephrosclerosis in Japan. Progression of CKD was defined as new-onset end-stage renal disease, decrease of estimated glomerular filtration rate (eGFR) by  ≥50% or doubling of serum creatinine, and the sub-distribution hazard ratio (SHR) with 95% confidence interval (CI) for CKD progression was determined for various clinical and histological characteristics in competing risks analysis. The incremental value of pathological information for predicting CKD progression was assessed by calculating Harrell's C-statistics, the Akaike information criterion (AIC), net reclassification improvement and integrated discrimination improvement. During a median follow-up period of 5.3 years, 117 patients showed progression of CKD and 10 patients died before the defined kidney event. Multivariable sub-distribution hazards model identified serum albumin (SHR 0.48; 95% CI 0.35-0.67), hemoglobin A1c (SHR 0.71; 95% CI 0.54-0.94), eGFR (SHR 0.98; 95% CI 0.97-0.99), urinary albumin/creatinine ratio (UACR) (SHR 1.18; 95% CI 1.08-1.29), percentage of segmental/global glomerulosclerosis (%GS) (SHR 1.01; 95% CI 1.00-1.02) and interstitial fibrosis and tubular atrophy (IFTA) (SHR 1.52; 95% CI 1.20-1.92) as risk factors for CKD progression. The C-statistic of a model with only clinical variables was improved by adding %GS (0.790 versus 0.796, P < 0.01) and IFTA (0.790 versus 0.811, P < 0.01). The reclassification statistic was also improved after adding the biopsy data to the clinical data. The model including IFTA was superior, with the lowest AIC. The study implies that in addition to the traditional markers of eGFR and UACR, we may explore the markers of serum albumin and hemoglobin A1c, which are widely available but not routinely measured in patients with nephrosclerosis, and the biopsy data, especially the data on the severity of interstitial damage, for the better prediction of CKD progression in patients with nephrosclerosis.

Littoral Combat Ship (LCS) Manpower Requirements Analysis

DTIC Science & Technology

2004-12-01

THIS PAGE INTENTIONALLY LEFT BLANK 183 APPENDIX W. ABBREVIATIONS AND ACRONYMS AFFF Aqueous Film Forming Foam AIC Aircraft Intercept Control ASW...181 APPENDIX W. ABBREVIATIONS AND ACRONYMS ..................... 183 LIST OF REFERENCES .........................................187 INITIAL...today. For example, the installed AFFF and CO2 systems inside critical spaces such as the main engineering and ordnance spaces. The Damage

75 FR 57756 - Combined Notice of Filings # 1

Federal Register 2010, 2011, 2012, 2013, 2014

2010-09-22

... Letter Agreement with NextEra Energy Resources, LLC for Engineering Study, to be effective 9/8/2010... tariff filing per 35.13(a)(2)(iii): AIC MBR and Short-Term Protocol to be effective 12/31/1998. Filed... at http://www.ferc.gov . To facilitate electronic service, persons with Internet access who will e...

78 FR 27181 - Notice of Intent To Grant Exclusive License

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-09

...Notice is hereby given that the U.S. Department of Agriculture, Agricultural Research Service, intends To grant to AIC Partners Group, LLC of Sylvester, Georgia, an exclusive license to U.S. Patent Application Serial No. 13/005,168, ``Method and Apparatus for Measuring Peanut Moisture Content,'' filed on January 12, 2011.

An information theory criteria based blind method for enumerating active users in DS-CDMA system

NASA Astrophysics Data System (ADS)

Samsami Khodadad, Farid; Abed Hodtani, Ghosheh

2014-11-01

In this paper, a new and blind algorithm for active user enumeration in asynchronous direct sequence code division multiple access (DS-CDMA) in multipath channel scenario is proposed. The proposed method is based on information theory criteria. There are two main categories of information criteria which are widely used in active user enumeration, Akaike Information Criterion (AIC) and Minimum Description Length (MDL) information theory criteria. The main difference between these two criteria is their penalty functions. Due to this difference, MDL is a consistent enumerator which has better performance in higher signal-to-noise ratios (SNR) but AIC is preferred in lower SNRs. In sequel, we propose a SNR compliance method based on subspace and training genetic algorithm to have the performance of both of them. Moreover, our method uses only a single antenna, in difference to the previous methods which decrease hardware complexity. Simulation results show that the proposed method is capable of estimating the number of active users without any prior knowledge and the efficiency of the method.

Self-determined, but not non-self-determined, motivation predicts activations in the anterior insular cortex: an fMRI study of personal agency

PubMed Central

Reeve, Johnmarshall

2013-01-01

Neuroscientific studies on agency focus rather exclusively on the notion of who initiates and regulates actions, not on the notion of why the person does. The present study focused on the latter to investigate two different reasons underlying personal agency. Using event-related functional magnetic resonance imaging, we scanned 16 healthy human subjects while they imagined the enactment of volitional, agentic behavior on the same task but either for a self-determined and intrinsically motivated reason or for a non-self-determined and extrinsically motivated reason. Results showed that the anterior insular cortex (AIC), known to be related to the sense of agency, was more activated during self-determined behavior while the angular gyrus, known to be related to the sense of loss of agency, was more activated during non-self-determined behavior. Furthermore, AIC activities during self-determined behavior correlated highly with participants’ self-reported intrinsic satisfactions. We conclude that self-determined behavior is more agentic than is non-self-determined behavior and that personal agency arises only during self-determined, intrinsically motivated action. PMID:22451482

The interaction of 2,3-diphosphoglycerate with various human hemoglobins

PubMed Central

Bunn, H. Franklin; Briehl, Robin W.

1970-01-01

Oxygen equilibria were measured on a number of human hemoglobins, which had been “stripped” of organic phosphates and isolated by column chromatography. In the presence of 2 × 10-4 M 2,3-diphosphoglycerate (2,3-DPG), the P50 of hemoglobins A, A2, S, and C increased about twofold, signifying a substantial and equal decrease in oxygen affinity. Furthermore, hemoglobins Chesapeake and MMilwaukee-1 which have intrinsically high and low oxygen affinities, respectively, also showed a twofold increase in P50 in the presence of 2 × 10-4 M 2,3-DPG. In comparison to these, hemoglobins AIC and F were less reactive with 2,3-DPG while hemoglobin FI showed virtually no reactivity. The N-terminal amino of each β-chain of hemoglobin AIC is linked to a hexose. In hemoglobin FI the N-terminal amino of each γ-chain is acetylated. These results suggest that the N-terminal amino groups of the non-α-chains are involved in the binding of 2,3-DPG to hemoglobin. PMID:5422014

Self-determined, but not non-self-determined, motivation predicts activations in the anterior insular cortex: an fMRI study of personal agency.

PubMed

Lee, Woogul; Reeve, Johnmarshall

2013-06-01

Neuroscientific studies on agency focus rather exclusively on the notion of who initiates and regulates actions, not on the notion of why the person does. The present study focused on the latter to investigate two different reasons underlying personal agency. Using event-related functional magnetic resonance imaging, we scanned 16 healthy human subjects while they imagined the enactment of volitional, agentic behavior on the same task but either for a self-determined and intrinsically motivated reason or for a non-self-determined and extrinsically motivated reason. Results showed that the anterior insular cortex (AIC), known to be related to the sense of agency, was more activated during self-determined behavior while the angular gyrus, known to be related to the sense of loss of agency, was more activated during non-self-determined behavior. Furthermore, AIC activities during self-determined behavior correlated highly with participants' self-reported intrinsic satisfactions. We conclude that self-determined behavior is more agentic than is non-self-determined behavior and that personal agency arises only during self-determined, intrinsically motivated action.

Spot counting on fluorescence in situ hybridization in suspension images using Gaussian mixture model

NASA Astrophysics Data System (ADS)

Liu, Sijia; Sa, Ruhan; Maguire, Orla; Minderman, Hans; Chaudhary, Vipin

2015-03-01

Cytogenetic abnormalities are important diagnostic and prognostic criteria for acute myeloid leukemia (AML). A flow cytometry-based imaging approach for FISH in suspension (FISH-IS) was established that enables the automated analysis of several log-magnitude higher number of cells compared to the microscopy-based approaches. The rotational positioning can occur leading to discordance between spot count. As a solution of counting error from overlapping spots, in this study, a Gaussian Mixture Model based classification method is proposed. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) of GMM are used as global image features of this classification method. Via Random Forest classifier, the result shows that the proposed method is able to detect closely overlapping spots which cannot be separated by existing image segmentation based spot detection methods. The experiment results show that by the proposed method we can obtain a significant improvement in spot counting accuracy.

Active control: an investigation method for combustion instabilities

NASA Astrophysics Data System (ADS)

Poinsot, T.; Yip, B.; Veynante, D.; Trouvé, A.; Samaniego, J. M.; Candel, S.

1992-07-01

Closed-loop active control methods and their application to combustion instabilities are discussed. In these methods the instability development is impeded with a feedback control loop: the signal provided by a sensor monitoring the flame or pressure oscillations is processed and sent back to actuators mounted on the combustor or on the feeding system. Different active control systems tested on a non-premixed multiple-flame turbulent combustor are described. These systems can suppress all unstable plane modes of oscillation (i.e. low frequency modes). The active instability control (AIC) also constitutes an original and powerful technique for studies of mechanisms leading to instability or resulting from the instability. Two basic applications of this kind are described. In the first case the flame is initially controlled with AIC, the feedback loop is then switched off and the growth of the instability is analysed through high speed Schlieren cinematography and simultaneous sound pressure and reaction rate measurements. Three phases are identified during th growth of the oscillations: (1) a linear phase where acoustic waves induce a flapping motion of the flame sheets without interaction between sheets, (2) a modulation phase, where flame sheets interact randomly and (3) a nonlinear phase where the flame sheets are broken and a limit cycle is reached. In the second case we investigate different types of flame extinctions associated with combustion instability. It is shown that pressure oscillations may lead to partial or total extinctions. Extinctions occur in various forms but usually follow a rapid growth of pressure oscillations. The flame is extinguished during the modulation phase observed in the initiation experiments. In these studies devoted to transient instability phenomena, the control system constitutes a unique investigation tool because it is difficult to obtain the same information by other means. Implications for modelling and prediction of combustion instabilities are discussed.

Dynamics of axial torsional libration under the mantle-inner core gravitational interaction

NASA Astrophysics Data System (ADS)

Chao, B. F.

2017-01-01

The aims of this paper are (i) formulating the dynamics of the mantle-inner core gravitational (MICG) interaction in terms of the spherical-harmonic multipoles of mass density. The modeled MICG system is composed of two concentric rigid bodies (mantle and inner core) of near-spherical but otherwise heterogeneous configuration, with a fluid outer core in between playing a passive role. We derive the general equation of motion for the vector rotation but only focus on the polar component that describes the MICG axial torsional libration. The torsion constant and hence the square of the natural frequency of the libration is proportional to the product of the equatorial ellipticities of the mantle and inner-core geoid embodied in their multipoles (of two different types) of degree 2 and order 2 (such as the Large Low-Shear-Velocity Provinces above the core-mantle boundary) and (ii) studying the geophysical implications upon equating the said MICG libration to the steady 6 year oscillation that are observed in the Earth's spin rate or the length-of-day variation (ΔLOD). In particular, the MICG torsion constant is found to be Γ>˜z = CIC σz2 ≈ 6.5 × 1019 N m, while the inner core's (BIC - AIC) ≈ 1.08 × 1031 kg m2 gives the inner core triaxiality (BIC - AIC)/CIC ≈ 1.8 × 10-4, about 8 times the whole-Earth value. It is also asserted that the required inner-core ellipticity amounts to no more than 140 m in geoid height, much smaller than the sensitivity required for the seismic wave travel time to resolve the variation of the inner core.

Bayesian model evidence as a model evaluation metric

NASA Astrophysics Data System (ADS)

Guthke, Anneli; Höge, Marvin; Nowak, Wolfgang

2017-04-01

When building environmental systems models, we are typically confronted with the questions of how to choose an appropriate model (i.e., which processes to include or neglect) and how to measure its quality. Various metrics have been proposed that shall guide the modeller towards a most robust and realistic representation of the system under study. Criteria for evaluation often address aspects of accuracy (absence of bias) or of precision (absence of unnecessary variance) and need to be combined in a meaningful way in order to address the inherent bias-variance dilemma. We suggest using Bayesian model evidence (BME) as a model evaluation metric that implicitly performs a tradeoff between bias and variance. BME is typically associated with model weights in the context of Bayesian model averaging (BMA). However, it can also be seen as a model evaluation metric in a single-model context or in model comparison. It combines a measure for goodness of fit with a penalty for unjustifiable complexity. Unjustifiable refers to the fact that the appropriate level of model complexity is limited by the amount of information available for calibration. Derived in a Bayesian context, BME naturally accounts for measurement errors in the calibration data as well as for input and parameter uncertainty. BME is therefore perfectly suitable to assess model quality under uncertainty. We will explain in detail and with schematic illustrations what BME measures, i.e. how complexity is defined in the Bayesian setting and how this complexity is balanced with goodness of fit. We will further discuss how BME compares to other model evaluation metrics that address accuracy and precision such as the predictive logscore or other model selection criteria such as the AIC, BIC or KIC. Although computationally more expensive than other metrics or criteria, BME represents an appealing alternative because it provides a global measure of model quality. Even if not applicable to each and every case, we aim at stimulating discussion about how to judge the quality of hydrological models in the presence of uncertainty in general by dissecting the mechanism behind BME.

Testing the potential significance of different scion/rootstock genotype combinations on the ecology of old cultivated olive trees in the southeast Mediterranean area.

PubMed

Barazani, Oz; Waitz, Yoni; Tugendhaft, Yizhar; Dorman, Michael; Dag, Arnon; Hamidat, Mohammed; Hijawi, Thameen; Kerem, Zohar; Westberg, Erik; Kadereit, Joachim W

2017-02-06

A previous multi-locus lineage (MLL) analysis of SSR-microsatellite data of old olive trees in the southeast Mediterranean area had shown the predominance of the Souri cultivar (MLL1) among grafted trees. The MLL analysis had also identified an MLL (MLL7) that was more common among rootstocks than other MLLs. We here present a comparison of the MLL combinations MLL1 (scion)/MLL7 (rootstock) and MLL1/MLL1 in order to investigate the possible influence of rootstock on scion phenotype. A linear regression analysis demonstrated that the abundance of MLL1/MLL7 trees decreases and of MLL1/MLL1 trees increases along a gradient of increasing aridity. Hypothesizing that grafting on MLL7 provides an advantage under certain conditions, Akaike information criterion (AIC) model selection procedure was used to assess the influence of different environmental conditions on phenotypic characteristics of the fruits and oil of the two MLL combinations. The most parsimonious models indicated differential influences of environmental conditions on parameters of olive oil quality in trees belonging to the MLL1/MLL7 and MLL1/MLL1 combinations, but a similar influence on fruit characteristics and oil content. These results suggest that in certain environments grafting of the local Souri cultivar on MLL7 rootstocks and the MLL1/MLL1 combination result in improved oil quality. The decreasing number of MLL1/MLL7 trees along an aridity gradient suggests that use of this genotype combination in arid sites was not favoured because of sensitivity of MLL7 to drought. Our results thus suggest that MLL1/MLL7 and MLL1/MLL1 combinations were selected by growers in traditional rain-fed cultivation under Mediterranean climate conditions in the southeast Mediterranean area.

Biogeographical Interpretation of Elevational Patterns of Genus Diversity of Seed Plants in Nepal

PubMed Central

Li, Miao; Feng, Jianmeng

2015-01-01

This study tests if the biogeographical affinities of genera are relevant for explaining elevational plant diversity patterns in Nepal. We used simultaneous autoregressive (SAR) models to investigate the explanatory power of several predictors in explaining the diversity-elevation relationships shown in genera with different biogeographical affinities. Delta akaike information criterion (ΔAIC) was used for multi-model inferences and selections. Our results showed that both the total and tropical genus diversity peaked below the mid-point of the elevational gradient, whereas that of temperate genera had a nearly symmetrical, unimodal relationship with elevation. The proportion of temperate genera increased markedly with elevation, while that of tropical genera declined. Compared to tropical genera, temperate genera had wider elevational ranges and were observed at higher elevations. Water-related variables, rather than mid-domain effects (MDE), were the most significant predictors of elevational patterns of tropical genus diversity. The temperate genus diversity was influenced by energy availability, but only in quadratic terms of the models. Though climatic factors and mid-domain effects jointly explained most of the variation in the diversity of temperate genera with elevation, the former played stronger roles. Total genus diversity was most strongly influenced by climate and the floristic overlap of tropical and temperate floras, while the influences of mid-domain effects were relatively weak. The influences of water-related and energy-related variables may vary with biogeographical affinities. The elevational patterns may be most closely related to climatic factors, while MDE may somewhat modify the patterns. Caution is needed when investigating the causal factors underlying diversity patterns for large taxonomic groups composed of taxa of different biogeographical affinities. Right-skewed diversity-elevation patterns may be produced by the differential response of taxa with varying biogeographical affinities to climatic factors and MDE. PMID:26488164

Biogeographical Interpretation of Elevational Patterns of Genus Diversity of Seed Plants in Nepal.

PubMed

Li, Miao; Feng, Jianmeng

2015-01-01

This study tests if the biogeographical affinities of genera are relevant for explaining elevational plant diversity patterns in Nepal. We used simultaneous autoregressive (SAR) models to investigate the explanatory power of several predictors in explaining the diversity-elevation relationships shown in genera with different biogeographical affinities. Delta akaike information criterion (ΔAIC) was used for multi-model inferences and selections. Our results showed that both the total and tropical genus diversity peaked below the mid-point of the elevational gradient, whereas that of temperate genera had a nearly symmetrical, unimodal relationship with elevation. The proportion of temperate genera increased markedly with elevation, while that of tropical genera declined. Compared to tropical genera, temperate genera had wider elevational ranges and were observed at higher elevations. Water-related variables, rather than mid-domain effects (MDE), were the most significant predictors of elevational patterns of tropical genus diversity. The temperate genus diversity was influenced by energy availability, but only in quadratic terms of the models. Though climatic factors and mid-domain effects jointly explained most of the variation in the diversity of temperate genera with elevation, the former played stronger roles. Total genus diversity was most strongly influenced by climate and the floristic overlap of tropical and temperate floras, while the influences of mid-domain effects were relatively weak. The influences of water-related and energy-related variables may vary with biogeographical affinities. The elevational patterns may be most closely related to climatic factors, while MDE may somewhat modify the patterns. Caution is needed when investigating the causal factors underlying diversity patterns for large taxonomic groups composed of taxa of different biogeographical affinities. Right-skewed diversity-elevation patterns may be produced by the differential response of taxa with varying biogeographical affinities to climatic factors and MDE.

Non-Targeted Effects and the Dose Response for Heavy Ion Tumorigenesis

NASA Technical Reports Server (NTRS)

Chappelli, Lori J.; Cucinotta, Francis A.

2010-01-01

BACKGROUND: There is no human epidemiology data available to estimate the heavy ion cancer risks experienced by astronauts in space. Studies of tumor induction in mice are a necessary step to estimate risks to astronauts. Previous experimental data can be better utilized to model dose response for heavy ion tumorigenesis and plan future low dose studies. DOSE RESPONSE MODELS: The Harderian Gland data of Alpen et al.[1-3] was re-analyzed [4] using non-linear least square regression. The data set measured the induction of Harderian gland tumors in mice by high-energy protons, helium, neon, iron, niobium and lanthanum with LET s ranging from 0.4 to 950 keV/micron. We were able to strengthen the individual ion models by combining data for all ions into a model that relates both radiation dose and LET for the ion to tumor prevalence. We compared models based on Targeted Effects (TE) to one motivated by Non-targeted Effects (NTE) that included a bystander term that increased tumor induction at low doses non-linearly. When comparing fitted models to the experimental data, we considered the adjusted R2, the Akaike Information Criteria (AIC), and the Bayesian Information Criteria (BIC) to test for Goodness of fit.In the adjusted R2test, the model with the highest R2values provides a better fit to the available data. In the AIC and BIC tests, the model with the smaller values of the summary value provides the better fit. The non-linear NTE models fit the combined data better than the TE models that are linear at low doses. We evaluated the differences in the relative biological effectiveness (RBE) and found the NTE model provides a higher RBE at low dose compared to the TE model. POWER ANALYSIS: The final NTE model estimates were used to simulate example data to consider the design of new experiments to detect NTE at low dose for validation. Power and sample sizes were calculated for a variety of radiation qualities including some not considered in the Harderian Gland data set and with different background tumor incidences. We considered different experimental designs with varying number of doses and varying low doses dependant on the LET of the radiation. The optimal design to detect a NTE for an individual ion had 4 doses equally spaced below a maximal dose where bending due to cell sterilization was < 2%. For example at 100 keV/micron we would irradiate at 0.03 Gy, 0.065 Gy, 0.13 Gy, and 0.26 Gy and require 850 mice including a control dose for a sensitivity to detect NTE with 80% power. Sample sizes could be improved by combining ions similar to the methods used with the Harderian Gland data.

Projecting climate-driven increases in North American fire activity

NASA Astrophysics Data System (ADS)

Wang, D.; Morton, D. C.; Collatz, G. J.

2013-12-01

Climate regulates fire activity through controls on vegetation productivity (fuels), lightning ignitions, and conditions governing fire spread. In many regions of the world, human management also influences the timing, duration, and extent of fire activity. These coupled interactions between human and natural systems make fire a complex component of the Earth system. Satellite data provide valuable information on the spatial and temporal dynamics of recent fire activity, as active fires, burned area, and land cover information can be combined to separate wildfires from intentional burning for agriculture and forestry. Here, we combined satellite-derived burned area data with land cover and climate data to assess fire-climate relationships in North America between 2000-2012. We used the latest versions of the Global Fire Emissions Database (GFED) burned area product and Modern-Era Retrospective Analysis for Research and Applications (MERRA) climate data to develop regional relationships between burned area and potential evaporation (PE), an integrated dryness metric. Logistic regression models were developed to link burned area with PE and individual climate variables during and preceding the fire season, and optimal models were selected based on Akaike Information Criterion (AIC). Overall, our model explained 85% of the variance in burned area since 2000 across North America. Fire-climate relationships from the era of satellite observations provide a blueprint for potential changes in fire activity under scenarios of climate change. We used that blueprint to evaluate potential changes in fire activity over the next 50 years based on twenty models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). All models suggest an increase of PE under low and high emissions scenarios (Representative Concentration Pathways (RCP) 4.5 and 8.5, respectively), with largest increases in projected burned area across the western US and central Canada. Overall, near-term climate projections point to pronounced changes in fire season length, total burned area, and the frequency of extreme events across North America by 2050.

Demand for pneumococcal vaccination under subsidy program for the elderly in Japan

PubMed Central

2012-01-01

Background Vaccination programs often organize subsidies and public relations in order to obtain high uptake rates and coverage. However, effects of subsidies and public relations have not been studied well in the literature. In this study, the demand function of pneumococcal vaccination among the elderly in Japan is estimated, incorporating effects of public relations and subsidy. Methods Using a data from a questionnaire survey sent to municipalities, the varying and constant elasticity models were applied to estimate the demand function. The response variable is the uptake rate. Explanatory variables are: subsidy supported shot price, operating years of the program, target population size for vaccination, shot location intensity, income and various public relations tools. The best model is selected by c-AIC, and varying and constant price elasticities are calculated from estimation results. Results The vaccine uptake rate and the shot price have a negative relation. From the results of varying price elasticity, the demand for vaccination is elastic at municipalities with a shot price higher than 3,708 JPY (35.7 USD). Effects of public relations on the uptake rate are not found. Conclusions It can be suggested that municipalities with a shot price higher than 3,708 JPY (35.7 USD) could subsidize more and reduce price to increase the demand for vaccination. Effects of public relations are not confirmed in this study, probably due to measurement errors of variables used for public relations, and studies at micro level exploring individual’s response to public relations would be required. PMID:22970727

Clustering Genes of Common Evolutionary History

PubMed Central

Gori, Kevin; Suchan, Tomasz; Alvarez, Nadir; Goldman, Nick; Dessimoz, Christophe

2016-01-01

Phylogenetic inference can potentially result in a more accurate tree using data from multiple loci. However, if the loci are incongruent—due to events such as incomplete lineage sorting or horizontal gene transfer—it can be misleading to infer a single tree. To address this, many previous contributions have taken a mechanistic approach, by modeling specific processes. Alternatively, one can cluster loci without assuming how these incongruencies might arise. Such “process-agnostic” approaches typically infer a tree for each locus and cluster these. There are, however, many possible combinations of tree distance and clustering methods; their comparative performance in the context of tree incongruence is largely unknown. Furthermore, because standard model selection criteria such as AIC cannot be applied to problems with a variable number of topologies, the issue of inferring the optimal number of clusters is poorly understood. Here, we perform a large-scale simulation study of phylogenetic distances and clustering methods to infer loci of common evolutionary history. We observe that the best-performing combinations are distances accounting for branch lengths followed by spectral clustering or Ward’s method. We also introduce two statistical tests to infer the optimal number of clusters and show that they strongly outperform the silhouette criterion, a general-purpose heuristic. We illustrate the usefulness of the approach by 1) identifying errors in a previous phylogenetic analysis of yeast species and 2) identifying topological incongruence among newly sequenced loci of the globeflower fly genus Chiastocheta. We release treeCl, a new program to cluster genes of common evolutionary history (http://git.io/treeCl). PMID:26893301

Expression of p53 Breast Cancer in Kurdish Women in the West of Iran: a Reverse Correlation with Lymph Node Metastasis.

PubMed

Payandeh, Mehrdad; Sadeghi, Masoud; Sadeghi, Edris; Madani, Seyed-Hamid

2016-01-01

In breast cancer (BC), it has been suggested that nuclear overexpression of p53 protein might be an indicator of poor prognosis. The aim of the current study was to evaluate the expression of p53 BC in Kurdish women from the West of Iran and its correlation with other clinicopathology figures. In the present retrospective study, 231 patients were investigated for estrogen receptor (ER) and progesterone receptor (PR) positivity, defined as ≥10% positive tumor cells with nuclear staining. A binary logistic regression model was selected using Akaike Information Criteria (AIC) in stepwise selection for determination of important factors. ER, PR, the human epidermal growth factor receptor 2 (HER2) and p53 were positive in 58.4%, 55.4%, 59.7% and 45% of cases, respectively. Ki67 index was divided into two groups: 54.5% had Ki67<20% and 45.5% had Ki67 ≥20%. Of 214 patients, 137(64%) had lymph node metastasis and of 186 patients, 122(65.6%) had vascular invasion. Binary logistic regression analysis showed that there was inverse significant correlation between lymph node metastasis (P=0.008, OR 0.120 and 95%CI 0.025-0.574), ER status (P=0.006, OR 0.080, 95%CI 0.014-0.477) and a direct correlation between HER2 (P=005, OR 3.047, 95%CI 1.407-6.599) with the expression of p53. As in a number of studies, expression of p53 had a inverse correlation with lymph node metastasis and ER status and also a direct correlation with HER2 status. Also, p53-positivity is more likely in triple negative BC compared to other subtypes.

Modelling infant mortality rate in Central Java, Indonesia use generalized poisson regression method

NASA Astrophysics Data System (ADS)

Prahutama, Alan; Sudarno

2018-05-01

The infant mortality rate is the number of deaths under one year of age occurring among the live births in a given geographical area during a given year, per 1,000 live births occurring among the population of the given geographical area during the same year. This problem needs to be addressed because it is an important element of a country’s economic development. High infant mortality rate will disrupt the stability of a country as it relates to the sustainability of the population in the country. One of regression model that can be used to analyze the relationship between dependent variable Y in the form of discrete data and independent variable X is Poisson regression model. Recently The regression modeling used for data with dependent variable is discrete, among others, poisson regression, negative binomial regression and generalized poisson regression. In this research, generalized poisson regression modeling gives better AIC value than poisson regression. The most significant variable is the Number of health facilities (X1), while the variable that gives the most influence to infant mortality rate is the average breastfeeding (X9).

Amiodarone-Induced Liver Injury and Cirrhosis

PubMed Central

Kappus, Matthew; Lagoo, Anand S.; Brady, Carla W.

2015-01-01

We present a case report of an 80-year-old woman with volume overload thought initially to be secondary to heart failure, but determined to be amiodarone-induced acute and chronic liver injury leading to submassive necrosis and bridging fibrosis consistent with early cirrhosis. Her histopathology was uniquely absent of steatosis and phospholipidosis, which are commonly seen in AIC. PMID:26157932

Amiodarone-Induced Liver Injury and Cirrhosis.

PubMed

Buggey, Jonathan; Kappus, Matthew; Lagoo, Anand S; Brady, Carla W

2015-01-01

We present a case report of an 80-year-old woman with volume overload thought initially to be secondary to heart failure, but determined to be amiodarone-induced acute and chronic liver injury leading to submassive necrosis and bridging fibrosis consistent with early cirrhosis. Her histopathology was uniquely absent of steatosis and phospholipidosis, which are commonly seen in AIC.

Transnational Activities of Chinese Crime Organizations

DTIC Science & Technology

2003-04-01

www.aic.gov.au> 20 Lachlan Colquhoun. “Asian Underworld in Australia,” Bangkok Asia Times [Bangkok]: 25 February 1997 (FBIS Document FTS19970625002098...example, Chinese criminals now have permeated the black 27 Russell G. Smith . “Plastic Card Fraud...London: Routledge, 2000. Colquhoun, Lachlan . “Asian Underworld in Australia,” Bangkok Asia Times [Bangkok], 25 February 1997 (FBIS Document

M-DCPS Student Performance in International Baccalaureate and Cambridge Advanced International Certificate of Education Programs. Research Brief. Volume 1102

ERIC Educational Resources Information Center

Blazer, Christie

2011-01-01

This Research Brief summarizes the performance of M-DCPS students participating in the International Baccalaureate (IB) and Cambridge Advanced International Certificate of Education (AICE) programs. Outcome data are provided for the eight M-DCPS schools offering the two programs and corresponding examinations. Participation in international…

Employing in vitro directed molecular evolution for the selection of α-amylase variant inhibitors with activity toward cotton boll weevil enzyme.

PubMed

da Silva, Maria Cristina Mattar; Del Sarto, Rafael Perseghini; Lucena, Wagner Alexandre; Rigden, Daniel John; Teixeira, Fabíola Rodrigues; Bezerra, Caroline de Andrade; Albuquerque, Erika Valéria Saliba; Grossi-de-Sa, Maria Fatima

2013-09-20

Numerous species of insect pests attack cotton plants, out of which the cotton boll weevil (Anthonomus grandis) is the main insect in Brazil and must be controlled to avert large economic losses. Like other insect pests, A. grandis secretes a high level of α-amylases in the midgut lumen, which are required for digestion of carbohydrates. Thus, α-amylase inhibitors (α-AIs) represent a powerful tool to apply in the control of insect pests. Here, we applied DNA shuffling and phage display techniques and obtained a combinatorial library containing 10⁸ α-AI variant forms. From this library, variants were selected exhibiting in vitro affinity for cotton boll weevil α-amylases. Twenty-six variant sequences were cloned into plant expression vectors and expressed in Arabidopsis thaliana. Transformed plant extracts were assayed in vitro to select specific and potent α-amylase inhibitors against boll weevil amylases. While the wild type inhibitors, used to create the shuffled library, did not inhibit the A. grandis α-amylases, three α-AI mutants, named α-AIC3, α-AIA11 and α-AIG4 revealed high inhibitory activities against A. grandis α-amylases in an in vitro assay. In summary, data reported here shown the potential biotechnology of new α-AI variant genes for cotton boll weevil control. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Some statistical features of the aftershock temporal behavior after the M7.4 Izmit earthquake of august 17, 1999 in Turkey

NASA Astrophysics Data System (ADS)

Gospodinov, D.; Fajtin, H.; Rangelov, B.; Marekova, E.

2009-04-01

An earthquake of magnitude Mw=7.4 struck 8 km. southeast of Izmit, Turkey at 3:02 AM local time on August 17, 1999. The earthquake occurred on one of the world's longest and best studied strike-slip (horizontal motion) faults - the east-west trending North Anatolian fault. Seismologists are not able to predict the timing and sizes of individual aftershocks but stochastic modeling allows determinationof probabilities for aftershocks and larger mainshocks duringintervals following the mainshock. The most widely applied stochastic model to depict aftershocks temporal distribution is the non- homogenous Poisson process with a decaying intensity, which follows the Modified Omori Formula (MOF) (Utsu, 1961). A more complex model, considering the triggering potential of each aftershock was developed by Ogata (1988) and it was named Epidemic Type Aftershock Sequence (ETAS) model. Gospodinov and Rotondi (2006) elaborated a Restricted Epidemic Type Aftershock Sequence (RETAS) model. The latter follows the general idea that only aftershocks stronger than some cut-off magnitude possess the capability to induce secondary aftershock activity. In this work we shall consider the Restricted Epidemic Type Aftershock Sequence (RETAS) model, for which the conditional intensity function turns out to be ‘ K0eα(Mi-M0)- λ (t|Ht) = + (t- ti + c)p ti < t Mi ≥ Mth (1) Here the summation occurs for all aftershocks with magnitude bigger than or equal to Mth, which took place before time. Leaving Mth to take all possible values, one can examine all RETAS model versions between the MOF and the ETAS model on the basis of the Akaike Information Criterion AIC (Akaike, 1974) AIC = - 2max log L+ 2k (2) where k is the number of parameters used in the model and logL is the logarithm of the likelihood function. Then for the model providing the best fit, we choose the one with the smallest AIC value. The purpose of this paper is to verify versions of the RETAS model (including the MOF and the ETAS model) for the analysis of the aftershock sequence after the Mw=7.4 Izmit earthquake. The obtained results revealed that the best fit model is ETAS, for which the triggering magnitude coincides with the lower cut-off. In this case each event in the sequence can generate its own secondary events. The analysis points out that about 12 days before the Duzce earthquake a relative quiescence begins which then turns to a relative activization about 5 days before the strong shock. This can be explained by a possible realization of several stronger aftershocks before the Duzce event, which are considered as its foreshocks. Another result which can be outlined is that the MOF model is not good for a sequence in which we have secondary activity (strong aftershock or several main events). For a complex aftershock sequence as is the case for the Izmit-Duzce zone, it is adequate to apply the RETAS model which gives more possibilities to model the aftershock activity in time. This allows to perform a better geotectonic interpretation later. The analysis of the aftershock activity after the strong Izmit earthquake, executed through the RETAS model makes it possible to draw some conclusions when interpreting the results: - The RETAS model supplies a methodology to identify the best fit model of the relaxation temporal development and in our case this is the ETAS model (each aftershock is capable to trigger secondary activity). - The recognized best fit model for the sequence provides an opportunity to identify separate periods of relative quiescence and activization. A quiescence period of several days was identified before the strongest aftershock - the Duzce earthquake. - For standard aftershock sequences the different versions of the RETAS model (ETAS, MOF and the intermediate ones) are closer to each other. For a more complex sequence with strong aftershock activity the RETAS model provides better possibilities to model the temporal aspect of the relaxation process and to perform the most adequate geotectonic interpretation. References Akaike, H., 1974, A new look at the statistical model identification. IEEE Trans. Automat. Contr. AC-19, 716-723. Gospodinov, D., R. Rotondi, 2006, Statistical Analysis of Triggered Seismicity in the Kresna Region of SW Bulgaria (1904) and the Umbria-Marche Region of Central Italy (1997). Pure Appl. Geophys. 163, 1597-1615. Ogata, Y., 1988, Statistical models for earthquake occurrences and residual analysis for point processes. J. Am. Stat. Assoc. 83, 9-27. Utsu, T., 1961, A statistical study on the occurrence of aftershocks, Geophys. Mag., 30, 521-605

On the predictive information criteria for model determination in seismic hazard analysis

NASA Astrophysics Data System (ADS)

Varini, Elisa; Rotondi, Renata

2016-04-01

Many statistical tools have been developed for evaluating, understanding, and comparing models, from both frequentist and Bayesian perspectives. In particular, the problem of model selection can be addressed according to whether the primary goal is explanation or, alternatively, prediction. In the former case, the criteria for model selection are defined over the parameter space whose physical interpretation can be difficult; in the latter case, they are defined over the space of the observations, which has a more direct physical meaning. In the frequentist approaches, model selection is generally based on an asymptotic approximation which may be poor for small data sets (e.g. the F-test, the Kolmogorov-Smirnov test, etc.); moreover, these methods often apply under specific assumptions on models (e.g. models have to be nested in the likelihood ratio test). In the Bayesian context, among the criteria for explanation, the ratio of the observed marginal densities for two competing models, named Bayes Factor (BF), is commonly used for both model choice and model averaging (Kass and Raftery, J. Am. Stat. Ass., 1995). But BF does not apply to improper priors and, even when the prior is proper, it is not robust to the specification of the prior. These limitations can be extended to two famous penalized likelihood methods as the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), since they are proved to be approximations of -2log BF . In the perspective that a model is as good as its predictions, the predictive information criteria aim at evaluating the predictive accuracy of Bayesian models or, in other words, at estimating expected out-of-sample prediction error using a bias-correction adjustment of within-sample error (Gelman et al., Stat. Comput., 2014). In particular, the Watanabe criterion is fully Bayesian because it averages the predictive distribution over the posterior distribution of parameters rather than conditioning on a point estimate, but it is hardly applicable to data which are not independent given parameters (Watanabe, J. Mach. Learn. Res., 2010). A solution is given by Ando and Tsay criterion where the joint density may be decomposed into the product of the conditional densities (Ando and Tsay, Int. J. Forecast., 2010). The above mentioned criteria are global summary measures of model performance, but more detailed analysis could be required to discover the reasons for poor global performance. In this latter case, a retrospective predictive analysis is performed on each individual observation. In this study we performed the Bayesian analysis of Italian data sets by four versions of a long-term hazard model known as the stress release model (Vere-Jones, J. Physics Earth, 1978; Bebbington and Harte, Geophys. J. Int., 2003; Varini and Rotondi, Environ. Ecol. Stat., 2015). Then we illustrate the results on their performance evaluated by Bayes Factor, predictive information criteria and retrospective predictive analysis.

Timing of nest vegetation measurement may obscure adaptive significance of nest-site characteristics: A simulation study.

PubMed

McConnell, Mark D; Monroe, Adrian P; Burger, Loren Wes; Martin, James A

2017-02-01

Advances in understanding avian nesting ecology are hindered by a prevalent lack of agreement between nest-site characteristics and fitness metrics such as nest success. We posit this is a result of inconsistent and improper timing of nest-site vegetation measurements. Therefore, we evaluated how the timing of nest vegetation measurement influences the estimated effects of vegetation structure on nest survival. We simulated phenological changes in nest-site vegetation growth over a typical nesting season and modeled how the timing of measuring that vegetation, relative to nest fate, creates bias in conclusions regarding its influence on nest survival. We modeled the bias associated with four methods of measuring nest-site vegetation: Method 1-measuring at nest initiation, Method 2-measuring at nest termination regardless of fate, Method 3-measuring at nest termination for successful nests and at estimated completion for unsuccessful nests, and Method 4-measuring at nest termination regardless of fate while also accounting for initiation date. We quantified and compared bias for each method for varying simulated effects, ranked models for each method using AIC, and calculated the proportion of simulations in which each model (measurement method) was selected as the best model. Our results indicate that the risk of drawing an erroneous or spurious conclusion was present in all methods but greater with Method 2 which is the most common method reported in the literature. Methods 1 and 3 were similarly less biased. Method 4 provided no additional value as bias was similar to Method 2 for all scenarios. While Method 1 is seldom practical to collect in the field, Method 3 is logistically practical and minimizes inherent bias. Implementation of Method 3 will facilitate estimating the effect of nest-site vegetation on survival, in the least biased way, and allow reliable conclusions to be drawn.

The Extended Erlang-Truncated Exponential distribution: Properties and application to rainfall data.

PubMed

Okorie, I E; Akpanta, A C; Ohakwe, J; Chikezie, D C

2017-06-01

The Erlang-Truncated Exponential ETE distribution is modified and the new lifetime distribution is called the Extended Erlang-Truncated Exponential EETE distribution. Some statistical and reliability properties of the new distribution are given and the method of maximum likelihood estimate was proposed for estimating the model parameters. The usefulness and flexibility of the EETE distribution was illustrated with an uncensored data set and its fit was compared with that of the ETE and three other three-parameter distributions. Results based on the minimized log-likelihood ([Formula: see text]), Akaike information criterion (AIC), Bayesian information criterion (BIC) and the generalized Cramér-von Mises [Formula: see text] statistics shows that the EETE distribution provides a more reasonable fit than the one based on the other competing distributions.

Kinetic modelling of methane production during bio-electrolysis from anaerobic co-digestion of sewage sludge and food waste.

PubMed

Prajapati, Kalp Bhusan; Singh, Rajesh

2018-05-10

In present study batch tests were performed to investigate the enhancement in methane production under bio-electrolysis anaerobic co-digestion of sewage sludge and food waste. The bio-electrolysis reactor system (B-EL) yield more methane 148.5 ml/g COD in comparison to reactor system without bio-electrolysis (B-CONT) 125.1 ml/g COD. Whereas bio-electrolysis reactor system (C-EL) Iron Scraps amended yield lesser methane (51.2 ml/g COD) in comparison to control bio-electrolysis reactor system without Iron scraps (C-CONT - 114.4 ml/g COD). Richard and Exponential model were best fitted for cumulative methane production and biogas production rates respectively as revealed modelling study. The best model fit for the different reactors was compared by Akaike's Information Criterion (AIC) and Bayesian Information Criterion (BIC). The bioelectrolysis process seems to be an emerging technology with lesser the loss in cellulase specific activity with increasing temperature from 50 to 80 °C. Copyright © 2018 Elsevier Ltd. All rights reserved.

A Time Series Analysis: Weather Factors, Human Migration and Malaria Cases in Endemic Area of Purworejo, Indonesia, 2005–2014

PubMed Central

REJEKI, Dwi Sarwani Sri; NURHAYATI, Nunung; AJI, Budi; MURHANDARWATI, E. Elsa Herdiana; KUSNANTO, Hari

2018-01-01

Background: Climatic and weather factors become important determinants of vector-borne diseases transmission like malaria. This study aimed to prove relationships between weather factors with considering human migration and previous case findings and malaria cases in endemic areas in Purworejo during 2005–2014. Methods: This study employed ecological time series analysis by using monthly data. The independent variables were the maximum temperature, minimum temperature, maximum humidity, minimum humidity, precipitation, human migration, and previous malaria cases, while the dependent variable was positive malaria cases. Three models of count data regression analysis i.e. Poisson model, quasi-Poisson model, and negative binomial model were applied to measure the relationship. The least Akaike Information Criteria (AIC) value was also performed to find the best model. Negative binomial regression analysis was considered as the best model. Results: The model showed that humidity (lag 2), precipitation (lag 3), precipitation (lag 12), migration (lag1) and previous malaria cases (lag 12) had a significant relationship with malaria cases. Conclusion: Weather, migration and previous malaria cases factors need to be considered as prominent indicators for the increase of malaria case projection. PMID:29900134

Population dynamics of mottled sculpin (PISCES) in a variable environment: information theoretic approaches

Treesearch

Gary D. Grossman; Robert E Ratajczak; J. Todd Petty; Mark D. Hunter; James T. Peterson; Gael Grenouillet

2006-01-01

We used strong inference with Akaike's Information Criterion (AIC) to assess the processes capable of explaining long-term (1984-1995) variation in the per capita rate of change of mottled sculpin (Cottus bairdi) populations in the Coweeta Creek drainage (USA). We sampled two fourth- and one fifth-order sites (BCA [uppermost], BCB, and CC [lowermost])...

AICE Survey of USSR Air Pollution Literature, Volume 13: Technical Papers from the Leningrad International Symposium on the Meteorological Aspects of Atmospheric Pollution, Part 2.

ERIC Educational Resources Information Center

Nuttonson, M. Y., Ed.

Twelve papers were translated from Russian: Automation of Information Processing Involved in Experimental Studies of Atmospheric Diffusion, Micrometeorological Characteristics of Atmospheric Pollution Conditions, Study of theInfluence of Irregularities of the Earth's Surface on the Air Flow Characteristics in a Wind Tunnel, Use of Parameters of…

Toward objective image quality metrics: the AIC Eval Program of the JPEG

NASA Astrophysics Data System (ADS)

Richter, Thomas; Larabi, Chaker

2008-08-01

Objective quality assessment of lossy image compression codecs is an important part of the recent call of the JPEG for Advanced Image Coding. The target of the AIC ad-hoc group is twofold: First, to receive state-of-the-art still image codecs and to propose suitable technology for standardization; and second, to study objective image quality metrics to evaluate the performance of such codes. Even tthough the performance of an objective metric is defined by how well it predicts the outcome of a subjective assessment, one can also study the usefulness of a metric in a non-traditional way indirectly, namely by measuring the subjective quality improvement of a codec that has been optimized for a specific objective metric. This approach shall be demonstrated here on the recently proposed HDPhoto format14 introduced by Microsoft and a SSIM-tuned17 version of it by one of the authors. We compare these two implementations with JPEG1 in two variations and a visual and PSNR optimal JPEG200013 implementation. To this end, we use subjective and objective tests based on the multiscale SSIM and a new DCT based metric.

PAN AIR: A computer program for predicting subsonic or supersonic linear potential flows about arbitrary configurations using a higher order panel method. Volume 1: Theory document (version 3.0)

NASA Technical Reports Server (NTRS)

Epton, Michael A.; Magnus, Alfred E.

1990-01-01

An outline of the derivation of the differential equation governing linear subsonic and supersonic potential flow is given. The use of Green's Theorem to obtain an integral equation over the boundary surface is discussed. The engineering techniques incorporated in the Panel Aerodynamics (PAN AIR) program (a discretization method which solves the integral equation for arbitrary first order boundary conditions) are then discussed in detail. Items discussed include the construction of the compressibility transformation, splining techniques, imposition of the boundary conditions, influence coefficient computation (including the concept of the finite part of an integral), computation of pressure coefficients, and computation of forces and moments. Principal revisions to version 3.0 are the following: (1) appendices H and K more fully describe the Aerodynamic Influence Coefficient (AIC) construction; (2) appendix L now provides a complete description of the AIC solution process; (3) appendix P is new and discusses the theory for the new FDP module (which calculates streamlines and offbody points); and (4) numerous small corrections and revisions reflecting the MAG module rewrite.

Development and evaluation of a novel lossless image compression method (AIC: artificial intelligence compression method) using neural networks as artificial intelligence.

PubMed

Fukatsu, Hiroshi; Naganawa, Shinji; Yumura, Shinnichiro

2008-04-01

This study was aimed to validate the performance of a novel image compression method using a neural network to achieve a lossless compression. The encoding consists of the following blocks: a prediction block; a residual data calculation block; a transformation and quantization block; an organization and modification block; and an entropy encoding block. The predicted image is divided into four macro-blocks using the original image for teaching; and then redivided into sixteen sub-blocks. The predicted image is compared to the original image to create the residual image. The spatial and frequency data of the residual image are compared and transformed. Chest radiography, computed tomography (CT), magnetic resonance imaging, positron emission tomography, radioisotope mammography, ultrasonography, and digital subtraction angiography images were compressed using the AIC lossless compression method; and the compression rates were calculated. The compression rates were around 15:1 for chest radiography and mammography, 12:1 for CT, and around 6:1 for other images. This method thus enables greater lossless compression than the conventional methods. This novel method should improve the efficiency of handling of the increasing volume of medical imaging data.

COHERENT EVENTS AND SPECTRAL SHAPE AT ION KINETIC SCALES IN THE FAST SOLAR WIND TURBULENCE

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

Lion, Sonny; Alexandrova, Olga; Zaslavsky, Arnaud, E-mail: sonny.lion@obspm.fr

2016-06-10

In this paper we investigate spectral and phase coherence properties of magnetic fluctuations in the vicinity of the spectral transition from large, magnetohydrodynamic to sub-ion scales using in situ measurements of the Wind spacecraft in a fast stream. For the time interval investigated by Leamon et al. (1998) the phase coherence analysis shows the presence of sporadic quasi-parallel Alfvén ion cyclotron (AIC) waves as well as coherent structures in the form of large-amplitude, quasi-perpendicular Alfvén vortex-like structures and current sheets. These waves and structures importantly contribute to the observed power spectrum of magnetic fluctuations around ion scales; AIC waves contributemore » to the spectrum in a narrow frequency range whereas the coherent structures contribute to the spectrum over a wide frequency band from the inertial range to the sub-ion frequency range. We conclude that a particular combination of waves and coherent structures determines the spectral shape of the magnetic field spectrum around ion scales. This phenomenon provides a possible explanation for a high variability of the magnetic power spectra around ion scales observed in the solar wind.« less

Laboratory evaluations of erectile dysfunction: an evidence based approach.

PubMed

Bodie, Joshua; Lewis, Jean; Schow, Doug; Monga, Manoj

2003-06-01

We evaluate the prevalence of laboratory abnormalities in men presenting for initial evaluation and therapy of erectile dysfunction. The computerized charts of men receiving treatment for erectile dysfunction from 1987 to 2002 were retrospectively reviewed. We pooled laboratory data for 3,547 men with erectile dysfunction to assess the prevalence of laboratory abnormalities. Values of the common laboratory screening tests for erectile dysfunction were recorded for testosterone, prolactin, luteinizing hormone, thyroid-stimulating hormone, hemoglobin A(Ic), prostate specific antigen, hemoglobin, cholesterol and creatinine. Of those patients evaluated 18.7% had low testosterone, 4.6% had increased prolactin, 14.6% had abnormal luteinizing hormone, 4.0% had increased thyroid-stimulating hormone, 8.3% had increased prostate specific antigen, 26.5% had anemia and 11.9% tested had renal insufficiency. A high percentage of patients presenting with a primary complaint of erectile dysfunction had increased hemoglobin A(Ic) and total serum cholesterol levels (52.9% and 48.4%, respectively). An evidence based approach to standardization of laboratory evaluations for men presenting with erectile dysfunction is recommended. Laboratory screening should be directed to identify those risk factors that may benefit from lifestyle modification and pharmacological intervention.

Mature and old-growth riparian forests: structure, dynamics, and effects on Adirondack stream habitats.

PubMed

Keeton, William S; Kraft, Clifford E; Warren, Dana R

2007-04-01

Riparian forests regulate linkages between terrestrial and aquatic ecosystems, yet relationships among riparian forest development, stand structure, and stream habitats are poorly understood in many temperate deciduous forest systems. Our research has (1) described structural attributes associated with old-growth riparian forests and (2) assessed linkages between these characteristics and in-stream habitat structure. The 19 study sites were located along predominantly first- and second-order streams in northern hardwood-conifer forests in the Adirondack Mountains of New York (U.S.A.). Sites were classified as mature forest (6 sites), mature with remnant old-growth trees (3 sites), and old-growth (10 sites). Forest-structure attributes were measured over stream channels and at varying distances from each bank. In-stream habitat features such as large woody debris (LWD), pools, and boulders were measured in each stream reach. Forest structure was examined in relation to stand age using multivariate techniques, ANOVA, and linear regression. We investigated linkages between forest structure and stream characteristics using similar methods, preceded by information-theoretic modeling (AIC). Old-growth riparian forest structure is more complex than that found in mature forests and exhibits significantly greater accumulations of aboveground tree biomass, both living and dead. In-stream LWD volumes were significantly (alpha = 0.05) greater at old-growth sites (200 m3/ha) compared to mature sites (34 m3/ha) and were strongly related to the basal area of adjacent forests. In-stream large-log densities correlated strongly with debris-dam densities. AIC models that included large-log density, debris-dam density, boulder density, and bankfull width had the most support for predicting pool density. There were higher proportions of LWD-formed pools relative to boulder-formed pools at old-growth sites as compared to mature sites. Old-growth riparian forests provide in-stream habitat features that have not been widely recognized in eastern North America, representing a potential benefit from late-successional riparian forest management and conservation. Riparian management practices (including buffer delineation and restorative silvicultural approaches) that emphasize development and maintenance of late-successional characteristics are recommended where the associated in-stream effects are desired.

Coral reef disturbance and recovery dynamics differ across gradients of localized stressors in the Mariana Islands.

PubMed

Houk, Peter; Benavente, David; Iguel, John; Johnson, Steven; Okano, Ryan

2014-01-01

The individual contribution of natural disturbances, localized stressors, and environmental regimes upon longer-term reef dynamics remains poorly resolved for many locales despite its significance for management. This study examined coral reefs in the Commonwealth of the Northern Mariana Islands across a 12-year period that included elevated Crown-of-Thorns Starfish densities (COTS) and tropical storms that were drivers of spatially-inconsistent disturbance and recovery patterns. At the island scale, disturbance impacts were highest on Saipan with reduced fish sizes, grazing urchins, and water quality, despite having a more favorable geological foundation for coral growth compared with Rota. However, individual drivers of reef dynamics were better quantified through site-level investigations that built upon island generalizations. While COTS densities were the strongest predictors of coral decline as expected, interactive terms that included wave exposure and size of the overall fish assemblages improved models (R2 and AIC values). Both wave exposure and fish size diminished disturbance impacts and had negative associations with COTS. However, contrasting findings emerged when examining net ecological change across the 12-year period. Wave exposure had a ubiquitous, positive influence upon the net change in favorable benthic substrates (i.e. corals and other heavily calcifying substrates, R2 = 0.17 for all reeftypes grouped), yet including interactive terms for herbivore size and grazing urchin densities, as well as stratifying by major reeftypes, substantially improved models (R2 = 0.21 to 0.89, lower AIC scores). Net changes in coral assemblages (i.e., coral ordination scores) were more sensitive to herbivore size or the water quality proxy acting independently (R2 = 0.28 to 0.44). We conclude that COTS densities were the strongest drivers of coral decline, however, net ecological change was most influenced by localized stressors, especially herbivore sizes and grazing urchin densities. Interestingly, fish size, rather than biomass, was consistently a better predictor, supporting allometric, size-and-function relationships of fish assemblages. Management implications are discussed.

Coral Reef Disturbance and Recovery Dynamics Differ across Gradients of Localized Stressors in the Mariana Islands

PubMed Central

Houk, Peter; Benavente, David; Iguel, John; Johnson, Steven; Okano, Ryan

2014-01-01

The individual contribution of natural disturbances, localized stressors, and environmental regimes upon longer-term reef dynamics remains poorly resolved for many locales despite its significance for management. This study examined coral reefs in the Commonwealth of the Northern Mariana Islands across a 12-year period that included elevated Crown-of-Thorns Starfish densities (COTS) and tropical storms that were drivers of spatially-inconsistent disturbance and recovery patterns. At the island scale, disturbance impacts were highest on Saipan with reduced fish sizes, grazing urchins, and water quality, despite having a more favorable geological foundation for coral growth compared with Rota. However, individual drivers of reef dynamics were better quantified through site-level investigations that built upon island generalizations. While COTS densities were the strongest predictors of coral decline as expected, interactive terms that included wave exposure and size of the overall fish assemblages improved models (R2 and AIC values). Both wave exposure and fish size diminished disturbance impacts and had negative associations with COTS. However, contrasting findings emerged when examining net ecological change across the 12-year period. Wave exposure had a ubiquitous, positive influence upon the net change in favorable benthic substrates (i.e. corals and other heavily calcifying substrates, R2 = 0.17 for all reeftypes grouped), yet including interactive terms for herbivore size and grazing urchin densities, as well as stratifying by major reeftypes, substantially improved models (R2 = 0.21 to 0.89, lower AIC scores). Net changes in coral assemblages (i.e., coral ordination scores) were more sensitive to herbivore size or the water quality proxy acting independently (R2 = 0.28 to 0.44). We conclude that COTS densities were the strongest drivers of coral decline, however, net ecological change was most influenced by localized stressors, especially herbivore sizes and grazing urchin densities. Interestingly, fish size, rather than biomass, was consistently a better predictor, supporting allometric, size-and-function relationships of fish assemblages. Management implications are discussed. PMID:25165893

Optimal selection of MULTI-model downscaled ensembles for interannual and seasonal climate prediction in the eastern seaboard of Thailand

NASA Astrophysics Data System (ADS)

Bejranonda, W.; Koch, M.

2010-12-01

Because of the imminent threat of the water resources of the eastern seaboard of Thailand, a climate impact study has been carried out there. To that avail, a hydrological watershed model is being used to simulate the future water availability in the wake of possible climate change in the region. The hydrological model is forced by predictions from global climate models (GCMs) that are to be downscaled in an appropriate manner. The challenge at that stage of the climate impact analysis lies then the in the choice of the best GCM and the (statistical) downscaling method. In this study the selection of coarse grid resolution output of the GCMs, transferring information to the fine grid of local climate-hydrology is achieved by cross-correlation and multiple linear regression using meteorological data in the eastern seaboard of Thailand observed between 1970-1999. The grids of 20 atmosphere/ocean global climate models (AOGCM), covering latitude 12.5-15.0 N and longitude 100.0-102.5 E were examined using the Climate-Change Scenario Generator (SCENGEN). With that tool the model efficiency of the prediction of daily precipitation and mean temperature was calculated by comparing the 1980-1999 ECMWF reanalysis predictions with the observed data during that time period. The root means square errors of the predictions were considered and ranked to select the top 5 models, namely, BCCR-BCM2.0, GISS-ER, ECHO-G, ECHAM5/MPI-OM and PCM. The daily time-series of 338 predictors in 9 runs of the 5 selected models were gathered from the CMIP3 multi-model database. Monthly time-serial cross-correlations between the climate predictors and the meteorological measurements from 25 rainfall, 4 minimum and maximum temperature, 4 humidity and 2 solar radiation stations in the study area were then computed and ranked. Using the ranked predictors, a multiple-linear regression model (downscaling transfer model) to forecast the local climate was set up. To improve the prediction power of this GCM downscaling approach, the regression equations were considered as a dynamic regression model that can alter the predictor by seasonal variation. The possible seasonal effect was examined for the 1974-1999 period which was equally divided into a calibration and verification sub-period. The calibrated model using the whole observed time-series was compared with the models separated into 2 seasons; dry and wet, 3 seasons; winter, summer and rainy, and 4 seasons; dry, pre-monsoon, first monsoon and second monsoon. The verification power of the various model variants was measured considering Akaike's information criterion (AIC) and the Nash-Sutcliffe coefficient of the corresponding model fit. The results show that the 4-seasons-variation prediction works best. The highest efficiency for the prediction of rainfall is achieved for the dry season, Oct-Mar, whereas the smallest efficiency is obtained in the monsoon seasons. The overall number of predictor giving top efficiency lies between 3 and 20 in the regression models. In the next, still ongoing stage of the climate impact study the predictions from this new, seasonally optimized downscaling transfer model are being used in the simulations of the future hydrological water budget in that region of Thailand.

Modeling fecal bacteria transport and retention in agricultural and urban soils under saturated and unsaturated flow conditions.

PubMed

Balkhair, Khaled S

2017-03-01

Pathogenic bacteria, that enter surface water bodies and groundwater systems through unmanaged wastewater land application, pose a great risk to human health. In this study, six soil column experiments were conducted to simulate the vulnerability of agricultural and urban field soils for fecal bacteria transport and retention under saturated and unsaturated flow conditions. HYDRUS-1D kinetic attachment and kinetic attachment-detachment models were used to simulate the breakthrough curves of the experimental data by fitting model parameters. Results indicated significant differences in the retention and drainage of bacteria between saturated and unsaturated flow condition in the two studied soils. Flow under unsaturated condition retained more bacteria than the saturated flow case. The high bacteria retention in the urban soil compared to agricultural soil is ascribed not only to the dynamic attachment and sorption mechanisms but also to the greater surface area of fine particles and low flow rate. All models simulated experimental data satisfactorily under saturated flow conditions; however, under variably saturated flow, the peak concentrations were overestimated by the attachment-detachment model and underestimated by the attachment model with blocking. The good match between observed data and simulated concentrations by the attachment model which was supported by the Akaike information criterion (AIC) for model selection indicates that the first-order attachment coefficient was sufficient to represent the quantitative and temporal distribution of bacteria in the soil column. On the other hand, the total mass balance of the drained and retained bacteria in all transport experiments was in the range of values commonly found in the literature. Regardless of flow conditions and soil texture, most of the bacteria were retained in the top 12 cm of the soil column. The approaches and the models used in this study have proven to be a good tool for simulating fecal bacteria transport under a variety of initial and boundary flow conditions, hence providing a better understanding of the transport mechanism of bacteria as well as soil removal efficiency. Copyright © 2016 Elsevier Ltd. All rights reserved.

Investigation into the performance of different models for predicting stutter.

PubMed

Bright, Jo-Anne; Curran, James M; Buckleton, John S

2013-07-01

In this paper we have examined five possible models for the behaviour of the stutter ratio, SR. These were two log-normal models, two gamma models, and a two-component normal mixture model. A two-component normal mixture model was chosen with different behaviours of variance; at each locus SR was described with two distributions, both with the same mean. The distributions have difference variances: one for the majority of the observations and a second for the less well-behaved ones. We apply each model to a set of known single source Identifiler™, NGM SElect™ and PowerPlex(®) 21 DNA profiles to show the applicability of our findings to different data sets. SR determined from the single source profiles were compared to the calculated SR after application of the models. The model performance was tested by calculating the log-likelihoods and comparing the difference in Akaike information criterion (AIC). The two-component normal mixture model systematically outperformed all others, despite the increase in the number of parameters. This model, as well as performing well statistically, has intuitive appeal for forensic biologists and could be implemented in an expert system with a continuous method for DNA interpretation. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Spatial modeling on the upperstream of the Citarum watershed: An application of geoinformatics

NASA Astrophysics Data System (ADS)

Ningrum, Windy Setia; Widyaningsih, Yekti; Indra, Tito Latif

2017-03-01

The Citarum watershed is the longest and the largest watershed in West Java, Indonesia, located at 106°51'36''-107°51' E and 7°19'-6°24'S across 10 districts, and serves as the water supply for over 15 million people. In this area, the water criticality index is concerned to reach the balance between water supply and water demand, so that in the dry season, the watershed is still able to meet the water needs of the society along the Citarum river. The objective of this research is to evaluate the water criticality index of Citarum watershed area using spatial model to overcome the spatial dependencies in the data. The result of Lagrange multiplier diagnostics for spatial dependence results are LM-err = 34.6 (p-value = 4.1e-09) and LM-lag = 8.05 (p-value = 0.005), then modeling using Spatial Lag Model (SLM) and Spatial Error Model (SEM) were conducted. The likelihood ratio test show that both of SLM dan SEM model is better than OLS model in modeling water criticality index in Citarum watershed. The AIC value of SLM and SEM model are 78.9 and 51.4, then the SEM model is better than SLM model in predicting water criticality index in Citarum watershed.

The effects of segmentation algorithms on the measurement of 18F-FDG PET texture parameters in non-small cell lung cancer.

PubMed

Bashir, Usman; Azad, Gurdip; Siddique, Muhammad Musib; Dhillon, Saana; Patel, Nikheel; Bassett, Paul; Landau, David; Goh, Vicky; Cook, Gary

2017-12-01

Measures of tumour heterogeneity derived from 18-fluoro-2-deoxyglucose positron emission tomography/computed tomography ( 18 F-FDG PET/CT) scans are increasingly reported as potential biomarkers of non-small cell lung cancer (NSCLC) for classification and prognostication. Several segmentation algorithms have been used to delineate tumours, but their effects on the reproducibility and predictive and prognostic capability of derived parameters have not been evaluated. The purpose of our study was to retrospectively compare various segmentation algorithms in terms of inter-observer reproducibility and prognostic capability of texture parameters derived from non-small cell lung cancer (NSCLC) 18 F-FDG PET/CT images. Fifty three NSCLC patients (mean age 65.8 years; 31 males) underwent pre-chemoradiotherapy 18 F-FDG PET/CT scans. Three readers segmented tumours using freehand (FH), 40% of maximum intensity threshold (40P), and fuzzy locally adaptive Bayesian (FLAB) algorithms. Intraclass correlation coefficient (ICC) was used to measure the inter-observer variability of the texture features derived by the three segmentation algorithms. Univariate cox regression was used on 12 commonly reported texture features to predict overall survival (OS) for each segmentation algorithm. Model quality was compared across segmentation algorithms using Akaike information criterion (AIC). 40P was the most reproducible algorithm (median ICC 0.9; interquartile range [IQR] 0.85-0.92) compared with FLAB (median ICC 0.83; IQR 0.77-0.86) and FH (median ICC 0.77; IQR 0.7-0.85). On univariate cox regression analysis, 40P found 2 out of 12 variables, i.e. first-order entropy and grey-level co-occurence matrix (GLCM) entropy, to be significantly associated with OS; FH and FLAB found 1, i.e., first-order entropy. For each tested variable, survival models for all three segmentation algorithms were of similar quality, exhibiting comparable AIC values with overlapping 95% CIs. Compared with both FLAB and FH, segmentation with 40P yields superior inter-observer reproducibility of texture features. Survival models generated by all three segmentation algorithms are of at least equivalent utility. Our findings suggest that a segmentation algorithm using a 40% of maximum threshold is acceptable for texture analysis of 18 F-FDG PET in NSCLC.

Ventilation/Perfusion Positron Emission Tomography—Based Assessment of Radiation Injury to Lung

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

Siva, Shankar, E-mail: shankar.siva@petermac.org; Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville; Hardcastle, Nicholas

2015-10-01

Purpose: To investigate {sup 68}Ga-ventilation/perfusion (V/Q) positron emission tomography (PET)/computed tomography (CT) as a novel imaging modality for assessment of perfusion, ventilation, and lung density changes in the context of radiation therapy (RT). Methods and Materials: In a prospective clinical trial, 20 patients underwent 4-dimensional (4D)-V/Q PET/CT before, midway through, and 3 months after definitive lung RT. Eligible patients were prescribed 60 Gy in 30 fractions with or without concurrent chemotherapy. Functional images were registered to the RT planning 4D-CT, and isodose volumes were averaged into 10-Gy bins. Within each dose bin, relative loss in standardized uptake value (SUV) was recorded for ventilation andmore » perfusion, and loss in air-filled fraction was recorded to assess RT-induced lung fibrosis. A dose-effect relationship was described using both linear and 2-parameter logistic fit models, and goodness of fit was assessed with Akaike Information Criterion (AIC). Results: A total of 179 imaging datasets were available for analysis (1 scan was unrecoverable). An almost perfectly linear negative dose-response relationship was observed for perfusion and air-filled fraction (r{sup 2}=0.99, P<.01), with ventilation strongly negatively linear (r{sup 2}=0.95, P<.01). Logistic models did not provide a better fit as evaluated by AIC. Perfusion, ventilation, and the air-filled fraction decreased 0.75 ± 0.03%, 0.71 ± 0.06%, and 0.49 ± 0.02%/Gy, respectively. Within high-dose regions, higher baseline perfusion SUV was associated with greater rate of loss. At 50 Gy and 60 Gy, the rate of loss was 1.35% (P=.07) and 1.73% (P=.05) per SUV, respectively. Of 8/20 patients with peritumoral reperfusion/reventilation during treatment, 7/8 did not sustain this effect after treatment. Conclusions: Radiation-induced regional lung functional deficits occur in a dose-dependent manner and can be estimated by simple linear models with 4D-V/Q PET/CT imaging. These findings may inform future studies of functional lung avoidance using V/Q PET/CT.« less

AICE Survey of USSR Air Pollution Literature, Volume 12: Technical Papers from the Leningrad International Symposium on the Meteorological Aspects of Atmospheric Pollution, Part I.

ERIC Educational Resources Information Center

Nuttonson, M. Y.

Twelve papers dealing with the meteorological aspects of air pollution were translated. These papers were initially presented at an international symposium held in Leningrad during July 1968. The papers are: Status and prospective development of meteorological studies of atmospheric pollution, Effect of the stability of the atmosphere on the…

AICE Survey of USSR Air Pollution Literature, Volume 15: A Third Compilation of Technical Reports on the Biological Effects and the Public Health Aspects of Atmospheric Pollutants.

ERIC Educational Resources Information Center

Nuttonson, M. Y.

Ten papers were translated: Maximum permissible concentrations of noxious substances in the atmospheric air of populated areas; Some aspects of the biological effect of microconcentrations of two chloroisocyanates; The toxicology of low concentrations of aromatic hydrocarbons; Chronic action of low concentrations of acrolein in air on the…

Using GPS telemetry to determine roadways most susceptible to deer-vehicle collisions

USGS Publications Warehouse

Kramer, David W.; Prebyl, Thomas J.; Stickles, James H.; Osborn, David A.; Irwin, Brian J.; Nibbelink, Nathan P.; Warren, Robert J.; Miller, Karl V.

2016-01-01

More than 1 million wildlife-vehicle collisions occur annually in the United States. The majority of these accidents involve white-tailed deer (Odocoileus virginianus) and result in >US $4.6 billion in damage and >200 human fatalities. Prior research has used collision locations to assess sitespecific as well as landscape features that contribute to risk of deer-vehicle collisions. As an alternative approach, we calculated road-crossing locations from 25 GPS-instrumented white-tailed deer near Madison, Georgia (n=154,131 hourly locations). We identified crossing locations by creating movement paths between subsequent GPS points and then intersecting the paths with road locations. Using AIC model selection, we determined whether 10 local and landscape variables were successful at identifying areas where higher frequencies of deer crossings were likely to occur. Our findings indicate that traffic volume, distance to riparian areas, and the amount of forested area influenced the frequency of road crossings. Roadways that were predominately located in wooded landscapes and 200–300 m from riparian areas were crossed frequently. Additionally, we found that areas of low traffic volume (e.g., county roads) had the highest frequencies of deer crossings. Analyses utilizing only records of deer-vehicle collision locations cannot separate the relative contribution of deer crossing rates and traffic volume. Increased frequency of road crossings by deer in low-traffic, forested areas may lead to a greater risk of deer-vehicle collision than suggested by evaluations of deer-vehicle collision frequency alone.

Boundary methods for mode estimation

NASA Astrophysics Data System (ADS)

Pierson, William E., Jr.; Ulug, Batuhan; Ahalt, Stanley C.

1999-08-01

This paper investigates the use of Boundary Methods (BMs), a collection of tools used for distribution analysis, as a method for estimating the number of modes associated with a given data set. Model order information of this type is required by several pattern recognition applications. The BM technique provides a novel approach to this parameter estimation problem and is comparable in terms of both accuracy and computations to other popular mode estimation techniques currently found in the literature and automatic target recognition applications. This paper explains the methodology used in the BM approach to mode estimation. Also, this paper quickly reviews other common mode estimation techniques and describes the empirical investigation used to explore the relationship of the BM technique to other mode estimation techniques. Specifically, the accuracy and computational efficiency of the BM technique are compared quantitatively to the a mixture of Gaussian (MOG) approach and a k-means approach to model order estimation. The stopping criteria of the MOG and k-means techniques is the Akaike Information Criteria (AIC).

Elevational Gradients in Fish Diversity in the Himalaya: Water Discharge Is the Key Driver of Distribution Patterns

PubMed Central

Bhatt, Jay P.; Manish, Kumar; Pandit, Maharaj K.

2012-01-01

Background Studying diversity and distribution patterns of species along elevational gradients and understanding drivers behind these patterns is central to macroecology and conservation biology. A number of studies on biogeographic gradients are available for terrestrial ecosystems, but freshwater ecosystems remain largely neglected. In particular, we know very little about the species richness gradients and their drivers in the Himalaya, a global biodiversity hotspot. Methodology/Principal Findings We collated taxonomic and distribution data of fish species from 16 freshwater Himalayan rivers and carried out empirical studies on environmental drivers and fish diversity and distribution in the Teesta river (Eastern Himalaya). We examined patterns of fish species richness along the Himalayan elevational gradients (50–3800 m) and sought to understand the drivers behind the emerging patterns. We used generalized linear models (GLM) and generalized additive models (GAM) to examine the richness patterns; GLM was used to investigate relationship between fish species richness and various environmental variables. Regression modelling involved stepwise procedures, including elimination of collinear variables, best model selection, based on the least Akaike’s information criterion (AIC) and the highest percentage of deviance explained (D2). This maiden study on the Himalayan fishes revealed that total and non-endemic fish species richness monotonously decrease with increasing elevation, while endemics peaked around mid elevations (700–1500 m). The best explanatory model (synthetic model) indicated that water discharge is the best predictor of fish species richness patterns in the Himalayan rivers. Conclusions/Significance This study, carried out along one of the longest bioclimatic elevation gradients of the world, lends support to Rapoport’s elevational rule as opposed to mid domain effect hypothesis. We propose a species-discharge model and contradict species-area model in predicting fish species richness. We suggest that drivers of richness gradients in terrestrial and aquatic ecosystems are likely to be different. These studies are crucial in context of the impacts of unprecedented on-going river regulation on fish diversity and distribution in the Himalaya. PMID:23029444

Forecasting typhoid fever incidence in the Cordillera administrative region in the Philippines using seasonal ARIMA models

NASA Astrophysics Data System (ADS)

Cawiding, Olive R.; Natividad, Gina May R.; Bato, Crisostomo V.; Addawe, Rizavel C.

2017-11-01

The prevalence of typhoid fever in developing countries such as the Philippines calls for a need for accurate forecasting of the disease. This will be of great assistance in strategic disease prevention. This paper presents a development of useful models that predict the behavior of typhoid fever incidence based on the monthly incidence in the provinces of the Cordillera Administrative Region from 2010 to 2015 using univariate time series analysis. The data used was obtained from the Cordillera Office of the Department of Health (DOH-CAR). Seasonal autoregressive moving average (SARIMA) models were used to incorporate the seasonality of the data. A comparison of the results of the obtained models revealed that the SARIMA (1,1,7)(0,0,1)12 with a fixed coefficient at the seventh lag produces the smallest root mean square error (RMSE), mean absolute error (MAE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The model suggested that for the year 2016, the number of cases would increase from the months of July to September and have a drop in December. This was then validated using the data collected from January 2016 to December 2016.

Modelling space of spread Dengue Hemorrhagic Fever (DHF) in Central Java use spatial durbin model

NASA Astrophysics Data System (ADS)

Ispriyanti, Dwi; Prahutama, Alan; Taryono, Arkadina PN

2018-05-01

Dengue Hemorrhagic Fever is one of the major public health problems in Indonesia. From year to year, DHF causes Extraordinary Event in most parts of Indonesia, especially Central Java. Central Java consists of 35 districts or cities where each region is close to each other. Spatial regression is an analysis that suspects the influence of independent variables on the dependent variables with the influences of the region inside. In spatial regression modeling, there are spatial autoregressive model (SAR), spatial error model (SEM) and spatial autoregressive moving average (SARMA). Spatial Durbin model is the development of SAR where the dependent and independent variable have spatial influence. In this research dependent variable used is number of DHF sufferers. The independent variables observed are population density, number of hospitals, residents and health centers, and mean years of schooling. From the multiple regression model test, the variables that significantly affect the spread of DHF disease are the population and mean years of schooling. By using queen contiguity and rook contiguity, the best model produced is the SDM model with queen contiguity because it has the smallest AIC value of 494,12. Factors that generally affect the spread of DHF in Central Java Province are the number of population and the average length of school.

Fire regime in Mediterranean ecosystem

NASA Astrophysics Data System (ADS)

Biondi, Guido; Casula, Paolo; D'Andrea, Mirko; Fiorucci, Paolo

2010-05-01

The analysis of burnt areas time series in Mediterranean regions suggests that ecosystems characterising this area consist primarily of species highly vulnerable to the fire but highly resilient, as characterized by a significant regenerative capacity after the fire spreading. In a few years the area burnt may once again be covered by the same vegetation present before the fire. Similarly, Mediterranean conifer forests, which often refers to plantations made in order to reforest the areas most severely degraded with high erosion risk, regenerate from seed after the fire resulting in high resilience to the fire as well. Only rarely, and usually with negligible damages, fire affects the areas covered by climax species in relation with altitude and soil types (i.e, quercus, fagus, abies). On the basis of these results, this paper shows how the simple Drossel-Schwabl forest fire model is able to reproduce the forest fire regime in terms of number of fires and burned area, describing whit good accuracy the actual fire perimeters. The original Drossel-Schwabl model has been slightly modified in this work by introducing two parameters (probability of propagation and regrowth) specific for each different class of vegetation cover. Using model selection methods based on AIC, the model with the optimal number of classes with different fire behaviour was selected. Two different case studies are presented in this work: Regione Liguria and Regione Sardegna (Italy). Both regions are situated in the center of the Mediterranean and are characterized by a high number of fires and burned area. However, the two regions have very different fire regimes. Sardinia is affected by the fire phenomenon only in summer whilst Liguria is affected by fires also in winter, with higher number of fires and larger burned area. In addition, the two region are very different in vegetation cover. The presence of Mediterranean conifers, (Pinus Pinaster, Pinus Nigra, Pinus halepensis) is quite spread in Liguria and is limited in Sardinia. What is common in the two regions is the widespread presence of shrub species frequently spread by fire. The analysis in the two regions thus allows in a rather limited area to study almost all the species that characterize the Mediterranean region. This work shows that the fire regime in Mediterranean area is strongly related with vegetation patterns, is almost totally independent by the cause of ignition, and only partially dependent by fire extinguishing actions.

Prevalence and predictors for musculoskeletal discomfort in Malaysian office workers: Investigating explanatory factors for a developing country.

PubMed

Maakip, Ismail; Keegel, Tessa; Oakman, Jodi

2016-03-01

Musculoskeletal disorders (MSDs) are a major occupational health issue for workers in developed and developing countries, including Malaysia. Most research related to MSDs has been undertaken in developed countries; given the different regulatory and cultural practices it is plausible that contributions of hazard and risk factors may be different. A population of Malaysian public service office workers were surveyed (N = 417, 65.5% response rate) to determine prevalence and associated predictors of MSD discomfort. The 6-month period prevalence of MSD discomfort was 92.8% (95%CI = 90.2-95.2%). Akaike's Information Criterion (AIC) analyses was used to compare a range of models and determine a model of best fit. Contributions associated with MSD discomfort in the final model consisted of physical demands (61%), workload (14%), gender (13%), work-home balance (9%) and psychosocial factors (3%). Factors associated with MSD discomfort were similar in developed and developing countries but the relative contribution of factors was different, providing insight into future development of risk management strategies. Copyright © 2015 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Replication of a gene-environment interaction Via Multimodel inference: additive-genetic variance in adolescents' general cognitive ability increases with family-of-origin socioeconomic status.

PubMed

Kirkpatrick, Robert M; McGue, Matt; Iacono, William G

2015-03-01

The present study of general cognitive ability attempts to replicate and extend previous investigations of a biometric moderator, family-of-origin socioeconomic status (SES), in a sample of 2,494 pairs of adolescent twins, non-twin biological siblings, and adoptive siblings assessed with individually administered IQ tests. We hypothesized that SES would covary positively with additive-genetic variance and negatively with shared-environmental variance. Important potential confounds unaddressed in some past studies, such as twin-specific effects, assortative mating, and differential heritability by trait level, were found to be negligible. In our main analysis, we compared models by their sample-size corrected AIC, and base our statistical inference on model-averaged point estimates and standard errors. Additive-genetic variance increased with SES-an effect that was statistically significant and robust to model specification. We found no evidence that SES moderated shared-environmental influence. We attempt to explain the inconsistent replication record of these effects, and provide suggestions for future research.

Replication of a Gene-Environment Interaction via Multimodel Inference: Additive-Genetic Variance in Adolescents’ General Cognitive Ability Increases with Family-of-Origin Socioeconomic Status

PubMed Central

Kirkpatrick, Robert M.; McGue, Matt; Iacono, William G.

2015-01-01

The present study of general cognitive ability attempts to replicate and extend previous investigations of a biometric moderator, family-of-origin socioeconomic status (SES), in a sample of 2,494 pairs of adolescent twins, non-twin biological siblings, and adoptive siblings assessed with individually administered IQ tests. We hypothesized that SES would covary positively with additive-genetic variance and negatively with shared-environmental variance. Important potential confounds unaddressed in some past studies, such as twin-specific effects, assortative mating, and differential heritability by trait level, were found to be negligible. In our main analysis, we compared models by their sample-size corrected AIC, and base our statistical inference on model-averaged point estimates and standard errors. Additive-genetic variance increased with SES—an effect that was statistically significant and robust to model specification. We found no evidence that SES moderated shared-environmental influence. We attempt to explain the inconsistent replication record of these effects, and provide suggestions for future research. PMID:25539975

Model averaging techniques for quantifying conceptual model uncertainty.

PubMed

Singh, Abhishek; Mishra, Srikanta; Ruskauff, Greg

2010-01-01

In recent years a growing understanding has emerged regarding the need to expand the modeling paradigm to include conceptual model uncertainty for groundwater models. Conceptual model uncertainty is typically addressed by formulating alternative model conceptualizations and assessing their relative likelihoods using statistical model averaging approaches. Several model averaging techniques and likelihood measures have been proposed in the recent literature for this purpose with two broad categories--Monte Carlo-based techniques such as Generalized Likelihood Uncertainty Estimation or GLUE (Beven and Binley 1992) and criterion-based techniques that use metrics such as the Bayesian and Kashyap Information Criteria (e.g., the Maximum Likelihood Bayesian Model Averaging or MLBMA approach proposed by Neuman 2003) and Akaike Information Criterion-based model averaging (AICMA) (Poeter and Anderson 2005). These different techniques can often lead to significantly different relative model weights and ranks because of differences in the underlying statistical assumptions about the nature of model uncertainty. This paper provides a comparative assessment of the four model averaging techniques (GLUE, MLBMA with KIC, MLBMA with BIC, and AIC-based model averaging) mentioned above for the purpose of quantifying the impacts of model uncertainty on groundwater model predictions. Pros and cons of each model averaging technique are examined from a practitioner's perspective using two groundwater modeling case studies. Recommendations are provided regarding the use of these techniques in groundwater modeling practice.

Dynamics and regulation of the southern brook trout (Salvelinus fontinalis) population in an Appalachian stream

Treesearch

Gary D. Grossman; Robert E. Ratajczak; C. Michael Wagner; J. Todd Petty

2010-01-01

1. We used information theoretic statistics [Akaike’s Information Criterion (AIC)] and regression analysis in a multiple hypothesis testing approach to assess the processes capable of explaining long-term demographic variation in a lightly exploited brook trout population in Ball Creek, NC. We sampled a 100-m-long second-order site during both spring and autumn 1991–...

TRADOC Library and Information Network (TRALINET)

DTIC Science & Technology

1979-03-01

by the Library of Congress, Dewey materials that have beer photographically reduced Decimal , or any other classification scheme adopted in size for...sites at Forts Hood, TX; Gordon, GA; Monroe, VA; Knox, KY, and Leavenworth, KS. DTIC, formally Defense Documentation Center ( DDC ), serves as the primary...locally expanded subject schedules, whether schedules aic for Dewey , Library of Congress, etc., particularly in the are& of Military Arts and Sciences. 1 4

Neck Injury in Advanced Military Aircraft Environments

DTIC Science & Technology

1990-02-01

injury alibhis the Fast 2 nortbs In atstlitied by type of oihcrafr. This table demonstrates a statistirally significant trend in frequency (P- S5 aud...it appears that ransitional vertebrae aic relatively coarnon and equally distributed bhtweon the thoracico-lumbal (9.0%) and the lumbo- sacral area...unilateral contact of asymmetrical lumbar sacralization which increases torque forces with consequent strain on the spine and risk of disc herniation above

AICE Survey of USSR Air Pollution Literature, Volume 14: Technical Papers from the Leningrad International Symposium on the Meteorological Aspects of Atmospheric Pollution, Part 3.

ERIC Educational Resources Information Center

Nuttonson, M. Y.

Fifteen papers were translated: On the removal of impurities from the atmosphere by clouds and precipitation; Some aspects of the adoption of automatic methods of determining atmospheric pollutants; Recording of sulfur dioxide content at the outskirts of a city. Comparison of measurement results for a valley and an elevation; Theoretical and…

Differential functional connectivity of rostral anterior cingulate cortex during emotional interference

PubMed Central

Szekely, Akos; Silton, Rebecca L.; Heller, Wendy; Miller, Gregory A.

2017-01-01

Abstract The rostral-ventral subdivision of the anterior cingulate cortex (rACC) plays a key role in the regulation of emotional processing. Although rACC has strong anatomical connections with anterior insular cortex (AIC), amygdala, prefrontal cortex and striatal brain regions, it is unclear whether the functional connectivity of rACC with these regions changes when regulating emotional processing. Furthermore, it is not known whether this connectivity changes with deficits in emotion regulation seen in different kinds of anxiety and depression. To address these questions regarding rACC functional connectivity, non-patients high in self-reported anxious apprehension (AP), anxious arousal (AR), anhedonic depression (AD) or none (CON) indicated the ink color of pleasant, neutral and unpleasant words during functional magnetic resonance imaging. While ignoring task-irrelevant unpleasant words, AD and CON showed an increase in the functional connectivity of rACC with AIC, putamen, caudate and ventral pallidum. There was a decrease in this connectivity in AP and AR, with AP showing greater reduction than AR. These findings provide support for the role of rACC in integrating interoceptive, emotional and cognitive functions via interactions with insula and striatal regions during effective emotion regulation in healthy individuals and a failure of this integration that may be specific to anxiety, particularly AP. PMID:27998997

Resting-state functional connectivity in combat veterans suffering from impulsive aggression

PubMed Central

Heesink, Lieke; van Honk, Jack; Geuze, Elbert

2017-01-01

Abstract Impulsive aggression is common among military personnel after deployment and may arise because of impaired top-down regulation of the amygdala by prefrontal regions. This study sought to further explore this hypothesis via resting-state functional connectivity analyses in impulsively aggressive combat veterans. Male combat veterans with (n = 28) and without (n = 30) impulsive aggression problems underwent resting-state functional magnetic resonance imaging. Functional connectivity analyses were conducted with the following seed-regions: basolateral amygdala (BLA), centromedial amygdala, anterior cingulate cortex (ACC), and anterior insular cortex (AIC). Regions-of-interest analyses focused on the orbitofrontal cortex and periaqueductal gray, and yielded no significant results. In exploratory cluster analyses, we observed reduced functional connectivity between the (bilateral) BLA and left dorsolateral prefrontal cortex in the impulsive aggression group, relative to combat controls. This finding indicates that combat-related impulsive aggression may be marked by weakened functional connectivity between the amygdala and prefrontal regions, already in the absence of explicit emotional stimuli. Group differences in functional connectivity were also observed between the (bilateral) ACC and left cuneus, which may be related to heightened vigilance to potentially threatening visual cues, as well as between the left AIC and right temporal pole, possibly related to negative memory association in impulsive aggression. PMID:29040723

Re-assess Vector Indices Threshold as an Early Warning Tool for Predicting Dengue Epidemic in a Dengue Non-endemic Country

PubMed Central

Hsu, Pi-Shan; Chen, Chaur-Dong; Lian, Ie-Bin; Chao, Day-Yu

2015-01-01

Background Despite dengue dynamics being driven by complex interactions between human hosts, mosquito vectors and viruses that are influenced by climate factors, an operational model that will enable health authorities to anticipate the outbreak risk in a dengue non-endemic area has not been developed. The objectives of this study were to evaluate the temporal relationship between meteorological variables, entomological surveillance indices and confirmed dengue cases; and to establish the threshold for entomological surveillance indices including three mosquito larval indices [Breteau (BI), Container (CI) and House indices (HI)] and one adult index (AI) as an early warning tool for dengue epidemic. Methodology/Principal Findings Epidemiological, entomological and meteorological data were analyzed from 2005 to 2012 in Kaohsiung City, Taiwan. The successive waves of dengue outbreaks with different magnitudes were recorded in Kaohsiung City, and involved a dominant serotype during each epidemic. The annual indigenous dengue cases usually started from May to June and reached a peak in October to November. Vector data from 2005–2012 showed that the peak of the adult mosquito population was followed by a peak in the corresponding dengue activity with a lag period of 1–2 months. Therefore, we focused the analysis on the data from May to December and the high risk district, where the inspection of the immature and mature mosquitoes was carried out on a weekly basis and about 97.9% dengue cases occurred. The two-stage model was utilized here to estimate the risk and time-lag effect of annual dengue outbreaks in Taiwan. First, Poisson regression was used to select the optimal subset of variables and time-lags for predicting the number of dengue cases, and the final results of the multivariate analysis were selected based on the smallest AIC value. Next, each vector index models with selected variables were subjected to multiple logistic regression models to examine the accuracy of predicting the occurrence of dengue cases. The results suggested that Model-AI, BI, CI and HI predicted the occurrence of dengue cases with 83.8, 87.8, 88.3 and 88.4% accuracy, respectively. The predicting threshold based on individual Model-AI, BI, CI and HI was 0.97, 1.16, 1.79 and 0.997, respectively. Conclusion/Significance There was little evidence of quantifiable association among vector indices, meteorological factors and dengue transmission that could reliably be used for outbreak prediction. Our study here provided the proof-of-concept of how to search for the optimal model and determine the threshold for dengue epidemics. Since those factors used for prediction varied, depending on the ecology and herd immunity level under different geological areas, different thresholds may be developed for different countries using a similar structure of the two-stage model. PMID:26366874

Effect of ultrasound pre-treatment on the drying kinetics of brown seaweed Ascophyllum nodosum.

PubMed

Kadam, Shekhar U; Tiwari, Brijesh K; O'Donnell, Colm P

2015-03-01

The effect of ultrasound pre-treatment on the drying kinetics of brown seaweed Ascophyllum nodosum under hot-air convective drying was investigated. Pretreatments were carried out at ultrasound intensity levels ranging from 7.00 to 75.78 Wcm(-2) for 10 min using an ultrasonic probe system. It was observed that ultrasound pre-treatments reduced the drying time required. The shortest drying times were obtained from samples pre-treated at 75.78 Wcm(-2). The fit quality of 6 thin-layer drying models was also evaluated using the determination of coefficient (R(2)), root means square error (RMSE), AIC (Akaike information criterion) and BIC (Bayesian information criterion). Drying kinetics were modelled using the Newton, Henderson and Pabis, Page, Wang and Singh, Midilli et al. and Weibull models. The Newton, Wang and Singh, and Midilli et al. models showed the best fit to the experimental drying data. Color of ultrasound pretreated dried seaweed samples were lighter compared to control samples. It was concluded that ultrasound pretreatment can be effectively used to reduce the energy cost and drying time for drying of A. nodosum. Copyright © 2014 Elsevier B.V. All rights reserved.

Using generalized additive (mixed) models to analyze single case designs.

PubMed

Shadish, William R; Zuur, Alain F; Sullivan, Kristynn J

2014-04-01

This article shows how to apply generalized additive models and generalized additive mixed models to single-case design data. These models excel at detecting the functional form between two variables (often called trend), that is, whether trend exists, and if it does, what its shape is (e.g., linear and nonlinear). In many respects, however, these models are also an ideal vehicle for analyzing single-case designs because they can consider level, trend, variability, overlap, immediacy of effect, and phase consistency that single-case design researchers examine when interpreting a functional relation. We show how these models can be implemented in a wide variety of ways to test whether treatment is effective, whether cases differ from each other, whether treatment effects vary over cases, and whether trend varies over cases. We illustrate diagnostic statistics and graphs, and we discuss overdispersion of data in detail, with examples of quasibinomial models for overdispersed data, including how to compute dispersion and quasi-AIC fit indices in generalized additive models. We show how generalized additive mixed models can be used to estimate autoregressive models and random effects and discuss the limitations of the mixed models compared to generalized additive models. We provide extensive annotated syntax for doing all these analyses in the free computer program R. Copyright © 2013 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Assessing variation in life-history tactics within a population using mixture regression models: a practical guide for evolutionary ecologists.

PubMed

Hamel, Sandra; Yoccoz, Nigel G; Gaillard, Jean-Michel

2017-05-01

Mixed models are now well-established methods in ecology and evolution because they allow accounting for and quantifying within- and between-individual variation. However, the required normal distribution of the random effects can often be violated by the presence of clusters among subjects, which leads to multi-modal distributions. In such cases, using what is known as mixture regression models might offer a more appropriate approach. These models are widely used in psychology, sociology, and medicine to describe the diversity of trajectories occurring within a population over time (e.g. psychological development, growth). In ecology and evolution, however, these models are seldom used even though understanding changes in individual trajectories is an active area of research in life-history studies. Our aim is to demonstrate the value of using mixture models to describe variation in individual life-history tactics within a population, and hence to promote the use of these models by ecologists and evolutionary ecologists. We first ran a set of simulations to determine whether and when a mixture model allows teasing apart latent clustering, and to contrast the precision and accuracy of estimates obtained from mixture models versus mixed models under a wide range of ecological contexts. We then used empirical data from long-term studies of large mammals to illustrate the potential of using mixture models for assessing within-population variation in life-history tactics. Mixture models performed well in most cases, except for variables following a Bernoulli distribution and when sample size was small. The four selection criteria we evaluated [Akaike information criterion (AIC), Bayesian information criterion (BIC), and two bootstrap methods] performed similarly well, selecting the right number of clusters in most ecological situations. We then showed that the normality of random effects implicitly assumed by evolutionary ecologists when using mixed models was often violated in life-history data. Mixed models were quite robust to this violation in the sense that fixed effects were unbiased at the population level. However, fixed effects at the cluster level and random effects were better estimated using mixture models. Our empirical analyses demonstrated that using mixture models facilitates the identification of the diversity of growth and reproductive tactics occurring within a population. Therefore, using this modelling framework allows testing for the presence of clusters and, when clusters occur, provides reliable estimates of fixed and random effects for each cluster of the population. In the presence or expectation of clusters, using mixture models offers a suitable extension of mixed models, particularly when evolutionary ecologists aim at identifying how ecological and evolutionary processes change within a population. Mixture regression models therefore provide a valuable addition to the statistical toolbox of evolutionary ecologists. As these models are complex and have their own limitations, we provide recommendations to guide future users. © 2016 Cambridge Philosophical Society.

Forecasting the Incidence of Mumps in Zibo City Based on a SARIMA Model.

PubMed

Xu, Qinqin; Li, Runzi; Liu, Yafei; Luo, Cheng; Xu, Aiqiang; Xue, Fuzhong; Xu, Qing; Li, Xiujun

2017-08-17

This study aimed to predict the incidence of mumps using a seasonal autoregressive integrated moving average (SARIMA) model, and provide theoretical evidence for early warning prevention and control in Zibo City, Shandong Province, China. Monthly mumps data from Zibo City gathered between 2005 and 2013 were used as a training set to construct a SARIMA model, and the monthly mumps in 2014 were defined as a test set for the model. From 2005 to 2014, a total of 8722 cases of mumps were reported in Zibo City; the male-to-female ratio of cases was 1.85:1, the age group of 1-20 years old accounted for 94.05% of all reported cases, and students made up the largest proportion (65.89%). The main serious endemic areas of mumps were located in Huantai County, Linzi District, and Boshan District of Zibo City. There were two epidemic peaks from April to July and from October to January in next year. The fitted model SARIMA (0, 1, 1) (0, 1, 1) 12 was established (AIC = 157.528), which has high validity and reasonability. The SARIMA model fitted dynamic changes of mumps in Zibo City well. It can be used for short-term forecasting and early warning of mumps.

Forecasting the Incidence of Mumps in Zibo City Based on a SARIMA Model

PubMed Central

Xu, Qinqin; Li, Runzi; Liu, Yafei; Luo, Cheng; Xu, Aiqiang; Xue, Fuzhong; Xu, Qing; Li, Xiujun

2017-01-01

This study aimed to predict the incidence of mumps using a seasonal autoregressive integrated moving average (SARIMA) model, and provide theoretical evidence for early warning prevention and control in Zibo City, Shandong Province, China. Monthly mumps data from Zibo City gathered between 2005 and 2013 were used as a training set to construct a SARIMA model, and the monthly mumps in 2014 were defined as a test set for the model. From 2005 to 2014, a total of 8722 cases of mumps were reported in Zibo City; the male-to-female ratio of cases was 1.85:1, the age group of 1–20 years old accounted for 94.05% of all reported cases, and students made up the largest proportion (65.89%). The main serious endemic areas of mumps were located in Huantai County, Linzi District, and Boshan District of Zibo City. There were two epidemic peaks from April to July and from October to January in next year. The fitted model SARIMA (0, 1, 1) (0, 1, 1)12 was established (AIC = 157.528), which has high validity and reasonability. The SARIMA model fitted dynamic changes of mumps in Zibo City well. It can be used for short-term forecasting and early warning of mumps. PMID:28817101

Modeling the influence of Chevron alignment sign on young male driver performance: A driving simulator study.

PubMed

Wu, Yiping; Zhao, Xiaohua; Chen, Chen; He, Jiayuan; Rong, Jian; Ma, Jianming

2016-10-01

In China, the Chevron alignment sign on highways is a vertical rectangle with a white arrow and border on a blue background, which differs from its counterpart in other countries. Moreover, little research has been devoted to the effectiveness of China's Chevron signs; there is still no practical method to quantitatively describe the impact of Chevron signs on driver performance in roadway curves. In this paper, a driving simulator experiment collected data on the driving performance of 30 young male drivers as they navigated on 29 different horizontal curves under different conditions (presence of Chevron signs, curve radius and curve direction). To address the heterogeneity issue in the data, three models were estimated and tested: a pooled data linear regression model, a fixed effects model, and a random effects model. According to the Hausman Test and Akaike Information Criterion (AIC), the random effects model offers the best fit. The current study explores the relationship between driver performance (i.e., vehicle speed and lane position) and horizontal curves with respect to the horizontal curvature, presence of Chevron signs, and curve direction. This study lays a foundation for developing procedures and guidelines that would allow more uniform and efficient deployment of Chevron signs on China's highways. Copyright © 2016 Elsevier Ltd. All rights reserved.

Autoregressive modelling of species richness in the Brazilian Cerrado.

PubMed

Vieira, C M; Blamires, D; Diniz-Filho, J A F; Bini, L M; Rangel, T F L V B

2008-05-01

Spatial autocorrelation is the lack of independence between pairs of observations at given distances within a geographical space, a phenomenon commonly found in ecological data. Taking into account spatial autocorrelation when evaluating problems in geographical ecology, including gradients in species richness, is important to describe both the spatial structure in data and to correct the bias in Type I errors of standard statistical analyses. However, to effectively solve these problems it is necessary to establish the best way to incorporate the spatial structure to be used in the models. In this paper, we applied autoregressive models based on different types of connections and distances between 181 cells covering the Cerrado region of Central Brazil to study the spatial variation in mammal and bird species richness across the biome. Spatial structure was stronger for birds than for mammals, with R(2) values ranging from 0.77 to 0.94 for mammals and from 0.77 to 0.97 for birds, for models based on different definitions of spatial structures. According to the Akaike Information Criterion (AIC), the best autoregressive model was obtained by using the rook connection. In general, these results furnish guidelines for future modelling of species richness patterns in relation to environmental predictors and other variables expressing human occupation in the biome.

Application of Time-series Model to Predict Groundwater Quality Parameters for Agriculture: (Plain Mehran Case Study)

NASA Astrophysics Data System (ADS)

Mehrdad Mirsanjari, Mir; Mohammadyari, Fatemeh

2018-03-01

Underground water is regarded as considerable water source which is mainly available in arid and semi arid with deficient surface water source. Forecasting of hydrological variables are suitable tools in water resources management. On the other hand, time series concepts is considered efficient means in forecasting process of water management. In this study the data including qualitative parameters (electrical conductivity and sodium adsorption ratio) of 17 underground water wells in Mehran Plain has been used to model the trend of parameters change over time. Using determined model, the qualitative parameters of groundwater is predicted for the next seven years. Data from 2003 to 2016 has been collected and were fitted by AR, MA, ARMA, ARIMA and SARIMA models. Afterward, the best model is determined using information criterion or Akaike (AIC) and correlation coefficient. After modeling parameters, the map of agricultural land use in 2016 and 2023 were generated and the changes between these years were studied. Based on the results, the average of predicted SAR (Sodium Adsorption Rate) in all wells in the year 2023 will increase compared to 2016. EC (Electrical Conductivity) average in the ninth and fifteenth holes and decreases in other wells will be increased. The results indicate that the quality of groundwater for Agriculture Plain Mehran will decline in seven years.

Exploring the limits of cryospectroscopy: Least-squares based approaches for analyzing the self-association of HCl

NASA Astrophysics Data System (ADS)

De Beuckeleer, Liene I.; Herrebout, Wouter A.

2016-02-01

To rationalize the concentration dependent behavior observed for a large spectral data set of HCl recorded in liquid argon, least-squares based numerical methods are developed and validated. In these methods, for each wavenumber a polynomial is used to mimic the relation between monomer concentrations and measured absorbances. Least-squares fitting of higher degree polynomials tends to overfit and thus leads to compensation effects where a contribution due to one species is compensated for by a negative contribution of another. The compensation effects are corrected for by carefully analyzing, using AIC and BIC information criteria, the differences observed between consecutive fittings when the degree of the polynomial model is systematically increased, and by introducing constraints prohibiting negative absorbances to occur for the monomer or for one of the oligomers. The method developed should allow other, more complicated self-associating systems to be analyzed with a much higher accuracy than before.

Vital: Vanguard Investigations of Therapeutic Approaches to Lung Cancer

DTIC Science & Technology

2007-01-01

corresponding AIC values were We analyzed these data sets using different methods . We -106.28, -106.39, and -106.72, respectively. The backward calculated the...old 8 considerable progress in generating 60-70 years old 11 immortalized HBECs from now 36 >70 years old 9 different individuals that were Age TBD 5...Aim 1. We will determine the potential role of different chemopreventive agents [e.g., celecoxib, N-(4- hydroxyphenyl]retinamide (4-HPR), Iressa

Parsimony and goodness-of-fit in multi-dimensional NMR inversion

NASA Astrophysics Data System (ADS)

Babak, Petro; Kryuchkov, Sergey; Kantzas, Apostolos

2017-01-01

Multi-dimensional nuclear magnetic resonance (NMR) experiments are often used for study of molecular structure and dynamics of matter in core analysis and reservoir evaluation. Industrial applications of multi-dimensional NMR involve a high-dimensional measurement dataset with complicated correlation structure and require rapid and stable inversion algorithms from the time domain to the relaxation rate and/or diffusion domains. In practice, applying existing inverse algorithms with a large number of parameter values leads to an infinite number of solutions with a reasonable fit to the NMR data. The interpretation of such variability of multiple solutions and selection of the most appropriate solution could be a very complex problem. In most cases the characteristics of materials have sparse signatures, and investigators would like to distinguish the most significant relaxation and diffusion values of the materials. To produce an easy to interpret and unique NMR distribution with the finite number of the principal parameter values, we introduce a new method for NMR inversion. The method is constructed based on the trade-off between the conventional goodness-of-fit approach to multivariate data and the principle of parsimony guaranteeing inversion with the least number of parameter values. We suggest performing the inversion of NMR data using the forward stepwise regression selection algorithm. To account for the trade-off between goodness-of-fit and parsimony, the objective function is selected based on Akaike Information Criterion (AIC). The performance of the developed multi-dimensional NMR inversion method and its comparison with conventional methods are illustrated using real data for samples with bitumen, water and clay.

Estimation of interplate coupling along Nankai trough considering the block motion model based on onland GNSS and seafloor GPS/A observation data using MCMC method

NASA Astrophysics Data System (ADS)

Kimura, H.; Ito, T.; Tadokoro, K.

2017-12-01

Introduction In southwest Japan, Philippine sea plate is subducting under the overriding plate such as Amurian plate, and mega interplate earthquakes has occurred at about 100 years interval. There is no occurrence of mega interplate earthquakes in southwest Japan, although it has passed about 70 years since the last mega interplate earthquakes: 1944 and 1946 along Nankai trough, meaning that the strain has been accumulated at plate interface. Therefore, it is essential to reveal the interplate coupling more precisely for predicting or understanding the mechanism of next occurring mega interplate earthquake. Recently, seafloor geodetic observation revealed the detailed interplate coupling distribution in expected source region of Nankai trough earthquake (e.g., Yokota et al. [2016]). In this study, we estimated interplate coupling in southwest Japan, considering block motion model and using seafloor geodetic observation data as well as onland GNSS observation data, based on Markov Chain Monte Carlo (MCMC) method. Method Observed crustal deformation is assumed that sum of rigid block motion and elastic deformation due to coupling at block boundaries. We modeled this relationship as a non-linear inverse problem that the unknown parameters are Euler pole of each block and coupling at each subfault, and solved them simultaneously based on MCMC method. Input data we used in this study are 863 onland GNSS observation data and 24 seafloor GPS/A observation data. We made some block division models based on the map of active fault tracing and selected the best model based on Akaike's Information Criterion (AIC): that is consist of 12 blocks. Result We find that the interplate coupling along Nankai trough has heterogeneous spatial distribution, strong at the depth of 0 to 20km at off Tokai region, and 0 to 30km at off Shikoku region. Moreover, we find that observed crustal deformation at off Tokai region is well explained by elastic deformation due to subducting Izu Micro Plate. We will present more details of our result, and discuss about not only interplate coupling but also rigid block motion, elastic deformation due to inland fault coupling, and resolution of estimated parameters.

Quantitative structure - mesothelioma potency model ...

EPA Pesticide Factsheets

Cancer potencies of mineral and synthetic elongated particle (EP) mixtures, including asbestos fibers, are influenced by changes in fiber dose composition, bioavailability, and biodurability in combination with relevant cytotoxic dose-response relationships. A unique and comprehensive rat intra-pleural (IP) dose characterization data set with a wide variety of EP size, shape, crystallographic, chemical, and bio-durability properties facilitated extensive statistical analyses of 50 rat IP exposure test results for evaluation of alternative dose pleural mesothelioma response models. Utilizing logistic regression, maximum likelihood evaluations of thousands of alternative dose metrics based on hundreds of individual EP dimensional variations within each test sample, four major findings emerged: (1) data for simulations of short-term EP dose changes in vivo (mild acid leaching) provide superior predictions of tumor incidence compared to non-acid leached data; (2) sum of the EP surface areas (ÓSA) from these mildly acid-leached samples provides the optimum holistic dose response model; (3) progressive removal of dose associated with very short and/or thin EPs significantly degrades resultant ÓEP or ÓSA dose-based predictive model fits, as judged by Akaike’s Information Criterion (AIC); and (4) alternative, biologically plausible model adjustments provide evidence for reduced potency of EPs with length/width (aspect) ratios 80 µm. Regar

A New First Break Picking for Three-Component VSP Data Using Gesture Sensor and Polarization Analysis

PubMed Central

Li, Huailiang; Tuo, Xianguo; Shen, Tong; Wang, Ruili; Courtois, Jérémie; Yan, Minhao

2017-01-01

A new first break picking for three-component (3C) vertical seismic profiling (VSP) data is proposed to improve the estimation accuracy of first arrivals, which adopts gesture detection calibration and polarization analysis based on the eigenvalue of the covariance matrix. This study aims at addressing the problem that calibration is required for VSP data using the azimuth and dip angle of geophones, due to the direction of geophones being random when applied in a borehole, which will further lead to the first break picking possibly being unreliable. Initially, a gesture-measuring module is integrated in the seismometer to rapidly obtain high-precision gesture data (including azimuth and dip angle information). Using re-rotating and re-projecting using earlier gesture data, the seismic dataset of each component will be calibrated to the direction that is consistent with the vibrator shot orientation. It will promote the reliability of the original data when making each component waveform calibrated to the same virtual reference component, and the corresponding first break will also be properly adjusted. After achieving 3C data calibration, an automatic first break picking algorithm based on the autoregressive-Akaike information criterion (AR-AIC) is adopted to evaluate the first break. Furthermore, in order to enhance the accuracy of the first break picking, the polarization attributes of 3C VSP recordings is applied to constrain the scanning segment of AR-AIC picker, which uses the maximum eigenvalue calculation of the covariance matrix. The contrast results between pre-calibration and post-calibration using field data show that it can further improve the quality of the 3C VSP waveform, which is favorable to subsequent picking. Compared to the obtained short-term average to long-term average (STA/LTA) and the AR-AIC algorithm, the proposed method, combined with polarization analysis, can significantly reduce the picking error. Applications of actual field experiments have also confirmed that the proposed method may be more suitable for the first break picking of 3C VSP. Test using synthesized 3C seismic data with low SNR indicates that the first break is picked with an error between 0.75 ms and 1.5 ms. Accordingly, the proposed method can reduce the picking error for 3C VSP data. PMID:28925981

Testing the equivalence of modern human cranial covariance structure: Implications for bioarchaeological applications.

PubMed

von Cramon-Taubadel, Noreen; Schroeder, Lauren

2016-10-01

Estimation of the variance-covariance (V/CV) structure of fragmentary bioarchaeological populations requires the use of proxy extant V/CV parameters. However, it is currently unclear whether extant human populations exhibit equivalent V/CV structures. Random skewers (RS) and hierarchical analyses of common principal components (CPC) were applied to a modern human cranial dataset. Cranial V/CV similarity was assessed globally for samples of individual populations (jackknifed method) and for pairwise population sample contrasts. The results were examined in light of potential explanatory factors for covariance difference, such as geographic region, among-group distance, and sample size. RS analyses showed that population samples exhibited highly correlated multivariate responses to selection, and that differences in RS results were primarily a consequence of differences in sample size. The CPC method yielded mixed results, depending upon the statistical criterion used to evaluate the hierarchy. The hypothesis-testing (step-up) approach was deemed problematic due to sensitivity to low statistical power and elevated Type I errors. In contrast, the model-fitting (lowest AIC) approach suggested that V/CV matrices were proportional and/or shared a large number of CPCs. Pairwise population sample CPC results were correlated with cranial distance, suggesting that population history explains some of the variability in V/CV structure among groups. The results indicate that patterns of covariance in human craniometric samples are broadly similar but not identical. These findings have important implications for choosing extant covariance matrices to use as proxy V/CV parameters in evolutionary analyses of past populations. © 2016 Wiley Periodicals, Inc.

Evaluation of leaf litter leaching kinetics through commonly-used mathematical models

NASA Astrophysics Data System (ADS)

Montoya, J. V.; Bastianoni, A.; Mendez, C.; Paolini, J.

2012-04-01

Leaching is defined as the abiotic process by which soluble compounds of the litter are released into the water. Most studies dealing with leaf litter breakdown and leaching kinetics apply the single exponential decay model since it corresponds well with the understanding of the biology of decomposition. However, during leaching important mass losses occur and mathematical models often fail in describing this process adequately. During the initial hours of leaching leaf litter experience high decay rates which are not properly modelled. Adjusting leaching losses to mathematical models has not been investigated thoroughly and the use of models assuming constant decay rates leads to inappropriate assessments of leaching kinetics. We aim to describe, assess, and compare different leaching kinetics models fitted to leaf litter mass losses from six Neotropical riparian forest species. Leaf litter from each species was collected in the lower reaches of San Miguel stream in Northern Venezuela. Air-dried leaves from each species were incubated in 250 ml of water in the dark at room temperature. At 1h, 6h, 1d, 2d, 4d, 8d and 15d, three jars were removed from the assay in a no-replacement experimental design. At each time leaves from each jar were removed and oven-dried. Afterwards, dried up leaves were weighed and remaining dry mass was determined and expressed as ash-free dry mass. Mass losses of leaf litter showed steep declines for the first two days followed by a steady decrease in mass loss. Data was fitted to three different models: single-exponential, power and rational. Our results showed that the mass loss predicted with the single-exponential model did not reflect the real data at any stage of the leaching process. The power model showed a better adjustment, but fails predicting successfully the behavior during leaching's early stages. To evaluate the performance of our models we used three criteria: Adj-R2, Akaike's Information Criteria (AIC), and residual distribution. Higher Adj-R2 were obtained for the power and the rational-type models. However, when AIC and residuals distribution were used, the only model that could satisfactory predict the behavior of our dataset was the rational-type. Even if the Adj-R2 was higher for some species when using the power model compared to the rational-type; our results showed that this criterion alone cannot demonstrate the predicting performance of any model. Usually Adj-R2 is used when assessing the goodness of fit for any mathematical model disregarding the fact that a good Adj-R2 could be obtained even when statistical assumptions required for the validity of the model are not satisfied. Our results showed that sampling at the initial stages of leaching is necessary to adequately describe this process. We also provided evidence that using traditional mathematical models is not the best option to evaluate leaching kinetics because of its mathematical inability to properly describe the abrupt changes that occur during the early stages of leaching. We also found useful applying different criteria to evaluate the goodness-of-fit and performance of any model considered taking into account both statistical and biological meaning of the results.

Considering the Creation of a Domestic Intelligence Agency in the United States: Lessons from the Experiences of Australia, Canada, France, Germany, and the United Kingdom

DTIC Science & Technology

2009-01-01

Police AG Attorney-General AIC Australian intelligence community ANAO Australian National Audit Office AQMI Al-Qaida pour le Maghreb Islamique [al...Democratic Republic GIA Groupe Islamique Armé [Armed Islamic Group] xviii Considering the Creation of a Domestic Intelligence Agency in the United...health, energy, utilities, transport, manufacturing, communications, banking and finance , government services and icons, and public gatherings. 17

[Autoimmune hepatitis: Immunological diagnosis].

PubMed

Brahim, Imane; Brahim, Ikram; Hazime, Raja; Admou, Brahim

2017-11-01

Autoimmune hepatopathies (AIHT) including autoimmune hepatitis (AIH), primary biliary cirrhosis (PBC), primary sclerosing cholangitis (PSC) and autoimmune cholangitis (AIC), represent an impressive entities in clinical practice. Their pathogenesis is not perfectly elucidated. Several factors are involved in the initiation of hepatic autoimmune and inflammatory phenomena such as genetic predisposition, molecular mimicry and/or abnormalities of T-regulatory lymphocytes. AIHT have a wide spectrum of presentation, ranging from asymptomatic forms to severe acute liver failure. The diagnosis of AIHT is based on the presence of hyperglobulinemia, cytolysis, cholestasis, typical even specific circulating auto-antibodies, distinctive of AIH or PBC, and histological abnormalities as well as necrosis and inflammation. Anti-F actin, anti-LKM1, anti-LC1 antibodies permit to distinguish between AIH type 1 and AIH type 2. Anti-SLA/LP antibodies are rather associated to more severe hepatitis, and particularly useful for the diagnosis of seronegative AIH for other the antibodies. Due to the relevant diagnostic value of anti-M2, anti-Sp100, and anti-gp210 antibodies, the diagnosis of PBC is more affordable than that of PSC and AIC. Based on clinical data, the immunological diagnosis of AIHT takes advantage of the various specialized laboratory techniques including immunofluorescence, immunodot or blot, and the Elisa systems, provided of a closer collaboration between the biologist and the physician. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

More environment-friendly and safer working gas mixtures for Bakelite RPCs operated in streamer mode

NASA Astrophysics Data System (ADS)

Zhang, Qingmin; Lv, Zhipeng; Lv, Jinge; Zhang, Jiawen; Xu, Jilei; Ning, Zhe

2017-08-01

This paper presents experimental results of RPCs performances with different working gas mixtures. Owing to Freon's high global warming potential, its threat to RPCs aging and its large consumption in large particle physics experiments, studies to minimize the concentration of HFC-134A, and even its complete replacement, have been undertaken. In addition, the reduction of iso-butane is also a favorable strategy, due to the flammability level of the gas mixture. Freon-less working gas mixture of Ar/HFC-134A/i-C4H10/CO2=20/0/8/72 was chosen with plateau efficiency of 86.3% and noise rate of 0.61 Hz/cm2. For working gas with lower ratio of Freon, Ar/HFC-134A/i-C4H10/CO2=20/20/8/52 was suggested with plateau efficiency of 91.0% and noise rate of 0.19 Hz/cm2, in which Freon was decreased by 22% compared to the BESIII RPC gas mixture. Furthermore, iso-butane was decreased to 6% with RPC's efficiency of 90% and noise rate of 0.20 Hz/cm2 achieved. Finally, the explanation of RPC's different performances at various working gas mixtures has been validated by the investigation of secondary streamers. This study will be helpful for RPC's application in future large particle physics experiments, in which RPCs can run in streamer mode.

Dysfunctional representation of expected value is associated with reinforcement-based decision-making deficits in adolescents with conduct problems.

PubMed

White, Stuart F; Tyler, Patrick M; Erway, Anna K; Botkin, Mary L; Kolli, Venkata; Meffert, Harma; Pope, Kayla; Blair, James R

2016-08-01

Previous work has shown that patients with conduct problems (CP) show impairments in reinforcement-based decision-making. However, studies with patients have not previously demonstrated any relationships between impairment in any of the neurocomputations underpinning reinforcement-based decision-making and specific symptom sets [e.g. level of CP and/or callous-unemotional (CU) traits]. Seventy-two youths [20 female, mean age = 13.81 (SD = 2.14), mean IQ = 102.34 (SD = 10.99)] from a residential treatment program and the community completed a passive avoidance task while undergoing functional MRI. Greater levels of CP were associated with poorer task performance. Reduced representation of expected values (EV) when making avoidance responses within bilateral anterior insula cortex/inferior frontal gyrus (AIC/iFG) and striatum was associated with greater levels of CP but not CU traits. The current data indicate that difficulties in the use of value information to motivate decisions to avoid suboptimal choices are associated with increased levels of CP (though not severity of CU traits). Moreover, they account for the behavioral deficits observed during reinforcement-based decision-making in youth with CP. In short, an individual's relative failure to utilize value information within AIC/iFG to avoid bad choices is associated with elevated levels of CP. © 2016 Association for Child and Adolescent Mental Health.

The U.S. home infusion market.

PubMed

Monk-Tutor, M R

1998-10-01

Medicare legislation stimulated the development of home care services but also resulted in fragmentation of service components. In the 1980s, prospective pricing and diagnosis-related groups, and resulting pressures to reduce inpatient length of stay, prompted additional growth of the industry. Even so, in 1995 home care represented only 3% of total national expenditures on health care. The annual growth rate of the home infusion industry dropped from 64% in 1982-86 to 24% in 1986-93. While revenue per patient for home infusion is expected to decrease under managed care, an increasing number of patients will support continued market growth. The home infusion market is highly competitive, with only a few large national providers and many small local providers. In 1996, 29% of acute care hospitals provided or were developing a home care program. Community pharmacists' options in the home infusion area include independent services, partnerships, joint ventures, contracts with hospitals, and franchises. The home infusion market is being integrated into alternative sites, such as ambulatory infusion centers (AICs), as providers attempt to diversify to maintain managed care contracts. AICs provide infusion therapy and nursing to noninstitutionalized, nonhome-bound patients. Untapped sources for future growth of the infusion market include long-term-care facilities. More consistent studies of the home care market are needed. Despite slowed growth in recent years, home care has a strong market in the United States.

Mucus-penetrating solid lipid nanoparticles for the treatment of cystic fibrosis: Proof of concept, challenges and pitfalls.

PubMed

Nafee, N; Forier, K; Braeckmans, K; Schneider, M

2018-03-01

Nanocarrier-mediated transmucosal drug delivery based on conventional mucoadhesive, muco-inert or mucus-penetrating nanoparticles (NPs) is a growing field especially in challenging diseases like cystic fibrosis (CF). Efficacy of such systems dictates profound investigation of particle-mucus interaction and factors governing the whole process. Although variable techniques studying particle diffusion in mucus have been introduced, standardized procedures are lacking. The study comprised different methods based on micro- and macro-displacement as well as colloidal stability and turbidimetric experiments. Artificial sputum medium (ASM), CF sputum and mucus-secreting cell line (Calu-3 air interface culture, AIC) were applied. Solid lipid nanoparticles (SLNs) coated with variable hydrophilic sheath (poloxamer, Tween 80 or PVA) represented the nanocarriers under investigation. Both micro-displacement studies based on single particle tracking and macro-displacement experiments based on 3D-time laps confocal imaging revealed faster diffusion of poloxamer- > Tween- > PVA-coated SLNs. Compared to ASM, CF sputum showed not only lower diffusion rates but also remarkable discrepancies in particle-mucus diffusion rate due to sputum heterogenicity. Meanwhile, in case of Calu-3 AIC, thickness of the mucosal layer as well as density of mucus network were key determinants in the diffusion process. The points emphasized in this study highlight the road towards in vivo relevant particle-mucus interaction research. Copyright © 2018 Elsevier B.V. All rights reserved.

Differential functional connectivity of rostral anterior cingulate cortex during emotional interference.

PubMed

Szekely, Akos; Silton, Rebecca L; Heller, Wendy; Miller, Gregory A; Mohanty, Aprajita

2017-03-01

The rostral-ventral subdivision of the anterior cingulate cortex (rACC) plays a key role in the regulation of emotional processing. Although rACC has strong anatomical connections with anterior insular cortex (AIC), amygdala, prefrontal cortex and striatal brain regions, it is unclear whether the functional connectivity of rACC with these regions changes when regulating emotional processing. Furthermore, it is not known whether this connectivity changes with deficits in emotion regulation seen in different kinds of anxiety and depression. To address these questions regarding rACC functional connectivity, non-patients high in self-reported anxious apprehension (AP), anxious arousal (AR), anhedonic depression (AD) or none (CON) indicated the ink color of pleasant, neutral and unpleasant words during functional magnetic resonance imaging. While ignoring task-irrelevant unpleasant words, AD and CON showed an increase in the functional connectivity of rACC with AIC, putamen, caudate and ventral pallidum. There was a decrease in this connectivity in AP and AR, with AP showing greater reduction than AR. These findings provide support for the role of rACC in integrating interoceptive, emotional and cognitive functions via interactions with insula and striatal regions during effective emotion regulation in healthy individuals and a failure of this integration that may be specific to anxiety, particularly AP. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Determination of Original Infection Source of H7N9 Avian Influenza by Dynamical Model

NASA Astrophysics Data System (ADS)

Zhang, Juan; Jin, Zhen; Sun, Gui-Quan; Sun, Xiang-Dong; Wang, You-Ming; Huang, Baoxu

2014-05-01

H7N9, a newly emerging virus in China, travels among poultry and human. Although H7N9 has not aroused massive outbreaks, recurrence in the second half of 2013 makes it essential to control the spread. It is believed that the most effective control measure is to locate the original infection source and cut off the source of infection from human. However, the original infection source and the internal transmission mechanism of the new virus are not totally clear. In order to determine the original infection source of H7N9, we establish a dynamical model with migratory bird, resident bird, domestic poultry and human population, and view migratory bird, resident bird, domestic poultry as original infection source respectively to fit the true dynamics during the 2013 pandemic. By comparing the date fitting results and corresponding Akaike Information Criterion (AIC) values, we conclude that migrant birds are most likely the original infection source. In addition, we obtain the basic reproduction number in poultry and carry out sensitivity analysis of some parameters.

Nested radiations and the pulse of angiosperm diversification: increased diversification rates often follow whole genome duplications.

PubMed

Tank, David C; Eastman, Jonathan M; Pennell, Matthew W; Soltis, Pamela S; Soltis, Douglas E; Hinchliff, Cody E; Brown, Joseph W; Sessa, Emily B; Harmon, Luke J

2015-07-01

Our growing understanding of the plant tree of life provides a novel opportunity to uncover the major drivers of angiosperm diversity. Using a time-calibrated phylogeny, we characterized hot and cold spots of lineage diversification across the angiosperm tree of life by modeling evolutionary diversification using stepwise AIC (MEDUSA). We also tested the whole-genome duplication (WGD) radiation lag-time model, which postulates that increases in diversification tend to lag behind established WGD events. Diversification rates have been incredibly heterogeneous throughout the evolutionary history of angiosperms and reveal a pattern of 'nested radiations' - increases in net diversification nested within other radiations. This pattern in turn generates a negative relationship between clade age and diversity across both families and orders. We suggest that stochastically changing diversification rates across the phylogeny explain these patterns. Finally, we demonstrate significant statistical support for the WGD radiation lag-time model. Across angiosperms, nested shifts in diversification led to an overall increasing rate of net diversification and declining relative extinction rates through time. These diversification shifts are only rarely perfectly associated with WGD events, but commonly follow them after a lag period. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

Zero adjusted models with applications to analysing helminths count data.

PubMed

Chipeta, Michael G; Ngwira, Bagrey M; Simoonga, Christopher; Kazembe, Lawrence N

2014-11-27

It is common in public health and epidemiology that the outcome of interest is counts of events occurrence. Analysing these data using classical linear models is mostly inappropriate, even after transformation of outcome variables due to overdispersion. Zero-adjusted mixture count models such as zero-inflated and hurdle count models are applied to count data when over-dispersion and excess zeros exist. Main objective of the current paper is to apply such models to analyse risk factors associated with human helminths (S. haematobium) particularly in a case where there's a high proportion of zero counts. The data were collected during a community-based randomised control trial assessing the impact of mass drug administration (MDA) with praziquantel in Malawi, and a school-based cross sectional epidemiology survey in Zambia. Count data models including traditional (Poisson and negative binomial) models, zero modified models (zero inflated Poisson and zero inflated negative binomial) and hurdle models (Poisson logit hurdle and negative binomial logit hurdle) were fitted and compared. Using Akaike information criteria (AIC), the negative binomial logit hurdle (NBLH) and zero inflated negative binomial (ZINB) showed best performance in both datasets. With regards to zero count capturing, these models performed better than other models. This paper showed that zero modified NBLH and ZINB models are more appropriate methods for the analysis of data with excess zeros. The choice between the hurdle and zero-inflated models should be based on the aim and endpoints of the study.

Multilevel joint competing risk models

NASA Astrophysics Data System (ADS)

Karunarathna, G. H. S.; Sooriyarachchi, M. R.

2017-09-01

Joint modeling approaches are often encountered for different outcomes of competing risk time to event and count in many biomedical and epidemiology studies in the presence of cluster effect. Hospital length of stay (LOS) has been the widely used outcome measure in hospital utilization due to the benchmark measurement for measuring multiple terminations such as discharge, transferred, dead and patients who have not completed the event of interest at the follow up period (censored) during hospitalizations. Competing risk models provide a method of addressing such multiple destinations since classical time to event models yield biased results when there are multiple events. In this study, the concept of joint modeling has been applied to the dengue epidemiology in Sri Lanka, 2006-2008 to assess the relationship between different outcomes of LOS and platelet count of dengue patients with the district cluster effect. Two key approaches have been applied to build up the joint scenario. In the first approach, modeling each competing risk separately using the binary logistic model, treating all other events as censored under the multilevel discrete time to event model, while the platelet counts are assumed to follow a lognormal regression model. The second approach is based on the endogeneity effect in the multilevel competing risks and count model. Model parameters were estimated using maximum likelihood based on the Laplace approximation. Moreover, the study reveals that joint modeling approach yield more precise results compared to fitting two separate univariate models, in terms of AIC (Akaike Information Criterion).

Modelling lactation curve for milk fat to protein ratio in Iranian buffaloes (Bubalus bubalis) using non-linear mixed models.

PubMed

Hossein-Zadeh, Navid Ghavi

2016-08-01

The aim of this study was to compare seven non-linear mathematical models (Brody, Wood, Dhanoa, Sikka, Nelder, Rook and Dijkstra) to examine their efficiency in describing the lactation curves for milk fat to protein ratio (FPR) in Iranian buffaloes. Data were 43 818 test-day records for FPR from the first three lactations of Iranian buffaloes which were collected on 523 dairy herds in the period from 1996 to 2012 by the Animal Breeding Center of Iran. Each model was fitted to monthly FPR records of buffaloes using the non-linear mixed model procedure (PROC NLMIXED) in SAS and the parameters were estimated. The models were tested for goodness of fit using Akaike's information criterion (AIC), Bayesian information criterion (BIC) and log maximum likelihood (-2 Log L). The Nelder and Sikka mixed models provided the best fit of lactation curve for FPR in the first and second lactations of Iranian buffaloes, respectively. However, Wood, Dhanoa and Sikka mixed models provided the best fit of lactation curve for FPR in the third parity buffaloes. Evaluation of first, second and third lactation features showed that all models, except for Dijkstra model in the third lactation, under-predicted test time at which daily FPR was minimum. On the other hand, minimum FPR was over-predicted by all equations. Evaluation of the different models used in this study indicated that non-linear mixed models were sufficient for fitting test-day FPR records of Iranian buffaloes.

Space Launch Complex 6 Wastewater Treatment Facilities Evaluation, Vandenberg AFB, California.

DTIC Science & Technology

1987-02-01

Sgt Tammy Johnson, AiC Roberto Rolon and AlC Pete Davis without whose valuable assistance this survey could never have been accomplished. We also...lLt Francis E. Slavich, MSgt Horace C. Burbage, Sgt Tammy Johnson, AlCs Roberto Rolon and Pete Davis. The objectives of the survey were to evaluate the...TSK Brooks AFB TX 78235-5301 Defense Technical Information Center (DTIC) 2 Cameron Station Alexandria VA 22319 HQ USAF/LEEV 1 Bolling AFB DC 20330

Planetary Geomorphology

NASA Technical Reports Server (NTRS)

Malin, Michael C.

1990-01-01

One of the major problems in the series of ice runs was that the subsurface temperature probes did not function. AIC re-evaluated the design and, after testing several suitable sensors, installed 50 type T thermocouples, each 2 m long. In this design, each thermocouple was soldered to a rectangular copper foil spreader 0.3 com wide by 2.8 cm long to ensure an acute reading. The long rectangular shape was used because it had a large area for good thermal connection to the test material.

Potential end-to-end imaging information rate advantages of various alternative communication systems

NASA Technical Reports Server (NTRS)

Rice, R. F.

1978-01-01

Various communication systems were considered which are required to transmit both imaging and a typically error sensitive, class of data called general science/engineering (gse) over a Gaussian channel. The approach jointly treats the imaging and gse transmission problems, allowing comparisons of systems which include various channel coding and data compression alternatives. Actual system comparisons include an Advanced Imaging Communication System (AICS) which exhibits the rather significant potential advantages of sophisticated data compression coupled with powerful yet practical channel coding.

Conventional Weapons Underwater Explosions

DTIC Science & Technology

1988-12-01

Nitromethane," UCRL 52903, December 1980. 22 I >I I 20 0--0 AIcN 23 0 I0 0 0 c W * ’S * / 0 o ---. 0 / nEil~ 24 Unreacted explosive Shock front t t...1976. 57 7. B. M. Dobratz LLNL Explosives Handbook - Properties of Explosives and Ex- plosive Simulants, UCRL -52997, March 1981. 8. M. H. Rice and J...Canada (403) 549- 3701 Ext. 4787 39. Joel C. W. Rogers Dept. of Mathemantics Polytechnic University 333 Jay Street Brooklyn, NY 11201 (718) 260-3501 40

Will They or Won't They? Secret Telling in Interpersonal Interactions.

PubMed

Kowalski, Robin Marie; Morgan, Chad Alan; Whittaker, Elizabeth; Zaremba, Brittany; Frazee, Laura; Dean, Jessica

2015-01-01

This study investigated predictors of within-gender secret telling. Eighty-eight participants were exposed to either a "positive" or a "negative" secret about another individual. Just under 20% of participants told the secret. Conscientiousness, secret condition, empathy, and the conscientiousness by secret condition interaction had effects on the rate of secret telling, χ(2) (5,82) = 17.78, p = .003, AIC = 80.60. Conscientiousness had a negative effect on secret telling among participants that told the "negative" secret.

Computer Programs for Helicopter Aerodynamic Stability Evaluation

DTIC Science & Technology

1976-08-01

11I.46 CCHflQz-16 .*C tAS*tH5P/BMF MIDCIO=- MUHS*DCHDW-CTAS DCTnR=-CTn,.*MIHS DCYfP=OMUHS/4.)/11.-AS4*DLDCT) ASS=AS4* SIGMAR /SIGMA COI=W/(VTIP*CTAS*DP...CYOP-16**CAS*Y3PlBMFOCTDP*BMF*(YIP+2.*AS4*Y2P*DCLDT)*CTAs DCYDV=OCYf)V-OC TrW*AIC*BMF*CTAS* ( IP.?. *Y2P) CTWTR=*25/(l.’-ASS*.25) CYVTR=C9I*CT %TR* SIGMAR

Proceedings of the International Workshop on Multistrategy Learning (2nd) Held in Harpers Ferry, West Virginia on May 26-29, 1993

DTIC Science & Technology

1993-05-26

aic.gmu.edu, Tel: 703 993-1719, Fax: 703 993-3729 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or... Public Health Data ....................... 309 John W. Sheppard Author Index vi I. General Issues 3 Multitype Inference in Multistrategy Task-adaptive...0.7 values were chosen because experience This description has appeared already in many showed they facilitated GenNet dynamics). publications (e.g. de

Using Statistical Multivariable Models to Understand the Relationship Between Interplanetary Coronal Mass Ejecta and Magnetic Flux Ropes

NASA Technical Reports Server (NTRS)

Riley, P.; Richardson, I. G.

2012-01-01

In-situ measurements of interplanetary coronal mass ejections (ICMEs) display a wide range of properties. A distinct subset, "magnetic clouds" (MCs), are readily identifiable by a smooth rotation in an enhanced magnetic field, together with an unusually low solar wind proton temperature. In this study, we analyze Ulysses spacecraft measurements to systematically investigate five possible explanations for why some ICMEs are observed to be MCs and others are not: i) An observational selection effect; that is, all ICMEs do in fact contain MCs, but the trajectory of the spacecraft through the ICME determines whether the MC is actually encountered; ii) interactions of an erupting flux rope (PR) with itself or between neighboring FRs, which produce complex structures in which the coherent magnetic structure has been destroyed; iii) an evolutionary process, such as relaxation to a low plasma-beta state that leads to the formation of an MC; iv) the existence of two (or more) intrinsic initiation mechanisms, some of which produce MCs and some that do not; or v) MCs are just an easily identifiable limit in an otherwise corntinuous spectrum of structures. We apply quantitative statistical models to assess these ideas. In particular, we use the Akaike information criterion (AIC) to rank the candidate models and a Gaussian mixture model (GMM) to uncover any intrinsic clustering of the data. Using a logistic regression, we find that plasma-beta, CME width, and the ratio O(sup 7) / O(sup 6) are the most significant predictor variables for the presence of an MC. Moreover, the propensity for an event to be identified as an MC decreases with heliocentric distance. These results tend to refute ideas ii) and iii). GMM clustering analysis further identifies three distinct groups of ICMEs; two of which match (at the 86% level) with events independently identified as MCs, and a third that matches with non-MCs (68 % overlap), Thus, idea v) is not supported. Choosing between ideas i) and iv) is more challenging, since they may effectively be indistinguishable from one another by a single in-situ spacecraft. We offer some suggestions on how future studies may address this.

Model-independent limits and constraints on extended theories of gravity from cosmic reconstruction techniques

NASA Astrophysics Data System (ADS)

de la Cruz-Dombriz, Álvaro; Dunsby, Peter K. S.; Luongo, Orlando; Reverberi, Lorenzo

2016-12-01

The onset of dark energy domination depends on the particular gravitational theory driving the cosmic evolution. Model independent techniques are crucial to test the both the present ΛCDM cosmological paradigm and alternative theories, making the least possible number of assumptions about the Universe. In this paper we investigate whether cosmography is able to distinguish between different gravitational theories, by determining bounds on model parameters for three different extensions of General Relativity, namely quintessence, F(𝒯) and f(R) gravitational theories. We expand each class of theories in powers of redshift z around the present time, making no additional assumptions. This procedure is an extension of previous work and can be seen as the most general approach for testing extended theories of gravity through the use of cosmography. In the case of F(𝒯) and f(R) theories, we show that some assumptions on model parameters often made in previous works are superfluous or even unjustified. We use data from the Union 2.1 supernovae catalogue, baryonic acoustic oscillation data and H(z) differential age compilations, which probe cosmology on different scales of the cosmological evolution. We perform a Monte Carlo analysis using a Metropolis-Hastings algorithm with a Gelman-Rubin convergence criterion, reporting 1-σ and 2-σ confidence levels. To do so, we perform two distinct fits, assuming only data within z < 1 first and then without limitations in redshift. We obtain the corresponding numerical intervals in which coefficients span, and find that the data is compatible the ΛCDM limit of all three theories at the 1-σ level, while still compatible with quite a large portion of parameter space. We compare our results to the truncated ΛCDM paradigm, demonstrating that our bounds divert from the expectations of previous works, showing that the permitted regions of coefficients are significantly modified and in general widened with respect to values usually reported in the existing literature. Finally, we test the extended theories through the Bayesian selection criteria AIC and BIC.

Model-independent limits and constraints on extended theories of gravity from cosmic reconstruction techniques

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

Cruz-Dombriz, Álvaro de la; Dunsby, Peter K.S.; Luongo, Orlando

The onset of dark energy domination depends on the particular gravitational theory driving the cosmic evolution. Model independent techniques are crucial to test the both the present ΛCDM cosmological paradigm and alternative theories, making the least possible number of assumptions about the Universe. In this paper we investigate whether cosmography is able to distinguish between different gravitational theories, by determining bounds on model parameters for three different extensions of General Relativity, namely quintessence, F (Τ) and f ( R ) gravitational theories. We expand each class of theories in powers of redshift z around the present time, making no additionalmore » assumptions. This procedure is an extension of previous work and can be seen as the most general approach for testing extended theories of gravity through the use of cosmography. In the case of F (Τ) and f ( R ) theories, we show that some assumptions on model parameters often made in previous works are superfluous or even unjustified. We use data from the Union 2.1 supernovae catalogue, baryonic acoustic oscillation data and H ( z ) differential age compilations, which probe cosmology on different scales of the cosmological evolution. We perform a Monte Carlo analysis using a Metropolis-Hastings algorithm with a Gelman-Rubin convergence criterion, reporting 1-σ and 2-σ confidence levels. To do so, we perform two distinct fits, assuming only data within z < 1 first and then without limitations in redshift. We obtain the corresponding numerical intervals in which coefficients span, and find that the data is compatible the ΛCDM limit of all three theories at the 1-σ level, while still compatible with quite a large portion of parameter space. We compare our results to the truncated ΛCDM paradigm, demonstrating that our bounds divert from the expectations of previous works, showing that the permitted regions of coefficients are significantly modified and in general widened with respect to values usually reported in the existing literature. Finally, we test the extended theories through the Bayesian selection criteria AIC and BIC.« less

Comparison of Weibull and Lognormal Cure Models with Cox in the Survival Analysis Of Breast Cancer Patients in Rafsanjan.

PubMed

Hoseini, Mina; Bahrampour, Abbas; Mirzaee, Moghaddameh

2017-02-16

Breast cancer is the most common cancer after lung cancer and the second cause of death. In this study we compared Weibull and Lognormal Cure Models with Cox regression on the survival of breast cancer. A cohort study. The current study retrospective cohort study was conducted on 140 patients referred to Ali Ibn Abitaleb Hospital, Rafsanjan southeastern Iran from 2001 to 2015 suffering from breast cancer. We determined and analyzed the effective survival causes by different models using STATA14. According to AIC, log-normal model was more consistent than Weibull. In the multivariable Lognormal model, the effective factors like smoking, second -hand smoking, drinking herbal tea and the last breast-feeding period were included. In addition, using Cox regression factors of significant were the disease grade, size of tumor and its metastasis (p-value<0.05). As Rafsanjan is surrounded by pistachio orchards and pesticides applied by farmers, people of this city are exposed to agricultural pesticides and its harmful consequences. The effect of the pesticide on breast cancer was studied and the results showed that the effect of pesticides on breast cancer was not in agreement with the models used in this study. Based on different methods for survival analysis, researchers can decide how they can reach a better conclusion. This comparison indicates the result of semi-parametric Cox method is closer to clinical experiences evidences.

Time dependent model of magma intrusion in and around Miyake and Kozu Islands, Central Japan in June August, 2000

NASA Astrophysics Data System (ADS)

Murase, Masayuki; Irwan, Meilano; Kariya, Shinichi; Tabei, Takao; Okuda, Takashi; Miyajima, Rikio; Oikawa, Jun; Watanabe, Hidefumi; Kato, Teruyuki; Nakao, Shigeru; Ukawa, Motoo; Fujita, Eisuke; Okayama, Muneo; Kimata, Fumiaki; Fujii, Naoyuki

2006-02-01

A time-dependent model of magma intrusion is presented for the Miyake-Kozu Island area in central Japan based on global positioning system (GPS) measurements at 28 sites recorded between June 27 and August 27, 2000. A model derived from a precise hypocenter distribution map indicates the presence of three dikes between Miyake and Kozu Islands. Other dike intrusion models, including a dike with aseismic creep and a dike associated with a deep deflation source are also discussed. The optimal parameters for each model are estimated using a genetic algorithm (GA) approach. Using Akaike's information criteria (AIC), the three-dike model is shown to provide the best solution for the observed deformation. Volume changes in spherical inflation and deflation sources, as well as three dikes, are calculated for seven discretized periods after GA optimization of the dike geometry. The optimization suggests a concentration of dike expansion near Miyake Island in the period from June 27 to July 1 associated with large deflation at a depth of about 7 km below Miyake volcano, indicating magma supply from depth below Miyake Island. In the period from July 9 to August 10, a huge dike intrusion near Kozu Island is inferred, accompanied by expansion of the lower parts of a central dike, suggesting magma supply from depth in the region between Miyake and Kozu Islands.

Visibility Modeling and Forecasting for Abu Dhabi using Time Series Analysis Method

NASA Astrophysics Data System (ADS)

Eibedingil, I. G.; Abula, B.; Afshari, A.; Temimi, M.

2015-12-01

Land-Atmosphere interactions-their strength, directionality and evolution-are one of the main sources of uncertainty in contemporary climate modeling. A particularly crucial role in sustaining and modulating land-atmosphere interaction is the one of aerosols and dusts. Aerosols are tiny particles suspended in the air ranging from a few nanometers to a few hundred micrometers in diameter. Furthermore, the amount of dust and fog in the atmosphere is an important measure of visibility, which is another dimension of land-atmosphere interactions. Visibility affects all form of traffic, aviation, land and sailing. Being able to predict the change of visibility in the air in advance enables relevant authorities to take necessary actions before the disaster falls. Time Series Analysis (TAS) method is an emerging technique for modeling and forecasting the behavior of land-atmosphere interactions, including visibility. This research assess the dynamics and evolution of visibility around Abu Dhabi International Airport (+24.4320 latitude, +54.6510 longitude, and 27m elevation) using mean daily visibility and mean daily wind speed. TAS has been first used to model and forecast the visibility, and then the Transfer Function Model has been applied, considering the wind speed as an exogenous variable. By considering the Akaike Information Criterion (AIC) and Mean Absolute Percentage Error (MAPE) as a statistical criteria, two forecasting models namely univarite time series model and transfer function model, were developed to forecast the visibility around Abu Dhabi International Airport for three weeks. Transfer function model improved the MAPE of the forecast significantly.

A generic model for a single strain mosquito-transmitted disease with memory on the host and the vector.

PubMed

Sardar, Tridip; Rana, Sourav; Bhattacharya, Sabyasachi; Al-Khaled, Kamel; Chattopadhyay, Joydev

2015-05-01

In the present investigation, three mathematical models on a common single strain mosquito-transmitted diseases are considered. The first one is based on ordinary differential equations, and other two models are based on fractional order differential equations. The proposed models are validated using published monthly dengue incidence data from two provinces of Venezuela during the period 1999-2002. We estimate several parameters of these models like the order of the fractional derivatives (in case of two fractional order systems), the biting rate of mosquito, two probabilities of infection, mosquito recruitment and mortality rates, etc., from the data. The basic reproduction number, R0, for the ODE system is estimated using the data. For two fractional order systems, an upper bound for, R0, is derived and its value is obtained using the published data. The force of infection, and the effective reproduction number, R(t), for the three models are estimated using the data. Sensitivity analysis of the mosquito memory parameter with some important responses is worked out. We use Akaike Information Criterion (AIC) to identify the best model among the three proposed models. It is observed that the model with memory in both the host, and the vector population provides a better agreement with epidemic data. Finally, we provide a control strategy for the vector-borne disease, dengue, using the memory of the host, and the vector. Copyright © 2015 Elsevier Inc. All rights reserved.

Possible Causes of a Harbour Porpoise Mass Stranding in Danish Waters in 2005

PubMed Central

Wright, Andrew J.; Maar, Marie; Mohn, Christian; Nabe-Nielsen, Jacob; Siebert, Ursula; Jensen, Lasse Fast; Baagøe, Hans J.; Teilmann, Jonas

2013-01-01

An unprecedented 85 harbour porpoises stranded freshly dead along approximately 100 km of Danish coastline from 7–15 April, 2005. This total is considerably above the mean weekly stranding rate for the whole of Denmark, both for any time of year, 1.23 animals/week (ranging from 0 to 20 during 2003–2008, excluding April 2005), and specifically in April, 0.65 animals/week (0 to 4, same period). Bycatch was established as the cause of death for most of the individuals through typical indications of fisheries interactions, including net markings in the skin and around the flippers, and loss of tail flukes. Local fishermen confirmed unusually large porpoise bycatch in nets set for lumpfish (Cyclopterus lumpus) and the strandings were attributed to an early lumpfish season. However, lumpfish catches for 2005 were not unusual in terms of season onset, peak or total catch, when compared to 2003–2008. Consequently, human activity was combined with environmental factors and the variation in Danish fisheries landings (determined through a principal component analysis) in a two-part statistical model to assess the correlation of these factors with both the presence of fresh strandings and the numbers of strandings on the Danish west coast. The final statistical model (which was forward selected using Akaike information criterion; AIC) indicated that naval presence is correlated with higher rates of porpoise strandings, particularly in combination with certain fisheries, although it is not correlated with the actual presence of strandings. Military vessels from various countries were confirmed in the area from the 7th April, en route to the largest naval exercise in Danish waters to date (Loyal Mariner 2005, 11–28 April). Although sonar usage cannot be confirmed, it is likely that ships were testing various equipment prior to the main exercise. Thus naval activity cannot be ruled out as a possible contributing factor. PMID:23460787

Factors associated with utilization of antenatal care services in Balochistan province of Pakistan: An analysis of the Multiple Indicator Cluster Survey (MICS) 2010.

PubMed

Ghaffar, Abdul; Pongponich, Sathirakorn; Ghaffar, Najma; Mehmood, Tahir

2015-01-01

The study was conducted to identify factors affecting the utilization of Antenatal Care (ANC) in Balochistan Province, Pakistan. Data on ANC utilization, together with social and economic determinants, were derived from a Multiple Indicator Cluster Survey (MICS) conducted in Balochistan in 2010. The analysis was conducted including 2339 women who gave birth in last two years preceding the survey. The researchers established a model to identify influential factors contributing to the utilization of ANC by logistic regression; model selection was by Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Household wealth, education, health condition, age at first marriage, number of children and spouse violence justification were found to be significantly associated with ANC coverage. Literate mothers are 2.45 times more likely to have ANC, and women whose newborns showed symptoms of illness at birth that needed hospitalization are 0.47 times less likely to access ANC. Women with an increase in the number of surviving children are 1.07 times less likely to have ANC, and those who think their spouse violence is socially justified are 1.36 times less likely to have ANC. The results draw attention towards evidence based planning of factors associated with utilization of ANC in the Balochistan province. The study reveals that women from high wealth index and having education had more chances to get ANC. Factors like younger age of the women at first marriage, increased number of children, symptoms of any illness to neonates at birth that need hospitalization and women who justify spouse violence had less chances to get ANC. Among components of ANC urine sampling and having tetanus toxoid (TT) in the last pregnancy increased the frequency of visits. ANC from a doctor decreased the number of visits. There is dire need to reduce disparities for wealth index, education and urban/rural living.

Factors associated with utilization of antenatal care services in Balochistan province of Pakistan: An analysis of the Multiple Indicator Cluster Survey (MICS) 2010

PubMed Central

Ghaffar, Abdul; Pongponich, Sathirakorn; Ghaffar, Najma; Mehmood, Tahir

2015-01-01

Objective: The study was conducted to identify factors affecting the utilization of Antenatal Care (ANC) in Balochistan Province, Pakistan. Methods: Data on ANC utilization, together with social and economic determinants, were derived from a Multiple Indicator Cluster Survey (MICS) conducted in Balochistan in 2010. The analysis was conducted including 2339 women who gave birth in last two years preceding the survey. The researchers established a model to identify influential factors contributing to the utilization of ANC by logistic regression; model selection was by Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Results: Household wealth, education, health condition, age at first marriage, number of children and spouse violence justification were found to be significantly associated with ANC coverage. Literate mothers are 2.45 times more likely to have ANC, and women whose newborns showed symptoms of illness at birth that needed hospitalization are 0.47 times less likely to access ANC. Women with an increase in the number of surviving children are 1.07 times less likely to have ANC, and those who think their spouse violence is socially justified are 1.36 times less likely to have ANC. The results draw attention towards evidence based planning of factors associated with utilization of ANC in the Balochistan province. Conclusion: The study reveals that women from high wealth index and having education had more chances to get ANC. Factors like younger age of the women at first marriage, increased number of children, symptoms of any illness to neonates at birth that need hospitalization and women who justify spouse violence had less chances to get ANC. Among components of ANC urine sampling and having tetanus toxoid (TT) in the last pregnancy increased the frequency of visits. ANC from a doctor decreased the number of visits. There is dire need to reduce disparities for wealth index, education and urban/rural living. PMID:26870113

Structural basis of empathy and the domain general region in the anterior insular cortex

PubMed Central

Mutschler, Isabella; Reinbold, Céline; Wankerl, Johanna; Seifritz, Erich; Ball, Tonio

2013-01-01

Empathy is key for healthy social functioning and individual differences in empathy have strong implications for manifold domains of social behavior. Empathy comprises of emotional and cognitive components and may also be closely linked to sensorimotor processes, which go along with the motivation and behavior to respond compassionately to another person's feelings. There is growing evidence for local plastic change in the structure of the healthy adult human brain in response to environmental demands or intrinsic factors. Here we have investigated changes in brain structure resulting from or predisposing to empathy. Structural MRI data of 101 healthy adult females was analyzed. Empathy in fictitious as well as real-life situations was assessed using a validated self-evaluation measure. Furthermore, empathy-related structural effects were also put into the context of a functional map of the anterior insular cortex (AIC) determined by activation likelihood estimate (ALE) meta-analysis of previous functional imaging studies. We found that gray matter (GM) density in the left dorsal AIC correlates with empathy and that this area overlaps with the domain general region (DGR) of the anterior insula that is situated in-between functional systems involved in emotion–cognition, pain, and motor tasks as determined by our meta-analysis. Thus, we propose that this insular region where we find structural differences depending on individual empathy may play a crucial role in modulating the efficiency of neural integration underlying emotional, cognitive, and sensorimotor information which is essential for global empathy. PMID:23675334

Staphylococcus argensis sp. nov., a novel staphylococcal species isolated from an aquatic environment.

PubMed

Heß, Stefanie; Gallert, Claudia

2015-08-01

A staphylocoagulase-negative, novobiocin-susceptible strain (M4S-6T) of a species of the genus Staphylococcus was isolated from the river Argen in Southern Germany. It was assigned to the genus Staphylococcus due to the presence of the fatty acids, ai-C15 : 0, i-C15 : 0, i-C17 : 0, ai-C17 : 0, and of menaquinone (MK-7) in the cytoplasmic membrane, which are typical of coagulase-negative staphylococci. The polar lipid profile consisted of phosphatidylglycerol, diphosphatidylglycerol, an unknown phospholipid and an unknown glycolipid. Although the 16S gene sequence of strain M4S-6T revealed a 98% similarity with its closest relative, Staphylococcus pettenkoferi, it could be distinguished by several phenotypical and physiological markers. In contrast to S. pettenkoferi, M4S-6T was ornithine decarboxylase-positive, urease-negative and could use formiate and l-histidine as carbon-sources; nitrate was not reduced. Whereas S. pettenkoferi could grow with d(-)-mannitol, d-sorbitol, gluconic acid, l-proline, carboxymethylcellulose and lignosulfonate, M4S-6T was not able to grow with these substances. The results of 16S rRNA gene sequence analysis and of phenotypic testing indicated that M4S-6T was a representative of a novel species for which the name Staphylococcus argensis sp. nov., is proposed with the type strain M4S-6T (DSM 29875T = CIP 110904T).

Evolving ONe WD+He star systems to intermediate-mass binary pulsars

NASA Astrophysics Data System (ADS)

Liu, D.; Wang, B.; Chen, W.; Zuo, Z.; Han, Z.

2018-06-01

It has been suggested that accretion-induced collapse (AIC) is a non-negligible path for the formation of the observed neutron stars (NSs). An ONe white dwarf (WD) that accretes material from a He star may experience AIC process and eventually produce intermediate-mass binary pulsars (IMBPs), named as the ONe WD+He star scenario. Note that previous studies can only account for part of the observed IMBPs with short orbital periods. In this work, we investigate the evolution of about 900 ONe WD+He star binaries to explore the distribution of IMBPs. We found that the ONe WD+He star scenario could form IMBPs including pulsars with 5-340 ms spin periods and 0.75-1.38 M_{⊙} WD companions, in which the orbital periods range from 0.04 to 900 d. Compared with the 20 observed IMBPs, this scenario can cover the parameters of 13 sources in the final orbital period-WD mass plane and the Corbet diagram, most of which have short orbital periods. We found that the ONe WD+He star scenario can explain almost all the observed IMBPs with short orbital periods. This work can well match the observed parameters of PSR J1802-2124 (one of the two precisely observed IMBPs), providing a possible evolutional path for its formation. We also speculate that the compact companion of HD 49798 (a hydrogen depleted sdO6 star) may be not a NS based on this work.

Cuckoos vs. top predators as prime bioindicators of biodiversity in disturbed environments.

PubMed

Morelli, Federico; Mousseau, Timothy A; Møller, Anders Pape

2017-10-01

We studied the abundance of the common cuckoo Cuculus canorus L. little cuckoo Cuculus poliocephalus L. and Asian cuckoo Cuculus saturatus L. and avian top predators as indicators of bird species richness (surrogate of biodiversity) in disturbed environments caused by radioactive contamination in Chernobyl, Ukraine and Fukushima, Japan, comparing their efficiency as indicators of local biodiversity hotspots. Bird species richness and birds abundance were quantified in each sample site during the breeding seasons between 2006 and 2015 and the level of background radiation was measured at every site. The correlation between number of cuckoos, top predators, land use composition and level of background radiation with bird species richness as response variable were examined using Generalized Linear Mixed Models. The strength of correlation between species richness and abundance and the covariates obtained from the model outputs were used as measure of the efficiency of each predictor, as well as the AIC of each model. Background radiation was negatively correlated with bird species richness and bird abundance in both countries, while number of top predators and cuckoos were both positively correlated with bird species richness and abundance. However, model with number of cuckoos was more performant than model with number of avian top predators. These differences in performance supports the hypothesis that cuckoos are a largely superior bioindicator than top predators. Copyright © 2017 Elsevier Ltd. All rights reserved.

Online writer identification using alphabetic information clustering

NASA Astrophysics Data System (ADS)

Tan, Guo Xian; Viard-Gaudin, Christian; Kot, Alex C.

2009-01-01

Writer identification is a topic of much renewed interest today because of its importance in applications such as writer adaptation, routing of documents and forensic document analysis. Various algorithms have been proposed to handle such tasks. Of particular interests are the approaches that use allographic features [1-3] to perform a comparison of the documents in question. The allographic features are used to define prototypes that model the unique handwriting styles of the individual writers. This paper investigates a novel perspective that takes alphabetic information into consideration when the allographic features are clustered into prototypes at the character level. We hypothesize that alphabetic information provides additional clues which help in the clustering of allographic prototypes. An alphabet information coefficient (AIC) has been introduced in our study and the effect of this coefficient is presented. Our experiments showed an increase of writer identification accuracy from 66.0% to 87.0% when alphabetic information was used in conjunction with allographic features on a database of 200 reference writers.

Was there an early reionization component in our universe?

DOE PAGES

Villanueva-Domingo, Pablo; Gariazzo, Stefano; Gnedin, Nickolay Y.; ...

2018-04-06

A deep understanding of the Epoch of Reionization is still missing in our knowledge of the universe. While future probes will allow us to test the precise evolution of the free electron fraction from redshifts betweenmore » $$z\\simeq 6$$ and $$z\\simeq 20$$, at present one could ask what kind of reionization processes are allowed by present Cosmic Microwave Background temperature and polarization measurements. An early contribution to reionization could imply a departure from the standard picture where star formation determines the reionization onset. BBy considering a broad class of possible reionization parameterizations, we find that current data do not require an early reionization component in our universe and that only one marginal class of models, based on a particular realization of reionization, may point to that. In addition, the frequentist Akaike Information Criterion (AIC) provides strong evidence against alternative reionization histories, favoring the most simple reionization scenario, which describes reionization by means of only one (constant) reionization optical depth $$\\tau$$.« less

Estimating the probability distribution of the incubation period for rabies using data from the 1948-1954 rabies epidemic in Tokyo.

PubMed

Tojinbara, Kageaki; Sugiura, K; Yamada, A; Kakitani, I; Kwan, N C L; Sugiura, K

2016-01-01

Data of 98 rabies cases in dogs and cats from the 1948-1954 rabies epidemic in Tokyo were used to estimate the probability distribution of the incubation period. Lognormal, gamma and Weibull distributions were used to model the incubation period. The maximum likelihood estimates of the mean incubation period ranged from 27.30 to 28.56 days according to different distributions. The mean incubation period was shortest with the lognormal distribution (27.30 days), and longest with the Weibull distribution (28.56 days). The best distribution in terms of AIC value was the lognormal distribution with mean value of 27.30 (95% CI: 23.46-31.55) days and standard deviation of 20.20 (15.27-26.31) days. There were no significant differences between the incubation periods for dogs and cats, or between those for male and female dogs. Copyright © 2015 Elsevier B.V. All rights reserved.

An electrochemical sensing approach for scouting microbial chemolithotrophic metabolisms.

PubMed

Saavedra, Albert; Figueredo, Federico; Cortón, Eduardo; Abrevaya, Ximena C

2018-05-01

The present study was aimed to test an electrochemical sensing approach for the detection of an active chemolithotrophic metabolism (and therefore the presence of chemolithotrophic microorganisms) by using the corrosion of pyrite by Acidithiobacillus ferrooxidans as a model. Different electrochemical techniques were combined with adhesion studies and scanning electron microscopy (SEM). The experiments were performed in presence or absence of A. ferrooxidans and without or with ferrous iron in the culture medium (0 and 0.5 g L -1 , respectively). Electrochemical parameters were in agreement with voltammetric studies and SEM showing that it is possible to distinguish between an abiotically-induced corrosion process (AIC) and a microbiologically-induced corrosion process (MIC). The results show that our approach not only allows the detection of chemolithotrophic activity of A. ferrooxidans but also can characterize the corrosion process. This may have different kind of applications, from those related to biomining to life searching missions in other planetary bodies. Copyright © 2018 Elsevier B.V. All rights reserved.

Was there an early reionization component in our universe?

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

Villanueva-Domingo, Pablo; Gariazzo, Stefano; Gnedin, Nickolay Y.

A deep understanding of the Epoch of Reionization is still missing in our knowledge of the universe. While future probes will allow us to test the precise evolution of the free electron fraction from redshifts betweenmore » $$z\\simeq 6$$ and $$z\\simeq 20$$, at present one could ask what kind of reionization processes are allowed by present Cosmic Microwave Background temperature and polarization measurements. An early contribution to reionization could imply a departure from the standard picture where star formation determines the reionization onset. BBy considering a broad class of possible reionization parameterizations, we find that current data do not require an early reionization component in our universe and that only one marginal class of models, based on a particular realization of reionization, may point to that. In addition, the frequentist Akaike Information Criterion (AIC) provides strong evidence against alternative reionization histories, favoring the most simple reionization scenario, which describes reionization by means of only one (constant) reionization optical depth $$\\tau$$.« less

Exploring the limits of cryospectroscopy: Least-squares based approaches for analyzing the self-association of HCl.

PubMed

De Beuckeleer, Liene I; Herrebout, Wouter A

2016-02-05

To rationalize the concentration dependent behavior observed for a large spectral data set of HCl recorded in liquid argon, least-squares based numerical methods are developed and validated. In these methods, for each wavenumber a polynomial is used to mimic the relation between monomer concentrations and measured absorbances. Least-squares fitting of higher degree polynomials tends to overfit and thus leads to compensation effects where a contribution due to one species is compensated for by a negative contribution of another. The compensation effects are corrected for by carefully analyzing, using AIC and BIC information criteria, the differences observed between consecutive fittings when the degree of the polynomial model is systematically increased, and by introducing constraints prohibiting negative absorbances to occur for the monomer or for one of the oligomers. The method developed should allow other, more complicated self-associating systems to be analyzed with a much higher accuracy than before. Copyright © 2015 Elsevier B.V. All rights reserved.

Was there an early reionization component in our universe?

NASA Astrophysics Data System (ADS)

Villanueva-Domingo, Pablo; Gariazzo, Stefano; Gnedin, Nickolay Y.; Mena, Olga

2018-04-01

A deep understanding of the epoch of reionization is still missing in our knowledge of the universe. While future probes will allow us to test the precise evolution of the free electron fraction from redshifts between zsimeq 6 and 0zsimeq 2, at present one could ask what kind of reionization processes are allowed by present cosmic microwave background temperature and polarization measurements. An early contribution to reionization could imply a departure from the standard picture where star formation determines the reionization onset. By considering a broad class of possible reionization parameterizations, we find that current data do not require an early reionization component in our universe and that only one marginal class of models, based on a particular realization of reionization, may point to that. In addition, the frequentist Akaike information criterion (AIC) provides strong evidence against alternative reionization histories, favoring the most simple reionization scenario, which describes reionization by means of only one (constant) reionization optical depth τ.

Environmental effects on vertebrate species richness: testing the energy, environmental stability and habitat heterogeneity hypotheses.

PubMed

Luo, Zhenhua; Tang, Songhua; Li, Chunwang; Fang, Hongxia; Hu, Huijian; Yang, Ji; Ding, Jingjing; Jiang, Zhigang

2012-01-01

Explaining species richness patterns is a central issue in biogeography and macroecology. Several hypotheses have been proposed to explain the mechanisms driving biodiversity patterns, but the causes of species richness gradients remain unclear. In this study, we aimed to explain the impacts of energy, environmental stability, and habitat heterogeneity factors on variation of vertebrate species richness (VSR), based on the VSR pattern in China, so as to test the energy hypothesis, the environmental stability hypothesis, and the habitat heterogeneity hypothesis. A dataset was compiled containing the distributions of 2,665 vertebrate species and eleven ecogeographic predictive variables in China. We grouped these variables into categories of energy, environmental stability, and habitat heterogeneity and transformed the data into 100 × 100 km quadrat systems. To test the three hypotheses, AIC-based model selection was carried out between VSR and the variables in each group and correlation analyses were conducted. There was a decreasing VSR gradient from the southeast to the northwest of China. Our results showed that energy explained 67.6% of the VSR variation, with the annual mean temperature as the main factor, which was followed by annual precipitation and NDVI. Environmental stability factors explained 69.1% of the VSR variation and both temperature annual range and precipitation seasonality had important contributions. By contrast, habitat heterogeneity variables explained only 26.3% of the VSR variation. Significantly positive correlations were detected among VSR, annual mean temperature, annual precipitation, and NDVI, whereas the relationship of VSR and temperature annual range was strongly negative. In addition, other variables showed moderate or ambiguous relations to VSR. The energy hypothesis and the environmental stability hypothesis were supported, whereas little support was found for the habitat heterogeneity hypothesis.

Regional analysis and derivation of copula-based drought Severity-Area-Frequency curve in Lake Urmia basin, Iran.

PubMed

Amirataee, Babak; Montaseri, Majid; Rezaie, Hossein

2018-01-15

Droughts are extreme events characterized by temporal duration and spatial large-scale effects. In general, regional droughts are affected by general circulation of the atmosphere (at large-scale) and regional natural factors, including the topography, natural lakes, the position relative to the center and the path of the ocean currents (at small-scale), and they don't cover the exact same effects in a wide area. Therefore, drought Severity-Area-Frequency (S-A-F) curve investigation is an essential task to develop decision making rule for regional drought management. This study developed the copula-based joint probability distribution of drought severity and percent of area under drought across the Lake Urmia basin, Iran. To do this end, one-month Standardized Precipitation Index (SPI) values during the 1971-2013 were applied across 24 rainfall stations in the study area. Then, seven copula functions of various families, including Clayton, Gumbel, Frank, Joe, Galambos, Plackett and Normal copulas, were used to model the joint probability distribution of drought severity and drought area. Using AIC, BIC and RMSE criteria, the Frank copula was selected as the most appropriate copula in order to develop the joint probability distribution of severity-percent of area under drought across the study area. Based on the Frank copula, the drought S-A-F curve for the study area was derived. The results indicated that severe/extreme drought and non-drought (wet) behaviors have affected the majority of study areas (Lake Urmia basin). However, the area covered by the specific semi-drought effects is limited and has been subject to significant variations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predictability of Western Himalayan river flow: melt seasonal inflow into Bhakra Reservoir in northern India

NASA Astrophysics Data System (ADS)

Pal, I.; Lall, U.; Robertson, A. W.; Cane, M. A.; Bansal, R.

2013-06-01

Snowmelt-dominated streamflow of the Western Himalayan rivers is an important water resource during the dry pre-monsoon spring months to meet the irrigation and hydropower needs in northern India. Here we study the seasonal prediction of melt-dominated total inflow into the Bhakra Dam in northern India based on statistical relationships with meteorological variables during the preceding winter. Total inflow into the Bhakra Dam includes the Satluj River flow together with a flow diversion from its tributary, the Beas River. Both are tributaries of the Indus River that originate from the Western Himalayas, which is an under-studied region. Average measured winter snow volume at the upper-elevation stations and corresponding lower-elevation rainfall and temperature of the Satluj River basin were considered as empirical predictors. Akaike information criteria (AIC) and Bayesian information criteria (BIC) were used to select the best subset of inputs from all the possible combinations of predictors for a multiple linear regression framework. To test for potential issues arising due to multicollinearity of the predictor variables, cross-validated prediction skills of the best subset were also compared with the prediction skills of principal component regression (PCR) and partial least squares regression (PLSR) techniques, which yielded broadly similar results. As a whole, the forecasts of the melt season at the end of winter and as the melt season commences were shown to have potential skill for guiding the development of stochastic optimization models to manage the trade-off between irrigation and hydropower releases versus flood control during the annual fill cycle of the Bhakra Reservoir, a major energy and irrigation source in the region.

Environmental Effects on Vertebrate Species Richness: Testing the Energy, Environmental Stability and Habitat Heterogeneity Hypotheses

PubMed Central

Luo, Zhenhua; Tang, Songhua; Li, Chunwang; Fang, Hongxia; Hu, Huijian; Yang, Ji; Ding, Jingjing; Jiang, Zhigang

2012-01-01

Background Explaining species richness patterns is a central issue in biogeography and macroecology. Several hypotheses have been proposed to explain the mechanisms driving biodiversity patterns, but the causes of species richness gradients remain unclear. In this study, we aimed to explain the impacts of energy, environmental stability, and habitat heterogeneity factors on variation of vertebrate species richness (VSR), based on the VSR pattern in China, so as to test the energy hypothesis, the environmental stability hypothesis, and the habitat heterogeneity hypothesis. Methodology/Principal Findings A dataset was compiled containing the distributions of 2,665 vertebrate species and eleven ecogeographic predictive variables in China. We grouped these variables into categories of energy, environmental stability, and habitat heterogeneity and transformed the data into 100×100 km quadrat systems. To test the three hypotheses, AIC-based model selection was carried out between VSR and the variables in each group and correlation analyses were conducted. There was a decreasing VSR gradient from the southeast to the northwest of China. Our results showed that energy explained 67.6% of the VSR variation, with the annual mean temperature as the main factor, which was followed by annual precipitation and NDVI. Environmental stability factors explained 69.1% of the VSR variation and both temperature annual range and precipitation seasonality had important contributions. By contrast, habitat heterogeneity variables explained only 26.3% of the VSR variation. Significantly positive correlations were detected among VSR, annual mean temperature, annual precipitation, and NDVI, whereas the relationship of VSR and temperature annual range was strongly negative. In addition, other variables showed moderate or ambiguous relations to VSR. Conclusions/Significance The energy hypothesis and the environmental stability hypothesis were supported, whereas little support was found for the habitat heterogeneity hypothesis. PMID:22530038

Real time detection of farm-level swine mycobacteriosis outbreak using time series modeling of the number of condemned intestines in abattoirs.

PubMed

Adachi, Yasumoto; Makita, Kohei

2015-09-01

Mycobacteriosis in swine is a common zoonosis found in abattoirs during meat inspections, and the veterinary authority is expected to inform the producer for corrective actions when an outbreak is detected. The expected value of the number of condemned carcasses due to mycobacteriosis therefore would be a useful threshold to detect an outbreak, and the present study aims to develop such an expected value through time series modeling. The model was developed using eight years of inspection data (2003 to 2010) obtained at 2 abattoirs of the Higashi-Mokoto Meat Inspection Center, Japan. The resulting model was validated by comparing the predicted time-dependent values for the subsequent 2 years with the actual data for 2 years between 2011 and 2012. For the modeling, at first, periodicities were checked using Fast Fourier Transformation, and the ensemble average profiles for weekly periodicities were calculated. An Auto-Regressive Integrated Moving Average (ARIMA) model was fitted to the residual of the ensemble average on the basis of minimum Akaike's information criterion (AIC). The sum of the ARIMA model and the weekly ensemble average was regarded as the time-dependent expected value. During 2011 and 2012, the number of whole or partial condemned carcasses exceeded the 95% confidence interval of the predicted values 20 times. All of these events were associated with the slaughtering of pigs from three producers with the highest rate of condemnation due to mycobacteriosis.

Job Tasks as Determinants of Thoracic Aerosol Exposure in the Cement Production Industry.

PubMed

Notø, Hilde; Nordby, Karl-Christian; Skare, Øivind; Eduard, Wijnand

2017-12-15

The aims of this study were to identify important determinants and investigate the variance components of thoracic aerosol exposure for the workers in the production departments of European cement plants. Personal thoracic aerosol measurements and questionnaire information (Notø et al., 2015) were the basis for this study. Determinants categorized in three levels were selected to describe the exposure relationships separately for the job types production, cleaning, maintenance, foreman, administration, laboratory, and other jobs by linear mixed models. The influence of plant and job determinants on variance components were explored separately and also combined in full models (plant&job) against models with no determinants (null). The best mixed models (best) describing the exposure for each job type were selected by the lowest Akaike information criterion (AIC; Akaike, 1974) after running all possible combination of the determinants. Tasks that significantly increased the thoracic aerosol exposure above the mean level for production workers were: packing and shipping, raw meal, cement and filter cleaning, and de-clogging of the cyclones. For maintenance workers, time spent with welding and dismantling before repair work increased the exposure while time with electrical maintenance and oiling decreased the exposure. Administration work decreased the exposure among foremen. A subjective tidiness factor scored by the research team explained up to a 3-fold (cleaners) variation in thoracic aerosol levels. Within-worker (WW) variance contained a major part of the total variance (35-58%) for all job types. Job determinants had little influence on the WW variance (0-4% reduction), some influence on the between-plant (BP) variance (from 5% to 39% reduction for production, maintenance, and other jobs respectively but an 79% increase for foremen) and a substantial influence on the between-worker within-plant variance (30-96% for production, foremen, and other workers). Plant determinants had little influence on the WW variance (0-2% reduction), some influence on the between-worker variance (0-1% reduction and 8% increase), and considerable influence on the BP variance (36-58% reduction) compared to the null models. Some job tasks contribute to low levels of thoracic aerosol exposure and others to higher exposure among cement plant workers. Thus, job task may predict exposure in this industry. Dust control measures in the packing and shipping departments and in the areas of raw meal and cement handling could contribute substantially to reduce the exposure levels. Rotation between low and higher exposed tasks may contribute to equalize the exposure levels between high and low exposed workers as a temporary solution before more permanent dust reduction measures is implemented. A tidy plant may reduce the overall exposure for almost all workers no matter of job type. © The Author 2017. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.

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

PubMed

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

2009-12-01

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

Demographic, reproductive, and dietary determinants of perfluorooctane sulfonic (PFOS) and perfluorooctanoic acid (PFOA) concentrations in human colostrum

PubMed Central

Jusko, Todd A.; Oktapodas, Marina; Murinová, L’ubica Palkovičová; Babinská, Katarina; Babjaková, Jana; Verner, Marc-André; DeWitt, Jamie C.; Thevenet-Morrison, Kelly; Čonka, Kamil; Drobná, Beata; Chovancová, Jana; Thurston, Sally W.; Lawrence, B. Paige; Dozier, Ann M.; Järvinen, Kirsi M.; Patayová, Henrieta; Trnovec, Tomáš; Legler, Juliette; Hertz-Picciotto, Irva; Lamoree, Marja H.

2017-01-01

To determine demographic, reproductive, and maternal dietary factors that predict perfluoroalkyl substance (PFAS) concentrations in breast milk, we measured perfluorooctane sulfonic (PFOS) and perfluorooctanoic acid (PFOA) concentrations, using liquid chromatography-mass spectrometry, in 184 colostrum samples collected from women participating in a cohort study in eastern Slovakia between 2002 and 2004. During their hospital delivery stay, mothers completed a food frequency questionnaire, and demographic and reproductive data were also collected. PFOS and PFOA predictors were identified by optimizing multiple linear regression models using Akaike’s information criterion (AIC). The geometric mean concentration in colostrum was 35.3 pg/ml for PFOS and 32.8 pg/ml for PFOA., In multivariable models, parous women had 40% lower PFOS (95% CI: −56 to −17%) and 40% lower PFOA (95% CI: −54 to −23%) concentrations compared with nulliparous women. Moreover, fresh/frozen fish consumption, longer birth intervals, and Slovak ethnicity were associated with higher PFOS and PFOA concentrations in colostrum. These results will help guide the design of future epidemiologic studies examining milk PFAS concentrations in relation to health endpoints in children. PMID:27244128

A comparative study of mixed exponential and Weibull distributions in a stochastic model replicating a tropical rainfall process

NASA Astrophysics Data System (ADS)

Abas, Norzaida; Daud, Zalina M.; Yusof, Fadhilah

2014-11-01

A stochastic rainfall model is presented for the generation of hourly rainfall data in an urban area in Malaysia. In view of the high temporal and spatial variability of rainfall within the tropical rain belt, the Spatial-Temporal Neyman-Scott Rectangular Pulse model was used. The model, which is governed by the Neyman-Scott process, employs a reasonable number of parameters to represent the physical attributes of rainfall. A common approach is to attach each attribute to a mathematical distribution. With respect to rain cell intensity, this study proposes the use of a mixed exponential distribution. The performance of the proposed model was compared to a model that employs the Weibull distribution. Hourly and daily rainfall data from four stations in the Damansara River basin in Malaysia were used as input to the models, and simulations of hourly series were performed for an independent site within the basin. The performance of the models was assessed based on how closely the statistical characteristics of the simulated series resembled the statistics of the observed series. The findings obtained based on graphical representation revealed that the statistical characteristics of the simulated series for both models compared reasonably well with the observed series. However, a further assessment using the AIC, BIC and RMSE showed that the proposed model yields better results. The results of this study indicate that for tropical climates, the proposed model, using a mixed exponential distribution, is the best choice for generation of synthetic data for ungauged sites or for sites with insufficient data within the limit of the fitted region.

The Transmission of Vertical Vibration to the Heads and Shoulders of Seated Men.

DTIC Science & Technology

1977-05-01

was fed into an SEL ac carrier amp l i f i e r , which contained the other hail of the bridge together with a balancing potentiometer which was used as...transport etc — you will be told in advance if the other 1% is to be used). People who have had recent transfusions , intestinal operations , a his...A052 009 ROYAL AIRCRAFT ESTABLISI CNT FARNSCROUS4I (ENILAIC) F/S 515TIC TRANSMISSION Off VERTICAL VIBRATION TO ThE CADS AIC SHOILOC—ETCIUP NAY 77

Studying relationships between environment and malaria incidence in Camopi (French Guiana) through the objective selection of buffer-based landscape characterisations

PubMed Central

2011-01-01

Background Malaria remains a major health problem in French Guiana, with a mean of 3800 cases each year. A previous study in Camopi, an Amerindian village on the Oyapock River, highlighted the major contribution of environmental features to the incidence of malaria attacks. We propose a method for the objective selection of the best multivariate peridomestic landscape characterisation that maximises the chances of identifying relationships between environmental features and malaria incidence, statistically significant and meaningful from an epidemiological point of view. Methods A land-cover map, the hydrological network and the geolocalised inhabited houses were used to characterise the peridomestic landscape in eleven discoid buffers with radii of 50, 100, 200, 300, 400, 500, 600, 700, 800, 900 and 1000 metres. Buffer-based landscape characterisations were first compared in terms of their capacity to discriminate between sites within the geographic space and of their effective multidimensionality in variable space. The Akaike information criterion (AIC) was then used to select the landscape model best explaining the incidences of P. vivax and P. falciparum malaria. Finally, we calculated Pearson correlation coefficients for the relationships between environmental variables and malaria incidence, by species, for the more relevant buffers. Results The optimal buffers for environmental characterisation had radii of 100 m around houses for P. vivax and 400 m around houses for P. falciparum. The incidence of P. falciparum malaria seemed to be more strongly linked to environmental features than that of P. vivax malaria, within these buffers. The incidence of P. falciparum malaria in children was strongly correlated with proportions of bare soil (r = -0.69), land under high vegetation (r = 0.68) and primary forest (r = 0.54), landscape division (r = 0.48) and the number of inhabited houses (r = -0.60). The incidence of P. vivax malaria was associated only with landscape division (r = 0.49). Conclusions The proposed methodology provides a simple and general framework for objective characterisation of the landscape to account for field observations. The use of this method enabled us to identify different optimal observation horizons around houses, depending on the Plasmodium species considered, and to demonstrate significant correlations between environmental features and the incidence of malaria. PMID:22151738

Studying relationships between environment and malaria incidence in Camopi (French Guiana) through the objective selection of buffer-based landscape characterisations.

PubMed

Stefani, Aurélia; Roux, Emmanuel; Fotsing, Jean-Marie; Carme, Bernard

2011-12-13

Malaria remains a major health problem in French Guiana, with a mean of 3800 cases each year. A previous study in Camopi, an Amerindian village on the Oyapock River, highlighted the major contribution of environmental features to the incidence of malaria attacks. We propose a method for the objective selection of the best multivariate peridomestic landscape characterisation that maximises the chances of identifying relationships between environmental features and malaria incidence, statistically significant and meaningful from an epidemiological point of view. A land-cover map, the hydrological network and the geolocalised inhabited houses were used to characterise the peridomestic landscape in eleven discoid buffers with radii of 50, 100, 200, 300, 400, 500, 600, 700, 800, 900 and 1000 metres. Buffer-based landscape characterisations were first compared in terms of their capacity to discriminate between sites within the geographic space and of their effective multidimensionality in variable space. The Akaike information criterion (AIC) was then used to select the landscape model best explaining the incidences of P. vivax and P. falciparum malaria. Finally, we calculated Pearson correlation coefficients for the relationships between environmental variables and malaria incidence, by species, for the more relevant buffers. The optimal buffers for environmental characterisation had radii of 100 m around houses for P. vivax and 400 m around houses for P. falciparum. The incidence of P. falciparum malaria seemed to be more strongly linked to environmental features than that of P. vivax malaria, within these buffers. The incidence of P. falciparum malaria in children was strongly correlated with proportions of bare soil (r = -0.69), land under high vegetation (r = 0.68) and primary forest (r = 0.54), landscape division (r = 0.48) and the number of inhabited houses (r = -0.60). The incidence of P. vivax malaria was associated only with landscape division (r = 0.49). The proposed methodology provides a simple and general framework for objective characterisation of the landscape to account for field observations. The use of this method enabled us to identify different optimal observation horizons around houses, depending on the Plasmodium species considered, and to demonstrate significant correlations between environmental features and the incidence of malaria. © 2011 Stefani et al; licensee BioMed Central Ltd.

Inter-regional comparison of land-use effects on stream metabolism

USGS Publications Warehouse

Bernot, M.J.; Sobota, D.J.; Hall, R.O.; Mulholland, P.J.; Dodds, W.K.; Webster, J.R.; Tank, J.L.; Ashkenas, L.R.; Cooper, L.W.; Dahm, Clifford N.; Gregory, S.V.; Grimm, N. B.; Hamilton, S.K.; Johnson, S.L.; McDowell, W.H.; Meyer, J.L.; Peterson, B.; Poole, G.C.; Maurice, Valett H.M.; Arango, C.; Beaulieu, J.J.; Burgin, A.J.; Crenshaw, C.; Helton, A.M.; Johnson, L.; Merriam, J.; Niederlehner, B.R.; O'Brien, J. M.; Potter, J.D.; Sheibley, R.W.; Thomas, S.M.; Wilson, K.

2010-01-01

1. Rates of whole-system metabolism (production and respiration) are fundamental indicators of ecosystem structure and function. Although first-order, proximal controls are well understood, assessments of the interactions between proximal controls and distal controls, such as land use and geographic region, are lacking. Thus, the influence of land use on stream metabolism across geographic regions is unknown. Further, there is limited understanding of how land use may alter variability in ecosystem metabolism across regions.2. Stream metabolism was measured in nine streams in each of eight regions (n = 72) across the United States and Puerto Rico. In each region, three streams were selected from a range of three land uses: agriculturally influenced, urban-influenced, and reference streams. Stream metabolism was estimated from diel changes in dissolved oxygen concentrations in each stream reach with correction for reaeration and groundwater input.3. Gross primary production (GPP) was highest in regions with little riparian vegetation (sagebrush steppe in Wyoming, desert shrub in Arizona/New Mexico) and lowest in forested regions (North Carolina, Oregon). In contrast, ecosystem respiration (ER) varied both within and among regions. Reference streams had significantly lower rates of GPP than urban or agriculturally influenced streams.4. GPP was positively correlated with photosynthetically active radiation and autotrophic biomass. Multiple regression models compared using Akaike's information criterion (AIC) indicated GPP increased with water column ammonium and the fraction of the catchment in urban and reference land-use categories. Multiple regression models also identified velocity, temperature, nitrate, ammonium, dissolved organic carbon, GPP, coarse benthic organic matter, fine benthic organic matter and the fraction of all land-use categories in the catchment as regulators of ER.5. Structural equation modelling indicated significant distal as well as proximal control pathways including a direct effect of land-use on GPP as well as SRP, DIN, and PAR effects on GPP; GPP effects on autotrophic biomass, organic matter, and ER; and organic matter effects on ER.6. Overall, consideration of the data separated by land-use categories showed reduced inter-regional variability in rates of metabolism, indicating that the influence of agricultural and urban land use can obscure regional differences in stream metabolism. ?? 2010 Blackwell Publishing Ltd.

Predicting Local Dengue Transmission in Guangzhou, China, through the Influence of Imported Cases, Mosquito Density and Climate Variability

PubMed Central

Sang, Shaowei; Yin, Wenwu; Bi, Peng; Zhang, Honglong; Wang, Chenggang; Liu, Xiaobo; Chen, Bin; Yang, Weizhong; Liu, Qiyong

2014-01-01

Introduction Each year there are approximately 390 million dengue infections worldwide. Weather variables have a significant impact on the transmission of Dengue Fever (DF), a mosquito borne viral disease. DF in mainland China is characterized as an imported disease. Hence it is necessary to explore the roles of imported cases, mosquito density and climate variability in dengue transmission in China. The study was to identify the relationship between dengue occurrence and possible risk factors and to develop a predicting model for dengue’s control and prevention purpose. Methodology and Principal Findings Three traditional suburbs and one district with an international airport in Guangzhou city were selected as the study areas. Autocorrelation and cross-correlation analysis were used to perform univariate analysis to identify possible risk factors, with relevant lagged effects, associated with local dengue cases. Principal component analysis (PCA) was applied to extract principal components and PCA score was used to represent the original variables to reduce multi-collinearity. Combining the univariate analysis and prior knowledge, time-series Poisson regression analysis was conducted to quantify the relationship between weather variables, Breteau Index, imported DF cases and the local dengue transmission in Guangzhou, China. The goodness-of-fit of the constructed model was determined by pseudo-R2, Akaike information criterion (AIC) and residual test. There were a total of 707 notified local DF cases from March 2006 to December 2012, with a seasonal distribution from August to November. There were a total of 65 notified imported DF cases from 20 countries, with forty-six cases (70.8%) imported from Southeast Asia. The model showed that local DF cases were positively associated with mosquito density, imported cases, temperature, precipitation, vapour pressure and minimum relative humidity, whilst being negatively associated with air pressure, with different time lags. Conclusions Imported DF cases and mosquito density play a critical role in local DF transmission, together with weather variables. The establishment of an early warning system, using existing surveillance datasets will help to control and prevent dengue in Guangzhou, China. PMID:25019967

Predicting local dengue transmission in Guangzhou, China, through the influence of imported cases, mosquito density and climate variability.

PubMed

Sang, Shaowei; Yin, Wenwu; Bi, Peng; Zhang, Honglong; Wang, Chenggang; Liu, Xiaobo; Chen, Bin; Yang, Weizhong; Liu, Qiyong

2014-01-01

Each year there are approximately 390 million dengue infections worldwide. Weather variables have a significant impact on the transmission of Dengue Fever (DF), a mosquito borne viral disease. DF in mainland China is characterized as an imported disease. Hence it is necessary to explore the roles of imported cases, mosquito density and climate variability in dengue transmission in China. The study was to identify the relationship between dengue occurrence and possible risk factors and to develop a predicting model for dengue's control and prevention purpose. Three traditional suburbs and one district with an international airport in Guangzhou city were selected as the study areas. Autocorrelation and cross-correlation analysis were used to perform univariate analysis to identify possible risk factors, with relevant lagged effects, associated with local dengue cases. Principal component analysis (PCA) was applied to extract principal components and PCA score was used to represent the original variables to reduce multi-collinearity. Combining the univariate analysis and prior knowledge, time-series Poisson regression analysis was conducted to quantify the relationship between weather variables, Breteau Index, imported DF cases and the local dengue transmission in Guangzhou, China. The goodness-of-fit of the constructed model was determined by pseudo-R2, Akaike information criterion (AIC) and residual test. There were a total of 707 notified local DF cases from March 2006 to December 2012, with a seasonal distribution from August to November. There were a total of 65 notified imported DF cases from 20 countries, with forty-six cases (70.8%) imported from Southeast Asia. The model showed that local DF cases were positively associated with mosquito density, imported cases, temperature, precipitation, vapour pressure and minimum relative humidity, whilst being negatively associated with air pressure, with different time lags. Imported DF cases and mosquito density play a critical role in local DF transmission, together with weather variables. The establishment of an early warning system, using existing surveillance datasets will help to control and prevent dengue in Guangzhou, China.

Strongly Coupled Models with a Higgs-like Boson

NASA Astrophysics Data System (ADS)

Pich, Antonio; Rosell, Ignasi; José Sanz-Cillero, Juan

2013-11-01

Considering the one-loop calculation of the oblique S and T parameters, we have presented a study of the viability of strongly-coupled scenarios of electroweak symmetry breaking with a light Higgs-like boson. The calculation has been done by using an effective Lagrangian, being short-distance constraints and dispersive relations the main ingredients of the estimation. Contrary to a widely spread believe, we have demonstrated that strongly coupled electroweak models with massive resonances are not in conflict with experimentalconstraints on these parameters and the recently observed Higgs-like resonance. So there is room for these models, but they are stringently constrained. The vector and axial-vector states should be heavy enough (with masses above the TeV scale), the mass splitting between them is highly preferred to be small and the Higgs-like scalar should have a WW coupling close to the Standard Model one. It is important to stress that these conclusions do not depend critically on the inclusion of the second Weinberg sum rule. We wish to thank the organizers of LHCP 2013 for the pleasant conference. This work has been supported in part by the Spanish Government and the European Commission [FPA2010-17747, FPA2011- 23778, AIC-D-2011-0818, SEV-2012-0249 (Severo Ochoa Program), CSD2007-00042 (Consolider Project CPAN)], the Generalitat Valenciana [PrometeoII/2013/007] and the Comunidad de Madrid [HEPHACOS S2009/ESP-1473].

Molecular Detection of Hematozoa Infections in Tundra Swans Relative to Migration Patterns and Ecological Conditions at Breeding Grounds

PubMed Central

Ramey, Andrew M.; Ely, Craig R.; Schmutz, Joel A.; Pearce, John M.; Heard, Darryl J.

2012-01-01

Tundra swans (Cygnus columbianus) are broadly distributed in North America, use a wide variety of habitats, and exhibit diverse migration strategies. We investigated patterns of hematozoa infection in three populations of tundra swans that breed in Alaska using satellite tracking to infer host movement and molecular techniques to assess the prevalence and genetic diversity of parasites. We evaluated whether migratory patterns and environmental conditions at breeding areas explain the prevalence of blood parasites in migratory birds by contrasting the fit of competing models formulated in an occupancy modeling framework and calculating the detection probability of the top model using Akaike Information Criterion (AIC). We described genetic diversity of blood parasites in each population of swans by calculating the number of unique parasite haplotypes observed. Blood parasite infection was significantly different between populations of Alaska tundra swans, with the highest estimated prevalence occurring among birds occupying breeding areas with lower mean daily wind speeds and higher daily summer temperatures. Models including covariates of wind speed and temperature during summer months at breeding grounds better predicted hematozoa prevalence than those that included annual migration distance or duration. Genetic diversity of blood parasites in populations of tundra swans appeared to be relative to hematozoa prevalence. Our results suggest ecological conditions at breeding grounds may explain differences of hematozoa infection among populations of tundra swans that breed in Alaska. PMID:23049862

Molecular detection of hematozoa infections in tundra swans relative to migration patterns and ecological conditions at breeding grounds

USGS Publications Warehouse

Ramey, Andrew M.; Ely, Craig R.; Schmutz, Joel A.; Pearce, John M.; Heard, Darryl J.

2012-01-01

Tundra swans (Cygnus columbianus) are broadly distributed in North America, use a wide variety of habitats, and exhibit diverse migration strategies. We investigated patterns of hematozoa infection in three populations of tundra swans that breed in Alaska using satellite tracking to infer host movement and molecular techniques to assess the prevalence and genetic diversity of parasites. We evaluated whether migratory patterns and environmental conditions at breeding areas explain the prevalence of blood parasites in migratory birds by contrasting the fit of competing models formulated in an occupancy modeling framework and calculating the detection probability of the top model using Akaike Information Criterion (AIC). We described genetic diversity of blood parasites in each population of swans by calculating the number of unique parasite haplotypes observed. Blood parasite infection was significantly different between populations of Alaska tundra swans, with the highest estimated prevalence occurring among birds occupying breeding areas with lower mean daily wind speeds and higher daily summer temperatures. Models including covariates of wind speed and temperature during summer months at breeding grounds better predicted hematozoa prevalence than those that included annual migration distance or duration. Genetic diversity of blood parasites in populations of tundra swans appeared to be relative to hematozoa prevalence. Our results suggest ecological conditions at breeding grounds may explain differences of hematozoa infection among populations of tundra swans that breed in Alaska.

Predictive occurrence models for coastal wetland plant communities: Delineating hydrologic response surfaces with multinomial logistic regression

NASA Astrophysics Data System (ADS)

Snedden, Gregg A.; Steyer, Gregory D.

2013-02-01

Understanding plant community zonation along estuarine stress gradients is critical for effective conservation and restoration of coastal wetland ecosystems. We related the presence of plant community types to estuarine hydrology at 173 sites across coastal Louisiana. Percent relative cover by species was assessed at each site near the end of the growing season in 2008, and hourly water level and salinity were recorded at each site Oct 2007-Sep 2008. Nine plant community types were delineated with k-means clustering, and indicator species were identified for each of the community types with indicator species analysis. An inverse relation between salinity and species diversity was observed. Canonical correspondence analysis (CCA) effectively segregated the sites across ordination space by community type, and indicated that salinity and tidal amplitude were both important drivers of vegetation composition. Multinomial logistic regression (MLR) and Akaike's Information Criterion (AIC) were used to predict the probability of occurrence of the nine vegetation communities as a function of salinity and tidal amplitude, and probability surfaces obtained from the MLR model corroborated the CCA results. The weighted kappa statistic, calculated from the confusion matrix of predicted versus actual community types, was 0.7 and indicated good agreement between observed community types and model predictions. Our results suggest that models based on a few key hydrologic variables can be valuable tools for predicting vegetation community development when restoring and managing coastal wetlands.

Molecular detection of hematozoa infections in tundra swans relative to migration patterns and ecological conditions at breeding grounds.

PubMed

Ramey, Andrew M; Ely, Craig R; Schmutz, Joel A; Pearce, John M; Heard, Darryl J

2012-01-01

Tundra swans (Cygnus columbianus) are broadly distributed in North America, use a wide variety of habitats, and exhibit diverse migration strategies. We investigated patterns of hematozoa infection in three populations of tundra swans that breed in Alaska using satellite tracking to infer host movement and molecular techniques to assess the prevalence and genetic diversity of parasites. We evaluated whether migratory patterns and environmental conditions at breeding areas explain the prevalence of blood parasites in migratory birds by contrasting the fit of competing models formulated in an occupancy modeling framework and calculating the detection probability of the top model using Akaike Information Criterion (AIC). We described genetic diversity of blood parasites in each population of swans by calculating the number of unique parasite haplotypes observed. Blood parasite infection was significantly different between populations of Alaska tundra swans, with the highest estimated prevalence occurring among birds occupying breeding areas with lower mean daily wind speeds and higher daily summer temperatures. Models including covariates of wind speed and temperature during summer months at breeding grounds better predicted hematozoa prevalence than those that included annual migration distance or duration. Genetic diversity of blood parasites in populations of tundra swans appeared to be relative to hematozoa prevalence. Our results suggest ecological conditions at breeding grounds may explain differences of hematozoa infection among populations of tundra swans that breed in Alaska.

Time-dependent oral absorption models

NASA Technical Reports Server (NTRS)

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

2001-01-01

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

An approach to 3D model fusion in GIS systems and its application in a future ECDIS

NASA Astrophysics Data System (ADS)

Liu, Tao; Zhao, Depeng; Pan, Mingyang

2016-04-01

Three-dimensional (3D) computer graphics technology is widely used in various areas and causes profound changes. As an information carrier, 3D models are becoming increasingly important. The use of 3D models greatly helps to improve the cartographic expression and design. 3D models are more visually efficient, quicker and easier to understand and they can express more detailed geographical information. However, it is hard to efficiently and precisely fuse 3D models in local systems. The purpose of this study is to propose an automatic and precise approach to fuse 3D models in geographic information systems (GIS). It is the basic premise for subsequent uses of 3D models in local systems, such as attribute searching, spatial analysis, and so on. The basic steps of our research are: (1) pose adjustment by principal component analysis (PCA); (2) silhouette extraction by simple mesh silhouette extraction and silhouette merger; (3) size adjustment; (4) position matching. Finally, we implement the above methods in our system Automotive Intelligent Chart (AIC) 3D Electronic Chart Display and Information Systems (ECDIS). The fusion approach we propose is a common method and each calculation step is carefully designed. This approach solves the problem of cross-platform model fusion. 3D models can be from any source. They may be stored in the local cache or retrieved from Internet, or may be manually created by different tools or automatically generated by different programs. The system can be any kind of 3D GIS system.

Body Size Evolution in Insular Speckled Rattlesnakes (Viperidae: Crotalus mitchellii)

PubMed Central

Meik, Jesse M.; Lawing, A. Michelle; Pires-daSilva, André

2010-01-01

Background Speckled rattlesnakes (Crotalus mitchellii) inhabit multiple islands off the coast of Baja California, Mexico. Two of the 14 known insular populations have been recognized as subspecies based primarily on body size divergence from putative mainland ancestral populations; however, a survey of body size variation from other islands occupied by these snakes has not been previously reported. We examined body size variation between island and mainland speckled rattlesnakes, and the relationship between body size and various island physical variables among 12 island populations. We also examined relative head size among giant, dwarfed, and mainland speckled rattlesnakes to determine whether allometric differences conformed to predictions of gape size (and indirectly body size) evolving in response to shifts in prey size. Methodology/Principal Findings Insular speckled rattlesnakes show considerable variation in body size when compared to mainland source subspecies. In addition to previously known instances of gigantism on Ángel de la Guarda and dwarfism on El Muerto, various degrees of body size decrease have occurred frequently in this taxon, with dwarfed rattlesnakes occurring mostly on small, recently isolated, land-bridge islands. Regression models using the Akaike information criterion (AIC) showed that mean SVL of insular populations was most strongly correlated with island area, suggesting the influence of selection for different body size optima for islands of different size. Allometric differences in head size of giant and dwarf rattlesnakes revealed patterns consistent with shifts to larger and smaller prey, respectively. Conclusions/Significance Our data provide the first example of a clear relationship between body size and island area in a squamate reptile species; among vertebrates this pattern has been previously documented in few insular mammals. This finding suggests that selection for body size is influenced by changes in community dynamics that are related to graded differences in area over what are otherwise similar bioclimatic conditions. We hypothesize that in this system shifts to larger prey, episodic saturation and depression of primary prey density, and predator release may have led to insular gigantism, and that shifts to smaller prey and increased reproductive efficiency in the presence of intense intraspecific competition may have led to insular dwarfism. PMID:20209105